SearcharxivSearch

arXiv · 2401.08697

The role of the surface evapotranspiration in regional climate modelling: Evaluation and near-term future changes

Abstract

The surface evapotranspiration (SFCEVP) plays an essential role in climate, being the link between the hydrological and energy cycles. Therefore, how it is approximated and its implication in the regional climate are important aspects to understand the effects of climate change, especially over transitional zones such as the Iberian Peninsula (IP). This study aims to investigate the spatiotemporal patterns of the SFCEVP using a regional climate model (RCM), the Weather Research and Forecasting (WRF) model. With this purpose, a set of WRF simulations were completed using different driving data. On the first hand, a recent present (1980-2017) simulation driven by the ERA-Interim reanalysis was carried out to evaluate the suitability of the RCM performance. On the other hand, two global climate models (GCMs) from the CMIP5 initiative, the CCSM4 and the MPI-ESM-LR, were used as driving data to evaluate the GCM-RCM couplings, which is essential to climate change applications. Finally, projected changes were also investigated for a near-term future (2021-2050) paradigm. In general, the results pointed out the WRF model as a valuable tool to study the spatiotemporal patterns of the SFCEVP in the IP, showing an overall and acceptable ability at different spatial and temporal scales. Concerning projections, the results indicate that the IP is likely to undergo significant changes in the SFCEVP in the near future. These changes will be more apparent over the southernmost, and particularly during spring and summer, being in the latter season the SFCEVP fundamentally reduced. These results agree with projected changes in soil moisture, which is probably associated with changes in precipitation patterns. Additionally, the results reveal the major role of SFCEVP in modulating the climate over this region, which is involved in the complex land-atmosphere processes.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matilde García-Valdecasas Ojeda, Juan José Rosa-Cánovas, Emilio Romero-Jiménez, P. Yeste, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, María Jesús Esteban-Parra. 2024-01-14. The role of the surface evapotranspiration in regional climate modelling: Evaluation and near-term future changes. https://doi.org/10.1016/j.atmosres.2020.104867

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph