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The pan-African Great Green Wall for the Sahara and the Sahel initiative (GGW) is a reforestation program to reverse the degradation of land. We investigate characteristics of mean precipitation due to proposed land-use changes to woody savannah with three hypothetical courses of the GGW, with an area between 0.8 and 1.25 million km2, and between the 100- and 400-mm isohyets. The global Model for Prediction Across Scales (MPAS) was applied for this investigation, employing ensembles with 40 members for the rainy season from June to September and 50 members for August when precipitation is at its peak. In comparison with the observational reference, the results show that a wet bias on the order of 33% in the eastern Sahel and a moderate dry bias of −41% in the western Sahel are present in the MPAS simulations. Our simulations do not provide any significant evidence for GGW-induced changes in the characteristics of the summer precipitation, for positive changes within the Sahel supporting the forestation activities, or for potentially adverse changes in the neighboring regions. Changes are present at the regional scale, but they are not significant at the 5% level. Also, changes simulated for further hydrometeorological variables such as temperature, radiation fluxes, or runoff are comparatively small. [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]

Climate models face limitations in their ability to accurately represent highly variable atmospheric phenomena. To resolve fine‐scale physical processes, allowing for local impact assessments, downscaling techniques are essential. We propose spateGAN, a novel approach for spatio‐temporal downscaling of precipitation data using conditional generative adversarial networks. Our method is based on a video super‐resolution approach and trained on 10 years of country‐wide radar observations for Germany. It simultaneously increases the spatial and temporal resolution of coarsened precipitation observations from 32 to 2 km and from 1 hr to 10 min. Our experiments indicate that the ensembles of generated temporally consistent rainfall fields are in high agreement with the observational data. Spatial structures with plausible advection were accurately generated. Compared to trilinear interpolation and a classical convolutional neural network, the generative model reconstructs the resolution‐dependent extreme value distribution with high skill. It showed a high fractions skill score of 0.6 (spatio‐temporal scale: 32 km and 1 hr) for rainfall intensities over 15 mm h−1 and a low relative bias of 3.35%. A power spectrum analysis confirmed that the probabilistic downscaling ability of our model further increased its skill. We observed that neural network predictions may be interspersed by recurrent structures not related to rainfall climatology, which should be a known issue for future studies. We were able to mitigate them by using an appropriate model architecture and model selection process. Our findings suggest that spateGAN offers the potential to complement and further advance the development of climate model downscaling techniques, due to its performance and computational efficiency. Plain Language Summary: Natural disasters like floods, hail, or landslides originate from precipitation. Global climate models are an important tool to understand these hazards and derive expected changes in a future climate. However, they operate on spatial and temporal scales that limit the regional ability to reflect their small‐scale characteristics. This has led to the development of dynamical and statistical downscaling methods. Due to their computational efficiency, machine learning algorithms recently got increased attention as a method for improving the spatial resolution of climate data. Here, we describe a new deep learning model that allows to simultaneously increasing both the temporal and spatial resolution of precipitation data. Our presented approach enhances the spatial resolution by a factor of 16 and the temporal resolution by a factor of 6. The generated rain fields are hardly identifiable as artificially generated and exhibit the typical structure, movement, and distribution of observed rain fields. Key Points: High performance simultaneous spatial and temporal precipitation downscaling enabled by 3D convolution approachGeneration of realistic high‐resolution ensembles using probabilistic conditional generative adversarial networksLow computational effort compared to dynamical downscaling approaches [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]

This research examines the suitability of four land surface schemes (LSSs) in Weather Research and Forecasting (WRF) model over different West Africa's (WA) climatological zones, namely Guinea, Savanna, and Sahel. The four LSSs include Noah, Noah (Multiparameterization) MP, Noah MP with the groundwater option (Noah GW), and Community Land Model Version 4 (CLM4). A 3-month simulation was carried out with each LSS during July–September 2012. Temperature and dew point temperature (Dpt) were evaluated using the ERA5 dataset, while precipitation was evaluated using the TRMM product. Based on the three variables, CLM4 is most suitable for the Guinea zone; Noah, Noah MP, and CLM4 perform equally well for Savanna; and Noah MP is most suitable for the Sahel zone. In general, and over all zones, Noah MP is most suitable for precipitation simulation, while CLM4 is the best for dew point temperature as Noah MP and Noah GW are equally suitable for 2 m temperature. Noah GW overestimates surface moisture, altering surface fluxes, increasing evaporative fraction, and increasing convective activities (especially in semi-arid areas), which led to a significant bias in temperature and precipitation. Also, over the Savanna and Sahel zone, a strong African Easterly Jet (AEJ) with a weak Tropical Easterly Jet (TEJ) in Noah led to lower rainfall. In contrast, weak AEJ with strong TEJ in Noah MP and Noah GW caused higher rainfall. Further studies would be to evaluate the sensitivity of each LSS under different initial conditions, and their land-atmosphere interactions strength over the zones. [ABSTRACT FROM AUTHOR]

We investigate the reproduction of observed river discharge in a coupled system consisting from a regional climate model (RCM) and a distributed hydrology model (HM), where the HM is applied to route the runoff simulated by the RCM. The coupled system joins MPI and OpenMP based parallel codes of the COSMO-CLM (CCLM) regional climate model version 5 and the hydrology model WaSiM-ETH version 9 within the coupling environment provided by the OpenPALM model coupler.Application area is the complex terrain catchment of the Ammer River (600 km2) located in the Ammergau Alps in Southern Germany, where the HM runs on a 100 m grid in UTM projection with RCM input on rotated grid coordinates that is received every full model hour and interpolated on to this grid by the IDW method. Our contribution addresses general issues of hydrological simulations driven with biased RCM data input. Then it presents the coupled model system, its advantages and limitations, and the evaluation of the obtained results in comparison with measurements from river gauges and climate stations operated within the long term TERENO preAlpine observatory. Special attention is given to the investigation of periods with extremely high river flows applying highly resolved RCM runs in convection permitting mode at 0.03 degree and 0.01 degree grids. [ABSTRACT FROM AUTHOR]

Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections. [ABSTRACT FROM AUTHOR]

We apply a complex hydro-meteorological modelling chain for investigating the impact of climate change on future hydrological extremes in Central Vietnam, a region characterized by limited data availability. The modelling chain consists of six General Circulation Models (GCMs), six Regional Climate Models (RCMs), six bias correction (BC) approaches, the fully distributed Water Flow and Balance Simulation Model (WaSiM), and extreme values analysis. Bias corrected and raw climate data are used as input for WaSiM. To derive hydrological extremes, the generalized extreme value distribution is fitted to the annual maxima/minima discharge. We identify limitations according to the fitting procedure and the BC methods, and suggest the usage of the delta change approach for hydrological decision support. Tendencies towards increased high- and decreased low flows are concluded. Our study stresses the challenges in using current GCMs/RCMs in combination with state-of-the-art BC methods and extreme value statistics for local impact studies. [ABSTRACT FROM AUTHOR]

Using 2001 and 2016 land use (LU) data and three developed deforestation LU scenarios, namely Deforest I, II and III, the possible impacts of the ongoing land use land cover change (LULCC) over West Africa (WA) and the uncertainty regarding the impacts on regional climate were assessed. Twelve Weather Research and Forecasting (WRF) model simulations were configured with the default and integrated MODIS satellite dataset for two sets of 3 months (December–February DJF 2011–2012 and July–September JAS 2012) over WA. Between 2001 and 2016, changes that occurred in precipitation, temperature and dew point temperature due to LULCC are mostly not significant at the 95% level with a decrease of 0.8, 0.0 and 0.1%, respectively. Deforest I, II and III caused significant changes with an increase of 44.4, 44.7, 41.5%; 0.8, 0.9, 1.1%; and 0.3, 0.1, −0.2%, respectively, for precipitation, temperature and dew point temperature. Precipitation was lower in the simulation with 2016 LU than it was with 2001 LU, which may have also been influenced by the reforestation that occurred between the periods. However, Deforest III resulted in a widespread nonsignificant rise in precipitation, as the modified areas showed a significant decrease during JAS period. Areas where forests were converted into barren lands revealed severe increase in the surface albedo which is a strong factor that its alteration is capable of influencing precipitation and other variables over WA. LULCC also altered the atmospheric column by modifying the strength of the African Easterly Jet (AEJ), Tropical Easterly Jet (TEJ), wind speed, and monsoonal, vertical, meridional and zonal winds. Heat related fields, such as dew point and air temperature, and moisture fields such as relative humidity were also modified by LULCC. Therefore, as WA reacts strongly to LULCC, sustainable land management practices should be encouraged to control its adverse effects. [ABSTRACT FROM AUTHOR]

Precipitation change is critical for the Three-River Headwaters (TRH) region, which serves downstream communities in East Asia. The spring (March–May) precipitation over the TRH region shows an increasing trend from 1979 to 2018, as revealed by a Chinese gridded precipitation product (CN05.1). However, the physical processes responsible for this precipitation change are still unclear. This study investigated the characteristics of spring precipitation and the water budget over the TRH region using the ERA5 global reanalysis and the Weather Research and Forecast (WRF) Model. The WRF version employed in this study includes online calculations of the atmospheric water budget and an evapotranspiration (ET) tagging procedure to trace evapotranspired water in the atmosphere. Both ERA5 and WRF reproduce the spring precipitation increase. Moreover, WRFD02 (with a 3-km domain) reduces the wet bias by around 60% and 77% compared to WRFD01 (9 km) and ERA5 (30 km). Both ERA5 and WRF demonstrate that the increase of spring precipitation is dominated by moisture convergence, especially the atmospheric water fluxes from the southern boundary. The enhanced moisture inflow is sustained by enhanced mass flux while the enhanced moisture outflow is sustained by increased moisture. The ET-tagging results further demonstrate the weakened precipitation recycling process because of the significant increase of precipitation produced by external moisture. Compared to ERA5, the reduced wet bias with WRF is attributed to a better spatial resolution of orographic barrier effects, which reduce the southerly water fluxes. The results highlight the potential of regional climate downscaling to better represent the atmospheric water budget in complex terrain. [ABSTRACT FROM AUTHOR]

Numerical climate models have been upgraded by the improved description of terrestrial hydrological processes across different scales. The goal of this study is to explore the role of terrestrial hydrological processes on land–atmosphere interactions within the context of modeling uncertainties related to model physics parameterization. The models applied are the Weather Research and Forecasting (WRF) model and its coupled hydrological modeling system WRF-Hydro, which depicts the lateral terrestrial hydrological processes and further allows their feedback to the atmosphere. We conducted convection-permitting simulations (3 km) over the Heihe River Basin in Northwest China for the period 2008–2010, and particularly focused on its upper reach area of complex high mountains. In order to account for the modeling uncertainties associated with model physics parameterization, an ensemble of simulations is generated by varying the planetary boundary layer (PBL) schemes. We embedded the fully three-dimensional atmospheric water tagging method in both WRF and WRF-Hydro for quantifying the strength of land–atmosphere interactions. The impact of PBL parameterization on land–atmosphere interactions is evaluated through its direct effect on vertical mixing. Results suggest that enabled lateral terrestrial flow in WRF-Hydro distinctly increases soil moisture and evapotranspiration near the surface in the high mountains, thereby modifies the atmospheric condition regardless of the applied PBL scheme. The local precipitation recycling ratio in the study area increases from 1.52 to 1.9% due to the description of lateral terrestrial flow, and such positive feedback processes are irrespective of the modeling variability caused by PBL parameterizations. This study highlights the non-negligible contribution of lateral terrestrial flow to local precipitation recycling, indicating the potential of the fully coupled modeling in land–atmosphere interactions research. [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]

Water isotopologues, as natural tracers of the hydrological cycle on Earth, provide a unique way to assess the skill of climate models in representing realistic atmospheric‐terrestrial water pathways. This study presents the newly developed WRF‐Hydro‐iso, which is a version of the coupled atmospheric‐hydrological WRF‐Hydro model enhanced with a joint soil‐vegetation‐atmospheric description of water isotopologue motions. It allows the consideration of isotopic fractionation processes during water phase changes in the atmosphere, the land surface, and the subsurface. For validation, WRF‐Hydro‐iso is applied to two different climate zones, namely Europe and Southern Africa under the present climate conditions. Each case is modeled with a domain employing a 5 km grid‐spacing coupled with a terrestrial subgrid employing a 500 m grid‐spacing in order to represent lateral terrestrial water flow. A 10‐year slice is simulated for 2003–2012, using ERA5 reanalyses as driving data. The boundary condition of isotopic variables is prescribed with mean values from a 10‐year simulation with the Community Earth System Model Version 1. WRF‐Hydro‐iso realistically reproduces the climatological variations of the isotopic concentrations δPO18 and δPH2 from the Global Network of Isotopes in Precipitation. In a sensitivity analysis, it is found that land surface evaporation fractionation increases the isotopic concentrations in the rootzone soil moisture and slightly decreases the isotopic concentrations in precipitation. Lateral terrestrial water flow minorly affects these isotopic concentrations through changes in evaporation‐transpiration partitioning. Plain Language Summary: Global climate models are limited by their coarse resolution, which may reduce their meaningfulness. This problem can be circumvented for a specific region with regional climate models, which provide, for example, a detailed description of clouds and land‐atmosphere interactions. But it remains a question: How realistic is the model representation of water transport through the different compartments of the hydrological cycle, the atmosphere, the land, and the sea? A unique way to assess modeled water transport is the comparison to natural tracers, such as water isotopologues, which requires to include the fate of these water isotopologues in the model. This is what we pursue here with the newly developed WRF‐Hydro‐iso model. A model description and a proof of concept are provided for two climate zones, using the Global Network of Isotopes in Precipitation data set as reference. Key Points: A new coupled atmospheric‐hydrological regional modeling system of water isotopologues is presentedLand surface evaporation fractionation increases the isotopic concentrations in the rootzoneLateral terrestrial water flow has a minor effect on isotopic concentrations in the rootzone [ABSTRACT FROM AUTHOR]

Seasonal forecasts have the potential to substantially improve water management particularly in water-scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1 ∘. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco River (Brazil), the Tekeze–Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo–Chira river (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts as well as remarkably reduced lead-dependent drift effects. But our analysis also shows that computing monthly averages from daily bias-corrected forecasts particularly during periods with strong temporal climate gradients or heteroscedasticity can lead to remaining biases especially in the lowest- and highest-lead forecasts. Our SEAS5 BCSD forecasts cover the whole (re-)forecast period from 1981 to 2019 and include bias-corrected and spatially disaggregated daily and monthly ensemble forecasts for precipitation, average, minimum, and maximum temperature as well as for shortwave radiation from the issue date to the next 215 d and 6 months, respectively. This sums up to more than 100 000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the five analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at 10.26050/WDCC/SaWaM_D01_SEAS5_BCSD Domain D01, Karun Basin (Iran), , 10.26050/WDCC/SaWaM_D02_SEAS5_BCSD Domain D02: São Francisco Basin (Brazil), , 10.26050/WDCC/SaWaM_D03_SEAS5_BCSD Domain D03: basins of the Tekeze–Atbara and Blue Nile (Ethiopia, Eritrea, Sudan), , and 10.26050/WDCC/SaWaM_D04_SEAS5_BCSD Domain D04: Catamayo–Chira Basin (Ecuador, Peru),. It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions. [ABSTRACT FROM AUTHOR]

Coupled numerical models, which simulate water and energy fluxes in the subsurface-land surface-atmosphere system in a physically consistent way are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a virtual reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform TerrSysMP, which couples the meteorological model COSMO, the land-surface model CLM, and the subsurface model ParFlow, to generate a virtual catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany. Simulations for this catchment are made for the period 2007-2015, and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modelling challenges, the model performance is evaluated based on real observations covering several variables of the water cycle. We find that the simulated (virtual) catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g. concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement such as overland flow, and in the realism of some observation operators like the satellite based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar%5FVCS%5Fv1. [ABSTRACT FROM AUTHOR]

This study aims at a new supplementary approach to identify optimal configurations of physics parameterizations in regional climate models (RCMs) and earth system models (ESMs). Traditional approaches separately evaluate variable performance, which may lead to an inappropriate selection of physics parameterization combinations. Besides traditional approaches, we suggest an additional selection approach by considering the joint dependence structure (covariance structure) between key meteorological variables, i.e., precipitation P and temperature T. This is accomplished by empirical P and T copula functions and the χ 2 -test, and is demonstrated in two locations in Kenya with different major precipitation processes. It is shown that the selection based on traditional approaches alone may lead to nonoptimal decisions in terms of joint dependence structure between P and T. It was found that the copula-based approach may reduce the need for complex multivariate bias correction, as demonstrated using local intensity scaling for P and linear scaling for T. The new approach may contribute to improving RCM and ESM simulations and climate-impact studies worldwide. [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]

An increase in the spatial resolution of regional climate model simulations improves the representation of land surface characteristics andmay allow the explicit calculation of important physical processes such as convection. The present study investigates further potential benefits with respect to precipitation, based on a small ensemble of high-resolution simulations with WRF with grid spacings up to 1 km. The skill of each experiment is evaluated regarding the temporal and spatial performance of the simulation of precipitation of one year over both a mountainous region in southwestern Germany and a mainly flat region in northern Germany. This study allows us to differentiate between the impact of grid spacing, topography, and convection parameterization. Furthermore, the performance of a state-of-the-art convection parameterization scheme in the gray zone of convection is evaluated against an explicit calculation of convection only. Our evaluation demonstrates the following: high-resolution simulations (5 and 1 km) are generally able to represent the diurnal cycle, structure, and intensity distribution of precipitation, when compared to observational datasets such as radar data and interpolated station data. The influence of the improved representation of the topography at higher resolution (1 km) becomes apparent in complex terrain, where the localization of precipitation maxima ismore accurate, although these maxima are overestimated. In flat areas, differences in spatial evaluations arise between simulationswith parameterized and explicitly calculated convection, whereas smaller grid spacings (1 km vs 5km) show hardly any impact on precipitation results. [ABSTRACT FROM AUTHOR]

ABSTRACT Regional climate models ( RCMs) include both terrestrial and atmospheric compartments and thereby allow studying land-atmosphere feedback, in particular, the impact of land-use land cover driven by biogeophysical processes on regional climate. In this study, a method is developed to separate the signals from the noise in RCM simulations of the effects of changes in land use, using perturbed initial boundary conditions ( PICs). We want to know how many ensemble members are required to identify robust and statistically significant land-use land cover change ( LULCC) effects from RCM LULCC studies. The method is applied to a case study of urbanization and deforestation, for which LULCC scenarios are implemented in the RCM Weather Research and Forecasting ( WRF). Based on WRF ensemble simulations with PICs for 2010, the signal-to-noise ratio ( SNR) is used to identify areas with pronounced effect of an LULCC or, rather, the parametrization of the land-use classes. While in the urbanization scenarios clear and statistically significant signals are found for air temperature and for both latent- and sensible heat ( SNR values up to 24), the effects are less pronounced for precipitation, and for deforestation in general ( SNR values < 1). For the case study of urbanization and precipitation, the impact of the ensemble size is studied in order to derive robust conclusions about the effects of LULCC on precipitation. We conclude that single RCM realizations of different land-use representations are not sufficient to derive LULCC-induced signals, particularly not for precipitation. Small ensemble sizes led to concluding there were significant LULCC-induced precipitation signals, but these disappeared when the ensemble size was increased. Our regional analysis suggests the need for ensemble sizes well above 10 for precipitation. [ABSTRACT FROM AUTHOR]

The analysis of land-atmosphere feedbacks requires detailed representation of land processes in atmospheric models. The focus here is on runoff-infiltration partitioning and resolved overland flow. In the standard version of WRF, runoff-infiltration partitioning is described as a purely vertical process. In WRF-Hydro, runoff is enhanced with lateral water flows. The study region is the Sissili catchment (12 800 km2) in West Africa, and the study period is from March 2003 to February 2004. The WRF setup here includes an outer and inner domain at 10- and 2-km resolution covering the West Africa and Sissili regions, respectively. In this WRF-Hydro setup, the inner domain is coupled with a subgrid at 500-m resolution to compute overland and river flow. Model results are compared with TRMM precipitation, model tree ensemble (MTE) evapotranspiration, Climate Change Initiative (CCI) soil moisture, CRU temperature, and streamflow observation. The role of runoff-infiltration partitioning and resolved overland flow on land-atmosphere feedbacks is addressed with a sensitivity analysis of WRF results to the runoff-infiltration partitioning parameter and a comparison between WRF and WRF-Hydro results, respectively. In the outer domain, precipitation is sensitive to runoff-infiltration partitioning at the scale of the Sissili area (~100 × 100 km2), but not of area A (500 × 2500 km2). In the inner domain, where precipitation patterns are mainly prescribed by lateral boundary conditions, sensitivity is small, but additionally resolved overland flow here clearly increases infiltration and evapotranspiration at the beginning of the wet season when soils are still dry. The WRF-Hydro setup presented here shows potential for joint atmospheric and terrestrial water balance studies and reproduces observed daily discharge with a Nash-Sutcliffe model efficiency coefficient of 0.43. [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]

• Bias correction (BC) methods can better represent the spatial patterns of the climate and derived agricultural impacts. • Uncertainties from BC can be twice as high compared to the uncertainties from the GCM-RCM combinations. • Climate change impacts studies (CCIS) without BC or relying on any single BC approach should be interpreted with great caution. • In CCIS, a BC ensemble is suggested for generating climate projections following a super-ensemble probabilistic assessment. Many open questions and unresolved issues surround the topic of bias correction (BC) in climate change impact studies (CCIS). One question relates to the contribution of downscaling of climate change scenarios on the uncertainties in results obtained using impact models for agriculture. In particular, for large area or regional agricultural impact assessments, the question of bias correction is of high relevance. Relatively few studies exist looking at the quantification of the impacts of BC methods in general circulation model (GCM) and regional climate model (RCM) data on results of such impact studies. Here, we quantify the impact of different BC methods on temperature (T) and precipitation (P) from different CORDEX GCM-RCM combinations, and how the debiased T&P signal may propagate through agricultural impact models. Specifically, we estimate the impact of BC on (i) an empirical P- and fuzzy rule-based algorithm to estimate the crop planting date, and (ii) a mechanistic T&P-based approach to quantify the crop suitability index (CSI) for the main staple crops in West Africa (i.e. groundnut, maize, pearl millet, sorghum). Both approaches serve as a proxy for more complex process-based ecophysiological crop models. Depending on the choice of the BC method, the uncertainties in the assessment of the CSI can be more than twice as high compared to the uncertainties from the GCM-RCM model selection. Comparing the estimated CSI values with observed harvest area, it is found that BC generally improves the performance for models with low hit rates (< 30 –35%), but decreases the performance for models with relatively high hit rates (> 35 %). Such consequences can also be expected for process-based crop models, which are developed to operate on field-scale but are driven by coarser scale RCMs. It is concluded that such agriculturally oriented climate impact models as investigated here should be interpreted with great caution if applied without BC or relying on a single BC approach only. Rather, we suggest to include different BC approaches in the generation of climate projections for CCIS and quantify their uncertainties following a super-ensemble probabilistic assessment. [ABSTRACT FROM AUTHOR]

The spatial and temporal distribution of soil water is a key constituent for the coupled moisture processes between the land surface and the atmospheric boundary layer. The scales of soil moisture observations and simulations with land-surface models are typically mismatching with respect to spatial support. Cosmic-ray neutron sensing (CRNS) offers a competitive method to observe soil moisture at the spatial scales of modern LSMs as typically used in local area climate and atmospheric models. In our study, we investigate how the assimilation of CRNS observations helps to improve the spatial structures of soil moisture in these models and the local soil moisture – atmospheric boundary layer interaction. The modeling chain consists of Noah-MP LSM, the WRF-Hydro model in both, observation driven and fully WRF coupled configuration, and the Data Assimilation Research Testbed (DART) coupled to WRF-Hydro (Hydro-DART). The data assimilation is based on Ensemble-Kalman Fitler (EnKF) with the fully coupled WRF-Hydro-Dart model system.Extensive observations from the Terrestrial Environmental Observatories (TERENO, www.tereno.net), will be used along with data from MOSES (Helmholtz Modular Observation Solutions for Earth Science https://moses.eskp.de/), and with data from the DFG research unit Cosmic Sense. We present the structure and a first evaluation of the model system for the Ammer river catchment in southern Germany based on observations from the TERENO preAlpine network. [ABSTRACT FROM AUTHOR]