Ben Harvey, Len Shaffrey, Tim Woollings. SRM Annual meeting, 2 nd November 2011
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1 Ben Harvey, Len Shaffrey, Tim Woollings SRM Annual meeting, 2 nd November 2011
2 Position within TEMPEST WP4 Integration and pull-through Lead Shaffrey WP1 Assessing Storms Lead Hodges Giuseppe Zappa WP2 Quantifying Processes Lead Shaffrey Ben Harvey WP3 Impact of Resolution Lead Palmer Andrew Dawson Student Statistical Modelling of Trends Exeter Phil Sansom Student Latent Heat Release in Storms Reading Matt Hawcroft WP2 aim: Understand which processes are leading to the large spread of climate predictions for European extra-tropical cyclones
3 CMIP3 23 models The database only contains daily mean data - Cannot track individual storms - Instead use Eulerian diagnostics - Here focus on time-filtered variance statistics
4 CMIP3 storm tracks Data: J. Pinto & M. Reyers (Uni. Cologne) (only 15 models used)
5 Possible drivers of change Global drivers Upper level pole-equator T Lower level pole-equator T Stratification Local moisture content Regional drivers Atlantic SSTs Arctic sea ice extent Land-sea contrast Tropically-forced stationary waves AMOC?
6 Possible drivers of change SRESA1B IPCC AR4
7 Possible drivers of change + - SRESA1B IPCC AR4
8 Possible drivers of change SRESA1B IPCC AR4
9 Possible drivers of change Global drivers Upper level pole-equator T Lower level pole-equator T Stratification Local moisture content Regional drivers Atlantic SSTs Arctic sea ice extent Land-sea contrast Tropically-forced stationary waves AMOC?
10 Possible drivers of change
11 Experimental design We force HadGAM1.2 with SSTs and sea ice patterns taken from the CMIP3 multi-model ensemble Key questions: 1. How much of the mean response is reproduced using the CMIP3 multi-model mean SST and sea ice extent responses? 2. How much of the spread is reproduced using forcing fields that represent the range of SST and sea ice responses?
12 Experimental design CONTROL Late C20 multi-model mean SST & sea ice edge position A1B Late C21 multi-model mean SST & median ice edge retreat Late C20 GHGs Late C21 GHGs
13 Experimental design A1B+ A1B + Atlantic EOF1 SST Strongly retreated ice edge CONTROL Late C20 multi-model mean SST & sea ice edge position A1B Late C21 multi-model mean SST & median ice edge retreat A1B- A1B - Atlantic EOF1 SST Weakly retreated ice edge Late C20 GHGs Late C21 GHGs
14 Experimental design CONTROL Late C20 multi-model mean SST & sea ice edge position A1B Late C21 multi-model mean SST & median ice edge retreat A1B- A1B - Atlantic EOF1 SST Weakly retreated ice edge A1B+ A1B + Atlantic EOF1 SST Strongly retreated ice edge
15 Results CONTROL Late C20 multi-model mean SST & sea ice edge position A1B Late C21 multi-model mean SST & median ice edge retreat
16 Results CONTROL Late C20 multi-model mean SST & sea ice edge position A1B Late C21 multi-model mean SST & median ice edge retreat Does this look like the multi-model mean response?
17 Results CONTROL Late C20 multi-model mean SST & sea ice edge position A1B Late C21 multi-model mean SST & median ice edge retreat Does this look like the multi-model mean response? - Yes in the SH and the Pacific - Not so much in the Atlantic, but there are qualitative similarities
18 Results CONTROL Late C20 multi-model mean SST & sea ice edge position A1B Late C21 multi-model mean SST & median ice edge retreat A1B- A1B - Atlantic EOF1 SST Weakly retreated ice edge A1B+ A1B + Atlantic EOF1 SST Strongly retreated ice edge
19 Results
20 Summary We have forced an atmosphere model with SST and sea-ice fields based on those produced by the CMIP3 models - The multi-model mean storm track response is qualitatively reproduced using the multi-model mean SSTs and sea ice extents - The magnitude of the inter-model spread is captured by the range of SSTs and sea ice extents What s next? - Analyse the structure of the responses in more detail - Run ice-only and SST-only experiments - Design experiments to look at the large-scale drivers of change
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