[1] This study provides estimates of the human contribution to the observed widespread intensification of precipita-tion extremes. We consider the annual maxima of daily (RX1day) and 5 day consecutive (RX5day) precipitation amounts over the Northern Hemisphere land area for 1951–2005 and compare observed changes with expected responses to external forcings as simulated by multiple coupled climate models participating in Coupled Model Intercomparison Project Phase 5. The effect of anthropo-genic forcings can be detected in extreme precipitation observations, both individually and when simultaneously estimating anthropogenic and naturally forced changes. The effect of natural forcings is not detectable. We estimate that human influence has intensified annual maximum 1 day precipitation in sampled Northern Hemisphere locations by 3.3 % [1.1 % to 5.8%,>90 % confidence interval] on average. This corresponds to an average intensification in RX1day of 5.2 % [1.3%, 9.3%] per degree increase in observed global mean surface temperature consistent with the

[1] While changes in land precipitation during the last 50 years have been attributed in part to human influences, results vary by season, are affected by data uncertainty and do not account for changes over ocean. One of the more physically robust responses of the water cycle to warming is the expected amplification of existing patterns of precipitation minus evaporation. Here, precipitation changes in wet and dry regions are analyzed from satellite data for 1988–2010, covering land and ocean. We derive fingerprints for the expected change from climate model simulations that separately track changes in wet and dry regions. The simulations used are driven with anthropogenic and natural forcings combined, and greenhouse gas forcing or natural forcing only. Results of detection and attribution analysis show that the fingerprint of combined external forcing is detectable in observations and that this intensification of the water cycle is partly attributable to greenhouse gas forcing. Citation: Polson, D., G. C. Hegerl, R. P. Allan, and B. Balan Sarojini (2013), Have greenhouse gases intensified the contrast between wet and dry regions?, Geophys. Res. Lett., 40, 4783–4787, doi:10.1002/grl.50923. 1.

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Assessment ” to ensure consistency with the approved Summary for Policymakers (IPCC-XXVI/Doc.4) and presented to the Panel at its 36th Session. This document lists the changes necessary to ensure consistency between the full Report and the Summary for Policymakers, which was approved line-by-line by Working Group I and accepted by the Panel at the abovementioned Sessions. Before publication the Report will undergo final copyediting as well as any error correction as necessary, consistent with the IPCC Protocol for Addressing Possible Errors. Publication of the Report is foreseen in January 2014. Disclaimer: The designations employed and the presentation of material on maps do not imply the expression of any opinion whatsoever on the part of the Intergovernmental Panel on Climate Change concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

In inference for max-stable processes in regional frequency analysis, it is found that, when the dependence model is misspecified, the pairwise likelihood method leads to bias in estimating the shape parameter of the generalized extreme value (GEV) distribution. The bias can be serious when the dependence is strong. Motivated by the fact that the primary interest in many studies is the inference about marginal GEV parameters and that the spatial dependence is a nuisance, we propose a combined score equations (CSE) approach that does not need dependence assumptions beyond the univariate GEV distribution. The CSE method combines the score equations of GEV model at each site with an approximate correlation function of the scores to improve the estimation efficiency. Applied to fingerprinting of changes in climate extremes with a coordinate descent algorithm to estimate a large number of parameters, the CSE method provides a close analog to the optimal fingerprinting in detection and attribution of changes in Zhuo Wang – University of Connecticut, 2015 climate extremes. The approach is applied on extreme temperature in Australia under