scholarly journals Evaluation of coupled regional climate models in representing the local biophysical effects of afforestation over continental China

2021 ◽  
pp. 1-62
Author(s):  
Jun Ge ◽  
Bo Qiu ◽  
Bowen Chu ◽  
Duzitian Li ◽  
Lingling Jiang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Climate Models ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Heat Fluxes ◽  
Test Bed ◽  
Area Index ◽  
Induced Changes ◽  
Biophysical Effects

AbstractRegional climate models have been widely used to examine the biophysical effects of afforestation, but their performances in this respect have rarely been evaluated. To fill this knowledge gap, an evaluation method based on the “space for time” strategy is proposed here. Using this method, we validate the performances of three regional models, the Regional Climate Model (RegCM), Weather Research and Forecasting (WRF) model and the WRF model run at a convection-permitting resolution (WRF-CP), in representing the local biophysical effects of afforestation over continental China against satellite observations. The results show that WRF and WRF-CP can not accurately describe afforestation-induced changes in surface biophysical properties, e.g. albedo or leaf area index. Second, all models exhibit poor simulations of afforestation-induced changes in latent and sensible heat fluxes. In particular, the observed increase in the summer latent heat due to afforestation is substantially underestimated by all models. Third, the models are basically reasonable in representing the biophysical impact of afforestation on temperature. The cooling of the daily mean surface temperature and 2-meter temperature in summer are reproduced well. Nevertheless, the mechanism driving the cooling effect may be improperly represented by the models. Moreover, the models perform relatively poorly in representing the response of the daily minimum surface temperature to afforestation. This highlights the necessity of evaluating the representation of the biophysical effects by a model before the model is employed to carry out afforestation experiments. This study serves as a test bed for validating regional model performance in this respect.

scholarly journals Effects of Forest Changes on Summer Surface Temperature in Changbai Mountain, China

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1551
Author(s):  
Jiaqi Zhang ◽  
Xiangjin Shen ◽  
Yanji Wang ◽  
Ming Jiang ◽  
Xianguo Lu
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface ◽  
Climate Models ◽  
Regional Climate ◽  
Changbai Mountain ◽  
Vegetation Coverage ◽  
Area Index ◽  
Forest Coverage ◽  
Cooling Effects

The area and vegetation coverage of forests in Changbai Mountain of China have changed significantly during the past decades. Understanding the effects of forests and forest coverage change on regional climate is important for predicting climate change in Changbai Mountain. Based on the satellite-derived land surface temperature (LST), albedo, evapotranspiration, leaf area index, and land-use data, this study analyzed the influences of forests and forest coverage changes on summer LST in Changbai Mountain. Results showed that the area and vegetation coverage of forests increased in Changbai Mountain from 2003 to 2017. Compared with open land, forests could decrease the summer daytime LST (LSTD) and nighttime LST (LSTN) by 1.10 °C and 0.07 °C, respectively. The increase in forest coverage could decrease the summer LSTD and LSTN by 0.66 °C and 0.04 °C, respectively. The forests and increasing forest coverage had cooling effects on summer temperature, mainly by decreasing daytime temperature in Changbai Mountain. The daytime cooling effect is mainly related to the increased latent heat flux caused by increasing evapotranspiration. Our results suggest that the effects of forest coverage change on climate should be considered in climate models for accurately simulating regional climate change in Changbai Mountain of China.

The Mesoscale Response to Global Warming over the Pacific Northwest Evaluated Using a Regional Climate Model Ensemble

2021 ◽  
pp. 1-56
Keyword(s):  
Global Warming ◽  
Pacific Northwest ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Winter Precipitation ◽  
Historical Period ◽  
Continental Interior

This paper describes the downscaling of an ensemble of twelve GCMs using the WRF model at 12-km grid spacing over the period 1970-2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP 8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. April 1 snowpack declines are large over the lower to middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCM’s producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the 21st century.

scholarly journals Assessment of Bias Assumptions for Climate Models

2014 ◽  
Vol 27 (17) ◽  
pp. 6799-6818 ◽  
Author(s):  
Christian Kerkhoff ◽  
Hans R. Künsch ◽  
Christoph Schär
Keyword(s):  
Surface Temperature ◽  
Climate Models ◽  
Climate Model ◽  
Spatial Scales ◽  
Regional Climate ◽  
Lower Troposphere ◽  
Regional Climate Models ◽  
Added Value ◽  
Model Biases ◽  
Interaction Component

Abstract Climate scenarios make implicit or explicit assumptions about the extrapolation of climate model biases from current to future time periods. Such assumptions are inevitable because of the lack of future observations. This manuscript reviews different bias assumptions found in the literature and provides measures to assess their validity. The authors explicitly separate climate change from multidecadal variability to systematically analyze climate model biases in seasonal and regional surface temperature averages, using global and regional climate models (GCMs and RCMs) from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project over Europe. For centennial time scales, it is found that a linear bias extrapolation for GCMs is best supported by the analysis: that is, it is generally not correct to assume that model biases are independent of the climate state. Results also show that RCMs behave markedly differently when forced with different drivers. RCM and GCM biases are not additive, and there is a significant interaction component in the bias of the RCM–GCM model chain that depends on both the RCM and GCM considered. This result questions previous studies that deduce biases (and ultimately projections) in RCM–GCM combinations from reanalysis-driven simulations. The authors suggest that the aforementioned interaction component derives from the refined RCM representation of dynamical and physical processes in the lower troposphere, which may nonlinearly depend upon the larger-scale circulation stemming from the driving GCM. The authors’ analyses also show that RCMs provide added value and that the combined RCM–GCM approach yields, in general, smaller biases in seasonal surface temperature and interannual variability, particularly in summer and even for spatial scales that are, in principle, well resolved by the GCMs.

scholarly journals Lake and climate models linkage: a 3-D hydrodynamic contribution

2005 ◽  
Vol 4 ◽  
pp. 57-62 ◽  
Author(s):  
L. F. Leon ◽  
D. Lam ◽  
W. Schertzer ◽  
D. Swayne
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Heat Fluxes ◽  
Large Lakes ◽  
Atmospheric Sciences ◽  
Lake Model ◽  
Realistic Representation ◽  
Heat Exchanges

Abstract. Under a Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) project, targeted to study the feasibility to link regional climate models with lake models, one of the tasks was to consider such a coupling in large lakes. The objective is to provide detailed information on temperature and circulation distributions of the lake to take into account the spatial variability for temperature and the heat exchange through the water's surface. The major contribution of this work is focused on realistic representation of the heat fluxes and temperature distributions to and from lakes especially during the thermally stratified ice-free periods. This paper presents the detailed 3-D ELCOM model applied in Lake Erie in order to produce, at the surface layer of the lake, the spatial distribution of temperature and heat exchanges that eventually can be coupled with a regional climate model (CRCM). Preliminary results will be presented on how this lake model may improve the regional climate models, which currently do not consider such large lake circulation effects.

scholarly journals A Maieutic Exploration of Nudging Strategies for Regional Climate Applications Using the WRF Model

2018 ◽  
Vol 57 (8) ◽  
pp. 1883-1906 ◽  
Author(s):  
Tanya L. Spero ◽  
Christopher G. Nolte ◽  
Megan S. Mallard ◽  
Jared H. Bowden
Keyword(s):  
United States ◽  
Climate Models ◽  
Weather Prediction ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
The United States ◽  
Spectral Nudging ◽  
Temperature And Precipitation ◽  
Quality Health

AbstractThe use of nudging in the Weather Research and Forecasting (WRF) Model to constrain regional climate downscaling simulations is gaining in popularity because it can reduce error and improve consistency with the driving data. While some attention has been paid to whether nudging is beneficial for downscaling, very little research has been performed to determine best practices. In fact, many published papers use the default nudging configuration (which was designed for numerical weather prediction), follow practices used by colleagues, or adapt methods developed for other regional climate models. Here, a suite of 45 three-year simulations is conducted with WRF over the continental United States to systematically and comprehensively examine a variety of nudging strategies. The simulations here use a longer test period than did previously published works to better evaluate the robustness of each strategy through all four seasons, through multiple years, and across nine regions of the United States. The analysis focuses on the evaluation of 2-m temperature and precipitation, which are two of the most commonly required downscaled output fields for air quality, health, and ecosystems applications. Several specific recommendations are provided to effectively use nudging in WRF for regional climate applications. In particular, spectral nudging is preferred over analysis nudging. Spectral nudging performs best in WRF when it is used toward wind above the planetary boundary layer (through the stratosphere) and temperature and moisture only within the free troposphere. Furthermore, the nudging toward moisture is very sensitive to the nudging coefficient, and the default nudging coefficient in WRF is too high to be used effectively for moisture.

scholarly journals How does bias correction of regional climate model precipitation affect modelled runoff?

2015 ◽  
Vol 19 (2) ◽  
pp. 711-728 ◽  
Author(s):  
J. Teng ◽  
N. J. Potter ◽  
F. H. S. Chiew ◽  
L. Zhang ◽  
B. Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Mapping Method ◽  
Future Climate Change ◽  
Runoff Generation ◽  
Simulated Precipitation

Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

scholarly journals Simulation of summer climate over Central Asia shows high sensitivity to different land surface schemes in WRF

2021 ◽  
Author(s):  
Sha Lu ◽  
Weidong Guo ◽  
Yongkang Xue ◽  
Fang Huang ◽  
Jun Ge
Keyword(s):  
Central Asia ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Global Analysis ◽  
Regional Climate ◽  
Wrf Model ◽  
Heat Fluxes ◽  
Ensemble Average ◽  
Adjustment Process

AbstractLand surface processes are vital to the performance of regional climate models in dynamic downscaling application. In this study, we investigate the sensitivity of the simulation by using the weather research and forecasting (WRF) model at 10-km resolution to the land surface schemes over Central Asia. The WRF model was run for 19 summers from 2000 to 2018 configured with four different land surface schemes including CLM4, Noah-MP, Pleim-Xiu and SSiB, hereafter referred as Exp-CLM4, Exp-Noah-MP, Exp-PX and Exp-SSiB respectively. The initial and boundary conditions for the WRF model simulations were provided by the National Centers for Environmental Prediction Final (NCEP-FNL) Operational Global Analysis data. The ERA-Interim reanalysis (ERAI), the GHCN-CAMS and the CRU gridded data were used to comprehensively evaluate the WRF simulations. Compared with the reanalysis and observational data, the WRF model can reasonably reproduce the spatial patterns of summer mean 2-m temperature, precipitation, and large- scale atmospheric circulation. The simulations, however, are sensitive to the option of land surface scheme. The performance of Exp-CLM4 and Exp-SSiB are better than that of Exp-Noah-MP and Exp-PX assessed by Multivariable Integrated Evaluation (MVIE) method. To comprehensively understand the dynamic and physical mechanisms for the WRF model’s sensitivity to land surface schemes, the differences in the surface energy balance between Ave-CLM4-SSiB (the ensemble average of Exp-CLM4 and Exp-SSiB) and Ave-NoanMP-PX (the ensemble average of Exp-Noah-MP and Exp-PX) are analyzed in detail. The results demonstrate that the sensible and latent heat fluxes are respectively lower by 30.42 W·m−2 and higher by 14.86 W·m−2 in Ave-CLM4-SSiB than that in Ave-NoahMP-PX. As a result, large differences in geopotential height occur over the simulation domain. The simulated wind fields are subsequently influenced by the geostrophic adjustment process, thus the simulations of 2-m temperature, surface skin temperature and precipitation are respectively lower by about 2.08 ℃, 2.23 ℃ and 18.56 mm·month−1 in Ave-CLM4-SSiB than that in Ave-NoahMP-PX over Central Asia continent.

scholarly journals Gains and losses in surface solar radiation with dynamic aerosols in regional climate simulations for Europe

2020 ◽  
Author(s):  
Sonia Jerez ◽  
Laura Palacios-Peña ◽  
Claudia Gutiérrez ◽  
Pedro Jiménez-Guerrero ◽  
Jose María López-Romero ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Atmospheric Aerosol ◽  
Climate Models ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Surface Solar Radiation ◽  
Global Circulation ◽  
Global Circulation Models ◽  
Gains And Losses

Abstract. The solar resource can be highly influenced by clouds and atmospheric aerosol, which has been named by the IPCC as the most uncertainty climate forcing agent. Nonetheless, Regional Climate Models (RCMs) hardly ever model dynamically atmospheric aerosol concentration and their interaction with radiation and clouds, in contrast to Global Circulation Models (GCMs). The objective of this work is to evince the role of the interactively modeling of aerosol concentrations and their interactions with radiation and clouds in Weather Research and Forecast (WRF) model simulations with a focus on summer mean surface downward solar radiation (RSDS) and over Europe. The results show that the response of RSDS is mainly led by the aerosol effects on cloudiness, which explain well the differences between the experiments in which aerosol-radiation and aerosol-radiation-cloud interactions are taken into account or not. Under present climate, a reduction about 5% in RSDS was found when aerosols are dynamically solved by the RCM, which is larger when only aerosol-radiation interactions are considered. However, for future projections, the inclusion of aerosol-radiation-cloud interactions results in the most negative RSDS change pattern (while with slight values), showing noticeable differences with the projections from either the other RCM experiments or from their driving GCM (which do hold some significant positive signals). Differences in RSDS among experiments are much more softer under clear-sky conditions.

scholarly journals Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 281 ◽  
Author(s):  
Jose Manuel Jiménez-Gutiérrez ◽  
Francisco Valero ◽  
Sonia Jerez ◽  
Juan Pedro Montávez
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Spatial Average ◽  
Area Index ◽  
Near Surface ◽  
Vegetation Fraction ◽  
Selection Of

The representation of vegetation in land surface models (LSM) is crucial for modeling atmospheric processes in regional climate models (RCMs). Vegetation is characterized by the green fractional vegetation cover (FVC) and/or the leaf area index (LAI) that are obtained from nearest difference vegetation index (NDVI) data. Most regional climate models use a constant FVC for each month and grid cell. In this work, three FVC datasets have been constructed using three methods: ZENG, WETZEL and GUTMAN. These datasets have been implemented in a RCM to explore, through sensitivity experiments over the Iberian Peninsula (IP), the effects of the differences among the FVC data-sets on the near surface temperature (T2m). Firstly, we noted that the selection of the NDVI database is of crucial importance, because there are important bias in mean and variability among them. The comparison between the three methods extracted from the same NDVI database, the global inventory modeling and mapping studies (GIMMS), reveals important differences reaching up to 12% in spatial average and and 35% locally. Such differences depend on the FVC magnitude and type of biome. The methods that use the frequency distribution of NDVI (ZENG and GUTMAN) are more similar, and the differences mainly depends on the land type. The comparison of the RCM experiments exhibits a not negligible effect of the FVC uncertainty on the monthly T2m values. Differences of 30% in FVC can produce bias of 1 ∘ C in monthly T2m, although they depend on the time of the year. Therefore, the selection of a certain FVC dataset will introduce bias in T2m and will affect the annual cycle. On the other hand, fixing a FVC database, the use of synchronized FVC instead of climatological values produces differences up to 1 ∘ C, that will modify the T2m interannual variability.

scholarly journals Brief communication: Evaluation of the near-surface climate in ERA5 over the Greenland Ice Sheet

2020 ◽  
Vol 14 (3) ◽  
pp. 957-965 ◽  
Author(s):  
Alison Delhasse ◽  
Christoph Kittel ◽  
Charles Amory ◽  
Stefan Hofer ◽  
Dirk van As ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Climate Models ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
The Arctic ◽  
Homogeneous Surface ◽  
Near Surface ◽  
Surface Climate

Abstract. The ERA5 reanalysis, recently made available by the European Centre for Medium-Range Weather Forecasts (ECMWF), is a new reanalysis product at a high resolution replacing ERA-Interim and is considered to provide the best climate reanalysis over Greenland to date. However, so far little is known about the performance of ERA5 over the Greenland Ice Sheet (GrIS). In this study, we compare the near-surface climate from the new ERA5 reanalysis to ERA-Interim, the Arctic System Reanalysis (ASR) as well as to a state-of-the-art polar regional climate model (MAR). The results show (1) that ERA5 does not outperform ERA-Interim significantly when compared with near-surface climate observations over GrIS, but ASR better models the near-surface temperature than both ERA reanalyses. (2) Polar regional climate models (e.g., MAR) are still a useful tool to downscale the GrIS climate compared to ERA5, as in particular the near-surface temperature in summer has a key role for representing snow and ice processes such as the surface melt. However, assimilating satellite data and using a more recent radiative scheme enable both ERA and ASR reanalyses to represent more satisfactorily than MAR the downward solar and infrared fluxes. (3) MAR near-surface climate is not affected when forced at its lateral boundaries by either ERA5 or ERA-Interim. Therefore, forcing polar regional climate models with ERA5 starting from 1950 will enable long and homogeneous surface mass balance reconstructions.

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