Land Surface Parameterization Schemes Research Articles

Impact of Convective and Land Surface Parameterization Schemes on the Simulation of Surface Temperature and Precipitation Using RegCM4.7 During Summer Period Over the DPR Korea

Impact of Convective and Land Surface Parameterization Schemes on the Simulation of Surface Temperature and Precipitation Using RegCM4.7 During Summer Period Over the DPR Korea

Impact of canopy environmental variables on the diurnal dynamics of water and carbon dioxide exchange at leaf and canopy level

Abstract. Quantifying water vapor and carbon dioxide (CO2) exchange dynamics between the land and the atmosphere through observations and modeling is necessary in order to reproduce and project near-surface climate in coupled land–atmosphere models. The exchange of water and CO2 occurs at the leaf surface (leaf level) and in a net manner through exchanges at all the leaf surfaces composing the vegetation canopy and at the soil surface (canopy level). These exchanges depend on the meteorological forcings imposed by the overlying atmosphere (atmospheric boundary layer level). In this paper, we investigate the effect of four canopy environmental variables (photosynthetic active radiation (PAR), water vapor pressure deficit (VPD), air temperature (T), and atmospheric CO2 concentration (Ca)) on the local individual leaf exchange and canopy exchange of water and CO2 at hourly timescales. Additionally, we investigate the effect of atmospheric boundary layer (ABL) processes on the local exchange. To that end, we simultaneously investigated the exchanges of water and CO2 at leaf level and canopy level for an alfalfa field in northern Spain over 1 day in summer 2021. We used comprehensive observations ranging from stomatal conductance to ABL measurements collected during the Land Surface Interactions with the Atmosphere in the Iberian Semi-Arid Environment (LIAISE) experiment. To support the observational analysis, we used a coupled land–atmosphere model (CLASS model) that has representations at all levels considered. To relate how temporal changes of the four environmental variables modify the fluxes of water and CO2, we studied tendency equations of the leaf gas exchange. These mathematical expressions quantify the temporal evolution of the leaf gas exchange as a function of the temporal evolution of PAR, VPD, T, and Ca. To investigate the effects of ABL processes on the local exchange, we developed three modeling experiments that impose surface radiative perturbations by a cloud passage (which perturbed PAR, T, and VPD), entrainment of dry air from the free troposphere (which perturbed VPD), and advection of cold air (which perturbed T and VPD). The model results and observations matched the leaf gas exchange (r2 between 0.23 and 0.67) and canopy gas exchange (r2 between 0.90 and 0.95). The tendency equations of the modeled leaf gas exchange during the study day revealed that the temporal dynamics of PAR were the main contributor to the temporal dynamics of the leaf gas exchange, with atmospheric CO2 temporal dynamics being the least important contributor. From the three modeling experiments with ABL perturbations, the surface radiative changes induced by a cloud perturbed the CO2 exchange the most, whereas all of them perturbed the water exchange to a similar extent. Second-order effects on the dynamics of the leaf gas exchange were also identified using the tendency equations. For instance, the decrease in the net CO2 assimilation rate during the cloud passage caused by a decrease in surface radiation was further enhanced due to the decrease in air temperature also associated with the cloud. With this research we showcase that the proposed tendency equations can disentangle the effect of environmental variables on the leaf exchange of water and CO2 with the atmosphere, as represented in land–surface parameterization schemes. As such, this framework can become a useful tool with which to analyze these schemes in weather and climate models.

Improving the Asian dust storm prediction using WRF-Chem through combinational optimization of physical parameterization schemes

This study aims to enhance the accuracy of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in forecasting Asian dust storms (ADSs) by using the micro-Genetic Algorithm (μGA). We developed an optimization system---the WRF-Chem-μGA system---to seek the optimal combination of the planetary boundary layer (PBL) and land surface parameterization schemes, which are crucial for numerical forecast of dust storms. The optimization was conducted concerning meteorological and air quality variables, i.e., aerosol optical depth, PBL height, 2 m temperature, 2 m relative humidity, and 10 m wind speed, simultaneously for three ADS cases over the optimization domain, including South Korea. Among a total of 32 available combinations of physical parameterization scheme options (8 from PBL and 4 from land surface schemes), the optimized set through the WRF-Chem-μGA system consists of the Asymmetrical Convective Model version 2 (ACM2) for the PBL scheme and the Noah land surface model with Multiple Parameterization options (Noah-MP) for the land surface scheme. The optimized set showed an improvement ratio of up to 22.5 % in terms of the normalized RMSE for all meteorological and air quality variables, compared to various non-optimized sets of physical parameterization schemes for two additional ADS cases. The optimal set proposed in this study can be used comprehensively in numerical forecasts of various meteorological and air quality problems in the East Asian region, using the WRF-Chem model.

Simulations of a Heavy Snowfall Event in Xinjiang via the WRF Model Coupled with Different Land Surface Parameterization Schemes

Frequent heavy snowfall in Xinjiang plays an important role in the land water cycle. In this study, 18 groups of simulation experiments are conducted on the heavy snowfall event in Xinjiang during 9–13 December of 2015 using the Weather Research and Forecasting (WRF) model. In these experiments, the combination of six land surface parameterization schemes (the Noah scheme, Noah-MP scheme, RUC scheme, CLM4 scheme, PX scheme, and TD scheme) with three microphysical parameterization schemes (the WSM6 scheme, Thompson scheme, and Lin scheme) are adopted, where the observed snowfall data are used for performance evaluation. Results show that the simulated snowfall intensity and snowfall range in different areas are very sensitive to the selection of the land surface scheme. The snowfall in southern Xinjiang is overestimated by almost all six schemes, where the Noah-MP scheme performs more reasonably than the others. The Noah scheme shows its advantage in northwestern Xinjiang. The three different microphysical schemes vary significantly in producing snowfall amount. The WSM6 scheme produced the largest snowfall amount, and the Lin scheme resulted in the smallest snowfall amount. In addition, the accumulated snowfall amounts above 10 mm are generally underestimated by all six land surface schemes, while the accumulated snowfall amounts below 10 mm are overestimated by most of the schemes. The Noah-MP scheme performs the best in the simulation of the snowfall amount in the whole region. However, the Noah scheme shows an advantage in areas with a large snowfall amount.

Quantification of model uncertainty in sub-daily extreme precipitation projections

Sub-daily precipitation is projected to intensify more rapidly with global warming, that in turn may lead to frequent disasters such as flash floods, landslides, etc. However, the precipitation projections from Global Climate Model (GCMs) are known to be highly uncertain, with an increasing magnitude at finer spatio-temporal scales. Hence, realistic quantification of the inherent uncertainty is vital to improve the confidence in these projections, for the mitigation/adaptation applications. In the present study, the model uncertainty in sub-daily (3-hourly) precipitation extremes is estimated, by considering 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, during four different seasons for the near (2026–45) and far-future (2081–99) periods. An uncertainty quantification approach using “mutual information based independence weight” is proposed in this study, to account the linear and nonlinear dependence between GCMs along with the equitability property. The CMCC-CM model is found to be the most independent model having highest independence weight (0.064) in the ensemble. The uncertainty in northern hemisphere is found to be more during June to August period, whereas in southern hemispheres during December to February period. Overall, there is an increase in model uncertainty in far-future, especially over the equatorial regions. However, the behaviour of different indices is not in agreement with each other. For example, extreme characteristics such as monthly maximum 3hourly-precipitation has increasing uncertainty; whereas the mean characteristic like, simple hourly-precipitation intensity index possesses unchanging uncertainty. Model uncertainty is most sensitive to the precipitation characteristics of the regions, followed by elevation, a significant geophysical factor. The study also highlighted the need for model improvement over the grids having elevation <2 km and above 4 km. The study identifies the possible regions, seasons, factors etc. where model improvement can be made. Moreover, it is anticipated that improvement of resolution, updation of convective parameterization schemes and land surface parameterization schemes in GCMs may enhance the skill of GCMs and can improve the confidence in the projection of precipitation on sub-daily scale. • Seasonal model uncertainty is estimated to assess the reliability in the projections of sub-daily precipitation extremes. • Mutual information-based weighting technique is used to establish the independence weight of models. • Different aspects of sub-daily precipitation exhibit differing uncertainty, in terms of magnitude and also temporal evolution. • Precipitation intensity remains the dominant factor behind model uncertainty, but geophysical influences cannot be ignored. • Reliability on the sub-daily mean characteristics improves with time; whereas that on extreme characteristics declines.

Non-linear Routing Scheme at Grid Cell Level for Large Scale Hydrologic Models: A Review

New tools and concepts in the form of mathematical models, remote sensing and Geographic Information System (GIS), communication and telemetering have been developed for the complex hydrologic systems that permit a different analysis of processes and allow watershed to be considered as an integrated planning and management unit. Hydrological characteristics can be generated through spatial analysis, and ready for input into a distributed hydrologic models to define adequately the hydrological response of a watershed that can be related back to the specific environmental, climatic, and geomorphic conditions. In the present paper, some recent development in hydrologic modeling will be reviewed with recognition of the role of horizontal routing scheme in large scale hydrologic modeling. Among others, these developments indicated the needs of alternative horizontal routing models at grid scale level that can be coupled to land surface parameterization schemes that presently still employed the linear routing model. Non-linear routing scheme will be presented and discussed in this paper as possible extension.

Fidelity of Regional Climate Model v4.6 in capturing seasonal and subseasonal variability of Indian summer monsoon

Climate modeling studies in the context of Indian summer monsoon (ISM) variability have usually been performed on the seasonal and interannual timescales. The present study assesses the fidelity of the Regional Climate Model (RegCM v4.6) in capturing the subseasonal active and break spells along with the seasonal mean rainfall during the ISM season. The model fields are obtained from 24 years (1982–2005) of simulation and validated against the observations and latest reanalyzed ERA5 data products. Our analysis indicates that RegCM v4.6 fairly captures the large scale features of ISM and improvement in seasonal rainfall is noted as compared to its precedent RegCM v4.4. At subseasonal timescales, though the model captures the active and break spells of ISM, the length and frequency of these events seem inconsistent as compared to the observations. Occurrences of breaks and associated circulation features are mostly consistent but the active spells are significantly misconstrued in the model. The dry air intrusion from the western region and lack of monsoon low over the mainland and Bay of Bengal seem to suppress the precipitation in the model. This subseasonal bias might persist due to systematic errors linked to the lack of ocean coupling, inefficiency of land surface and cumulus parameterization schemes in the model. Overall, RegCM v4.6 offers improvements at seasonal timescale but needs further improvements to realistically represent the subseasonal variability of ISM.

Sensitivity of convective and land surface parameterization in the simulation of contrasting monsoons over CORDEX-South Asia domain using RegCM-4.4.5.5

An effort for testing the capability and suitability of different cumulus and land surface parameterization schemes in simulating the Indian summer monsoon (ISM) and associated features during contrasting monsoon years using a regional climate model framework is presented. The latest Regional Climate Model system (hereafter RegCM, Vn4.4.5.5) developed and managed by the International Centre for Theoretical Physics (ICTP) has been used to downscale the ERA-Interim reanalysis dataset for three different ISM years, namely, normal (1990), deficit (1987), and excess (1988). Three different convection schemes, namely, Grell (GFC), Emanuel (MIT), and mixed type (i.e., Grell over land and Emanuel over ocean (MIX)), have been used for the simulation of ISM and its associated features. Moreover, three different land surface schemes, namely, the Biosphere–Atmosphere Transfer Scheme or BATS (control), subgrid disaggregation of BATS (SUB-BATS), and Community Land Model 4.5 (CLM4.5), have also been tested for their performance. With these combinations, 27 different suites of experiments were simulated at a horizontal resolution of 50 km over the COordinated Regional Climate Downscaling Experiment (CORDEX)-South Asia domain. The validation of experiments was performed against the India Meteorological Department (IMD) and Climatic Research Unit (CRU) daily dataset for precipitation and near surface air temperature, respectively. In light of different performance metrics, it is found that the near surface air temperature and precipitation in the seasonal-scale simulations are well represented in space for most of the experiments with inherent biases. Among all the experiments, the SUB-BATS scheme in association with GFC cumulus scheme is found to perform consistently among all the years in simulating the ISM precipitation. In most of the cases for the simulation of precipitation, the SUB-BATS seems to outperform the control and CLM4.5 experiments. The domination of dry bias in CLM4.5 experiments is attributed to the improper representation of mean sea level pressure, weaker ISM circulation, and higher atmospheric stability, thus inhibiting the convective activity. Compared to other convection schemes, the MIT scheme is more suitable in representing the features of ISM simulation while using CLM4.5 as a land surface scheme. However, the GFC scheme was found suitable for the simulation of different precipitation years while using SUB-BATS as a land surface model. Such simulations portray reduced biases and improved spatial patterns compared to other schemes. Additionally, CLM4.5 experiments display more utility for the simulation of near surface air temperature. A more comprehensive process-based approach is envisaged to investigate the performance of CLM4.5 in the simulation of ISM for climate-scale simulations.

Application of weather forecasting model WRF for operational electric power network management—a case study for Phailin cyclone

Extreme weather events like tropical cyclone result in colossal catastrophe during landfall causing widespread inland flooding due to storm surge and also the post-landfall event result in extensive damage to infrastructural facilities and property hinterland. The state of Odisha located in east coast of India experienced a Very Severe Cyclonic Storm (VSCS) named Phailin during the post-monsoon season of October 2013. Timely warnings and alertness on storm surge coordinated with a massive evacuation effort by National Disaster Management Authorities (NDMA) were quite effective in minimizing the loss of human life. However, there was a trial of destruction due to extremely high winds and rainfall that followed during post-landfall causing extensive damage to property and major infrastructure facilities in the Odisha State. This study critically investigated the Phailin post-landfall phase focusing on the impact of high winds and rainfall on the power distribution network using the Weather Research and Forecasting (WRF) model. The study evaluated the spatial and temporal variability of wind speed and rainfall distribution from the WRF model configured for three different spatial domains and selecting the best available microphysics and land surface parameterization schemes. The proposed outer, intermediate, and inner domains had spatial resolutions of 27, 9, and 3 km respectively and that provided the best estimate for onshore wind speed, track forecast, and rainfall distribution highly relevant for the management of power distribution and transmission network. In context to weather model application for the Indian region, this effort is novel and probably for the first time that linked a suitable customized weather model output to evaluate its impact on observed tripping in transmission network of electric power grids. The dynamic model outputs from WRF were compared with data from synchrophasors used in electrical technology that monitored the transient and dynamic behavior of power systems in real-time operations. A close examination of the results signifies that the atmospheric model performed exceptionally well in capturing the tripping time of power lines, and the overall knowledge obtained from this study has a broader scope to develop a framework for efficient planning operations of the power network, resource allocation, and emergency preparedness.

The impacts of different land surface parameterization schemes on Northeast China snowfall simulation

In this paper, hindcast experiments were carried out on the snowfall that occurred on 12–13 April 2010 in Northeast China using the WRF model. Three land surface parameterization schemes (Noah, Noah-MP, and SSiB) and three microphysics schemes (WSM5, Lin, and CAM5.1) were chosen for nine ensemble members. The results indicate that spatial distribution of snowfall is sensitive to the choice of land surface schemes. The Noah-MP land surface scheme yields the smallest spatial distribution among the three land surface schemes. The simulations with the Noah and Noah-MP land surface schemes are better than those with the SSiB land surface scheme in terms of spatial correlation coefficient. Microphysics schemes have an influence on snowfall amount in the nine simulation experiments. The Lin and CAM5.1 microphysics schemes lead to much less snowfall than the WSM5 microphysics scheme. Overall, the ensemble means of simulations with three different land surface schemes can capture the features of snowfall distribution, especially when combined with the CAM5.1 microphysics scheme.

The Multibudget Soil, Vegetation, and Snow (SVS) Scheme for Land Surface Parameterization: Offline Warm Season Evaluation

Abstract A new land surface parameterization scheme, named the Soil, Vegetation, and Snow (SVS) scheme, was recently developed at Environment and Climate Change Canada to replace the operationally used Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme. The new scheme is designed to address a number of weaknesses and limitations of ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single energy budget for the different land surface components, SVS introduces a new tiling approach that includes separate energy budgets for bare ground, vegetation, and two different snowpacks (over bare ground and low vegetation and under high vegetation). The inclusion of a photosynthesis module as an option to determine the surface stomatal resistance is another significant addition in SVS. The representation of vertical water transport through soil has also been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline simulations conducted in the present study demonstrated clear improvements in warm season meteorological predictions with SVS compared to the ISBA scheme. The results also revealed considerable reduction of standard error in the SVS-predicted L-band brightness temperature. This demonstrates the scheme’s ability for better hydrological prediction and its potential for providing more accurate soil moisture analysis. The impact of the photosynthesis module within the current implementation of SVS is, however, found to be negligible on near-surface meteorological prediction and slightly negative for brightness temperature.

Sensitivity of a regional climate model to land surface parameterization schemes for East Asian summer monsoon simulation

Land surface processes play an important role in the East Asian Summer Monsoon (EASM) system. Parameterization schemes of land surface processes may cause uncertainties in regional climate model (RCM) studies for the EASM. In this paper, we investigate the sensitivity of a RCM to land surface parameterization (LSP) schemes for long-term simulation of the EASM. The Weather Research and Forecasting (WRF) Model coupled with four different LSP schemes (Noah-MP, CLM4, Pleim–Xiu and SSiB), hereafter referred to as Sim-Noah, Sim-CLM, Sim-PX and Sim-SSiB respectively, have been applied for 22-summer EASM simulations. The 22-summer averaged spatial distributions and strengths of downscaled large-scale circulation, 2-m temperature and precipitation are comprehensively compared with ERA-Interim reanalysis and dense station observations in China. Results show that the downscaling ability of RCM for the EASM is sensitive to LSP schemes. Furthermore, this study confirms that RCM does add more information to the EASM compared to reanalysis that imposes the lateral boundary conditions (LBC) because it provides 2-m temperature and precipitation that are with higher resolution and more realistic compared to LBC. For 2-m temperature and monsoon precipitation, Sim-PX and Sim-SSiB simulations are more consistent with observation than simulations of Sim-Noah and Sim-CLM. To further explore the physical and dynamic mechanisms behind the RCM sensitivity to LSP schemes, differences in the surface energy budget between simulations of Ens-Noah-CLM (ensemble mean averaging Sim-Noah and Sim-CLM) and Ens-PX-SSiB (ensemble mean averaging Sim-PX and Sim-SSiB) are investigated and their subsequent impacts on the atmospheric circulation are analyzed. It is found that the intensity of simulated sensible heat flux over Asian continent in Ens-Noah-CLM is stronger than that in Ens-PX-SSiB, which induces a higher tropospheric temperature in Ens-Noah-CLM than in Ens-PX-SSiB over land. The adaptive modulation of geopotential height gradients affects wind field (through geostrophic balance) simulation especially at lower levels, which subsequently affects the simulation of large-scale circulation, 2-m temperature and monsoon precipitation as well as RCM’s downscaling ability.

Customization of regional climate model (RegCM4) over Indian region

The regional climate model (RegCM4) is customized for 10-year climate simulation over Indian region through sensitivity studies on cumulus convection and land surface parameterization schemes. The model is configured over 30° E–120° E and 15° S–45° N at 30-km horizontal resolution with 23 vertical levels. Six 10-year (1991–2000) simulations are conducted with the combinations of two land surface schemes (BATS, CLM3.5) and three cumulus convection schemes (Kuo, Grell, MIT). The simulated annual and seasonal climatology of surface temperature and precipitation are compared with CRU observations. The interannual variability of these two parameters is also analyzed. The results indicate that the model simulated climatology is sensitive to the convection as well as land surface parameterization. The analysis of surface temperature (precipitation) climatology indicates that the model with CLM produces warmer (dryer) climatology, particularly over India. The warmer (dryer) climatology is due to the higher sensible heat flux (lower evapotranspiration) in CLM. The model with MIT convection scheme simulated wetter and warmer climatology (higher precipitation and temperature) with smaller Bowen ratio over southern India compared to that with the Grell and Kuo schemes. This indicates that a land surface scheme produces warmer but drier climatology with sensible heating contributing to warming where as a convection scheme warmer but wetter climatology with latent heat contributing to warming. The climatology of surface temperature over India is better simulated by the model with BATS land surface model in combination with MIT convection scheme while the precipitation climatology is better simulated with BATS land surface model in combination with Grell convection scheme. Overall, the modeling system with the combination of Grell convection and BATS land surface scheme provides better climate simulation over the Indian region.

The role of land surface schemes in the regional climate model (RegCM) for seasonal scale simulations over Western Himalaya

Climate prediction over the Western Himalaya is a challenging task due to the highly variable altitude and orientation of orographic barriers. Surface characteristics also play a vital role in climate simulations and need appropriate representation in the models. In this study, two land surface parameterization schemes (LSPS), the Biosphere-Atmosphere Transfer Scheme (BATS) and the Common Land Model (CLM, version 3.5) in the regional climate model (RegCM, version 4) have been tested over the Himalayan region for nine distinct winter seasons in respect of seasonal precipitation (three years each for excess, normal and deficit). Reanalysis II data of the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) have been used as initial and lateral boundary conditions for the RegCM model. In order to provide land surface boundary conditions in the RegCM model, geophysical parameters (10 min resolution) obtained from the United States Geophysical Survey were used. The performance of two LSPS (CLM and BATS) coupled with the RegCM is evaluated against gridded precipitation and surface temperature data sets from the India Meteorological Department (IMD). It is found that the simulated surface temperature and precipitation are better represented in the CLM scheme than in the BATS when compared with observations. Further, several statistical analysis such as bias, root mean square error (RMSE), spatial correlation coefficient (CC) and skill scores like the equitable threat score (ETS) and the probability of detection (POD) are estimated for evaluating RegCM simulations using both LSPS. Results indicate that the RMSE decreases and the CC increases with the use of the CLM compared to BATS. ETS and POD also indicate that the performance of the model is better with the CLM than with the BATS in simulating seasonal scale precipitation. Overall, results suggest that the performance of the RegCM coupled with the CLM scheme improves the model skill in predicting winter precipitation (by 15-25%) and temperature (by 10-20%) over the Western Himalaya.

Surface Water and Energy Budgets for the Mississippi River Basin in Three NCEP Reanalyses

Abstract Surface water and energy budgets from the National Centers for Environmental Prediction–U.S. Department of Energy (NCEP–DOE) Atmospheric Model Intercomparison Project (AMIP-II) Global Reanalysis 2 (GR2), the North American Regional Reanalysis (NARR), and the NCEP Climate Forecast System Reanalysis (CFSR) are compared here with each other and with available observations over the Mississippi River basin. The comparisons in seasonal cycle, interannual variation, and annual mean over a 31-yr period show that there are a number of noticeable differences and similarities in the large-scale basin averages. Warm season precipitation and runoff in the GR2 are too large compared to the observations, and seasonal surface water variation is small. By contrast, the precipitation in both NARR and CFSR is more reasonable and in better agreement with the observation, although the corresponding seasonal runoff is very small. The main causes of the differences in both surface parameterization and approach used in assimilating the observed precipitation datasets and snow analyses are then discussed. Despite the discrepancies in seasonal water budget components, seasonal energy budget terms in the three reanalyses are close to each other and to available observations. The interannual variations in both water and energy budgets are comparable. This study shows that the CFSR achieves a large improvement over the GR2, indicating that the CFSR dataset can be used in climate variability studies. Nonetheless, improved land surface parameterization schemes and data assimilation techniques are needed to depict the surface water and energy climates better, in particular, the variation in seasonal runoff.

Variability in evaporation across the Canadian Prairie region during drought and non-drought periods

Knowledge of changes in spatial and temporal distributions of actual evaporation would be useful for land surface parameterizations in the Prairie region of Canada. Yet challenges persist for examining the variability of evaporation from land surfaces and vegetation over such a large region. This is due in part to the existence of numerous methods of varying complexity for obtaining estimates of evaporation and a general lack of sufficient measurements to drive detailed models. Integrated approaches may be applied for distributing evaporation over vast regions using energy and mass balance methods that integrate remote sensing imagery and surface reference data. Whilst informative, previous studies have not considered the variability of actual evaporation under drought and above normal moisture conditions. Continuous physically-based simulations were conducted for a 46 year period using the Cold Regions Hydrological Model (CRHM) platform. The Penman–Monteith model was applied in this platform to calculate estimates of actual evaporation at point locations which had sufficient hourly measurements. Variations in the statistical properties and mapped distributions derived from point-scale modelling via CRHM were instructional for understanding how evaporation varied spatially and temporally for a baseline normal period (1971–2000) and the years 1999–2005 which included both drought and above normal moisture conditions. The modelling approach was applied successfully for examining the historical variability of evaporation and can be applied to constrain land surface parameterization schemes; validate more empirical predictive model outputs; inform operational agrometeorological and hydrological applications in the Canadian Prairies.

Sensitivity of regional climate simulations to land-surface schemes on the Tibetan Plateau

To investigate the effects of land-surface schemes in 2 regional climate models (RegCM3 and RegCM4.3.4) on the Tibetan Plateau (TP) climate simulation, 3 groups of 10 yr (1992-2001) simulation experiments were performed based on 2 land-surface schemes (BATS and CLM3.5). The simulations (RegCM3_BATS, RegCM4_BATS, and RegCM4_CLM) were compared with observed data by setting the same domain and initial and lateral atmospheric boundary conditions and using 30 km spatial resolution. The results showed that, compared to observed data, regional average annual mean temperature was underestimated by 1.22, 2.11, and 1.32 degrees C, and regional average annual precipitation was overestimated by 43.2, 49.8, and 18.4% in RegCM3_BATS, RegCM4_BATS, and RegCM4_CLM respectively. There were significant differences in simulated energy and water budget components among the 3 models resulting from the influence of the 2 different land-surface parameterization schemes, which impacted the simulated precipitation and temperature results through interaction between the land surface and the atmosphere. Therefore, climate simulation over the TP is very sensitive to the use of different land-surface schemes in regional climate models. Overall, use of RegCM4_CLM instead of RegCM_BATS resulted in a warmer and drier land surface and a better simulation of annual average spatial patterns of temperature and precipitation.

A Study on the Influence of the Land Surface Processes on the Southwest Monsoon Simulations using a Regional Climate Model

Influence of the land surface processes as an important mechanism in the development of the Indian Summer Monsoon is studied by performing simulations with a regional atmospheric model. Seasonal scale simulations are conducted for two contrasting summer monsoons (MJJAS months) in 2008 & 2009 with the Weather Research and Forecasting-Advanced Research regional model at a high resolution of 15 km using the boundary conditions derived from the National Centers for Environmental Prediction (NCEP) reanalysis data and using the NOAH land surface parameterization scheme. Simulations are evaluated by comparison of precipitation with 0.5° India Meteorological Department gridded rainfall data over land, atmospheric circulation fields with 1° resolution NCEP global final analysis, and surface fluxes with 0.75° resolution Era-Interim reanalysis. Results indicated significant variation in the evolution of the surface fluxes, air temperatures and flux convergence in the 2 contrasting years. A lower albedo, higher heating (sensible, latent heat fluxes), higher air temperatures, stronger flow and higher moisture flux convergence are noted over the subcontinent during the monsoon 2008 relative to the monsoon 2009. The simulated surface fluxes are in good comparison with observations. The stronger flow in 2008 is found to be associated with stronger heat flux gradients as well as stronger north-south geopotential/pressure gradients. The simulations revealed notable differences in many features such as zonal and meridional surface sensible heat gradients which, in turn, influenced the low-level pressure gradients, wind flow, and moisture transport. The present study reveals that, even at a regional scale, the physical processes of land-surface energy partitioning do influence the regional behavior of the monsoon system to a certain extent.

Impact of Land Surface Processes on the South Asian Monsoon Simulations Using WRF Modeling System

The Weather Research and Forecasting model has been used to examine the role of land surface processes on Indian summer monsoon simulations. Isolated experiments have been carried out with physical parameterization schemes (land surface and planetary boundary layer) and data assimilation to examine their relative roles in the representation of regional hydroclimate in model simulations. The impact of vegetation green fraction on the model simulations has been extensively studied by replacing the default United States Geological Survey (USGS) vegetation cover data with that of Indian Space Research Organisation (ISRO) data. Results indicate that differences in the treatment of surface processes in the model lead to large differences in precipitation simulation over the Indian domain. Several hydroclimate parameters from the simulations using ISRO and USGS vegetation green fractions were examined. It is seen that the role of vegetation green fraction in these experiments has been to increase latent heat flux to the atmosphere. Two sets of data assimilation experiments were also carried out for an entire year using the same set of observed data but with different land surface parameterization schemes. It is found that evenwhen using the same observed data, the differences in land surface schemes reduce the impact and contribution of observed data being assimilated into the model. The hydroclimate over the region becomes a function of the land surface scheme. This study highlights the importance of vegetation green fraction and land surface schemes in the context of the regional hydroclimate over South Asia.

Assessment of uncertainties in the response of the African monsoon precipitation to land use change simulated by a regional model

Land use and land cover (LULC) over Africa have changed substantially over the last 60 years and this change has been proposed to affect monsoon circulation and precipitation. This study examines the uncertainties of model simulated response in the African monsoon system and Sahel precipitation due to LULC change using a set of regional model simulations with different combinations of land surface and cumulus parameterization schemes. Although the magnitude of the response covers a broad range of values, most of the simulations show a decline in Sahel precipitation due to the expansion of pasture and croplands at the expense of trees and shrubs and an increase in surface air temperature. The relationship between the model responses to LULC change and the climatologists of the control simulations is also examined. Simulations that are climatologically too dry or too wet compared to observations and reanalyses have weak response to land use change because they are in moisture or energy limited regimes respectively. The ones that lie in between have stronger response to the LULC changes, showing a more significant role in land–atmosphere interactions. Much of the change in precipitation is related to changes in circulation, particularly to the response of the intensity and latitudinal position of the African Easterly Jet, which varies with the changes in meridional surface temperature gradients. The study highlights the need for measurements of the surface fluxes across the meridional cross-section of the Sahel to evaluate models and thereby allowing human impacts such as land use change on the monsoon to be projected more realistically.