Noah Land Surface Model Research Articles

Comparison and evaluation of multiple land surface products for the water budget in the Yellow River Basin

Multiple global datasets simulated by different land surface models (LSMs) are useful for providing information to decision-makers on the water balance in different subareas of a river basin. As a large basin covering a vast area, the Yellow River Basin (YRB) represents a typical case to evaluate the water cycle components in LSM products. The main objective of this study is to evaluate and compare multiple LSM products with respect to precipitation, runoff, evapotranspiration (ET) and terrestrial water storage anomaly (TWSA). The datasets analyzed include different Global Land Data Assimilation System (GLDAS) products, ERA-Interim/Land datasets, and augmented Noah LSM products with multiple parameterization options (Noah-MP) driven by different GLDAS forcing datasets. The evaluation is conducted with reference to gauge-based precipitation datasets, reconstructed natural streamflow and Gravity Recovery and Climate Experiment (GRACE)-derived TWSA. Several statistical metrics and the partitioning of runoff and ET were emphasized. The results show that the products generated diverse spatial patterns for the water cycle components. The ERA-Interim/Land data present the best performance almost for all water cycle components. The three models (variable infiltration capacity (VIC), Mosaic and community land model (CLM)) driven by GLDAS version 1 generated different but large systematic biases, and the Noah model performs the best among the four models. The Noah simulation of GLDAS version 2.0 (GLDAS-2.0) and the Noah-MP simulation with the Princeton Global Forcing also have systematically large biases for runoff. The different Noah-MP simulations perform better for runoff (although overestimated) than other models used in GLDAS, but present the poorest correlations of TWSA to GRACE-derived TWSA. The ensemble averages of multiple products perform the best for precipitation and ET than any other individual product but not for runoff.

Simulation and Analysis of Land-Surface Processes in the Taklimakan Desert Based on Noah LSM

This study evaluated the Noah land-surface model performance to simulate the land-surface process during different weather conditions in the hinterland of the Taklimakan Desert. This study is based on observation data from the Taklimakan Desert Meteorology Field Experiment Station in 2014. The results illustrated that the energy-exchange process between the land surface and the atmosphere in the drifting desert can be simulated by Noah effectively. However, the effects of soil moisture and latent heat flux were very poor. For sunny days, the soil temperature and heat flux were underestimated significantly in the nighttime and overestimated in the daytime. The simulation results are very good in sand-dust weather. The simulation of heat flux and net radiation is very consistent with the observation during cloudy days. For rainy days, the model can successfully model the diurnal variation of soil moisture, but it has obvious deviations in the net radiation, heat flux, and soil heat flux.

Numerical Simulation of the Irrigation Effects on Surface Fluxes and Local Climate in Typical Mountain‐Oasis‐Desert Systems in the Central Asia Arid Area

AbstractIn this study, the Weather Research and Forecasting model was coupled with an improved Noah land surface model (WRF‐Noah) where dynamic flood and drip irrigation processes were implemented firstly. We simulated the different irrigation effects on surface water‐heat processes in a typical mountain‐oasis‐desert system in Central Asia for both wet and drought years, respectively, using the modified WRF‐Noah. The modified WRF‐Noah model can dynamically generate amounts of irrigation in agreement with actual values. The statistically significant decrease in the root mean square error and increase in the Pearson correlation coefficient for the 2‐m temperature (T2), relative humidity (RH), latent heat flux (LE), and precipitation suggest that the modified WRF‐Noah model was improved by implementing irrigation processes. During the irrigation season, flood and drip irrigation decreased the average sensible heat flux by −80.69 and −50.50 W/m2 and T2 by 1.09 and 0.82 °C over the irrigated area and increased LE by 88.47 and 66.70 W/m2 and RH by 6.23% and 4.65%, respectively. Throughout the domain, flood irrigation had equivalent or slightly weaker effects on near‐surface temperature and humidity due to the smaller irrigated area. Both flood and drip irrigation increased precipitation throughout the domain, especially in the mountainous area, thereby accelerating the hydrological cycle in the mountain‐oasis‐desert system. The local oasis breeze circulation that plays a role in maintaining the oasis stability is still counteracted by the dominant background circulation even with irrigation processes. Thus, more effort should be exerted to maintain the future sustainability of the oasis.

Evaluation of the CABLEv2.3.4 Land Surface Model Coupled to NU‐WRFv3.9.1.1 in Simulating Temperature and Precipitation Means and Extremes Over CORDEX AustralAsia Within a WRF Physics Ensemble

AbstractThe Community Atmosphere Biosphere Land Exchange (CABLE) model is a third‐generation land surface model (LSM). CABLE is commonly used as a stand‐alone LSM, coupled to the Australian Community Climate and Earth Systems Simulator global climate model and coupled to the Weather Research and Forecasting (WRF) model for regional applications. Here, we evaluate an updated version of CABLE within a WRF physics ensemble over the COordinated Regional Downscaling EXperiment (CORDEX) AustralAsia domain. The ensemble consists of different cumulus, radiation and planetary boundary layer (PBL) schemes. Simulations are carried out within the NASA Unified WRF modeling framework, NU‐WRF. Our analysis did not identify one configuration that consistently performed the best for all diagnostics and regions. Of the cumulus parameterizations the Grell‐Freitas cumulus scheme consistently overpredicted precipitation, while the new Tiedtke scheme was the best in simulating the timing of precipitation events. For the radiation schemes, the RRTMG radiation scheme had a general warm bias. For the PBL schemes, the YSU scheme had a warm bias, and the MYJ PBL scheme a cool bias. Results are strongly dependent on the region of interest, with the northern tropics and southwest Western Australia being more sensitive to the choice of physics options compared to southeastern Australia which showed less overall variation and overall better performance across the ensemble. Comparisons with simulations using the Unified Noah LSM showed that CABLE in NU‐WRF has a more realistic simulation of evapotranspiration when compared to GLEAM estimates.

Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data

The accuracy of land surface hydrological simulations using an offline land surface model (LSM) depends largely on the quality of the atmospheric forcing data. In this study, Global Land Data Assimilation System (GLDAS) forcing data and the newly developed China Meteorological Administration Land Data Assimilation System (CLDAS) forcing data are used to drive the Noah LSM with multiple parameterizations (Noah-MP) and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over mainland China. The monthly soil moisture (SM) and evapotranspiration (ET) simulations are then compared and evaluated against observations. The results show that the Noah-MP driven by the CLDAS forcing data (referred to as CLDAS_Noah-MP) significantly improves the simulations in most cases over mainland China and its eight river basins. CLDAS_Noah-MP increases the correlation coefficient (R) values from 0.451 to 0.534 for the SM simulations at a depth range of 0–ss10 cm in mainland China, especially in the eastern monsoon area such as the Huang-Huai-Hai Plain, the southern Yangtze River basin, and the Zhujiang River basin. Moreover, the root-mean-square error is reduced from 0.078 to 0.068 m3 m−3 for the SM simulations, and from 12.9 to 11.4 mm month−1 for the ET simulations over mainland China, especially in the southern Yangtze River basin and Zhujiang River basin. This study demonstrates that, by merging more in situ and remote sensing observations in regional atmospheric forcing data, offline LSM simulations can better simulate regional-scale land surface hydrological processes.

Assessing snow simulation performance of typical combination schemes within Noah-MP in northern Xinjiang, China

The performance of the Noah land surface model (LSM) with multi-parameterization options (Noah-MP) in simulating snow depth was evaluated in northern Xinjiang, China. A total number of 13,824 Noah-MP physics-ensemble simulations were conducted at the Altay site by combining different parameterization schemes of physical processes while disregarding the uncertainties of forcing data and model parameters. The natural selection approach and Tukey’s test, which are two sensitivity analysis methods, were used to analyze the sensitivity of snow to parameterization schemes. Then, the uncertainty intervals of the ensemble simulation experiments were compared. According to the results of the sensitivity and uncertainty experiments, snow depth could be simulated by three typical combination schemes at the regional scale: the longest snow melting time scheme (LT), the shortest snow melting time scheme (ST) and the default combination scheme (DT). Observation data of snow depth from thirty-nine meteorological stations in northern Xinjiang were used to evaluate the snow simulation performance of typical combination schemes. The simulation performances of the three typical combination schemes were examined and compared in groups that were divided according to elevation and land cover. The results demonstrated that the simulation results of snow depth and snow water equivalent (SWE) were sensitive to four of the eleven physics options within Noah-MP. The exclusion of the parameterization schemes that notably reduced the simulation performance in the sensitive physical processes can significantly reduce uncertainty. Snow simulation performances of three typical combination schemes were diverse in northern Xinjiang, China; no single scheme performed best at all sites, but the length of the snow melting phase exhibited the best performance.

Examining the Impact of SMAP Soil Moisture Retrievals on Short-Range Weather Prediction under Weakly and Strongly Coupled Data Assimilation with WRF-Noah

AbstractRemotely sensed soil moisture data are typically incorporated into numerical weather models under a framework of weakly coupled data assimilation (WCDA), with a land surface analysis scheme independent from the atmospheric analysis component. In contrast, strongly coupled data assimilation (SCDA) allows simultaneous correction of atmospheric and land surface states but has not been sufficiently explored with land surface soil moisture data assimilation. This study implemented a variational approach to assimilate the Soil Moisture Active Passive (SMAP) 9-km enhanced retrievals into the Noah land surface model coupled with the Weather Research and Forecasting (WRF) Model under a framework of both WCDA and SCDA. The goal of the study is to quantify the relative impact of assimilating SMAP data under different coupling frameworks on the atmospheric forecasts in the summer. The results of the numerical experiments during July 2016 show that SCDA can provide additional benefits on the forecasts of air temperature and humidity compared to WCDA. Over the U.S. Great Plains, assimilation of SMAP data under WCDA reduces a warm bias in temperature and a dry bias in humidity by 7.3% and 19.3%, respectively, while the SCDA case contributes an additional bias reduction of 2.2% (temperature) and 3.3% (humidity). While WCDA leads to a reduction of RMSE in temperature forecasts by 4.1%, SCDA results in additional reduction of RMSE by 0.8%. For the humidity, the reduction of RMSE is around 1% for both WCDA and SCDA.

An optimal ensemble of the Noah-MP land surface model for simulating surface heat fluxes over a typical subtropical forest in South China

Accurate estimation of land surface heat fluxes is critical to climate and air quality forecasting, which needs an appropriate parameterization in land surface models. The benchmarking method was applied to search for the optimal combination of the physical parameterization based on Noah land surface model with multi-physics options (Noah-MP). The effect of integrated physical mechanisms on the simulation of land surface energy fluxes in a typical subtropical forest (Dinghushan Forest Ecosystem Research Station) over South China is further investigated. The most sensitive process is the soil moisture threshold for evaporation while there are six processes have little or no effect on heat simulation. The optimal combination options are referred as follow: prescribed table leaf area index (LAI) and vegetation fraction, Jarvis scheme for canopy stomatal resistance, Noah soil moisture scheme, SIMGM model for runoff and groundwater, original Noah consistent scheme for surface layer drag coefficient and two-stream applied to grid-cell scheme for radiation transfer.

The Role of Winter Warming in Permafrost Change Over the Qinghai‐Tibet Plateau

AbstractWinter warming is fast than summer warming on the Qinghai‐Tibet Plateau (QTP). However, no assessment of winter warming effects on permafrost has been attempted. Here we conducted hypothetical control experiments and used the Noah land surface model to evaluate the impacts of winter warming on the QTP permafrost. The results show that air temperature in winter (November–April) was increasing at a rate of 0.66 °C/decade during 1980s−2000s, over double that in summer (May–October). The mean annual ground temperature of permafrost increased by 0.13 °C/decade. The summer warming dominated the variations in thermal regime of permafrost before 2000. After that, the influence of winter warming on permafrost thermal regime has gradually grown and exceeded that of summer warming. Winter warming has amplified the thermal degradation of permafrost. Our findings reveal that alpine continuous permafrost on the northern QTP has experienced a prominent regional warming due to rapid winter warming since 2000.

Systematic Hydrological Evaluation of the Noah-MP Land Surface Model over China

We evaluate water budget components—namely, soil moisture, runoff, evapotranspiration, and terrestrial water storage (TWS)—simulated by the Noah land surface model with multi-parameterization options (Noah-MP) in China, a large geographic domain challenging for hydrological modeling due to poor observational data and a lack of one single parameterization that can fit for complex hydrological processes. By comparing the model simulations with multi-source reference data, we show that Noah-MP can generally reproduce the overall spatiotemporal patterns of runoff and evapotranspiration over six major river basins, with the annual correlation coefficients generally greater than 0.8 and the Nash-Sutcliffe model efficiency coefficient exceeding 0.5. Among the six basins evaluated, the best model performance is seen over the Huaihe River basin. The temporal trend of the modeled TWS anomalies agrees well with GRACE (Gravity Recovery and Climate Experiment) observations, capturing major flood and drought events in different basins. Experiments with 12 selected physical parameterization options show that the runoff parameterization has a stronger impact on the simulated soil moisture-runoff-evapotranspiration relationships than the soil moisture factor for stomatal resistance schemes, a result consistent with previous studies. Overall, Noah-MP driven by GLDAS forcing simulates the hydrological variables well, except for the Songliao basin in northeastern China, likely because this is a transitional region with extensive freeze-thaw activity, while representations of human activities may also help improve the model performance.

Effects of Mosaic Land Use on Dynamically Downscaled WRF Simulations of the Contiguous U.S.

The representation of land use (LU) in meteorological modeling strongly influences the simulation of fluxes of heat, moisture, and momentum; affecting the accuracy of 2-m temperature and precipitation. Here, the Weather Research and Forecasting (WRF) model is used with the Noah land surface model to compare a mosaic approach, which accounts for subgrid scale variability of LU types, to the default option which only considers the dominant category in each grid cell. Three-year historical dynamically downscaled WRF simulations are generated using a 12-km domain over the contiguous U.S. to assess the sensitivities to using mosaic LU and to changes to parameters associated with LU and soil categories. Compared to dominant LU, mosaic LU features decreased coverage of forest and agricultural types and increased low-density urban LU throughout much of the eastern and central U.S. However, highly urbanized areas show the opposite trend, as mosaic LU represents partial greenspace within areas that are exclusively urban within dominant LU. Mosaic LU results in widespread increases in sensible heat fluxes and 2-m temperatures, with reductions in latent heat flux, 2-m mixing ratio, and monthly precipitation across the central and eastern U.S. These changes exacerbate an existing warm bias found with dominant LU but reduce overestimations of precipitation. Highly urbanized areas in the eastern U.S. tend to have cooler, more realistic temperatures with mosaic LU relative to dominant LU. A pair of runs with updated surface parameters corroborates these results. Overall, differences between the simulations are largely attributable to their representations of urban LU.

+50 Years of Terrestrial Hydroclimatic Variability in Africa\u2019s Transboundary Waters

GRACE Terrestrial Water Storage (TWS) provides unique and unprecedented perspectives about freshwater availability and change globally. However, GRACE-TWS records are relatively short for long-term hydroclimatic variability studies, dating back to April 2002. In this paper, we made use of Noah Land Surface Model (LSM), and El Niño–Southern Oscillation (ENSO) data in an autoregressive model with exogenous variables (ARX) to reconstruct a 66-year record of TWS for nine major transboundary river basins (TRBs) in Africa. Model performance was evaluated using standard indicators, including the Nash Sutcliffe Efficiency criteria, cumulative density frequency, standardized residuals plots, and model uncertainty bounds. Temporally, the reconstruction results were evaluated for trend, cycles, and mode of variability against ancillary data from the WaterGAP Model (WGHM-TWS) and GPCC-based precipitation anomalies. The temporal pattern reveals good agreement between the reconstructed TWS, WGHM-TWS, and GPCC, (p-value < 0.0001). The reconstructed TWS suggests a significant declining trend across the northern and central TRBs since 1951, while the southern basins show an insignificant trend. The mode of variability analysis indicates short storage periodicity of four to sixteen-month in the northern basins, while strong intra-annual variability in the central and southern basins. The long-term TWS records provide additional support to Africa’s water resources research on hydroclimatic variability and change in shared transboundary water basins.

Diagnosing Moisture Sources for Flash Floods in the United States. Part II: Terrestrial and Oceanic Sources of Moisture

Abstract Backward trajectories were derived from North American Regional Reanalysis data for 19 253 flash flood reports published by the National Weather Service to determine the along-path contribution of the land surface to the moisture budget for flash flood events in the conterminous United States. The impact of land surface interactions was evaluated seasonally and for six regions: the West Coast, Arizona, the Front Range, Flash Flood Alley, the Missouri Valley, and the Appalachians. Parcels were released from locations that were impacted by flash floods and traced backward in time for 120 h. The boundary layer height was used to determine whether moisture increases occurred within the boundary layer or above it. Moisture increases occurring within the boundary layer were attributed to evapotranspiration from the land surface, and surface properties were recorded from an offline run of the Noah land surface model. In general, moisture increases attributed to the land surface were associated with anomalously high surface latent heat fluxes and anomalously low sensible heat fluxes (resulting in a positive anomaly of evaporative fraction) as well as positive anomalies in top-layer soil moisture. Over the ocean, uptakes were associated with positive anomalies in sea surface temperatures, the magnitude of which varies both regionally and seasonally. Major oceanic surface-based source regions of moisture for flash floods in the United States include the Gulf of Mexico and the Gulf of California, while boundary layer moisture increases in the southern plains are attributable in part to interactions between the land surface and the atmosphere.

A Spatial Pattern Analysis of Land Surface Roughness Heterogeneity and its Relationship to the Initiation of Weak Tornadoes

Abstract North Alabama is among the most tornado-prone regions in the United States and is composed of more spatially variable terrain and land cover than the frequently studied North American Great Plains region. Because of the high tornado frequency observed across north Alabama, there is a need to understand how land surface roughness heterogeneity influences tornadogenesis, particularly for weak-intensity tornadoes. This study investigates whether horizontal gradients in land surface roughness exist surrounding locations of tornadogenesis for weak (EF0–EF1) tornadoes. The existence of the horizontal gradients could lead to the generation of positive values of the vertical components of the 3D vorticity vector near the surface that may aid in the tornadogenesis process. In this study, surface roughness was estimated using parameterizations from the Noah land surface model with inputs from MODIS 500-m and Landsat 30-m data. Spatial variations in the parameterized roughness lengths were assessed using GIS-based grid and quadrant pattern analyses to quantify observed variation of land surface features surrounding tornadogenesis locations across spatial scales. This analysis determined that statistically significant horizontal gradients in surface roughness exist surrounding tornadogenesis locations.

Assessment and Reduction of the Physical Parameterization Uncertainty for Noah‐MP Land Surface Model

AbstractThe community Noah land surface model with multiparameterization options (Noah‐MP) provides a plethora of model configurations with varying complexity for land surface modeling. The practical application of this model requires a basic understanding of the relative abilities of its various parameterization configurations in representing spatiotemporal variability and hydrologic connectivity. We designed an ensemble of 288 experiments from multiparameterization schemes of six physical processes to assess and reduce the structural uncertainty for land surface modeling over 10 hydrologic regions in China for the period 2001–2010. The observed latent heat (LH) was well reproduced by the ensemble. Meanwhile, most experiments underestimated sensible heat (SH) throughout the year and overestimated the cold season but underestimated the warm season terrestrial water storage anomaly (TWSA). The sensitive processes and best‐performing schemes varied not only with regions but also among variables. The LH and SH were most sensitive to runoff‐groundwater (RUN), surface heat exchange coefficient (SFC), and radiation transfer (RAD). The TWSA was dominated by RUN and RAD while largely influenced by soil moisture factor for stomatal resistance (BTR) and frozen soil permeability (INF) over some limited regions. By contrast, supercooled liquid water (FRZ) had little influence on all variables. Our optimization for individual variables produced high mean Taylor skill scores that ranged from 0.95–0.99 for LH, 0.82–0.99 for SH, and 0.63–0.95 for TWSA depending on regions. The simultaneous optimization made trade‐off among the three variables, which improved TWSA performance at the cost of reducing the skill for LH and SH over a few regions.

Estimating High-Resolution Groundwater Storage from GRACE: A Random Forest Approach

Gravity Recovery and Climate Experiment (GRACE) data have become a widely used global dataset for evaluating the variability in groundwater storage for the different major aquifers. Moreover, the application of GRACE has been constrained to the local scale due to lower spatial resolution. The current study proposes Random Forest (RF), a recently developed unsupervised machine learning method, to downscale a GRACE-derived groundwater storage anomaly (GWSA) from 1° × 1° to 0.25° × 0.25° in the Northern High Plains aquifer. The RF algorithm integrated GRACE to other satellite-based geospatial and hydro-climatological variables, obtained from the Noah land surface model, to generate a high-resolution GWSA map for the period 2009 to 2016. This RF approach replicates local groundwater variability (the combined effect of climatic and human impacts) with acceptable Pearson correlation (0.58 ~ 0.84), percentage bias (−14.67 ~ 2.85), root mean square error (15.53 ~ 46.69 mm), and Nash-Sutcliffe efficiency (0.58 ~ 0.84). This developed RF model has significant potential to generate finer scale GWSA maps for managing groundwater at both local and regional scales, especially for areas with sparse groundwater monitoring wells.

A surface temperature and moisture intercomparison study of the Weather Research and Forecasting model, in‐situ measurements and satellite observations over the Atacama Desert

AbstractGood knowledge of the environmental conditions of deserts on Earth is relevant for climate studies. The Atacama Desert is of particular interest as it is considered to be the driest region on Earth. We have performed simulations using the Weather Research and Forecasting (WRF) model over the Atacama Desert for two week‐long periods in the austral winter season coincident with surface temperature and relative humidity in‐situ observations at three sites. We found that the WRF model generally overestimates the daytime surface temperature, with biases of up to 11°C, despite giving a good simulation of the relative humidity. In order to improve the agreement with observed measurements, we conducted sensitivity experiments in which the surface albedo, soil moisture content and five tuneable parameters in the Noah Land Surface Model (namely soil porosity, soil suction, saturated soil hydraulic conductivity, the b parameter used in hydraulic functions and the quartz fraction) are perturbed. We concluded that an accurate simulation is not possible, most likely because the Noah Land Surface Model does not have a groundwater table that may be shallow in desert regions. The WRF‐predicted land surface temperature is also evaluated against that estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. While at night the satellite‐derived and ground‐based measurements are generally in agreement, during the day MODIS estimates are typically lower by as much as 17°C. This is attributed to the large uncertainty in the MODIS‐estimated land surface temperatures in arid and semi‐arid regions. The findings of this work highlight the need for ground‐based observational networks in remote regions such as the Atacama Desert where satellite‐derived and model products may not be very accurate.

Evaluation of urban surface parameterizations in WRF model using energy fluxes measurements in Portugal

The performance of WRF model was investigated for the simulation of urban microclimate, with particular focus on energy fluxes, using different urban surface parameterizations. The model performance was evaluated using measurements carried out between August and December 2014 in Portugal. Several simulations were performed over two different areas, Porto urban area and Aveiro suburban area, for the entire measurement period. Distinct simulations were performed using different urban parametrizations: (i) the Noah Land Surface Model (LSM); (ii) a single-layer urban canopy model (UCM); and (iii) a modelling system composed by WRF and SUEWS models (WRF-SUEWS).The results showed that both UCM and SUEWS are able to simulate the energy partitioning over the low and high intensity residential. At the low intensity residential area, the majority of energy is partitioned to latent heat flux, accounting on average for 47% and 49% of the daytime available energy, for UCM and SUEWS, respectively. At the high intensity residential area, the greatest share of energy goes to sensible heat flux (42% [UCM] and 50% [SUEWS]), followed by the storage heat flux (33% [UCM] and 43% [SUEWS]). For both areas, a completely different energy partitioning was obtained when the LSM was used. The analysis performed showed that the UCM are able to provide a more accurate turbulent energy partitioning (sensible and latent heat), which contribute to enhance the urban microclimate simulation results; the systematic model biases in the LSM simulation was reduced by 1–2 °C in air temperature and by 0.5–1 m s−1 in wind speeds at near surface layer, on average, depending on the urban density. The overall results suggest that an appropriate representation of urban physical processes are crucial to improve numerical tools suited for the modelling of the urban atmospheric boundary layer.

Implementation of the Land Surface Processes into a Vector Vorticity Equation Model (VVM) to Study its Impact on Afternoon Thunderstorms over Complex Topography in Taiwan

In this study, we aim to evaluate the impact of fast land-atmosphere interactions on the afternoon thunderstorm in Taiwan through high-resolution meteorological simulations. For this purpose, the Noah land surface model (LSM) is implemented into the vector vorticity equation cloud-resolving model (VVM) with corresponding realistic land surface data of Taiwan into the coupling system, called TaiwanVVM. Two idealized experiments are conducted by giving the same surface forcing but one with direct land-atmosphere coupling from Noah LSM (called Coupled experiment) and the other with prescribed surface fluxes (called Prescribed experiment). Our results show that the fast land-atmosphere interaction over complex topography has a significant influence on rainfall intensity, especially in the heavy precipitating region where the interaction is strong. Without direct coupling between the land surface and the atmosphere in the Prescribed experiment, the diurnal intensity is suppressed by 50% over whole Taiwan and 70% for East Taiwan. Our findings demonstrate that the intensity of the afternoon thunderstorm is sensitive to fast land-atmosphere interactions by modifying local circulation in the mountainous region of Taiwan.

Effects of vegetation fraction variation on regional climate simulation over Eastern China

Abstract Vegetation-atmosphere interaction is thought to have significant impacts on modulating regional climate. The main purpose of this paper is to reveal the impact of vegetation change on regional climate simulation over Eastern China. Therefore, we have performed three 30-year simulations from 1982 to 2011 with the WRF model coupled with the Noah land surface model as follows: a control experiment with climatology green vegetation fraction (GVF) (CTL), a sensitive experiment with WRF default climatology GVF (CLMV), and a sensitive experiment with dynamic vegetation (DYNV). The climatology difference between the CTL and CLMV experiment points out that during 1982–2011 over Eastern China, the increase of vegetation causes the decrease of temperature; on the other hand, the increase of temperature often corresponds to the reduction of vegetation. In the meantime, the increase/decrease in GVF is quite consistent with the simulated increase/decrease in precipitation, which is caused by both circulations and water vapor distributions at lower levels. The difference in the temperature trend between the CTL and DYNV experiment is almost opposite to that between the DYNV and GVF trend, which means that the decreased/increased vegetation will increase/decrease the surface air temperature. However, the trend of precipitation variation shows no clear relation with that of GVF. In response to GVF change, the results show that in the vegetation-climate feedback the moisture feedback is dominant over the radiation and momentum feedback. Significant decrease in GVF and LAI is detected across the East China, which leads to the decrease in the latent heat flux and increase in the sensible heat flux. For extremes and heat waves, it is shown that DYNV leads to better stimulation on the maximum of daily maximum temperature (TXx), warm spell days (WSDI), heat wave days (HWNY) and the averaged heat wave duration (HWDU) than CTL does over most of the Southeast China, although it may introduce some new biases in the Eastern coastal regions as well as the Northeastern China.