1. Tropical Oceanic Influences on Global Climate Prashant. D. Sardeshmukh @ noaa.govClimate Diagnostics Center, CIRES, University of Coloradoand Physical Sciences Division/ESRL/NOAAThanks to: Joe Barsugli, Gil Compo, and Sang-Ik ShinGEOSS meeting Quebec 15 September 2008

2. Some Basic Questions 1. What parts of the climate system need to be observed and modeled most accurately to improve regional climate predictions ? The tropical oceans ? Is there more sensitivity to SST changes in some tropical regions than others ? What is the best way to estimate such sensitivities ? One way is to perturb an atmospheric GCM with the same prescribed localized SST anomaly at different locations and assess its impact. Are the responses to SST linear enough to justify such an analysis ? The notion of “sensitivity” becomes murky if the response to SST fields is not the sum of the responses to the SSTs at individual locations, because we cannot then speak unambiguously of the effect of a local SST change in isolation. 5. Finally, since everything in the climate system depends on everything else to some degree, can the sensitivities to SST changes be summarized in a meaningful way ?

3. Global and Continental Temperature Change IPCCSummary for PolicyMakers2007 To what degree has the oceanic warming influenced the continental warming ?

4. Most Recent Surface Temperature Change (1991-2006 minus 1961-1990) Observed Simulated by ECHAM AGCM with prescribed observed SST changes (but no GHG changes) As above, but simulated by NASA AGCM From Compo and Sardeshmukh Climate Dynamics 2008

5. The IPCC models have not captured recent 50-yr regional climate trends Figure currently unavailable Observed Trends IPCC model simulated trends (Coupled models) (From Shin and Sardeshmukh2008)

6. The IPCC models have not captured recent 50-yr regional climate trends On the other hand, atmospheric GCMs with prescribed observed SSTs (either globally or just in the tropics) have done much better in this regard. Figure currently unavailable Observed Trends IPCC model simulated trends (Coupled models) Atmospheric Model simulated trends (Uncoupled models with prescribed observed SSTs) (From Shin and Sardeshmukh2008)

7. The IPCC models have also not captured recent 50-yr Tropical SST trends Figure currently unavailable (From Shin and Sardeshmukh2008)

8. Evidence for the Dominance, Linearity, and Low-Dimensionality of Tropical SST influences on regional climates around the globe Dominance : Tropical SSTs account for most of the global SST-forced responses Linearity : The response to tropical SSTs is approximately linear; y = Gx Low-Dimensionality: G is a “low-dimensional” linear operator G = U S VT = s1 u1 v1T + s2 u2 v2T + . . In other words, tropical forcing/global response relationships can be characterized by just a few forcing-response singular vector pairs

9. To demonstrate the important role of the tropical oceans, we have used the NCAR/CCM3 atmospheric GCM, which realistically captures the observed Land-Average 850 mb Temp. changes with prescribed observed global SST changes (and without prescribed GHG changes) SBS-1 Blue Curve Observed 850 mb land-average temperature (NCEP/NCAR reanalysis) Red Curve Ensemble-mean response to global SSTs with + 1 sigma of ensemble spread

10. An Array of localized SST patches A comprehensive analysis of atmospheric sensitivity to Tropical SST “Fuzzy Green’s Functions” : Global CCM3 responses to an array of localized tropical SST anomaly patches (Barsugli, Shin, and Sardeshmukh, Climate Dynamics, 2006)

11. CCM3’s GLOBAL MEAN temperature and precipitation responses to observed global SST changes over the last 50 years (GOGA runs) are well captured by linearly combining the responses to our tropical SST patches BSS Winter (DJF) Summer (JJA) Red Curves Response to global SSTs Black Curves Linear response To tropical SSTs 850 mb Temperature Precipitation This is an important result. It demonstrates both the dominance and linearity of Tropical SST influences on the global mean climate.

12. Local correlations of our “linear reconstructions” with the CCM3’s annual-mean global SST-forced responses over the last 50 years BSS These high correlations demonstrate the dominance and linearity of Tropical SST influences also on REGIONAL climate changes

13. BSS Sensitivity of GLOBAL MEAN Temperature and Precipitation to SST increases at different tropical locations Temperature Precipitation This implies that Global Warming will be very sensitive to the precise pattern of tropical ocean warming (Barsugli, Shin, Sardeshmukh Clim. Dyn. 2006)

14. “Low-Dimensionality”of DJF teleconnections The first two forcing-response Singular-Vector (SV) pairs of G account for 72% of the structure of G Top and Middle Panels The “response” part of the SV pairs: 500 mb z, land surface temperature, and land precipitation These are “optimal” response patterns Bottom Panels: The “forcing” part of the SV pairs: Tropical SST These are “optimal” SST forcing patterns for generating responses with the largest amplitude

15. Dominant SST sensitivity patterns are VERY DIFFERENT from the dominant pattern of observed interannual SST variability EOF 1 of Observed SST 32% DJF 57% MAM 30% There is much greater sensitivity to SST changes in the Central Pacific and the Warm Pool. The sensitivity is generally opposite to SST changes in the Indian and Western Pacific portions of the Warm Pool . JJA 40% SON 41%

16. Summary 1. Tropical SSTs account for most of the global responses to global SST variations. 2. The response to tropical SSTs is approximately linear; y = Gx G contains all the sensitivity information. 3. G is a “low-dimensional” operator : That is, tropical forcing/global response relationships can be characterized by just a few “optimal” forcing-response Singular Vector pairs Our analysis reveals the largest sensitivity - often with opposite signs - to SST changes in the Extended Tropical Warm Pool area of the largest recent and projected SST trends. Climate models need to simulate and predict SST changes accurately in this area, and also to correctly represent the global atmosphere’s sensitivity to them.