Barriopedro

Branstator

Chikamoto

Christidis

Conil

Doug M. Smith

Díaz

D’Ippoliti

Eade

Efron

Fink

Fischer

Fouillet

Gabriele C. Hegerl

Goddard

Gordon

Grize

Hamilton

Hawkins

Haylock

Hegerl

Helen M. Hanlon

Keenlyside

Matei

Meehl

Murphy

Palmer

Pascal

Pohlmann

Pope

Quesada

Seneviratne

Simon F. B. Tett

Smith

Solomon

Teuling

Uppala

van Oldenborgh

Vautard

Weisheimer

English

Hanlon, Helen M.

Hegerl, Gabriele C.

Tett, Simon F. B.

Smith, Doug M.

Edinburgh Research Explorer

Can a Decadal Forecasting System Predict Temperature Extreme Indices?

131, 2961–3012, doi:10.1256/ qj.04.176. vanderLinden,P.,andJ.F.B.Mitchell,Eds.,2009:ENSEMBLES: Climate change and its impacts: Summary of research and results from the ENSEMBLES project.

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Decadal prediction.

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Abstract
               Daily maximum and minimum summer temperatures have increased throughout the majority of Europe over the past few decades, along with the frequency and intensity of heat waves. It is essential to learn whether this rise is expected to continue in the future for adaptation purposes. A study of predictability of European temperature indices with the Met Office Hadley Centre Decadal Prediction System (DePreSys) has revealed significant skill in predictions of 5- and 10-yr average indices of the summer mean and maximum 5-day average temperatures based on daily maximum and minimum temperatures for a large area of Europe, particularly in the Mediterranean. In contrast, the decadal forecasts of winter mean/minimum 5-day average temperature indices show poorer skill than the summer indices. Significant skill is shown for the United Kingdom in some cases but less than for the European/Mediterranean regions.
               Comparison of two parallel ensembles, one initialized with observations and one without initialization, has shown that the skill largely originates from external forcing. However, there were a few cases with hints of additional skill in forecasts of decadal mean indices due to the initialization.
               Model realizations of extreme indices can have large biases compared to observations that are different from those of the mean climate indices. Several methods were tested for correcting biases, as well as for testing the significance and quantifying uncertainty of the results to rule out cases of spurious skill. Bias correction of each index individually is required as biases vary across different extremes.</jats:p

Crossref

Journal of Climate

Can a Decadal Forecasting System Predict Temperature Extreme Indices?*

https://www.pure.ed.ac.uk/ws/files/8296438/PDF_Hanlon_Hegerl_Tett2013.pdf

Can a Decadal Forecasting System Predict Temperature Extreme Indices?

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