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Yonghua Chen
,
Anthony D. Del Genio
, and
Junye Chen

Abstract

Aspects of the tropical atmospheric response to El Niño related to the global energy and water cycle are examined using satellite retrievals from the Tropical Rainfall Measuring Mission and the Advanced Microwave Scanning Radiometer-E and simulations from the Goddard Institute for Space Studies (GISS) general circulation model (GCM). The El Niño signal is extracted from climate fields using a linear cross-correlation technique that captures local and remote in-phase and lagged responses. Passive microwave and radar precipitation anomalies for the 1997/98 and 2002/03 El Niños and the intervening La Niña are highly correlated, but anomalies in stratiform–convective rainfall partitioning in the two datasets are not. The GISS GCM produces too much rainfall in general over ocean and too little over land. Its atmospheric response to El Niño is weaker and decays a season too early. Underestimated stratiform rainfall fraction (SRF) and convective downdraft mass flux in the GISS GCM and excessive shallow convective and low stratiform cloud result in latent heating that peaks at lower altitudes than inferred from the data. The GISS GCM also underestimates the column water vapor content throughout the Tropics, which causes it to overestimate outgoing longwave radiation. The response of both quantities to interannual Hadley circulation anomalies is too weak. The GISS GCM’s Walker circulation also exhibits a weak remote response to El Niño, especially over the Maritime Continent and western Indian Ocean. This appears to be a consequence of weak static stability due to the model’s lack of upper-level stratiform anvil heating, excessive low-level heating, and excessive dissipation due to cumulus momentum mixing. Our results suggest that parameterizations of mesoscale updrafts, convective downdrafts, and cumulus-scale pressure gradient effects on momentum transport are keys to a reasonable GISS GCM simulation of tropical interannual variability.

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Michael G. Bosilovich
,
Franklin R. Robertson
, and
Junye Chen

Abstract

Reanalyses, retrospectively analyzing observations over climatological time scales, represent a merger between satellite observations and models to provide globally continuous data and have improved over several generations. Balancing the earth’s global water and energy budgets has been a focus of research for more than two decades. Models tend to their own climate while remotely sensed observations have had varying degrees of uncertainty. This study evaluates the latest NASA reanalysis, the Modern Era Retrospective-Analysis for Research and Applications (MERRA), from a global water and energy cycles perspective, to place it in context of previous work and demonstrate the strengths and weaknesses.

MERRA was configured to provide complete budgets in its output diagnostics, including the incremental analysis update (IAU), the term that represents the observations influence on the analyzed states, alongside the physical flux terms. Precipitation in reanalyses is typically sensitive to the observational analysis. For MERRA, the global mean precipitation bias and spatial variability are more comparable to merged satellite observations [the Global Precipitation and Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP)] than previous generations of reanalyses. MERRA ocean evaporation also has a much lower value, which is comparable to independently derived estimate datasets. The global energy budget shows that MERRA cloud effects may be generally weak, leading to excess shortwave radiation reaching the ocean surface.

Evaluating the MERRA time series of budget terms, a significant change occurs that does not appear to be represented in observations. In 1999, the global analysis increments of water vapor changes sign from negative to positive and primarily lead to more oceanic precipitation. This change is coincident with the beginning of Advanced Microwave Sounding Unit (AMSU) radiance assimilation. Previous and current reanalyses all exhibit some sensitivity to perturbations in the observation record, and this remains a significant research topic for reanalysis development. The effect of the changing observing system is evaluated for MERRA water and energy budget terms.

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Michael G. Bosilovich
,
Junye Chen
,
Franklin R. Robertson
, and
Robert F. Adler

Abstract

Retrospective-analysis (or reanalysis) systems merge observations and models to provide global four-dimensional earth system data encompassing many physical and dynamical processes. Precipitation is one critical diagnostic that is not only sensitive to the observing system and model physics, but also reflects the general circulation. Climate records of observed precipitation through a merged satellite and gauge dataset provide a reference for comparison, though not without their own uncertainty. In this study, five reanalyses precipitation fields are compared with two observed data products to assess the strengths and weaknesses of the reanalyses. Taylor diagrams show the skill of the reanalyses relative to the reference dataset. While there is a general sense that the reanalyses precipitation data are improving in recent systems, it is not always the case. In some ocean regions, NCEP–NCAR reanalysis spatial patterns are closer to observed precipitation than NCEP–Department of Energy. The 40-yr ECMWF reanalysis (ERA-40) produces reasonable comparisons over Northern Hemisphere continents, but less so in the tropical oceans. On the other hand, the most recent reanalysis, the Japanese 25-yr reanalysis (JRA-25), shows good comparisons in both the Northern Hemisphere continents and the tropical oceans but contains distinct variation according to the available observing systems. The statistics and methods used are also tested on short experiments from a data assimilation system proposed to perform a satellite-era reanalysis.

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Hailan Wang
,
Siegfried Schubert
,
Max Suarez
,
Junye Chen
,
Martin Hoerling
,
Arun Kumar
, and
Philip Pegion

Abstract

The observed climate trends over the United States during 1950–2000 exhibit distinct seasonality and regionality. The surface air temperature exhibits a warming trend during winter, spring, and early summer and a modest countrywide cooling trend in late summer and fall, with the strongest warming occurring over the northern United States in spring. Precipitation trends are positive in all seasons, with the largest trend occurring over the central and southern United States in fall. This study investigates the causes of the seasonality and regionality of those trends, with a focus on the cooling and wetting trends in the central United States during late summer and fall. In particular, the authors examine the link between the seasonality and regionality of the climate trends over the United States and the leading patterns of sea surface temperature (SST) variability, including a global warming (GW) pattern and a Pacific decadal variability (PDV) pattern.

A series of idealized atmospheric general circulation model (AGCM) experiments were performed forced by SST trends associated with these leading SST patterns, as well as the residual trend pattern (obtained by removing the GW and PDV contributions). The results show that the observed seasonal and spatial variations of the climate trends over the United States are to a large extent explained by changes in SST. Among the leading patterns of SST variability, the PDV pattern plays a prominent role in producing both the seasonality and regionality of the climate trends over the United States. In particular, it is the main contributor to the apparent cooling and wetting trends over the central United States. The residual SST trend, a manifestation of phase changes of the Atlantic multidecadal SST variation during 1950–2000, also exerts influences that show strong seasonality with important contributions to the central U.S. temperature and precipitation during the summer and fall seasons. In contrast, the response over the United States to the GW SST pattern is an overall warming with little seasonality or regional variation. These results highlight the important contributions of decadal and multidecadal variability in the Pacific and Atlantic in explaining the observed seasonality and regionality of the climate trends over the United States during the period of 1950–2000.

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Franklin R. Robertson
,
Michael G. Bosilovich
,
Junye Chen
, and
Timothy L. Miller

Abstract

Like all reanalysis efforts, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) must contend with an inhomogeneous observing network. Here the effects of the two most obvious observing system epoch changes, the Advanced Microwave Sounding Unit-A (AMSU-A) series in late 1998 and, to a lesser extent, the earlier advent of the Special Sensor Microwave Imager (SSM/I) in late 1987 are examined. These sensor changes affect model moisture and enthalpy increments and thus water and energy fluxes, since the latter result from model physics processes that respond sensitively to state variable forcing. Inclusion of the analysis increments in the MERRA dataset is a unique feature among reanalyses that facilitates understanding the relationships between analysis forcing and flux response.

In stepwise fashion in time, the vertically integrated global-mean moisture increments change sign from drying to moistening and heating increments drop nearly 15 W m−2 over the 30 plus years of the assimilated products. Regression of flux quantities on an El Niño–Southern Oscillation sea surface temperature (SST) index analysis reveals that this mode of climate variability dominates interannual signals and its leading expression is minimally affected by satellite observing system changes. Conversely, precipitation patterns and other fluxes influenced by SST changes associated with Pacific decadal variability (PDV) are significantly distorted. Observing system changes also induce a nonstationary component to the annual cycle signals.

Principal component regression is found useful for identifying artifacts produced by changes of satellite sensors and defining appropriate adjustments. After the adjustments are applied, the spurious flux trend components are greatly diminished. Time series of the adjusted precipitation and the Global Precipitation Climatology Project (GPCP) data compare favorably on a global basis. The adjustments also provide a much better depiction of precipitation spatial trends associated with PDV-like forcing. The utility as well as associated drawbacks of this statistical adjustment and the prospects for future improvements of the methodology are discussed.

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Junye Chen
,
Anthony D. Del Genio
,
Barbara E. Carlson
, and
Michael G. Bosilovich

Abstract

The dominant interannual El Niño–Southern Oscillation (ENSO) phenomenon and the short length of climate observation records make it difficult to study long-term climate variations in the spatiotemporal domain. Based on the fact that the ENSO signal spreads to remote regions and induces delayed climate variation through atmospheric teleconnections, an ENSO-removal method is developed through which the ENSO signal can be approximately removed at the grid box level from the spatiotemporal field of a climate parameter. After this signal is removed, long-term climate variations are isolated at mid- and low latitudes in the climate parameter fields from observed and reanalysis datasets. This paper addresses the long-term global warming trend (GW); a companion paper concentrates on Pacific pan-decadal variability (PDV).

The warming that occurs in the Pacific basin (approximately 0.4 K in the twentieth century) is much weaker than in surrounding regions and the other two ocean basins (approximately 0.8 K). The modest warming in the Pacific basin is likely due to its dynamic nature on the interannual and decadal time scales and/or the leakage of upper ocean water through the Indonesian Throughflow.

Based on the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), a comprehensive atmospheric structure associated with the GW trend is given. Significant discrepancies exist between the two datasets, especially in the tightly coupled dynamics and water vapor fields. The dynamics fields based on NCEP–NCAR, which show a change in the Walker Circulation, are consistent with the GW change in the surface temperature field. However, intensification in the Hadley Circulation is associated with GW trend in ERA-40 instead.

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Junye Chen
,
Anthony D. Del Genio
,
Barbara E. Carlson
, and
Michael G. Bosilovich

Abstract

The spatiotemporal structure of Pacific pan-decadal variability (PDV) is isolated in global long-term surface temperature (ST) datasets and reanalysis atmospheric parameter fields from which El Niño–Southern Oscillation (ENSO) effects have been removed. Empirical orthogonal function (EOF) and combined EOF analysis of the resulting time series identify PDV as one of two primary modes of long-term variability, the other being a global warming (GW) trend, which is addressed in a companion paper (Part I).

In this study, it is shown that one of several PDV interdecadal regime shifts occurred during the 1990s. This significant change in the Pacific basin is comparable but antiphase to the well-known 1976 climate regime shift and is consistent with the observed changes in biosystems and ocean circulation. A comprehensive picture of PDV as manifested in the troposphere and at the surface is described. In general, the PDV spatial patterns in different parameter fields share some similarities with the patterns associated with ENSO, but important differences exist. First, the PDV circulation pattern is shifted westward by about 20° and is less zonally extended than that for ENSO. The westward shift of the PDV wave train produces a different North American teleconnection pattern that is more west–east oriented. The lack of a strong PDV surface temperature (ST) signal in the west equatorial Pacific and the relatively strong ST signal in the subtropical regions are consistent with an atmospheric overturning circulation response that differs from the one associated with ENSO. The analysis also suggests that PDV is a combination of decadal and/or interdecadal oscillations interacting through teleconnections.

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Aaron D. Kennedy
,
Xiquan Dong
,
Baike Xi
,
Shaocheng Xie
,
Yunyan Zhang
, and
Junye Chen

Abstract

Atmospheric states from the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the North American Regional Reanalysis (NARR) are compared with data from the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site, including the ARM continuous forcing product and Cloud Modeling Best Estimate (CMBE) soundings, during the period 1999–2001 to understand their validity for single-column model (SCM) and cloud-resolving model (CRM) forcing datasets. Cloud fraction, precipitation, and radiation information are also compared to determine what errors exist within these reanalyses. For the atmospheric state, ARM continuous forcing and the reanalyses have good agreement with the CMBE sounding information, with biases generally within 0.5 K for temperature, 0.5 m s−1 for wind, and 5% for relative humidity. Larger disagreements occur in the upper troposphere (p < 300 hPa) for temperature, humidity, and zonal wind, and in the boundary layer (p > 800 hPa) for meridional wind and humidity. In these regions, larger errors may exist in derived forcing products. Significant differences exist for vertical pressure velocity, with the largest biases occurring during the spring upwelling and summer downwelling periods. Although NARR and MERRA share many resemblances to each other, ARM outperforms these reanalyses in terms of correlation with cloud fraction. Because the ARM forcing is constrained by observed precipitation that gives the adequate mass, heat, and moisture budgets, much of the precipitation (specifically during the late spring/early summer) is caused by smaller-scale forcing that is not captured by the reanalyses. While reanalysis-based forcing appears to be feasible for the majority of the year at this location, it may have limited usage during the late spring and early summer, when convection is common at the ARM SGP site. Both NARR and MERRA capture the seasonal variation of cloud fractions (CFs) observed by ARM radar–lidar and Geostationary Operational Environmental Satellite (GOES) with high correlations (0.92–0.78) but with negative biases of 14% and 3%, respectively. Compared to the ARM observations, MERRA shows better agreement for both shortwave (SW) and longwave (LW) fluxes except for LW-down (due to a negative bias in water vapor): NARR has significant positive bias for SW-down and negative bias for LW-down under clear-sky and all-sky conditions. The NARR biases result from a combination of too few clouds and a lack of sufficient extinction by aerosols and water vapor in the atmospheric column. The results presented here represent only one location for a limited period, and more comparisons at different locations and longer periods are needed.

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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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