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output variable names) and the surface net downward shortwave flux assuming clear sky (SWGNTCLR): (1) SWCRE = SWGNT − SWGNTCLR . Longwave CRE (LWCRE) is calculated as the difference between the surface absorbed longwave radiation (LWGAB) and the model-defined surface absorbed longwave radiation assuming clear sky, that is, with cloud effects removed (LWGABCLR): (2) LWCRE = LWGAB − LWGABCLR . 3. Results a. Heat-wave climatology and trends in MERRA-2 Prior to the examination of heat-wave climatology
output variable names) and the surface net downward shortwave flux assuming clear sky (SWGNTCLR): (1) SWCRE = SWGNT − SWGNTCLR . Longwave CRE (LWCRE) is calculated as the difference between the surface absorbed longwave radiation (LWGAB) and the model-defined surface absorbed longwave radiation assuming clear sky, that is, with cloud effects removed (LWGABCLR): (2) LWCRE = LWGAB − LWGABCLR . 3. Results a. Heat-wave climatology and trends in MERRA-2 Prior to the examination of heat-wave climatology
modeled and observed AOD for the lowest maximum are within the reported instrumental error (±0.02 for SEAC 4 RS). d. Clear-sky aerosol direct radiative effects Atmospheric aerosols, both natural and anthropogenic, impact climate through scattering and absorption of radiation [direct radiative effect (DRE)], modification of cloud microphysics (indirect effects), and thermodynamic effects (semidirect effect of aerosol absorption). Estimating the direct radiative effect requires knowledge of the three
modeled and observed AOD for the lowest maximum are within the reported instrumental error (±0.02 for SEAC 4 RS). d. Clear-sky aerosol direct radiative effects Atmospheric aerosols, both natural and anthropogenic, impact climate through scattering and absorption of radiation [direct radiative effect (DRE)], modification of cloud microphysics (indirect effects), and thermodynamic effects (semidirect effect of aerosol absorption). Estimating the direct radiative effect requires knowledge of the three
1. Introduction The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is NASA’s latest reanalysis for the satellite era (1980 onward) using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. MERRA-2 provides several improvements over its predecessor MERRA-1 ( Rienecker et al. 2011 ), including online aerosol fields that interact with model radiation fields (i.e., aerosol direct and semidirect effects) for the entire period ( Randles
1. Introduction The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is NASA’s latest reanalysis for the satellite era (1980 onward) using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. MERRA-2 provides several improvements over its predecessor MERRA-1 ( Rienecker et al. 2011 ), including online aerosol fields that interact with model radiation fields (i.e., aerosol direct and semidirect effects) for the entire period ( Randles
.g., Son et al. 2010 ; Arblaster et al. 2011 ; McLandress et al. 2011 ). Waugh et al. (2015) showed that the extent to which the models are capable of reproducing observed trends in jet position depends strongly on their accuracy in representing ozone depletion and tropical sea surface temperatures. Current models generally do not capture the full magnitude of observed changes, though this may be more closely related to natural internal variability than to incorrect representation of anthropogenic
.g., Son et al. 2010 ; Arblaster et al. 2011 ; McLandress et al. 2011 ). Waugh et al. (2015) showed that the extent to which the models are capable of reproducing observed trends in jet position depends strongly on their accuracy in representing ozone depletion and tropical sea surface temperatures. Current models generally do not capture the full magnitude of observed changes, though this may be more closely related to natural internal variability than to incorrect representation of anthropogenic
pressure vanish locally, as shown in section 3 of this paper and discussed in further detail by Bosilovich et al. (2017) . g. Observation-corrected precipitation forcing The precipitation generated by the atmospheric model during the IAU segment of the assimilation procedure is subject to considerable errors that can propagate into land surface hydrological fields and beyond ( Reichle et al. 2011 ). To mitigate these effects in MERRA-2, the model-generated precipitation is corrected with
pressure vanish locally, as shown in section 3 of this paper and discussed in further detail by Bosilovich et al. (2017) . g. Observation-corrected precipitation forcing The precipitation generated by the atmospheric model during the IAU segment of the assimilation procedure is subject to considerable errors that can propagate into land surface hydrological fields and beyond ( Reichle et al. 2011 ). To mitigate these effects in MERRA-2, the model-generated precipitation is corrected with
; Ting et al. 2011 ] also modulate moisture transport. Anthropogenic radiative forcing changes and the consequent hydrologic cycle effects are expected to produce regional variations, encapsulated in the “wet get wetter and dry get drier” paradigm ( Chou and Neelin 2004 ) wherein hydrologic extremes are expected to increase. As yet, evidence for this behavior in observational datasets is weak at best ( Greve et al. 2014 ). There is also substantial uncertainty as to trends in soil moisture dryness
; Ting et al. 2011 ] also modulate moisture transport. Anthropogenic radiative forcing changes and the consequent hydrologic cycle effects are expected to produce regional variations, encapsulated in the “wet get wetter and dry get drier” paradigm ( Chou and Neelin 2004 ) wherein hydrologic extremes are expected to increase. As yet, evidence for this behavior in observational datasets is weak at best ( Greve et al. 2014 ). There is also substantial uncertainty as to trends in soil moisture dryness
wet season is rather pristine as has been noted in previous studies and MERRA-2 estimates monthly mean aerosol radiative effects below 10 W m −2 , peaking in September (not shown). The annual maximum in the downwelling component of the surface SW radiative flux occurs at the same time as the maximum cooling resulting from aerosols in MERRA-2. The largest discrepancies between MERRA-2 and the observations, exceeding 100 W m −2 , occur in June and July, which is when MERRA-2 is the most accurate in
wet season is rather pristine as has been noted in previous studies and MERRA-2 estimates monthly mean aerosol radiative effects below 10 W m −2 , peaking in September (not shown). The annual maximum in the downwelling component of the surface SW radiative flux occurs at the same time as the maximum cooling resulting from aerosols in MERRA-2. The largest discrepancies between MERRA-2 and the observations, exceeding 100 W m −2 , occur in June and July, which is when MERRA-2 is the most accurate in
-sky surface fluxes have improved going from MERRA to MERRA-2, the disagreement from EBAF in ATOTNET is much worse in MERRA-2. This is because the large biases in MERRA ASWDN and ALWDN are of opposite sign and thus offsetting, while the smaller biases in MERRA-2 reinforce each other. Overall, the MERRA and MERRA-2 clear-sky surface radiative fluxes agree better with EBAF. The chief exception is CSWDN term in MERRA-2, for which the difference has increased from −0.9 to 5.0 W m −2 . Apparently, cloud effects
-sky surface fluxes have improved going from MERRA to MERRA-2, the disagreement from EBAF in ATOTNET is much worse in MERRA-2. This is because the large biases in MERRA ASWDN and ALWDN are of opposite sign and thus offsetting, while the smaller biases in MERRA-2 reinforce each other. Overall, the MERRA and MERRA-2 clear-sky surface radiative fluxes agree better with EBAF. The chief exception is CSWDN term in MERRA-2, for which the difference has increased from −0.9 to 5.0 W m −2 . Apparently, cloud effects
streamflow data provide an estimate of the streamflow that would have occurred in the absence of anthropogenic hydrologic effects such as regulation at dams, evaporation from reservoir surfaces, and water withdrawals and return flows. The evaluation approach is the same as that of Reichle et al. (2011) , except that here the comparison starts in 1980 instead of 1989 because we are no longer limited by the availability of ERA-Interim (hereafter ERA-I; Dee et al. 2011 ) data (which at that time was
streamflow data provide an estimate of the streamflow that would have occurred in the absence of anthropogenic hydrologic effects such as regulation at dams, evaporation from reservoir surfaces, and water withdrawals and return flows. The evaluation approach is the same as that of Reichle et al. (2011) , except that here the comparison starts in 1980 instead of 1989 because we are no longer limited by the availability of ERA-Interim (hereafter ERA-I; Dee et al. 2011 ) data (which at that time was
