A multisatellite climatology of clouds, radiation, and precipitation in southern West Africa and comparison to climate models

Abstract

Southern West Africa (SWA) has a large population that relies on highly variable monsoon rainfall, yet climate models show little consensus over projected precipitation in this region. Understanding of the current and future climate of SWA is further complicated by rapidly increasing anthropogenic emissions and a lack of surface observations. Using multiple satellite observations, the ERA‐Interim reanalysis, and four climate models, we document the climatology of cloud, precipitation, and radiation over SWA in June–July, highlight discrepancies among satellite products, and identify shortcomings in climate models and ERA‐Interim. Large differences exist between monthly mean cloud cover estimates from satellites, which range from 68 to 94%. In contrast, differences among satellite observations in top of atmosphere outgoing radiation and surface precipitation are smaller, with monthly means of about 230 W m−2 of longwave radiation, 145 W m−2 of shortwave radiation, and 5.87 mm d−1 of precipitation. Both ERA‐Interim and the climate models show less total cloud cover than observations, mainly due to underestimating low cloud cover. Errors in cloud cover, along with uncertainty in surface albedo, lead to a large spread of outgoing shortwave radiation. Both ERA‐Interim and the climate models also show signs of convection developing too early in the diurnal cycle, with associated errors in the diurnal cycles of precipitation and outgoing longwave radiation. Clouds, radiation, and precipitation are linked in an analysis of the regional energy budget, which shows that interannual variability of precipitation and dry static energy divergence are strongly linked.

The onset and cessation of seasonal rainfall over Africa

“…Other studies have indicated that precipitation amount and variability in ERA‐I differs from observational data sets over West Africa (and other areas, e.g., central Africa [ Nikulin et al , ; Sylla et al , ]). Although the large‐scale features of the West African Monsoon are captured, a lack of northward propagation of precipitation results in an ERA‐I overestimation of precipitation along the coast and an underestimation further north [ Diaconescu et al , ; Paeth et al , ; Nikulin et al , ; Hill et al , ]. A weaker first rainfall maxima along the coast [ Diaconescu et al , ; Nikulin et al , ], matched by a shorter first season resulting from the later onset in our results, suggests that this rainfall overestimation is associated with the second wet season.…”

Spatio‐temporal variability of cloud cover types in West Africa with satellite‐based and reanalysis data

“…This behaviour may be linked to the failure of reanalysis products to efficiently represent (in this case underestimate) the northward shift of the WA Monsoon (WAM) as indicated by both Hill et al . () and Thorncroft et al . ().…”

“…This is perhaps explained by the difficulty of detecting optically thin cirrus clouds by MODIS as revealed by previous studies (e.g. Lee et al ., ; Sun et al ., ; Hill et al ., ), leading to the lower mean fractions of HCC. The slightly lower values of TCC shown by CERES than ERA5 could be attributed to this same reason but needs to be investigated further.…”

“…This absence of a well‐marked diurnal cycle in ERA5 is unexpected considering the presence of distinctly marked diurnal cycles of HCC and MCC in previous studies (e.g. Hill et al ., ; Bourgeois et al ., ). CERES, however, shows some diurnal variation of HCC (but not for MCC) in both regions with higher cloud fractions in the night than during the day, more consistent with some previous studies in the region (e.g.…”

Determination of global Earth outgoing radiation at high temporal resolution using a theoretical constellation of satellites

“…Since radiation shows distinct diurnal signatures directly linked to the evolution of weather and climate system processes, CERES and geostationary satellite observations have been combined to generate a temporally interpolated data set [ Doelling et al ., , ] that has been used in studies of cloud and aerosol radiative forcing [ Taylor , ; Su et al ., 2013], explaining TOA diurnal cycle variability [ Taylor , ; Dodson and Taylor , ], and to make comparisons with models and reanalyses [ Itterly and Taylor , ; Hill et al ., ]. However, incorporating different geostationary observations with unique sensor characteristics and varying degrees of quality can produce significant artifacts resulting in unnatural spatial patterns in radiation fields [ Doelling et al ., ].…”