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Clark J. Weaver
,
Paul Ginoux
,
N. Christina Hsu
,
Ming-Dah Chou
, and
Joanna Joiner

Abstract

This study uses information on Saharan aerosol from a dust transport model to calculate radiative forcing values. The transport model is driven by assimilated meteorological fields from the Goddard Earth Observing System Data Assimilation System. The model produces global three-dimensional dust spatial information for four different mineral aerosol sizes. These dust fields are input to an offline radiative transfer calculation to obtain the direct radiative forcing due to the dust fields. These estimates of the shortwave reduction of radiation at the top of the atmosphere (TOA) compare reasonably well with the TOA reductions derived from Earth Radiation Budget Experiment (ERBE) and Total Ozone Mapping Spectrometer (TOMS) satellite data. The longwave radiation also agrees with the observations; however, potential errors in the assimilated temperatures complicate the comparison. Depending on the assumptions used in the calculation and the dust loading, the summertime forcing ranges from 0 to −18 W m−2 over ocean and from 0 to +20 W m−2 over land.

Increments are terms in the assimilation general circulation model (GCM) equations that force the model toward observations. They are differences between the observed analyses and the GCM forecasts. Off west Africa the analysis temperature increments produced by the assimilation system show patterns that are consistent with the dust spatial distribution. It is not believed that radiative heating of dust is influencing the increments. Instead, it is suspected that dust is affecting the Television Infrared Observational Satellite (TIROS) Operational Vertical Sounder (TOVS) satellite temperature retrievals that provide the basis of the assimilated temperatures used by the model.

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J. Le Marshall
,
J . Jung
,
J. Derber
,
M. Chahine
,
R. Treadon
,
S J. Lord
,
M Goldberg
,
W Wolf
,
H C. Liu
,
J Joiner
,
J. Woollen
,
R. Todling
,
P. van Delst
, and
Y. Tahara
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L. Coy
,
I. Štajner
,
A. M. DaSilva
,
J. Joiner
,
R. B. Rood
,
S. Pawson
, and
S. J. Lin

Abstract

The 4-day wave often dominates the large-scale wind, temperature, and constituent variability in the high-latitude Southern Hemisphere winter near the stratopause. This study examines the winter Southern Hemisphere vortex of 1998 using 4-times-daily output from a data assimilation system to focus on the polar 2-day, wavenumber-2 component of the 4-day wave. The data assimilation system products are from a test version of the finite volume data assimilation system (fvDAS) being developed at the Goddard Space Flight Center (GSFC) and include an ozone assimilation system. Results show that the polar 2-day wave in temperature and ozone dominates over other planetary-scale disturbances during July 1998 at 70°S. The period of the quasi-2-day wave is somewhat shorter than 2 days (about 1.7 days) during July 1998 with an average perturbation temperature amplitude for the month of over 2.5 K. The 2-day wave propagates more slowly than the zonal mean zonal wind, consistent with Rossby wave theory, and has Eliassen–Palm (EP) flux divergence regions associated with regions of negative horizontal potential vorticity gradients, as expected from linear instability theory. Results for the assimilation-produced ozone mixing ratio show that the 2-day wave represents a major source of ozone variation in this region. The ozone wave in the assimilation system is in good agreement with the wave seen in the Polar Ozone and Aerosol Measurement (POAM) ozone observations for the same time period. Some differences from linear instability theory are noted, as well as spectral peaks in the ozone field, not seen in the temperature field, that may be a consequence of advection.

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Arthur Y. Hou
,
David V. Ledvina
,
Arlindo M. da Silva
,
Sara Q. Zhang
,
Joanna Joiner
,
Robert M. Atlas
,
George J. Huffman
, and
Christian D. Kummerow

Abstract

This article describes a variational framework for assimilating the SSM/I-derived surface rain rate and total precipitable water (TPW) and examines their impact on the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The SSM/I observations consist of tropical rain rates retrieved using the Goddard Profiling Algorithm and tropical TPW estimates produced by Wentz.

In a series of assimilation experiments for December 1992, results show that the SSM/I-derived rain rate, despite current uncertainty in its intensity, is better than the model-generated precipitation. Assimilating rainfall data improves cloud distributions and the cloudy-sky radiation, while assimilating TPW data reduces a moisture bias in the lower troposphere to improve the clear-sky radiation. Together, the two data types reduce the monthly mean spatial bias by 46% and the error standard deviation by 26% in the outgoing longwave radiation (OLR) averaged over the Tropics, as compared with the NOAA OLR observation product. The improved cloud distribution, in turn, improves the solar radiation at the surface. There is also evidence that the latent heating change associated with the improved precipitation improves the large-scale circulation in the Tropics. This is inferred from a comparison of the clear-sky brightness temperatures for TIROS Operational Vertical Sounder channel 12 computed from the GEOS analyses with the observed values, suggesting that rainfall assimilation reduces a prevailing moist bias in the upper-tropospheric humidity in the GEOS system through enhanced subsidence between the major convective centers.

This work shows that assimilation of satellite-derived precipitation and TPW can reduce state-dependent systematic errors in the OLR, clouds, surface radiation, and the large-scale circulation in the assimilated dataset. The improved analysis also leads to better short-range forecasts, but the impact is modest compared with improvements in the time-averaged signals in the analysis. The study shows that, in the presence of biases and other errors of the forecast model, it is possible to improve the time-averaged “climate content” in the data without comparable improvements in forecast. The full impact of these data types on the analysis cannot be measured solely in terms of forecast skills.

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Michele M. Rienecker
,
Max J. Suarez
,
Ronald Gelaro
,
Ricardo Todling
,
Julio Bacmeister
,
Emily Liu
,
Michael G. Bosilovich
,
Siegfried D. Schubert
,
Lawrence Takacs
,
Gi-Kong Kim
,
Stephen Bloom
,
Junye Chen
,
Douglas Collins
,
Austin Conaty
,
Arlindo da Silva
,
Wei Gu
,
Joanna Joiner
,
Randal D. Koster
,
Robert Lucchesi
,
Andrea Molod
,
Tommy Owens
,
Steven Pawson
,
Philip Pegion
,
Christopher R. Redder
,
Rolf Reichle
,
Franklin R. Robertson
,
Albert G. Ruddick
,
Meta Sienkiewicz
, and
Jack Woollen

Abstract

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) was undertaken by NASA’s Global Modeling and Assimilation Office with two primary objectives: to place observations from NASA’s Earth Observing System satellites into a climate context and to improve upon the hydrologic cycle represented in earlier generations of reanalyses. Focusing on the satellite era, from 1979 to the present, MERRA has achieved its goals with significant improvements in precipitation and water vapor climatology. Here, a brief overview of the system and some aspects of its performance, including quality assessment diagnostics from innovation and residual statistics, is given.

By comparing MERRA with other updated reanalyses [the interim version of the next ECMWF Re-Analysis (ERA-Interim) and the Climate Forecast System Reanalysis (CFSR)], advances made in this new generation of reanalyses, as well as remaining deficiencies, are identified. Although there is little difference between the new reanalyses in many aspects of climate variability, substantial differences remain in poorly constrained quantities such as precipitation and surface fluxes. These differences, due to variations both in the models and in the analysis techniques, are an important measure of the uncertainty in reanalysis products. It is also found that all reanalyses are still quite sensitive to observing system changes. Dealing with this sensitivity remains the most pressing challenge for the next generation of reanalyses.

Production has now caught up to the current period and MERRA is being continued as a near-real-time climate analysis. The output is available online through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC).

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