scholarly journals Assessment of the Urban Impact on Surface and Screen-Level Temperature in the ALADIN-Climate Driven SURFEX Land Surface Model for Budapest

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 709
Author(s):  
Gabriella Zsebeházi ◽  
Sándor István Mahó
Keyword(s):  
Surface Temperature ◽  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Land Surface Model ◽  
Surface Model ◽  
Urban Impact ◽  
Model Driven ◽  
The Impact

Land surface models with detailed urban parameterization schemes provide adequate tools to estimate the impact of climate change in cities, because they rely on the results of the regional climate model, while operating on km scale at low cost. In this paper, the SURFEX land surface model driven by the evaluation and control runs of ALADIN-Climate regional climate model is validated over Budapest from the aspect of urban impact on temperature. First, surface temperature of SURFEX with forcings from ERA-Interim driven ALADIN-Climate was compared against the MODIS land surface temperature for a 3-year period. Second, the impact of the ARPEGE global climate model driven ALADIN-Climate was assessed on the 2 m temperature of SURFEX and was validated against measurements of a suburban station for 30 years. The spatial extent of surface urban heat island (SUHI) is exaggerated in SURFEX from spring to autumn, because the urbanized gridcells are generally warmer than their rural vicinity, while the observed SUHI extent is more variable. The model reasonably simulates the seasonal means and diurnal cycle of the 2 m temperature in the suburban gridpoint, except summer when strong positive bias occurs. However, comparing the two experiments from the aspect of nocturnal UHI, only minor differences arose. The thorough validation underpins the applicability of SURFEX driven by ALADIN-Climate for future urban climate projections.

Evaluation of Temperature Simulation over Northern Thailand from Regional Climate Model Coupled with Land Surface Model

2017 ◽  
Vol 866 ◽  
pp. 108-111
Author(s):  
Theerapan Saesong ◽  
Pakpoom Ratjiranukool ◽  
Sujittra Ratjiranukool
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Global Analysis ◽  
Regional Climate ◽  
Land Surface Model ◽  
Analysis Data ◽  
Surface Model ◽  
Northern Thailand ◽  
Weather Model

Numerical Weather Model called The Weather Research and Forecasting model, WRF, developed by National Center for Atmospheric Research (NCAR) is adapted to be regional climate model. The model is run to perform the daily mean air surface temperatures over northern Thailand in 2010. Boundery dataset provided by National Centers for Environmental Prediction, NCEP FNL, (Final) Operational Global Analysis data which are on 10 x 10. The simulated temperatures by WRF with four land surface options, i.e., no land surface scheme (option 0), thermal diffusion (option 1), Noah land-surface (option 2) and RUC land-surface (option 3) were compared against observational data from Thai Meteorological Department (TMD). Preliminary analysis indicated WRF simulations with Noah scheme were able to reproduce the most reliable daily mean temperatures over northern Thailand.

scholarly journals Land Surface Model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v.3.9 model over North America

2020 ◽  
Author(s):  
Almudena García-García ◽  
Francisco José Cuesta-Valero ◽  
Hugo Beltrami ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
...  
Keyword(s):  
North America ◽  
Regional Climate Model ◽  
Extreme Events ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Temperature And Precipitation

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 evaluate land-atmosphere interactions within four simulations performed by the WRF model using three different LSMs from 1980 to 2012 over North America. 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. 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. 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 temperature and precipitation extreme events, which in turn will help to reduce uncertainties in climate model projections.

scholarly journals Evaluation of Snow Water Equivalent in NARCCAP Simulations, Including Measures of Observational Uncertainty

2017 ◽  
Vol 18 (9) ◽  
pp. 2425-2452 ◽  
Author(s):  
Rachel R. McCrary ◽  
Seth McGinnis ◽  
Linda O. Mearns
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Dominant Role ◽  
Regional Climate ◽  
Land Surface Model ◽  
Surface Model ◽  
Observational Uncertainty ◽  
Snow Water ◽  
Noah Land Surface Model

Abstract This study evaluates snow water equivalent (SWE) over North America in the reanalysis-driven NARCCAP regional climate model (RCM) experiments. Examination of SWE in these runs allows for the identification of bias due to RCM configuration, separate from inherited GCM bias. SWE from the models is compared to SWE from a new ensemble observational product to evaluate the RCMs’ ability to capture the magnitude, spatial distribution, duration, and timing of the snow season. This new dataset includes data from 14 different sources in five different types. Consideration of the associated uncertainty in observed SWE strongly influences the appearance of bias in RCM-generated SWE. Of the six NARCCAP RCMs, the version of MM5 run by Iowa State University (MM5I) is found to best represent SWE despite its use of the Noah land surface model. CRCM overestimates SWE because of cold temperature biases and surface temperature parameterization options, while RegCM3 (RCM3) does so because of excessive precipitation. HadRM3 (HRM3) underestimates SWE because of warm temperature biases, while in the version of WRF using the Grell scheme (WRFG) and ECPC-RSM (ECP2), the misrepresentation of snow in the Noah land surface model plays the dominant role in SWE bias, particularly in ECP2 where sublimation is too high.

scholarly journals Advancing land surface model development with satellite-based Earth observations

2017 ◽  
Vol 21 (5) ◽  
pp. 2483-2495 ◽  
Author(s):  
Rene Orth ◽  
Emanuel Dutra ◽  
Isabel F. Trigo ◽  
Gianpaolo Balsamo
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Weather Forecasting ◽  
Model Performance ◽  
Land Surface Model ◽  
Climate System ◽  
Surface Model ◽  
Earth Observations ◽  
The Impact

Abstract. The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts, we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability, and understanding of climate system feedbacks.

scholarly journals Land surface model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v3.9 model over North America

2020 ◽  
Vol 13 (11) ◽  
pp. 5345-5366
Author(s):  
Almudena García-García ◽  
Francisco José Cuesta-Valero ◽  
Hugo Beltrami ◽  
Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Extreme Temperature ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Temperature And Precipitation ◽  
Model Ensembles ◽  
Precipitation Events

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.

scholarly journals Impact of the use of a CO<sub>2</sub> responsive land surface model in simulating the effect of climate change on the hydrology of French Mediterranean basins

2011 ◽  
Vol 11 (10) ◽  
pp. 2803-2816 ◽  
Author(s):  
S. Queguiner ◽  
E. Martin ◽  
S. Lafont ◽  
J.-C. Calvet ◽  
S. Faroux ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Regional Climate ◽  
Regional Scale ◽  
Land Surface Model ◽  
Climate Scenario ◽  
Climate Impact ◽  
Surface Model ◽  
Present Climate ◽  
The Impact

Abstract. In order to evaluate the uncertainty associated with the impact model in climate change studies, a CO2 responsive version of the land surface model ISBA (ISBA-A-gs) is compared with its standard version in a climate impact assessment study. The study is performed over the French Mediterranean basin using the Safran-Isba-Modcou chain. A downscaled A2 regional climate scenario is used to force both versions of ISBA, and the results of the two land surface models are compared for the present climate and for that at the end of the century. Reasonable agreement is found between models and with discharge observations. However, ISBA-A-gs has a lower mean evapotranspiration and a higher discharge than ISBA-Standard. Results for the impact of climate change are coherent on a yearly basis for evapotranspiration, total runoff, and discharge. However, the two versions of ISBA present contrasting seasonal variations. ISBA-A-gs develops a different vegetation cycle. The growth of the vegetation begins earlier and reaches a slightly lower maximum than in the present climate. This maximum is followed by a rapid decrease in summertime. In consequence, the springtime evapotranspiration is significantly increased when compared to ISBA-Standard, while the autumn evapotranspiration is lower. On average, discharge changes are more significant at the regional scale with ISBA-A-gs.

scholarly journals Afforestation impact on soil temperature in regional climate model simulations over Europe

2021 ◽  
Author(s):  
Giannis Sofiadis ◽  
Eleni Katragkou ◽  
Edouard L. Davin ◽  
Diana Rechid ◽  
Nathalie de Noblet-Ducoudre ◽  
...  
Keyword(s):  
Soil Temperature ◽  
Land Surface ◽  
Seasonal Cycle ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Model ◽  
European Continent ◽  
Societal Relevance ◽  
Ground Heat Flux ◽  
The Impact

Abstract. In the context of the first phase of the Euro-CORDEX Flagship Plot Study (FPS) Land Use and Climate Across Scales (LUCAS), we investigate the afforestation impact on the seasonal cycle of soil temperature over the European continent with an ensemble of ten regional climate models (RCMs). For this purpose, each ensemble member performed two idealized land cover experiments in which Europe is covered either by forests or grasslands. The multi-model mean exhibits a reduction of the annual amplitude of soil temperature (AAST) over all European regions, although this not a robust feature among the models. In Mediterranean, the simulated AAST response to afforestation is between −4 K and +2 K while in Scandinavia the inter-model spread ranges from −7 K to +1 K. We then examine the role of changes in the annual amplitude of ground heat flux (AAGHF) and summer soil moisture content (SMC) in determining the effect of afforestation on AAST response. In contrast with the diverging results in AAST, all the models consistently indicate a widespread AAGHF decrease and summer SMC decline due to afforestation. The AAGHF changes effectively explain the largest part of the inter-model variance in AAST response in most regions, while the changes in summer SMC determine the sign of AAST response within a group of three simulations sharing the same land surface model. Finally, we pair FLUXNET sites to compare the simulated results with observation-based evidence of the impact of forest on soil temperature. In line with models, observations indicate a summer ground cooling in forested areas compared to open lands. The vast majority of models agree with the sign of the observed reduction in AAST, although with a large variation in the magnitude of changes. Overall, we aspire to emphasize the effects of afforestation on soil temperature profile with this study, given that changes in the seasonal cycle of soil temperature potentially perturb crucial biochemical processes. Such perturbations can be of societal relevance as afforestation is proposed as a climate change mitigation strategy.

scholarly journals Validating the water vapour content from a reanalysis product and a regional climate model over Europe based on GNSS observations

2018 ◽  
Author(s):  
Julie Berckmans ◽  
Roeland Van Malderen ◽  
Eric Pottiaux ◽  
Rosa Pacione ◽  
Rafiq Hamdi
Keyword(s):  
Regional Climate Model ◽  
Water Vapour ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Horizontal Resolution ◽  
Global Navigation Satellite Systems ◽  
Surface Model ◽  
Water Vapour Content

Abstract. The use of ground-based observations is suitable for the assessment of atmospheric water vapour in climate models. Global Navigation Satellite Systems (GNSS) provide information on the Integrated Water Vapour (IWV), at a high temporal and spatial resolution. We used IWV observations at 100 European GNSS sites to evaluate the regional climate model ALARO running at 20 km horizontal resolution and coupled to the land surface model SURFEX, driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) data. The observations recorded in the selected stations span from 1996 to 2014 (with minimum 10 years) and were homogeneously reprocessed during the second reprocessing campaign of the EUREF Permanent Network (EPN Repro2). The outcome of the reprocessing was then used to compute IWV time series at these stations. The yearly cycle of the IWV for the 19-year period from 1996 to 2014 reveals that the model simulates well the seasonal variation. Although the model overestimates IWV during winter and spring, it is consistent with the driving field of ERA-Interim. However, the agreement with ERA-Interim is less in summer, when the model demonstrates an underestimation of the IWV. The model presents a cold and dry bias in summer that feedbacks to a lower evapotranspiration and results in too few water vapour. The spatial variability among the sites is high and shows a dependence on the altitude of the stations which is strongest in summer and by ALARO-SURFEX. The IWV diurnal cycle presents best results with ERA-Interim in the morning, whereas ALARO-SURFEX presents best results at midnight.

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