Ben Harvey, Len ShaffreyNCAS-Climate, University of Reading

Tim WoollingsAOPP, University of Oxford

EMS Annual Meeting, Reading, September 2013

Large-scale temperature gradients and the extratropical

storm track responses in CMIP5

Data from 24 modelsOne run per model

Scenario:RCP8.5 – HISTORICAL

Storm track measure:2-6 day MSLP std dev

CMIP5 storm track responses

Global drivers Regional driversUpper level equator-pole ∆T Atlantic SSTs

AMOC?Lower level equator-pole ∆T Arctic sea ice extentStratification Land-sea contrastLocal moisture content Tropically-forced stationary waves

Possible `drivers’ of the spread

Global drivers Regional driversUpper level equator-pole ∆T Atlantic SSTs

AMOC?Lower level equator-pole ∆T Arctic sea ice extentStratification Land-sea contrastLocal moisture content Tropically-forced stationary waves

Possible `drivers’ of the spread

Are there correlations between the responses of the storm tracks and the responses of the drivers in the CMIP5 models?

Here, focus on equator-to-pole ∆T

Talk outline

Equator-to-pole ∆T in the CMIP5 models

The North Atlantic wintertime storm track

A quick look at other regions

CMIP5 temperature responsesDJF North Atlantic only: 60W-10W

Scenario:RCP8.5 – HISTORICAL

See also…Harvey et al (2013)

CMIP5 temperature responsesDJF North Atlantic only: 60W-10W

Scenario:RCP8.5 – HISTORICAL

See also…Harvey et al (2013)

CMIP5 temperature responsesDJF North Atlantic only: 60W-10W

Scenario:RCP8.5 – HISTORICAL

See also…Harvey et al (2013)

Perform a simple linear regression across the ensemble:

CMIP5 temperature responsesDJF North Atlantic only: 60W-10W

Scenario:RCP8.5 – HISTORICAL

See also…Harvey et al (2013)

CMIP5 storm track regressionRegression slopes (shading) and significance of correlation (p=0.05)

CMIP5 storm track regression

Comparison with mean storm track response:

Regression slopes (shading) and significance of correlation (p=0.05)

CMIP5 storm track regression

Fraction of inter-model variance “explained”:

Regression slopes (shading) and significance of correlation (p=0.05)

Summary – North Atlantic winterThere are large regions with significant correlation between the storm track responses and both the ∆T850 and ∆T250 temperature difference responses.

The regression slopes are mostly positive, suggesting:

1. The storm track responses are driven by the responses of the baroclinicity

2. The impact of ∆T850 on the storm track response is negative across most of the hemisphere, whereas the impact of ∆T250 is positive but confined to the ocean basins.

Together, the two linear regression maps qualitatively capture the spatial pattern of the multi-model mean response.

These results suggest there is potential to reduce the spread in the storm track responses by constraining the relative magnitudes of the warming in the tropical and polar regions.

Other regionsA similar analysis has been performed for both summer and winter of all the extratropical storm track regions – see Harvey et al (2013)

e.g. NH summer

Other regionsA similar analysis has been performed for both summer and winter of all the extratropical storm track regions – see Harvey et al (2013)

e.g. SH summer

Summary - continuedThe North Atlantic winter is unique in that both the ∆T850 and ∆T250 regressions are needed to capture the pattern of the mean response. This more complex behaviour may go some way towards explaining the particularly large inter-model spread in the North Atlantic region.

One limitation of this study is that the causality of the correlations cannot be determined. It is not clear whether the storm tracks respond directly to the equator-to-pole temperature difference, or instead to more local baroclinicity changes (e.g. SST, sea-ice or land-sea contrast changes), which may themselves be correlated with the equator-to-pole temperature difference.

Summary - continuedThe North Atlantic winter is unique in that both the ∆T850 and ∆T250 regressions are needed to capture the pattern of the mean response. This more complex behaviour may go some way towards explaining the particularly large inter-model spread in the North Atlantic region.

One limitation of this study is that the causality of the correlations cannot be determined. It is not clear whether the storm tracks respond directly to the equator-to-pole temperature difference, or instead to more local baroclinicity changes (e.g. SST, sea-ice or land-sea contrast changes), which may themselves be correlated with the equator-to-pole temperature difference.Thank you for listening