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Climate–land interaction over West Africa has often been assessed using climate simulations, although the model-based approach suffers from the limitations of climate models for the region. In this paper, an alternative method based on the analysis of historical land cover data and standardized climatic indices is used to investigate climate–land interactions, in order to establish climatic conditions and their corresponding land cover area changes. The annual variation in land cover area changes and climatic changes are first estimated separately and then linked using various spatiotemporal scales. The results show that incidences of land cover change result from abrupt changes in climatic conditions. Interannual changes of −1.0–1.0 °C, 0–1.5 °C, and −0.5–0.5 °C, and up to ± 50 mm changes in precipitation and climatic water balance, lead to 45,039–52,133 km2, 20,935–22,127 km2, and approximately 32,000 km2 changes, respectively, while a ± 0.5 °C and ± 20 mm change represents normal climate conditions with changes below 20,000 km2. Conversely, conversions of cropland, forest, grassland, and shrubland are the main land cover change types affecting the climate. The results offer a basis for the re-evaluation of land cover change and climate information used in regional climate models simulating land–climate interactions over West Africa. [ABSTRACT FROM AUTHOR]

Climate model simulations typically exhibit a bias, which can be corrected using statistical approaches. In this study, a geostatistical approach for bias correction of daily precipitation at ungauged locations is presented. The method utilizes a double quantile mapping with dry day correction for future periods. The transfer function of the bias correction for the ungauged locations is established using distribution functions estimated by ordinary kriging with anisotropic variograms. The methodology was applied to the daily precipitation simulations of the entire CORDEX‐Africa ensemble for a study region located in the West African Sudanian Savanna. This ensemble consists of 23 regional climate models (RCM) that were run for three different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). The evaluation of the approach for a historical 50‐year period (1950–2005) showed that the method can reduce the inherent strong precipitation bias of RCM simulations, thereby reproducing the main climatological features of the observed data. Moreover, the bias correction technique preserves the climate change signal of the uncorrected RCM simulations. However, the ensemble spread is increased due to an overestimation of the rainfall probability of uncorrected RCM simulations. The application of the bias correction method to the future period (2006–2100) revealed that annual precipitation increases for most models in the near (2020–2049) and far future (2070–2099) with a mean increase of up to 165mm⋅a−1 (18%). An analysis of the monthly and daily time series showed a slightly delayed onset and intensification of the rainy season. [ABSTRACT FROM AUTHOR]

Droughts are among the most relevant natural disasters related to climate change. We evaluated different regional climate model outputs and their ability to reproduce observed drought indices in Germany and the near surroundings between 1980-2009. Both, outputs of an ensemble of six EURO-CORDEX models of 12.5 km grid resolution and outputs from a high resolution (5 km) WRF run were employed. The latter was especially tailored for the study region regarding the physics configuration. We investigated drought related variables and derived the 3 month Standardized Precipitation Evapotranspiration Index (SPEI-3) to account for meteorological droughts. Based on that, we analyzed correlations, the 2003 event, trends and drought characteristics (frequency, duration and severity) and compared the results to E-OBS. Methods used imply Taylor diagrams, the Mann-Kendall trend test and the Spatial Efficiency (SPAEF) metric to account for spatial agreement of patterns. Averaged over the domain, meteorological droughts were found to occur approx. 16 times in the study period with an average duration of 3.1 months and average severity of 1.47 SPEI units. WRF's resolution and setup was shown to be less important for the reproduction of the single drought event and overall drought characteristics. Depending on the specific goals of drought analyses, computation resources could therefore be saved, since a coarser resolution can provide similar results. Benefits of WRF were found in the correlation analysis. Greatest benefits were identified in the trend analysis: Only WRF was able to reproduce the observed negative SPEI trends in a fairly high spatial accuracy, while the other RCMs completely failed in this regard. This was mainly due to the WRF model settings, highlighting the importance of appropriate model configuration tailored to the target region. Our findings are especially relevant in the context of climate change studies, where the appropriate reproduction of trends is of high importance. [ABSTRACT FROM AUTHOR]

With large elevation gradients and high hydrometeorological variability, Alpine catchments pose special challenges to hydrological climate change impact assessment. Data from seven regional climate models run within the Coordinated Regional Climate Downscaling Experiments (CORDEX), each driven with a different boundary forcing, are used to exemplarily evaluate the reproduction of observed flow duration curves and access the future discharge of the Ammer River located in Alpine southern Germany applying the hydrological simulation model called the Water Flow and Balance Simulation Model (WaSiM). The results show that WaSiM reasonably reproduces the observed runoff for the entire catchment when driven with observed precipitation. When applied with CORDEX evaluation data (1989–2008) forced by ERA-Interim, the simulations underestimate the extreme runoff and reproduce the high percentile values with errors in the range from −37% to 55% with an ensemble mean of around 15%. Runs with historical data 1975–2005 reveal larger errors, up to 120%, with an ensemble mean of around 50% overestimation. Also, the results show a large spread between the simulations, primarily resulting from deficiencies in the precipitation data. Results indicate future changes for 2071–2100 in the 99.5th percentile runoff value of up to 9% compared to 1975–2005. An increase in high flows is also supported by flow return periods obtained from a larger sample of highest flows over 50 years, which reveals for 2051–2100 lower return periods for high runoff values compared to 1956–2005. Obtained results are associated with substantial uncertainties leading to the conclusion that CORDEX data at 0.11° resolution are likely inadequate for driving hydrologic analyses in mesoscale catchments that require a high standard of fidelity for hydrologic simulation performance. [ABSTRACT FROM AUTHOR]

Precipitation is affected by soil moisture spatial variability. However, this variability is not well represented in atmospheric models that do not consider soil moisture transport as a three-dimensional process. This study investigates the sensitivity of precipitation to the uncertainty in the representation of terrestrial water flow. The tools used for this investigation are the Weather Research and Forecasting (WRF) Model and its hydrologically enhanced version, WRF-Hydro, applied over central Europe during April-October 2008. The model grid is convection permitting, with a horizontal spacing of 2.8 km. The WRF-Hydro subgrid employs a 280-m resolution to resolve lateral terrestrial water flow. A WRF/WRF-Hydro ensemble is constructed by modifying the parameter controlling the partitioning between surface runoff and infiltration and by varying the planetary boundary layer (PBL) scheme. This ensemble represents terrestrial water flow uncertainty originating from the consideration of resolved lateral flow, terrestrial water flow uncertainty in the vertical direction, and turbulence parameterization uncertainty. The uncertainty of terrestrial water flow noticeably increases the normalized ensemble spread of daily precipitation where topography is moderate, surface flux spatial variability is high, and the weather regime is dominated by local processes. The adjusted continuous ranked probability score shows that the PBL uncertainty improves the skill of an ensemble subset in reproducing daily precipitation from the E-OBS observational product by 16%-20%. In comparison to WRF, WRF-Hydro improves this skill by 0.4%-0.7%. The reproduction of observed daily discharge with Nash-Sutcliffe model efficiency coefficients generally above 0.3 demonstrates the potential of WRF-Hydro in hydrological science. [ABSTRACT FROM AUTHOR]

The impact of climate change on three small- to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021-50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member en-semble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is estab-lished. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one re-alization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM-realization-RCM-HM) at least two members and allows the investigation of quali-tative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer. [ABSTRACT FROM AUTHOR]

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution. [ABSTRACT FROM AUTHOR]