Sahel Climate Change in the IPCC AR4 models Michela Biasutti email@example.com in collaboration with : Alessandra Giannini, Adam Sobel, Isaac Held OUTLINE • 20th Century: Was the Sahel drought internal noise? Forced Signal? Anthropogenic? GHG or Aerosols? • 21st Century: What is the source of model disagreement ? Different SST forcing? Different response to the same SST forcing? SST-forced Sahel drought: natural? AMIP coupled CTL Fig. 5. The 1950–99 trends of (left) observed and (middle) atmospheric GCM simulated seasonal African rainfall for JAS. Plotted is the total seasonal rainfall change (mm) over the 50-yr period. (right) The empirical PDFs of JAS 50-yr rainfall trends averaged over the Sahel region. The data given by the red curve are from the 80 individual members of the AGCM simulations forced with the history of global observed SSTs. The data given by the blue curve are from 15 individual members of unforced coupled atmosphere–ocean model Hoerling et al., 2006 simulations. The observed trend value is indicated by the gray bar. IPCC Simulations PI XX A1B Pre-Industrial 20th Century Global Warming Control (PI) Simulation (XX) Scenario (A1B) NASA/GISS GCMs IPCC Simulations 1950 2000 2050 “[The ensamble mean] fails to simulate the pattern or amplitude of the twentieth-century African drying, indicating that the drought conditions were likely of natural origin.” Hoerling et al., 2006 Importance of Internal Variability 1950-1985 Trend 1950-1999 Trend 60 XX Simulations 1930-1999 Trend 1. reduced variability 2. predominance of drying trend Forced Signal: (1975-1999 mean) minus (PI mean) XX-PI Rainfall Change XX-PI SST Change OUTLINE • 20th Century: Was the Sahel drought internal noise? Forced? Anthropogenic? GHG or Aerosols? • 21st Century: What is the source of model disagreement ? Different SST forcing? Different response to the same SST forcing? Effect of GHG 4x(yrs50:70)-PI Surface Temperature Mean Rainfall Change Robustness of Rainfall Change 20 Effect of Reflective Aerosols SULFATE AEROSOL FORCINGS (1850-1997) Temp RESPONSE Precip RESPONSE QuickTime™ an d a TIFF (LZW) decomp ressor are need ed to see this p icture . Quic kTime™ and a TIFF ( LZW) dec omp resso r ar e need ed to s ee this picture. ROTSTAYN AND LOHMANN „02 NASA/GISS Some Conclusions • 20th Century drying of the Sahel is reproduced by almost all IPCC AR4 models it is (partly) externally forced. (But natural, internal variability is substantial.) • The forcing was anthropogenic, with the most robust signal coming from the sulfate aerosol forcing. • The response to GHG increase alone is inconsistent across models, which implies an uncertain outlook for the Sahel. Precipitation Response in the Sahel GFDL What are the possible causes of discrepancy? Given the role of SST in simulations of the 20th Century, is it SST?: different SST anomalies? different sensitivity to same SST anomalies? Relationship of Sahel rainfall & SST (pre-industrial, not forced) Biasutti et al., 2007 goodness of model Linear Multi-Regressive Model: from SST PI (Indo-Pacific & Atlantic Gradient) (training run) to Sahel Rainfall XX A1B interannual (=detrended) goodness of model Linear Multi-Regressive Model trained on (detrended) PI: PI from SST (Indo-Pacific & Atlantic Gradient) to Sahel Rainfall XX nb: same results if NTA & STA are used (3 predictors) and/or if model is trained on XX. interannual A1B interannual + trend Simulated &Predicted Sahel Rainfall Linear Regression Coefficients obs CM2 North Atlantic miroc AM2 CM2 miroc Uniform Warming Held & Lu, 2007 Conclusions • ~30%(?) of 20th Century drying of the Sahel was externally forced. The forcing was anthropogenic, with the most robust signal coming from the sulfate aerosol forcing. • In the 21st Century, when GHG are the dominant forcing, the Sahel response is inconsistent across models. • Global SST changes can explain the 20th Century trend, but, in most models, not the 21st Century one (at least not through the same mechanisms active in the past). • A model’s good representation of the past is no indication of a trustworthy prediction of the future. How can we reduce the uncertainty of our climate outlook?