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Land 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⁻²and higher by 14.86 W·m⁻²in 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⁻¹in Ave-CLM4-SSiB than that in Ave-NoahMP-PX over Central Asia continent.

 

Earlier studies of land use land cover change (LULCC) normally used only a specified LULCC map with no interannual variations. In this study, using an Atmospheric General Circulation Model (AGCM) coupled with a land surface model, biophysical impacts of LULCC on global and regional climate are investigated by using a LULCC map which covers 63 years from 1948 to 2010 with interannual variation. A methodology has been developed to convert a recently developed LULCC fraction map with 1° × 1° resolution to the AGCM grid points in which only one dominant type is allowed. Comprehensive evaluations are conducted to ensure consistency of the trend of the original LULCC fraction change and the trend of the fraction of grid point changes over different regions. The model was integrated with a potential vegetation map (CTL) and the map with LULCC, in which a set of surface parameters such as leaf area index, albedo and other soil and vegetation parameters were accordingly changed with interannual variation. The results indicate that the interannual LULCC map simulation is able to reproduce better interannual variability of surface temperature and rainfall when compared to the control simulation. LULCC causes negative effect on global precipitation, with the strongest significant signals over degraded regions such as East Asia, West Africa and South America, and some of these changes are consistent with observed regional anomalies for certain time periods. LULCC causes reduction in net radiation and evapotranspiration which leads to changes in monsoon circulation and variation in magnitude and pattern of moisture flux convergence and subsequent reduction in precipitation. Meanwhile, LULCC enhances surface warming during the summer in the LULCC regions due to greatly reduced evapotranspiration. In contradiction to the surface, upper troposphere temperatures are cool because of less latent heat released into the upper troposphere, which leads to weaker circulation in LULCC regions.

 

Abstract Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations. 

Abstract When compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees. 

Abstract. Frozen soil processes are of great importance incontrolling surface water and energy balances during the cold season and incold regions. Over recent decades, considerable frozen soil degradation andsurface soil warming have been reported over the Tibetan Plateau and NorthChina, but most land surface models have difficulty in capturing thefreeze–thaw cycle, and few validations focus on the effects of frozen soil processes on soil thermal characteristics in these regions. This paperaddresses these issues by introducing a physically more realistic andcomputationally more stable and efficient frozen soil module (FSM) into aland surface model – the third-generation Simplified Simple Biosphere Model (SSiB3-FSM). To overcome the difficulties in achieving stable numericalsolutions for frozen soil, a new semi-implicit scheme and a physics-basedfreezing–thawing scheme were applied to solve the governing equations. The performance of this model as well as the effects of frozen soil process onthe soil temperature profile and soil thermal characteristics were investigated over the Tibetan Plateau and North China using observationsites from the China Meteorological Administration and models from 1981 to 2005. Results show that the SSiB3 model with the FSM produces a more realistic soiltemperature profile and its seasonal variation than that without FSM duringthe freezing and thawing periods. The freezing process in soil delays thewinter cooling, while the thawing process delays the summer warming. Thetime lag and amplitude damping of temperature become more pronounced withincreasing depth. These processes are well simulated in SSiB3-FSM. Thefreeze–thaw processes could increase the simulated phase lag days and land memory at different soil depths as well as the soil memory change with the soil thickness. Furthermore, compared with observations, SSiB3-FSM producesa realistic change in maximum frozen soil depth at decadal scales. This study shows that the soil thermal characteristics at seasonal to decadal scalesover frozen ground can be greatly improved in SSiB3-FSM, and SSiB3-FSM can be used as an effective model for TP and NC simulation during cold season. Overall, this study could help understand the vertical soil thermalcharacteristics over the frozen ground and provide an important scientificbasis for land–atmosphere interactions.