scholarly journals Sensitivity of physical parameterization schemes in WRF model for dynamic downscaling of climatic variables over the MRB

2020 ◽  
Author(s):  
Lia Pervin ◽  
Thian Yew Gan
Keyword(s):  
Land Surface ◽  
Reference Data ◽  
Climate Modeling ◽  
Regional Climate ◽  
Wrf Model ◽  
Climatic Variables ◽  
Surface Physics ◽  
Dynamic Downscaling ◽  
Radiation Physics ◽  
Mountainous Regions

Abstract The Weather Research and Forecasting (WRF) model was tested through 18 different combinations of physics parameters to simulate the regional climate over the Mackenzie River Basin (MRB). The objective was to investigate the response to the physics parameters for dynamic downscaling of climatic variables. The rainfall, temperature, albedo, and surface pressure from the 18 different WRF setups were compared with the reference data and were found sensitive to land surface physics and microphysics and to the radiation physics. The combination of Noah Land Surface Physics with the WRF Single-moment 6-class microphysics and CAM shortwave and longwave schemes produced comparable results for summer 2009. This WRF setup was further tested for summers 1979–1991 and it was found that WRF could simulate air temperature more accurately than the rainfall, since the rainfall over the mountainous regions was over-simulated. Then the selected combinations of WRF parameterizations were used to downscale the CanESM2 historical temperature and rainfall for summers 1979–2005, which showed good agreement with the reference data. The suggested WRF parameters from this study could be utilized for regional climate modeling of MRB.

Modeling the Influence of Upstream Land-atmosphere Coupling on the 2017 Persistent Drought over Northeast China

2021 ◽  
pp. 1-62
Author(s):  
Dingwen Zeng ◽  
Xing Yuan
Keyword(s):  
Land Surface ◽  
Northeast China ◽  
Large Scale ◽  
Climate Modeling ◽  
Regional Climate ◽  
Critical Role ◽  
Wrf Model ◽  
Regional Climate Modeling ◽  
Anticyclonic Circulation ◽  
Persistent Drought

AbstractPersistent drought events that cause serious damages to economy and environment are usually intensified by the feedback between land surface and atmosphere. Therefore, reasonably modeling land-atmosphere coupling is critical for skillful prediction of persistent droughts. However, most high-resolution regional climate modeling focused on the amplification effect of land-atmosphere coupling on local anticyclonic circulation anomaly, while less attention was paid to the non-local influence through altering large-scale atmospheric circulation. Here we investigate how the antecedent land-atmosphere coupling over the area south to Lake Baikal (ASLB) influences the drought events occurred over its downstream region (Northeast China; NEC) by using Weather Research and Forecasting (WRF) model and linear baroclinic model (LBM). When the ASLB is artificially forced to be wet in the WRF simulations during March-May, the surface sensible heating is weakened and results in a cooling anomaly in low level atmosphere during May-July. Consequently, the anticyclonic circulation anomalies over ASLB and NEC are weakened, and the severity of NEC drought during May-July cannot be captured due to the upstream wetting in March-May. In the LBM experiments, idealized atmospheric heating anomaly that mimics the diabatic heating associated with surface wetness is imposed over ASLB, and the quasi-steady response pattern of 500-hPa geopotential height to the upstream wetting is highly consistent with that in the WRF simulation. In addition, the lower level heating instead of the upper level cooling makes a major contribution to the high pressure anomaly over NEC. This study implies the critical role of modeling upstream land-atmosphere coupling in capturing downstream persistent droughts.

Hydrological evaluation of high-resolution precipitation estimates from the WRF model in the Third Pole river basins

2021 ◽  
Author(s):  
He Sun ◽  
Fengge Su ◽  
Zhihua He ◽  
Tinghai Ou ◽  
Deliang Chen ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Infiltration Capacity ◽  
Flood Prediction ◽  
Daily Streamflow ◽  
Precipitation Estimates

AbstractIn this study, two sets of precipitation estimates based on the regional Weather Research and Forecasting model (WRF) –the high Asia refined analysis (HAR) and outputs with a 9 km resolution from WRF (WRF-9km) are evaluated at both basin and point scales, and their potential hydrological utilities are investigated by driving the Variable Infiltration Capacity (VIC) large-scale land surface hydrological model in seven Third Pole (TP) basins. The regional climate model (RCM) tends to overestimate the gauge-based estimates by 20–95% in annual means among the selected basins. Relative to the gauge observations, the RCM precipitation estimates can accurately detect daily precipitation events of varying intensities (with absolute bias < 3 mm). The WRF-9km exhibits a high potential for hydrological application in the monsoon-dominated basins in the southeastern TP (with NSE of 0.7–0.9 and bias of -11% to 3%), while the HAR performs well in the upper Indus (UI) and upper Brahmaputra (UB) basins (with NSE of 0.6 and bias of -15% to -9%). Both the RCM precipitation estimates can accurately capture the magnitudes of low and moderate daily streamflow, but show limited capabilities in flood prediction in most of the TP basins. This study provides a comprehensive evaluation of the strength and limitation of RCMs precipitation in hydrological modeling in the TP with complex terrains and sparse gauge observations.

Configuration and Evaluation of the WRF Model for the Study of Hawaiian Regional Climate

2012 ◽  
Vol 140 (10) ◽  
pp. 3259-3277 ◽  
Author(s):  
Chunxi Zhang ◽  
Yuqing Wang ◽  
Axel Lauer ◽  
Kevin Hamilton
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Model Simulation ◽  
Regional Climate ◽  
Hawaiian Islands ◽  
Wrf Model ◽  
Trade Wind ◽  
Preliminary Evaluation ◽  
The Mean

Abstract The Weather Research and Forecasting (WRF) model V3.3 has been configured for the Hawaiian Islands as a regional climate model for the region (HRCM). This paper documents the model configuration and presents a preliminary evaluation based on a continuous 1-yr simulation forced by observed boundary conditions with 3-km horizontal grid spacing in the inner nested domain. The simulated vertical structure of the temperature and humidity are compared with twice-daily radiosonde observations at two stations. Generally the trade wind inversion (TWI) height and occurrence days are well represented. The simulation over the islands is compared with observations from nine surface climatological stations and a dense network of precipitation stations. The model simulation has generally small biases in the simulated surface temperature, relative humidity, and wind speed. The model realistically simulated the magnitude and geographical distribution of the mean rainfall over the Hawaiian Islands. In addition, the model simulation reproduced reasonably well the individual heavy rainfall events as seen from the time series of pentad mean rainfall averaged over island scales. Also the model reproduced the geographical variation of the mean diurnal rainfall cycle even though the observed diurnal cycle displays quite different features over different islands. Comparison with results obtained using the land surface dataset from the official release of the WRF model confirmed that the newly implemented land surface dataset generally improved the simulation of surface variables. These results demonstrate that the WRF can be a useful tool for dynamical downscaling of regional climate over the Hawaiian Islands.

scholarly journals A Regional Climate Simulation Study Using WRF-ARW Model over Europe and Evaluation for Extreme Temperature Weather Events

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Hari Prasad Dasari ◽  
Rui Salgado ◽  
Joao Perdigao ◽  
Venkata Srinivas Challa
Keyword(s):  
Land Surface ◽  
Heat Waves ◽  
Regional Climate ◽  
Extreme Temperature ◽  
Wrf Model ◽  
Initial Boundary ◽  
Analysis Data ◽  
Climate Simulation ◽  
Cold Wave ◽  
Local Climate

In this study regional climate simulations of Europe over the 60-year period (1950–2010) made using a 25 km resolution WRF model with NCEP 2.5 degree analysis for initial/boundary conditions are presented for air temperature and extreme events of heat and cold waves. The E-OBS 25 km analysis data sets are used for model validation. Results suggest that WRF could simulate the temperature trends (mean, maximum, minimum, seasonal maximum, and minimum) over most parts of Europe except over Iberian Peninsula, Mediterranean, and coastal regions. Model could simulate the slight fall of temperatures from 1950 to 1970 as well as steady rise in temperatures from 1970 to 2010 over Europe. Simulations show occurrence of about 80% of the total heat waves in the period 1970–2010 with maximum number of heat/cold wave episodes over Eastern and Central Europe in good agreement with observations. Relatively poor correlations and high bias are found for heat/cold wave episodes over the complex topographic areas of Iberia and Mediterranean regions where land surface processes play important role in local climate. The poor simulation of temperatures over the above regions could be due to deficiencies in representation of topography and surface physics which need further sensitivity studies.

scholarly journals Effects of Land Surface Schemes on WRF-Simulated Geopotential Heights over China in Summer 2003*

2016 ◽  
Vol 17 (3) ◽  
pp. 829-851 ◽  
Author(s):  
Xin-Min Zeng ◽  
B. Wang ◽  
Y. Zhang ◽  
Y. Zheng ◽  
N. Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Regional Climate ◽  
Sensible Heat Flux ◽  
Wrf Model ◽  
Surface Fluxes ◽  
Forecast Errors ◽  
Large Area ◽  
Pressure Fields ◽  
Temperature And Pressure

Abstract To quantify and explain effects of different land surface schemes (LSSs) on simulated geopotential height (GPH) fields, we performed simulations over China for the summer of 2003 using 12-member ensembles with the Weather Research and Forecasting (WRF) Model, version 3. The results show that while the model can generally simulate the seasonal and monthly mean GPH patterns, the effects of the LSS choice on simulated GPH fields are substantial, with the LSS-induced differences exceeding 10 gpm over a large area (especially the northwest) of China, which is very large compared with climate anomalies and forecast errors. In terms of the assessment measures for the four LSS ensembles [namely, the five-layer thermal diffusion scheme (SLAB), the Noah LSS (NOAH), the Rapid Update Cycle LSS (RUC), and the Pleim–Xiu LSS (PLEX)] in the WRF, the PLEX ensemble is the best, followed by the NOAH, RUC, and SLAB ensembles. The sensitivity of the simulated 850-hPa GPH is more significant than that of the 500-hPa GPH, with the 500-hPa GPH difference fields generally characterized by two large areas with opposite signs due to the smoothly varying nature of GPHs. LSS-induced GPH sensitivity is found to be higher than the GPH sensitivity induced by atmospheric boundary layer schemes. Moreover, theoretical analyses show that the LSS-induced GPH sensitivity is mainly caused by changes in surface fluxes (in particular, sensible heat flux), which further modify atmospheric temperature and pressure fields. The temperature and pressure fields generally have opposite contributions to changes in the GPH. This study emphasizes the importance of choosing and improving LSSs for simulating seasonal and monthly GPHs using regional climate models.

Impact of Soil Moisture Initialization on Temperature Extreme Detection in the context of Regional Climate Modeling

2020 ◽  
Author(s):  
Matilde García-Valdecasas Ojeda ◽  
Juan José Rosa-Cánovas ◽  
Emilio Romero-Jiménez ◽  
Patricio Yeste ◽  
Sonia R. Gámiz-Fortis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Climate Modeling ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Modeling ◽  
Weather Research And Forecasting ◽  
Temperature Extremes ◽  
Temperature Extreme ◽  
The Impact

&lt;p&gt;Land surface-related processes play an essential role in the climate conditions at a regional scale. In this study, the impact of soil moisture (SM) initialization on regional climate modeling has been explored by using a dynamical downscaling experiment. To this end, the Weather Research and Forecasting (WRF) model was used to generate a set of high-resolution climate simulations driven by the ERA-Interim reanalysis for a period from 1989 to 2009. As the spatial configuration, two one-way nested domains were used, with the finer domain being centered over the Iberian Peninsula (IP) at a spatial resolution of about 10 km, and nested over a coarser domain that covers the Euro-CORDEX region at 50 km of spatial resolution.&lt;/p&gt;&lt;p&gt;The sensitivity experiment consisted of two control runs (CTRL) performed using as SM initial conditions those provided by ERA-Interim, and initialized for two different dates times (January and June). Additionally, another set of runs was completed driven by the same climate data but using as initial conditions prescribed SM under wet and dry scenarios.&lt;/p&gt;&lt;p&gt;The study is based on assessing the WRF performance by comparing the CTRL simulations with those performed with the different prescribed SM, and also, comparing them with the observations from the Spanish Temperature At Daily scale (STEAD) dataset. In this sense, we used two temperature extreme indices within the framework of decadal predictions: the warm spell index (WSDI) and the daily temperature range (DTR).&lt;/p&gt;&lt;p&gt;These results provide valuable information about the impact of the SM initial conditions on the ability of the WRF model to detect temperature extremes, and how long these affect the regional climate in this region. Additionally, these results may provide a source of knowledge about the mechanisms involved in the occurrence of extreme events such as heatwaves, which are expected to increase in frequency, duration, and magnitude under the context of climate change.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: soil moisture initial conditions, temperature extremes, regional climate, Weather Research and Forecasting model&lt;/p&gt;&lt;p&gt;Acknowledgments: This work has been financed by the project CGL2017-89836-R (MINECO-Spain, FEDER). The WRF simulations were performed in the Picasso Supercomputer at the University of M&amp;#225;laga, a member of the Spanish Supercomputing Network.&lt;/p&gt;

scholarly journals Examining Interior Grid Nudging Techniques Using Two-Way Nesting in the WRF Model for Regional Climate Modeling

2012 ◽  
Vol 25 (8) ◽  
pp. 2805-2823 ◽  
Author(s):  
Jared H. Bowden ◽  
Tanya L. Otte ◽  
Christopher G. Nolte ◽  
Martin J. Otte
Keyword(s):  
Climate Modeling ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Modeling ◽  
Atmospheric Model ◽  
Meridional Wind ◽  
Department Of Energy ◽  
Monthly Precipitation ◽  
Spectral Nudging ◽  
Regional Reanalysis

Abstract This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R-2) data are downscaled to 36 km × 36 km by nudging only at the lateral boundaries, using gridpoint (i.e., analysis) nudging and using spectral nudging. Seven annual simulations are conducted and evaluated for 1988 by comparing 2-m temperature, precipitation, 500-hPa geopotential height, and 850-hPa meridional wind to the 32-km North American Regional Reanalysis (NARR). Using interior nudging reduces the mean biases for those fields throughout the CONUS compared to the simulation without interior nudging. The predictions of 2-m temperature and fields aloft behave similarly when either analysis or spectral nudging is used. For precipitation, however, analysis nudging generates monthly precipitation totals, and intensity and frequency of precipitation that are closer to observed fields than spectral nudging. The spectrum of 250-hPa zonal winds simulated by the WRF model is also compared to that of the R-2 and NARR. The spatial variability in the WRF model is reduced by using either form of interior nudging, and analysis nudging suppresses that variability more strongly than spectral nudging. Reducing the nudging strengths on the inner domain increases the variability but generates larger biases. The results support the use of interior nudging on both domains of a two-way nest to reduce error when the inner nest is not otherwise dominated by the lateral boundary forcing. Nevertheless, additional research is required to optimize the balance between accuracy and variability in choosing a nudging strategy.

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 Regional Climate Variability Responses to Future Land Surface Forcing in the Brazilian Amazon

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Tao Zhang ◽  
Jinyan Zhan ◽  
Feng Wu ◽  
Jiao Luo ◽  
Juan Huang
Keyword(s):  
Heat Flux ◽  
Surface Temperature ◽  
Land Surface ◽  
Regional Climate ◽  
Sensible Heat Flux ◽  
Wrf Model ◽  
Brazilian Amazon ◽  
Close Link ◽  
Study Results ◽  
Surface Changes

Tropical deforestation could destabilize regional climate changes. This paper aimed to model the potential climatological variability caused by future forest vulnerability in the Brazilian Amazon over the 21th century. The underlying land surface changes between 2005 and 2100 are first projected based on the respectable output produced by Hurtt et al. Then the weather research and forecasting (WRF) model is applied to assess the impacts of future deforestation on regional climate during 2090–2100. The study results show that the forests in the Brazilian Amazon will primarily be converted into dryland cropland and pasture in the northwest part and into cropland/woodland mosaic in the southeast part, with 5.12% and 13.11%, respectively. These land surface changes will therefore lead to the significant reduction of the sum of sensible heat flux and latent heat flux and precipitation and the increase of the surface temperature. Furthermore, the variability of surface temperature is observed with close link to the deforested areas.

Detecting evapotranspiration biases in reanalyses and regional climate modeling

2021 ◽  
Author(s):  
Jingyu Dan ◽  
Yanhong Gao
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Water Cycle ◽  
Climate Modeling ◽  
Global Climate ◽  
Monsoon Season ◽  
Regional Climate ◽  
Horizontal Resolution ◽  
The Tibetan Plateau ◽  
Free Atmosphere

&lt;p&gt;As the highest plateau in the world, the Tibetan Plateau (TP) exerts great impacts on regional and global climate and water cycle through interactions between land and free atmosphere. Terrestrial evapotranspiration is a critical component of the Earth's water cycle. To better understand the heterogeneity of the evapotranspiration over the Tibetan Plateau and its influences, we conducted a whole year dynamical downscale modelling (DDM) with the horizontal resolution at 28km and a convection permitting modelling (CPM) at 4km for 2014. DDM and CPM simulation results are compared with an satellite retrieving dataset, which is referred as OBS in the following, the global land surface data assimilation system (GLDAS) and two commonly used reanalyses ERA-Interim and ERA5, as well. The annual and seasonal means and seasonal variabilities are inter-compared. The evapotranspiration over ten dominant land use types are investigated based on six datasets. Differences with the satellite dataset are illustrated and relationships with soil moisture and temperature, precipitation and radiation are explored. The followings are obtained. GLDAS generally reproduces magnitude and pattern of the OBS; reanalyses overestimate, DDM and CPM underestimate compared to the OBS and GLDAS.&lt;/p&gt;&lt;p&gt;The overestimations in reanalyses occur in the monsoon season and the underestimations in DDM and CPM occur in the non-monsoon season. Large evapotranspiration biases exist over the vegetated ground which exert large impacts on the TP-average biases for growing season.&lt;/p&gt;

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