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© Crown copyright 2007 Global changes in extreme daily temperature since 1950 using non-stationary extreme value analysis Simon Brown, John Caesar and Chris Ferro Met Office Hadley Centre May 2008
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© Crown copyright 2007 Outline Data and methods Trends in extreme temperatures with time Dependence of temperature extremes on NAO Future non-linear changes in extreme temperatures
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© Crown copyright 2007 Observed data - Gridded daily temperature Daily Tmax and Tmin from 1946-2004. On a 2.5 x 3.75 grid. Anomalies gridded using an angular-distance weighting technique. Based on network of ~2500 stations with quality controlled observations. Here we use annual and seasonal anomalies
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© Crown copyright 2007 The method Describe the distribution of suitably extreme values by a marked point process following S. Coles “An introduction to statistical modelling of extreme values” The expected no of exceedances per year for any x, given x>u is: For threshold use a linear regression through all data, suitably biased to leave 1.5% of data exceeding the threshold. Results robust to threshold choice Reduce serial correlation by extracting maximum in ten day window. Results robust to window size Apply KS goodness of fit test with bootstrapped critical values
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© Crown copyright 2007 The method 2 Allow parameters to depend linearly with time and/or NAO Test validity of increasingly complex distribution models using the likelihood ratio test. Test field significance using 30x50year samples from HadCM3 control run No time/NAO dependence found for scale or shape parameter
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© Crown copyright 2007 Location scale and shape for: xTmax, nTmax, xTmin, nTmin – Annual anomalies xTmax nTmax xTmin nTmin
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© Crown copyright 2007 Location trend xTmax, nTmax, xTmin, nTmin (units DegC/58 years)
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© Crown copyright 2007 Location - mean trend xTmax, nTmax, xTmin, nTmin
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© Crown copyright 2007 Location dependence on NAO xTmax, nTmax, xTmin, nTmin - DJF anomalies
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© Crown copyright 2007 Location trend by season for xTmin, nTmin MAM SON JJA DJF xTmin nTmin
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© Crown copyright 2007 Future global temperature change may not be linear Approach guided by the MOHC probabilistic framework (UKCIP) Perturbed physics ensemble of +240 slab versions run at 1x & 2x CO2 A subset of 17 run as fully coupled transient GCMs used to train an emulator to give pattern of change through time for any parameter set Use global temperature as the covariate, to give: Label these as Mu_t and Mu_tSig_t Fit to all transient + 1xCO2 + 2xCO2 simultaneously – “Joint” Compare with fit to just 1xCO2 + 2xCO2 data – “Slab” Extreme models for future changes
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© Crown copyright 2007 Example data & threshold Threshold (spline) Global mean temperature
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© Crown copyright 2007 Example data & threshold Threshold (spline) Global mean temperature
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© Crown copyright 2007 xTmax 20y return level (Mu_t)
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© Crown copyright 2007 xTmax 20y return level (Mu_tSig_t)
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© Crown copyright 2007 Location scale and shape for xTmax, nTmax, xTmin, nTmin Mu_t
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© Crown copyright 2007 Location scale and shape for xTmax, nTmax, xTmin, nTmin Mu_t
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© Crown copyright 2007 Location scale and shape for xTmax, nTmax, xTmin, nTmin Mu_t
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© Crown copyright 2007 Location scale and shape for xTmax, nTmax, xTmin, nTmin Mu_t Joint
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© Crown copyright 2007 Location scale and shape for xTmax, nTmax, xTmin, nTmin Mu_t
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© Crown copyright 2007 Location scale and shape for xTmax, nTmax, xTmin, nTmin Mu_t
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© Crown copyright 2007 Location scale and shape for xTmax, nTmax, xTmin, nTmin Mu_t
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© Crown copyright 2007 End
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