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Abstract
Reanalysis model output is extensively used in atmospheric research and must be rigorously and continuously evaluated to understand the strengths and weaknesses. This paper evaluates the tropical top-of-atmosphere (TOA) flux diurnal cycle in NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) and the ECMWF Interim Re-Analysis (ERA-Interim) against Clouds and the Earth’s Radiant Energy System (CERES) synoptic edition 3A (SYN Ed3A) TOA flux data. MERRA and ERA-Interim are able to reproduce large-scale features of the diurnal cycle, including land–ocean contrast. MERRA and ERA-Interim, however, fail to reproduce many regional features of the climatological annual diurnal cycle. The TOA flux diurnal cycle errors in regions dominated by convective diurnal cycles are 5–10 times larger than in nonconvective regions. These errors in the TOA radiative flux diurnal cycle are primarily attributed to errors in the cloud diurnal evolution and specifically the failure to reproduce diurnally forced propagating convection. The largest diurnal cycle errors are found in ocean convective regions (e.g., Indian and equatorial Pacific Oceans); the observed longwave cloud forcing (LWCF) diurnal evolution in several oceanic convective regions shows two peaks: an afternoon and a near midnight peak; however, the reanalysis models produce a single midnight peak. The outgoing longwave radiation (OLR) diurnal cycle over tropical land is 20%–30% too weak in both reanalyses. The small diurnal cycle errors in marine stratocumulus regions are a result of two common misrepresentations in MERRA and ERA-Interim: 1) the dissipation of marine stratocumulus clouds from morning to afternoon is too slow and 2) the cloud diurnal cycle is too weak. Overall, the intermodel differences in the representation of the TOA flux diurnal cycle are smaller than the differences between reanalysis models and observations.
Abstract
Reanalysis model output is extensively used in atmospheric research and must be rigorously and continuously evaluated to understand the strengths and weaknesses. This paper evaluates the tropical top-of-atmosphere (TOA) flux diurnal cycle in NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) and the ECMWF Interim Re-Analysis (ERA-Interim) against Clouds and the Earth’s Radiant Energy System (CERES) synoptic edition 3A (SYN Ed3A) TOA flux data. MERRA and ERA-Interim are able to reproduce large-scale features of the diurnal cycle, including land–ocean contrast. MERRA and ERA-Interim, however, fail to reproduce many regional features of the climatological annual diurnal cycle. The TOA flux diurnal cycle errors in regions dominated by convective diurnal cycles are 5–10 times larger than in nonconvective regions. These errors in the TOA radiative flux diurnal cycle are primarily attributed to errors in the cloud diurnal evolution and specifically the failure to reproduce diurnally forced propagating convection. The largest diurnal cycle errors are found in ocean convective regions (e.g., Indian and equatorial Pacific Oceans); the observed longwave cloud forcing (LWCF) diurnal evolution in several oceanic convective regions shows two peaks: an afternoon and a near midnight peak; however, the reanalysis models produce a single midnight peak. The outgoing longwave radiation (OLR) diurnal cycle over tropical land is 20%–30% too weak in both reanalyses. The small diurnal cycle errors in marine stratocumulus regions are a result of two common misrepresentations in MERRA and ERA-Interim: 1) the dissipation of marine stratocumulus clouds from morning to afternoon is too slow and 2) the cloud diurnal cycle is too weak. Overall, the intermodel differences in the representation of the TOA flux diurnal cycle are smaller than the differences between reanalysis models and observations.
Abstract
This study shows that the African easterly wave (AEW) activity over the African monsoon region and the northern tropical Atlantic can be divided in two distinct temporal bands with time scales of 2.5–6 and 6–9 days. The results are based on a two-dimensional ensemble empirical mode decomposition (2D-EEMD) of the Modern-Era Retrospective Analysis for Research and Applications (MERRA). The novel result of this investigation is that the 6–9-day waves appear to be located predominantly to the north of the African easterly jet (AEJ), originate at the jet level, and are different in scale and structure from the well-known low-level 2.5–6-day waves that develop baroclinically on the poleward flank of the AEJ. Moreover, they appear to interact with midlatitude eastward-propagating disturbances, with the strongest interaction taking place at the latitudes where the core of the Atlantic high pressure system is located. Composite analyses applied to the mode decomposition indicate that the interaction of the 6–9-day waves with midlatitude systems is characterized by enhanced southerly (northerly) flow from (toward) the tropics. This finding agrees with independent studies focused on European floods, which have noted enhanced moist transport from the ITCZ toward the Mediterranean region on time scales of about a week as important precursors of extreme precipitation.
Abstract
This study shows that the African easterly wave (AEW) activity over the African monsoon region and the northern tropical Atlantic can be divided in two distinct temporal bands with time scales of 2.5–6 and 6–9 days. The results are based on a two-dimensional ensemble empirical mode decomposition (2D-EEMD) of the Modern-Era Retrospective Analysis for Research and Applications (MERRA). The novel result of this investigation is that the 6–9-day waves appear to be located predominantly to the north of the African easterly jet (AEJ), originate at the jet level, and are different in scale and structure from the well-known low-level 2.5–6-day waves that develop baroclinically on the poleward flank of the AEJ. Moreover, they appear to interact with midlatitude eastward-propagating disturbances, with the strongest interaction taking place at the latitudes where the core of the Atlantic high pressure system is located. Composite analyses applied to the mode decomposition indicate that the interaction of the 6–9-day waves with midlatitude systems is characterized by enhanced southerly (northerly) flow from (toward) the tropics. This finding agrees with independent studies focused on European floods, which have noted enhanced moist transport from the ITCZ toward the Mediterranean region on time scales of about a week as important precursors of extreme precipitation.
Abstract
The Modern-Era Retrospective Analysis for Research and Application (MERRA) is a reanalysis designed to produce an improved representation of the Earth’s hydrologic cycle. This study examines the representation of deep convective clouds in MERRA, comparing analyzed liquid and ice clouds with deep convective cloud objects observed by instruments on the Tropical Rainfall Measuring Mission satellite. Results show that MERRA contains deep convective cloud in 98.1% of the observed cases. MERRA-derived probability density functions (PDFs) of cloud properties have a similar form as the observed PDFs and exhibit a similar trend with changes in object size. Total water path, optical depth, and outgoing shortwave radiation (OSR) in MERRA are found to match the cloud object observations quite well; however, there appears to be a bias toward higher-than-observed cloud tops in the MERRA. The reanalysis fits the observations most closely for the largest class of convective systems, with performance generally decreasing with a transition to smaller convective systems. Comparisons of simulated total water path, optical depth, and OSR are found to be highly sensitive to the assumed subgrid distribution of condensate and indicate the need for caution when interpreting model-data comparisons that require disaggregation of grid-scale cloud to satellite pixel scales.
Abstract
The Modern-Era Retrospective Analysis for Research and Application (MERRA) is a reanalysis designed to produce an improved representation of the Earth’s hydrologic cycle. This study examines the representation of deep convective clouds in MERRA, comparing analyzed liquid and ice clouds with deep convective cloud objects observed by instruments on the Tropical Rainfall Measuring Mission satellite. Results show that MERRA contains deep convective cloud in 98.1% of the observed cases. MERRA-derived probability density functions (PDFs) of cloud properties have a similar form as the observed PDFs and exhibit a similar trend with changes in object size. Total water path, optical depth, and outgoing shortwave radiation (OSR) in MERRA are found to match the cloud object observations quite well; however, there appears to be a bias toward higher-than-observed cloud tops in the MERRA. The reanalysis fits the observations most closely for the largest class of convective systems, with performance generally decreasing with a transition to smaller convective systems. Comparisons of simulated total water path, optical depth, and OSR are found to be highly sensitive to the assumed subgrid distribution of condensate and indicate the need for caution when interpreting model-data comparisons that require disaggregation of grid-scale cloud to satellite pixel scales.
Abstract
The Modern-Era Reanalysis for Research and Applications (MERRA) is realistic, including its Madden–Julian oscillation (MJO), which the underlying model [Goddard Earth Observing System, version 5 (GEOS-5)] lacks. In the MERRA budgets, analysis tendencies (ATs) make evolution realistic despite model shortcomings. The ATs are the negative of physical process errors, if dynamical tendencies are accurate. Pattern resemblances between ATs and physical tendencies suggest which processes are erroneous. The authors examined patterns of tropical ATs in four dimensions and found several noteworthy features. Temperature AT profiles show that moist physics has erroneous sharp cooling at 700 hPa, a signature of misplaced melting and perhaps excessive precipitation evaporation. This excites a distinctive (fingerprint) erroneous short vertical wavelength temperature structure, perhaps a cause of the GEOS-5 too-slow convectively coupled waves. The globe’s largest AT of 200-hPa wind stems from overactive heating over the intra-Americas seas region in summer, with the same moist physics fingerprint. The erroneous heating produces a baroclinic vortex that is countered by ATs opposing its temperature and momentum fields in a thermal wind balanced sense. Lack of restraint in the deep convection scheme is also indicated in MJO composites, where the water vapor AT is anomalously positive on the leading edge, indicating a premature vapor sink. Since GEOS-5 lacks an MJO, this diagnosis suggests that the transition from shallow to deep convection (moistening to drying) is crucial in the real-world MJO. This is not news, but its diagnosis by ATs provides an objective, repeatable way to measure the effect that could be a useful guide in model development.
Abstract
The Modern-Era Reanalysis for Research and Applications (MERRA) is realistic, including its Madden–Julian oscillation (MJO), which the underlying model [Goddard Earth Observing System, version 5 (GEOS-5)] lacks. In the MERRA budgets, analysis tendencies (ATs) make evolution realistic despite model shortcomings. The ATs are the negative of physical process errors, if dynamical tendencies are accurate. Pattern resemblances between ATs and physical tendencies suggest which processes are erroneous. The authors examined patterns of tropical ATs in four dimensions and found several noteworthy features. Temperature AT profiles show that moist physics has erroneous sharp cooling at 700 hPa, a signature of misplaced melting and perhaps excessive precipitation evaporation. This excites a distinctive (fingerprint) erroneous short vertical wavelength temperature structure, perhaps a cause of the GEOS-5 too-slow convectively coupled waves. The globe’s largest AT of 200-hPa wind stems from overactive heating over the intra-Americas seas region in summer, with the same moist physics fingerprint. The erroneous heating produces a baroclinic vortex that is countered by ATs opposing its temperature and momentum fields in a thermal wind balanced sense. Lack of restraint in the deep convection scheme is also indicated in MJO composites, where the water vapor AT is anomalously positive on the leading edge, indicating a premature vapor sink. Since GEOS-5 lacks an MJO, this diagnosis suggests that the transition from shallow to deep convection (moistening to drying) is crucial in the real-world MJO. This is not news, but its diagnosis by ATs provides an objective, repeatable way to measure the effect that could be a useful guide in model development.
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
The following study examines the position and intensity differences of tropical cyclones (TCs) among the Best-Track and five atmospheric reanalysis datasets to evaluate the degree to which reanalyses are appropriate for studying TCs. While significant differences are found in both reanalysis TC intensity and position, the representation of TC intensity within reanalyses is found to be most problematic owing to its underestimation beyond what can be attributed solely to the coarse grid resolution. Moreover, the mean life cycle of normalized TC intensity within reanalyses reveals an underestimation of both prepeak intensification rates as well as a delay in peak intensity relative to the Best-Track. These discrepancies between Best-Track and reanalysis TC intensity and position can further be described through correlations with such parameters as Best-Track TC age, Best-Track TC intensity, Best-Track TC location, and the extended Best-Track TC size. Specifically, TC position differences within the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), ECMWF Interim Re-Analysis (ERA-I), and Modern Era Retrospective-Analysis for Research and Applications (MERRA) exhibit statistically significant correlations (0.27 ≤ R ≤ 0.38) with the proximity of TCs to observation dense areas in the North Atlantic (NATL) and western North Pacific (WPAC). Reanalysis TC intensity is found to be most strongly correlated with Best-Track TC size (0.53 ≤ R ≤ 0.70 for maximum 10-m wind speed; −0.71 ≤ R ≤ −0.53 for minimum mean sea level pressure) while exhibiting smaller, yet significant, correlations with Best-Track TC age, Best-Track TC intensity, and Best-Track TC latitude. Of the three basins examined, the eastern North Pacific (EPAC) has the largest reanalysis TC position differences and weakest intensities possibly due to a relative dearth of observations, the strong nearby terrain gradient, and the movement of TCs away from the most observation dense portion of the basin over time. The smaller mean Best-Track size and shorter mean lifespan of Best-Track EPAC TCs may also yield weaker reanalysis TC intensities. Of the five reanalyses, the smaller position differences and stronger intensities found in the Climate Forecast System Reanalysis (CFSR) and Japanese 25-year Reanalysis (JRA-25) are attributed to the use of vortex relocation and TC wind profile retrievals, respectively. The discrepancies in TC position between the Best-Track and reanalyses combined with the muted magnitude of TC intensity and its partially nonphysical life cycle within reanalyses suggests that caution should be exercised when utilizing these datasets for studies that rely either on TC intensity (raw or normalized) or track. Finally, several cases of nonphysical TC structure also argue that further work is needed to improve TC representation while implying that studies focusing solely on TC intensity and track do not necessarily extend to other aspects of TC representation.
Abstract
The following study examines the position and intensity differences of tropical cyclones (TCs) among the Best-Track and five atmospheric reanalysis datasets to evaluate the degree to which reanalyses are appropriate for studying TCs. While significant differences are found in both reanalysis TC intensity and position, the representation of TC intensity within reanalyses is found to be most problematic owing to its underestimation beyond what can be attributed solely to the coarse grid resolution. Moreover, the mean life cycle of normalized TC intensity within reanalyses reveals an underestimation of both prepeak intensification rates as well as a delay in peak intensity relative to the Best-Track. These discrepancies between Best-Track and reanalysis TC intensity and position can further be described through correlations with such parameters as Best-Track TC age, Best-Track TC intensity, Best-Track TC location, and the extended Best-Track TC size. Specifically, TC position differences within the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), ECMWF Interim Re-Analysis (ERA-I), and Modern Era Retrospective-Analysis for Research and Applications (MERRA) exhibit statistically significant correlations (0.27 ≤ R ≤ 0.38) with the proximity of TCs to observation dense areas in the North Atlantic (NATL) and western North Pacific (WPAC). Reanalysis TC intensity is found to be most strongly correlated with Best-Track TC size (0.53 ≤ R ≤ 0.70 for maximum 10-m wind speed; −0.71 ≤ R ≤ −0.53 for minimum mean sea level pressure) while exhibiting smaller, yet significant, correlations with Best-Track TC age, Best-Track TC intensity, and Best-Track TC latitude. Of the three basins examined, the eastern North Pacific (EPAC) has the largest reanalysis TC position differences and weakest intensities possibly due to a relative dearth of observations, the strong nearby terrain gradient, and the movement of TCs away from the most observation dense portion of the basin over time. The smaller mean Best-Track size and shorter mean lifespan of Best-Track EPAC TCs may also yield weaker reanalysis TC intensities. Of the five reanalyses, the smaller position differences and stronger intensities found in the Climate Forecast System Reanalysis (CFSR) and Japanese 25-year Reanalysis (JRA-25) are attributed to the use of vortex relocation and TC wind profile retrievals, respectively. The discrepancies in TC position between the Best-Track and reanalyses combined with the muted magnitude of TC intensity and its partially nonphysical life cycle within reanalyses suggests that caution should be exercised when utilizing these datasets for studies that rely either on TC intensity (raw or normalized) or track. Finally, several cases of nonphysical TC structure also argue that further work is needed to improve TC representation while implying that studies focusing solely on TC intensity and track do not necessarily extend to other aspects of TC representation.
Abstract
With continual advancements in data assimilation systems, new observing systems, and improvements in model parameterizations, several new atmospheric reanalysis datasets have recently become available. Before using these new reanalyses it is important to assess the strengths and underlying biases contained in each dataset. A study has been performed to evaluate and compare cloud fractions (CFs) and surface radiative fluxes in several of these latest reanalyses over the Arctic using 15 years (1994–2008) of high-quality Baseline Surface Radiation Network (BSRN) observations from Barrow (BAR) and Ny-Alesund (NYA) surface stations. The five reanalyses being evaluated in this study are (i) NASA's Modern-Era Retrospective analysis for Research and Applications (MERRA), (ii) NCEP's Climate Forecast System Reanalysis (CFSR), (iii) NOAA's Twentieth Century Reanalysis Project (20CR), (iv) ECMWF's Interim Reanalysis (ERA-I), and (v) NCEP–Department of Energy (DOE)'s Reanalysis II (R2). All of the reanalyses show considerable bias in reanalyzed CF during the year, especially in winter. The large CF biases have been reflected in the surface radiation fields, as monthly biases in shortwave (SW) and longwave (LW) fluxes are more than 90 (June) and 60 W m−2 (March), respectively, in some reanalyses. ERA-I and CFSR performed the best in reanalyzing surface downwelling fluxes with annual mean biases less than 4.7 (SW) and 3.4 W m−2 (LW) over both Arctic sites. Even when producing the observed CF, radiation flux errors were found to exist in the reanalyses suggesting that they may not always be dependent on CF errors but rather on variations of more complex cloud properties, water vapor content, or aerosol loading within the reanalyses.
Abstract
With continual advancements in data assimilation systems, new observing systems, and improvements in model parameterizations, several new atmospheric reanalysis datasets have recently become available. Before using these new reanalyses it is important to assess the strengths and underlying biases contained in each dataset. A study has been performed to evaluate and compare cloud fractions (CFs) and surface radiative fluxes in several of these latest reanalyses over the Arctic using 15 years (1994–2008) of high-quality Baseline Surface Radiation Network (BSRN) observations from Barrow (BAR) and Ny-Alesund (NYA) surface stations. The five reanalyses being evaluated in this study are (i) NASA's Modern-Era Retrospective analysis for Research and Applications (MERRA), (ii) NCEP's Climate Forecast System Reanalysis (CFSR), (iii) NOAA's Twentieth Century Reanalysis Project (20CR), (iv) ECMWF's Interim Reanalysis (ERA-I), and (v) NCEP–Department of Energy (DOE)'s Reanalysis II (R2). All of the reanalyses show considerable bias in reanalyzed CF during the year, especially in winter. The large CF biases have been reflected in the surface radiation fields, as monthly biases in shortwave (SW) and longwave (LW) fluxes are more than 90 (June) and 60 W m−2 (March), respectively, in some reanalyses. ERA-I and CFSR performed the best in reanalyzing surface downwelling fluxes with annual mean biases less than 4.7 (SW) and 3.4 W m−2 (LW) over both Arctic sites. Even when producing the observed CF, radiation flux errors were found to exist in the reanalyses suggesting that they may not always be dependent on CF errors but rather on variations of more complex cloud properties, water vapor content, or aerosol loading within the reanalyses.
Abstract
Reanalysis products produced at the various centers around the globe are utilized for many different scientific endeavors, including forcing land surface models and creating surface flux estimates. Here, flux tower observations of temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes are used to evaluate the performance of various reanalysis products [NCEP–NCAR reanalysis and Climate Forecast System Reanalysis (CFSR) from NCEP; 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) from ECMWF; and Modern-Era Retrospective Analysis for Research and Applications (MERRA) and Global Land Data Assimilation System (GLDAS) from the Goddard Space Flight Center (GSFC)]. To combine the biases and standard deviation of errors from the separate stations, a ranking system is utilized. It is found that ERA-Interim has the lowest overall bias in 6-hourly air temperature, followed closely by MERRA and GLDAS. The variability in 6-hourly air temperature is again most accurate in ERA-Interim. ERA-40 is found to have the lowest overall bias in latent heat flux, followed closely by CFSR, while ERA-40 also has the lowest 6-hourly sensible heat bias. MERRA has the second lowest and is close to ERA-40. The variability in 6-hourly precipitation is best captured by GLDAS and ERA-Interim, and ERA-40 has the lowest precipitation bias. It is also found that at monthly time scales, the bias term in the reanalysis products are the dominant cause of the mean square errors, while at 6-hourly and daily time scales the dominant contributor to the mean square errors is the correlation term. Also, it is found that the hourly CFSR data have discontinuities present due to the assimilation cycle, while the hourly MERRA data do not contain these jumps.
Abstract
Reanalysis products produced at the various centers around the globe are utilized for many different scientific endeavors, including forcing land surface models and creating surface flux estimates. Here, flux tower observations of temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes are used to evaluate the performance of various reanalysis products [NCEP–NCAR reanalysis and Climate Forecast System Reanalysis (CFSR) from NCEP; 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) from ECMWF; and Modern-Era Retrospective Analysis for Research and Applications (MERRA) and Global Land Data Assimilation System (GLDAS) from the Goddard Space Flight Center (GSFC)]. To combine the biases and standard deviation of errors from the separate stations, a ranking system is utilized. It is found that ERA-Interim has the lowest overall bias in 6-hourly air temperature, followed closely by MERRA and GLDAS. The variability in 6-hourly air temperature is again most accurate in ERA-Interim. ERA-40 is found to have the lowest overall bias in latent heat flux, followed closely by CFSR, while ERA-40 also has the lowest 6-hourly sensible heat bias. MERRA has the second lowest and is close to ERA-40. The variability in 6-hourly precipitation is best captured by GLDAS and ERA-Interim, and ERA-40 has the lowest precipitation bias. It is also found that at monthly time scales, the bias term in the reanalysis products are the dominant cause of the mean square errors, while at 6-hourly and daily time scales the dominant contributor to the mean square errors is the correlation term. Also, it is found that the hourly CFSR data have discontinuities present due to the assimilation cycle, while the hourly MERRA data do not contain these jumps.
Abstract
This study investigates the structure of the African easterly jet, focusing on instability processes on a seasonal and subseasonal scale, with the goal of identifying features that could provide increased predictability of Atlantic tropical cyclogenesis. The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is used as the main investigating tool. MERRA is compared with other reanalyses datasets from major operational centers around the world and was found to describe very effectively the circulation over the African monsoon region. In particular, a comparison with precipitation datasets from the Global Precipitation Climatology Project shows that MERRA realistically reproduces seasonal precipitation over that region. The verification of the generalized Kuo barotropic instability condition computed from seasonal means is found to have the interesting property of defining well the location where observed tropical storms are detected. This property does not appear to be an artifact of MERRA and is present also in the other adopted reanalysis datasets. Therefore, the fact that the areas where the mean flow is unstable seems to provide a more favorable environment for wave intensification, could be another factor to include—in addition to sea surface temperature, vertical shear, precipitation, the role of Saharan air, and others—among large-scale forcings affecting development and tropical cyclone frequency. In addition, two prominent modes of variability are found based on a spectral analysis that uses the Hilbert–Huang transform: a 2.5–6-day mode that corresponds well to the African easterly waves and also a 6–9-day mode that seems to be associated with tropical–extratropical interaction.
Abstract
This study investigates the structure of the African easterly jet, focusing on instability processes on a seasonal and subseasonal scale, with the goal of identifying features that could provide increased predictability of Atlantic tropical cyclogenesis. The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is used as the main investigating tool. MERRA is compared with other reanalyses datasets from major operational centers around the world and was found to describe very effectively the circulation over the African monsoon region. In particular, a comparison with precipitation datasets from the Global Precipitation Climatology Project shows that MERRA realistically reproduces seasonal precipitation over that region. The verification of the generalized Kuo barotropic instability condition computed from seasonal means is found to have the interesting property of defining well the location where observed tropical storms are detected. This property does not appear to be an artifact of MERRA and is present also in the other adopted reanalysis datasets. Therefore, the fact that the areas where the mean flow is unstable seems to provide a more favorable environment for wave intensification, could be another factor to include—in addition to sea surface temperature, vertical shear, precipitation, the role of Saharan air, and others—among large-scale forcings affecting development and tropical cyclone frequency. In addition, two prominent modes of variability are found based on a spectral analysis that uses the Hilbert–Huang transform: a 2.5–6-day mode that corresponds well to the African easterly waves and also a 6–9-day mode that seems to be associated with tropical–extratropical interaction.
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.
