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Selected annual climate indicators under different CO2 regimes

PI: Paul Switzer
Institution: Stanford University
Additional Investigators: Sarah Shafer
Model runs produce inter-annual variation, such as the inter-annual variation of global mean surface temperature. For a model in equilibrium there are long-term frequency distributions for annual indicators such as the global mean surface temperature. Inter-annual variations are an important aspect of climate. Model runs of limited length will produce [imprecise] estimates of the long-term distribution of annual indicators. I propose to perform statistical analyses of selected climate indicators that address the following questions: How do models differ from one another with respect to their distributions of selected annual climate indicators, under different CO2 regimes? How is the statistical precision of these estimated indicator distributions related to model run length? How do models differ with respect to specific distributional characteristics such as the inter-annual median and upper/lower quartiles? Toward this end I will explore model run estimates of the inter-annual variation of global mean surface temperature, zonal mean surface temperatures, zonal and seasonal 24-hour extreme temperature frequencies, and possibly precipitation indicators. For purposes of statistical inference I will use modern statistical tools based on data resampling. I will use equilibrium model outputs that are beyond the period of transient response to CO2 forcing. Monthly output values are sufficient for mean value indicators, but for 24-hour extremes it will be necessary to use the more limited daily model output values. It appears that relevant data should be available from experiments numbered 6,7,8,9 as described in the IPCC Standard Output document.

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    Lawrence Livermore National Laboratory  |  Physical & Life Sciences Directorate