The northern Sub-Saharan African region (NSSA) is an area of intense study due to the recent severe droughts that have dire consequences on the population, which relies mostly on rain fed agriculture for its food supply. This region’s weather and hydrologic cycle are very complex and are dependent on the West African Monsoon. Different regional processes affect the West African Monsoon cycle and variability. One of the areas of current investigation is the water cycle response to the variability of land surface characteristics. Land surface characteristics are often altered in NSSA due to agricultural practices, grazing, and the fires that occur during the dry season. To better understand the effects of biomass burning on the hydrologic cycle of the sub-Saharan environment, an interdisciplinary team sponsored by NASA is analyzing potential feedback mechanisms due to the fires. As part of this research, this study focuses on the effects of land surface changes, particularly albedo and skin temperature, that are influenced by biomass burning. Surface temperature anomalies can influence the initiation of convective rainfall and surface albedo is linked to the absorption of solar radiation. To capture the effects of fire perturbations on the land surface, NASA’s Unified Weather and Research Forecasting (NU-WRF) model coupled with NASA’s Land Information System (LIS) is being used to simulate some of the fire-induced surface temperature anomalies and other environmental processes. The strategy and preliminary results are presented. Investigating the impacts of surface temperature anomalies due to wildfires in Northern sub-Saharan Africa Abstract Trisha Gabbert 1, Charles Ichoku 2, Toshi Matsui 2,3, and William Capehart 1 1 South Dakota School of Mines & Technology (SDSMT), 2 National Aeronautics and Space Administration (NASA), 3 Earth System Science Interdisciplinary Center (ESSIC), 4 Science Systems and Applications, Inc. (SSAI) Data Strategy & Model Description Preliminary Results Maps References & Acknowledgments Gatebe, C., Ichoku, C., Poudyal, R., Roman, M., and Wilcox, E., 2014: Surface albedo darkening from wildfires in northern sub-Saharan Africa. Environ. Res. Lett., accepted for publication, Ichoku, C., R. Kahn, and M. Chin, 2012: Satellite contributions to the quantitative characterization of biomass burning for climate modeling. Atmos. Res., 111, LIS User’s Guide, Revised NASA, Greenbelt, MD, USA, 127 pp. National Centre for Atmospheric Research (NCAR), 2014: ARW Version 3 Modeling System’s User’s Guide, NCAR, Boulder, Colo, USA, 423 pp. NASA-Unified Weather Research and Forecasting (NU-WRF) Version 6­3.4.1 User Guide, Revised NASA, Greenbelt, MD, USA, 24 pp. I wish to express my sincere gratitude to the International Association of Wildland Fire for support of this project. Many thanks to the South Dakota Space Grant Consortium for continued support throughout this project. The NASA GES DISC maintains MERRA products used for NUWRF runs. I have great appreciation for the MERRA, GDAS, CMAP, TRMM, NRL, PERSIANN, and CMORPH mission scientists and associated personnel for the production of the data used in this research effort. Step 1 – Establish control run from April 2006 thru March 2007 (strong fire season) Step 2 – Incorporate fire perturbations into NUWRF Step 3 – Complete NUWRF run with perturbation from April 2006 thru March 2007 Step 4 – Compare results with control and satellite observations The NASA-Unified Weather Research and Forecasting (NU-WRF) model is developed from NCAR’s WRF ARW model with additional available components such as Goddard Bulk Microphysics, Goddard shortwave/longwave radiation, Goddard Chemistry Aerosols Radiation Transport (GOCART) model, Goddard Satellite Data Simulator Unit (G-SDSU), G-SDSU Maximum Likelihood Ensemble Filter (MLEF), and Goddard Space Flight Center (GSFC) Land Information System (LIS). The NUWRF model is an observationally driven, non-hydrostatic, regional model but can be used to simulate over a broad range of scales. This simulation is coupled with GSFC LIS. Lateral boundary conditions: MERRA global analysis driven LIS surface forcing: GDAS and CMAP rainfall rate Grid spacing: 9km Vertical levels: 61 sigma Discussion The NUWRF plots above are all from the control run, recently completed. Further analysis of the control run is ongoing. This consists of several parameters compared from the control to satellite observations, such as precipitation, albedo, upper air wind speeds, and surface skin temperature. The next phase will be the addition of fire radiative power to perturb the model. Of particular interest is the potential feedback mechanism between fire radiative power and surface interactions that affect the hydrologic cycle, specifically surface albedo and skin temperature. More information on surface temperatures and albedo changes and their potential influences due to biomass burning may shed light on what to expect regarding the regional water cycle. MERRA: Modern Era-Retrospective Analysis For Research And Applications (NUWRF model) GDAS: Global Data Assimilation System (LIS model) gdas CMAP: CPC Merged Analysis of Precipitation (LIS model) TRMM 3B42: Tropical Rainfall Measuring Mission Product 3B42 tml NRL: Naval Research Laboratory – Blend Satellite Rainfall Estimates PERSIANN: Precipitation Estimation from Remote Sensing Information using Artificial Neural Network CMORPH: Climate Prediction Center Morphing Technique Figure 1: Area averaged albedo over study region during first week of control run. Figure 3: Area averaged surface temperature [K] over study region during first week of control run. Figure 2: Area averaged albedo over study region during a selection from fire season of control run. Figure 4: Area averaged surface temperature [K] over study region during a selection from fire season of control run. Figure 7: Four independent rainfall estimates [mm/hr] over study region during time first week of control run for observational comparison. Figure 8: Four independent rainfall estimates [mm/hr] over study region during a time period from fire season for observational comparison. Figure 5: Area average non- convective rainfall [mm/hr] over study region during first week of control run for comparison (figure 7). Figure 6: Area average non- convective rainfall [mm/hr] over study region during a selection from fire season of control run for comparison (figure 8). Contact info: Trisha Gabbert,