Federal Departement of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Present-day interannual variability of surface climate in CMIP3 models and its relation to future warming* Simon C. Scherrer Federal of Office of Meteorology and Climatology MeteoSwiss 15 July th International Meeting on Statistical Climatology, Edinburgh UK * partly based on a publication in the International Journal of Climatology (doi: /joc.2170)

2 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Outline  Can CMIP3 models adequately represent interannual variability in surface temp, precip, SLP? Problem regions?  Are there differences in variability representation for large areas (tropics, extratropics, land, sea)?  Can future temperature changes be constrained using only the „good“ models based on present day variability skill?

3 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Observed interannual variability spread: standard deviation of seasonal averages K (top) mm/day (bottom) 2m Temp ERA DJFJJA Precip GPCP dotted: precip < 1mm/season time signal time signal small large

4 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Simple variability metrics: VI  Variability index (VI) cf. Gleckler et al. (2008) is used to analyze variability performance for each model, variable and grid-point  “multi-model mean” index defined as the average over all model run’s #1 for 21 GCMs of the CMIP3 archive What is a good VI? s: standard deviation estimate x, y: grid point coordinate var: variable (e.g. temperature) ref: reference data set (e.g. ERA-40) VI-range: 0 (perfect) to 

5 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Precip dotted: precip < 1mm/season Multi-model mean variability index 2m Temp (ERA-40, ) and Precip (GPCP, ) 2m Temp DJFJJA goodbad 0[-29;+41][-38;+61][-48;+93][-62;+161][-71;+245] %

6 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Multi-model mean variability index sea level pressure (ERA-40, ) 2m Temp DJFJJA SLP 0[-29;+41][-38;+61][-48;+93][-62;+161][-71;+245] %

7 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Outline  Can CMIP3 models adequately represent interannual variability in surface temp, precip, SLP? Problem regions?  Are there differences in variability representation for large areas (tropics, extratropics, land, sea)?  Can scenario range be constrained using only information of the „good“ models using present day variability skill?

8 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Variability index for 2m Temp tropics vs extratropics tropics worse  good models in tropics are in general not also good models in extratropics

9 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Variability index for 2m Temp land vs sea DJFJJA  good models over land are not necessarily also good models over sea sea worse

10 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Outline  Can CMIP3 models adequately represent interannual variability in surface temp, precip, SLP? Problem regions?  Are there differences in variability representation for large areas (tropics, extratropics, land, sea)?  Can scenario range be constrained using only information of the „good“ models using present day variability skill?

11 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Variability index vs. temperature change TROPICS, dT DJFJJA no constraining potential dT [K] good VI bad

12 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Variability index vs. temperature change EXTRA-TROPICS, dT DJFJJA no constraining potential dT [K] good VI bad

13 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Variability index vs. temperature change LAND, dT DJFJJA no real constraining potential dT [K] good VI bad

14 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Variability index vs. temperature change SEA, dT DJFJJA no constraining potential dT [K] good VI bad

15 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Conclusions  Can CMIP3 models adequately represent interannual variability in surface temp, precipitation, sea level pressure? Problem regions? T/SLP pretty much, Precip hardly. Problems with sea ice boundary, ENSO, Central Africa, monsoon regions  Are there differences in variability representation for large areas (tropics, extratropics, land, sea)? Yes. Surface variability better on land than on sea and in the extra- tropics than in the tropics, but: “good” models in tropics (over sea) are not necessarily also “good” in extratropics (over land).  Can scenario range of dT on large scale be constrained using only information of models that represent present day variability skill well? Rather not. Weak negative relation between good IAV representation and more warming not enough to do constraining.  Model combination: Equal model weighting safest and most transparent. “Omitting” really bad models may be option.

16 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Thank you!

17 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Constraining the scenario change range? Simple concept (abs(r)>>0) 1) no constraining abs(r)>>0 goodbad skill r: Pearson correlation coefficient 2) constraining „potential?“ abs(r)>>0 goodbad skill change physics? small large 3) constraining „possible“ goodbad skill abs(r)>>0 reduced = full  reduced < full ? ? ? gap reduced < full

18 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh CMIP3 models 21 models considered

19 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Are skill - change relations real? If yes, can they be used to constrain climate scenarios?  YES:  Regional relation found: e.g. Coquard et al. (2004), Giorgi & Mearns (2002, 2003)  better models show stronger T changes but: no relation/narrower distribution for precipitation!  Global (land only) relation found: Shukla et al. (2006)  better models show stronger T changes (r = -0.74)  NO:  Grid point scale relation skill-change:  only where clearly wrong model, Räisänen (2007)

20 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Types of uncertainty in climate projections Hawkins and Sutton, 2009 emission scenarios models internal variability short term (up to 2050): models are key driver of uncertainty Long term (2050&beyond): Emissions are key driver of uncertainty

21 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Motivation Changes in Temp/Precip Sahel JAS CRUTS dT [K] wrt dPrecip [%] wrt to +55% +1.8 to +5.6 K warmer wetter drier

22 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh Changes in Temp/Precip Winter , 50-90N land precip change ~+4%/K ! CRUTS dT [K] wrt dPrecip [%] wrt

23 Interannual variability of surface climate in CMIP3 | 11 th IMSC, 15 July 2010, Edinburgh dPrecip ~ 0-1%/K ? dTemp ~ +3-5K dPrecip ~ +3%/K ? dTemp ~ K dPrecip ~ +5%/K? dTemp ~ K Temp/Precip changes JJA , 50-90N land Observation

24 MMM T2 biases ( ) wrt ERA-40 too cold too warm Temp too low -> westerlies not strong enough? related too sea ice? K 20C3m runs

25 MMM mean sea level pressure biases wrt ERA-40 ( ) too low too high NAO too weak H L H L H H L H LLL hPa 20C3m runs

26 MMM PRECIP biases wrt GPCP ( ) too dry too wet Precip deficit: westerlies not strong enough… mm/day 20C3m runs