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The Modern-Era Retrospective Analysis for Research and Applications, 
Version 2 (MERRA-2) 


Ronald Gelaro!, Will McCarty!, Max J. Suarez”, Ricardo Todling', Andrea Molod!, 
Lawrence Takacs’, Cynthia Randles*!, Anton Darmenov!, Michael G. Bosilovich', Rolf 
Reichle!, Krzysztof Wargan?, Lawrence Coy, Richard Cullather®, Clara Draper’, 
Santha Akella®?, Virginie Buchard?, Austin Conaty?, Arlindo da Silva, Wei Gu’, 
Gi-Kong Kim!, Randal Koster!, Robert Lucchesi?, Dagmar Merkova*', Jon Eric 
Nielsen®, Gary Partyka?, Steven Pawson!, William Putman!, Michele Rienecker', 
Siegfried D. Schubert!, Meta Sienkiewicz?, and Bin Zhao® 


‘Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, 
Greenbelt, MD 
2 Universities Space Research Association, Columbia, MD 
3 Science Systems and Applications, Inc., Lanham, MD 
4 Morgan State University, Baltimore, MD 
> Earth System Science Interdisciplinary Center, College Park, MD 
® Science Applications International Corporation, Beltsville, MD 
‘Current affiliation: ExxonMobil Corporate Strategic Research, Annandale, NJ 
' Current affiliation: ILM. System Group, Inc., Rockville, MD 


Revised 23 March 2017 


Accepted for publication in Journal of Climate on 29 March 2017 
MERRA-2 Special Collection 


Corresponding author: Ronald Gelaro, Global Modeling and Assimilation Office, NASA 
Goddard Space Flight Center, Greenbelt, MD 20771, USA. Email: ron.gelaro@nasa.gov 


Abstract 


The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA- 


2) is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s 
Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation 
types not available to its predecessor, MERRA, and includes updates to the Goddard 
Earth Observing System (GEOS) model and analysis scheme so as to provide a viable 
ongoing climate analysis beyond MERRA’s terminus. While addressing known limita- 
tions of MERRA, MERRA-2 is also intended to be a development milestone for a future 
integrated Earth system analysis (IESA) currently under development at GMAO. This 
paper provides an overview of the MERRA-2 system and various performance metrics. 
Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol 
observations, several improvements to the representation of the stratosphere including 
ozone, and improved representations of cryospheric processes. Other improvements in 
the quality of MERRA-2 compared with MERRA include the reduction of some spuri- 
ous trends and jumps related to changes in the observing system, and reduced biases 
and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. 
Production of MERRA-2 began in June 2014 in four processing streams, and converged 
to a single near-real time stream in mid 2015. MERRA-2 products are accessible online 
through the NASA Goddard Earth Sciences Data Information Services Center (GES 
DISC). 


1. Introduction 


Reanalysis is the process whereby an unchanging data assimilation system is used to 
provide a consistent reprocessing of meteorological observations, typically spanning an 
extended segment of the historical data record. The process relies on an underlying 
forecast model to combine disparate observations in a physically consistent manner, en- 
abling production of gridded data sets for a broad range of variables including ones that 
are sparsely or not directly observed. As such, and with appropriate consideration of the 
inherent uncertainties, reanalysis products have not only become a staple of the atmo- 
spheric research community, but are used increasingly for climate monitoring as well as 
for business applications in, for example, energy and agriculture. Recent reanalyses from 
the National Oceanic and Atmospheric Administration/National Centers for Environ- 
mental Prediction (NOAA/NCEP), the European Centre for Medium-Range Weather 
Forecasts (ECMWF), the National Aeronautics and Space Administration/Global Mod- 
eling and Assimilation Office (NASA/GMAO), and the Japan Meteorological Agency 
(JMA) provide a rich ensemble of climate data products beginning more or less with 
the period of regular conventional and satellite observations in the mid to late twentieth 
century (Saha et al. 2010; Dee et al. 2011; Rienecker et al. 2011; Kobayashi et al. 2015). 
However, there have also been successful efforts to extend atmospheric reanalyses back 
to the late nineteenth and early twentieth centuries using only surface pressure obser- 
vations (Compo et al. 2011) or surface and mean sea level pressure observations plus 
surface marine winds (Poli et al. 2013). As noted by Dee et al. (2011), these century- 
long reanalyses have also sparked remarkable data recovery and digitization efforts by 


various groups around the world. 


The GMAO’s reanalysis development effort began (under its predecessor organization, 


the Data Assimilation Office) with the production of the Goddard Earth Observing 


System, version 1 (GEOS-1) reanalysis (Schubert et al. 1993), but advanced significantly 
with the more recent production of the Modern-Era Retrospective Analysis for Research 
and Applications (MERRA, Rienecker et al. 2011). MERRA encompassed the period 
1979-2016 and was undertaken with two primary objectives: to place NASA’s Earth 
Observing System (EOS) satellite observations in a climate context and to improve 
the representation of the atmospheric branch of the hydrological cycle compared with 
previous reanalyses. MERRA succeeded in meeting these objectives overall and was 
found to be of comparable quality to contemporaneous reanalyses produced by NCEP 
and ECMWF (e.g., Decker et al. 2011). However, it also suffered from a number 
of known, but not necessarily unique, deficiencies. These include unphysical jumps 
and trends in precipitation in response to changes in the observing system, biases and 
imbalances in certain atmospheric and land surface hydrological quantities, and a poor 
representation of the upper stratosphere (e.g., Bosilovich et al. 2011; Robertson et al. 
2011; Reichle et al. 2011; Rienecker et al. 2011). In addition, the long-term viability 
of MERRA was limited by system constraints that precluded the incorporation of new 
satellite data sources beyond NOAA-18, which launched in 2005. At the time of its 
termination in March 2016, MERRA was at risk of suffering a significant degradation 
in quality were certain observing platforms to fail, including, for example, EOS Aqua, 
which was already well beyond its designed lifetime and provided MERRA with its only 


sources of hyperspectral infrared and afternoon-orbit microwave radiances. 


The Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA- 


2) was undertaken to provide a timely replacement for MERRA and to sustain GMAO’s 
commitment to having an ongoing near-real-time climate analysis. MERRA-2 is in- 
tended as an intermediate reanalysis; one that leverages recent developments at GMAO 
in modeling and data assimilation to address some of the known limitations of MERRA, 


but also provides a stepping stone to GMAO’s longer term goal of developing an in- 


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tegrated Earth system analysis (IESA) capability that couples assimilation systems for 
the atmosphere, ocean, land and chemistry. Toward the latter goal MERRA-2 includes 
aerosol data assimilation, thereby providing a multi-decadal reanalysis in which aerosol 
and meteorological observations are jointly assimilated within a global data assimilation 
system. Other new developments in MERRA-2 relevant to IESA focus on aspects of 
the cryosphere and stratosphere, including the representation of ozone, and on the use 
of precipitation observations to force the land surface. At the same time, basic aspects 
of the MERRA-2 system, such as the variational analysis algorithm and observation 
handling, are largely unchanged since MERRA. Also unchanged is the preparation of 


most conventional data sources used originally in MERRA. 


This paper presents an overview of MERRA-2, including a description of the data as- 
similation system and various measures of performance. Some of these measures focus 
on difficulties encountered in MERRA while others highlight new capabilities such as 
the assimilation of aerosol observations. This paper also serves as an introduction to a 
series of companion papers that provide more detailed analyses of the topics covered in 
this overview as well as others. For example, a detailed description of the MERRA-2 
aerosol analysis system and its validation are presented in Randles et al. (2017) and 
Buchard et al. (2017). Reichle et al. (2017a,b) assess the land surface precipitation 
and land surface hydrology, while Draper et al. (2017) examine the land surface en- 
ergy budget. Bosilovich et al. (2017) evaluate the global water balance and water cycle 
variability in MERRA-2. Collow et al. (2016) examine MERRA-2’s representation of 
US summertime extreme precipitation events, and Lim et al. (2017) investigate aspects 
of major El Nino events. Collow and Miller (2016) examine the radiation budget and 
cloud radiative effect over the Amazon. Segal-Rosenhemier et al. (2017) examine surface 
radiative fluxes in polar marginal ice zones. Several papers investigate aspects of the 


stratosphere in MERRA-2: Wargan et al. (2017) examine the representation of lower 


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stratospheric ozone and the effect of assimilating ozone observations from NASA’s Aura 
satellite; Coy et al. (2016) examine the representation of the quasi-biennial oscillation 
in MERRA-2; and Wargan and Coy (2016) present a case study of the 2009 sudden 


stratospheric warming. 


Section 2 provides an overview of the MERRA-2 data assimilation system, focusing pri- 
marily on developments since MERRA, including new observation sources. Basic met- 
rics of the assimilation system performance are presented in section 3. The MERRA-2 
aerosol analysis is described in section 4, along with sample results and validation statis- 
tics. Section 5 examines global and regional aspects of the representation of precipitation 
in MERRA-2, focusing on areas of difficulty in MERRA. Stratospheric processes and 
the representation of ozone are discussed in section 6. Section 7 addresses the represen- 
tation of the cryosphere in MERRA-2, with focus on glaciated land surface processes. 
Section 8 provides information about MERRA-2 products and how they can be accessed. 
It is noted here that each MERRA-2 data collection has its own digital object identifier 
(DOI) number, so data used in scientific publications can be cited exactly. Most of the 
results shown for MERRA-2 in this paper are derived from these collections, which are 
individually cited in the corresponding figure captions. Finally, a brief summary and 
perspective on future work are presented in section 9. A list of acronyms is given in the 


Appendix. 


2. MERRA-2 system description 


MERRA-2 is produced with version 5.12.4 of the Goddard Earth Observing System 
(GEOS-5.12.4) atmospheric data assimilation system. The key components of the system 
are the GEOS atmospheric model (Rienecker et al. 2008; Molod et al. 2015) and the 


Gridpoint Statistical Interpolation (GSI) analysis scheme (Wu et al. 2002; Kleist et 


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al. 2009b). The model includes the finite-volume dynamical core of Putman and Lin 
(2007), which uses a cubed sphere horizontal discretization at an approximate resolution 
of 0.5° x 0.625° and 72 hybrid-eta levels from the surface to 0.01 hPa. The analysis is 
computed on a latitude-longitude grid at the same spatial resolution as the atmospheric 
model using a three-dimensional variational (3 DVAR) algorithm based on the GSI with a 
6-h update cycle and the so-called first-guess-at-appropriate-time (FGAT) procedure for 
computing temporally accurate observation-minus-background departures. The analysis 
is applied as a correction to the background state using an incremental analysis update 


(IAU) procedure (Bloom et al. 1996). 


The MERRA-2 system has many of the same basic features as the MERRA system 
(GEOS-5.2.0) described in Rienecker et al. (2011) but includes a number of important 
updates. An overview of these updates is provided here, with additional details provided 
in companion publications as cited. Unless otherwise stated, other aspects of the system 
configuration and preparation of the input data are as described in Rienecker et al. 
(2011). The updates discussed here include changes to the forecast model (section 2a), 
the analysis algorithm (section 2b), the observing system (section 2c), the radiance 
assimilation (section 2d), the bias correction of aircraft observations (section 2e), the 
mass conservation and water balance (section 2f), the precipitation used to force the land 
surface and drive wet aerosol deposition (section 2g), the boundary conditions for sea 
surface temperature and sea ice concentration (section 2h), and reanalysis production 


(section 2i). 


a. Forecast model 


Since MERRA, the GEOS model has undergone changes to both its dynamical core 


and its physical parameterizations. Whereas in MERRA the horizontal discretization of 


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the model was computed on a latitude-longitude grid, MERRA-2 uses a cubed sphere 
grid. This allows relatively uniform grid spacing at all latitudes and mitigates the more 
severe grid spacing singularities that occur on a latitude-longitude grid. Upgrades to 
the physical parameterization schemes include increased re-evaporation of frozen pre- 
cipitation and cloud condensate, changes to the background gravity wave drag, and 
an improved relationship between the ocean surface roughness and ocean surface stress 
(Molod et al. 2015). The MERRA-2 model also includes a Tokioka-type trigger on deep 
convection as part of the Relaxed Arakawa-Schubert (RAS, Moorthi and Suarez 1992) 
convective parameterization scheme, which governs the lower limit on the allowable en- 
trainment plumes (Bacmeister and Stephens 2011). A new glaciated land representation 
and seasonally-varying sea ice albedo have been implemented, leading to improved air 
temperatures and reduced biases in the net energy flux over these surfaces (Cullather et 


al. 2014). 


b. Analysis algorithm 


The control variable for moisture used in recent versions of GSI and MERRA-2 differs 
from the one used in MERRA. Whereas MERRA used the so-called pseudo-relative 
humidity (Dee and da Silva, 2003) defined by the water vapor mixing ratio scaled by 
its saturation value, MERRA-2 uses the normalized pseudo-relative humidity (Holm 
2003) defined by the pseudo-relative humidity scaled by its background error standard 
deviation. The latter has a near Gaussian error distribution, making it more suitable for 
the minimization procedure employed in the assimilation scheme. Also within the GSI, 
a tangent linear normal mode constraint (TLNMC, Kleist et al. 2009a) is applied during 
the minimization procedure to control noise and improve the overall use of observations. 


The background error statistics used in the GSI have been updated as well in MERRA- 


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2. As in MERRA, the statistics are estimated using the ‘NMC’ method (Parrish and 
Derber, 1992) by calculating variances and covariances from the differences between 
24-h and 48-h forecasts, but from a more recent version of GEOS. Compared with 
the MERRA system, the background error statistics for the MERRA-2 system exhibit 
generally smaller standard deviations for most variables, but both larger and smaller 


correlation length scales depending on the variable, latitude and vertical level. 


c. Observing system 


MERRA included no new satellite observation sources after the introduction of NOAA- 
18 in 2005. MERRA-2, in contrast, includes numerous additional satellite observations 
both before and after this time. The complete set of input observations assimilated in 
MERRA-2 is summarized in Table 1, while a detailed description of their use is provided 
in McCarty et al. (2016). Additions to the MERRA-2 observing system compared with 


MERRA include: 


Atmospheric motion vectors from the Advanced Very High Resolution Radiometer 


(AVHRR): 


Surface wind speeds from the Special Sensor Microwave Imager /Sounder (SSMIS):; 


Surface wind vectors from the Meteorological Operational Satellite-A (Metop-A) 


Advanced Scatterometer (ASCAT) and WindSat; 


Temperature and ozone profiles from the EOS Aura Microwave Limb Sounder 


(MLS); 


Total column ozone from the EOS Aura Ozone Monitoring Instrument (OMI); 


Bending angle from Global Positioning System radio occultations (GPSRO); 


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e Microwave and infrared sounding radiances from the Advanced TIROS Operational 


Vertical Sounder (ATOVS) on NOAA-19, Metop-A and -B; 


e Microwave sounding radiances from the Advanced Technology Microwave Sounder 


(ATMS) on the Suomi National Polar-orbiting Partnership (SNPP); 


e Hyperspectral infrared radiances from the Infrared Atmospheric Sounding Inter- 
ferometer (IASI) on Metop-A and -B, and from the Cross-track Infrared Sounder 


(CrIS) on SNPP; 


e Geostationary radiances from the Meteosat Second Generation (MSG) Spinning 
Enhanced Visible Infrared Imager (SEVIRI) and Geostationary Operational En- 


vironmental Satellites (GOES-11, -13 and -15). 


Time series of the various types of observations assimilated in MERRA and MERRA-2 
are shown in Figure 1. The number of assimilated observations in MERRA-2 grows 
from approximately two million per 6-h cycle in 2002 to almost five million in 2015, 
while MERRA assimilates approximately 1.5 million observations per 6-h cycle from 
2002 onward. The GSI in MERRA-2 is also capable of assimilating microwave and hy- 
perspectral infrared radiances from planned future satellites including Metop-C and the 
Joint Polar Satellite System (JPSS). The temporary spike in the number of QuikSCAT 
data assimilated in MERRA-2 in late 2000 is due to an error in preprocessing which 
led to observations beyond the mid-swath “sweet spot” being used in the analysis. This 
has no discernible impact on the quality of the analyzed fields or on the use of other 


observations in the assimilation system. 


MERRA-2 also assimilates reprocessed versions of some of the same satellite observation 
types used in MERRA. In MERRA-2, Remote Sensing Systems version 7 (RSS v7) 


recalibrated radiances and retrieved surface wind speeds from the Defense Meteorological 


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Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) are used, whereas 
MERRA used RSS v6. The use of retrieved ozone from the Solar Backscatter Ultraviolet 
Radiometer (SBUV) also differs, with MERRA-2 assimilating version 8.6 on 21 layers 
from 1980 thru 2004 before switching to OMI and MLS in October 2004. In contrast, 
MERRA used SBUV version 8 throughout, in a form degraded from its original 21 layers 


to 12. 


d. Radiance assimilation 


Radiative transfer calculations necessary for the assimilation of satellite radiances in 
MERRA-2 are performed using the Community Radiative Transfer Model (CRTM, Han 
et al. 2006, Chen et al. 2008). MERRA-2 uses version 2.1.3 of the CRTM for assimilation 
of all satellite radiances, whereas MERRA used a prototype version of the CRTM for 
all radiances except those from the Stratospheric Sounding Unit (SSU), for which the 
Goddard Laboratory for Atmospheres TOVS forward model (GLATOVS, Susskind et 
al. 1983) was used. Differences between the prototype and version 2.1.3 of the CRTM 
are too numerous to mention here, but a detailed description of the latter can be found 


in Liu and Boukabara (2014). 


The actively assimilated channels for each satellite sensor type in MERRA-2 are sum- 
marized in Table 2. Microwave temperature sounding channels with strong surface 
sensitivity—so-called window channels—are not assimilated in MERRA-2, in part be- 
cause of the strong sensitivity of global precipitation and humidity to these data found in 
MERRA (Robertson et al. 2011). These include channels 1-3 and 15 on the Advanced 
Microwave Sounding Unit-A (AMSU-A), channels 1-4 and 16 on ATMS, and channel 
1 on the Microwave Sounding Unit (MSU). For microwave humidity sounders includ- 


ing the the Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humid- 


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ity Sounder (MHS), window channels are actively assimilated along with the sounding 
channels. For heritage infrared sounders, channels 13-15 on the High-resolution Infrared 
Radiation Sounder (HIRS) were assimilated in MERRA but are excluded in MERRA-2. 
The channel selections for hyper-spectral infrared sounders and performance assessments 


for selected instruments are provided in McCarty et al. (2016). 


Like MERRA, MERRA-2 uses an automated bias correction scheme for the assimilation 
of most satellite radiance observations. Bias estimates for individual sensor channels 
are represented by a small number of predictors which can depend on the atmospheric 
state, the radiative transfer model, and the sensor characteristics. Air-mass- and viewing 
angle-dependent biases are estimated using a variational scheme in which the predictor 
coefficients are updated as part of the control vector used to minimize the analysis cost 
function (Derber and Wu, 1998). Satellite scan-position-dependent bias is estimated 
directly as an exponential moving average filter of the observation-minus-background 
departures for brightness temperature. For both the variational and scan-position predic- 
tors, initial values of the coefficients for MERRA-2 were derived from GEOS operations 
and other long production runs using system versions similar to that used for MERRA- 
2. In the few cases where no recent coefficient information was available, initial values 
were derived from MERRA. Note that no bias correction is applied to a small number of 
sensor channels that peak in the upper stratosphere, including channel 14 on AMSU-A, 
channel 15 on ATMS, and channel 3 on SSU. This is done to prevent the variational 
bias correction scheme—which is formulated to remove systematic discrepancies between 
the observations and the background state irrespective of the source—from making er- 
roneous adjustments to the observations at levels where model biases are known to be 


large. 


e. Bias correction of aircraft temperature observations 


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A bias correction scheme for aircraft temperature observations has been implemented in 
MERRA-2, motivated by the known warm bias of these measurements compared with 
other data sources (Cardinali et al. 2003, Ballish and Kumar 2008; Rienecker et al. 
2011). The scheme uses the mean observed-minus-background departures to estimate 
the bias for temperature reports from individual aircraft, identified by their tail number. 
The bias estimates are updated after each analysis. The scheme is used to correct 
Aircraft Meteorological Data Relay (AMDAR) and Aircraft Communications Addressing 
and Reporting (ACARS) reports only, since other sources of aircraft observations in 
MERRA-2 do not have unique identifiers by which they can be tracked. As of 2015, bias 


corrections for approximately 3700 separate aircraft are tracked in MERRA-2. 


The performance of the scheme is discussed in McCarty et al (2016). As expected, 
the scheme is shown to reduce the bias between the corrected aircraft observations 
and the background forecast, as well as reduce the variance of the corrected background 
departures, allowing more aircraft observations to be used in the analysis. Unfortunately, 
the MERRA-2 background state was found to have a larger than expected positive bias 
in the mid- to upper troposphere, which feeds back to the bias estimates. The result 
is that the bias correction actually increases the aircraft temperatures in some cases, 
and the fit to other unbiased observation types such as radiosondes is degraded. This is 


discussed further in section 3. 


f. Mass conservation and water balance 


Studies have documented the difficulty of maintaining realistic balances between varia- 


tions in total mass and total water content in previous reanalyses (e.g., Trenberth and 


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Smith 2005; Bosilovich et al. 2011; Berrisford et al. 2011). These studies show that 
analysis adjustments to moisture are often large (when, ideally, they should be small), 
highly sensitive to changes in the observing system, and mostly balanced by unphysical 
changes in precipitation. Takacs et al. (2016) argue that, in attempting to analyze the 
total mass of the atmosphere from surface pressure observations, reanalyses may violate 
the simple physical constraint that, to an excellent approximation, the total dry mass of 
the atmosphere is invariant, and so changes in total mass must be essentially equivalent 
to changes in total water mass. At the same time, Berrisford et al. (2011) argue that, 
while the observing system may not provide the data to determine exactly the total mass 
of the atmosphere, the degree to which dry mass is preserved in a reanalysis provides a 


useful diagnostic of reanalysis quality. 


Reconsideration of these issues during the development of MERRA-2 prompted mod- 
ifications to GEOS to conserve atmospheric dry mass and to guarantee that the net 
source of water from precipitation and surface evaporation equals the change in total 
atmospheric water. As described by Takacs et al. (2016), this has been achieved by 


making the following changes to the forecast model and assimilation procedure: 


e Sources and sinks of atmospheric water have been added to the model continuity 


equation so that changes in total mass are driven purely by changes in total water. 


e A constraint that penalizes analysis increments of dry air has been added to the 


GSI. 
e Tendencies in the IAU are rescaled so that the global mean is removed from the 


analysis increment of water. 


The global impact of these modifications is illustrated in Figures 2 and 3, which compare 


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different components of atmospheric mass in MERRA and MERRA-2. Figure 2 shows 
monthly mean anomalies from the mean seasonal cycle for total mass, total water, and 
dry-air mass in the two reanalyses. In MERRA, there is an increase in total water over 
the period, with significant inter-annual variations, but these features do not necessarily 
match the changes in total mass. There also are spurious anomalies in dry-air mass 
throughout, some of which track closely with the changes in total mass. In MERRA-2, 
changes in total mass and total water track each other almost perfectly, by design, and 
the dry-air mass remains a constant whose value must be specified. For the latter, the 
value 983.24 hPa is chosen based on MERRA. This value falls within 0.1% of the values 


derived from other recent reanalyses (Takacs et al. 2016). 


Figure 3 shows monthly mean values of evaporation minus precipitation (£— P, or water 
source term), the vertically integrated analysis increment of water, and the atmospheric 
water storage. Note that the atmospheric water storage has similar magnitude in both 
reanalyses and is dominated by the seasonal cycle. In MERRA, however, the storage 
is determined by a near balance between the large and highly variable contributions 
from the analysis increment on the one hand, and unphysical variations in F — P of 
the opposite sign on the other hand. This includes an abrupt change in the sign of 
these quantities after the introduction of AMSU-A in 1998 (Robertson et al. 2011). In 
MERRA-2, the globally integrated analysis increment is zero, by design, and the water 
storage is determined as in nature by small seasonal differences in F and P. It should be 
noted that removing the global mean analysis increments of total mass and water mass 
does not imply that the analysis increments of water vapor or surface pressure vanish 
locally, as shown in Section 3 of this paper and discussed in further detail by Bosilovich 


et al. (2017). 


g. Observation-corrected precipitation forcing 


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The precipitation generated by the atmospheric model during the IAU segment of the 
assimilation procedure is subject to considerable errors that can propagate into land 
surface hydrological fields and beyond (Reichle et al. 2011). To mitigate these effects 
in MERRA-2, the model-generated precipitation is corrected with observations before 
being used to force the land surface or affect the wet deposition of aerosols over land 
and ocean. Both the model-generated precipitation and the precipitation seen by the 
land surface and the aerosols are available in the MERRA-2 output. MERRA-2 is 
one of several recent applications of GEOS that uses observation-corrected precipitation 
estimates. Others include the GMAO seasonal forecasting system (Ham et al. 2014), the 
MERRA-Land data product (Reichle et al. 2011), and the MERRAero aerosol reanalysis 
(Buchard et al. 2015). Precipitation observations have also been used in reanalyses 
produced by NOAA, including the North American Regional Reanalyis (Mesinger et al. 
2006) and in the Climate Forecast System Reanalysis (CFSR, Saha et al. 2010; Meng 
et al. 2012), although in both cases the approaches differ from that used in MERRA-2. 
Some discussion of the differences between the approaches used in MERRA-2 and CFSR 


can be found in Reichle et al. (2017a). 


The corrected precipitation in MERRA-2 is derived from publicly available, observa- 
tionally based global precipitation products disaggregated from daily or pentad totals 
to hourly accumulations using precipitation estimates from MERRA (Reichle and Liu 
2014; Reichle et al. 2017a). The land surface in MERRA-2 sees a combination of cor- 
rected and model-generated precipitation depending on latitude, with the land surface 
forced primarily by the corrected estimates at low to mid-latitudes, by the MERRA-2 
model-generated precipitation at high latitudes, and by a weighted mixture in between 


to prevent spatial discontinuities in climatological means. This is illustrated in Figure 4, 


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which shows the annual average adjustment made to the model-generated precipitation 
in MERRA-2 for the period 1980-2015 using this technique. The greatest adjustments 
are made in the tropics, where precipitation is greatest and the corrected estimates are 
given most weight, while no adjustments are made poleward of 62.5° in either hemi- 


sphere. 


Based on the evaluation of several metrics, Reichle et al. (2017a) found the observation- 
corrected precipitation to be more realistic overall than that generated by the model 
within the cycling MERRA-2 system, or that of the MERRA and MERRA-Land data 
products. Exceptions include discontinuities in the MERRA-2 corrected precipitation 
that result from errors in the underlying gauge products, for example, in Myanmar and 
South America. Another issue is the high bias in MERRA-2 summer precipitation in the 
high latitudes (where precipitation observations are not used). Moreover, the diurnal 
cycle of the MERRA-2 corrected precipitation has reasonable amplitudes compared to 
independent observations, but the time-of-day of maximum precipitation is inherited 


from MERRA and is unrealistic. 


The improvements in the precipitation forcing are also reflected in the MERRA-2 land 
surface estimates. Reichle et al. (2017b) show that soil moisture, snow, terrestrial 
water storage, and runoff in MERRA-2 agree better with independent observations than 
estimates from MERRA. Draper et al. (2017) further demonstrate that the temporal 
behavior and long term mean values of the land-atmosphere turbulent fluxes in MERRA- 
2 are improved. Moreover, by applying the precipitation corrections within the coupled 
atmosphere-land modeling system, MERRA-2 can provide more self-consistent surface 
meteorological data than were used for MERRA-Land (Reichle et al. 2017a). This self- 


consistency is important for applications such as forcing land-only model simulations. 


15 


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429 


Finally, it should be noted that the atmospheric water and energy prognostic variables 
associated with the creation of precipitation in MERRA-2 are not directly modified by 
the corrected estimates, although they can be indirectly modified through subsequent 


feedback with the land surface. 


h. Sea surface temperature and sea ice concentration 


The boundary conditions for sea surface temperature (SST) and sea ice concentration 
(SIC) in MERRA were based on the 1° weekly (or monthly) product of Reynolds et al. 
(2002). In MERRA-2, SST and SIC boundary conditions are instead based on currently 
available high-resolution (finer than 1°) daily products. However, as there exists no 
continuous source of daily global high-resolution SST and SIC for the entire period 
of MERRA-2—and no source of daily data whatsoever prior to 1982—the following 
products were used in combination (Table 3): monthly 1° data from the Coupled Model 
Intercomparison Project (CMIP) as in Taylor et al. (2000) for the period prior to 
1982; daily 1/4° data from the NOAA Optimum Interpolation Sea Surface Temperature 
(OISST) as in Reynolds et al. (2007) from 1982 thru March 2006; and daily 1/20° 
data from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) as 
in Donlon et al. (2012) from April 2006 onwards. Note that different versions of the 
NOAA OISST product are used prior to and after January 2003, the latter including 
satellite data from both AVHRR and the Advanced Microwave Scanning Radiometer- 
EOS (AMSR-E) on NASA’s Aqua satellite, and the former including satellite data from 
AVHRR only. The processing of these products into a unified gridded set of daily SST 
and SIC boundary conditions for MERRA-2 is described in Bosilovich et al. (2015). 
Care was taken to use both SST and SIC from the same data source to avoid potential 


inconsistencies, especially in marginal ice zones. 


16 


443 


Figure 5 shows 12-month running averaged values of SST between 60°N and 60°S for 
MERRA-2 and several other reanalyses, including MERRA. In all cases, there is a 
positive trend in SST throughout the period. The running means for all the reanalyses 
are within 1 K for the 30 years spanning 1980-2010, and the anomalies (not shown) are 
separated by less than 0.2 K. At the same time, there are clear systematic differences 
between reanalyses, with the MERRA-2 SST’s on the one hand being cooler than those 
used in the other reanalyses shown except CFSR (which used similar input data sets), 
especially before the transition to OSTIA in 2006. The values for JRA-55, on the other 
hand, are on the order of 0.1 K higher than other reanalyses throughout the 35-year 
period. It can also be seen that the MERRA-2 SSTs increase slightly with the change 
in NOAA OISST versions after 2003. The reader is referred to Bosilovich et al. (2015) 
for a more detailed list of known issues with the SST and SIC boundary conditions for 


MERRA-2. 


1. Production 


MERRA-2 was produced in four separate streams, each of which was spun up for a year 
at full resolution beginning on 1 January 1979 (stream 1), 1 January 1991 (stream 2), 
1 January 2000 (stream 3) and 1 January 2010 (stream 4). The land surface restart 
files for each MERRA-2 stream were themselves spun up for at least 20 years using the 
off-line MERRA-2 land model forced by MERRA surface meteorological fields, and with 
the precipitation replaced by the observation-corrected estimates described in section 
2g. The final MERRA-2 product distribution is from stream 1 for 1 January 1980-31 
December 1991, followed by stream 2 for 1 January 1992-31 December 2000, then stream 
3 for 1 January 2001-31 December 2010, and finally stream 4 for 1 January 2011—present. 


With streams 1-3 complete, MERRA-2 production continues as a near-real time climate 


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analysis from stream 4 alone. The decision to begin stream 1 in January 1979 and 
distribute products beginning in January 1980—a year later than the schedule followed 
in MERRA—was based on the fact that the products used to create the observation- 
corrected precipitation estimates for MERRA-2 only start on 1 January 1979, leaving no 
viable way to initialize the land surface properly before this time (which requires several 


months of spin-up, after initialization from climatological conditions). 


The overlap periods between successive streams were examined to determine the ad- 
equacy of the spin-up procedure and to quantify the uncertainty in individual fields. 
Differences between overlapping MERRA-2 streams were found to be minimal for most 
fields after one year, with the exception of certain land surface variables including the 
deep-level soil temperature and land surface soil moisture storage at high latitudes. 
The spin-up of the land surface is addressed separately in Reichle et al. (2017a); sec- 
tion 3d and Figure 13 of that paper discuss specific examples of the aforementioned 
discontinuities across consecutive MERRA-2 streams. Users should be aware of these 


discontinuities when the data are used for specific applications. 


3. Data assimilation diagnostics 


By-products of the data assimilation procedure in the form of differences between fore- 
casts and observations, analysis increments, and estimates of bias can be used effectively 
to monitor the quality of both the input and output of the assimilation. In this section, 
examples of such diagnostics are presented for MERRA-2, focused mainly on feedbacks 
with respect to in-situ conventional observations and on the net correction, or incre- 
ment, brought by the entirety of the assimilated observations. The reader is referred 
to McCarty et al. (2016) for examples of feedbacks related to the treatment of satellite 


radiance observations. 


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a. Background departure statistics 


Differences between the assimilated observations and the background forecast, referred 
to as innovations or background departures, provide important information about the 
quality of the assimilation. In particular, it is important that the assimilation system be 
able to predict high-quality observations, especially for conventional data types which 
provide direct measurements of the analyzed variables. In addition to affecting the anal- 
ysis directly, many conventional data play an important role in anchoring the variational 
bias estimates used in the assimilation of satellite radiances. Generally speaking, smaller 
background departures indicate a higher quality assimilation. The results shown here 
are selected to highlight both strengths and weaknesses of MERRA-2 in this regard. 
As in MERRA, for convenience, gridded versions of the observations and corresponding 


departures used in MERRA-2 will be made available to users. 


Figure 6 shows time series of monthly mean and root mean square (RMS) background 
departure statistics for all assimilated surface pressure observations in MERRA and 
MERRA-2 for both the Northern and Southern Hemisphere. Also shown are the monthly 
mean numbers of surface pressure observations assimilated in each 6-h assimilation cycle 
in MERRA-2. The RMS values decrease with time in both reanalyses, especially in the 
Southern Hemisphere after the early to mid 1990’s when the number of observations 
begins to increase significantly. The RMS values in the Northern Hemisphere (Fig- 
ure 6a) are smaller than in the Southern Hemisphere initially and decrease more slowly 
with time, reflecting the greater number of conventional observations available over land 
throughout the period. This decrease is slightly more pronounced in MERRA-2 after 
the mid 1990’s when the number of surfaces pressure observations from land stations 
increases significantly. In the Southern Hemisphere (Figure 6b), the RMS values are 
larger in MERRA-2 than in MERRA before the mid 1990’s but smaller by the end of 


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the period. The larger values early on are due to the use of larger observation errors 
for surface ship observations (and some other conventional data types) in MERRA-2, 
allowing more “outliers” with larger departure values to pass the quality control pro- 
cedure in the analysis.! The impact diminishes by the mid 1990’s as other observation 
types, including from satellites, become more abundant. There is no similar effect in the 
Northern Hemisphere where surface pressure observations from land stations are domi- 
nant early in the period; the observation errors specified for these data are the same in 
MERRA and MERRA-2. Finally, the jump in RMS values in the Southern Hemisphere 
evident in both reanalyses at the beginning of 1985 coincides with the introduction of 


regularly spaced synthetic surface pressure observations over southern ocean areas. 


The mean background departures for surface pressure in the Northern Hemisphere are 
consistently less biased in MERRA-2 than in MERRA, especially after the mid 1990’s. 
In the Southern Hemisphere, however, the departures for MERRA-2 show a negative 
bias throughout the period; this is discussed further in section 3b. The mean departures 
in MERRA-2 also show a more pronounced annual cycle in this hemisphere. As a 
point of reference, the background departure statistics for other reanalyses including, 
for example, ERA-Interim (Dee at al. 2011) exhibit a clear annual cycle, but with 


somewhat smaller amplitude than in MERRA-2. 


Figure 7 shows global background departure statistics for radiosonde temperatures for 
MERRA and MERRA-2 at selected pressure levels in the troposphere (300 hPa and 
700 hPa) and stratosphere (10 hPa and 50 hPa). Also shown for each level are the 
monthly mean numbers of radiosonde temperature observations assimilated in each 6-h 
assimilation cycle in MERRA-2. In the troposphere (Figures 7c and d), the performance 
of MERRA-2 is degraded compared to that of MERRA, especially at 300 hPa. The 


'The observation errors for conventional data types have been adjusted since MERRA-2. 


20 


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550 


RMS values for MERRA-2 decrease with time but remain 10-20% larger than those for 
MERRA during much of the period. Again, this is due at least partially to the use of 
larger observation errors for radiosonde temperatures and other conventional data types 
in MERRA-2. Noticeable improvements occur first in the mid 1990’s when satellite 
observations become more abundant, and again in 2006 when the number of GPSRO 


observations increases significantly. 


The mean departure values at 300 hPa for both MERRA and MERRA-2 exhibit a clear 
negative bias. The bias is generally larger in MERRA-2, reaching a maximum amplitude 
of greater than 0.5 K during the early 2000’s. This is due to a warm model bias in the 
upper troposphere which worsened during the course of development between MERRA 
and MERRA-2 (see also Figure 10). However, aspects of the assimilation process may 
exacerbate the problem. It can be seen for example that the bias in the background 
departures at 300 hPa increases noticeably after the mid 1990’s, especially in MERRA-2, 
when the numbers of both aircraft temperature observations and satellite radiances begin 
to increase significantly (Figure 1). The design of the bias correction procedures for both 
observation types is such that they result in an adjustment of the observations regardless 
of the source of the bias. In the presence of a strong model bias this can reinforce the 
actual observational bias and cause the assimilation system to drift further toward the 
model state, as noted in the case of the aircraft bias corrections described in section 2e. 
A similar, though less direct, effect may occur through the observational bias corrections 
used to assimilate satellite radiances, although other aspects of the variational scheme 
used to adjust these data act to reduce this risk (Dee and Uppala 2009). At 700 hPa, the 
mean departures for both reanalyses are generally more comparable and considerably 


less biased. 


In the stratosphere (Figures 7a and b), there are fewer significant differences between 


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the results for MERRA and MERRA-2 although the departures at 10 hPa for MERRA-2 
show a larger negative bias of —0.2 K to —0.3 K prior to the early 2000’s. After 2002, 
when assimilation of AIRS radiances begins, the biases at 10 hPa in both reanalyses 
exhibit an upward trend and eventually become positive, first in MERRA around 2003 
and then in MERRA-2 in 2005. There is a discernible jump in the mean departures 
at this level for MERRA-2 in 2005. This is around the time when assimilation of both 
MLS temperature retrievals (above 5 hPa) and GPSRO bending angle observations (up 
to approximately 10 hPa) begins in MERRA-2, but this does not appear to improve 
the fit to radiosondes at 10 hPa compared with MERRA. After 2006, the biases in both 
reanalyses have average values of 0.2 K to 0.3 K. Finally, at 50 hPa, the departure values 


for both reanalyses are very similar and exhibit only a small positive bias throughout. 


Figure 8 shows statistics for radiosonde specific humidity background departures at 500 
and 850 hPa in the tropics. The performance of MERRA-2 is slightly worse than that 
of MERRA in the middle troposphere in terms of both RMS and bias, but similar or 
slightly better in the lower troposphere. Again, the mean departure values are consistent 


with known biases in the GEOS model. 


b. Analysis increments 


The analysis increments represent the net adjustment to the background state by the 
assimilation scheme in response to all the observations. As this adjustment depends in 
a complex way on assumed or crudely estimated errors in the observations and back- 
ground state, and on the forward operator that transforms the model variables to obser- 
vation space, the increments do not necessarily represent errors in the background state. 
Nonetheless, their spatial and temporal variations provide an important diagnostic of 


system performance, including how changes in the observing system may affect the con- 


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sistency of the analysis. Systematic increments often indicate the presence of biases in 
the model or observations which may complicate the use of reanalyses for estimating 


budgets and identifying trends (Dee et al. 2011). 


As described in section 2, the GEOS assimilation system uses an IAU procedure which, 
instead of correcting the initial condition, applies the analysis increment to the model 
as a constant tendency term during the 6-h assimilation window. It is this contribution 
to the time tendency from the analysis that is provided as a standard output quantity 
in MERRA-2, examples of which are presented here. For convenience, these are referred 


to as simply the analysis increments in the discussion that follows. 


Figure 9 shows the mean and standard deviation in time of the monthly mean analysis 
increment of surface pressure in MERRA-2 for the period January 1980 through De- 
cember 2015. The monthly means themselves have been computed from sub-daily data, 
eight times per day. The pattern of the mean increments indicates that the analysis 
tends to move mass from the oceans to the continents, as noted also by Takacs et al. 
(2016), although this pattern is arguably most robust in the Southern Hemisphere. (The 
mostly negative surface pressure increments over Canada provide an obvious counter ex- 
ample.) These results are consistent with those in Figure 6 showing a negative bias in 
the Southern Hemisphere background departures in MERRA-2. The standard devia- 
tion of the increments shows that the largest variations in surface pressure occur in the 
middle and high latitudes, and especially over coastal Antarctica and the mountainous 


regions of southern and eastern Asia, as well as southern Alaska. 


Time series of the global monthly mean and standard deviation of the analysis increments 
of temperature from the surface to 70 hPa in MERRA-2 are shown in Figure 10. The 


most striking feature in the mean increments is the persistent cooling by the analysis 


23 


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606 


618 


625 


in the layer between 250 and 400 hPa. This is consistent with the negative bias in the 
background departures at 300 hPa shown in Figure 7 and provides further evidence of 
the warm model bias at these levels. Except for seasonal variations, the magnitude 
of the cooling remains relatively constant throughout much of the period, although 
noticeable changes occur, for example, beginning in the mid to late 1990’s as the number 
of aircraft and satellite observations increase, and again in 2006, possibly in response 
to the introduction of data from IASI and GPSRO. Warming by the analysis is evident 
above 200 hPa and below 700 hPa. In this global view, the mean increments close to the 
surface exhibit a negative trend with strong warming before the early 1990’s turning to 
slight cooling after 2010, but this is in fact the net effect of distinct regional differences 
in the increments (not shown). In particular, near-surface warming by the analysis in 
response to a cold model bias over northern midlatitude land masses is offset by cooling 
over southern oceans that generally increases with time beginning with the assimilation 
of data from the first microwave humidity sensors in the late 1980’s. These differences 
also contribute to the large variability of the increments below 700 hPa (Figure 10b). 
The variability in the mid troposphere is noticeable but small compared with that at 
low levels, again highlighting the consistency of the cooling by the observations between 


250 and 400 hPa. 


The increments of specific humidity in the tropics are shown in Figure 11 for levels 
between the surface and 250 hPa (the values become exceedingly small above this level). 
The mean increments indicate distinct biases in the middle and lower troposphere, with 
systematic drying between 600 and 300 hPa, and mostly moistening below 700 hPa. The 
corrections are generally larger during the second half of the period and especially after 
the late 1990’s as more satellite observations of humidity become available. There is an 
abrupt increase in the variability of the increments corresponding to the introduction of 


the first SSM/I instrument in mid 1987, with additional increases corresponding to the 


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639 


640 


641 


649 


use of a second and third SSM/T instrument in late 1990 and mid-1995, respectively. The 
use of multiple SSM/TI instruments from the early 1990’s to late 2000’s also corresponds 
to a strong drying and a marked increase in variability at levels very close to the surface. 
The introduction of AMSU-B data in 1998 corresponds to marked increases in the mean 
and variability of the increments, the latter being most pronounced in the layer between 
800 and 900 hPa. The sensitivity of the precipitation to these observing system changes 


is discussed in section 5. 


4. Aerosol data assimilation 


In addition to a standard meteorological analysis, MERRA-2 includes an aerosol analysis 
as described in Randles et al. (2016, 2017) and Buchard et al. (2017). The multi-decadal 
coverage and the coupling between aerosols and the circulation is a step forward com- 
pared to previous EOS-era reanalyses such as MERRAero, the Navy Aerosol Analysis 
and Prediction System (NAAPS) reanalysis (Lynch et al. 2016), the Monitoring At- 
mospheric Composition and Climate (MACC) reanalysis (Inness et al. 2013), and the 
more recent Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (Flemming 
et al. 2017). The MERRA-2 system produces 3-hourly analyses and gridded output of 
both observable parameters and aerosol diagnostics not easily observed, especially on a 
global scale, with potential applications ranging from air quality forecasting to studies 


of aerosol-climate and aerosol-weather interactions (e.g., Bocquet et al. 2015). 


An analysis splitting technique (Randles et al. 2017) is used to assimilate aerosol optical 
depth (AOD) at 550 nm, in which a two-dimensional analysis is performed first using 
error covariances derived from innovation data and then the horizontal increments are 
projected vertically and across species using an ensemble method. AOD observations 


are derived from several sources, including 


25 


650 e Reflectances from AVHRR (1979-2002, ocean-only, Heidinger et al. 2002); 


651 e Reflectances from the Moderate Resolution Imaging Spectroradiometer (MODIS) 
652 on Terra (2000—present) and Aqua (2002—present) (Remer et al. 2005; Levy et. al. 
653 2007); 


654 e AOD retrievals from the Multi-angle SpectroRadiometer (MISR) (2000-2014, bright, 


655 desert regions only, Kahn et al. 2005); 


656 e Direct AOD measurements from the ground-based Aerosol Robotics Network (AERONET) 
657 (1999-2014, Holben et. al. 1998). 


6s MODIS provides the vast majority of AOD observations assimilated in MERRA-2, es- 
69 pecially after 2002 when data from both the Terra and Aqua satellites become available. 
«o Prior to 2000, only AVHRR reflectances over ocean are used in MERRA-2. AOD for 
61 both MODIS and AVHRR are derived from cloud-cleared reflectances using a neural net 
62 procedure trained on AERONET measurements (Randles et al. 2017). By construction, 
«3 these AOD retrievals are unbiased with respect to AERONET observations. AOD from 
6a MISR and AERONET observations are used without bias correction. Additional details 
6s about the aerosol observing system in MERRA-2 can be found in Randles et al. (2016, 
6s 2017). 


67 ‘The Goddard Chemistry, Aerosol, Radiation and Transport model (GOCART; Chin et 
cs al. 2002; Colarco et al. 2010) is coupled with the GEOS atmospheric model to sim- 
«9 Ulate the life cycles of five externally-mixed aerosol species, including dust, sea salt, 
67 black carbon, organic carbon, and sulfate. The model carries three-dimensional mass 
67 mixing ratios of these five aerosol species as prognostic aerosol tracers. The AOD at 
672 900 nm is a column- and species-integrated optical quantity, which is calculated as the 


673 Summed product of each species mass and its extinction coefficient based on aerosol 


26 


680 


optical properties derived largely from the Optical Properties of Aerosols and Clouds 
(OPAC) dataset (see Randles et al. 2017 and references within.) Emissions of both dust 
and sea salt are wind-driven for each of five size bins, parameterized following Martio- 
corena and Bergametti (1995) and Gong (2003), respectively. Sulfate and carbonaceous 
aerosol emissions derive from both natural and anthropogenic sources as described in 
Randles et al. (2017). In particular, MERRA-2 includes volcanic sources (Diehl et al., 
2012) and biomass burning emissions that utilize satellite observations, and are based on 
the Reanalysis of the Tropospheric chemical composition, version 2 (RETRO-2, Schultz 
et al. 2008), the Global Fire Emissions Database, version 3.1 (GFED-3.1, van der Werf et 
al. 2006), and the Quick Fire Emission Dataset, version 2.416 (QFED-2.4.r6, Darmenov 
and da Silva, 2015). 


It should be noted that AOD observations can only directly constrain the total, species- 
integrated and vertically-integrated aerosol extinction — a quantity that can be related 
to column aerosol mass by assuming a set of optical properties. Non-analyzed aerosol 
properties such as the vertical distribution, aerosol speciation, and absorption are not 
fully constrained by the observations and are chiefly determined by the underlying model 
physics and error covariance assumptions. Despite this fact, Buchard et al. (2017) 
show that the MERRA-2 aerosol reanalysis has considerable skill in simulating numer- 
ous observable aerosol properties. Randles et al. (2017) show that the AOD fields in 
MERRA-2 generally have both high correlation and low bias relative to independent 


(non-assimilated) sun-photometer and aircraft observations. 


As in the case of the meteorological analysis discussed in section 3, statistics of back- 
ground and analysis departures provide a basic metric of the quality of the aerosol as- 
similation. Figure 12 shows probability distribution functions of collocated observation- 


minus-forecast and observation-minus-analysis departures from MERRA-2 for each sen- 


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712 


713 


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723 


sor in the aerosol observing system. Statistics are shown in terms of the log-transform 
AOD analysis variable (i.e., In[AOD + 0.01]) which is approximately normally dis- 
tributed (Randles et al., 2017). Note that AOD is a dimensionless quantity and log- 
transformed AOD is typically in the range (—4, 2). As expected, compared to the forecast 
departures, the analysis departures show reduced bias with respect to the observations. 
Note also that the innovation variances are much larger over land than ocean, a direct 


consequence of the signal-to-noise limitation of aerosol retrievals over land. 


Regional aspects of the global distribution of aerosols are illustrated in Figure 13, which 
shows time series of analyzed AOD from MERRA-2 area-averaged over several major 
aerosol source regions. The contribution of each aerosol species to the total AOD is 
indicated by the colored shading. The seasonal cycles of dust and biomass burning 
(carbonaceous) AOD are apparent in all regions. Large increases in sulfate aerosol occur 
in all regions after the El Chichon (1982) and Pinatubo (1991) volcanic eruptions. Over 
the Asian region (Figure 13a), the analysis captures high carbonaceous aerosol associated 
with the 2003 Siberian fires and the increasing trend in AOD between the late 1990s 
and present (commensurate with increasing anthropogenic aerosol emissions reported by 
Diehl et al. 2012). The AOD over northern Africa (Figure 13b) is dominated by dust, 
and major dust transport events such as in 2010 are captured (see Buchard et al. 2017 
for details). Carbonaceous aerosol from biomass burning in major source regions such 
as the Amazon Basin are also well captured (Figure 13c), especially after 2000 when 


emissions inventories derive from MODIS observations (Darmenov and da Silva, 2015). 


Figure 14 compares values of AOD from several recent aerosol reanalyses for the pe- 
riod 2003-2010. Where such information is available, the results are partitioned by 
species and identified as either fine or coarse mode (see caption for details). Also shown 


are multi-model average results from Phase I of the Aerosol Comparison (AeroCom) 


28 


730 


736 


742 


743 


748 


inter-comparison project (Kinne et al. 2006), as well as both model and observational 
estimates from Yu et al. (2006). The latter study includes an attempt to account for 
satellite clear-sky biases by combining MODIS and MISR observations with the GO- 
CART model. Compared to MERRAero, for example, MERRA-2 has slightly higher 
global average AOD due to increased contributions from dust (related to the assimi- 
lation of MISR AOD over bright surfaces) and sea salt (related to changes in model 
physics). MERRA-2 and NAAPS show similar global average AOD, both for fine and 
coarse mode aerosol. Models without assimilation (AeroCom and Yu_Model) underesti- 
mate global average AOD compared to both observational estimates (Yu_Obs) and the 
aerosol reanalyses. The MACC aerosol reanalysis has the highest global mean AOD 
(Bellouin et al. 2013), which is close to the MODIS-only value of 0.188 for the period 
2003-2010 (Yu et al. 2006). MACC also has more dust and sea salt aerosol compared 


to the other reanalyses, particularly over the ocean (not shown). 


The direct aerosol impact on the radiative energy balance of Earth is dependent on the 
vertical distribution of aerosol scattering and absorption, which is not fully constrained 
by the vertically integrated AOD measurements that MERRA-2 assimilates. An as- 
sessment of the aerosol vertical structure and absorption is presented in a companion 
paper (Buchard et al. 2017). Long-term aerosol reanalyses can potentially reduce un- 
certainty in how aerosol direct effects have changed over time, particularly once better 
observational constraints on aerosol absorption become available. The direct radiative 
effect (DRE) of all aerosols is defined as the flux difference in W m~? between clear- 
sky and clear clean-sky conditions (no aerosols or clouds). In the absence of clouds, 
this quantity is less sensitive to the vertical distribution of aerosol absorption, although 
it remains sensitive to absorbing aerosols over surfaces with high albedo (Chylek and 


Coakley, 1974). 


29 


749 


754 


755 


756 


765 


772 


Table 4 compares the DRE from MERRA-2, MERRAero, MACC, model inter-comparisons, 
and the observationally constrained estimate of Yu et al. (2006). Listed are the top-of- 
the atmosphere (TOA), surface (SFC), and atmospheric (ATM) estimates of DRE for the 
period 2003-2010, averaged over land and ocean separately. Note that the atmospheric 
contribution to the DRE is defined as the difference between top-of-the-atmosphere 
and surface values, ATM = TOA — SFC. Over land, the DRE estimate from MACC 
best agrees with the observationally-constrained estimate. TOA and SFC forcing in 
MERRA-2 and MERRAero are lower than in MACC due to their lower AOD, although 
the atmospheric forcing is similar. Over ocean, the DRE estimates from MERRA-2 and 
MACC are lower and higher, respectively, than the observational estimate, and both re- 
analyses have lower estimates of atmospheric absorption. Much of the uncertainty in the 
DRE reported by the Intergovernmental Panel on Climate Change (IPCC) arises from 
differences between estimates from global models and satellite-based estimates (Myhre 
2009). However, as aerosol reanalyses such as MERRA-2 continue to mature and incor- 
porate additional observations (e.g., from lidars and multi-spectral sensors), we expect 


a narrowing of the gap between simulated and satellite-based estimates of the DRE. 


5. Precipitation 


The representation of precipitation in a reanalysis is key to applications in weather 
and climate as it ties together aspects of both the water and energy cycles. It also 
presents a significant challenge, however, as estimates of precipitation are only indi- 
rectly constrained by observations and are strongly dependent on model physics whose 
parameterizations have known errors and can be highly sensitive to even small changes 
in large-scale temperature and humidity fields. The observations themselves can some- 


times introduce additional uncertainty in these estimates as a result of heterogeneous 


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778 


779 


782 


783 


785 


786 


795 


796 


sampling, changes in instrumentation, and time-varying calibration (Bosilovich et al. 


2017). 


While improved representation of the hydrological cycle was a primary focus of MERRA, 
the character of its global precipitation in particular was found to be highly sensitive to 
the assimilated observations and thus to changes in the observing system (e.g., Robert- 
son et al. 2011). Among the development aspects of MERRA-2 intended to address 
this issue are modifications to GEOS to conserve atmospheric dry mass and ensure that 
changes in global atmospheric total mass are equivalent to changes in total water (sec- 
tion 2f), exclusion from the analysis of microwave temperature sounding channels with 
strong surface sensitivity (section 2d) and, less directly, forcing of the land surface by 


observation-corrected precipitation estimates (section 2g). 


a. Global aspects 


Bosilovich et al. (2015, 2017) have investigated the global water cycle variability in 
MERRA-2 using comparisons with observational data sets and other recent reanalyses. 
Those studies present a broad range of metrics on this topic, a small subset of which 
are summarized here. Figure 15 shows time series of global mean precipitation for sev- 
eral recent reanalyses and the observation-based estimates from the Global Precipitation 
Climatology Project (GPCP, Adler et al. 2003). MERRA-2 exhibits larger temporal 
variability than GPCP but similar temporal variability as other recent reanalyses, and 
noticeably less spurious temporal variability than MERRA. The largest improvements 
compared with MERRA in this regard relate to the decreased sensitivity of MERRA-2 
to the introduction of AMSU-A radiances on NOAA-15 and -16 in the late 1990s, and 
to the loss of SSM/I radiances in the late 2000s. There is still an obvious sensitivity 


in MERRA-2 to the introduction of SSM/I in 1987, but the response to these data 


dl 


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is comparable in magnitude to those of the other reanalyses shown. The response in 
MERRA-2 appears accentuated due to the decrease and subsequent recovery of precip- 
itation through the mid 1980s. This behavior is not reflected in the GPCP time series, 
but is evident to lesser degrees in CFSR and ERA-Interim, especially after 1983. For 
MERRA-2 and CFSR, this may be related to the fact that the SST boundary condi- 
tions used in these reanalyses reach their global minimum value for the entire reanalysis 
period after 1985 (Figure 5), but further investigation is required to confirm this. The 
increasing trend in global precipitation in MERRA-2 from approximately 2.9 mm day~' 
in 1988 to approximately 3.0 mm day~! in 1998 is likely due to increasing evaporation 
over oceans driven by the assimilation of additional SSM/I wind speed observations and 
the tight coupling of evaporation and precipitation in MERRA-2 through the global 
mass constraint (Bosilovich et al. 2017). Overall, the global mean precipitation values 


are higher than those of GPCP but well within the envelope of other recent reanalyses. 


Spatial comparisons provide additional insight into the strengths and weaknesses of the 
representation of precipitation globally in MERRA-2. Figure 16 shows maps of time- 
averaged differences in precipitation during boreal summer for MERRA and MERRA-2 
compared with GPCP. MERRA-2 shows general improvement compared to MERRA 
over oceanic regions in both the tropics and extratropics, but an increase in positive 
bias over northern high latitudes. A notable deficiency in MERRA-2 is the excessive 
precipitation in the vicinity of high topography in the tropics, especially along the Andes 
and over the maritime continent. This is related to the partitioning between resolved 
(large scale) and parameterized (convective) precipitation in the MERRA-2 model which, 
being more heavily skewed toward the former, results in large-scale precipitation over 
high topography that is difficult to control. In comparing these features with available 
gauge data, Bosilovich et al. (2015) point out that the maximum precipitation values 


in MERRA-2 do not always coincide with the maximum terrain height, so that other 


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effects also may play a role locally. Despite this deficiency over tropical land areas, the 
positive bias over the warm pool present in MERRA is slightly improved in MERRA-2. 
Additionally, the high precipitation bias over the Central America Sea in MERRA has 
been reduced significantly in MERRA-2 and precipitation over the Bay of Bengal and 
Arabian Sea is slightly improved. Results for other seasons (not shown) are qualitatively 


similar to those in Figure 16. 


b. US summertime precipitation variability 


Deficiencies in the ability of MERRA to reproduce certain aspects of the summer- 
time seasonal precipitation over the United States (US) have been well documented 
(Bosilovich 2013). In particular, MERRA was unable to produce seasonal highs and 
lows in regional precipitation that were similar to observations. For example, droughts 


and floods were only weakly reproduced. 


Figure 17 shows the time series of summertime seasonal precipitation anomalies over 
the midwestern US as derived from the NOAA Climate Prediction Center (CPC) gauge 
observations and from MERRA and MERRA-2 model-generated precipitation. (The 
correlation values between various reanalyses and the gauge data for this and other 
regions of the US are shown in Figure 18.) The limitations of MERRA are apparent, 
especially when comparing values for 1988 (regional drought) and 1993 (large-scale flood- 
ing) with the observed values. In contrast, MERRA-2 is able to reproduce the 1988 and 
1993 anomalies and is generally much better at tracking the overall variability of the 
observed anomalies. The poor performance of MERRA-2 in 1980 is a notable exception. 
A significant drought occurred in the southern Great Plains that year, but its location 


in MERRA-2 extended too far northeastward into the midwestern US. 


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Figure 18 presents regional summary statistics for US summer seasonal precipitation 
anomalies for selected reanalyses. The regions are defined as in Bosilovich (2013). For 
each region, the temporal mean, standard deviation, and anomaly correlation with re- 
spect to the CPC data are derived from time series like those shown in Figure 17. In 
general, precipitation mean values across the US are improved in MERRA-2 compared 
with MERRA (Figure 18a), and in many regions the values for MERRA-2 improve over 
those of other reanalyses as well. There is also a marked increase in the standard de- 
viation of the MERRA-2 time series relative to MERRA (Figure 18b). As discussed 
above, for example, MERRA-2 more realistically reproduces the seasonal extremes in 
midwestern US precipitation. Note, however, that MERRA-2 overestimates the stan- 
dard deviation with respect to the CPC estimates in some regions. Ancillary results 
indicate that this is due to an excess in the number of days with rain in MERRA-2. 
Improvements in MERRA-2 are most evident in the anomaly correlation of the seasonal 
time series (Figure 18c). In this measure, the two most recent reanalyses, JRA-55 and 
MERRA-2, generally outperform the others. MERRA-2 produces the highest values of 
the reanalyses shown in most regions, with substantially higher values in a few of these 


regions. 


The detection and analysis of extreme weather, including extreme precipitation events, 
is a topic of societal interest and another potential application of reanalyses. At least 
some of this interest is related to assessing changes in the risk of such events in the con- 
text of climate change. For example, observation-based studies cite strong evidence of 
an upward trend in the frequency and intensity of extreme precipitation events averaged 
over the US during the last 50 years (Kunkel et al. 2013), although the causes of the 
observed trends are less certain. Figure 19 shows the accumulated precipitation amounts 
for the largest precipitation events (at the 99th percentile) as derived from gauge ob- 


servations, MERRA, and MERRA-2. Compared with the observations, MERRA shows 


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very low values, and very little structure across the continental US. MERRA-2, on the 
other hand, exhibits a spatial pattern more similar to the observations, and the magni- 
tude of the extreme rainfall is also more similar to the observations. MERRA-2 does, 
however, overestimate the precipitation values over the Midwestern US. While the re- 
sults in Figure 19 provide an indicator of how the representation of extreme events has 
improved in MERRA-2 compared with MERRA, the relatively coarse resolution of both 
reanalyses limits their utility for studying such events in detail. Presumably, the trend 
toward increasing resolution, among other improvements, will reduce these limitations 


in future global reanalyses. 


6. The stratosphere 


In MERRA-2 the stratospheric meteorology and ozone have benefited from improve- 
ments to the GEOS atmospheric model and GSI analysis scheme, as well as from the 
addition of observations that were not incorporated into MERRA. The model changes 
most relevant to the stratosphere are the use of the cubed sphere grid and the re-tuning 
of the gravity wave drag (GWD) parameterization. The amplitude of the non-orographic 
GWD was increased in the tropics, enabling a model-generated Quasi-Biennial Oscilla- 
tion (QBO) that was not found in the model version used for MERRA (Molod et al. 
2015). Having a model-generated QBO, in turn, results in smaller lower-stratospheric 
analysis wind increments in MERRA-2 than in MERRA (Coy et al. 2016). The strength 
of the orographic GWD was also increased in the Southern Hemisphere to better model 


the strong, late-winter westerlies found there (Molod et al. 2015). 


The main GSI change relevant to the stratosphere is the use in MERRA-2 of the CRTM 
for the assimilation of SSU radiances while in MERRA the SSU assimilation was based 


on GLATOVS (section 2d). These SSU radiance channels are a major source of strato- 


35 


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spheric information during the 1980’s and 1990’s, although the SSU instruments during 
these decades span several satellite platforms, each with different bias characteristics 
(Kobayashi et al. 2009). The CRTM has been enhanced for SSU data assimilation since 


MERRA and now accounts for these biasing factors. 


The main additional observations relevant to the stratosphere for MERRA-2 are GPSRO 
bending angle observations from the suite of platforms beginning in July 2004, and 
temperature and ozone measurements of the middle atmosphere from MLS and OMI on 
the EOS Aura satellite beginning later the same year (Froidevaux et al. 2006; Schwartz 
et al. 2008; McPeters et al. 2008). MERRA-2 assimilates GPSRO bending angle 
observations up to 30 km. Details of the GPSRO platforms assimilated by MERRA-2 
can be found in McCarty et al. (2016). The GPSRO observations aid lower stratospheric 
bias correction by providing a stable source of temperature and moisture measurements. 
The MLS-retrieved temperature profiles are assimilated in MERRA-2 at altitudes above 
5 hPa, providing a strong constraint on the dynamics of the stratopause and lower 
mesosphere. As shown below in section 6a, this improves the quality of the synoptic 
meteorological fields at these altitudes but may complicate the study of trends. The 


MLS and OMI contributions to ozone assimilation are discussed in section 6b. 


a. Meteorology 


The cubed sphere discretization of the MERRA-2 model eliminates computational insta- 
bilities near the poles, a characteristic of latitude-longitude grids. This is especially im- 
portant for stratospheric analysis where strong cross-polar flow events occur frequently, 
especially during major sudden warming events, as planetary-scale Rossby waves disturb 
the polar vortex. Ertel’s Potential Vorticity (EPV), a scalar based on the horizontal vor- 


ticity, is often used to characterize the stratospheric circulation (Andrews et al. 1987), 


36 


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where stronger EPV gradients imply stronger flow. Figure 20 illustrates a case where 
the analyzed wind speeds in MERRA-2 reached nearly 170 m s~' close to the polar 
stratopause on 2 January 1995 at 12 UTC. On a global scale (Figure 20a and b), the 
MERRA and MERRA-2 EPV fields appear similar, with the polar vortex (indicated by 
green and orange colors) displaced well off the pole. In both cases, strong winds cross 
the North Pole as they circle around the region of high EPV. However, a closer look 
reveals that the EPV in MERRA (Figure 20c) has anomalous radial perturbations near 
the pole, while the EPV in MERRA-2 (Figure 20d) shows a smooth and strong EPV 
gradient in this region. Note also that while the largest discontinuities in the MERRA 


EPV field occur close to the pole itself, their effects can extend well beyond this location. 


Figure 21 provides an example of how the assimilation of MLS temperature measure- 
ments in MERRA-2 improves the representation of the dynamics near the stratopause. 
The figure shows the time-height evolution of polar temperatures during the 2005-2006 
Northern Hemisphere winter in which a major stratospheric sudden warming occurred. 
In a comprehensive study of this winter based on MLS observations, Manney et al. 
(2008) documented the disappearance of the warm polar stratopause during the warm- 
ing and its later high-altitude reformation and subsequent descent. This breakdown 
and high-altitude reformation in early February 2006 is now well captured in MERRA-2 
(Figure 21b), in contrast to MERRA (Figure 21a). 


The characteristics of the assimilation on longer time scales is illustrated in Figure 22, 
which shows the time-height evolution of global monthly averaged temperature anomalies 
in MERRA-2. The 35-year mean and annual cycle for the period 1980-2015 have been 
subtracted from each pressure level. The global temperatures in the lower stratosphere 
(100-10 hPa) show no obvious discontinuities as different instruments become available. 


There is a slight cooling with time over the 35 years, which is generally consistent with 


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957 


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recent analyses of the satellite-based stratospheric climate data record (see Seidel et al., 
2016 and references therein). There are also episodic temperature increases associated 
with the two large volcanic eruptions, El Chichon in 1982 and Pinatubo in 1991. In 
the upper stratosphere, several discontinuities can be seen. There is a marked decrease 
in temperature near 1 hPa in 1995 when the transition from assimilating NOAA-11 
to NOAA-14 SSU channel 3 radiances occurs. The latter are demonstrably cooler (see 
Figure 16 of McCarty et al. 2016) and are assimilated without bias correction because of 
the relatively large model errors at this level. There is an overall increase in temperature 
when AMSU-A data are first assimilated in 1998, which was not as apparent in MERRA 
(Rienecker et al. 2011) due to the overlapping use of SSU channel 3 and AMSU-A 
channel 14 radiances in that reanalysis. The overall effect of assimilating the MLS 
temperature profiles beginning in 2004 is to sharpen the stratopause with warming at 


approximately 1 hPa and cooling above and below this level. 


b. Ozone 


The most notable aspects of the MERRA-2 ozone analysis, and those that constitute 
the main differences with MERRA, are the use of the improved version of SBUV data 
prior to October 2004 and subsequent assimilation of OMI and MLS observations. The 
latter provides high vertical resolution (~2.5 km) measurements of stratospheric ozone 
profiles during both night and day. The specification of background errors for ozone has 
also been upgraded to account for flow dependent error standard deviations as described 


in Wargan et al. (2015). 


Many ozone data sets exist for various periods between 1980 and present. The decision 
to use only SBUV, MLS and OMI observations in MERRA-2 was motivated by the 


desire to avoid introducing multiple discontinuities into the ozone observing system 


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while taking advantage of high-quality data offered by SBUV and EOS Aura retrievals. 
This approach leads to a relatively homogeneous MERRA-2 ozone record with only one 
major discontinuity in 2004 when MLS and OMI data replace SBUV observations. The 
price is a degraded quality of the analyzed ozone during the short periods when the 
selected data are not available, most notably in the Southern Hemisphere in late 1994, 


as discussed below. 


An initial evaluation of the representation of ozone in MERRA-2 was presented in 
Bosilovich et al (2015). A more comprehensive validation against independent satel- 
lite and ozonesonde data, including evaluation of the vertical structure and variability, 
is given in Wargan et al. (2017). In particular, it is shown there that the assimilation of 
MLS observations in MERRA-2 leads to significant improvements in the representation 
of lower stratospheric ozone when compared with MERRA or compared with the period 
of SBUV assimilation in MERRA-2. The QBO signal in ozone is discussed in Coy et al. 
(2016), who demonstrate an improvement in the vertical structure of the ozone QBO 
signature from 2004 onward, when MLS data are assimilated in MERRA-2. The focus 
here is on the Antarctic total column ozone in order to illustrate that MERRA-2 has 
realistic climatic ozone in a poorly observed region, while also highlighting some of its 
uncertainties. Two examples are presented: a comparative evaluation of the South Pole 
ozone in MERRA and MERRA-2 and the representation of Antarctic ozone holes in the 


present reanalysis. The former follows Wargan et al. (2017). 


Figure 23a shows the time series of total ozone derived from ozonesonde measurements at 
the South Pole, along with MERRA and MERRA-2 output sampled at the ozonesonde 
times and location between 1986 and 2015. The ozonesonde data, including the inte- 
grated column values were obtained from the Earth System Research Laboratory website 


(http: //www.esrl.noaa.gov/gmd/ozwv/ozsondes/spo.html). Note that the vertical range 


39 


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of balloon-borne measurements typically does not extend to pressure levels above 10 hPa 
and so the upper-stratospheric portion of the column is obtained by extrapolating the 
mixing ratios from 7 hPa or from the highest observed altitude, whichever is lower. For 
completeness, Figure 23a also shows the reanalysis data between 1980 and 1985. In 
the absence of ozonesondes, the reanalyses are sampled four times monthly in one-week 
intervals for that period. The differences between each reanalysis and the ozonesonde 
values are plotted in Figure 23b. Overall, both reanalyses capture the annual cycle and 
much of the interannual variability observed in the ozonesonde data, although there 
are large discrepancies (greater than 50%) during austral summer months in MERRA-2 
prior to 2005 and in MERRA throughout the period of comparison. This is consis- 
tent with the fact that the reanalyses are not constrained by SBUV data during polar 
night. In addition, in late 1994, the SBUV coverage was limited to latitudes north of 
approximately 30°S owing to an orbital drift of the NOAA-11 satellite, which left the 
middle and high southern latitudes unobserved in both reanalyses. Nonetheless, these 
differences are reduced in MERRA-2 compared to MERRA. MERRA-2 performs sig- 
nificantly better than MERRA relative to the South Pole ozonesondes from October 
2004 onward, when EOS Aura ozone data are assimilated. In particular, the standard 
deviation of the differences between MERRA-2 and the ozonesonde values drops from 
12.5% between 1991 and 2004 to 5% between 2005 and 2014. At the same time, the 
correlation between MERRA-2 and the ozonesonde measurements increases from 0.88 
to 0.98. The large excursions seen in Figure 23b in MERRA between 2008 and 2012 are 
due to degraded coverage of the NOAA-17 SBUV instrument. In contrast, the behavior 
of the MERRA-2 South Pole ozone is remarkably steady relative to the ozonesondes in 
the period when MLS and OMI data are assimilated. Only small seasonal variations 
are seen during that period. The MERRA-2 South Pole total ozone exhibits a small 


negative bias of approximately 6.7 Dobson units (DU), or roughly 2%, throughout the 


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period of comparison. This bias does not vary significantly between the periods when 


either SBUV or EOS Aura ozone data are assimilated. 


As discovered by Molina and Rowland (1974), anthropogenic emissions of chlorofluo- 
rocarbons provide the main contribution to the chlorine loading in the stratosphere, 
leading to destruction of the ozone layer. One prominent feature of the ozone loss in 
recent decades is the occurrence of springtime ozone holes over Antarctica since the 
early 1980’s (Farman et al. 1985). Ozone holes are regions of extremely low values of 
total ozone forming inside the polar vortex due to a series of chlorine-catalyzed reactions 
(WMO 2014). The climatological importance of this phenomenon warrants its accurate 
representation in long-term reanalyses. The discussion here focuses on only one simple 
diagnostic, the ozone hole area, defined as the region with total ozone values less than 


22) lst 


Figure 24 shows the time series of the ozone hole area calculated from the MERRA-2 
total ozone averaged between 20 September and 10 October in each year between 1980 
and 2015. Also plotted in Figure 24 are the ozone hole area values derived from the Total 
Ozone Mapping Spectrometer (TOMS) instruments on Nimbus-7 (1980-1992), Meteor-3 
(1992-1994) and Earth Probe (1996-2005), and from OMI (2004-2015). Note that OMI 
data are assimilated in MERRA-2 but TOMS observations are not. With the excep- 
tion of 1994 there is remarkable agreement between MERRA-2 and these observations. 
In particular, MERRA-2 realistically captures the ozone hole interannual variability 
throughout the period of the reanalysis. There is an upward trend between 1980 and 
the mid-1990s followed by a plateau with the area oscillating around 22 x 10° km?. 
This is consistent with the late twentieth century increase of anthropogenic chlorine and 
bromine loadings and the subsequent slow recovery after the gradual implementation of 


the Montreal Protocol of 1986 (WMO 2014). The Protocol, which went into effect in the 


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late 1990s, banned the release of the main ozone depleting substances. Because the rate 
of the springtime polar ozone depletion depends on temperature and the strength of the 
Antarctic polar vortex in a given year, the size of the ozone hole exhibits a dynamically 
driven interannual variability superimposed on decadal-scale trends. This dynamical 
modulation is also evident in Figure 24. The extremely small (less than 3 x 10° km?) 
ozone hole in 2002 occurred in conjunction with the only major sudden stratospheric 


warming in the Southern Hemisphere on record (Newman and Nash 2005). 


It should be noted that the southern high-latitude ozone for 1994 in MERRA-2 is not 
recommended for scientific use. The degraded result for that year is due to limited 
SBUV data coverage, as explained above, and the decision not to use data sources other 
than SBUV, OMI and MLS throughout the reanalysis. This particular deficiency is 
not shared with other major reanalyses (except MERRA), which replaced the missing 
data with other available observations such as from the short-lived Meteor-3 TOMS 
instrument (ERA-Interim) or NOAA-9 SBUV (CFSR and JRA-55). The latter were not 
considered in MERRA-2 because of the poorer quality of its partial columns compared 


to other SBUV instruments. 


Realistic ozone hole interannual variability is also present in MERRA (Sean M. Davis, 
personal communication 2016) with the exception of 1993, 1994 (as in MERRA-2), and 
the period between 2010 and 2012 when poor coverage from NOAA-17 SBUV resulted 
in degraded quality of the Antarctic ozone. The inferior performance of MERRA in 
1993 compared to MERRA-2 is a consequence of applying more stringent data quality 
criteria to the older version of the SBUV data, resulting in limited data coverage near 


the terminator. 


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7. Representation of the cryosphere 


Reanalyses provide a global context for assessing recent, pronounced high latitude cli- 
mate variability and provide seamless information on linkages to lower latitudes. As 
compared to midlatitudes, reanalyses in polar regions are particularly challenged by the 
paucity of the in-situ observational network, by the difficulty of satellite microwave and 
infrared sensors to profile the lower atmosphere over snow and ice surfaces, and by an 
inadequate representation of physical processes in models that are specific to these areas. 
Of these three challenges, improvement of model representations of physical processes— 
particularly as they relate to ice and snow surfaces—was seen as the most tractable in 


the development of MERRA-2. 


Several changes in the representation of physical processes between MERRA and MERRA- 
2 are directly relevant to polar regions. These include the use in MERRA-2 of the 
cubed-sphere computational grid (e.g., Putman and Lin 2007), which removes the need 
for gravity wave filtering at high latitudes, as well as daily sea ice concentration and 
sea surface temperature boundary conditions (Donlon et al. 2012; Reynolds et al. 2007; 


Taylor et al. 2000), as compared with the weekly fields used in MERRA. 


In MERRA, a fixed surface albedo of 0.6 was used with sea-ice cover. This resulted in 
erroneously warm surface temperatures in the Arctic spring, when the observed albedo 
is typically much higher (Cullather and Bosilovich 2012). In MERRA-2, Northern Hemi- 
sphere sea-ice albedo varies seasonally based on flux tower observations from the Surface 
Heat Budget of the Arctic Ocean (SHEBA) field experiment (Duynkerke and de Roode 
2001). Monthly values are computed and then linearly interpolated in time to produce 
instantaneous values. Sea-ice albedo in the Southern Hemisphere remains fixed as in 


MERRA, as there are few reliable albedo observations there. Sea ice in the Southern 


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Hemisphere also does not endure an extended period of surface melting and a resulting 
decreased albedo as in the Northern Hemisphere. Comparisons with SHEBA observa- 
tions indicate a substantial reduction in 2-m air temperature biases during boreal spring 


in MERRA-2. 


These comparisons also find a warm bias in winter months over sea ice in MERRA-2 
of approximately 1.2°C in comparison to SHEBA. Larger air temperature differences 
of greater than 3°C are found in comparison to Soviet ice drifting station observations 
made during the 1980’s (Colony et al. 1992). Simmons et al. (2016) showed that 
MERRA-2 is an outlier in near-surface temperature trends in polar regions as compared 
to ERA-Interim, JRA-55, and several conventional data sets. For the period 1980— 
2009, annual 2-m air temperatures for the north polar cap bounded by 60°N increased 
by 0.35 + 0.08°C per decade in MERRA-2. This is the trend determined from linear 
regression; the uncertainty denotes the standard error of the trend. By comparison, 
north polar cap temperatures increased by 0.46 + 0.09°C per decade in NOAA CFSR, 
by 0.55 + 0.10°C per decade in ERA-Interim, and by 0.56 + 0.09°C per decade in JRA- 
55. The behavior in MERRA-2 may be attributable to spurious changes in the SST and 
SIC boundary conditions and the response of the model to changes in surface forcing. 


Investigation of these issues is ongoing. 


A particular focus during the development of MERRA-2 was on the representation of 
glaciated land surfaces (Cullather et al. 2014). In MERRA, ice sheets had an unrealistic 
design, with a fixed surface albedo and no representation of surface hydrology. Surface 
energy fluxes were computed using a fixed sub-surface temperature of 230 K (—43°C). In 
MERRA-2, energy conduction properties of the upper 15 meters of ice are represented, 
as well as the energy and hydrologic properties of an overlying, variable snow cover. 


Snow hydrology follows a modified version of the Stieglitz model that is also used over 


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terrestrial land surfaces (Lynch-Stieglitz 1994; Stieglitz et al. 2001). This provides an 
explicit representation of snow densification, meltwater runoff, percolation, refreezing, 


and a prognostic surface albedo based on Greuell and Konzelmann (1994). 


Figure 25 shows the effects of the different surface configurations in MERRA and 
MERRA-2 on near-surface air temperatures over ice sheets. In MERRA, biases are 
found when the observed surface temperature differs markedly from the fixed sub-surface 
temperature of —43°C. This includes South Pole station in winter (Figure 25a), where 
MERRA values are more than 5 K too warm; over the central Ross Ice Shelf in summer 
(Figure 25b), where MERRA is 8 K too cold; and over central Greenland in summer 
(Figure 25c), where MERRA is 4 K too cold. It may be seen from Figure 25 that these 
seasonal air temperature differences between MERRA and the station values are signifi- 
cant over interannual time periods. In contrast, 2-m air temperatures for these locations 


in MERRA-2 more closely agree with the observed values. 


The surface representation in MERRA-2 also allows for the computation of surface 
mass balance over ice sheets, which may be defined as the net of precipitation minus 
evaporation minus runoff. The MERRA system does not provide runoff over land ice 
and, as seen in Figure 26, lacks ablation areas (in which the annual surface mass balance 
is negative) along the periphery of the ice sheet. For Greenland these occur mostly as a 
result of runoff from surface melt. The corresponding fields in MERRA-2, on the other 
hand, compare well with those from the widely-used Modele Atmosphérique Régional 
regional climate model (MAR; Fettweis 2007), particularly in terms of the accumulation 
distribution in southeastern and western Greenland and the location of the zero-contour 
line along the western coast. However, some differences are also evident. For example, 
the regional climate model indicates average annual mean ablation values of up to 4 m 


yr! in southwestern Greenland, as compared with values of approximately 1 m yr~? in 


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MERRA-2. In addition to differing surface representations, differences in grid spacing 
between MAR (25 km) and MERRA-2 (roughly 50 km) may also play a role. A final 
point of comparison in Figure 26 is with regard to topography. The MERRA system used 
a dated topography which contained large errors of up to 600 m over the Greenland Ice 
Sheet (Box and Rinke 2003). These differences are apparent in the topography contours 
shown for MERRA and MERRA-2 in Figure 26. 


8. MERRA-2 products and access 


The complete list of analyzed and diagnosed fields produced by MERRA-2 is given in 
the product file specification document available at the GMAO’s MERRA-2 web site 
(https: //gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf). The GEOS IAU procedure 
allows for higher-frequency products than just the 6-hourly ones generated directly from 
the analysis. There are three time-invariant and 39 time-varying product collections, 
all produced on a 0.625° x 0.5° horizontal grid. Variables are provided on either the 
native vertical grid (at 72 model layers or the 73 edges), or interpolated to 42 standard 
pressure levels. Detailed information and a description of each variable are available in 
the MERRA-2 file specification document. As in MERRA, MERRA-2 provides closed 
atmospheric budgets, including the analysis increment terms. The observational forcing 
from the assimilation increments during the IAU segment is summed in the output 
budgets of the model. Bosilovich et al. (2015) show the magnitudes of these terms in 


water and energy budgets. 


The NASA Goddard Earth Sciences Data Information Services Center (GES DISC) 
provides access to MERRA-2 products through a new unified user interface connected 
to three different search engines. Many of the tools will be familiar to MERRA users, 


such as the popular Giovanni visualization and analysis tool, web based FTP servers 


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and OpenDAP web services. The subsetting capability has been updated to include 
grid transformation options, while retaining the essential functionality of selecting lev- 
els, variables, time and domain. Citations for the individual MERRA-2 data collections 
are included in the GES DISC MERRA-2 data access pages. As noted in section 1, 
these citations are included in the figure captions of this paper (except where results for 
MERRA-2 are derived from other sources such as diagnostic output from the data assim- 
ilation scheme). Results shown for MERRA are from similarly named data collections, 


as described by Rienecker et al. (2011). 


9. Summary and outlook 


The Modern Era Retrospective Analysis for Research Applications Version 2 (MERRA- 
2) was developed with two primary objectives: to provide an ongoing near-real time cli- 
mate analysis of the satellite era that addresses known limitations of the now-completed 
MERRA reanalysis (January 1979-February 2016), and to demonstrate progress toward 
development of a future integrated Earth system analysis (IESA) capability. MERRA-2 
has achieved those objectives in several respects. These include the assimilation of satel- 
lite observations not available to MERRA—which assimilated no new satellite observa- 
tions after NOAA-18 (launched in 2005)—the reduction of certain biases and imbalances 
in the water cycle, and the reduction of spurious trends and jumps in precipitation related 
to changes in the observing system. As a step toward a future IESA, MERRA-2 includes 
aerosol data assimilation and improved representations of aspects of the cryosphere and 


stratosphere, including ozone, as compared with MERRA. 


At the same time, because of the fairly rapid development schedule required to produce 
a timely replacement for MERRA, other aspects of the MERRA-2 development received 


less attention. For example, there was little focus on the preparation and improvement of 


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input conventional data types and minimal tuning of the model physics for the current 
application. Notable shortcomings of MERRA-2 compared with MERRA include an 
increased warm bias in the upper troposphere—as revealed by the background forecast fit 
to radiosonde temperature observations and mean analysis increments of temperature— 
as well as excessive precipitation over high topography in the tropics and, to a lesser 
extent, over northern high latitudes. Subsequent experimentation indicates that these 
behaviors are most affected by the model parameterizations of deep convection and 
gravity wave drag in GEOS, as well as the representation of topography. They are being 


addressed in more recent model versions. 


Ongoing development in other aspects of modeling and data assimilation are likely to pro- 
vide benefit for reanalyses in the near future. For example, while MERRA-2 assimilates 
only clear-sky satellite radiances, the use of cloud- and rain-affected radiances—referred 
to as all-sky assimilation (Bauer et al. 2010)—has matured or become operational at 
several centers including GMAO. This should improve the assimilation of moisture- 
sensitive data types which, as shown here and by Bosilovich et al. (2017), can still 
induce unexpected changes in global precipitation and moisture fields. Direct assimi- 
lation of land surface observations, including remotely sensed soil moisture and snow 
cover fraction, is another area of improving capability that is likely to provide bene- 
fit to reanalysis, especially for capturing extreme events like droughts and heat waves. 
Implementation of an improved land model that includes dynamic phenology and pho- 
tosynthesis is a key component of the GMAO’s land surface modeling and assimilation 
efforts (Koster et al. 2014). To improve the specification of ocean surface boundary 
conditions, many centers are developing some form of coupled ocean-atmosphere anal- 
ysis system. The GMAO has recently implemented a coupled data assimilation scheme 
for analyzing ocean skin temperature within the existing atmospheric analysis (Akella 


et al. 2016). It uses background fields from a near-surface ocean diurnal layer model 


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to assimilate surface-sensitive radiances plus in-situ observations along with all other 
observations in the atmospheric assimilation system. The scheme may be described as 
being weakly coupled in the sense that the atmospheric observations do not affect the 
ocean fields directly, but only through the increment of ocean skin temperature during 


the next analysis cycle. 


Improving the representation of aerosol effects on climate is another important area of 
development for reanalysis. As the aerosol observing system continues to evolve and 
provide additional global information on aerosol absorption, size and vertical distribu- 
tion, the discrepancy among reanalyses and satellite-only estimates of aerosol radiative- 
climate effects should decrease. For example, the GMAO is working to incorporate 
aerosol vertical distribution information from space-based lidars, as well as implicit spe- 
ciation and size information from multi-channel radiometers on low-orbiting and geosta- 
tionary satellites. Unlike satellite estimates alone, reanalyses like MERRA-2 can provide 
detailed information on how the anthropogenic component of aerosols, and thus radia- 
tive forcing, has changed during the modern satellite era, as well as its interaction with 
the circulation and the climate at large. This should lead to reduced uncertainty in 


assessing, for example, the human impact on climate. 


More extensive analysis coupling between the atmosphere, ocean, land and chemistry as 
envisioned for IESA, while progressing, still presents significant challenges (e.g., Brass- 
ington et al. 2015). These include model biases that can be exacerbated when coupled, 
component systems with different physical characteristics and different spatial and tem- 
poral scales, and component observations in different media with different spatial and 
temporal frequencies and different latencies. These challenges may be offset at least 
partially by the fact that, in practice, where the time scales and observation laten- 


cies between components differ greatly—as between the deep ocean and atmosphere for 


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example—a weak coupling approach may suffice. Prospects for success are also bolstered 
by the fact that the numerical weather prediction community is placing increasing focus 
on the need to analyze currently uncoupled components of the Earth system in a more 
consistent manner. The GMAO strategy is to progress incrementally toward an IESA 
through an evolving combination of coupled systems and offline component reanalyses 


driven by, for example, MERRA-2 atmospheric forcing. 


Quantifying uncertainty in reanalyses remains important for expanding their utility, 
especially as a potential tool for climate change assessment. Dee et al. (2011) argued 
that advances in observational bias correction and other aspects of data assimilation 
have reduced uncertainty in the representation of low-frequency variability to the point 
where ERA-Interim can be used to estimate certain atmospheric temperature trends. 
More recently, Simmons et al. (2014) compared multi-annual variability and trends 
in atmospheric temperature from ERA-Interim, JRA-55 and MERRA and found them 
to be in generally good agreement in the upper troposphere and lower stratosphere 
but more uncertain in the middle stratosphere. Nonetheless, for less well constrained 
quantities such as precipitation and surface fluxes, there still appear to be substantial 
differences between recent reanalyses. For example, the 12-month running mean values 
of global precipitation in ERA-Interim, MERRA-2, and JRA-55 can at times differ by 
almost 20%. Uncertainty in sea surface temperature, as illustrated by the surprising 
differences between the prescribed values used in different reanalyses (Figure 5) is likely 
to be a contributing factor. Impacts from observing system changes also appear to play 
a significant role in explaining these precipitation differences, pointing to the need for 
new sources of high-quality observations of these or closely related variables not only 
for assimilation but for improving our understanding and modeling of the underlying 
physical processes. Ongoing efforts to improve the quality of existing historical data 


sets are also critical in this regard. 


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The increasing use of ensemble and hybrid ensemble-variational methods in Earth sys- 
tem data assimilation has the potential to make at least some measures of uncertainty a 
standard component of reanalysis data sets (e.g., Compo et al. 2011; Poli et al. 2013). 
The GMAO has recently implemented a hybrid four-dimensional ensemble-variational 
(4D-ENVAR) assimilation scheme with similar capability. Finally, ECMWF, JMA and 
GMAO are conducting multi-decadal atmospheric model integrations (without data as- 
similation) for comparison with reanalyses as a means of assessing internal variability 
and distinguishing boundary-forced climate signals from those imposed by changes in 
the observing system. All these efforts will benefit from the continued assessment of ex- 
isting reanalysis products by the research community, and from the sharing of key data 
assimilation diagnostic quantities (e.g., background departures, analysis increments, bias 


estimates) between both reanalysis developers and data providers. 


Acknowledgments 


Development of the GEOS data assimilation system and the MERRA-2 project were 
funded by NASA’s Modeling Analysis and Prediction program. Computational resources 
and support for the execution of MERRA-2 were provided by the NASA High-End 
Computing Capability Project and NASA Center for Climate Simulation. GEOS and 
MERRA-2 are the result of years of dedicated research, development and analysis by 
many individuals at GMAO whose efforts are greatly appreciated. We gratefully ac- 
knowledge the GMAO operations group for monitoring the production of MERRA-2 
and the GMAO software integration group who helped improve the performance and 
flexibility of GEOS. We thank Julio Bacmeister for his contribution to the development 
of the model physics, Qing Liu for processing the precipitation input data, and Allison 


Collow and Edmond Brent Smith for providing some of the figures for this paper. We 


51 


vor also thank the GES DISC for providing on-line access to MERRA-2 products. Finally, 
vo2 we thank the three reviewers, whose comments and suggestions helped improve the paper 


ves substantially. 


52 


vos Appendix: Acronyms 


1295 


3DVAR 
4DENVAR 
AAOD 
ACARS 
AeroCom 
AERONET 
AIREP 
AIRS 
AMDAR 
AMSR-E 
AMSU-A 


AMSU-B 
AOD 
ASCAT 
ASDAR 
ATMS 
ATOVS 
AVHRR 
CFSR 
CAMS 
CMIP 
CPC 
CrIS 
CRTM 


Three-dimensional variational data assimilation 


Four-dimensional ensemble-variational data assimilation 


Aerosol absorption optical depth 


Aircraft Communications Addressing and Reporting 


Aerosol Comparison Project 

Aerosol Robotics Network 

Aircraft report 

Advanced Infrared Sounder 

Aircraft Meteorological Data Relay 

Advanced Microwave Scanning Radiometer-EOS 
Advanced Microwave Sounding Unit-A 
Advanced Microwave Sounding Unit-B 

Aerosol optical depth 

Advanced Scatterometer 

Aircraft to Satellite Data Relay 

Advanced Technology Microwave Sounder 
Advanced TIROS Operational Vertical Sounder 
Advanced Very High Resolution Radiometer 
Climate Forecast System Reanalysis 
Copernicus Atmosphere Monitoring Service 
Coupled Model Intercomparison Project 
Climate Prediction Center 

Cross-track Infrared Sounder 


Community Radiative Transfer Model 


53 


1296 


DMSP 
DRE 
ECMWF 
EOS 
ERA-20C 


ERA-Interim 


ERS 
FGAT 
GEOS 
GES DISC 
GLATOVS 
GMAO 
GMS 
GOCART 
GOES 
GPCP 
GPSRO 
GSI 

GWD 
HIRS 

IASI 

TAU 

IESA 
IPCC 
JMA 


Defense Meteorological Satellite Program 

Direct radiative effect 

European Centre for Medium-Range Weather Forecasts 
Earth Observing System 

ECMWFE Reanalysis from 1900-2010 

ECMWFE Reanalysis from 1979—present 

Environmental Research Satellite 

First guess at appropriate time 

Goddard Earth Observing System 

Goddard Earth Sciences Data Information Services Center 
Goddard Laboratory for Atmospheres TOVS forward model 
Global Modeling and Assimilation Office 

Geostationary Meteorological Satellite 

Goddard Chemistry, Aerosol, Radiation and Transport model 
Geostationary Operational Environmental Satellites 
Global Precipitation Climatology Project 

Global Positioning System radio occultation 

Gridpoint Statistical Interpolation 

Gravity wave drag 

High-resolution Infrared Radiation Sounder 

Infrared Atmospheric Sounding Interferometer 
Incremental analysis update 

Integrated Earth system analysis 

Intergovernmental Panel on Climate Change 


Japan Meteorological Agency 


54 


1297 


JPSS 
JRA-55 
MACC 
MAR 
MDCRS 
MERRA 
MERRA-2 
Metop 
MHS 
MISR 
MLS 
MODIS 
MSG 
MSU 
MTSAT 
NAAPS 
NASA 
NCEP 
NEXRAD 
NOAA 


NRL 
OISST 
OMI 
OPAC 
OSTIA 
PAOB 


Joint Polar Satellite System 

Japanese 55-year Reanalysis 

Monitoring Atmospheric Composition and Climate project 
Modéle Atmosphérique Régional regional climate model 
Meteorological Data Collection and Reporting System 
Modern-Era Retrospective Analysis for Research and Applications 
Modern-Era Retrospective Analysis for Research and Applications, Version 2 
Meteorological Operational Satellite 

Microwave Humidity Sounder 

Multi-angle SpectroRadiometer 

Microwave Limb Sounder 

Moderate Resolution Imaging Spectroradiometer 

Meteosat Second Generation satellite 

Microwave Sounding Unit 

Multifunctional Transport Satellite 

Navy Aerosol Analysis and Prediction System 

National Aeronautics and Space Administration 

National Centers for Environmental Prediction 

Next-Generation Radar 


National Oceanic and Atmospheric Administration 


Naval Research Laboratory 

Optimum Interpolation Sea Surface Temperature 

Ozone Monitoring Instrument 

Optical Properties of Aerosols and Clouds 

Operational Sea Surface Temperature and Sea Ice Analysis 


Synthetic surface pressure observation 


59 


1298 


Pibal 
PIREP 
QBO 
QFED 
Raob 
RAS 
RMS 
RSS 
SBUV 
SEVIRI 
SHEBA 
SIC 
SMAP 
SMOS 
SNPP 
SSM/I 
SSMIS 
SST 
SSU 
TIROS 
TLNMC 
TMI 
TOA 
TOMS 
VAD 
WMO 


Pilot balloon 

Pilot report 

Quasi-Biennial Oscillation 

Quick Fire Emission Dataset 

Radiosonde observation 

Relaxed Arakawa-Schubert convection scheme 
Root mean square 

Remote Sensing Systems 

Solar Backscatter Ultraviolet Radiometer 
Spinning Enhanced Visible Infrared Imager 
Surface Heat Budget of the Arctic Ocean 
Sea ice concentration 

Soil Moisture Active Passive satellite 

Soil Moisture and Ocean Salinity satellite 
Suomi National Polar-orbiting Partnership 
Special Sensor Microwave Imager 

Special Sensor Microwave Imager /Sounder 
Sea surface temperature 

Stratospheric Sounding Unit 

Television Infrared Observation Satellite 
Tangent linear normal mode constraint 
Tropical Rainfall Measuring Mission Microwave Imager 
Top of the atmosphere 

Total Ozone Mapping Spectrometer 
Velocity Azimuth Display 


World Meteorological Organization 


56 


1299 


1325 


1326 


1327 


1328 


1329 


References 


Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Cimatology 
Project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeor., 
4, 1147-1167. 


Akella, S., R. Todling, M., and M. Suarez, 2016: Assimilation for skin SST in the NASA 
GEOS atmospheric data assimilation system. Quart. J. Roy. Meteor. Soc., 
doi:10.1002/qj.2988. 


Andrews, D. G., J. R. Holton, and C. B. Leovy, 1987: Middle Atmosphere Dynamics. 
Academic Press, 489 pages. 


Bacmeister, J. T. and Stephens, G., 2011: Spatial statistics of likely convective clouds 
in CloudSat data. J. Geophys. Res., 116, D04104, doi:10.1029/2010JD014444. 


Ballish, B. A., and V. K. Kumar, 2008: Systematic differences in aircraft and radiosonde 
temperatures. Bull. Amer. Meteor. Soc., 89, 1689-1707. 


Bauer, P., A. J. Geer, P. Lopez, and D. Salmond, 2010: Direct 4D-Var assimilation of 
all-sky radiances. Part I: Implementation. Quart. J. Roy. Meteor. Soc., 136, 
1868-1885. doi:10.1002/qj.659 


Bellouin, N., J. Quaas, J.-J. Morcrette and O. Boucher, 2013: Estimates of aerosol 
radiative forcing from the MACC re-analysis. Atmos. Chem. Phys., 13, 2045— 
2062, doi:10.5194 /acp-13-2045-2013. 


Berrisford, P., P. Kallberg, S. Kobayashi, D. Dee, S. Uppala, A. J. Simmons, P. Poli, 
and H. Sato, 2011: Atmospheric conservation properties in ERA-Interim. Quart. 
J. Roy. Meteor. Soc., 137, 1381-1399. 


Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina, 1996: Data assimilation using incre- 
mental analysis updates. Mon. Wea. Rev., 124, 1256-1271. 


Bocquet M., and Coauthors, 2015:, Data assimilation in atmospheric chemistry models: 
Current and future prospects for coupled chemistry meteorology models. Atmos. 
Chem. Phys., 15 (10), 5325-5358, doi:10.5194/acp-15-5325-2015. 

Bosilovich, M. G., 2013: Regional climate and variability in NASA MERRA and recent 
reanalyses: US summertime precipitation and temperature, J. Appl. Meteor. 


Climatol., 52, 1939-1951, doi: http://dx.doi.org/10.1175/JAMC-D-12-0291.1. 


Bosilovich, M.G., F. R. Robertson, and J. Chen, 2011: Global energy and water budgets 


57 


330 


in MERRA. J. Climate, 24, 282-300. 


Bosilovich, M.G., and Coauthors, 2015: MERRA-2: Initial Evaluation of the Climate. 
NASA/TM2015104606, Vol. 43, 139 pp. 
https: //gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf. 


Bosilovich, M., F. Robertson, L. Takacs, A. Molod, and D. Mocko, 2017: Atmospheric 
water balance and variability in the MERRA-2 reanalysis. J. Climate, 30, 1177— 
1196, doi: 10.1175/JCLI-D-16-0338.1. 


Box, J. E., and A. Rinke, 2003: Evaluation of Greenland Ice Sheet surface climate in 
the HIRHAM regional climate model using automatic weather station data. J. 
Climate, 16, 1302-1319, doi:10.1175/1520-0442-16.9.1302. 


Brassington, G. B., M. J. Martin, H. L. Tolman, S. Akella, M. Balmeseda, C. R. S. Cham- 
bers, J. A. Cummings, Y. Drillet, P. A. E. M. Jansen, P. Laloyaux, D. Lea, A. 
Mehra, I. Mirouze, H. Ritchie, G. Samson, P. A. Sandery, G. C. Smith, M. Suarez, 
and R. Todling, 2015: Progress and challenges in short- to medium- range coupled 
prediction, J. Op. Oceanogr., 8, 239-258, doi:10.1080/1755876X.2015.1049875. 


Buchard, V., and Coauthors, 2015: Using the OMI aerosol index and absorption aerosol 
optical depth to evaluate the NASA MERRA Aerosol Reanalysis. Atmos. Chem. 
Phys., 15 (10), 5743-5760, 10.5194/acp-15-5743-2015. 


Buchard, V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, R. Govin- 
daraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemka, and H. Yu, 2017: 
The MERRA-2 Aerosol Reanalysis, 1980-onward, Part 2: Evaluation and case 
studies. J. Climate, in review. 


Cardinali, C., L. Isaksen, and E. Anderson, 2003: Use and impact of automated aircraft 
data in a global 4DVAR data assimilation system. Mon. Wea. Rev., 131, 1865- 
1877. 


Chen Y., F. Weng, Y. Han, and Q. Liu, 2008: Validation of the community radiative 
transfer model (CRTM) by using CloudSat Data. J. Geophys. Res., 113 (D8), 
2156-2202. 


Chin, M., P, Ginoux, S. Kinne, O. Torres, B. N. Holben, B. N. Duncan, R. V. Martin, 
J. A. Logan, A. Higurashi, and T. Nakajima, 2002: Tropospheric aerosol op- 
tical thickness from the GOCART model and comparisons with satellite and 
sun photometer measurements. J. Atmos. Sci., 59, 461-483, 10.1175/1520- 
0469(2002)059 <0461:TAOTFT>2.0.CO;2, 
http: //dx.doi.org10.1175 /1520-0469(2002)059<0461:TAOTFT>2.0.CO;2. 


58 


1364 


1365 


1366 


1367 


1368 


1369 


1370 


1371 


1372 


1373 


1374 


1375 


1376 


1377 


1378 


1379 


1380 


1381 


1382 


1383 


1384 


1385 


1386 


1387 


1388 


1389 


1390 


1391 


1392 


1393 


1394 


1395 


Chylek, P., and J. A. Coakley, 1974: Aerosol and climate. Science, 183, 75-77. 


Colarco, P., A. da Silva, M. Chin, and T. Diehl, 2010: Online simulations of global 
aerosol distributions in the NASA GEOS-4 model and comparisons to satel- 
lite and ground-based aerosol optical depth. J. Geophys. Res., 115 (D14207), 
10.1029/2009JD012820, http: //dx.doi.org/10.1029/2009JD012820. 


Collow, A. B. M., M. G. Bosilovich, and R. D. Koster, 2016: Large scale influences on 
summertime extreme precipitation in the northeastern United States. To appear 
in J. Hydromet., doi: 10.1175/JHM-D-16-0091.1. 


Collow, A. B. M., and M. A. Miller, 2016: The seasonal cycle of the radiation budget 
and cloud radiative effect in the Amazon rainforest of Brazil. J. Climate, doi: 
10.1175 /JCLI-D-16-0089.1. 


Colony, R., I. Appel, and I. Rigor, 1992: Surface air temperature observations in the 
Arctic Basin. Tech. Memo. TM 1-92, 120 pp. Available from Applied Physics 
Laboratory, University of Washington, Seattle, WA 98195. 


Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. 
J. Roy. Meteor. Soc., 137, 1-28, doi:10.1002/qj.776. 


Coy, L., K. Wargan, A. M. Molod, W. R. McCarty, and S. Pawson, 2016: Structure 
and dynamics of the quasi-biennial oscillation in MERRA-2. J. Climate, 29, 
5339-5354, doi:10.1175/JCLI-D-15-0809.1. 


Cullather, R. I., and M. G. Bosilovich, 2012: The energy budget of the polar atmosphere 
in MERRA. J. Climate, 25, 5-24, doi:10.1175/2011JCLI4138.1. 


Cullather, R.I., S.M.J. Nowicki, B. Zhao, and M. J. Suarez, 2014: Evaluation of the 
surface representation of the Greenland Ice Sheet in a general circulation model. 
J. Climate, 27, 4835-4856, doi: 10.1175/JCLI-D-13-00635.1. 


Darmenoyv, Anton, and Arlindo da Silva, 2015. The Quick Fire Emissions Dataset 
(QFED): Documentation of versions 2.1, 2.2 and 2.4. NASA/TM2015104606, 
Vol. 38, 201 pp. 


Decker, M., M. A. Brunke, Z. Wang, K. Sakaguchi, X. Zeng, and M. G. Bosilovich, 2011: 
Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux 
tower observations. J. Climate, 24, 221-249, doi:10.1175/JCLI-D-11-00004.1. 


Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: configuration and per- 
formance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553-597, 


59 


1396 


doi:10.1002/qj.828. 


Dee D. P., and A. M. da Silva, 2003: The choice of variable for atmospheric moisture 
analysis. Mon. Weather Rev., 131 155-171. 


Dee, D, and S. Uppala, 2009: Variational bias correction of satellite radiance data in 
the ERA-Interim reanalysis. Quart. J. Roy. Meteor. Soc., 135, 1830-1841. 


Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the 
NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287-2299. 


Diehl, T., A. Heil, M. Chin, X. Pan, D. Streets, M. Schultz, and S. Kinne, 2012: An- 
thropogenic, biomass burning, and volcanic emissions of black carbon, organic 
carbon, and SO2 from 1980 to 2010 for hindcast model experiments. Atmos. 
Chem. Phys. Discuss., 12 (9), 24 895-24 954, 10.5194/acpd-12-24895-2012, 
http://www.atmos-chem-phys-discuss.net/12/ 24895/2012/. 


Donlon, C.J., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, 2012: 
The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. 
Remote Sens. Environ., 116, 140-158, doi:10.1016/j.rse.2010.10.017. 


Draper, C., R. Reichle, and R. Koster, 2017: Assessment of the MERRA-2 land surface 
energy flux estimates. J. Climate, in review. 


Duynkerke, P., and S. de Roode, 2001: Surface energy balance and turbulence charac- 
teristics observed at the SHEBA Ice Camp during FIRE III. J. Geophys. Res., 
106, 15313-15322, doi:10.1029/2000JD900537. 


Farman, J., B. Gardiner, B., and J. Shanklin, 1985: Large losses of total ozone in 
Antarctica reveal seasonal ClOx/NOx interaction Nature, 315 (6016), 207-210, 
doi: 10.1038/315207a0. 


Fettweis, X., 2007: Reconstruction of the 1979-2006 Greenland ice sheet surface mass 
balance using the regional climate model MAR. The Cryosphere, 1, 21-40, 
doi:10.5194/tc-1-21-2007. 


Flemming, J., A. Benedetti, A. Inness, R. Engelen, L. Jones, V. Huijnen, S. Remy, M. 
Parrington, M. Suttie, A. Bozzo, V.-H. Peuch, D. Akritidis, and E. Katragkou, 
2017: The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 
2003-2015, Atmos. Chem. Phys., 17, 1945-1983, doi:10.5194/acp-17-1945-2017. 


Froidevaux, L., and Coauthors, 2006: Early validation analyses of atmospheric profiles 
from EOS MLS on the Aura satellite. I[EEE Transactions on Geoscience and 


60 


1428 


1429 


1430 


1431 


1432 


1433 


1434 


1435 


1436 


1437 


1438 


1439 


1440 


1441 


1442 


1443 


1444 


1445 


1446 


1447 


1448 


1449 


1450 


1451 


1452 


1453 


1454 


1455 


1456 


1457 


1458 


1459 


1460 


1461 


1462 


Remote Sensing 44, no. 5, doi:10.1109/TGRS.2006.864366. 


Global Modeling and Assimilation Office (GMAO), 2015a: MERRA-2 inst1_2d_asm_Nx: 
2d, 3-Hourly, Instantaneous, Single-Level,Assimilation, Single-Level Diagnostics 
V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information 
Services Center (GES DISC), accessed June 2016, doi:10.5067/3Z173KIE2TPD. 


Global Modeling and Assimilation Office (GMAO), 2015b: MERRA-2 inst1_2d_int_Nx: 
2d,1-Hourly, Instantaneous, Single-Level, Assimilation, Vertically Integrated Di- 
agnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and In- 
formation Services Center (GES DISC), accessed June 2016, 
doi:10.5067/GOU6NGQ3BLEO0. 


Global Modeling and Assimilation Office (GMAO), 2015c: MERRA-2 inst3_3d_asm_Np: 
3d, 3-Hourly, Instantaneous, Pressure-Level, Assimilation, Assimilated Meteo- 
rological Fields, V5.12.4, Greenbelt, MD, USA: Goddard Space Flight Center 
Distributed Active Archive Center (GSFC DAAC), accessed June 2016, 
doi:10.5067/QBZ6MG944HWO0. 


Global Modeling and Assimilation Office (GMAO), 2015d: MERRA-2 tavg1_2d_flx_Nx: 
2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Surface Flux Diagnos- 
tics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information 
Services Center (GES DISC), accessed June 2016, doi:10.5067/7MCPBJ41Y0K6. 


Global Modeling and Assimilation Office (GMAO), 2015e: MERRA-2 tavg1_2d_int_Nx: 
2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Vertically Integrated 
Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and 
Information Services Center (GES DISC), accessed June 2016, 
doi:10.5067/Q5GVUVUIVGO7. 


Global Modeling and Assimilation Office (GMAO), 2015f: MERRA-2 tavg1_2d_slv_Nx: 
2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Single-Level Diagnos- 
tics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information 
Services Center (GES DISC), accessed June 2016, doi:10.5067/VJAFPLILCSIV. 


Global Modeling and Assimilation Office (GMAO), 2015g: MERRA-2 tavgM_2d_aer_Nx: 
2d, Monthly mean, Time-averaged, Single-Level, Assimilation, Aerosol Diagnos- 
tics V5.12.4, Greenbelt, MD, USA: Goddard Space Flight Center Distributed 
Active Archive Center (GSFC DAAC), accessed June 2016, 
doi:10.5067/FHIAOMLJPC7N. 


Global Modeling and Assimilation Office (GMAO), 2015h: MERRA-2 tavgM_2d_flx_Nx: 
2d, Monthly mean, Time-Averaged, Single-Level, Assimilation, Surface Flux Di- 


61 


1474 


1475 


1476 


1477 


1478 


1479 


1480 


1481 


1482 


1483 


1484 


1485 


1486 


1487 


agnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and In- 
formation Services Center (GES DISC), accessed June 2016, 
doi:10.5067/0OJRLVL8Y V2Y4. 


Global Modeling and Assimilation Office (GMAO), 2015i: MERRA-2 tavgM_2d_glc_Nx: 
2d, Monthly mean, Land Ice Surface Diagnostics, V5.12.4, Greenbelt, MD, USA: 
Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), 
accessed June 2015, doi:10.5067/5W8Q3I9WUFGX. 


Global Modeling and Assimilation Office (GMAO), 2015j: MERRA-2 tavgM_2d_Ind_Nx: 
2d, Monthly mean, Land Surface Diagnostics, V5.12.4, Greenbelt, MD, USA: 
Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), 
accessed June 2015, doi:10.5067/8S35XF81C28F. 


Global Modeling and Assimilation Office (GMAO), 2015k: MERRA-2 tavgM_2d_int_Nx: 
2d, Monthly mean, Time-Averaged, Single-Level, Assimilation, Vertically Inte- 
grated Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data 
and Information Services Center (GES DISC), accessed June 2015, 
doi:10.5067/FQPTQ40J22TL. 


Global Modeling and Assimilation Office (GMAO), 20151: MERRA-2 tavgM_3d_qdt_Np: 
3d, Monthly mean, Time-Averaged, Pressure-Level, Assimilation, Moist Tenden- 
cies V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Informa- 
tion Services Center (GES DISC), accessed June 2016, 
doi:10.5067/2ZTU87V69ATP. 


Global Modeling and Assimilation Office (GMAO), 2015m: MERRA-2 tavgM_2d_slv_Nx: 
2d, Monthly mean, Single-Level Diagnostics, V5.12.4, Greenbelt, MD, USA: God- 
dard Space Flight Center Distributed Active Archive Center (GSFC DAAC), 
accessed April 2015, doi:10.5067/AP1BOBA5PD2K. 


Global Modeling and Assimilation Office (GMAO), 2015n: MERRA-2 tavgM_3d_tdt_Np: 
3d, Monthly mean, Time-Averaged, Pressure-Level, Assimilation, Temperature 
Tendencies V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and 
Information Services Center (GES DISC), accessed June 2016, 
doi:10.5067/VILT59HI2MOY. 


Gong, S. L., 2003: A parameterization of sea-salt aerosol source function for sub- and 
super-micron particles. Global Biogeochemical Cycles, 17 (4), 


doi:10.1029/2003GB002079. 


Greuell, W., and T. Konzelmann, 1994: Numerical modelling of the energy balance 
and englacial temperature of the Greenland Ice Sheet. Calculations for the ETH- 


62 


1498 Camp location (West Greenland, 1155m a.s.l.). Global Planet. Change, 9, 91-114, 
1499 doi:10.1016/0921-8181(94)90010-8. 


100 Ham, Y.-G., S. Schubert, Y. Vikhliaev, and M. J. Suarez, 2014: An assessment of the 
1501 ENSO forecast skill of GEOS-5 system. Clim. Dynam., doi:10.1007 /s00382-014- 
1502 2063-2. 


103 ~Han, Y., P. van Delst, Q. Liu, F. Weng, B. Yan, R. Treadon, and J. Derber, 2006: 
1504 JCSDA Community Radiative TransferModel (CRTM)-Version 1. NOAA Tech. 
1505 Rep. 122, 33 pp. 


ise Heidinger, A. K., C. Cao, and J. T. Sullivan, 2002: Using Moderate Resolution Imaging 


1507 Spectrometer (MODIS) to calibrate Advanced Very High Resolution Radiometer 
1508 reflectance channels. J. Geophys. Res. Atmos., 107 (D23), 10.1029/2001JD002035, 
1509 http: //dx.doi.org/10.1029/2001JD002035. 

10 Hoch, S.W., 2005: Radiative flux divergence in the surface boundary layer. A study 
1511 based on observations at Summit, Greenland. Ph.D. dissertation, Swiss Federal 
1512 Institute of Technology (ETH), Zurich, 164 pp. 

113 Holben, B., and Coauthors, 1998: AERONET - A federated instrument network and 
1514 data archive for aerosol characterization. Remote Sens. Environ., 66 (1), 1-16, 
1515 http: //dx.doi.org/10.1016/S0034-4257(98)00031-5, 

1516 http: //www.sciencedirect.com/science/article/ pii/S0034425798000315. 

iz Holm, E. V., 2003: Revision of the ECMWF humidity analysis: Construction of a 
1518 Gaussian control variable. In Proceedings of Workshop on Humidity Analysis, 
1519 1-3. ECMWF/GEWEX: Reading, UK. 

1520 Inness, A., F. Baier, A. Benedetti, I. Bouarar, S. Chabrillat, H. Clark, H., and Coauthors, 
1521 2013: The MACC reanalysis: An 8 yr data set of atmospheric composition. 
1522 Atmos. Chem. Phys., 13, 4073-4109, doi:10.5194/acp-13-4073-2013. 

i223 Kahn, R. A., B. J. Gaitley, J. V. Martonchik, D. J. Diner, K. A. Crean, and B. Holben, 
1524 2005: Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth 
1525 validation based on 2 years of coincident Aerosol Robotic Network (AERONET) 
1526 observations. J. Geophys. Res. Atmos., 110 (D10), 10.1029/2004JD004706, 
1527 URL http://dx.doi.org/10.1029/ 2004JD004706. 

28 Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, R. M. Errico, and R. Yang, 2009a: 
1529 Improving incremental balance in the GSI 3DVAR analysis system. Mon. Wea. 
1530 Rev., 137, 1046-1060. 


63 


1531 


1532 


1533 


1534 


1535 


1536 


Kleist D. T., D. F. Parrish, J. C. Derber, R. Treadon, W.-S. Wu, and 8. Lord, 2009b: 
Introduction of the GSI into the NCEPs Global Data Assimilation System. Wea. 
Forecasting, 24, 1691-1705. 


Kinne, S., and Coauthors, 2006:, An AeroCom initial assessment - optical properties in 
aerosol component modules of global models. Atmos. Chem. Phys., 6, 1815- 
1834, doi:10.5194/acp-6-1815-2006. 


Kobayashi, S., M. Matricardi, D. Dee, and S. Uppala, 2009: Toward a consistent reanal- 
ysis of the upper stratosphere based on radiance measurements from SSU and 
AMSU-A. Quart. J. Roy. Meteor. Soc., 135, 2086-2099, doi:10.1002/qj.514. 


Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya, H. Onoda, K. Onogi, H. 
Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi, 2015: The 
JRA-55 reanalysis: General specifications and basic characteristics. J. Meteorol. 
Soc. Japan, 93 (1), 5-48. doi:10.2151/jmsj.2015-001. 


Koster, R. D., G. Walker, G. J. Collatz, and P. E. Thornton, 2014: Hydroclimatic 
controls on the means and variability of vegetation phenology and carbon uptake. 
J. Climate, 27, 5632-5652. doi: 10.1175/JCLI-D-13-00477. 


Kunkel, K. E., and Coauthors, 2013: Monitoring and understanding trends in ex- 
treme storms: State of knowledge. Bull. Amer. Meteor. Soc., 94, 499-514, 
doi:10.1175/BAMS-D-11-00262.1. 


Levy, R. C., L. A. Remer, S. Mattoo, E. F. Vermote, and Y. J. Kaufman, 2007: 
Second-generation operational algorithm: Retrieval of aerosol properties over land 
from inversion of Moderate Res- olution Imaging Spectroradiometer spectral re- 
flectance. J. Geophys. Res. Atmos., 112 (D13), 10.1029/2006JD007811, URL 
http: //dx.doi.org/10.1029/2006JD007811. 


Lim, Y.-K., R. Kovach, S$. Pawson, and G. Vernieres, 2017: The 2015/2016 El Nino event 
in context of the MERRA-2 reanalysis: A comparison of the tropical Pacific with 
1982/1983 and 1997/1998. J. Climate, doi:10.1175/JCLI-D-16-0800.1, in press. 


Liu, Q. and S. Boukabara, 2014: Community Radiative Transfer Model (CRTM) ap- 
plications in supporting the Suomi National Polar-orbiting Partnership (SNPP) 
mission validation and verification. Remote Sens. Environ., 140, 744-754. 


Lynch, P., and Coauthors, 2016:, An 11-year global gridded aerosol optical thickness 


reanalysis (v1.0) for atmospheric and climate sciences. Geosci. Model Dev., 9, 
1489-1522, doi:10.5194/gmd-9-1489-2016. 


64 


1564 


1565 


1566 


1567 


1568 


1569 


1570 


1571 


1572 


1573 


1574 


1575 


1576 


1577 


1578 


1579 


1580 


1581 


1582 


1583 


1584 


1585 


1586 


1587 


1588 


1589 


1590 


1591 


1592 


1593 


1594 


1595 


1596 


Lynch-Stieglitz, M., 1994: The development and validation of a simple snow model for 
the GISS GCM. J. Climate, '7, 1842-1855, 
doi:10.1175/1520-0442(1994)007,1842: TDAVOA.2.0.CO;2. 


Manney, G. L., K. Kruger, S. Pawson, K. Minschwaner, M. J. Schwartz, W. H. Daffer, 
N. J. Livesey, M. G. Mlynczak, E. E. Remsberg, J. M. Russell, J.W. Waters, 
2008: The evolution of the stratopause during the 2006 major warming: Satellite 
data and assimilated meteorological analyses. J. Geophys. Res., 113, D11115, 
doi:10.1029/2007JD00909. 


Marticorena, B., and G. Bergametti, 1995: Modeling the atmospheric dust cycle: 1. 
Design of a soil-derived dust emission scheme. J. Geophys. Res. Atmos., 100 
(D8), 16415-16430, doi:10.1029/95JD00690. 


McCarty, W., L. Coy, R. Gelaro, A, Huang, D. Merkova, E. B. Smith, M. Sienkiewicz, 
and K. Wargan, 2016: MERRA-2 input observations: Summary and initial assess- 
ment. NAS'A Technical Report Series on Global Modeling and Data Assimilation, 
NASA/TM-2016-104606, Vol. 46, 61 pp. 


McPeters, R., M. Kroon, G. Labow, E. Brinksma, D. Balis, I. Petropavlovskikh, J. P. 
Veefkind, P. K. Bhartia, and P. F. Levelt, 2008: Validation of the Aura Ozone 
Monitoring Instrument total column ozone product. J. Geophys. Res., 118, 
D15S14, doi:10.1029/2007J DO08802. 


Meng, J., R. Yang, H. Wei, M. Ek, G. Gayno, P. Xie, and K. Mitchell, 2012: The 
Land Surface Analysis in the NCEP Climate Forecast System Reanalysis. J. 
Hydrometeor., 13, 1621-1630, doi: 10.1175/JHM-D-11-090.1. 


Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. 
Meteor. Soc., 87, 343-360, doi:10.1175/BAMS-87-3-343. 


Molina, M. J., and F. S. Rowland, 1974: Stratospheric sink for chlorouoromethanes: 
Chlorine atom-catalysed destruction of ozone. Nature, 249 (28), 810-812. 


Molod, A., L. Takacs, M. Suarez, and J. Bacmeister, 2015: Development of the GEOS-5 
atmospheric general circulation model: evolution from MERRA to MERRA2, 
Geosci. Model Dev., 8, 1339-1356, doi:10.5194/gmd-8-1339-2015. 


Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa-Schubert: A parameterization of 
moist convection for general circulation models. Mon. Wea. Rev., 120, 978-1002. 


Newman, P. A., and E. R. Nash, 2005: The unusual southern hemisphere stratosphere 
winter of 2002. J. Atmos. Sci., 62, 614-628, doi: 10.1175/JAS-3323.1. 


65 


1597 


1598 


1599 


1600 


1601 


1602 


1603 


1604 


1605 


1606 


1607 


1608 


1609 


1610 


a a a a 
B w N BR 


1625 


1626 


1627 


1628 


Myhre, G., 2009:, Consistency between satellite-derived and modeled estimates of the 
Direct Aerosol Effect. Science, 325, 187-190. 


Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral 
statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763. 
http: //dx.doi.org/10.1175/ 1520-0493 (1992) 120;1747:TNMCS$S,.2.0.CO;2 


Poli, P., and Coauthors, 2013: The data assimilation system and initial performance 
evaluation of the ECMWF pilot reanalysis of the 20th-century assimilating surface 
observations only (ERA-20C). ERA Report Series 14 


Putman, W. and S$.-J. Lin, 2007: Finite Volume Transport on Various Cubed Sphere 
Grids. J. Comput. Phys., 2277, 55-78. doi:10.1016/j.jcp.2007.07.022. 


Randles, C. A., A. M. da Silva, V. Buchard, A. Darmenov, P. R. Colarco, V. Aquila, H. 
Bian, E. P. Nowottnick, X. Pan, A. Smirnov, H. Yu, and R. Govindaraju, 2016: 
The MERRA-2 aerosol assimilation. NASA Technical Report Series on Global 
Modeling and Data Assimilation, NASA/'TM-2016-104606, Vol. 45, 143 pp. 


Randles, C. A., A. da Silva, V. Buchard, P.R. Colarco, A. Darmenov, R. Govindaraju, 
A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y. Shinozuka, and C.J. Flynn, 2017: 
The MERRA-2 Aerosol Reanalysis, 1980-onward, Part 1: System description and 
data assimilation evaluation. J. Climate, in review. 


Reichle, R. H., R. D. Koster, G. J. M. De Lannoy, B. A. Forman, Q. Liu, S. Mahanama, 
and A. Toure, 2011: Assessment and enhancement of MERRA land surface hy- 
drology estimates. J. Climate, 24, 6322-6338. doi:10.1175/JCLI-D-10-05033.1. 


Reichle, R. H., and Q. Liu, 2014: Observation-Corrected Precipitation Estimates in 
GEOS-5. NASA/TM2014-104606, Vol. 35. 


Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, R. D. Koster, and 
G. J. M. De Lannoy, 2017b: Assessment of MERRA-2 land surface hydrology 
estimates, J. Climate, doi:10.1175/JCLI-D-16-0720.1. 


Reichle, R. H., Q. Liu, R. D. Koster, C. S. Draper, S. P. P. Mahanama, and G. S. 
Partyka, 2017a: Land surface precipitation in MERRA-2, J. Climate, 30, 1643- 
1664, doi:10.1175/JCLI-D-16-0570.1. 


Remer, L. A., and Coauthors, 2005: The MODIS aerosol algorithm, products, and 


validation. J. Atmos. Sci., 62, 947-973, 10.1175/JAS3385.1, http://dx.doi.org/ 
10.1175 /JAS3385.1. 


66 


1629 


Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An 
improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625. 


Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. 5S. Casey, and M. G. Schlax, 
2007: Daily high-resolution-blended analyses for sea surface temperature. J. 
Climate, 20, 5473-5496, doi:10.1175/2007JCLI1824.1. 


Rienecker, M. M., and Coauthors, 2008: The GEOS-5 Data Assimilation System— 
Documentation of versions 5.0.1 and 5.1.0, and 5.2.0. NASA Technical Report 
Series on Global Modeling and Data Assimilation, NASA/TM-2008-104606, Vol. 
27, 118 pp. 
https: //gmao.gsfc.nasa.gov/pubs/docs/Rienecker369.pdf. 


Rienecker and Coauthors, 2011: MERRA - NASA’s Modern-Era Retrospective Analysis 
for Research and Applications. J. Climate, 24, 3624-3648, doi:10.1175/JCLI-D- 
11-00015.1. 


Robertson, F. R., M. G. Bosilovich, J. Chen, and T. L. Miller, 2011: The effect of satellite 
observing system changes on MERRA water and energy fluxes. J. Climate, 24, 
5197-5217. doi: http://dx.doi.org/10.1175/2011JCL14227.1. 


Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. 
Amer. Meteor. Soc., 91, 1010-1057, doi:10.1175/2010BAMS3001.1. 


Schubert, S. D., R. Rood, and J. Pfaendtner, 1993: An assimilated dataset for earth sci- 
ence applications. Bull. Amer. Meteor. Soc., 74, 2331-2342, doi:10.1175/1520- 
0477(1993)0742.0.CO;2. 


Schultz, M. G., A. Heil, J. J. Hoelzemann, A. Spessa, K. Thonicke, J. Goldammer, 
A. C. Held, J. M. Pereira, and M. van het Bolscher, 2008: Global Wildland 
Fire Emissions from 1960 to 2000. Global Biogeochem. Cyc., 22, GB2002, 
doi:10.1029/2007GB003031. 


Schwartz, M.J., and Coauthors, 2008: Validation of the Aura Microwave Limb Sounder 
temperature and geopotential height measurements. J. Geophys. Res. 1138, 
D15S11, doi:10.1029/2007JD008783. 


Segal-Rosenhemier, M., N. Barton, J. Redmann, S. Schmidt, S. LeBlanc, B. Anderson, 
E. Winstead, C. Corr, R. Moore, L.K. Thornhill, and R.I. Cullather., 2017: Im- 
proving our understanding of surface radiative flux bias in Arctic reanalysis over 
the marginal ice zone: Observational based sensitivity analysis during ARISE. J. 
Clim., in review. 


67 


we2 Seidel, D. J., J. Li, C. Mears, I. Moradi, J. Nash, W. J. Randel, R. Saunders, D. W. J. 


1663 Thompson, and C.-Z. Zou, 2016: Stratospheric temperature changes during the 
1664 satellite era. J. Geophys. Res. Atmos., 121, doi:10.1002/2015JD024039. 

165 Simmons, A. J., P. Berrisford, D. P. Dee, H. Hersbach, S. Hirahara, and J.-N. Thpaut, 
166 2016: A reassessment of temperature variations and trends from global reanalyses 
1667 and monthly surface climatological datasets. Quart. J. Roy. Meteor. Soc., 
1668 doil0.1002/qj.2949. 

19 Simmons, A. J., P. Poli, D. P. Dee, P. Berrisford, H. Hersbach, S. Kobayashi, and 
670 C. Peubey, 2014: Estimating low-frequency variability and trends in atmospheric 
671 temperature using ERA-Interim. Q. J. R. Meteorol. Soc., 140, 329-353, doi:10.1002 /qj.2317. 
672 Stieglitz, M., A. Ducharne, R. D. Koster, and M. J. Suarez, 2001: The impact of de- 
1673 tailed snow physics on the simulation of snow cover and subsurface thermody- 
674 namics at continental scales. J. Hydrometeor., 2, 228-242, doi:10.1175/1525- 
675 7541 (2001)002,0228:TIODSP.2.0.CO;2. 

67 Susskind, J., J. Rosenfield, and D. Reuter, 1983: An accurate radiative transfer model 
677 for use in the direct physical inversion of HIRS and MSU temperature sounding 
678 data. J. Geophys. Res., 88, 8550-8568. 

679 ~‘Takacs, L. L., M. J. Suarez, and R. Todling, 2016: Maintaining atmospheric mass and 
680 water balance in reanalyses. Quart. J. Roy. Meteor. Soc., 142 1565-1573. doi: 
a 10.1002/qj.2763. 

ce Taylor, K. E., D. Williamson, and F. Zwiers, 2000: The sea surface temperature and 
683 sea ice concentration boundary conditions for AMIP II simulations. Program for 
684 Climate Model Diagnosis and Intercomparison (PCMDI). Report 60, Lawrence 
685 Livermore National Laboratory. 


«6 ‘Trenberth, K.E., and L. Smith, 2005: The mass of the atmosphere: A constraint on 


687 global analysis. J. Climate, 18, 864-875. 

vss Turner, J., S. Colwell, G. Marshall, T. Lachlan-Cope, A. Carleton, P. Jones, V. Lagun, 
689 P. Reid, and S. Iagovkina, 2004: The SCAR READER Project: Toward a High- 
1690 Quality Database of Mean Antarctic Meteorological Observations. J. Climate, 
601 17, 2890-2898, doi:10.1175/1520-0442(2004)017<2890:TSRPTA>2.0.CO;2. 

coo van der Werf, G. R., J. Y. Randerson, L. Giglio, G. J. Collatz, P. S. Kasibhatla, and A. 
1693 F. Arellano Jr., 2006: Interannual variability in global biomass burning emissions 
694 from 1997 to 2004. Atmos. Chem. Phys., 6, 3423-3441, doi:10.5194/acp-6-3423- 
695 2006. 


68 


1696 


1697 


1698 


1699 


1700 


1701 


1702 


1703 


1704 


1705 


1706 


1707 


1708 


1709 


1710 


1711 


1712 


1713 


1714 


1715 


Wargan, K., and L. Coy, 2016: Strengthening of the Tropopause Inversion Layer during 
the 2009 Sudden Stratospheric Warming: A MERRA-2 Study. J. Atmos. Sci., 
73, 1871-1887, doi: 10.1175/JAS-D-15-0333.1 


Wargan, K., G. Labow, S. Frith, S. Pawson, and G. Partyka, 2017: Evaluation of the 
ozone fields in NASAs MERRA-2 reanalysis. J. Climate, doi: 10.1175/JCLI-D- 
16-0699.1. 


Wargan, K., S. Pawson, M. A. Olsen, J. C. Witte, A. R. Douglass, J. R. Ziemke, S. E. 
Strahan, and J. E. Nielsen, 2015: The global structure of upper troposphere-lower 
stratosphere ozone in GEOS-5: A multiyear assimilation of EOS Aura data, J. 
Geophys. Res. Atmos., 120, 2013-2036, doi:10.1002/ 2014JD022493. 


World Meteorological Organization (WMO 2014): Scientific Assessment of Ozone De- 
pletion: 2014. Global Ozone Research and Monitoring Project - Report no. 55. 


Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis 
with spatially inhomogeneous covariances. Mon. Wea. Rev., 180, 2905-2916. 


Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu, and S. Yang, 2007: 
A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeorol., 
8, 607-626. 


Yu, H., and Coauthors, 2006: A review of measurement-based assessments of the 


aerosol direct radiative effect and forcing. Atmos. Chem. Phys., 6, 613-666, 
doi:10.5194/acp-6-613-2006. 


69 


Table 1: Observation types assimilated in MERRA-2, including their usage dates and 
sources. Bold fonts indicate observation types not assimilated in MERRA. Acronyms 


are defined in the Appendix. 


Data Type 


Raob, Pibal, Dropsonde 

AIREP, PIREP, ASDAR, MDCRS aircraft 
PAOB 

Surface land 

Surface ship and buoy 


MERRA-2 Dates 


Conventional 


1 Jan 1980—present 
1 Jan 1980—present 
1 Jan 1980-17 Aug 2010 
1 Jan 1980—present 
1 Jan 1980—present 


Ground-Based Remotely Sensed 


Wind profiler 
NEXRAD VAD wind 


14 May 1992-present 
16 June 1997—present 


Satellite-Derived Wind 


GMS, MTSAT, Himawari atmos. motion vector 
Meteosat atmos. motion vector 
GOES atmos. motion vector 
AVHRR atmos. motion vector 
SSM/I surface wind speed 

ERS-1 surface wind vector 

ERS-2 surface wind vector 
QuikSCAT surface wind vector 
MODIS atmos. motion vector 
SSMIS surface wind speed 
WindSat surface wind vector 
ASCAT surface wind vector 


1 Jan 1980—present 

1 Jan 1980—present 

1 Jan 1980—present 

1 Oct 1982—present 

9 Jul 1987-4 Nov 2009 

5 Aug 1991-21 May 1996 

19 Mar 1996-29 Mar 2011 
19 Jul 1999-22 Nov 2009 

2 Jul 2002—present 

23 Oct 2003-29 Oct 2013 
13 Aug 2007—4 Aug 2012 
15 Sep 2008—present 


Satellite-Retrieved 


SBUV, SBUV/2 ozone 
SSM/T rain rate 

TMI rain rate 

MLS temperature 

MLS ozone 

OMI total column ozone 


1 Jan 1980-31 Sep 2004 
9 Jul 1987-16 Sep 2009 
1 Jan 1998-8 Apr 2015 
13 Aug 2004—present 
1 Oct 2004—present 
1 Oct 2004—present 


Radio Occultation 


GPSRO bending angle 


14 July 2004—present 


Satellite Radiance 


TOVS 

SSM/I 

ATOVS (NOAA-15, -16, -17, -18) 
GOES (G08, G10, G11, G12 Low Res.) 
AMSU-A (Aqua) 

AIRS 

GOES (G11, G12, G13, G15 Full Res.) 
ATOVS (NOAA-19, Metop-A, -B) 
IASI 

ATMS 

SEVIRI 

CrIS 


1 Jan 1980-10 Oct 2006 
9 Jul 1987-4 Nov 2009 
21 Jul 1998—present 

24 April 2001-31 March 2007 
1 Sep 2002—present 

1 Sep 2002—present 

1 April 2007—present 
21 May 2007—present 
17 Sep 2008—present 
16 Nov 2011—present 
15 Feb 2012—present 
7 Apr 2012—present 


70 


Source 


See Rienecker et al. (2011) 


NCEP, ECMWF, JMA 
BOM 

NCEP 

ICOADS 


UCAR, NCEP 
NCEP 


NCEP, JMA 
NCEP, EUMETSAT 
NCEP 
CIMSS 

RSS 

ESA 

ESA 

JPL 

CIMSS, NCEP 
RSS 

NCEP 
NCEP 


NASA/GES DISC 
NASA/GES DISC 
NASA/GES DISC 
NASA/GES DISC 
NASA/GES DISC 
NASA/GES DISC 


NCAR, NCEP 


NCAR, NESDIS 
RSS 

NESDIS 

NCEP, NESDIS 
NASA/GES DISC 
NASA/GES DISC 
NESDIS 
NESDIS 
NESDIS 
NESDIS 
NESDIS 
NESDIS 


Table 2: Nominal channel selections for satellite radiances assimilated in MERRA-2. 
Usage can vary for individual satellite platforms as a result of sensor failure or quality 


control decisions. 


Sensor 
MSU 
AMSU-A 
ATMS 
AMSU-B 
MHS 
SSM/I 
Sou 
HIRS 
AIRS 
IASI 
CrIS 
GOES Sounder 
SEVIRI 


Assimilated Channels 
2-4 
4-14 
5-15, 17-22 
1-5 


2-8, 10-12 
See McCarty et al. 2016 
See McCarty et al. 2016 
See McCarty et al. 2016 
1-8, 10-12 
a) 


Table 3: Sea surface temperature and sea ice concentration data products used in 


MERRA-2. 


MERRA-2 dates 

1 January 1980 — 31 December 1981 
1 January 1982 — 31 December 2002 
1 January 2003 — 31 March 2006 

1 April 2006 — present 


SST and SIC product 

CMIP mid-monthly 1° 

NOAA OISST daily 1/4° (AVHRR) 

NOAA OISST daily 1/4° (AVHRR, AMSR-E) 
OSTIA daily 1/20° 


71 


Table 4: Clear-sky Direct Radiative Effect (DRE) from Reanalyses and Observations 


Yu et al. (2006) Yu et al. (2006) MERRA-2° MERR AeroS MACC4 
Obs.* Models? 


Land-area Average 


AOD 0.225 + 0.038 0.178 + 0.029 0.180 + 0.027 0.171 + 0.030 0.203 + 0.030 


AAOD = = 0.012 + 0.002 0.016 + 0.003 0.010 + 0.003 
TOA DRE -4.85 + 0.45 -2.80 + 1.19 -3.09 + 0.62 -3.11 + 0.70 -6.40 + 1.00 
SFC DRE -11.70 +£1.20 -7.20 + 1.86 -8.35 + 1.82 -8.64 + 2.04 -11.50 + 1.90 
ATM DRE 6.85 + 0.75 4.90 + 0.81 5.26 + 1.23 5.53 + 1.37 5.10 


Ocean-area Average 


AOD 0.138 + 0.024 0.100 + 0.042 0.123 40.008 0.111 + 0.010 0.170 + 0.030 
AAOD - — 0.005 + 0.001 0.005 + 0.001 0.007 + 0.001 
TOA DRE -5.45 + 0.70 -3.50 + 1.28 -3.65 + 0.21 -3.44 + 0.24 -7.70 + 1.50 
SFC DRE -8.80 + 1.65 -4.80 + 1.60 -5.74 + 0.41 -5.58 + 0.47 -10.60 + 1.90 
ATM DRE 3.60 + 1.30 1.30 + 0.72 2.09 + 0.27 2.14 + 0.29 2.90 


*Median and standard deviation from satellite-derived estimates in Yu et al. (2006). 

>Median and standard deviation from 4 global models in Yu et al. (2006). 

©Climatological global area-weighted average (+ monthly standard deviation) for Y2003—Y2010. 
4For MACC, the Y2003-Y2010 global mean and uncertainty is given following Bellouin et al. (2013). 


i 


1716 


List of Figures 


1 Observations assimilated per 6-hr cycle in (a) MERRA and (b) MERRA- 
2. The temporary spike in the number of surface wind observations assim- 
ilated in MERRA-2 in late 2000 is due to an error in the pre-processing 
OL OUI RS CA ata, = fo ope: her do Ble & dn neh Bk Ge enks Ssh le at Shae Bi 78 


2 Globally integrated monthly-mean mass anomalies from the mean sea- 
sonal cycle for (a) MERRA and (b) MERRA-2. Shown are the anoma- 
lies of total mass (black dotted), and their decomposition into atmo- 
spheric water (blue) and dry air (orange). The units are hPa. Results 
for MERRA-2 are derived from the data collection described in GMAO 
QUIS i Soles tert Oa ela had gee alehty SMe dts eee 79 


3S Globally integrated monthly-mean total water budget terms for (a) MERRA 
and (b) MERRA-2. Shown are the water source term (FE —P, blue), verti- 
cally integrated analysis increment of water (green), and atmospheric wa- 
ter storage (black dotted). The units are mm day~'. Results for MERRA- 
2 are derived from the data collections described in GMAO (2015b, d, e). 80 


4 Mean difference (1980-2015) between the (corrected) MERRA-2 precip- 
itation seen by the land surface and the model-generated precipitation 
within the MERRA-2 system. The units are mm d~'. Results are de- 
rived from the data collections described in GMAO (2015h, j). ..... 81 


5 Time series of 12-month running mean prescribed sea surface temperature 
for various reanalyses, averaged between 60°N and 60°S. The units are K. 
Results for MERRA-2 are derived from the data collection described in 
GMNOFO0LSE SS adie ise ktpeticd ts Denand te niente ath ind ate any 82 


6 Monthly mean (thick lines) and RMS (thin lines) background depar- 
tures for surface pressure observations assimilated in MERRA (blue) and 
MERRA-2 (red). Results are shown for the (a) Northern Hemisphere 
and (b) Southern Hemisphere. The units are hPa. Also shown are the 
corresponding monthly mean counts of surface pressure observations as- 
similated in MERRA-2 (gray shaded). ...............-0-4. 83 


73 


1764 


1765 


1766 


1767 


1768 


1769 


1770 


1771 


1772 


1773 


1774 


1775 


1776 


1777 


1778 


1779 


1780 


1781 


1782 


10 


11 


12 


13 


Global monthly mean (thick lines) and RMS (thin lines) background de- 
partures for radiosonde temperature observations assimilated in MERRA 
(blue) and MERRA-2 (red). Results are shown for the pressure levels 
(a) 10 hPa, (b) 50 hPa, (c) 300 hPa and (d) 700 hPa. The units are 
Kk. Also shown are the corresponding monthly mean counts of radiosonde 
temperature observations assimilated in MERRA-2 (gray shaded). 


As in Figure 7, except for radiosonde specific humidity observations in 
the tropics (20°N—20°S) at (a) 500 hPa and (b) 850 hPa. The units are g 
BOO, atts Ete we Oates eat olen actin oh, Aon gs GH An eles PS ech tha wet Bae cas 


(a) Mean and (b) standard deviation of the monthly mean analysis ten- 
dency of surface pressure for the period January 1980 through December 
2015. Monthly mean values are based on four synoptic times daily. The 
units are hPa day~'. Results are derived from the data collection de- 
scribed ii-GMAQ (015K e072 Gc Dee ge oe BS ee Be le 


Global (a) mean and (b) standard deviation of the monthly mean analysis 
tendency of temperature from 1000 to 70 hPa. Monthly means values are 
based on four synoptic times daily. The units are K day‘. Results are 
derived from the data collection described in GMAO (2015n). ...... 


As in Figure 10, except for specific humidity in the tropics (20°N—20°S) 
from 1000 to 250 hPa. The units are g kg~! day~!. Results are derived 
from the data collection described in GMAO (20151). ........... 


Probability distribution functions (PDFs) of observation minus forecast 
(O-F, dashed) and observation minus analysis (O-A, solid) differences in 
observation space, collocated in space and time for each sensor in the 
MERRA-2 aerosol observing system. The PDFs are calculated from in- 
novation data in log-transformed space (In(AOD+0.01)) to ensure distri- 
butions are positive and Gaussian. The time periods considered include 
AVHRR (1993-1999), MODIS Terra (2001-2014), MODIS Aqua (2003- 
2014), MISR (2001-2012), and AERONET (ANET 2000-2013). ..... 


Time series of area-weighted aerosol optical depth (AOD) from the MERRA- 
2 aerosol reanalysis averaged over major aerosol source regions: (a) South 
and East Asia [5°N-55°N, 65°W-160° W], (b) northern Africa [2.5°S—30°N, 
45°W-15°E], and (c) the Amazon Basin in South America [20°S—7.5°N, 
80°W-30°W]. The total AOD (thick black line) is the sum of contribu- 
tions from sea salt (blue), dust (yellow), carbonaceous (black and organic 
carbon, green), and sulfate (grey) AOD. Results are derived from the data 
collection described in GMAO (2015g). ..............-004. 


74 


84 


1783 


1784 


1785 


1786 


1787 


1788 


1789 


1790 


1791 


1792 


1793 


1794 


1795 


1796 


1797 


1798 


1799 


1800 


B @® YN fF Oo 


ior) ior) ice) ior) ior) ior) ior) ior) ior) ior) 


oO ior) N a oa 


14 


15 


16 


17 


18 


Aerosol optical depth (AOD) from aerosol reanalyses (MERRA-2, MER- 
RAero, NAAPS, MACC), inter-model comparisons (AeroCom Phase I, 
Yu_Model), and observations (Yu_Obs) for the period 2003-2010. Where 
available, total AOD is broken down by component species (left bar) and 
by fine and coarse mode (right bar). For MERRA-2 and MERRAcro, the 
error bar represents the standard deviation of the monthly-mean AOD 
for the period 2003-2010. For MACC, the error bar is the uncertainty in 
the total AOD from Bellouin et al. (2013). AeroCom (Kinne et al., 2006) 
and Yu et al. (2006) uncertainty are the inter-model or inter-observational 
standard deviations. Coarse mode is defined as the sum of dust plus sea 
salt AOD, with the remainder of the AOD assigned to the fine mode. 
Results for MERRA-2 are derived from the data collection described in 
GRO OU e).- en ehec che ty cn Brn en Puan mi eh Gace 


Time series of 12-month running mean globally averaged precipitation for 
several reanalyses and the GPCP merged gauge satellite data product. 
The units are mm day~!. Results for MERRA-2 are derived from the 
data collection described in GMAO (2015h). . 2... ......00.0. 


Time-averaged precipitation differences during June-July-August for (a) 
MERRA minus GPCP and (b) MERRA-2 minus GPCP for the period 
1980-2015. The units are mm day~!. Results for MERRA-2 are derived 
from the data collection described in GMAO (2015h). 2. ........ 


Time series of midwestern US summer seasonal precipitation anomalies, 
following Bosilovich (2013). The anomalies are computed from the June- 
July-August mean for the period 1980-2011. The gauge data are from 
NOAA/CPC gridded daily data for the US (Xie et al. 2007). The units 
are mm day~!. Results for MERRA-2 are derived from the data collection 
described in GMAQ: (2015H).. 6p eae a Me Se ERG ee EES 


Regional summary statistics for the US summer seasonal anomaly time 
series of precipitation: (a) mean (mm day~'), (b) standard deviation (mm 
day~'), and (c) anomaly correlation to CPC gauge observations. The 
anomalies are computed from the June-July-August mean for the period 
1980-2011. The regions lie within the continental US and are defined as 
in Bosilovich (2013): Northeast (NE), Southeast (SE), Midwest (MW), 
Great Plains (GP), Southern Great Plains (SGP), Northern Great Plains 
(NGP), Northwest (NW), Southwest (SW), and the accumulation of all 
area in these regions (US). Results for MERRA-2 are derived from the 
data collection described in GMAO (2015h). 2... . 2... 0. 


75 


1831 


1832 


1833 


1834 


1835 


1836 


1837 


1838 


1839 


1840 


1841 


1842 


1843 


1844 


1845 


1846 


1847 


848 


849 


850 


851 


852 


853 


19 


20 


Pall 


22 


23 


Average amount of precipitation that exceeds the 99th percentile during 
June-July-August for the period 1980-2013 for (a) MERRA, (b) MERRA- 
2, and (c) CPC gauge observations. Panel (d) shows the closeness of 
each reanalysis to the CPC observations for the same period, defined as 
|MERRA-2 — CPC| — |MERRA — CPC], where the vertical bars indicate 
absolute differences and the names indicate the set of time-averaged grid- 
point values for each data type. In (d), blue (red) shades indicate that 
MERRA-2 (MERRA) is closer to the CPC observations. The units in all 
panels are mm day~?. Results for MERRA-2 are derived from the data 
collection described in GMAO (2015d). 2... 2.2... . ee ee ee 


Ertel’s potential vorticiity (EPV, x10° potential vorticity units, PVU; 
1 PVU = 10°°m~’s"'K kg) at 0.7 hPa on 2 January 1995 12 UTC 
for (a) MERRA and (b) MERRA-2 for the Northern Hemisphere. Polar 
cap detail (80°-90°N) for (c) MERRA and (d) MERRA-2. Color shading 
interval is 2.5x 10? PVU. Black contour interval is 10x 10? PVU in (a) and 
(b) and 5 x 10° PVU in (c) and (d). Cyan circle denotes 80°N latitude. 
Results are derived from the data collection described in GMAO (2015c). 


Time-altitude section of zonally averaged temperature at 70°N for (a) 
MERRA and (b) MERRA-2. The time resolution is twice daily (00 and 
12 UTC) for December 2005—March 2006. The contour interval is 5 K,. 


Monthly and globally averaged temperature anomaly for MERRA-2 as a 
function of time. The annual cycle and mean for 1980-2015 have been 
removed. The MLS temperatures were introduced at levels above 5 hPa 
beginning in August 2004. Results are derived from the data collection 
described in'G MAO" (DOGG). » 2-43 4 Cea ot tend oe Se ave BB a 


Time series of (a) total ozone (Dobson units, DU) at the South Pole 
derived from individual ozonesonde measurements (gray) and from collo- 
cated values in MERRA (blue) and MERRA-2 (red). Note that ozonesonde 
measurements are unavailable prior to 1986; see text for details. The 
reanalysis-minus-ozonesonde differences divided by sonde total ozone are 
shown in (b) for MERRA (blue) and MERRA-2 (red). The black vertical 
line in (b) separates the SBUV and Aura periods. (Figure from Wargan 
et al. 2016.) Results for MERRA-2 are derived from the data collection 
deseribed an-G MAG: (20154): ic ears hate be he eee a eee 


76 


97 


98 


24 


25 


26 


Time series of the Antarctic ozone hole area calculated from MERRA-2 
ozone fields averaged between 20 September and 10 October for the years 
1980-2015 (red curve with circles). Also shown are values derived from 
TOMS (gray squares) and OMI (black triangles) observations. The units 
are 10° km?. Results for MERRA-2 in 1994 are excluded due to insuf- 
ficient SBUV data coverage in the Southern Hemisphere, which signifi- 
cantly degraded the analysis; see text for details. Results for MERRA-2 
are derived from the data collection described in GMAO (2015a). 


Average annual cycle of 2-m air temperature in MERRA and MERRA-2 
at (a) South Pole station (90°S; 1980-2014; Turner et al., 2004), (b) Gill 
automatic weather station (80°S, 179°W; 1985-2014; Turner et al., 2004), 
and (c) Summit, Greenland (73°N, 38°W; 2000-2002; Hoch, 2005). The 
units are °C. Vertical bars denote +1 standard deviation of the multi-year 
time series for each month. Results for MERRA-2 are derived from the 
data collections described in GMAO (2015i,j,m). ............ 


Surface mass balance for the Greenland Ice Sheet for the period 1980-2012 
in (a) MERRA, (b) MERRA-2, and (c) MAR regional climate model (Fet- 
tweis 2007). The units are mm yr! water-equivalent. Surface topography 
(including ice sheet) is contoured with dashed lines every 200 m. Results 
for MERRA-2 are derived from the data collections described in GMAO 
G5 ie FW. dees Se cers WG Been de a Pte ULE Ue oe ety col tne 


ee 


2 11 


ul 


x10° . (a) MERRA observations 


iN 


WwW 
T 
L 


N 
T 


Observation Count 
— 


pene 


1995 2000 2005 2010 2015 


1990 


0 = 
1980 


1985 


x10° (b) MERRA-2 observations 


N Ww 4 ul 


Observation Count 
i 


eM NR Is pj ent VINA A 


0 ™ 
1980 1985 1990 1995 2000 2005 2010 2015 
ME Conventional {9 AIRS MM Geo IR GMB Heritage MW © Precip 
Mm Aircraft Ml AMV ©) GPSRO ls IAS! Mim Sfc Wind 
Ml Advanced MW M@MCriS —__— Heritage IR MN Ozone fm SSMI 


Figure 1: Observations assimilated per 6-hr cycle in (a) MERRA and (b) MERRA-2. 
The temporary spike in the number of surface wind observations assimilated in MERRA- 
2 in late 2000 is due to an error in the pre-processing of QuikSCAT data. 


78 


1 
0.20 | Pere ee (a) MERRA 
0.15 + Water . - . 
| Dry Air 
0.10 | - as 
q 
2 0.05 ae 
0.00 fh uy Vi PA IY 
re Ss el WV AVY V 
~0.05 fh Wars 
0.10 + ve 
| 
-0.15 5 
-0.20 | 
1980 1985 1990 1995 2000 2005 2010 2015 
1 
0.20 | ers ee (b)- MERRA—2 
0.15 7 —— Water Z . : : : 
0.10 { Dry Air 
1 
0.05 4 veeede 
oO 0.00 +“ fo" hos Ke, NN pt | 
1 Maar 
-0.05 | . y 
-0.10 4 
| 
-0.15 4 
-0.20 4 
1980 1985 1990 1995 2000 2005 2010 2015 


Figure 2: Globally integrated monthly-mean mass anomalies from the mean seasonal 
cycle for (a) MERRA and (b) MERRA-2. Shown are the anomalies of total mass (black 
dotted), and their decomposition into atmospheric water (blue) and dry air (orange). 
The units are hPa. Results for MERRA-2 are derived from the data collection described 
in GMAO (2015b). 


79 


mm/day 
o 2 
S 


40 + . | 
1980 1985 1990 1995 2000 2005 2010 2015 


0.40 5 : 
(b) MERRA-2 


mm/day 
Oo 
S 


E-P 
Analysis Tendency 
acini stat Water Storage 


-0.40 | 
1980 1985 1990 1995 2000 2005 2010 2015 


Figure 3: Globally integrated monthly-mean total water budget terms for (a) MERRA 
and (b) MERRA-2. Shown are the water source term (£— P, blue), vertically integrated 
analysis increment of water (green), and atmospheric water storage (black dotted). The 
units are mm day~'. Results for MERRA-2 are derived from the data collections de- 
scribed in GMAO (2015b, d, e). 


80 


8.0 
2:0 

. 1.0 

of. 62 

2 0.5 
-30 “30 
es -8.0 


-60;80 -120.-60 0 60 120 180° 


Figure 4: Mean difference (1980-2015) between the (corrected) MERRA-2 precipitation 
seen by the land surface and the model-generated precipitation within the MERRA-2 
system. The units are mm d~'. Results are derived from the data collections described 
in GMAO (2015h, j). 


81 


Sea Surface Temperature (60S-60N) 


| —MERRA-2 
294.2 ——MERRA 
—— ERA-Interim 


1980 1985 1990 1995 2000 2005 2010 2015 


Figure 5: Time series of 12-month running mean prescribed sea surface temperature 
for various reanalyses, averaged between 60°N and 60°S. The units are K. Results for 
MERRA-2 are derived from the data collection described in GMAO (2015f). 


82 


T T T T T T T 
1980 1985 1990 1995 2000 2005 2010 2015 


(b) Southern Hemisphere Surface Pressure O-F 


1980 1985 1990 1995 2000 2005 2010 2015 


Figure 6: Monthly mean (thick lines) and RMS (thin lines) background departures for 
surface pressure observations assimilated in MERRA (blue) and MERRA-2 (red). Re- 
sults are shown for the (a) Northern Hemisphere and (b) Southern Hemisphere. The 
units are hPa. Also shown are the corresponding monthly mean counts of surface pres- 
sure observations assimilated in MERRA-2 (gray shaded). 


83 


(a) 10 hPa Global Radiosonde Temp O-F 


1980 1985 1990 1995 2000 2005 2010 2015 
(b) 50 hPa Global Radiosonde Temp O-F 


T 
rR 
fo) 
Oo 
Oo 


1980 1985 1990 1995 2000 2005 2010 2015 
(c) 300 hPa Global Radiosonde Temp O-F 


1980 1985 1990 1995 2000 2005 2010 2015 
(d) 700 hPa Global Radiosonde Temp O-F 


T T T T T T T 
1980 1985 1990 1995 2000 2005 2010 2015 


Figure 7: Global monthly mean (thick lines) and RMS (thin lines) background de- 
partures for radiosonde temperature observations assimilated in MERRA (blue) and 
MERRA-2 (red). Results are shown for the pressure levels (a) 10 hPa, (b) 50 hPa, (c) 
300 hPa and (d) 700 hPa. The units are K. Also shown are the corresponding monthly 
mean counts of radiosonde temperature observations assimilated in MERRA-2 (gray 


shaded). 


84 


(a) 500 hPa Tropical Radiosonde Humidity O-F 


2.0 
1.5 er rere Seer Seen Lee rere ree Tere 
1.0 7 
0.5 
oO 
~ 0.0 
oO 
-0.5 
-1.0 
-15 
—2.0 + T T T T T T T 0 
1980 1985 1990 1995 2000 2005 2010 2015 
(b) 850 hPa Tropical Radiosonde Humidity O-F 
f C} j : : 
Aap ara Pyyaslmlaalean Alte mall la mocha iM: : 
3 ay if ful ie villa ue | 1 Nay if ain “ Vn ws peda ip My L [vo gat, ¢ ayn 
o 1 ai Ai NINE tae asleeasevail sisnune agentes re eee ere Sy 
, : aud parades , 
= 4 ER LAT tel i agony Puffa a Had Beery pp 
oOo i J any ' 5) ae tT : 
1 : : aF 150 
: : + 100 
=9 - 50 
T T T T T T T 0 
1980 1985 1990 1995 2000 2005 2010 2015 


Figure 8: As in Figure 7, except for radiosonde specific humidity observations in the 
tropics (20°N-20°S) at (a) 500 hPa and (b) 850 hPa. The units are g kg“. 


85 


(a) Surface Pressure Increment Mean 


60E 120E 180 120W 60W 
(b) Surface Pressure Increment Std Dev 


60E 120E 180 : 120W 60W 


Figure 9: (a) Mean and (b) standard deviation of the monthly mean analysis tendency 
of surface pressure for the period January 1980 through December 2015. Monthly mean 
values are based on four synoptic times daily. The units are hPa day~'. Results are 
derived from the data collection described in GMAO (2015k). 


86 


(a) Global Temperature Increment Mean 


0.5 
0.4 
0.3 
j 0.1 


Pressure (hPa) 


1980 8 =1985 


ie | i i] Wea 
oP OR) PT AT] Ay 2 
Pel Wey 0.75 


Pressure (hPa) 


1985 1990 1995 2000 2005 2010 2015 


Figure 10: Global (a) mean and (b) standard deviation of the monthly mean analysis 
tendency of temperature from 1000 to 70 hPa. Monthly means values are based on 
four synoptic times daily. The units are K day~!. Results are derived from the data 
collection described in GMAO (2015n). 


87 


(a) Tropical Humidity Increment Mean 


250 
300 SK SIE OTR a RITA IPO OE ORT OIE ORT OOS 0.5 
0.4 
wo 0.3 
Re 400 b\w chided te 6's 5614 1 el Shale ehh alert emterdiens 0.2 
o 500 0.1 
4, : ; -0.1 
a 5 h ccenaitccshoe ee ee YS A. Au 
wv a la aca a : f pond 
Oo 7004-------- eRe ey SUL eee eer: | . : -0.3 
800 isan 
i CEE es ss hares aba , “0.5 
1000. -aananiapaent aT. 
1980 1985 1990 1995 2000 2005 2010 £2015 
(b) Tropical Humidity Increment Std Dev 
250 ; 3 : ; F $ 
° : ’ ‘ M r : 0.8 
300 seit x wake tent wiatd Ripe ater brere rs Tepe Pe ree ey! ; SSA ees Sa Siete * ey 
o ; ; 0.6 
ro 400 Paes: baaiebieted awaits sa epee ial iA - 
2 5007) 
a ene i 0.3 
wv 0.2 
oO 7004... 0.1 
800 ie 0.05 
900 eo caisweese- Sid 0 
1000 


1980 1985 1990 1995 2000 2005 2010 2015 


Figure 11: As in Figure 10, except for specific humidity in the tropics (20°N—20°S) 
from 1000 to 250 hPa. The units are g kg! day~!. Results are derived from the data 
collection described in GMAO (2015l). 


88 


PDFs in Log-transformed Observation Space 


5.0 
— AVHRR 

ae = AERONET 

4.0 — MISR 
> 3.5 == MObD!|STerra Land |! 
E == MODIS Terra Ocean 
= 3.0+ | 
2 == MODIS Aqua Land 
2 — MODIS Aqua Ocean 
30) 

1.57 : 

1.0} 

0.5 

Booch 


0.0 = : : i = 
-1.0 -0.75 -0.5 -0.25 0.0 0.25 0.5 0.75 1.00 
O - F (dashed) or O - A (solid) 


Figure 12: Probability distribution functions (PDF's) of observation minus forecast (O- 
F,, dashed) and observation minus analysis (O-A, solid) differences in observation space, 
collocated in space and time for each sensor in the MERRA-2 aerosol observing system. 
The PDFs are calculated from innovation data in log-transformed space (In(AOD+0.01)) 
to ensure distributions are positive and Gaussian. The time periods considered include 
AVHRR, (1993-1999), MODIS Terra (2001-2014), MODIS Aqua (2003-2014), MISR 
(2001-2012), and AERONET (ANET 2000-2013). 


89 


(a) Timeseries of global, monthly mean AOD: South and East Asia 


— MERRA-2 
Sea Salt 
0.5 Dust . : 
Carbonacesus Pinatubo Eruption 
ial Sulfate 2003 Siberian Fires 
El Chichon Eruption 
0.3 
0.2 
| Nya {| 
hear NANA | AW hI WW \ 
0.0" [981 1985 1989 1993 1997 2001 2005 2009 2013 
iG (b) Timeseries of global, monthly mean AOD: Northern Africa and Atlantic 
0.8 
0.6 
2010 Dust Event 
0.4 
AW "y Mv My Wy yY\ 
ome | " 1 it iN 
0.0" [981 1985 1989 1993 1997 2001 2005 2009 2013 
(c) Timeseries of global, monthly mean AOD: Amazon Basin 
0.5} 
0.4} 
0.3} 
0.2 
| | 
i | | | 
WU NAG VAM 
0.1} Wi | i | AV AWAY VMN VV AQWNG 
|| Ly \ NN wv) u WAV NMA yyy 
WWI AVG td t ay f| | Wh Vy OA NV pea a IV aN V 
0.0" 7981 1985 1989 1993 1997 2001 2005 2009 2013 


Figure 13: Time series of area-weighted aerosol optical depth (AOD) from the MERRA- 
2 aerosol reanalysis averaged over major aerosol source regions: (a) South and East 
Asia [5°N-55°N, 65°W-160°W], (b) northern Africa [2.5°S-30°N, 45°W-15°E], and (c) 
the Amazon Basin in South America [20°S-7.5°N, 80°W-30°W]. The total AOD (thick 
black line) is the sum of contributions from sea salt (blue), dust (yellow), carbonaceous 
(black and organic carbon, green), and sulfate (grey) AOD. Results are derived from the 
data collection described in GMAO (2015g). 


90 


AOD from Reanalyses, Models, and Observations 


0.25 : . 
a Saleh Fine Mode 
Black Carbon : a Coarse Mode 
6 0.20) “= Organic Carbon EE Total 
° ~ Dust 
= Sea Salt 
<= 
. 
$0.15} 
a 
8 
2 
6 9.10} 
3 
2 
€ 0.05} 
0.0 


MERRA-2 MERRAero NAAPS MACC AeroCom Yu_Model Yu_Obs 


Figure 14: Aerosol optical depth (AOD) from aerosol reanalyses (MERRA-2, MER- 
RAero, NAAPS, MACC), inter-model comparisons (AeroCom Phase I, Yu-_Model), and 
observations (Yu_Obs) for the period 2003-2010. Where available, total AOD is bro- 
ken down by component species (left bar) and by fine and coarse mode (right bar). 
For MERRA-2 and MERRAero, the error bar represents the standard deviation of the 
monthly-mean AOD for the period 2003-2010. For MACC, the error bar is the uncer- 
tainty in the total AOD from Bellouin et al. (2013). AeroCom (Kinne et al., 2006) and 
Yu et al. (2006) uncertainty are the inter-model or inter-observational standard devia- 
tions. Coarse mode is defined as the sum of dust plus sea salt AOD, with the remainder 
of the AOD assigned to the fine mode. Results for MERRA-2 are derived from the data 
collection described in GMAO (2015g). 


91 


Global Precipitation 


—— MERRA-2 
— MERRA 
—— ERA-Interim 
—CFSR 
——JRA-55 
—— GPCP 


1980 1985 1990 1995 2000 2005 2010 2015 


Figure 15: Time series of 12-month running mean globally averaged precipitation for 
several reanalyses and the GPCP merged gauge satellite data product. The units are 
mm day~'. Results for MERRA-2 are derived from the data collection described in 
GMAO (2015h). 


92 


(a) MERRA - GPCP 


= ~ “3 
> 5! et : > 


Figure 16: Time-averaged precipitation differences during June-July-August for (a) 
MERRA minus GPCP and (b) MERRA-2 minus GPCP for the period 1980-2015. The 


units are mm day. Results for MERRA-2 are derived from the data collection de- 
scribed in GMAO (2015h). 


93 


Midwestern US Summer Precipitation Anomaly 


PA ATA 


TAS A AN 
TVW VW SVM 
_* Vr Vv” VW 


Mower mnwoenrRKe Or DOW KR OD 
Bn OODOWDADA HD DODD Oo 
DNnOMnODnaAaDDDD DD ® 
Oe a SE Oe = ae 


2003 
2005 
2007 


2001 
2009 
2011 
2013 


Figure 17: Time series of midwestern US summer seasonal precipitation anomalies, 
following Bosilovich (2013). The anomalies are computed from the June-July-August 
mean for the period 1980-2011. The gauge data are from NOAA/CPC gridded daily 
data for the US (Xie et al. 2007). The units are mm day~!. Results for MERRA-2 are 
derived from the data collection described in GMAO (2015h). 


94 


Mean Precipitation JJA 1980-2011 


ee ee ee a ee 


Wilda 


NE SE MW GP SGP NGP NW SW US 
= CPC ma MERRA-2 & MERRA@ERA-Interim @ JRA-55 


Standard Deviation JJA 1980-2011 »5 


mm/day 


NE SE MW GP SGP NGP NW SW US 
= CPC #MERRA-2 #MERRA®ERA-Interim & JRA-55 


‘6 Anomaly Correlation JJA 1980-2011 <) 


0.9 
0.8 
0.7 
0.6 
0.5 4 
0.4 3 


NE SE MW GP SGP NGP NW SW US 
= MERRA-2 @MERRA @ERA-Interim @JRA-55 


Figure 18: Regional summary statistics for the US summer seasonal anomaly time series 
of precipitation: (a) mean (mm day~'), (b) standard deviation (mm day~'), and (c) 
anomaly correlation to CPC gauge observations. The anomalies are computed from the 
June-July-August mean for the period 1980-2011. The regions lie within the continental 
US and are defined as in Bosilovich (2013): Northeast (NE), Southeast (SE), Midwest 
(MW), Great Plains (GP), Southern Great Plains (SGP), Northern Great Plains (NGP), 
Northwest (NW), Southwest (SW), and the accumulation of all area in these regions 
(US). Results for MERRA-2 are derived from the data collection described in GMAO 
(2015h). 


95 


c) Observations 


- 
a 


Lae 
tea ee 


10 20 30 40 50 60 70 80 90 100110120 
JJA Precipitation > 99th Percentile 


Figure 19: Average amount of precipitation that exceeds the 99th percentile during 
June-July-August for the period 1980-2013 for (a) MERRA, (b) MERRA-2, and (c) 
CPC gauge observations. Panel (d) shows the closeness of each reanalysis to the CPC 
observations for the same period, defined as |MERRA-2 — CPC| — |MERRA — CPCI, 
where the vertical bars indicate absolute differences and the names indicate the set of 
time-averaged grid-point values for each data type. In (d), blue (red) shades indicate 
that MERRA-2 (MERRA) is closer to the CPC observations. The units in all panels 
are mm day~!. Results for MERRA-2 are derived from the data collection described in 
GMAO (2015d). 


96 


MERRA-2 


MERRA-2 


Figure 20: Ertel’s potential vorticiity (EPV, x 10° potential vorticity units, PVU; 1 PVU 
= 10°°m~’s~'K kg) at 0.7 hPa on 2 January 1995 12 UTC for (a) MERRA and (b) 
MERRA-2 for the Northern Hemisphere. Polar cap detail (80°-90°N) for (c) MERRA 
and (d) MERRA-2. Color shading interval is 2.5 x 10* PVU. Black contour interval is 
10 x 10° PVU in (a) and (b) and 5 x 10% PVU in (c) and (d). Cyan circle denotes 80°N 
latitude. Results are derived from the data collection described in GMAO (2015c). 


97 


(a) MERRA 70°N 


0.01 
0.1 e 
Ss = 
© o 
= 1 3 
S = 
2 <q 
@o 
a a 
2 
100 oa 
1000 
December 
2005 
CU Eee eee 
175 180 185 190 195 200 205 210 215 220 225 230 235 240 245 250 255 260 265 270 275 280 285 
(b) MERRA-2 70°N 
0.01 TT 80 
0.1 as 
_ co 
© o 
= 1 3 
- = 
5 40a 
7) 10 p nS 2 
g @ 3 
ee ; 
202 
100 oa 
1000 


December February 
2005 20' 2006 


175 180 185 190 195 200 205 210 215 220 225 230 235 240 245 250 255 260 265 270 275 280 285 


Figure 21: Time-altitude section of zonally averaged temperature at 70°N for (a) 
MERRA and (b) MERRA-2. The time resolution is twice daily (00 and 12 UTC) 
for December 2005—March 2006. The contour interval is 5 K. 


98 


MERRA-2 Global Temperature Anomalies 


- MA | 
int , , A IE | , 
bie LS . 
1000 - i , h | ‘th ! | 7 i 


1980 1985 1990 1995 2000 2005 2010 2015 

T (kK) 
Figure 22: Monthly and globally averaged temperature anomaly for MERRA-2 as a 
function of time. The annual cycle and mean for 1980-2015 have been removed. The 


MLS temperatures were introduced at levels above 5 hPa beginning in August 2004. 
Results are derived from the data collection described in GMAO (2015c). 


99 


(a) Total ozone at the South Pole 


400 : 
s50E—+ = 
S 300k i A hE ; ) 
2 AW | HAM NAY MAA RW A VG) = 
E- 4 q i | | } I } q f I Pant IF Hf i al 
2 250 \ NY Hs | i / 4 } NK) \ i! | : 
& 200= raed et | 1) FI = 
E | Station y | = 
150 E-|MERRA = 
100£ MERRA-2 = 
1980 1990 2000 2010 
(b) Relative difference with ozonesonde data 
150, —— : 
z 100 
8 e + 
5 50|- e 
2 F&F : 
a OF 1 
-50E + 
1980 


Figure 23: Time series of (a) total ozone (Dobson units, DU) at the South Pole derived 
from individual ozonesonde measurements (gray) and from collocated values in MERRA 
(blue) and MERRA-2 (red). Note that ozonesonde measurements are unavailable prior 
to 1986; see text for details. The reanalysis-minus-ozonesonde differences divided by 
sonde total ozone are shown in (b) for MERRA (blue) and MERRA-2 (red). The black 
vertical line in (b) separates the SBUV and Aura periods. (Figure from Wargan et al. 
2016.) Results for MERRA-2 are derived from the data collection described in GMAO 
(2015a). 


100 


Ozone hole area 


—e— MERRA-2 


vy OM 


[10°%km’] 
a 


OEN 


1980 1990 2000 2010 
Year 


Figure 24: Time series of the Antarctic ozone hole area calculated from MERRA-2 
ozone fields averaged between 20 September and 10 October for the years 1980-2015 
(red curve with circles). Also shown are values derived from TOMS (gray squares) and 
OMI (black triangles) observations. The units are 10° km?. Results for MERRA-2 in 
1994 are excluded due to insufficient SBUV data coverage in the Southern Hemisphere, 
which significantly degraded the analysis; see text for details. Results for MERRA-2 are 
derived from the data collection described in GMAO (2015a). 


101 


South Pole (90°S), 1980-2014 


-20°C + 
-30°C + 
40°C f 
50°C + 
-60°C + 


76°C 
}EMAMJJAS OND) F (a) 


Gill AWS (80°S, 179°W), 1985-2014 
O°C + 


-10°C | 
Ie = 
-30°C | 
-40°C | 


-50°C £ 
}FMAMJJAS OND) F (b) 


Summit (73°N, 38°W), 2000-2002 
-10°C + 


-20°C + 
-30°C £ 
-40°c | 
“50°C 
60°C | 


1FMAMJ1 AS ONDJe (¢) 
-Station —MERRA —MERRA-2 


Figure 25: Average annual cycle of 2-m air temperature in MERRA and MERRA-2 at 
(a) South Pole station (90°S; 1980-2014; Turner et al., 2004), (b) Gill automatic weather 
station (80°S, 179°W; 1985-2014; Turner et al., 2004), and (c) Summit, Greenland (73°N, 
38°W; 2000-2002; Hoch, 2005). The units are °C. Vertical bars denote +1 standard 
deviation of the multi-year time series for each month. Results for MERRA-2 are derived 
from the data collections described in GMAO (2015i, j, m). 


102 


4000 
2000 
1000 
700 
500 
300 
200 
100 
50 
20 
-20 
-50 
-100 
-200 
-300 
-500 
-700 
-1000 
-2000 
-4000 


(c) 


Figure 26: Surface mass balance for the Greenland Ice Sheet for the period 1980-2012 
in (a) MERRA, (b) MERRA-2, and (c) MAR regional climate model (Fettweis 2007). 
The units are mm yr! water-equivalent. Surface topography (including ice sheet) is 
contoured with dashed lines every 200 m. Results for MERRA-2 are derived from the 
data collections described in GMAO (2015i, j, m). 


103