N
2
23
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-
101
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
3
121
122
123
124
140
43
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
4
145
151
166
167
168
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
175
178
181
182
183
184
185
186
187
188
189
190
191
192
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-
199
207
208
212
213
214
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);
7
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
8
245
246
261
262
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-
263
265
266
267
268
269
270
272
273
275
276
277
278
279
280
282
283
284
285
286
287
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
10
288
289
290
291
292
293
294
295
296
298
299
300
301
302
303
305
306
308
309
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
11
310
312
313
314
315
316
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
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
12
333
335
336
337
338
339
340
342
343
344
345
346
347
348
349
350
352
353
354
355
356
357
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
13
358
359
365
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,
14
382
383
385
386
387
388
389
390
392
393
394
395
396
397
398
399
400
401
402
403
405
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
410
428
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
17
455
456
457
458
459
460
462
463
465
466
468
469
470
472
473
475
476
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.
18
479
480
482
483
485
486
488
489
490
492
493
495
496
498
499
500
502
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
19
503
505
506
507
508
509
510
512
513
514
515
516
518
519
520
522
523
525
526
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
533
545
546
547
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
ZL
552
553
554
555
556
557
558
562
568
572
573
574
575
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-
22
576
578
579
580
581
582
583
585
586
587
588
589
590
591
592
593
595
596
598
599
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
600
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
24
626
632
633
636
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-
Zt
699
705
712
713
722
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
30
773
775
776
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
803
809
822
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
32
823
829
833
835
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.
33
846
848
849
850
851
852
853
855
856
858
859
860
861
862
863
865
866
867
868
869
870
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
34
872
873
874
875
876
877
878
879
880
881
882
886
895
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
896
899
902
913
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
920
926
930
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
37
945
951
957
958
959
968
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
38
969
975
988
989
990
991
992
993
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
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
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
40
1020
1021
022
026
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
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
41
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
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.
42
1068
1069
1070
1071
1072
1073
075
076
1078
1079
1080
1081
1082
1083
1084
1085
1086
087
088
089
090
091
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
43
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
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
44
1117
1118
1119
1120
1121
1122
1123
28
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
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
45
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
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
46
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
76
77
78
79
80
1181
1182
1183
1184
1185
1186
1187
1188
1189
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
AT
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
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
48
1216
1217
1218
1219
1220
1221
1222
223
225
226
228
229
230
231
232
233
235
236
237
238
239
240
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
49
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
251
252
253
254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
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.
50
1267
1268
1269
270
271
272
273
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
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
Live Music Archive
Librivox Free Audio
Metropolitan Museum
Cleveland Museum of Art
Internet Arcade
Console Living Room
Books to Borrow
Open Library
TV News
Understanding 9/11