Antarctic sea ice changes natural or anthropogenic? Will Hobbs

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1 Antarctic sea ice changes natural or anthropogenic? Will Hobbs

2 When is a trend not a trend? Antarctic sea ice trends are significant (compared to our 35-year record), but are they significant compared to long term variability? SIC trend SIE trend

3 Are these climate models wrong? IPCC AR5 low confidence in our scientific understanding of the observed changes in Antarctic sea ice low confidence in Antarctic sea ice projections over the next century

4 Antarctic sea ice: climate response or natural variability? Is there an ongoing anthropogenic signal in SH sea ice changes? How large is that signal compared to internal variability? Do models capture that response? Can we trust their projections?

5 Linear model of observed trends observed change (e.g. SIC trends) Y = βx + u internal variability/ noise an expected response to some forcing (e.g. a model forced by GHG)

6 Linear model of observed trends Y = βx + u Internal variability is estimated from a control run with no external forcings Control run pseudo-observations are used to get the range of βfrom internal variability

7 Whatβcan tell us about models Y = βx + u If the range of βdoes not include 0, the observations are outside the range of internal variability detectable change If the range of βdoes not include 1, the model s response-plus-variability does not include the observed climate model must be wrong

8 Whatβcan tell us about models For the cold seasons, most models seem reliable with a small, undetectable response For the warmer seasons, only a few models have a signal-plus-noise that includes the observed climate.

9 Where are the model vs obs. differences?

10 Is there an anthropogenic signal in the models? For the cold seasons, most models seem reliable with a small, undetectable response For the warmer seasons, only a few models have a signal-plusnoise that includes the observed climate.

11 Insights from century scale proxies forced response Black ASO reconstructions and satellite records Red CMIP5 historical response

12 Insights from century scale proxies variability Black ASO reconstructions and satellite records Magenta CMIP5 control run

13 Projections in the context of internal variability

14 Summary: current understanding Global coupled models indicate an anthropogenic sea ice reduction in most sectors, but which is small compared to internal variability. The satellite record is too short for a confident evaluation of the anthropogenic influence on Southern Ocean sea ice Century-scale proxies suggest that the CMIP5 models do a reasonable job for most sectors, except the Ross Sea.

15 Summary: current understanding Global coupled models indicate an anthropogenic sea ice reduction in most sectors, but which is small compared to internal variability. The satellite record is too short for a confident evaluation of the anthropogenic influence on Southern Ocean sea ice Century-scale proxies suggest that the CMIP5 models do a reasonable job for most sectors, except the Ross Sea.

16 Summary: current understanding Global coupled models indicate an anthropogenic sea ice reduction in most sectors, but which is small compared to internal variability. The satellite record is too short for a confident evaluation of the anthropogenic influence on Southern Ocean sea ice Century-scale proxies suggest that the CMIP5 models do a reasonable job for most sectors, except the Ross Sea.

17 Summary: suggested pathways to better understanding Beyond statistics a process-based model evaluation Appropriate model framework high resolution regional models (especially for the atmosphere) are the low hanging fruit Understanding changes in the Ross Sea (including work towards reliable proxy reconstructions) are key for model evaluation

18 Summary: suggested pathways to better understanding Beyond statistics a process-based model evaluation Appropriate model framework high resolution regional models (especially for the atmosphere) are the low hanging fruit Understanding changes in the Ross Sea (including work towards reliable proxy reconstructions) are key for model evaluation

19 Summary: suggested pathways to better understanding Beyond statistics a process-based model evaluation Appropriate model framework high resolution regional models (especially for the atmosphere) are the low hanging fruit Understanding changes in the Ross Sea (including work towards reliable proxy reconstructions) are key for model evaluation