Why projecting regional precipitation trends is difficult

1. Why projecting regional precipitation trends is difficult A1B 2080-2099 minus 1980-1999 Stippling: 80% of CMIP3 models agree on sign of trend Chris Bretherton Department of Atmospheric Sciences University of Washington IPCC 2007

2. The gist of this talk Focus: Zonally asymmetric regional precipitation trends driven by well-mixed greenhouse gas increases. Won’t discuss, but important (Yi Ming’s talk): Precipitation is sensitive to uncertainties in regional aerosol emissions and their pattern of direct + indirect (cloud-related) radiativeforcing

3. IPCC 2007 Observationalcontext – precip trends visible, but lots of natural variability

4. Main points of this talk Tropical precipitation, circulation and SST depend on poorly modeled processes such as cumulus convection. Teleconnection of tropical precipitation changes to midlatitudes also varies between models. Land surface and clouds feed back on precipitation changes Precipitation trends modulated by overall global warming, so dependent on emissions changes and climate sensitivity. Bottom line: Strong internal feedbacks in the climate system make precipitation trends very sensitive to model details.

5. 1. Tropical precipitation challenges

6. Trends in tropical dryingon ITCZ margins… quite diverse between models Neelin et al. 2006

7. Precipitation trends are not just noise...each model has its own systematic response Neelin et al. 2006

8. Evaporation increases over all low-lat oceans

9. Climate model rainfall sensitive to SST coupling CAM3 Coupled dSST Plots: NCAR CESM web site CAM3 + ocean Double-ITCZ bias in rainfall and SST in coupled model.

10. Precip trends don’t require SST gradient trends • Climate models simulate different tropical rainfall responses even to a uniform +2K SST increase, including over land. -w500 is a rainfall proxy (10 units ~ 1 mm/d) Bony et al. 2004

11. Tropical rainfall pattern sensitive to Cu param. CAM3 simulations with different cumulus parameterizations have different rainfall biases

12. 2. Teleconnection challenges

13. Midlatitude circulation responds more strongly to tropical SST anomalies than midlatitude SST anomalies PNA GOGA TOGA 27% of winter ensemble-mean PNA variability explained by SSTA; of that 70% is explained by tropical SSTAs. Lau and Nath 1994

14. Tropical teleconnections depend on the subtropical jet structure, which is model-dependent 200 hPa circulation response of 3 ‘AMIP’ AGCMs to ENSO SSTA over 1979-1988; note large differences in midlatteleconnections despite similar tropical Pac response obs SUNYA MPI CCC Boyle et al. 1982

15. 3. Regional land surface and cloud feedbacks

16. Land processes (model-dependent) modify rainfall Example: Albedo increase from Amazon deforestation Zeng et al. 1996 Tropical land precipitation is sensitive to albedo (Charney 1975) via a positive ‘convergence feedback’ loop.

17. Example 2: Regional climate models - NARRCAP • Each RCM has different physical parameterizations but is driven at boundaries by same global climate model output. • Loosely interpret as giving precip sensitivity to local physics Global AOGCMs 50 km regional climate models over N America Courtesy Linda Mearns

18. Ideally, all RCMs should have same precip trends • Winter: decent agreement for 2041-2070 minus 1971-2000 Courtesy Linda Mearns

19. Summer: poor agreement for 2041-2070 minus 1971-2000 Courtesy Linda Mearns Model land and atmosphere representation uncertainties matter more in midlat summer

20. Regional cloud trends are circulation-driven • Clouds, like precipitation, are affected by vertical motion • Clouds affect the surface and atmospheric energy balance to positively feed back on atmospheric circulations. • Clouds are challenging for climate models to simulate • Clouds also dominate uncertainty in global climate sensitivity, which affects the amplitude of precip trends. Bony et al. 2004 More ascent = more deep cloud

21. Regional cloud vs. precipitation trends Cloud cover

22. 4. Precipitation uncertainty due to global warming uncertainty

23. The climate sensitivity problem All other things being equal, GHG-induced precip trends should scale with global temperature rise, which depends on uncertain emissions and has model uncertainty. IPCC 2007

24. For a given scenario, global precip increase scales with model-simulated global warming Held and Soden 2006 2% K-1

25. Rainfall trends depend on scenario via global ΔT CMIP3 multimodel means • A climate model will warm 4x more and give 4x large precip change for A2 than commitment scenario

26. Regional precipitation trends hard to model: • Tropical precipitation, circulation and SST depend on poorly modeled processes such as cumulus convection. • Teleconnection of tropical precipitation changes to midlatitudes also varies between models. • Land surface and clouds feed back on precipitation changes • Precipitation trends modulated by overall global warming, so dependent on emissions changes and climate sensitivity. Bottom line: Strong internal feedbacks in the climate system make precipitation trends very sensitive to model details.