[1] This study provides an overview of projected changes in climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The temperature-and precipitation-based indices are computed with a consistent methodology for climate change simulations using different emission scenarios in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) multimodel ensembles. We analyze changes in the indices on global and regional scales over the 21st century relative to the reference period 1981-2000. In general, changes in indices based on daily minimum temperatures are found to be more pronounced than in indices based on daily maximum temperatures. Extreme precipitation generally increases faster than total wet-day precipitation. In regions, such as Australia, Central America, South Africa, and the Mediterranean, increases in consecutive dry days coincide with decreases in heavy precipitation days and maximum consecutive 5 day precipitation, which indicates future intensification of dry conditions. Particularly for the precipitation-based indices, there can be a wide disagreement about the sign of change between the models in some regions. Changes in temperature and precipitation indices are most pronounced under RCP8.5, with projected changes exceeding those discussed in previous studies based on SRES scenarios. The complete set of indices is made available via the ETCCDI indices archive to encourage further studies on the various aspects of changes in extremes.

[1] This paper provides a first overview of the performance of state-of-the-art global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), and compares it to that in the previous model generation (CMIP3). For the first time, the indices based on daily temperature and precipitation are calculated with a consistent methodology across multimodel simulations and four reanalysis data sets (ERA40, ERA-Interim, NCEP/NCAR, and NCEP-DOE) and are made available at the ETCCDI indices archive website. Our analyses show that the CMIP5 models are generally able to simulate climate extremes and their trend patterns as represented by the indices in comparison to a gridded observational indices data set (HadEX2). The spread amongst CMIP5 models for several temperature indices is reduced compared to CMIP3 models, despite the larger number of models participating in CMIP5. Some improvements in the CMIP5 ensemble relative to CMIP3 are also found in the representation of the magnitude of precipitation indices. We find substantial discrepancies between the reanalyses, indicating considerable uncertainties regarding their simulation of extremes. The overall performance of individual models is summarized by a "portrait" diagram based on root-mean-square errors of model climatologies for each index and model relative to four reanalyses. This metric analysis shows that the median model climatology outperforms individual models for all indices, but the uncertainties related to the underlying reference data sets are reflected in the individual model performance metrics.

Citation:Curry, C. L., et al. (2014), A multimodel examination of climate extremes in an idealized geoengineering experiment, J. Geophys. Res. Atmos., 119, 3900-3923, doi:10.1002 However, it is also the case that extremes of temperature and precipitation in G1 differ significantly from those under preindustrial conditions. Probability density functions of standardized anomalies of monthly surface temperature  and precipitation  in G1 exhibit an extension of the high- tail over land, of the low- tail over ocean, and a shift of  to drier conditions. Using daily model output, we analyzed the frequency of extreme events, such as the coldest night (TNn), warmest day (TXx), and maximum 5 day precipitation amount, and also duration indicators such as cold and warm spells and consecutive dry days. The strong heating at northern high latitudes simulated under 4 × CO 2 is much alleviated in G1, but significant warming remains, particularly for TNn compared to TXx. Internal feedbacks lead to regional increases in absorbed solar radiation at the surface, increasing temperatures over Northern Hemisphere land in summer. Conversely, significant cooling occurs over the tropical oceans, increasing cold spell duration there. Globally, G1 is more effective in reducing changes in temperature extremes compared to precipitation extremes and for reducing changes in precipitation extremes versus means but somewhat less effective at reducing changes in temperature extremes compared to means. IntroductionThe persistent rise in atmospheric greenhouse gas concentrations over the last century, and the climate change it has wrought, has prompted discussions beyond those focusing solely on efforts to curtail greenhouse gas emissions. The notion of a more purposeful alteration of the Earth's radiation balance on the global scale, known as climate engineering or geoengineering, has received increased attention in recent years. In principle, there are many means by which this might be achieved [e.g., Royal Society, 2009]. Perhaps the simplest conceptual scheme is one in which incoming solar energy at the top of the planet's atmosphere is reduced by a certain fraction. Whether such a straightforward form of solar radiation management (SRM) could be achieved in practice is open to question [Angel, 2006; Royal Society, 2009], and the ethical dilemmas of intentional alteration of the Earth's climate continue to be widely discussed [e.g., Robock et al., 2009;Gardiner, 2010].The scientific investigation of the effect of SRM on the climate system has been undertaken using a variety of modeling approaches, ranging from simple zero-dimensional energy balance models [Lenton and Vaughan, 2009] to more complex simulations that have exposed marked differences in regional climate response to these equal but opposite global forcings [Irvine et al., 2010]. In an effort to compare these modeling results on a more equal footing, the Geoengineering Model Intercomparison Project (GeoMIP) was CURRY ET AL.