1. Evaluation of RM3 Weather Forecasts over Western Africa Dr. Leonard M. Druyan 1 ; Dr. Matthew B. Fulakeza 1 ; Ruben Worrell 2 ; Kristal Quispe 3, and Kush Dave 4 1 Team Principal Investigator (GISS), 2 Education Specialist (NYCRI), 3 Undergraduate (NYCRI), 4 High School Student (NYCRI) Sponsors: National Aeronautics and Space Administration (NASA) NASA’s Goddard Space Flight Center (GSFC) NASA’s Goddard Institute for Space Studies (GISS) New York City Research Initiative (NYCRI) Contributors: Leonard M. Druyan, Ph. D (PI) Matthew B. Fulakeza, Ph.D (PI) Ruben Worrell (Education Specialist) Kristal Quispe (Undergraduate) Kush Dave (High School Student) SST Variability and Sahel Rainfall During the past century West Africa has been subject to significant decadal and inter-decadal variations in precipitation. This variability is most apparent and strongest during the summer, when the Monsoon system is taking its toll over West Africa. The trend observed for the 20 th century is a decrease in precipitation with a small recovery of rainfall towards the end of the century and into the 21 st century (Paeth and Hense 2004). Most studies have reached the consensus that SST plays a major role in forcing rainfall variability in the southern part of West Africa (Camberlin et al. 2001; Nicholson 2001; Vizy and Cook 2001). Still, it is apparent that it isn’t the only factor. Changing patterns of SST’s, increasing greenhouse gas concentrations, and aerosols are possible variables affecting precipitation variability in the Sahel region of Western Africa. It has been noted that warm sea surface temperature anomalies in the Gulf of Guinea causes a lot of rainfall over the Guinea coast, and dryer conditions in the Sudan and Sahel zone (Camberlin et al. 2001). The latitudinal location of the tropical rainbelt also seems to have an effect on the variability of Sahelian rainfall. When the belt is located more towards the north the Sahel experiences a wet year and if the belt is located more southward then the Sahel experiences a drought (Nicholson and Webster 2007). It has been observed that the rainbelt has a tendency to be shifting southward, causing the Sahel to experience dryer conditions (Hwang et al. 2013). Still it is yet unclear if the shift of the tropical rainbelt is a cause or effect of changing rainfall variability. If indeed it is the cause, aerosols have been strongly suggested to be a major factor behind the rainfall patterns that have produced an overall wetter West Africa but a dryer Sahel when compared to the Guinea Region. Still, others suggestions have been made towards the shift of the rainbelt, including inertial instability, that deals with the development of a strong westerly Jet-like flow (Nicholson and Webster 2007). Most papers side towards SST’s playing the major role during the 20 th century while greenhouse gases and/or aerosols having a more pronounced effect towards the end of the 20 th century and into the 21 st century. Abstract The West African Monsoon (WAM) is a climatological moisture system in the Sahel region of Western Africa. The WAM supplies the annual source of precipitation to the countries within western Africa during the summer months of June to September, providing rich fertile soil for farmland. The agriculture in this region is the main source of income for the people residing within it. Due to the socio-economic impacts of Sahelian Rainfall it is important to understand the factors affecting its variability. This includes changing pattern of Sea Surface Temperature’s (SST’s), the position of the Intertropical Convergence zone (ITCZ), aerosols, and increasing concentrations of Green house gases. Thus, the development of reliable climatological models in order to predict rainfall trends in the near and distant future is crucial. Regional Climate Models (RCMs) are continuously being tested due to their higher spatial resolution. The Regional Model (RM3) developed at GISS is running and producing data for the area of interest, which stretches from Senegal to Niger. The objective of our study is to compare RM3 forecasts to observational station data to validate the accuracy of the model. Once the data from the RM3 was retrieved it was compared with data from 42 weather stations in Africa. In addition, generated maps of RM3 simulations were compared with TRMM satellite data due to inconsistencies in the reliability of observational data. While analyzing RM3 forecasts, attention was brought to the fact that data from model runs in the United States GISS computer (PIRO) were producing slightly different results from data at the African Center of Meteorological Application for Development’s (ACMAD) computer (G5). RM3 validation against weather station data revealed that the model was overestimating monthly rainfall rates, especially in the southern part of western Africa. Comparison of maps also showed that most RM3 data compared to coastal weather station data showed overestimation, while most RM3 data compared to inland weather station data showed underestimation of rainfall. Overestimations, has been suggested to be due to coastal moisture. Data: Validation RM3 vs. African Stations PIRO vs. ACMAD Figure 3: Piro (White) and ACMAD (Blue) precipitation comparisons for 14 days in August and September References Camberlin P, Janicot S, and Poccard I. 2001. Seasonality and Atmospheric Dynamics of the Teleconnection between African Rainfall and Tropical Sea-surface Temperature: Atlantic vs. ENSO. International Journal of Climatology. 21: 973–1005. Giannini A. 2010. Mechanisms of Climate Change in the Semiarid African Sahel: The Local View. Journal of Climate. 23: 743-756. Giannini A, Salack S, Lodoun T, Ali A, Gaye A, Ndiaye O. 2013. A unifying view of climate change in the Sahel linking intra-seasonal, interannual and longer time scales. Environmental Research Letters. 8: 024010. Hwang Y, Frierson D, Kang S. 2013. Anthropogenic Sulfate Aerosol and the Southward Shift of Tropical Precipitation in the late 20th Century. Geophysical Research Letters. 40: 1-6. Nicholson S. E. 2001. Climatic and Environmental Change in Africa during the Last Two Centuries. Climate Research. 17: 123–144. Paeth H, Hense A. 2004. SST versus Climate Change Signals in West African Rainfall: 20th-Century Variations and Future Projections. Climatic Change. 65: 179-208. Lebel T, Ali A. 2009. Recent trends in the Central and Western Sahel rainfall regime (1990–2007). Journal of Hydrology. 375: 52-64. Vizy E. K and Cook K. H. 2001. Mechanisms by which Gulf of Guinea and Eastern North Atlantic Sea Surface Temperature Anomalies Can Influence African Rainfall. Journal of Climate. 14: 795–821. Conclusion The RM3 model seems to be overestimating monthly rainfall rates, especially in the southern part of West Africa. The overestimation is partially due to the fact that the model tends to add precipitation when it is not actually raining in the area, mistakenly analyzing high humidity as precipitation. Yamoussokro and Gagnoa have the highest model overestimations for June and July. Overestimations are due to the addition of coastal moisture as well as high humidity values. Piro and ACMAD seem to have differences although the same model is running on both computers. This indicates that even though the same model is running, the structural differences of both computers can cause subtle differences in data. Differences in both model runs were found only when rounding the data to two decimal places. After evaluating the accuracy of the model in June and July of 2012, it was noted that areas in the southern part of West Africa were overestimated by the model and areas in the central and northeast part of West Africa were underestimated by the model. Analysis: Real time RM3 forecasts vs. TRM Forecasts June PrecipitationJuly Precipitation