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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 FROM AUTHOR]

Occasionally, a need arises to downscale a time series of data from a coarse temporal resolution to a finer one, a typical example being from monthly means to daily means. For this case, daily means derived as such are used as inputs of climatic or atmospheric models so that the model results may exhibit variance on the daily time scale and retain the monthly mean of the original data set without an abrupt change from the end of one month to the beginning of the next. Different methods have been developed which often need assumptions, free parameters and the solution of simultaneous equations. Here we derive a generalized formulation by means of Fourier transform and inversion so that it can be used to directly compute daily means from a series of an arbitrary number of monthly means. The formulation can be used to transform any coarse temporal resolution to a finer one. From the derived results, the original data can be recovered almost identically. As a real application, we use this method to derive the daily counterpart of the MAC-v1 aerosol climatology that provides monthly mean aerosol properties for 18 shortwave bands and 12 longwave bands for the years from 1860 to 2100. The derived daily means are to be used as inputs of the shortwave and longwave algorithms of the NASA GEWEX SRB project. [ABSTRACT FROM AUTHOR]

This study examines radiative flux distributions and local spread of values from three major observational datasets (CERES, ISCCP, and SRB) and compares them with results from climate modeling (CMIP3). Examinations of the spread and differences also differentiate among contributions from cloudy and clear-sky conditions. The spread among observational datasets is in large part caused by noncloud ancillary data. Average differences of at least 10 W m−2 each for clear-sky downward solar, upward solar, and upward infrared fluxes at the surface demonstrate via spatial difference patterns major differences in assumptions for atmospheric aerosol, solar surface albedo and surface temperature, and/or emittance in observational datasets. At the top of the atmosphere (TOA), observational datasets are less influenced by the ancillary data errors than at the surface. Comparisons of spatial radiative flux distributions at the TOA between observations and climate modeling indicate large deficiencies in the strength and distribution of model-simulated cloud radiative effects. Differences are largest for lower-altitude clouds over low-latitude oceans. Global modeling simulates stronger cloud radiative effects (CRE) by +30 W m−2 over trade wind cumulus regions, yet smaller CRE by about −30 W m−2 over (smaller in area) stratocumulus regions. At the surface, climate modeling simulates on average about 15 W m−2 smaller radiative net flux imbalances, as if climate modeling underestimates latent heat release (and precipitation). Relative to observational datasets, simulated surface net fluxes are particularly lower over oceanic trade wind regions (where global modeling tends to overestimate the radiative impact of clouds). Still, with the uncertainty in noncloud ancillary data, observational data do not establish a reliable reference. [ABSTRACT FROM AUTHOR]