Boris Orlowsky

Global changes in extreme events: regional and seasonal dimension

This study systematically analyzes the complete IPCC AR4 (CMIP3) ensemble of GCM simulations with respect to changes in extreme event characteristics at the end of the 21st century compared to present-day conditions. It complements previous studies by investigating a more comprehensive database and considering seasonal changes beside the annual time scale. Confirming previous studies, the agreement between the GCMs is generally high for temperature-related extremes, indicating increases of warm day occurrences and heatwave lengths, and decreases of cold extremes. However, we identify issues with the choice of indices used to quantify heatwave lengths, which do overall not affect the sign of the changes, but strongly impact the magnitude and patterns of projected changes in heatwave characteristics. Projected changes in precipitation and dryness extremes are more ambiguous than those in temperature extremes, despite some robust features, such as increasing dryness over the Mediterranean and increasing heavy precipitation over the Northern high latitudes. We also find that the assessment of projected changes in dryness depends on the index choice, and that models show less agreement regarding changes in soil moisture than in the commonly used ‘consecutive dry days’ index, which is based on precipitation data only. Finally an analysis of the scaling of changes of extreme temperature quantiles with global, regional and seasonal warming shows that much of the extreme quantile changes are due to a seasonal scaling of the regional annual-mean warming. This emphasizes the importance of the seasonal time scale also for extremes. Changes in extreme quantiles of temperature on land scale with changes in global annual mean temperature by a factor of more than 2 in some regions and seasons, implying large changes in extremes in several countries, even for the commonly discussed global 2°C-warming target.

Reconciling spatial and temporal soil moisture effects on afternoon rainfall

Soil moisture impacts on precipitation have been strongly debated. Recent observational evidence of afternoon rain falling preferentially over land parcels that are drier than the surrounding areas (negative spatial effect), contrasts with previous reports of a predominant positive temporal effect. However, whether spatial effects relating to soil moisture heterogeneity translate into similar temporal effects remains unknown. Here we show that afternoon precipitation events tend to occur during wet and heterogeneous soil moisture conditions, while being located over comparatively drier patches. Using remote-sensing data and a common analysis framework, spatial and temporal correlations with opposite signs are shown to coexist within the same region and data set. Positive temporal coupling might enhance precipitation persistence, while negative spatial coupling tends to regionally homogenize land surface conditions. Although the apparent positive temporal coupling does not necessarily imply a causal relationship, these results reconcile the notions of moisture recycling with local, spatially negative feedbacks.

Statistical Analyses of Land–Atmosphere Feedbacks and Their Possible Pitfalls

Abstract In some regions of the world, soil moisture has a typical memory for atmospheric processes and can also feed back to the latter. Thus, a better understanding of feedbacks between soil moisture and the atmosphere could provide promising perspectives for increased seasonal predictability. Besides numerical simulations, statistical analysis of existing GCM simulations or observational data has been used to study such feedbacks. By referring to a recent statistical analysis of soil moisture–precipitation feedbacks in GCM simulations, the authors illustrate potential pitfalls of statistical approaches in this context: (i) most importantly, apparent soil moisture–precipitation feedbacks can often as well or even better be attributed to the influence of sea surface temperatures (SSTs) on precipitation and (ii) the discrepancy between different GCMs is large, which makes the aggregation of individual model results difficult. These aspects need to be carefully evaluated in statistical analyses of land–atm...

Introducing a probabilistic Budyko framework

Water availability is of importance for a wide range of ecological, climatological, and socioeconomic applications. Over land, the partitioning of precipitation into evapotranspiration and runoff essentially determines the availability of water. At mean annual catchment scales, the widely used Budyko framework provides a simple, deterministic, first-order relationship to estimate this partitioning as a function of the prevailing climatic conditions. Here we extend the framework by introducing a method to specify probabilistic estimates of water availability that account for the nonlinearity of the underlying phase space. The new framework allows to evaluate the predictability of water availability that is related to varying catchment characteristics and conditional on the underlying climatic conditions. Corresponding results support the practical experience of low predictability of river runoff in transitional climates.

Future Climates from Bias-Bootstrapped Weather Analogs: An Application to the Yangtze River Basin

Abstract The authors describe a statistical analog resampling scheme, similar to the “intentionally biased bootstrap,” for future climate projections whose only constraint is a prescribed linear temperature trend. It provides a large ensemble of day-to-day time series of single-station weather variables and other climatological observations at low computational cost. Time series are generated by mapping time sequences from the observed past into the future. The Yangtze River basin, comprising all climatological subregions of central China, is used as a test bed. Based on daily station data (1961–2000), the bootstrap scheme is assessed in a cross-validation experiment that confirms its applicability. Results obtained for the projected future climates (2001–40) include climatological profiles along the Yangtze, annual cycles, and other weather-related phenomena (e.g., floods, droughts, monsoons, typhoons): (i) the annual mean temperature and, associated with that, precipitation increase; (ii) the annual cyc...

A Combination of Cluster Analysis and Kappa Statistic for the Evaluation of Climate Model Results

Abstract The authors present a combination of different statistical methods for the validation of climate simulation data with respect to observational data of the same spatial and temporal coverage. It is assumed that simulated data and observed data are both given as time series at locations such as grid cells or station locations. The aim of this approach is to quantify the agreement between the two spatial structures of observed and simulated data. These spatial structures consist of the spatial distributions of clusters (obtained from a cluster analysis) that contain climatologically similar locations. If the spatial distribution of clusters were identical for the observed and the simulated data, the simulation would describe the spatial structure of the observations perfectly. Differences from this ideal situation can be objectively quantified using the κ statistic. If the simulation data have shortcomings, the different κ variants can be used to diagnose where these are located. The method is demon...