The Multidecadal Atlantic SST - Sahel Rainfall Teleconnection in CMIP5 Simulations
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1 The Multidecadal Atlantic SST - Sahel Rainfall Teleconnection in CMIP5 Simulations A13M-03 Chris Thorncroft*, Elinor R. Martin* and Ben Booth^ *DAES, SUNY Albany ^Met Office, Hadley Centre, UK
2 Sahel Rainfall and Global SST Correlation between (filtered) Sahel rainfall and (filtered) global SST in observations Atlantic Ocean à AMO à warm SST wet Sahel Indian Ocean à warm SST dry Sahel Use CMIP5 historical simulations to understand these patterns 21 models, 72 individual ensemble members
3 Model Selection Criteria Why do some models with high decadal variance in the AMO have high Sahel rainfall decadal variance, but others do not? High AMO decadal variance High Sahel rain decadal variance + Correlation with NAtl SST Low Sahel rain decadal variance + No correlation with NAtl SST GOOD POOR
4 Sahel Rain AMO Teleconnection CRU GOOD MEAN POOR MEAN mm/day per SD Rainfall regressed onto AMO Index As N. Atlantic warms: Increased rainfall across Sahel and tropical Atlantic Band of decreased rainfall to South Poor models have little to no rainfall variability with the AMO
5 Spatial Structure of the AMO HadISST SST regressed onto AMO Index Largest difference in tropical North Atlantic & Mediterranean GOOD MEAN POOR MEAN C per SD 1. Large cross equatorial SST gradient 2. Large SLP gradient 3. Deeper Saharan heat low 4. Stronger monsoon 5. Increased Sahel rain
6 1) CLOUDS: Mean Cloud Structure ISCCP GOOD MEAN POOR MEAN Clouds: Positive Feedback é SST ê cloud amount Total cloud fraction (includes all levels) Larger total cloud amount & variability in good models à Larger AMO related SST variability Is total mean cloud amount related to simulation of SST variability?
7 2) DUST: Dust Response to AMO Dust: Positive Feedback é SST é Sahel rain ê Dust Dust load regressed onto AMO Index GOOD MEAN Good Models: Increase SST, increase SST, reduce dust Poor models do not have this positive feedback Cannot attribute dust as cause for low SST variability as dust, rain and SST all connected POOR MEAN
8 3) SULFATES: Sulfate Aerosol and Total Cloud Amount Sulfate Aerosols: é Sulfate ê SST Some CMIP5 models include interactive indirect effects Need cloud and sulfate in same location for indirect effect to occur Good Models Poor Models Mean sulfate load (shading) >50% total cloud faction (stippling) Colocation of sulfate and cloud important for SST variability in tropical Atlantic
9 Summary CMIP5 models must represent the spatial Atlantic SST pattern correctly in order to simulate decadal Sahel rainfall variability SST errors in tropical/subtropical Atlantic due to Poor simulation of mean cloud amount and variability with SST Incorrect SST-rain-dust relationship Non co-location of cloud and sulfate so no indirect effects possible even if model includes interactive indirect effects Paper: Martin, E., C. Thorncroft, and B. Booth, 2013: The Multidecadal Atlantic SST - Sahel Rainfall Teleconnection in CMIP5 Simulations. J. Climate. doi: /jcli- D , in press. Poster Today: GC11D Sahel Rainfall in Multimodel CMIP5 Decadal Hindcasts Elinor R. Martin and Chris D. Thorncroft
10 CMIP5: Sahel Rainfall and North Atlantic SST Correlation between (filtered) Sahel rainfall and (filtered) area averaged SST for all 72 ensemble members Color shows significance of correlation GREEN > 90 % YELLOW % ORANGE % RED < 50 % (e.g. opposite sign to observed) CMIP5 historical simulations have some success representing the positive and significant Sahel Atlantic SST teleconnection on decadal timescales
11 Decadal Variance Fraction: Sahel Rain Decadal variance / total variance JAS Sahel rainfall Observed value 45 % CMIP5 Historical mean 16 %
12 CMIP5 Control Simulations
13 AMO Cloud Relationship GOOD MEAN Regress total cloud fraction (includes all levels) onto AMO index Large cloud variability coincident with large SST variability in good models POOR MEAN Weak variability in poor models Larger (more realistic) total cloud amount in eastern basin of good models
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