Droughts in Germany: performance of regional climate models in reproducing observed characteristics

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 its 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) Weather Research and Forecasting (WRF) run were employed. The latter model 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 include 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 approximately 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 were 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. The greatest benefits were identified in the trend analysis: only WRF was able to reproduce the observed negative SPEI trends to a fairly high spatial accuracy, while the other regional climate models (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.</p

Is the soil moisture precipitation feedback enhanced by heterogeneity and dry soils? A comparative study

The interaction between the land surface and the atmosphere is a crucial driver of atmospheric processes. Soil moisture and precipitation are key components in this feedback. Both variables are intertwined in a cycle, that is, the soil moisture – precipitation feedback for which involved processes and interactions are still discussed. In this study the soil moisture – precipitation feedback is compared for the sempiternal humid Ammer catchment in Southern Germany and for the semiarid to subhumid Sissili catchment in West Africa during the warm season, using precipitation datasets from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), from the German Weather Service (REGNIE) and simulation datasets from the Weather Research and Forecasting (WRF) model and the hydrologically enhanced WRF-Hydro model. WRF and WRF-Hydro differ by their representation of terrestrial water flow. With this setup we want to investigate the strength, sign and variables involved in the soil moisture – precipitation feedback for these two regions. The normalized model spread between the two simulation results shows linkages between precipitation variability and diagnostic variables surface fluxes, moisture flux convergence above the surface and convective available potential energy in both study regions. The soil moisture – precipitation feedback is evaluated with a classification of soil moisture spatial heterogeneity based on the strength of the soil moisture gradients. This allows us to assess the impact of soil moisture anomalies on surface fluxes, moisture flux convergence, convective available potential energy and precipitation. In both regions the amount of precipitation generally increases with soil moisture spatial heterogeneity. For the Ammer region the soil moisture – precipitation feedback has a weak negative sign with more rain near drier patches while it has a positive signal for the Sissili region with more rain over wetter patches. At least for the observed moderate soil moisture values and the spatial scale of the Ammer region, the spatial variability of soil moisture is more important for surface-atmosphere interactions than the actual soil moisture content. Overall, we found that soil moisture heterogeneity can greatly affect the soil moisture – precipitation feedback

Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model

Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive method for estimating soil moisture at the field scale, in our case a few tens of hectares. The current study uses the Ensemble Adjustment Kalman Filter (EAKF) to assimilate neutron counts observed at four locations within a 655 km$^{2}$ pre-alpine river catchment into the Noah-MP land surface model (LSM) to improve soil moisture simulations and to optimize model parameters. The model runs with 100 m spatial resolution and uses the EU-SoilHydroGrids soil map along with the Mualem–van Genuchten soil water retention functions. Using the state estimation (ST) and joint state–parameter estimation (STP) technique, soil moisture states and model parameters controlling infiltration and evaporation rates were optimized, respectively. The added value of assimilation was evaluated for local and regional impacts using independent root zone soil moisture observations. The results show that during the assimilation period both ST and STP significantly improved the simulated soil moisture around the neutron sensors locations with improvements of the root mean square errors between 60 and 62% for ST and 55–66% for STP. STP could further enhance the model performance for the validation period at assimilation locations, mainly by reducing the Bias. Nevertheless, due to a lack of convergence of calculated parameters and a shorter evaluation period, performance during the validation phase degraded at a site further away from the assimilation locations. The comparison of modeled soil moisture with field-scale spatial patterns of a dense network of CRNS observations showed that STP helped to improve the average wetness conditions (reduction of spatial Bias from –0.038 cm$^{3}$ cm$^{-3}$ to –0.012 cm$^{3}$ cm$^{-3}$) for the validation period. However, the assimilation of neutron counts from only four stations showed limited success in enhancing the field-scale soil moisture patterns

Role of Runoff–Infiltration Partitioning and Resolved Overland Flow on Land–Atmosphere Feedbacks: A Case Study with the WRF-Hydro Coupled Modeling System for West Africa

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 km$^{2}$) 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 km$^{2}$). 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

Assimilation of GNSS and synoptic data in a convection permitting limited area model: improvement of simulated tropospheric water vapor content

The assimilation of observations in limited area models (LAMs) allows to find the best possible estimate of a region’s meteorological state. Water vapor is a crucial constituent in terms of cloud and precipitation formation. Its highly variable nature in space and time is often insufficiently represented in models. This study investigates the improvement of simulated water vapor content within the Weather Research and Forecasting model (WRF) in every season by assimilating temperature, relative humidity, and surface pressure obtained from climate stations, as well as geodetically derived Zenith Total Delay (ZTD) and precipitable water vapor (PWV) data from global navigation satellite system (GNSS) ground stations. In four case studies we analyze the results of high-resolution convection-resolving WRF simulations (2.1 km) between 2016 and 2018 each in every season for a 650 × 670 km domain in the tri-border-area Germany, France and Switzerland. The impact of 3D VAR assimilation of different variables and combinations thereof, background error option, as well as the temporal and spatial resolution of assimilation is evaluated. Both column values and profiles derived from radiosondes are addressed. Best outcome was achieved when assimilating ZTD and synoptic data at an hourly resolution and a spatial thinning distance of 10 km. It is concluded that the careful selection of assimilation options can additionally improve simulation results in every season. Clear effects of assimilation on the water budgets can also be seen

spateGAN: spatio‐temporal downscaling of rainfall fields using a cGAN approach

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

High-resolution fully coupled atmospheric-hydrological modeling: a cross-compartment regional water and energy cycle evaluation

Abstract. The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases, and energy. Nonlinear feedback and scale-dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local-area weather prediction. This study examines the ability of the hydrologically enhanced version of the Weather Research and Forecasting model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assesses the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identically calibrated parameter settings for the land surface model (Noah-Multiparametrization; Noah-MP). The simulations are evaluated based on extensive observations at the Terrestrial Environmental Observatories (TERENO) Pre-Alpine Observatory for the Ammer (600 km2) and Rott (55 km2) river catchments in southern Germany, covering a 5-month period (June–October 2016). The sensitivity of seven land surface parameters is tested using the Latin-Hypercube–One-factor-At-a-Time (LH-OAT) method, and six sensitive parameters are subsequently optimized for six different subcatchments, using the model-independent Parameter Estimation and Uncertainty Analysis software (PEST). The calibration of the offline WRF-Hydro gives Nash–Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of the classic WRF and fully coupled WRF-Hydro models, both using the calibrated parameters from the offline model, shows only tiny alterations for radiation and precipitation but considerable changes for moisture and heat fluxes. By comparison with TERENO Pre-Alpine Observatory measurements, the fully coupled model slightly outperforms the classic WRF model with respect to evapotranspiration, sensible and ground heat flux, the near-surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation, whereas soil moisture and precipitation change randomly