Calibrating Climate Change Time-Slice Projections with Estimates of Seasonal Forecast Reliability

AbstractIn earlier work, it was proposed that the reliability of climate change projections, particularly of regional rainfall, could be improved if such projections were calibrated using quantitative measures of reliability obtained by running the same model in seasonal forecast mode. This proposal is tested for fast atmospheric processes (such as clouds and convection) by considering output from versions of the same atmospheric general circulation model run at two different resolutions and forced with prescribed sea surface temperatures and sea ice. Here output from the high-resolution version of the model is treated as a proxy for truth. The reason for using this approach is simply that the twenty-first-century climate change signal is not yet known and, hence, no climate change projections can be verified using observations. Quantitative assessments of reliability of the low-resolution model, run in seasonal hindcast mode, are used to calibrate climate change time-slice projections made with the same ...

[1]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[2]  Mio Matsueda,et al.  Climate Simulations Using MRI-AGCM3.2 with 20-km Grid , 2012 .

[3]  T. N. Palmer,et al.  Oceanic Stochastic Parameterizations in a Seasonal Forecast System , 2015, 1506.09181.

[4]  Elizabeth C. Kent,et al.  Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century , 2003 .

[5]  N. Lau,et al.  Model Projections of the Changes in Atmospheric Circulation and Surface Climate over North America, the North Atlantic, and Europe in the Twenty-First Century , 2013 .

[6]  G. Vecchi,et al.  Simulated Climate and Climate Change in the GFDL CM2.5 High-Resolution Coupled Climate Model , 2012 .

[7]  David P. Rowell,et al.  Causes and uncertainty of future summer drying over Europe , 2006 .

[8]  F. Molteni,et al.  Atmospheric initial conditions and the predictability of the Arctic Oscillation , 2015 .

[9]  Andrew Dawson,et al.  Simulating regime structures in weather and climate prediction models , 2012 .

[10]  Andrew Dawson,et al.  Simulating weather regimes: impact of model resolution and stochastic parameterization , 2015, Climate Dynamics.

[11]  Tim N. Palmer,et al.  The economic value of ensemble forecasts as a tool for risk assessment: From days to decades , 2002 .

[12]  T. Palmer,et al.  Accuracy of climate change predictions using high resolution simulations as surrogates of truth , 2011 .

[13]  Paul Berrisford,et al.  The role of horizontal resolution in simulating drivers of the global hydrological cycle , 2014, Climate Dynamics.

[14]  T. N. Palmer,et al.  On the reliability of seasonal climate forecasts , 2013, Journal of The Royal Society Interface.

[15]  S. Kobayashi,et al.  The JRA-25 Reanalysis , 2007 .

[16]  Adam A. Scaife,et al.  The role of the stratosphere in the European climate response to El Niño , 2009 .

[17]  Adam A. Scaife,et al.  Skillful long‐range prediction of European and North American winters , 2014 .

[18]  Bernard A. Chouet,et al.  Identifying bubble collapse in a hydrothermal system using hidden Markov models , 2012 .

[19]  Raquel V. Francisco,et al.  Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM , 2000 .

[20]  John F. B. Mitchell,et al.  THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research , 2007 .

[21]  R. Mizuta,et al.  Future change in wintertime atmospheric blocking simulated using a 20‐km‐mesh atmospheric global circulation model , 2009 .

[22]  R. Mizuta,et al.  Future change in Southern Hemisphere summertime and wintertime atmospheric blockings simulated using a 20‐km‐mesh AGCM , 2010 .

[23]  M. Rodwell,et al.  Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts , 2008 .