scholarly journals Projected Impacts of Bioenergy-Demand-Induced Land Use and Cover Changes on Regional Climate in Central Europe

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Fang Yin ◽  
Yihui Xiong ◽  
Li Jiang ◽  
Zhiguo Pang
Keyword(s):  
Land Use ◽  
Central Europe ◽  
Regional Climate ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Bioenergy Production ◽  
Land System ◽  
Land Use Structure ◽  
Climatic Impacts ◽  
Main Input

Energy shortfalls are becoming more and more serious all over the world, and worldwide governments have tried to promote the development of biofuels in order to mitigate the climatic impacts of massive fossil fuel consumption. Since the land is the main input factor of the bioenergy production, the development of biofuels will inevitably lead to change of the land use structure and allocation and thereby affect the climate system. With Central Europe as the study area, this study explored the impacts of land use/land cover change (LUCC) on climate under the influence of demand of bioenergy production for land resources. First, the land use structure from 2010 to 2050 is simulated with the Agriculture and Land Use model in MiniCam. The result indicates that the main conversion will be mainly from grassland and forest to cropland and from cropland to grassland. Then the Dynamics of Land System model was used to spatially simulate the LUCC in the future. The impacts of LUCC on the climate were analyzed on the basis of simulation with the Weather Research and Forecasting (WRF) model. The climate change will be characterized by the increase of latent heat flux and temperature and the decrease of precipitation.

scholarly journals Relative impacts of land use and climate change on summer precipitation in the Netherlands

2016 ◽  
Vol 20 (10) ◽  
pp. 4129-4142 ◽  
Author(s):  
Emma Daniels ◽  
Geert Lenderink ◽  
Ronald Hutjes ◽  
Albert Holtslag
Keyword(s):  
Climate Change ◽  
Land Use ◽  
The Netherlands ◽  
Land Use Changes ◽  
Wrf Model ◽  
Summer Precipitation ◽  
Circulation Type ◽  
Weather Research And Forecasting ◽  
Temperature Perturbation ◽  
Change Scenario

Abstract. The effects of historic and future land use on precipitation in the Netherlands are investigated on 18 summer days with similar meteorological conditions. The days are selected with a circulation type classification and a clustering procedure to obtain a homogenous set of days that is expected to favor land impacts. Changes in precipitation are investigated in relation to the present-day climate and land use, and from the perspective of future climate and land use. To that end, the weather research and forecasting (WRF) model is used with land use maps for 1900, 2000, and 2040. In addition, a temperature perturbation of +1 °C assuming constant relative humidity is imposed as a surrogate climate change scenario. Decreases in precipitation of, respectively, 3–5 and 2–5 % are simulated following conversion of historic to present, and present to future, land use. The temperature perturbation under present land use conditions increases precipitation amounts by on average 7–8 % and amplifies precipitation intensity. However, when also considering future land use, the increase is reduced to 2–6 % on average, and no intensification of extreme precipitation is simulated. In all, the simulated effects of land use changes on precipitation in summer are smaller than the effects of climate change, but are not negligible.

scholarly journals CORDEX-WRF v1.3: development of a module for the Weather Research and Forecasting (WRF) model to support the CORDEX community

2019 ◽  
Vol 12 (3) ◽  
pp. 1029-1066 ◽  
Author(s):  
Lluís Fita ◽  
Jan Polcher ◽  
Theodore M. Giannaros ◽  
Torge Lorenz ◽  
Josipa Milovac ◽  
...  
Keyword(s):  
Regional Climate ◽  
Regional Scale ◽  
Wrf Model ◽  
Data Access ◽  
Quality Data ◽  
Weather Research And Forecasting ◽  
Time Step ◽  
Mitigation And Adaptation ◽  
Data Requirements ◽  
Weather Research

Abstract. The Coordinated Regional Climate Downscaling Experiment (CORDEX) is a scientific effort of the World Climate Research Program (WRCP) for the coordination of regional climate initiatives. In order to accept an experiment, CORDEX provides experiment guidelines, specifications of regional domains, and data access and archiving. CORDEX experiments are important to study climate at the regional scale, and at the same time, they also have a very prominent role in providing regional climate data of high quality. Data requirements are intended to cover all the possible needs of stakeholders and scientists working on climate change mitigation and adaptation policies in various scientific communities. The required data and diagnostics are grouped into different levels of frequency and priority, and some of them even have to be provided as statistics (minimum, maximum, mean) over different time periods. Most commonly, scientists need to post-process the raw output of regional climate models, since the latter was not originally designed to meet the specific CORDEX data requirements. This post-processing procedure includes the computation of diagnostics, statistics, and final homogenization of the data, which is often computationally costly and time-consuming. Therefore, the development of specialized software and/or code is required. The current paper presents the development of a specialized module (version 1.3) for the Weather Research and Forecasting (WRF) model capable of outputting the required CORDEX variables. Additional diagnostic variables not required by CORDEX, but of potential interest to the regional climate modeling community, are also included in the module. “Generic” definitions of variables are adopted in order to overcome the model and/or physics parameterization dependence of certain diagnostics and variables, thus facilitating a robust comparison among simulations. The module is computationally optimized, and the output is divided into different priority levels following CORDEX specifications (Core, Tier 1, and additional) by selecting pre-compilation flags. This implementation of the module does not add a significant extra cost when running the model; for example, the addition of the Core variables slows the model time step by less than a 5 %. The use of the module reduces the requirements of disk storage by about a 50 %. The module performs neither additional statistics over different periods of time nor homogenization of the output data.

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>

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.

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 A New Land-Use Dataset for the Weather Research and Forecasting (WRF) Model

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 350
Author(s):  
Huoqing Li ◽  
Hailiang Zhang ◽  
Ali Mamtimin ◽  
Shuiyong Fan ◽  
Chenxiang Ju
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Sensible Heat Flux ◽  
Wrf Model ◽  
Northwest China ◽  
Specific Humidity ◽  
Weather Research And Forecasting ◽  
United States Geological Survey ◽  
Parameter Values ◽  
Weather Research

The USGS (United States Geological Survey) land-use data used in the Weather Research and Forecasting (WRF) model have become obsolete as they are unable to accurately represent actual underlying surface features. Therefore, this study developed a new multi-satellite remote-sensing land-use dataset based on the latest GLC2015 (Global Land Cover, 2015) land-use data, which had 300 m spatial resolution. The new data were used to update the default USGS land-use dataset. Based on observational data from national meteorological observing stations in Xinjiang, northwest China, a comparison of the old USGS and new GLC2015 land-use datasets in the WRF model was performed for July 2018, where the simulated variables included the sensible heat flux (SHF), latent heat flux (LHF), surface skin temperature (Tsk), two-meter air temperature (T2), wind speed (Winds), specific humidity (Q2) and relative humidity (RH). The results indicated that there were significant differences between the two datasets. For example, our statistical verification results found via in situ observations made by the MET (model evaluation tools) illustrated that the bias of T2 decreased by 2.54%, the root mean square error (RMSE) decreased by 1.48%, the bias of Winds decreased by 10.46%, and the RMSE decreased by 6.77% when using the new dataset, and the new parameter values performed a net positive effect on land–atmosphere interactions. These results suggested that the GLC2015 land-use dataset developed in this study was useful in terms of improving the performance of the WRF model in the summer months.

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 Regional Climate Effects of Conversion from Grassland to Forestland in Southeastern China

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Rui Yu ◽  
Xinsheng Wang ◽  
Zhe Yan ◽  
Haiming Yan ◽  
Qunou Jiang
Keyword(s):  
Land Cover ◽  
Global Climate ◽  
Regional Climate ◽  
Wrf Model ◽  
Climatic Effects ◽  
Annual Average ◽  
Climate Effects ◽  
Global Climate Changes ◽  
Land System ◽  
Mutual Conversion

The land-use and land-cover change (LUCC) is the synthetic result of natural processes and human activities; it largely depends on the surface vegetation conditions, and the mutual conversion among land cover types can accelerate or alleviate the regional and global climate changes. Aiming at analyzing the regional climatic effects of the conversion from grassland to forestland, especially in the long term perspective, we carried out the comparison simulation using the Weather Research and Forecasting (WRF) Model in Fujian province, results indicated that this conversion had a significant influence on the regional climate; the annual average temperature decreased by 0.11°C and the annual average precipitation increased by 46 mm after 11.2% of the grassland was converted into the forestland in the study area from 2000 to 2008. In the future (form 2010 to 2050), the conversion from grassland to forestland is significant under two representative concentration pathways (RCPs) (RCP6 and RCP8.5); the spatial pattern of this conversion under the two scenarios is simulated by dynamic of land system (DLS); then, the regional climate effects of the conversion are simulated using WRF model.

scholarly journals Impact of Nudging Parameters on Dynamical Downscaling over CORDEX East Asia Phase II Domain: The Case of Summer 2003

2019 ◽  
Vol 58 (12) ◽  
pp. 2755-2771
Author(s):  
Linyun Yang ◽  
Shuyu Wang ◽  
Jianping Tang ◽  
Xiaorui Niu ◽  
Congbin Fu
Keyword(s):  
Relaxation Time ◽  
East Asia ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Wrf Model ◽  
Precipitation Event ◽  
Weather Research And Forecasting ◽  
Spectral Nudging ◽  
Thermodynamic Variables

AbstractIn this paper, the sensitivity of the Weather Research and Forecasting (WRF) Model to the nudging parameters in simulating July–August (JJA) precipitation was assessed with 16 experiments over the Coordinated Regional Climate Downscaling Experiment East Asia II (CORDEX-EA-II) domain. The effects of various nudging parameters in spectral nudging (referred to as SN) and grid nudging (referred to as AN) experiments are examined, including wavenumbers, relaxation time, nudging levels, and nudging variables for SN and relaxation time and nudging variables for AN. Results showed that the applications of spectral nudging and grid nudging methods in WRF simulations can improve the model’s ability to reproduce the JJA extreme precipitation event and accompanying large-scale fields in 2003. The major findings include 1) spectral nudging is superior to grid nudging in simulating heavy rainfall and low-level circulation, 2) nudging both kinematic and thermodynamic variables is efficient to better simulate the JJA precipitation for both SN and AN simulations, 3) in SN simulations, the options of wavenumbers display stronger impact on JJA precipitation if nudging solely the kinematic variables instead of both kinematic and thermodynamic variables over wet subregions, and 4) the free developed large-scale processes associated with small nudging wavenumbers can diminish the improvement from nudging both kinematic and thermodynamic variables in simulating subseasonal variations of precipitation. Overall, the experiment that adopts spectral nudging of both kinematic and thermodynamic variables, 1-h relaxation time, and four or eight nudging wavenumbers captures the characteristics of summer climate more reasonably.

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