scholarly journals Sensitivity of Precipitation Statistics to Resolution, Microphysics, and Convective Parameterization: A Case Study with the High-Resolution WRF Climate Model over Europe

2015 ◽  
Vol 16 (4) ◽  
pp. 1857-1872 ◽  
Author(s):  
Alexandre B. Pieri ◽  
Jost von Hardenberg ◽  
Antonio Parodi ◽  
Antonello Provenzale
Keyword(s):  
High Resolution ◽  
Seasonal Cycle ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Explicit Representation ◽  
Weather Research And Forecasting ◽  
Microphysics Scheme ◽  
The Impact

Abstract We explore the impact of different resolutions, convective closures, and microphysical parameterizations on the representation of precipitation statistics (climatology, seasonal cycle, and intense events) in 20-yr-long simulations over Europe with the regional climate Weather Research and Forecasting (WRF) Model. The simulations are forced in the period 1979–98, using as boundary conditions the ERA-Interim fields over the European region. Special attention is paid to the representation of precipitation in the Alpine area. We consider spatial resolutions ranging from 0.11° to 0.037°, allowing for an explicit representation of convection at the highest resolution. Our results show that while there is a good overall agreement between observed and modeled precipitation patterns, the model outputs display a positive precipitation bias, particularly in winter. The choice of the microphysics scheme is shown to significantly affect the statistics of intense events. High resolution and explicitly resolved convection help to considerably reduce precipitation biases in summer and the reproduction of precipitation statistics.

scholarly journals Contrasting seasonal changes in total and intense precipitation in the European Alps from 1903 to 2010

2020 ◽  
Author(s):  
Martin Ménégoz ◽  
Evgenia Valla ◽  
Nicolas C. Jourdain ◽  
Juliette Blanchet ◽  
Julien Beaumet ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Variability ◽  
Seasonal Changes ◽  
Seasonal Cycle ◽  
Climate Model ◽  
Regional Climate ◽  
Vertical Gradient ◽  
P Value ◽  
European Alps ◽  
High Elevations

Abstract. Changes of precipitation over the European Alps are investigated with the regional climate model MAR applied with a 7-km resolution over the period 1903–2010 using the reanalysis ERA-20C as forcing. A comparison with several observational datasets demonstrates that the model is able to reproduce the climatology as well as both the inter-annual variability and the seasonal cycle of precipitation over the European Alps. The relatively high resolution allows to estimate precipitation at high elevations. The vertical gradient of precipitation simulated by MAR over the European Alps reaches 33 % km−1 (1.21 mm.day−1.km−1) in summer and 38 % km−1 (1.15 mm.day−1.km−1) in winter, on average over 1971–2008 and shows a large spatial variability. A significant (p-value 

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

<p>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.</p><p>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.</p><p>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).</p><p>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.</p><p><strong>Keywords</strong>: soil moisture initial conditions, temperature extremes, regional climate, Weather Research and Forecasting model</p><p>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 Málaga, a member of the Spanish Supercomputing Network.</p>

Projected climate change over Kuwait simulated using a WRF high resolution regional climate model

2020 ◽  
Author(s):  
Hussain Alsarraf
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Regional Climate Model ◽  
Arabian Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Dynamic Downscaling ◽  
Model Simulations ◽  
Average Maximum ◽  
The Impact

<p>The purpose of this study is to examine the impact of climate change on the changes on summer surface temperatures between present (2000-2010) and future (2050-2060) over the Arabian Peninsula and Kuwait. In this study, the influence of climate change in the Arabian Peninsula and especially in Kuwait was investigated by high resolution (36, 12, and 4 km grid spacing) dynamic downscaling from the Community Climate System Model CCSM4 using the WRF Weather Research and Forecasting model. The downscaling results were first validated by comparing National Centers for Environmental Prediction NCEP model outputs with the observational data. The global climate change dynamic downscaling model was run using WRF regional climate model simulations (2000-2010) and future projections (2050-2060). The influence of climate change in the Arabian Peninsula can be projected from the differences between the two period’s model simulations. The regional model simulations of the average maximum surface temperature in summertime predicted an increase from 1◦C to 3 ◦C over the summertime in Kuwait by midcentury.</p><p><strong> </strong></p>

scholarly journals Technical challenges and solutions in representing lakes when using WRF in downscaling applications

2014 ◽  
Vol 7 (5) ◽  
pp. 7121-7150 ◽  
Author(s):  
M. S. Mallard ◽  
C. G. Nolte ◽  
T. L. Spero ◽  
O. R. Bullock ◽  
K. Alapaty ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Ice Cover ◽  
Alternative Methods ◽  
Weather Research And Forecasting ◽  
Inland Lakes ◽  
Air Temperatures

Abstract. The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional downscaled fields, inland lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for downscaling simulations are presented and contrasted.

Impact of regional climate model input and soil map resolution on projected winter wheat production in SW Germany

2020 ◽  
Author(s):  
Sebastian Gayler ◽  
Rajina Bajracharya ◽  
Tobias Weber ◽  
Thilo Streck
Keyword(s):  
High Resolution ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Grid Cell ◽  
Small Scale ◽  
Climate Projections ◽  
Soil Map ◽  
Soil Information ◽  
The Impact

<p>Agricultural ecosystem models, driven by climate projections and fed with soil information and plausible management scenarios are frequently used tools to predict future developments in agricultural landscapes. On the regional scale, the required soil parameters must be derived from soil maps that are available in different spatial resolutions, ranging from grid cell sizes of 50 m up to 1 km and more. The typical spatial resolution of regional climate projections is currently around 12 km. Given the small-scale heterogeneity in soil properties, using the most accurate soil representation could be important for predictions of crop growth. However, simulations with very highly resolved soil data requires greater computing time and higher effort for data organization and storage. Moreover, the higher resolution may not necessarily lead to better simulations due to redundant information of the land surface and because the impact of climate forcing could dominate over the effect of soil variability. This leads to the question if the use of high-resolution soil data leads to significantly different predictions of future yields and grain protein trends compared to simulations in which soil data is adapted to the resolution of the climate input.</p><p>This study investigated the impact of weather and soil input on simulated crop growth in an intensively used agricultural region in Southwest Germany. For all areas classified as ‘arable land’ (CLC10), winter wheat growth was simulated over a 44-year period (2006 to 2050) using weather projections from three regional climate models and soil information at two spatial resolutions. The simulations were performed with the model system Expert-N 5.0, where the crop model Gecros was combined with the Richards equation and the CN turnover module of the model Daisy. Soil hydraulic parameters as well as initial values of soil organic matter pools were estimated from BK50 soil map information on soil texture and soil organic matter content, using pedo-transfer functions and SOM pool fractionation following Bruun and Jensen (2002). The coarser soil map is derived from BK50 soil map (50m x 50m) by selecting only the dominant soil type in a 12km × 12km grid to be representative for that grid cell. The crop model was calibrated with field data of crop phenology, leaf area, biomass, yield and crop nitrogen, which were collected at a research station within the study area between 2009 and 2018.</p><p>The predicted increase in temperatures during the growing season correlated with earlier maturity, lower yields and a higher grain protein content. The regional mean values varied by +/- 0.5 t/ha or +/-0.3 percentage points of protein content depending to the climate model used. On the regional scale, the simulated trends remained unchanged using high-resolution or coarse resolution soil data. However, there are strong differences in both the forecasted averages and the distribution of forecasts, as the coarser resolution captures neither the small-scale heterogeneity nor the average of the high-resolution results.</p>

scholarly journals Technical challenges and solutions in representing lakes when using WRF in downscaling applications

2015 ◽  
Vol 8 (4) ◽  
pp. 1085-1096 ◽  
Author(s):  
M. S. Mallard ◽  
C. G. Nolte ◽  
T. L. Spero ◽  
O. R. Bullock ◽  
K. Alapaty ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Ice Cover ◽  
Alternative Methods ◽  
Weather Research And Forecasting ◽  
Air Temperatures ◽  
Impacts Of Climate Change

Abstract. The Weather Research and Forecasting (WRF) model is commonly used to make high-resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional downscaled fields, lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for downscaling simulations are presented and contrasted.

scholarly journals Multivariate statistical modelling of extreme coastal water levels and the effect of climate variability: a case study in the Netherlands

2020 ◽  
Author(s):  
Victor M. Santos ◽  
Mercè Casas-Prat ◽  
Benjamin Poschlod ◽  
Elisa Ragno ◽  
Bart van den Hurk ◽  
...  
Keyword(s):  
Time Series ◽  
Water Level ◽  
Climate Model ◽  
Regional Climate ◽  
Water Levels ◽  
Multivariate Statistical ◽  
Inland Water ◽  
Impact Function ◽  
The Impact

Abstract. The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a case study in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present and test a multivariate statistical framework to replicate the demonstrated dependence and the resulting return periods of inland water levels. We use the same 800-year data series to develop an impact function, which is able to empirically describe the relationship between high inland water levels (the impact) and its driving variables (precipitation and surge). In our study area, this relationship is complex because of the high degree of human management affecting the dynamics of the water level. By event sampling and conditioning the drivers, an impact function was created that can reproduce the water levels maintaining an unbiased performance at the full range of simulated water levels. The dependence structure between the driving variables is modeled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. The proposed framework is therefore able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50 year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.

scholarly journals A Sensitivity Study of High-Resolution Climate Simulations for Greece

Climate ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 44
Author(s):  
Nadia Politi ◽  
Athanasios Sfetsos ◽  
Diamando Vlachogiannis ◽  
Panagiotis T. Nastos ◽  
Stylianos Karozis
Keyword(s):  
Statistical Analysis ◽  
High Resolution ◽  
Climate Model ◽  
Sensitivity Study ◽  
Weather Research And Forecasting ◽  
Climate Simulations ◽  
One Year ◽  
The Impact ◽  
Of Greece ◽  
Selection Of

In the present study, the ability of the Advanced Weather Research and Forecasting numerical model (WRF-ARW) to perform climate regionalization studies in the topographically complex region of Greece, was examined in order to explore the possibility of a more reliable selection of physical schemes for the simulation of historical and future high resolution (5 km) climate model experiments to investigate the impact of climate change. This work is directly linked to a previous study investigating the performance of seven different model setups for one year, from which the need was derived for further examination of four different simulations to investigate the model sensitivity on the representation of surface variables statistics during a 5-year period. The results have been compared with observational data for maximum and minimum air temperature and daily precipitation through statistical analysis. Clear similarities were found in precipitation patterns among simulations and observations, yielding smoothly its inter-annual variability, especially during the wettest months and summer periods, with the lowest positive percentage BIAS calculated at about 19% for the selected combination of physics parameterizations (PP3). Regarding the maximum and minimum temperature, statistical analysis showed a high correlation above 0.9, and negative bias around 1−1.5 °C, and positive bias near 2 °C, respectively.

scholarly journals A High Resolution Coupled Ocean-atmosphere Simulation of the Regional Climate Over Central America

2021 ◽  
Author(s):  
Vasubandhu Misra ◽  
C. B. Jayasankar
Keyword(s):  
High Resolution ◽  
Central America ◽  
Seasonal Cycle ◽  
Climate Model ◽  
Regional Climate ◽  
Precipitable Water ◽  
Summer Drought ◽  
The Pacific ◽  
Ocean Atmosphere ◽  
Global Reanalysis

Abstract This study analyzes a relatively high resolution (15km grid spacing), regional coupled ocean-atmosphere simulation configured over Central America. The simulation is forced with global atmospheric and oceanic reanalysis for a period of 25 years (1986-2010). The spatial resolution and the time period of the Regional Climate Model (RCM) simulation are both unprecedented for the region. The highlights of the RCM simulation include the verifiable seasonal cycle of mesoscale features like the low level jets, the mid-summer drought and the seasonal tropical cyclone activity both in the Pacific and in the Atlantic Oceans. Similarly, the seasonal cycle of the robust surface ocean currents in the eastern Pacific and the Costa Rica Dome is also well captured in the RCM simulation. The RCM simulation also resolves the seasonal cycle of the Panama-Colombia Gyre, the Gulf of Papagayo and the Gulf of Tehuantepec Gyre. In many instances we find the RCM improves upon the global reanalysis forcing the simulation, indicating the potential value of dynamic downscaling. Furthermore, the co-evolving components of the atmosphere and ocean in the RCM is an added benefit to the atmosphere only and ocean only global reanalysis forcing the simulation. However, the model displays significant biases that manifest in precipitation, precipitable water, SST and winds which could potentially be improved.

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