Global trends in extreme precipitation: climate models versus observations

Abstract

Abstract. Precipitation events are expected to become substantially more intense under global warming, but few global comparisons of observations and climate model simulations are available to constrain predictions of future changes in precipitation extremes. We present a systematic global-scale comparison of changes in historical annualmaximum daily precipitation between station observations (compiled in HadEX2) and the suite of global climate models contributing to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). We use both parametric and non-parametric methods to quantify the strength of trends in extreme precipitation in observations and models, taking care to sample them spatially and temporally in comparable ways. We find that both observations and models show generally increasing trends in extreme precipitation since 1901, with the largest changes in the deep tropics. Annual-maximum daily precipitation (Rx1day) has increased faster in the observations than in most of the CMIP5 models. On a global scale, the observational annual-maximum daily precipitation has increased by an average of 5.73 mm over the last 110 years, or 8.5 % in relative terms. This corresponds to an increase of 10 % K −1 in global warming since 1901, which is larger than the average of climate models, with 8.3 % K −1 . The average rate of increase in extreme precipitation per K of warming in both models and observations is higher than the rate of increase in atmospheric water vapor content per K of warming expected from the Clausius-Clapeyron equation. We expect our findings to help inform assessments of precipitationrelated hazards such as flooding, droughts and storms.

Projected changes in US rainfall erosivity

“…Although barely significant (see the limited extent of the stippling), there is general drying in Mexico and Texas, extending into the southwest US, and more positive anomalies in the eastern gulf and Florida. The pattern of yearly anomalies shown here is consistent with the expectations reported in the literature for both CMIP3 and CMIP5 models (see for example Seager et al, 2007Seager et al, , 2014Biasutti et al, 2011;Christensen et al, 2013;Maloney et al, 2014). It can be interpreted as the sum of a weak US-wide summertime drying and the wintertime pattern of increase in rainfall north of 40 • N and decrease in precipitation in the southwest and Texas.…”

“…An increase in the intensity of rainfall, especially in the frequency of the most extreme events, is a robust expectation for future climate change, supported by theory (e.g., Trenberth, 1999;O'Gorman andSchneider, 2009), modeling (e.g., O'Gorman, 2012;Tebaldi et al, 2006;Sillmann et al, 2013), and observations (e.g., Alexander et al, 2006;Lenderink et al, 2011;Asadieh and Krakauer, 2015). The effect of such changes on erosivity can only be assessed by using daily rainfall data.…”

Effects of variability in probable maximum precipitation patterns on flood losses

“…Furthermore, critical infrastructure, such as power stations, has to be protected against extreme floods. Since floods are expected to increase due to climatic changes (Asadieh and Krakauer, 2015;Arnell and Gosling, 2016;Beniston et al, 2007;Bouwer, 2013;Fischer and Knutti, 2016;Millán, 2014;Pfahl et al, 2017;Rajczak et al, 2013;Scherrer et al, 2016), flood risk analyses and the management of extreme events will become even more relevant (Smolka, 2006;Yuan et al, 2017). Hence, insurance companies and governmental institutions are increasingly interested in quantifying flood risks, and especially in estimating the impacts of probable maximum floods leading to high cumulative losses (Burke et al, 2016;Morrill and Becker, 2017) or the destruction of critical infrastructure (Hasan and Foliente, 2015;Mechler et al, 2010;Michaelides, 2014).…”

Effects of variability in probable maximum precipitation patterns on flood losses

“…extreme floods. Since floods are expected to increase due to climatic changes (Asadieh and Krakauer, 2015;Arnell and Gosling, 2016;Beniston et al, 2007;Bouwer, 2013;Fischer and Knutti, 2016;Millán, 2014;Pfahl et al, 2017;Rajczak et al, 2013;Scherrer et al 2016), flood risk analyses and the management of extreme events will become even more relevant (Smolka, 2006;Yuan et al, 2017). Hence, insurance companies as well as governmental institutions are increasingly interested in quantifying flood risks, and especially in estimating the impacts of probable maximum floods leading to high 5 cumulative losses (Burke et al, 2016;Morrill and Becker, 2017) or the destruction of critical infrastructure (Hasan and Foliente, 2015;Mechler et al, 2010;Michaelides, 2014).…”