scholarly journals Evaluation of the Sensitivity of the Weather Research and Forecasting Model to Parameterization Schemes for Regional Climates of Europe over the Period 1990–95

2013 ◽  
Vol 26 (3) ◽  
pp. 1002-1017 ◽  
Author(s):  
P. A. Mooney ◽  
F. J. Mulligan ◽  
R. Fealy
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Correlation Coefficients ◽  
Longwave Radiation ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Land Surface Models ◽  
Surface Models ◽  
Regional Climates ◽  
Weather Research

Abstract The Weather Research and Forecasting model (WRF) is used to downscale interim ECMWF Re-Analysis (ERA-Interim) data for the climate over Europe for the period 1990–95 with grid spacing of 0.44° for 12 combinations of physical parameterizations. Two longwave radiation schemes, two land surface models (LSMs), two microphysics schemes, and two planetary boundary layer (PBL) schemes have been investigated while the remaining physics schemes were unchanged. WRF simulations are compared with Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) observations gridded dataset (E-OBS) for surface air temperatures (T2), precipitation, and mean sea level pressure (MSLP) in eight subregions within the model domain to assess the performance of the different parameterizations on widely varying regional climates. This work shows that T2 is modeled well by WRF with high correlation coefficients (0.8 < R < 0.95) and biases less than 4°C. T2 shows greatest sensitivity to land surface models, some sensitivity to longwave radiation schemes, and less sensitivity to microphysics and PBL schemes. Precipitation is not well modeled by WRF with low correlation coefficients (0.1 < R < 0.3) and high root-mean-square differences (RMSDs; 8–9 mm day−1). Precipitation shows sensitivity to LSMs in summer. No significant bias has been observed in the MSLP modeled by WRF. Correlation coefficients are typically in the range 0.7 < R < 0.8 while RMSDs are in the range 6–10 hPa. MSLP output is sensitive to longwave radiation scheme in summer but is relatively insensitive to either microphysics or the choice of LSM. The optimum combination of parameterizations for all three state variables examined is strongly dependent on subregion and demonstrates the need to carefully select parameterization combinations when attempting to use WRF as a regional climate model.

scholarly journals Impact of Scale and Aggregation on the Terrestrial Water Exchange: Integrating Land Surface Models and Rhône Catchment Observations

2007 ◽  
Vol 8 (5) ◽  
pp. 1002-1015 ◽  
Author(s):  
Reto Stöckli ◽  
Pier Luigi Vidale ◽  
Aaron Boone ◽  
Christoph Schär
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Climate Model ◽  
Good Representation ◽  
Subgrid Scale ◽  
Land Surface Models ◽  
Catchment Scale ◽  
Surface Models ◽  
Terrestrial Water

Abstract Land surface models (LSMs) used in climate modeling include detailed above-ground biophysics but usually lack a good representation of runoff. Both processes are closely linked through soil moisture. Soil moisture however has a high spatial variability that is unresolved at climate model grid scales. Physically based vertical and horizontal aggregation methods exist to account for this scaling problem. Effects of scaling and aggregation have been evaluated in this study by performing catchment-scale LSM simulations for the Rhône catchment. It is found that evapotranspiration is not sensitive to soil moisture over the Rhône but it largely controls total runoff as a residual of the terrestrial water balance. Runoff magnitude is better simulated when the vertical soil moisture fluxes are resolved at a finer vertical resolution. The use of subgrid-scale topography significantly improves both the timing of runoff on the daily time scale (response to rainfall events) and the magnitude of summer baseflow (from seasonal groundwater recharge). Explicitly accounting for soil moisture as a subgrid-scale process in LSMs allows one to better resolve the seasonal course of the terrestrial water storage and makes runoff insensitive to the used grid scale. However, scale dependency of runoff to above-ground hydrology cannot be ignored: snowmelt runoff from the Alpine part of the Rhône is sensitive to the spatial resolution of the snow scheme, and autumnal runoff from the Mediterranean part of the Rhône is sensitive to the spatial resolution of precipitation.

Longwave Radiation at the Cloud-Aerosol Transition Zone from Radiative Parameterizations in Weather Research and Forecasting Model (WRF)

2020 ◽  
Author(s):  
Babak Jahani ◽  
Josep Calbó ◽  
Josep-Abel González
Keyword(s):  
Transition Zone ◽  
Atmospheric Aerosol ◽  
Longwave Radiation ◽  
Suspended Particles ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Liquid Droplets ◽  
Mixing Ratios ◽  
Free Air ◽  
Weather Research

<p>There are conditions between cloudy and cloud-free air at which it is hard to define the suspended particles in the atmosphere either as a cloud or an atmospheric aerosol; it is called twilight or transition zone. This occurs when characteristics of the suspended particles are between those corresponding to a pure cloud and those corresponding to a pure atmospheric aerosol. However, in most meteorological and climate studies the condition of sky is assumed to be either cloudy (fully developed cloud) or cloud-free (dry aerosol), neglecting the transition zone. The present communication aims to show the uncertainties introduced by this simplified assumption in modeling longwave radiation. For this purpose, the parameterizations RRTMG, NewGoddard and FLG included in the Weather Research and Forecasting Model (WRF) version 4.0 were isolated from the whole model. These parameterizations were then used to perform a number of simulations under ideal “cloud” and “aerosol” modes, for different values of (i) cloud optical thicknesses resulting from different sizes of ice crystals or liquid droplets, cloud height, mixing ratios; and (ii) different aerosol optical thicknesses combined with various aerosol types. The differences in the resulting longwave radiative effects (RE) at the top of the atmosphere and at the Earth surface were analyzed. The primary results show: (1) the parameterization RRTMG is not capable of simulating the REs of the aerosols in the longwave region, (2) different assumptions of a situation corresponding to the transition zone lead to a mean relative uncertainty of about 170% in the estimated longwave irradiance at both top of the atmosphere and surface, (3) the absolute uncertainties observed in the surface downwelling irradiances are substantially greater than those relating to the upwelling irradiances at top of the atmosphere.</p>

scholarly journals Comparisons of WRF/Chem simulations in Mexico City with ground-based RAMA measurements during the MILAGRO-2006 period

2009 ◽  
Vol 9 (1) ◽  
pp. 1329-1376 ◽  
Author(s):  
Y. Zhang ◽  
M. K. Dubey ◽  
S. C. Olsen
Keyword(s):  
Relative Humidity ◽  
Land Surface ◽  
Surface Wind ◽  
Chemical Species ◽  
Correlation Coefficients ◽  
Monitoring Network ◽  
Weather Research And Forecasting ◽  
Wind Speeds ◽  
Surface Models ◽  
Pbl Scheme

Abstract. Comparison of the WRF/Chem (Weather Research and Forecasting – Chemistry) model simulations at 3-km resolution with measurements from the ground-based RAMA monitoring network during the MCMA-2006/MILAGRO field campaign is presented. The model resolves reasonably well the observed surface temperature, relative humidity and wind speed; however, large discrepancies are identified between the simulated and the observed surface wind direction for wind speeds below 2 m s−1. The simulated chemical species concentrations (CO, O3, NO, NO2 and NOy) compare favorably with the observations with the notable exception of SO2. Simulated O3 concentrations agree especially well with the observations. The model performs much better during daytime than nighttime for both chemical species and meteorological variables, although the model tends to underestimate daytime temperature and overestimate nighttime relative humidity. It is noted that the simulated nocturnal planetary boundary layer (PBL) height using the Yonsei University PBL scheme is unrealistically low. However, no combination of the available PBL schemes and land surface models (LSMs) is distinctly better than the others in reproducing the observations. The simulated meteorological fields under the O3-South, O3-North and EI Norte weather episodes exhibit similar correlation coefficients and biases for the same variable. However, the model performs best for the O3-South episode and performs poorest for the El Norte events in resolving the observed chemical species.

Applying Scaled Vegetation Greenness Metrics to Constrain Simulated Transpiration Anomalies: A Study over Australia*

2013 ◽  
Vol 15 (4) ◽  
pp. 1607-1623 ◽  
Author(s):  
Mark Decker ◽  
Andy J. Pitman ◽  
Jason Evans
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Land Surface Models ◽  
Model Simulations ◽  
Vegetation Greenness ◽  
Surface Models ◽  
The Mean ◽  
Climate Model Simulations

Abstract The feasibility of using vegetation greenness metrics as a proxy for transpiration variability over Australia is demonstrated. Several global evapotranspiration datasets, one of which provides transpiration data and is constructed independently of the vegetation greenness measurements, are compared to four satellite-based observations representative of the state of the vegetation over several regions in Australia. Further estimates of the transpiration are obtained by decomposing the evapotranspiration datasets using an ensemble of land surface model simulations. On monthly time scales, the greenness anomaly metrics show a near one-to-one relationship with the transpiration estimates when the time series are appropriately scaled by the mean. The authors demonstrate that anomalous vegetation greenness metrics, when properly scaled, provide a tool for evaluating transpiration variability simulated by land surface models and observation-based evapotranspiration datasets that include transpiration. These methods provide a new test to help constrain the dynamic behavior of the land surface in climate model simulations.

scholarly journals Development of “Physical Parametrizations with PYthon” (PPPY, version 1.1) and its usage to reduce the time-step dependency in a microphysical scheme

2020 ◽  
Vol 13 (2) ◽  
pp. 443-460
Author(s):  
Sébastien Riette
Keyword(s):  
Climate Model ◽  
Mesoscale Model ◽  
Weather Research And Forecasting ◽  
Small Scale ◽  
Forecasting Model ◽  
Time Step ◽  
Numerical Weather ◽  
Numerical Weather Model ◽  
Weather Model ◽  
Weather Research

Abstract. To help develop and compare physical parametrizations such as those found in a numerical weather or climate model, a new tool was developed. This tool provides a framework with which to plug external parametrizations, run them in an offline mode (using one of the two time-advance methods available), save the results and plot diagnostics. The software can be used in a 0-D and a 1-D mode with schemes originating from various models. As for now, microphysical schemes from the Meso-NH model, the AROME (Applications of Research to Operations at Mesoscale) model and the Weather Research and Forecasting model have been successfully plugged. As an application, Physical Parametrizations with PYthon (PPPY) is used in this paper to suppress the origin of the time-step dependency of the microphysical scheme used in the Météo-France small-scale operational numerical weather model. The tool helped to identify the origin of the dependency and to check the efficiency of the introduced corrections.

scholarly journals Inter-comparison of two land-surface schemes applied on different scales and their feedbacks while coupled with a regional climate model

2011 ◽  
Vol 8 (4) ◽  
pp. 7091-7136 ◽  
Author(s):  
F. Zabel ◽  
W. Mauser ◽  
T. Marke ◽  
A. Pfeiffer ◽  
G. Zängl ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Land Surface Models ◽  
Feedback Effects ◽  
Surface Models ◽  
Vegetation Water

Abstract. Feedback effects between the land surface and the atmosphere are an important issue in modelling the climate system. Therefore, in order to take land surface heterogeneity adequately into account, a representation of the land surface in sufficient spatial resolution is necessary. In order to analyze the impact of different land surface models on the atmosphere, we analyzed the differences of two physically based land surface models, which evolved from different disciplinary backgrounds, both fully coupled with the regional climate model MM5, providing the atmospheric drivers. While the NOAH-LSM originally was developed for atmosphere applications, PROMET is primarily used as a hydrological land surface model. Both use different physical approaches and different spatial resolutions of 45 km (NOAH) and 1 km (PROMET) respectively, to represent the land surface processes. The parameterization of soil and plant properties in terms of phenological behaviour and water-stress is treated with a higher level of detail in PROMET. Used with same atmospheric drivers over a four-year period for Central Europe, the model differences have strong impacts on simulated evapotranspiration and soil moisture both spatially and temporally. Regions with high proportion of impervious surfaces show the highest differences in simulated evapotranspiration (up to 30 %). Further, PROMET simulations show lower evapotranspiration rates e.g. in the Po Valley, caused mainly by a higher level of vegetation water stress. In order to study feedback effects, PROMET was then bilaterally coupled with MM5. The feedbacks result in increasing near surface air temperature and decreasing precipitation especially in Southern Europe and are a result of regional self-amplification effects due to decreasing soil moisture and increasing vegetation water stress.

scholarly journals Results from a full coupling of the HIRHAM regional climate model and the MIKE SHE hydrological model for a Danish catchment

2014 ◽  
Vol 11 (3) ◽  
pp. 3005-3047 ◽  
Author(s):  
M. A. D. Larsen ◽  
J. C. Refsgaard ◽  
M. Drews ◽  
M. B. Butts ◽  
K. H. Jensen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Research Field ◽  
Land Surface Models ◽  
Mike She ◽  
Simulated Precipitation ◽  
Surface Models

Abstract. In recent years research on the coupling of existing regional climate models and hydrology/land surface models has emerged. A major challenge in this emerging research field is the computational interaction between the models. In this study we present results from a full two-way coupling of the HIRHAM regional climate model over a 4000 km x 2800 km domain in 11 km resolution and the combined MIKE SHE-SWET hydrology and land surface models over the 2500 km2 Skjern river catchment. A total of 26 one-year runs were performed to assess the influence of the data transfer interval (DTI) between the two models and the internal HIRHAM model variability of ten variables. In general, the coupled model simulations exhibit less accurate performance than the uncoupled simulations which is to be expected as both models prior to this study have been individually refined or calibrated to reproduce observations. Four of six output variables from HIRHAM, precipitation, relative humidity, wind speed and air temperature, showed statistically significant improvements in RMSE with a reduced DTI as evaluated in the range of 12–120 min. For these four variables the perturbation induced HIRHAM variability was shown to correspond to 47% of the RMSE improvement when using a DTI of 120 min compared to a DTI of 12 min and the variability resulted in large ranges in simulated precipitation. Also, the DTI was shown to substantially affect computation time. The MIKE SHE energy flux and discharge output variables experienced little impact from the DTI.

scholarly journals Biogeochemical model of CO<sub>2</sub> and CH<sub>4</sub> production in anoxic Arctic soil microcosms

2016 ◽  
Author(s):  
Guoping Tang ◽  
Jianqiu Zheng ◽  
Xiaofeng Xu ◽  
Ziming Yang ◽  
David E. Graham ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Co2 Production ◽  
Biogeochemical Model ◽  
Land Surface Models ◽  
Soil Redox Potential ◽  
Community Land Model ◽  
Arctic Soils ◽  
Surface Models ◽  
The Impact

Abstract. Soil organic carbon turnover to CO2 and CH4 is sensitive to soil redox potential and pH conditions. However, land surface models do not consider redox and pH in the aqueous phase explicitly, thereby limiting their use for making predictions in anoxic environments. Using recent data from incubations of Arctic soils, we extend the Community Land Model Carbon Nitrogen (CLM-CN) decomposition cascade to include simple organic substrate turnover, fermentation, Fe(III) reduction, and methanogenesis reactions, and assess the efficacy of various temperature and pH response functions. Incorporating the Windermere Humic Aqueous Model (WHAM) enables us to approximately describe the observed pH evolution without additional parameterization. Although Fe(III) reduction is normally assumed to compete with methanogenesis, the model predicts that Fe(III) reduction raises the pH from acidic to neutral, thereby reducing environmental stress to methanogens and accelerating methane production when substrates are not limiting. The equilibrium speciation predicts a substantial increase in CO2 solubility as pH increases, and taking into account CO2 adsorption to surface sites of metal oxides further decreases the predicted headspace gas-phase fraction at low pH. Without adequate representation of these speciation reactions, and the impact of pH, temperature, and pressure, CO2 production from closed microcosms can be substantially underestimated based on headspace CO2 measurements only. Our results demonstrate the efficacy of geochemical models for simulating soil biogeochemistry and provide predictive understanding and mechanistic representations that can be tested in land surface models to improve climate model predictions.

Use of Satellite-Based Precipitation Observation in Improving the Parameterization of Canopy Hydrological Processes in Land Surface Models

2005 ◽  
Vol 6 (5) ◽  
pp. 745-763 ◽  
Author(s):  
Dagang Wang ◽  
Guiling Wang ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Land Surface ◽  
Rain Rate ◽  
Climate Models ◽  
Climate Model ◽  
Model Simulation ◽  
Heat Fluxes ◽  
Land Surface Models ◽  
Canopy Interception ◽  
Surface Models ◽  
Rain Rates

Abstract Precipitation exhibits significant spatial variability at scales much smaller than the typical size of climate model grid cells. Neglecting such subgrid-scale variability in climate models causes unrealistic representation of land–atmosphere flux exchanges. It is especially problematic over densely vegetated land. This paper addresses this issue by incorporating satellite-based precipitation observations into the representation of canopy interception processes in land surface models. Rainfall data derived from passive microwave (PM) observations are used to obtain realistic estimates of 1) conditional mean rain rates, which together with the modeled rain rate are used to estimate the rainfall coverage fraction at each model grid cell in this study, and 2) the probability density function (pdf) of rain rates within the rain-covered areas. Both of these properties significantly impact the land–atmosphere water vapor exchanges. Based on the above information, a statistical–dynamical approach is taken to incorporate the representation of precipitation subgrid variability into canopy interception processes in land surface models. The results reveal that incorporation of precipitation subgrid variability significantly alters the partitioning between runoff and total evapotranspiration as well as the partitioning among the three components of evapotranspiration (i.e., canopy interception loss, ground evaporation, and plant transpiration). This further influences soil water, surface temperature, and surface heat fluxes. It is shown that the choice of the rain-rate pdf within rain-covered areas has an effect on the model simulation of land–atmosphere flux exchanges. This study demonstrates that land surface and climate models can substantially benefit from the fine-resolution remotely sensed rainfall observations.

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