Andreas Dobler

Precipitation by a regional climate model and bias correction in Europe and South Asia

Because coarse-grid global circulation models do not allow for regional estimates of the water balance or trends of extreme precipitation, downscaling of global simulations is necessary to generate regional precipitation. This paper applies for downscaling the regional climate model CLM as a dynamical downscaling method (DDM) and two statistical downscaling methods (SDMs). Because the SDMs neglect information available to the DDM, and vice versa, a combination of the dynamical and statistical approaches is proposed here. In this combined approach, a simple statistical step is carried out to correct for the regional model biases in the dynamically downscaled simulations. To test the proposed methods, coarse-grid global re-analysis data (ERA40 with ∼ 1.125° grid spacing) is downscaled in two regions with different climatology and orography: one in South Asia and the other in Europe. All of the methods are tested on daily precipitation with 0.5° grid spacing. The SDMs are generally successful: the standardized root mean square error of rain day intensity is reduced from ERA40s 0.16 to 0.10 in a test area to the west of the European Alps. The CLM simulations perform less well (with a corresponding error of 0.14), but represent a promising approach if the user requires flexibility and independence from observational data. The proposed bias correction of the CLM simulations performs very well in European test areas (better than or at least comparable with the SDMs; i.e., with a corresponding error of 0.07), but fails in South Asia. An investigation of the observed and simulated precipitation climate in the test areas shows a strong dependence of the bias correction performance on sampling statistics (i.e., rain day frequency) and on the robustness of bias estimation.

Soil Moisture–Precipitation Feedback Processes in the Indian Summer Monsoon Season

AbstractSoil moisture can influence precipitation through a feedback loop with land surface evapotranspiration. A series of numerical simulations, including soil moisture sensitivity experiments, have been performed for the Indian summer monsoon season (ISM). The simulations were carried out with the nonhydrostatic regional climate model Consortium for Small-Scale Modeling (COSMO) in climate mode (COSMO-CLM), driven by lateral boundary conditions derived from the ECMWF Interim reanalysis (ERA-Interim). Positive as well as negative feedback processes through local and remote effects are shown to be important. The regional moisture budget studies have exposed that changes in precipitable water and changes in precipitation efficiency vary in importance, in time, and in space in the simulations for India. Overall, the results show that the premonsoonal soil moisture has a significant influence on the monsoonal precipitation, and thus confirmed that modeling of soil moisture is essential for reliable simulatio...

Discontinuous Daily Temperatures in the WATCH Forcing Datasets

AbstractThe Water and Global Change (WATCH) forcing datasets have been created to support the use of hydrological and land surface models for the assessment of the water cycle within climate change studies. They are based on 40-yr ECMWF Re-Analysis (ERA-40) or ECMWF interim reanalysis (ERA-Interim) with temperatures (among other variables) adjusted such that their monthly means match the monthly temperature dataset from the Climatic Research Unit. To this end, daily minimum, maximum, and mean temperatures within one calendar month have been subjected to a correction involving monthly means of the respective month. As these corrections can be largely different for adjacent months, this procedure potentially leads to implausible differences in daily temperatures across the boundaries of calendar months. We analyze day-to-day temperature fluctuations within and across months and find that across-months differences are significantly larger, mostly in the tropics and frigid zones. Average across-months differe...

Development and evaluation of a simple method to estimate evaporation from satellite data

Evaporation is a key factor in climate monitoring, agricultural meteorology, and the validation of climate models, numerical weather prediction models, and hydrological models. Methods using satellite data have the advantage that they provide data with a large, up to global coverage, while surface measurements typically provide local values only. Moreover, the density of surface measurements is usually quite low throughout the world. Hence, in many regions satellite instruments are the only available observation source for the estimation of evaporation. This paper compares different semi-empirical methods for determining potential and actual evaporation from satellite data with a spatial resolution of 0.05 degree over Europe. The results for potential evaporation are compared to data of the network of the Deutscher Wetterdienst in order to evaluate the best method. In a second step satellite-based actual evaporation is compared with data from different land-surface models and observations for the time period from September 2005 to August 2006. The proposed methods provide actual evaporation in good agreement with the other models and observations, but at a higher spatial resolution.

Regional Climate Projections

Regional climate projections are required to further downscale the results provided by Atmosphere-Ocean Global Circulation Models (AOGCM) from the global scale grid size to the mesoscale grid size used in ILWRM application for mesoscale and macroscale river basins. The results provided from this climate modelling study were inputs into the hydrological modelling exercises done by the means of DANUBIA hydrological river basin model.