Abstract
The grid size resolution effect on the annual and seasonal simulated mean, maximum and minimum daily temperatures and precipitation is assessed using the Advanced Research Weather Research and Forecasting model (ARW-WRF, hereafter WRF) that dynamically downscales the National Centers for Environmental Prediction’s final (NCEP FNL) Operational Global Analysis data. Simulations were conducted over central Europe for the year 2015 using 36, 12 and 4 km grid resolutions. Evaluation is done using daily E-OBS data. Several performance metrics and the bias adjusted equitable threat score (BAETS) for precipitation are used. Results show that model performance for mean, maximum and minimum temperature improves when increasing the spatial resolution from 36 to 12 km, with no significant added value when further increasing it to 4 km. Model performance for precipitation is slightly worsened when increasing the spatial resolution from 36 to 12 km while further increasing it to 4 km has minor effect. However, simulated and observed precipitation data are in quite good agreement in areas with precipitation rates below 3 mm/day for all three grid resolutions. The annual mean fraction of observed and/or forecast events that were correctly predicted (BAETS), when increasing the grid size resolution from 36 to 12 and 4 km, suggests a slight modification on average over the domain. During summer the model presents significantly lower BAETS skill score compared to the rest of the seasons.
Highlights
Earth system models (ESMs) and climate circulation models (GCMs) are still the principal tools of the scientific community for projecting future climate [1,2]
ESMs and GCMs do not satisfactorily represent vegetation variability, complex topography and coastlines, which are significant components of the physical system that govern the climate change signal on a local or regional scale. To cope with these deficiencies, dynamical downscaling techniques have been developed and are currently adopted, for effectively adapting the large-scale projections of the inferred climate components provided by an ESM or a GCM to regional or local scales, through explicitly solving the process-based physical dynamics of the regional climate system at high spatial resolution, when driven by the large-scale low-resolution data of the ESM/GCM [3,4]
The first approach is not flawless, given that GCMs are not forced by observed data, possible systematic errors developed by a GCM/ESM may propagate into the Regional climate models (RCMs) outputs
Summary
Earth system models (ESMs) and climate circulation models (GCMs) are still the principal tools of the scientific community for projecting future climate [1,2]. The added value of an RCM used for downscaling purposes might be diminished Still, such simulations are very useful, as they allow climate projections and assessments for various climate change scenarios on finer resolutions than those resolved by the GCMs/ESMs. On the other hand, reanalysis data are the most accurate representation of the archived climate observations at high temporal and spatial resolution forced by climatic observation, they are extensively used for evaluation purposes of the current climate. Despite the large number of studies examining the effect of the spatial resolution on models’ performances, the question of up to what spatial scales downscaling global data to local scales can improve local representation of temperature and precipitation and whether very fine resolutions (below 10 km) are necessary for their improved representation still remains inadequately addressed Addressing this challenge, in this study we assess the Advanced Research Weather Research and Forecasting model (ARW-WRF, hereafter WRF) [21,22] temperature and precipitation performance sensitivity to the grid size resolution. The selected domain is extended over central Europe, due to the significant number of observational data, for assessing both annual and seasonal impact
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