In much of East Africa, climatological rainfall follows a bimodal distribution characterized by the long rains(March–May) and short rains (October–December). Most CMIP5 coupled models fail to properly simulatethis annual cycle, typically reversing the amplitudes of the short and long rains relative to observations. Thisstudy investigates how CMIP5 climatological sea surface temperature (SST) biases contribute to simulationerrors in the annual cycle of East African rainfall. Monthly biases in CMIP5 climatological SSTs (508S–508N)are first identified in historical runs (1979–2005) from 31 models and examined for consistency. An atmo-spheric general circulation model (AGCM) is then forced with observed SSTs (1979–2005) generating a set ofcontrol runs and observed SSTs plus the monthly, multimodel mean SST biases generating a set of ‘‘bias’’ runsfor the same period. The control runs generally capture the observed annual cycle of East African rainfallwhile the bias runs capture prominent CMIP5 annual cycle biases, including too little (much) precipitationduring the long rains (short rains) and a 1-month lag in the peak of the long rains relative to observations.Diagnostics reveal the annual cycle biases are associated with seasonally varying north–south- and east–west-oriented SST bias patterns in Indian Ocean and regional-scale atmospheric circulation and stability changes,the latter primarily associated with changes in low-level moist static energy. Overall, the results indicate thatCMIP5 climatological SST biases are the primary driver of the improper simulation of the annual cycle of EastAfrican rainfall. Some implications for climate change projections are discussed
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Biases in sea surface temperature and the annual cycle of Greater Horn of Africa rainfall in CMIP6
Climatological rainfall across much of the Greater Horn of Africa has a bimodal annual cycle characterized by the short rains from October to December and the long rains from March to May. Previous generations of climate models from the Coupled Model Intercomparison Project (CMIP3 and CMIP5) generally misrepresented the bimodal rainfall distribution in this region by generating too much rainfall during the short rains and too little during the long rains. The peak of the long rains in these models also typically showed a pronounced 1-month lag relative to observations. Here, the ability of 21 CMIP6 models to properly simulate the observed, climatological annual cycle of Greater Horn rainfall is examined, comparing results with CMIP5 and CMIP3. As previous work has shown a connection between Greater Horn climatological rainfall biases and model biases in sea surface temperatures (SSTs), pattern correlations of climatological SST biases are also analysed. For the multi-model mean, it is found that the earlier biases in Greater Horn rainfall and associated SSTs persist in CMIP6. Examining only the three best performing models in each CMIP group reveals the CMIP6 models outperform those in CMIP3, with mixed results regarding improvements over CMIP5. For the best performing CMIP6 models, the SST and 850 hPa wind biases are reduced over the Indian Ocean relative to the other CMIP6 models examined. No statistically significant relationship was identified between CMIP6 model performance and the horizontal resolution of the model. Combined, these results indicate the importance of properly simulating the annual cycle of SSTs in order to successfully model the observed rainfall annual cycle in the Greater Horn.
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- Award ID(s):
- 1650037
- NSF-PAR ID:
- 10318428
- Date Published:
- Journal Name:
- International Journal of Climatology
- ISSN:
- 0899-8418
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The societies of the coastal regions of the Greater Horn of Africa (GHA) experience two distinct rainy seasons: the generally wetter “long” rains in the boreal spring and the generally drier “short” rains in the boreal fall. The GHA rainfall climatology is unique for its latitude in both its aridity and for the dynamical differences between its two rainy seasons. This study explains the drivers of the rainy seasons through the climatology of moist static stability, estimated as the difference between surface moist static energy
hs and midtropospheric saturation moist static energy. In areas and at times when this difference, , is higher, rainfall is more frequent and more intense. However, even during the rainy seasons, on average and the atmosphere remains largely stable, in line with the GHA’s aridity. The seasonal cycle of , to which the unique seasonal cycles of surface humidity, surface temperature, and midtropospheric temperature all contribute, helps explain the double-peaked nature of the regional hydroclimate. Despite tropospheric temperature being relatively uniform in the tropics, even small changes in can have substantial impacts on instability; for example, during the short rains, the annual minimum in GHA lowers the threshold for convection and allows for instability despite surface humidity anomalies being relatively weak. This framework can help identify the drivers of interannual variability in GHA mean rainfall or diagnose the origin of biases in climate model simulations of the regional climate. -
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Abstract The influence of coupled model sea surface temperature (SST) climatological biases and SST projections on daily convection over the Intra‐American Seas (IAS) during the May–November rainy season are examined by clustering (
k − means) daily OLR anomalies in ECHAM5 atmospheric global climate model (AGCM) experiments. The AGCM is first forced by 1980–2005 observed SSTs (GOGA), then with climatological, multi‐model mean monthly climatological SST bias from 31 CMIP5 coupled models (HIST) and projected SST changes for 2040–2059 (PROJ) and 2080–2099 (PROJ2) imposed on top of observed values. A typology of seven recurrent convection regimes is identified and consists of three dry and four wet regimes, including three regimes characterized by tropical‐midlatitude interactions between surface convection cells across the IAS and Rossby wave in the upper‐troposphere, and a regime of broad wettening typical of the ITCZ. Compared to an earlier observational study, all seven regimes are reasonably well reproduced in the HIST runs. However, the latter exhibit drier dry regimes, a less wet ITCZ‐like wet regime and a southeastward shift of convective anomalies developing across the IAS in the three other regimes, all result in a drier simulated IAS climate compared to GOGA. ECHAM5 projection runs for PROJ and PROJ2 are both characterized by the inhibition of the broad ITCZ‐like wet regime, indicating a significant trend towards more frequent dry weather. Meanwhile, convection anomalies related to tropical‐midlatitude interactions are shifted further east of the Caribbean as lead increases. These results suggest more frequent and intense extreme rainfall over the tropical Atlantic and the southeast US, while parts of the Caribbean are projected to experience drier climate. The projected drying, however, is of the same order of magnitude as results from historical SST biases, suggesting that the latter need to be considered in model projections, which might underestimate future IAS drying. -
The recent decline in Horn of Africa rainfall during the March–May “long rains” season has fomented drought and famine, threatening food security in an already vulnerable region. Some attribute this decline to anthropogenic forcing, whereas others maintain that it is a feature of internal climate variability. We show that the rate of drying in the Horn of Africa during the 20th century is unusual in the context of the last 2000 years, is synchronous with recent global and regional warming, and therefore may have an anthropogenic component. In contrast to 20th century drying, climate models predict that the Horn of Africa will become wetter as global temperatures rise. The projected increase in rainfall mainly occurs during the September–November “short rains” season, in response to large-scale weakening of the Walker circulation. Most of the models overestimate short rains precipitation while underestimating long rains precipitation, causing the Walker circulation response to unrealistically dominate the annual mean. Our results highlight the need for accurate simulation of the seasonal cycle and an improved understanding of the dynamics of the long rains season to predict future rainfall in the Horn of Africa.more » « less
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Abstract The persistent inter‐model spread in the response of global‐mean surface temperature to increased CO2(known as the “Equilibrium Climate Sensitivity,” or “ECS”) is a crucial problem across model generations. This work examines the influence of the models' present‐day atmospheric circulation climatologies, and the accompanying climatological cloud radiative effects, in explaining that spread. We analyze the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and find that they simulate a more poleward, and thus more realistic, edge of the Hadley cell (HC) in the Southern Hemisphere than the CMIP5 models, although the climatological shortwave cloud radiative effects are similar in the two generations of models. A few CMIP5 models with extreme equatorward biases in the HC edge exhibited high ECS due to strong Southern midlatitude shortwave cloud radiative warming in response to climate change, suggesting an ECS dependence on HC position. We find that such constraint no longer holds for the CMIP6 models, due to the absence of models with extreme HC climatologies. In spite of this, however, the CMIP6 models show an increased spread in ECS, with more models in the high ECS range. In addition, an improved representation of the climatological jet dynamics does not lead to a new emergent constraint in the CMIP6 models either.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/joc.7456
