Impacts of Climate Change and Variability on Agriculture: Using NASA Models for Regional Applications Radley Horton 1, Cynthia Rosenzweig 2, and David Rind 2 Columbia University 1, NASA Goddard Institute of Space Studies 2 Introduction Climate changes with major impacts on agriculture –including warmer temperatures, changes in the distribution of precipitation, and sea level rise– are expected during the 21 st Century. Major modes of climate variability, such as the El Niño Southern Oscillation (ENSO) and the Arctic/North Atlantic Oscillation (AO/NAO), are also known to have key regional impacts. However, the regional climate impacts of the interaction between global warming and possible changes in climate variability are less understood. Modeling Experiments Modeling experiments were conducted with various NASA GISS General Circulation Models (GCMs). The ability of the models to simulate the regional impacts of current climate variability is assessed, followed by a modeling study of how regional climate may change due to global warming and possible changes in climate variability. For this study, future increases in greenhouse gases were based on IPCC scenario A1B, which is considered an intermediate estimate. Results Simulation of Climatology The suite of NASA models used, which included coupled models, are able to simulate annual rainfall climatology, as shown in figure 1 for the Mediterranean region. ENSO and Indonesia: An Agriculture Case Study As was shown in figures 2 and 3, El Niño events in Indonesia are associated with reduced rainfall during the early rainy season of September through November. (Only during the most extreme El Niño events do negative rainfall anomalies persist throughout the entire rainy season). As a result, farmers delay planting of staple crops such as rice and corn until the rainy season is underway. As shown in figure 7 (top), there is a negative correlation between El Niño and planted area of irrigated rice during the first half of the principal growing season (left) for the key agricultural areas, but the correlation becomes positive during the second half of the growing season (right), once the rains become established. This same pattern is mirrored in the harvested area data for irrigated rice (figure 7, bottom), with the approximately four-month delay corresponding to the maturation time. These results show the effects of climate variability on individual livelihood in a regional vulnerable to food shortages during climate extremes.. Figure 4 shows rainfall distribution in the same model for the Mediterranean winters between 1978 and When the North Atlantic Oscillation is positive in the model (leftmost bar), there are no years in which rainfall is in the wettest tercile. Simulation of Interannual Variability in the Current Climate When the atmospheric portion of GISS ModelE is run in hindcast mode forced by observed SST for the period , interannual precipitation patterns in Indonesia are well simulated as well, with a statistically significant r value of.62, as shown in figure 2. Figure 3 explores the primary source of interannual rainfall variability in Indonesia: The El Niño Southern Oscillation. Figure 3 (top) shows Simulation of Interannual Variability in the Future Climate A NASA GISS atmospheric GCM was coupled to the Cane-Zebiak ocean model (Eichler, 2000). Figure 5 shows the tropical Pacific December- February climatology for the current climate (top) and with doubled CO 2 bottom. The combination of greenhouse gas increase and ENSO coupling produces a shift towards more “El Niño-like” mean conditions characterized by increased basin wide SST, and reduced zonal SST and SLP gradient. Most noteworthy though is a more than 100 percent increase in central Pacific precipitation, to more than 16 mm/day locally. observed results over Indonesia from a composite of El Niño years minus a composite of La Niña years, for rainfall and sea level pressure. Figure 3 (bottom) shows results for the GISS model forced by observed SST. The model is able to capture the reduced rainfall and increased sea level pressure associated with El Niño, although the magnitude of the precipitation anomalies is too small in the model. The rightmost bar corresponds to the observed dry tercile, and shows that the model likewise produced no wet years when actual conditions were dry. These results indicate that when forced by observed SST, the NASA GISS model produces realistic rainfall during extreme climate events. This tropical convection fuels major changes in teleconnection patterns, including an enhancement of the Pacific North American Pattern (PNA). As shown in figure 6, the PNA change brought about by a more “El Niño- like” mean state contributes to increased winter precipitation over the southern United States, due in part to reduced sea level pressure and an enhanced moisture-laden subtropical jet stream. These results suggest the southeastern U.S. might get some protection from increased evaporation associated with higher warm season temperatures under global warming. If farmers and water resource managers are able to capture increased winter precipitation, the region’s agriculture may be able to withstand the worst effects of global warming. Figure 1: Average rainfall by calendar month. Observed CMAP2 precipitation is shown in bold; the other six lines show coupled model results. Figure 2: Western Indonesia September-November rainfall. Observed CMAP2 (black), individual ModelE run forced by observed SST (green), four- member ensemble forced by observed SST (blue). Figure 3: El Niño minus La Niña precipitation (contour) and sea level pressure (shaded). Top: observed CMAP2 precipitation and NCEP reanalysis sea level pressure, bottom: GISS ModelE forced by observed SST. Figure 4: GISS ModelE (forced by observed SST) rainfall terciles for the Mediterranean, December- February. Shown in the left bars are rainfall during the model’s extreme NAO years, and in the right bars model rainfall during observed wet and dry years. Figure 5: December-February tropical Pacific sea surface temperature (shaded, C), precipitation (contour, mm/day) and 200 mb. wind (vector, m/s). Top: NASA GISS atmospheric model with Q-Flux ocean, and current greenhouse gas levels. Bottom: Same atmospheric model coupled to Cane-Zebiak ocean model, with approximately doubled CO 2. Figure 6: Change in precipitation (contour, mm/day) sea level pressure (shaded, mb), and 200 mb wind (vector, m/s), coupled 2x CO 2 run minus Q-Flux control. Conclusion NASA climate models are valuable tools for predicting the impacts of climate variability and climate change on key agricultural regions, including the Southeastern United States. As computing power and scientific understanding advance, these models will further assist farmers and agricultural planners with agricultural decision support. Figure 7: Correlation between irrigated rice by province in Indonesia and October-December NINO4 index. Top: Planted area, bottom: harvested area.