CONSORTIUM SUR LA CLIMATOLOGIE RÉGIONALE ET L’ADAPTATION AUX CHANGEMENTS CLIMATIQUES

1. CONSORTIUM SUR LA CLIMATOLOGIE RÉGIONALE ET L’ADAPTATION AUX CHANGEMENTS CLIMATIQUES 2m TemperatureinterannualVariability and ClimateChange Signal from the Narccap’sRCMs Sébastien Biner, Ramon de Elia and Anne Frigon May 2012

2. Motivations • Whylookingatinterannualvariability? • It is a fundamental part of the climate • It is variable over NorthAmerica • It is a « noise » to whichwecan compare the climate change « signal » era40 [1958-1999] From Scherrer 2010

3. TemperatureInterannualVariability DJF JJA • Willmot et Matsuura, 2009 • Synopticscale Chinook effect • Sea-ice • Snow cover

4. TemperatureInterannualVariability DJF JJA • Willmot et Matsuura, 2009 • Synopticscale Chinook effect • Sea-ice • Snow cover Not in CRU2 dataset

5. How well do RCMsreproduce the interannualVariability? • Narccap • 6 RCMs • Simulations driven by NCEP (1980-2003)

6. Definition of a new Index to compare interannualVariability Inspired by Gleckleret al 2008 and Scherrer 2010 wedefine a new Variability Index Ratio (VIR) : if if Example : VIR=-30% : underestimation by 30% VIR=50% : overestimation by 50%

7. VIR for Winter 2m Temperature

8. VIR for Summer 2m Temperature

9. How well do RCMsreproduce the interannualVariability? • Narccap • 6 RCMs • Simulations driven by NCEP (1980-2003) • Simulations driven by GCMs (1971-1999)

10. VIR for Winter 2m Temperature

11. VIR for Winter 2m Temperature ccsm cgcm hadcm3 gfdl

12. VIR for Winter 2m Temperature wrf crcm mm5 hadrm3 rcm3 ecp2

13. VIR for Winter 2m Temperature

14. VIR for Winter 2m Temperature CGCM3 drivenRCMssharecommonunderestimation

15. VIR for Winter 2m Temperature SomeRCMs are sensible to the driving GCM …

16. VIR for Winter 2m Temperature … whileother are less sensible

17. VIR for Summer 2m Temperature

18. VIR for Summer 2m Temperature CCSM drivenRCMssharecommonoverestimation

19. VIR for Summer 2m Temperature RCMs tend to overestimatevariability in the Gulf of Mexico region

20. Climate Change in a signal to noise Paradigm • In order to appreciate the strength of the climate change signal, it has to becompared to the variabilitywhichrepresents the range of temperatureinside of whichwe are used to live (adapted). • Climate change = signal = • Variability = noise = • Expectednumber of Yearsbefore Emergence (EYE) : • Wheretarepresent the studentdistribtution value for a givena % value (typicallya=95%)

21. CC for Winter Temperature North/South gradient

22. CC for SummerTemperature Maximum heating over US Minmumheating over northern part

23. EYE for Winter Temperature Values in 30-60 years range

24. EYE for Winter Temperature Values in 30-60 years range Pattern dominated by variability

25. EYE for SummerTemperature Values in the 20-40 years range over US and South Canada Region of low CC dominate pattern

26. Conclusions • Ability of RCMs to reproduce interannual variability • Ncep driven : • relatively small over/under estimation over some regions during winter. • general noticeable overestimation during summer, especially over southeastern US • GCMs driven : • underestimation across the domain during winter (particularly cgcm3 driven) • underestimation around Hudson Bay and overestimation over southeastern US during summer • Climate change signal and its perception • CC signal similar among RCMs during winter with northern gradient heating. • CC signal variable among RCMs during summer, heating generally more important over central US. Some cooling. • During winter high variability over northwest North America slows the perception of the important warming (high EYE values) • During summer no general EYE pattern except for RCMs with regions of low CC signal • Perception of CC is expected to occur faster during summer than during winter, especially over the US • General Conclusions similar to Hawkins and Sutton 2010