Search

It is well accepted that global circulation models equipped with stable water isotopologues help us better understand the relationships between atmospheric circulation changes and isotope records in paleoclimate archives. Still, isotope-enabled models do not disentangle the different processes affecting precipitation isotopic compositions. Furthermore, the relevance of this model-oriented approach relies on the realism of the modeled isotope results, which would support the interpretation of the proxy records in terms of modeled climate changes. To alleviate these limitations, the newly developed WRF-Hydro-iso-tag, a version of the isotope-enabled regional coupled model WRF-Hydro-iso enhanced with an isotope-tracing procedure, is presented. Physics-based WRF-Hydro-iso-tag ensembles are used to regionally downscale the isotope-enabled Community Earth System Model for southern Africa, for two 10-yr slices of mid-Holocene and preindustrial times. The isotope-tracing procedure is tailored to assess the origin of the hydrogen isotope deuterium contained in southern African precipitation, between the Atlantic and Indian Oceans. In comparison to the global model, WRF-Hydro-iso-tag simulates lower precipitation amounts with more regional details, as well as mid-Holocene-to-preindustrial changes in precipitation isotopic compositions that better match plant-wax deuterium records from two marine sediment cores off the Orange and Limpopo River basins. Linear relationships between mid-Holocene-to-preindustrial changes in temperature, precipitation amount, moisture source, and precipitation deuterium compositions are derived from the ensemble results. A deuterium enrichment in the Orange River-related sediment core may not be related to an aridification but rather indicate a summer circulation change enabling a larger contribution of more isotopically enriched moisture from the Atlantic Ocean. Significance Statement: The knowledge of past climates is crucial for understanding our Earth system and apprehending future climate change. Plant materials in sediment archives contain atoms of hydrogen from past precipitation that allow paleoclimate reconstructions, using compositions of the hydrogen isotope deuterium. However, in the tropics, deuterium-depleted plant remains can either denote a wetter climate phase or a change in atmospheric circulation patterns with longer distances between ocean evaporation and land precipitation. This work provides an innovative dynamical downscaling method of global paleoclimate models to disentangle the effects of precipitation amount change and moisture source change on deuterium records, and ultimately to improve paleoclimate reconstructions. The interpretation of a deuterium enrichment in a marine sediment core as a marker for aridification is revised. The enrichment caused by an atmospheric circulation change bringing a larger amount of more isotopically enriched moisture flow from the Atlantic Ocean to southern African precipitation would be a more physically sound explanation. [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]

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]

• Remarkable differences of soil texture in southern Africa are found in global soil data. • Land-atmosphere interactions are investigated by using coupled WRF-Hydro with four global soil data. • Soil texture changes lead to differences in temperature up to 1.7°C and sensible heat flux up to 25 W/m2 during austral summer. • Soil data discrepancies demonstrate physical feedback on atmospheric processes. • Consistent consideration of soil data uncertainties is needed in modeling applications. Numerical weather- and climate prediction models rely on soil data to accurately model land surface processes. However, as soil data are produced using soil profiles and maps with multiple sources of uncertainty, wide discrepancies prevail in global soil datasets. Comparison of four commonly used soil datasets in Earth system climate models, i.e., Food and Agriculture Organization soil data, Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids, yields widespread differences in southern Africa. This study investigates the simulated land-atmosphere interactions in southern Africa in the context of the uncertainties from applying different global soil datasets. We conducted ensemble simulations using the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) incorporated with each of the global soil datasets mentioned above. Model simulations were performed at 4-km convection-permitting scale from January 2015 to June 2016. By quantifying model's internal variability and comparing the modeling results, results show that the simulated temperature, soil moisture, and surface energy fluxes are largely impacted by soil texture differences. For instance, changes in soil texture and associated hydrophysical parameters result in large differences in air temperature up to 1.7°C and surface heat flux up to 25 W/m2, and disparities in averaged surface soil moisture differ up to 0.1 m3/m3 in austral summer months. Differences in soil texture characteristics also regulate local climatic conditions differently in the wet and dry seasons as well as in different climatic regions. Furthermore, the thermodynamic differences in surface energy fluxes caused by soil texture demonstrate physical feedback perspective on atmospheric processes, resulting in distinct changes in planetary boundary layer height. This study demonstrates the non-negligible impact of soil data on land surface-atmosphere coupled modeling and highlights the need for consistent consideration of modeling uncertainties from soil data in modeling applications. [ABSTRACT FROM AUTHOR]