AUTH Regional Climate Model Contributions to EURO-CORDEX
Eleni KatragkouI. GkotovouΣτέργιος ΚάρτσιοςVasileios PavlidisKostas TsigaridisM. TrailLarissa NazarenkoT. Karacostas
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This study performs dynamical downscaling simulations using a regional atmospheric model, WRF (the weather research and forecasting model), over the Gediz Basin at 6 km grid resolution in order to investigate changes in temperature and precipitation during the 21st century under two IPCC representative concentration pathway scenarios (RCP4.5 and RCP8.5). The global climate models (GCMs) used in this study are HadGEM2-ES and GFDL-ESM2M. Bias correction was applied for precipitation on a monthly basis by comparing the dynamically-downscaled precipitation obtained from the historical simulations of each GCM to the corresponding observed data. Historical temperature and precipitation data (1985–2005) were then compared to the temperature and precipitation projections (2017–2099). In addition, trends in annual projected temperature and precipitation were also analyzed.
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This study is the first evaluation of dynamical downscaling using the Weather Research and Forecasting (WRF) Model on a 4 km × 4 km high resolution scale in the eastern US driven by the new Community Earth System Model version 1.0 (CESM v1.0). First we examined the global and regional climate model results, and corrected an inconsistency in skin temperature during the downscaling process by modifying the land/sea mask. In comparison with observations, WRF shows statistically significant improvement over CESM in reproducing extreme weather events, with improvement for heat wave frequency estimation as high as 98%. The fossil fuel intensive scenario Representative Concentration Pathway (RCP) 8.5 was used to study a possible future mid-century climate extreme in 2057–9. Both the heat waves and the extreme precipitation in 2057–9 are more severe than the present climate in the Eastern US. The Northeastern US shows large increases in both heat wave intensity (3.05 °C higher) and annual extreme precipitation (107.3 mm more per year).
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Abstract. The representation and projection of extreme temperature and precipitation events in regional and global climate models are of major importance for the study of climate change impacts. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensity, duration and frequency of these extremes. Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) model to determine the influence of the land surface model (LSM) component on uncertainties associated with extreme events. First, we analyze land–atmosphere interactions within four simulations performed by the WRF model from 1980 to 2012 over North America, using three different LSMs. Results show LSM-dependent differences at regional scales in the frequency of occurrence of events when surface conditions are altered by atmospheric forcing or land processes. The inter-model range of extreme statistics across the WRF simulations is large, particularly for indices related to the intensity and duration of temperature and precipitation extremes. Our results show that the WRF simulation of the climatology of heat extremes can be 5 ∘C warmer and 6 d longer depending on the employed LSM component, and similarly for cold extremes and heavy precipitation events. Areas showing large uncertainty in WRF-simulated extreme events are also identified in a model ensemble from three different regional climate model (RCM) simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project, revealing the implications of these results for other model ensembles. Thus, studies based on multi-model ensembles and reanalyses should include a variety of LSM configurations to account for the uncertainty arising from this model component or to test the performance of the selected LSM component before running the whole simulation. This study illustrates the importance of the LSM choice in climate simulations, supporting the development of new modeling studies using different LSM components to understand inter-model differences in simulating extreme temperature and precipitation events, which in turn will help to reduce uncertainties in climate model projections.
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Abstract A significant challenge with dynamical downscaling of climate simulations is the ability to accurately represent convection and precipitation. The use of convection‐permitting resolutions avoids cumulus parameterization, which is known to be a large source of uncertainty. A regional climate model (RCM) based on the Weather Research and Forecasting model is configured with a 4 km grid spacing and applied to the U.S. Great Plains, a region characterized by many forms of weather and climate extremes. The 4 km RCM is evaluated by running it in a hindcast mode over the central U.S. region for a 10 year period, forced at the boundary by the 32 km North America Regional Reanalysis. The model is also run at a 25 km grid spacing, but with cumulus parameterization turned on for comparison. The 4 km run more successfully reproduces certain observed features of the Great Plains May‐through‐August precipitation. In particular, the magnitude of extreme precipitation and the diurnal cycle of precipitation over the Great Plains are better simulated. The 4 km run more realistically simulates the low‐level jet and related atmospheric circulations that transport and redistribute moisture from Gulf of Mexico. The convection‐permitting RCM may therefore produce better dynamical downscaling of future climate when nested within global model climate projections, especially for extreme precipitation magnitudes. The 4 km and 25 km simulations do share similar precipitation biases, including low biases over the central Great Plains and high biases over the Rockies. These biases appear linked to circulation biases in the simulations, but determining of the exact causes will require extensive, separate studies.
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Abstract The prediction skill of dynamical downscaling is evaluated for climate forecasts over southern Africa using the Advanced Research Weather Research and Forecasting (WRF) model. As a case study, forecasts for the December–February (DJF) season of 2011/12 are evaluated. Initial and boundary conditions for the WRF model were taken from the seasonal forecasts of the Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F) coupled general circulation model. In addition to sea surface temperature (SST) forecasts generated by nine-member ensemble forecasts of SINTEX-F, the WRF was also configured to use SST generated by a simple mixed layer Price–Weller–Pinkel ocean model coupled to the WRF model. Analysis of the ensemble mean shows that the uncoupled WRF model significantly increases the biases (errors) in precipitation forecasted by SINTEX-F. When coupled to a simple mixed layer ocean model, the WRF model improves the spatial distribution of precipitation over southern Africa through a better representation of the moisture fluxes. Precipitation anomalies forecasted by the coupled WRF are seen to be significantly correlated with the observed precipitation anomalies over South Africa, Zimbabwe, southern Madagascar, and parts of Zambia and Angola. This is in contrast to the SINTEX-F global model precipitation anomaly forecasts that are closer to observations only for parts of Zimbabwe and South Africa. Therefore, the dynamical downscaling with the coupled WRF adds value to the SINTEX-F precipitation forecasts over southern Africa. However, the WRF model yields positive biases (>2°C) in surface air temperature forecasts over the southern African landmass in both the coupled and uncoupled configurations because of biases in the net heat fluxes.
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A suite of eighteen simulations over the U.S. and Mexico, representing combinations of two mesoscale regional climate models (RCMs), two driving global general circulation models (GCMs), and the historical and four future anthropogenic forcings were intercompared. The RCMs' downscaling reduces significantly driving GCMs' present‐climate biases and narrows inter‐model differences in representing climate sensitivity and hence in simulating the present and future climates. Very high spatial pattern correlations of the RCM minus GCM differences in precipitation and surface temperature between the present and future climates indicate that major model present‐climate biases are systematically propagated into future‐climate projections at regional scales. The total impacts of the biases on trend projections also depend strongly on regions and cannot be linearly removed. The result suggests that the nested RCM‐GCM approach that offers skill enhancement in representing the present climate also likely provides higher credibility in downscaling the future climate projection.
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