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

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.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0272.1 ◽

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.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0239.1 ◽

2016 ◽

Vol 17 (3) ◽

pp. 829-851 ◽

Cited By ~ 8

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.

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Sensitivity of high resolution WRF model to land surface schemes in simulating boreal summer climate over Central Asia

10.5194/egusphere-egu2020-1877 ◽

2020 ◽

Author(s):

Sha Lu ◽

Weidong Guo ◽

Yongkang Xue ◽

Fang Huang

Keyword(s):

High Resolution ◽

Central Asia ◽

Land Surface ◽

Model Simulation ◽

Sensible Heat Flux ◽

Wrf Model ◽

Boreal Summer ◽

Adjustment Process ◽

Land Surface Scheme ◽

Surface Scheme

<p>The Land surface scheme is crucial for the performance of regional climate models in dynamic downscaling application. In this study, we investigate the sensitivity of the simulation &#160;with high resolution (10km) WRF model to the land surface schemes over Central Asia. The high resolution WRF simulations for 19 summers from 2000 to 2018 are conducted with four different land surface schemes (hereafter referred to as Exp-CLM, Exp-Noah-MP, Exp-PX and Exp-SSiB, respectively). The initial and boundary conditions for the WRF model simulations are provided from the NCEP-FNL analysis product. The ERA-Interim reanalysis (ERA), the GHCN-CAMS (CAMS) and the CRU gridded data are used to comprehensively evaluate the WRF simulations. Compared with verification data, the WRF model with high resolution can reasonably reproduce the spatial patterns of summer mean large scale atmospheric circulation, 2-m temperature and precipitation. The simulation results, 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 the multivariable integrated evaluation method. To comprehensively understand the dynamic and physical mechanisms behind the WRF model sensitivity to land surface schemes, the differences in the surface energy balance between the ensemble means Ens-CLM4-SSiB and Ens-NoanMP-PX are analyzed in detail. The results demonstrate that the intensity of the simulated sensible heat flux over Central Asia is weaker in Ens-CLM4-SSiB than that in Ens-NoahMP-PX. As a result, large differences in geopotential height occur over the model simulation domain. The simulated wind fields are subsequently affected due to the geostrophic adjustment process, thus the simulation of 2-m temperature, precipitation, surface soil moisture and surface skin temperature are all affected.</p>

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Differences between downscaling with spectral and grid nudging using WRF

Atmospheric Chemistry and Physics ◽

10.5194/acp-12-3601-2012 ◽

2012 ◽

Vol 12 (8) ◽

pp. 3601-3610 ◽

Cited By ~ 84

Author(s):

P. Liu ◽

A. P. Tsimpidi ◽

Y. Hu ◽

B. Stone ◽

A. G. Russell ◽

...

Keyword(s):

Large Scale ◽

Climate Models ◽

Dynamical Downscaling ◽

Global Climate ◽

Regional Climate ◽

Wrf Model ◽

Global Climate Models ◽

Small Scale ◽

Data Set ◽

Spectral Nudging

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

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

Journal of Climate ◽

10.1175/jcli-d-21-0462.1 ◽

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.

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

Journal of Climate ◽

10.1175/jcli-d-20-0650.1 ◽

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.

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Differences between downscaling with spectral and grid nudging using WRF

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-12-1191-2012 ◽

2012 ◽

Vol 12 (1) ◽

pp. 1191-1213 ◽

Cited By ~ 4

Author(s):

P. Liu ◽

A. P. Tsimpidi ◽

Y. Hu ◽

B. Stone ◽

A. G. Russell ◽

...

Keyword(s):

Large Scale ◽

Climate Models ◽

Dynamical Downscaling ◽

Global Climate ◽

Regional Climate ◽

Wrf Model ◽

Global Climate Models ◽

Small Scale ◽

Data Set ◽

Spectral Nudging

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data using Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

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Cities Exacerbate Climate Warming

Urban Science ◽

10.3390/urbansci5010027 ◽

2021 ◽

Vol 5 (1) ◽

pp. 27

Author(s):

Lahouari Bounoua ◽

Kurtis Thome ◽

Joseph Nigro

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Climate Models ◽

Warming Trend ◽

Climate Mitigation ◽

Temperature Changes ◽

Surface Warming ◽

The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

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Improving runoff estimates from regional climate models: a performance analysis in Spain

Hydrology and Earth System Sciences ◽

10.5194/hess-16-1709-2012 ◽

2012 ◽

Vol 16 (6) ◽

pp. 1709-1723 ◽

Cited By ~ 18

Author(s):

D. González-Zeas ◽

L. Garrote ◽

A. Iglesias ◽

A. Sordo-Ward

Keyword(s):

Time Series ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Aridity Index ◽

Regional Climate Models ◽

Hydrologic Model ◽

Direct Runoff ◽

Interpolation Methods ◽

Basin Scale

Abstract. An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results.

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Simulating future precipitation extremes in a complex Alpine catchment

Natural Hazards and Earth System Science ◽

10.5194/nhess-13-263-2013 ◽

2013 ◽

Vol 13 (2) ◽

pp. 263-277 ◽

Cited By ~ 9

Author(s):

C. Dobler ◽

G. Bürger ◽

J. Stötter

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Precipitation Extremes ◽

Climate Projections ◽

Research Station ◽

Climate Change Scenarios ◽

Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

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