Reliability of regional climate model simulations of extremes and of long-term climate

Abstract

Abstract. We present two case studies that demonstrate how a common evaluation methodology can be used to assess the reliability of regional climate model simulations from different fields of research. In Case I, we focused on the agricultural yield loss risk for maize in Northeastern Brazil during a drought linked to an El-Niño event. In Case II, the present-day regional climatic conditions in Europe for a 10-year period are simulated. To comprehensively evaluate the model results for both kinds of investigations, we developed a general methodology. On its basis, we elaborated and implemented modules to assess the quality of model results using both advanced visualization techniques and statistical algorithms. Besides univariate approaches for individual near-surface parameters, we used multivariate statistics to investigate multiple near-surface parameters of interest together. For the latter case, we defined generalized quality measures to quantify the model's accuracy. Furthermore, we elaborated a diagnosis tool applicable for atmospheric variables to assess the model's accuracy in representing the physical processes above the surface under various aspects. By means of this evaluation approach, it could be demonstrated in Case Study I that the accuracy of the applied regional climate model resides at the same level as that we found for another regional model and a global model. Excessive precipitation during the rainy season in coastal regions could be identified as a major contribution leading to this result. In Case Study II, we also identified the accuracy of the investigated mean characteristics for near-surface temperature and precipitation to be comparable to another regional model. In this case, an artificial modulation of the used initial and boundary data during preprocessing could be identified as the major source of error in the simulation. Altogether, the achieved results for the presented investigations indicate the potential of our methodology to be applied as a common test bed to different fields of research in regional climate modeling.

A Class of Indices and a Graphical Tool to Monitor Temperature Anomalies

“…Nonetheless, several studies during the past decade have attempted to identify previous extreme events and to project future extreme events. These studies have employed diverse temperature and precipitation data for identification of return periods [10][11][12] ; calculation of frequency-duration-intensity indices 13 ; analysis by multivariate statistics 14,15 ; and development of indices based on frequency and variance. 16,17 The Intergovernmental Panel on Climate Change 18 Fourth Assessment Report (AR4) focused on 6 types of "extreme weather events" in their discussions of observed changes in extreme events and projections of future extreme events 19,20 : (1) daily maximum and minimum temperatures (coldest and hottest 10% each year); (2) heat waves; (3) heavy precipitation events; (4) droughts; (5) intense tropical cyclone activity; and (6) incidences of extreme high sea levels.…”

Going to the Extremes

“…These have employed a range of temperature and precipitation data that included return periods (e.g., Ekstrom et al, 2005;Semmler and Jacob, 2004;Wehner, 2004); frequency-duration-intensity indices (Adamowski and Bougadis, 2003;Khaliq et al, 2005); multivariate statistics (Bohm et al, 2004;Huth and Pokorna, 2005); and indices based on frequency and variance (Palmer and Raisanen, 2002;.…”

Future change in the frequency of warm and cold spells over Pakistan simulated by the PRECIS regional climate model

“…These high-resolution dynamic RCMs nested in GCM are becoming an increasingly important tool in climate research (Giorgi et al 2001). As RCM can also be used for climate extreme indices (Bohm et al 2004;Huth and Pokorna 2005), a RCM based climate scenario experiment is used in this study to estimate the future variations and change in the temperature indices such as TXx, TXn, TNx, TNn, frequency of warm and cold spell duration (WSDI and CSDI) as well as percentile based extreme temperature indices. This study is centered over Pakistan which is a region having several climatic aspects.…”