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Abstract
This paper investigates intraseasonal variability as represented by the recent NASA Global Modeling and Assimilation Office (GMAO) reanalysis, the Modern-Era Retrospective analysis for Research and Applications (MERRA). The authors examine the behavior of heat, moisture, and radiative fluxes emphasizing their contribution to intraseasonal variations in heat and moisture balance integrated over the tropical oceans. MERRA successfully captures intraseasonal signals in both state variables and fluxes, though it depends heavily on the analysis increment update terms that constrain the reanalysis to be near the observations. Precipitation anomaly patterns evolve in close agreement with those from the Tropical Rainfall Measuring Mission (TRMM) though locally MERRA may occasionally be smaller by up to 20%. As in the TRMM observations, tropical convection increases lead tropospheric warming by approximately 7 days. Radiative flux anomalies are dominated by cloud forcing and are found to replicate the top-of-the-atmosphere (TOA) energy loss associated with increased convection found by other observationally based studies. However, MERRA’s convectively produced clouds appear to deepen too soon as precipitation increases. Total fractional cloud cover variations appear somewhat weak compared to observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Evolution of the surface fluxes, convection, and TOA radiation is consistent with the “discharge–recharge” paradigm that posits the importance of lower-tropospheric moisture accumulation prior to the expansion of organized deep convection. The authors conclude that MERRA constitutes a very useful representation of intraseasonal variability that will support a variety of studies concerning radiative–convective–dynamical processes and will help identify pathways for improved moist physical parameterization in global models.
Abstract
This paper investigates intraseasonal variability as represented by the recent NASA Global Modeling and Assimilation Office (GMAO) reanalysis, the Modern-Era Retrospective analysis for Research and Applications (MERRA). The authors examine the behavior of heat, moisture, and radiative fluxes emphasizing their contribution to intraseasonal variations in heat and moisture balance integrated over the tropical oceans. MERRA successfully captures intraseasonal signals in both state variables and fluxes, though it depends heavily on the analysis increment update terms that constrain the reanalysis to be near the observations. Precipitation anomaly patterns evolve in close agreement with those from the Tropical Rainfall Measuring Mission (TRMM) though locally MERRA may occasionally be smaller by up to 20%. As in the TRMM observations, tropical convection increases lead tropospheric warming by approximately 7 days. Radiative flux anomalies are dominated by cloud forcing and are found to replicate the top-of-the-atmosphere (TOA) energy loss associated with increased convection found by other observationally based studies. However, MERRA’s convectively produced clouds appear to deepen too soon as precipitation increases. Total fractional cloud cover variations appear somewhat weak compared to observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Evolution of the surface fluxes, convection, and TOA radiation is consistent with the “discharge–recharge” paradigm that posits the importance of lower-tropospheric moisture accumulation prior to the expansion of organized deep convection. The authors conclude that MERRA constitutes a very useful representation of intraseasonal variability that will support a variety of studies concerning radiative–convective–dynamical processes and will help identify pathways for improved moist physical parameterization in global models.
Abstract
Although El Niño events each have distinct evolutionary character, they typically provide systematic large-scale forcing for warming and increased drought frequency across the tropical continents. We assess this response in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis and in a 10-member-model Atmospheric Model Intercomparison Project (AMIP) ensemble. The lagged response (3–4 months) of mean tropical land temperature to El Niño warming in the Pacific Ocean is well represented. MERRA-2 reproduces the patterns of precipitation in the tropical regions, and the AMIP ensemble reproduces some regional responses that are similar to those observed and some regions that are not simulating the response well. Model skill is dependent on event forcing strength and temporal proximity to the peak of the sea surface warming. A composite approach centered on maximum Niño-3.4 SSTs and lag relationships to energy fluxes and transports is used to identify mechanisms supporting tropical land warming. The composite necessarily moderates weather-scale variability of the individual events while retaining the systematic features across all events. We find that reduced continental upward motions lead to reduced cloudiness and more shortwave radiation at the surface, as well as reduced precipitation. The increased shortwave heating at the land surface, along with reduced soil moisture, leads to warmer surface temperature, more sensible heating, and warming of the lower troposphere. The composite provides a broad picture of the mechanisms governing the hydrologic response to El Niño forcing, but the regional and temporal responses can vary substantially for any given event. The 2015/16 El Niño, one of the strongest events, demonstrates some of the forced response noted in the composite, but with shifts in the evolution that depart from the composite, demonstrating the limitations of the composite and individuality of El Niño.
Abstract
Although El Niño events each have distinct evolutionary character, they typically provide systematic large-scale forcing for warming and increased drought frequency across the tropical continents. We assess this response in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis and in a 10-member-model Atmospheric Model Intercomparison Project (AMIP) ensemble. The lagged response (3–4 months) of mean tropical land temperature to El Niño warming in the Pacific Ocean is well represented. MERRA-2 reproduces the patterns of precipitation in the tropical regions, and the AMIP ensemble reproduces some regional responses that are similar to those observed and some regions that are not simulating the response well. Model skill is dependent on event forcing strength and temporal proximity to the peak of the sea surface warming. A composite approach centered on maximum Niño-3.4 SSTs and lag relationships to energy fluxes and transports is used to identify mechanisms supporting tropical land warming. The composite necessarily moderates weather-scale variability of the individual events while retaining the systematic features across all events. We find that reduced continental upward motions lead to reduced cloudiness and more shortwave radiation at the surface, as well as reduced precipitation. The increased shortwave heating at the land surface, along with reduced soil moisture, leads to warmer surface temperature, more sensible heating, and warming of the lower troposphere. The composite provides a broad picture of the mechanisms governing the hydrologic response to El Niño forcing, but the regional and temporal responses can vary substantially for any given event. The 2015/16 El Niño, one of the strongest events, demonstrates some of the forced response noted in the composite, but with shifts in the evolution that depart from the composite, demonstrating the limitations of the composite and individuality of El Niño.
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.
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.
Abstract
Vertically integrated atmospheric moisture transport from ocean to land [vertically integrated atmospheric moisture flux convergence (VMFC)] is a dynamic component of the global climate system but remains problematic in atmospheric reanalyses, with current estimates having significant multidecadal global trends differing even in sign. Continual evolution of the global observing system, particularly stepwise improvements in satellite observations, has introduced discrete changes in the ability of data assimilation to correct systematic model biases, manifesting as nonphysical variability. Land surface models (LSMs) forced with observed precipitation P and near-surface meteorology and radiation provide estimates of evapotranspiration (ET). Since variability of atmospheric moisture storage is small on interannual and longer time scales, VMFC = P − ET is a good approximation and LSMs can provide an alternative estimate. However, heterogeneous density of rain gauge coverage, especially the sparse coverage over tropical continents, remains a serious concern.
Rotated principal component analysis (RPCA) with prefiltering of VMFC to isolate the artificial variability is used to investigate artifacts in five reanalysis systems. This procedure, although ad hoc, enables useful VMFC corrections over global land. The P − ET estimates from seven different LSMs are evaluated and subsequently used to confirm the efficacy of the RPCA-based adjustments. Global VMFC trends over the period 1979–2012 ranging from 0.07 to −0.03 mm day−1 decade−1 are reduced by the adjustments to 0.016 mm day−1 decade−1, much closer to the LSM P − ET estimate (0.007 mm day−1 decade−1). Neither is significant at the 90% level. ENSO-related modulation of VMFC and P − ET remains the largest global interannual signal, with mean LSM and adjusted reanalysis time series correlating at 0.86.
Abstract
Vertically integrated atmospheric moisture transport from ocean to land [vertically integrated atmospheric moisture flux convergence (VMFC)] is a dynamic component of the global climate system but remains problematic in atmospheric reanalyses, with current estimates having significant multidecadal global trends differing even in sign. Continual evolution of the global observing system, particularly stepwise improvements in satellite observations, has introduced discrete changes in the ability of data assimilation to correct systematic model biases, manifesting as nonphysical variability. Land surface models (LSMs) forced with observed precipitation P and near-surface meteorology and radiation provide estimates of evapotranspiration (ET). Since variability of atmospheric moisture storage is small on interannual and longer time scales, VMFC = P − ET is a good approximation and LSMs can provide an alternative estimate. However, heterogeneous density of rain gauge coverage, especially the sparse coverage over tropical continents, remains a serious concern.
Rotated principal component analysis (RPCA) with prefiltering of VMFC to isolate the artificial variability is used to investigate artifacts in five reanalysis systems. This procedure, although ad hoc, enables useful VMFC corrections over global land. The P − ET estimates from seven different LSMs are evaluated and subsequently used to confirm the efficacy of the RPCA-based adjustments. Global VMFC trends over the period 1979–2012 ranging from 0.07 to −0.03 mm day−1 decade−1 are reduced by the adjustments to 0.016 mm day−1 decade−1, much closer to the LSM P − ET estimate (0.007 mm day−1 decade−1). Neither is significant at the 90% level. ENSO-related modulation of VMFC and P − ET remains the largest global interannual signal, with mean LSM and adjusted reanalysis time series correlating at 0.86.
Abstract
The assimilation of observations in reanalyses incurs the potential for the physical terms of budgets to be balanced by a term relating the fit of the observations relative to a forecast first guess analysis. This may indicate a limitation in the physical processes of the background model or perhaps assimilating data from an inconsistent observing system. In the MERRA reanalysis, an area of long-term moisture flux divergence over land has been identified over the central United States. Here, the water vapor budget is evaluated in this region, taking advantage of two unique features of the MERRA diagnostic output: 1) a closed water budget that includes the analysis increment and 2) a gridded diagnostic output dataset of the assimilated observations and their innovations (e.g., forecast departures).
In the central United States, an anomaly occurs where the analysis adds water to the region, while precipitation decreases and moisture flux divergence increases. This is related more to a change in the observing system than to a deficiency in the model physical processes. MERRA’s Gridded Innovations and Observations (GIO) data narrow the observations that influence this feature to the ATOVS and Aqua satellites during the 0600 and 1800 UTC analysis cycles, when radiosonde information is not prevalent. Observing system experiments further narrow the instruments that affect the anomalous feature to AMSU-A (mainly window channels) and Atmospheric Infrared Sounder (AIRS). This effort also shows the complexities of the observing system and the reactions of the regional water budgets in reanalyses to the assimilated observations.
Abstract
The assimilation of observations in reanalyses incurs the potential for the physical terms of budgets to be balanced by a term relating the fit of the observations relative to a forecast first guess analysis. This may indicate a limitation in the physical processes of the background model or perhaps assimilating data from an inconsistent observing system. In the MERRA reanalysis, an area of long-term moisture flux divergence over land has been identified over the central United States. Here, the water vapor budget is evaluated in this region, taking advantage of two unique features of the MERRA diagnostic output: 1) a closed water budget that includes the analysis increment and 2) a gridded diagnostic output dataset of the assimilated observations and their innovations (e.g., forecast departures).
In the central United States, an anomaly occurs where the analysis adds water to the region, while precipitation decreases and moisture flux divergence increases. This is related more to a change in the observing system than to a deficiency in the model physical processes. MERRA’s Gridded Innovations and Observations (GIO) data narrow the observations that influence this feature to the ATOVS and Aqua satellites during the 0600 and 1800 UTC analysis cycles, when radiosonde information is not prevalent. Observing system experiments further narrow the instruments that affect the anomalous feature to AMSU-A (mainly window channels) and Atmospheric Infrared Sounder (AIRS). This effort also shows the complexities of the observing system and the reactions of the regional water budgets in reanalyses to the assimilated observations.
Abstract
A methodology is developed for deriving atmospheric water vapor transports over the World Oceans from satellite-retrieved precipitation (P) and evaporation (E) datasets. The motivation for developing the method is to understand climatically varying properties of transports, that is, year-to-year changes of the seasonally averaged divergent transport distribution fields, over regions where conventional data, in particular, winds, are sparse. Ultimately, the method is intended to take advantage of the relatively complete and consistent coverage, as well as continuity in sampling, associated with E − P datasets obtained from satellite measurements. Separate P and E retrievals from Special Sensor Microwave Imager (SSM/I) measurements, along with P retrievals from Tropical Rainfall Measuring Mission (TRMM) measurements, are used to obtain the transport solutions.
In this opening study, a 7-yr climatological normal is derived for the January–February–March (JFM) period for years 1988–94, providing the basis for comparing vapor transport anomalies from the 1997/98 El Niño and 1999/2000 La Niña events. These are derived from JFM-averaged transport solutions for 1998 and 1999, respectively. These two periods correspond to times when the Multivariate ENSO Index (MEI) provided by the NOAA Climatic Data Center (CDC) was first at a relative maximum and then at a relative minimum in conjunction with back-to-back west Pacific warm and cold events. Because the El Niño–La Niña events produce such highly contrasting behavior in the transports, shifting from a largely meridionally oriented solution to a largely zonally oriented solution, focusing on this pairing, helps to explain why the methodology is reliable and effective in capturing important details embedded in full-coverage E − P fields.
The analysis includes a sensitivity study of the transport solution technique based on 20 combinations of four precipitation datasets (two satellite based and two model based) and five evaporation datasets (two satellite based, one in situ observation based, and two model based), which helps to explain the reliability of the method. The analysis also includes a comparison to water vapor transports derived with the same method, but applied to E − P datasets obtained from global analysis products prepared by the National Centers of Environmental Prediction (NCEP), again to help explain the reliability of the method. The study concludes by first showing how the anomaly JFM 1998 El Niño solution behaves in close correspondence to associated SST anomalies and is generally more realistic in comparison to the corresponding NCEP solution. Finally, its reliability is discussed in terms of the implications of the vapor transport features for the El Niño–La Niña transition, vis-à-vis north–south and east–west circulations and their accompanying impact on the atmospheric hydrological cycle.
Abstract
A methodology is developed for deriving atmospheric water vapor transports over the World Oceans from satellite-retrieved precipitation (P) and evaporation (E) datasets. The motivation for developing the method is to understand climatically varying properties of transports, that is, year-to-year changes of the seasonally averaged divergent transport distribution fields, over regions where conventional data, in particular, winds, are sparse. Ultimately, the method is intended to take advantage of the relatively complete and consistent coverage, as well as continuity in sampling, associated with E − P datasets obtained from satellite measurements. Separate P and E retrievals from Special Sensor Microwave Imager (SSM/I) measurements, along with P retrievals from Tropical Rainfall Measuring Mission (TRMM) measurements, are used to obtain the transport solutions.
In this opening study, a 7-yr climatological normal is derived for the January–February–March (JFM) period for years 1988–94, providing the basis for comparing vapor transport anomalies from the 1997/98 El Niño and 1999/2000 La Niña events. These are derived from JFM-averaged transport solutions for 1998 and 1999, respectively. These two periods correspond to times when the Multivariate ENSO Index (MEI) provided by the NOAA Climatic Data Center (CDC) was first at a relative maximum and then at a relative minimum in conjunction with back-to-back west Pacific warm and cold events. Because the El Niño–La Niña events produce such highly contrasting behavior in the transports, shifting from a largely meridionally oriented solution to a largely zonally oriented solution, focusing on this pairing, helps to explain why the methodology is reliable and effective in capturing important details embedded in full-coverage E − P fields.
The analysis includes a sensitivity study of the transport solution technique based on 20 combinations of four precipitation datasets (two satellite based and two model based) and five evaporation datasets (two satellite based, one in situ observation based, and two model based), which helps to explain the reliability of the method. The analysis also includes a comparison to water vapor transports derived with the same method, but applied to E − P datasets obtained from global analysis products prepared by the National Centers of Environmental Prediction (NCEP), again to help explain the reliability of the method. The study concludes by first showing how the anomaly JFM 1998 El Niño solution behaves in close correspondence to associated SST anomalies and is generally more realistic in comparison to the corresponding NCEP solution. Finally, its reliability is discussed in terms of the implications of the vapor transport features for the El Niño–La Niña transition, vis-à-vis north–south and east–west circulations and their accompanying impact on the atmospheric hydrological cycle.
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.
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.
Abstract
Turbulent fluxes of heat and moisture across the atmosphere–ocean interface are fundamental components of the earth’s energy and water balance. Characterizing both the spatiotemporal variability and the fidelity of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere–ocean feedbacks, and improving model predictability. This study examines the veracity of the recently completed NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) product in terms of its turbulent surface fluxes. This assessment employs a large dataset of directly measured turbulent fluxes as well as other turbulent surface flux datasets. The spatial and temporal variability of the surface fluxes are examined in terms of their annual-mean climatologies, their seasonal covariability of near-surface bulk parameters, and their representation of extremes. The impact of data assimilation on the near-surface parameters is assessed through evaluation of the incremental analysis update tendencies. It is found that MERRA turbulent surface fluxes are relatively accurate for typical conditions but have systematically weak vertical gradients in moisture and temperature and a weaker covariability between the near-surface gradients and wind speed than found in observations. This results in an underestimate of the surface latent and sensible heat fluxes over the western boundary current and storm-track regions. The assimilation of observations generally acts to bring MERRA closer to observational products by increasing moisture and temperature near the surface and decreasing the near-surface wind speeds.
Abstract
Turbulent fluxes of heat and moisture across the atmosphere–ocean interface are fundamental components of the earth’s energy and water balance. Characterizing both the spatiotemporal variability and the fidelity of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere–ocean feedbacks, and improving model predictability. This study examines the veracity of the recently completed NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) product in terms of its turbulent surface fluxes. This assessment employs a large dataset of directly measured turbulent fluxes as well as other turbulent surface flux datasets. The spatial and temporal variability of the surface fluxes are examined in terms of their annual-mean climatologies, their seasonal covariability of near-surface bulk parameters, and their representation of extremes. The impact of data assimilation on the near-surface parameters is assessed through evaluation of the incremental analysis update tendencies. It is found that MERRA turbulent surface fluxes are relatively accurate for typical conditions but have systematically weak vertical gradients in moisture and temperature and a weaker covariability between the near-surface gradients and wind speed than found in observations. This results in an underestimate of the surface latent and sensible heat fluxes over the western boundary current and storm-track regions. The assimilation of observations generally acts to bring MERRA closer to observational products by increasing moisture and temperature near the surface and decreasing the near-surface wind speeds.
Abstract
Closing and balancing Earth’s global water cycle remains a challenge for the climate community. Observations are limited in duration, global coverage, and frequency, and not all water cycle terms are adequately observed. Reanalyses aim to fill the gaps through the assimilation of as many atmospheric water vapor observations as possible. Former generations of reanalyses have demonstrated a number of systematic problems that have limited their use in climate studies, especially regarding low-frequency trends. This study characterizes the NASA Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) water cycle relative to contemporary reanalyses and observations. MERRA-2 includes measures intended to minimize the spurious global variations related to inhomogeneity in the observational record. The global balance and cycling of water from ocean to land is presented, with special attention given to the water vapor analysis increment and the effects of the changing observing system. While some systematic regional biases can be identified, MERRA-2 produces temporally consistent time series of total column water and transport of water from ocean to land. However, the interannual variability of ocean evaporation is affected by the changing surface-wind-observing system, and precipitation variability is closely related to the evaporation. The surface energy budget is also strongly influenced by the interannual variability of the ocean evaporation. Furthermore, evaluating the relationship of temperature and water vapor indicates that the variations of water vapor with temperature are weaker in satellite data reanalyses, not just MERRA-2, than determined by observations, atmospheric models, or reanalyses without water vapor assimilation.
Abstract
Closing and balancing Earth’s global water cycle remains a challenge for the climate community. Observations are limited in duration, global coverage, and frequency, and not all water cycle terms are adequately observed. Reanalyses aim to fill the gaps through the assimilation of as many atmospheric water vapor observations as possible. Former generations of reanalyses have demonstrated a number of systematic problems that have limited their use in climate studies, especially regarding low-frequency trends. This study characterizes the NASA Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) water cycle relative to contemporary reanalyses and observations. MERRA-2 includes measures intended to minimize the spurious global variations related to inhomogeneity in the observational record. The global balance and cycling of water from ocean to land is presented, with special attention given to the water vapor analysis increment and the effects of the changing observing system. While some systematic regional biases can be identified, MERRA-2 produces temporally consistent time series of total column water and transport of water from ocean to land. However, the interannual variability of ocean evaporation is affected by the changing surface-wind-observing system, and precipitation variability is closely related to the evaporation. The surface energy budget is also strongly influenced by the interannual variability of the ocean evaporation. Furthermore, evaluating the relationship of temperature and water vapor indicates that the variations of water vapor with temperature are weaker in satellite data reanalyses, not just MERRA-2, than determined by observations, atmospheric models, or reanalyses without water vapor assimilation.
Abstract
Four state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.
Abstract
Four state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.
