Representation Of Land Surface Processes Research Articles

Evolution of the Convective Boundary Layer in a WRF Simulation Nested Down to 100 m Resolution During a Cloud‐Free Case of LAFE, 2017 and Comparison to Observations

AbstractThe Weather Research and Forecasting model was applied in a nested configuration from the convection‐permitting resolution of 2.5 km, via an intermediate resolution of 500 m down to the 100 m turbulence‐permitting scale to investigate the spatial and temporal evolution of the convective boundary layer and their interaction with the underlying land surface. This included detailed comparisons with observations collected during the Land‐Atmosphere Feedback Experiment, performed at the Central Facility in Oklahoma. The simulation was driven by the operational analysis of the European Center for Medium‐range Weather Forecasting for a cloud‐free case on 23 August 2017 for which the measurement operation was extended into the following night to include the evening decay of the daytime convective boundary layer. The mesoscale 2.5 km resolution was capable to represent the correct boundary layer height and its temporal evolution. Details of the morning and evening transitions between the nighttime and daytime boundary layer as well as its internal structure were only simulated by the 100 m turbulence‐permitting simulation. Although systematic differences between the simulation and lidar observations were found, the model captured the temporal and spatial structure and the statistics of turbulence rather well. Comparison with data from Eddy‐Covariance stations showed that the model was able to reproduce the evolution of many near‐surface meteorological fields. Systematically higher surface temperatures and related differences in the surface fluxes suggest weaknesses in the representation of land surface processes although the simulation was initialized with accurate and high‐resolution initial fields.

Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1

Abstract. With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems. However, they are still limited with respect to the impact of agriculture on land surface variables. This is particularly true for agro-hydrological processes related to a strong human control on freshwater. While many LSMs consider natural processes only, the development of human-related processes, e.g. crop phenology and irrigation in LSMs, is key. In this study, we present the implementation of a new crop phenology and irrigation scheme in the ISBA (interactions between soil–biosphere–atmosphere) LSM. This highly flexible scheme is designed to account for various configurations and can be applied at different spatial scales. For each vegetation type within a model grid cell, three irrigation systems can be used at the same time. A limited number of parameters are used to control (1) the amount of water used for irrigation, (2) irrigation triggering (based on the soil moisture stress), and (3) crop seasonality (emergence and harvesting). A case study is presented over Nebraska (USA). This region is chosen for its high irrigation density and because independent observations of irrigation practices can be used to verify the simulated irrigation amounts. The ISBA simulations with and without the new crop phenology and irrigation scheme are compared to different satellite-based observations. The comparison shows that the irrigation scheme improves the simulated vegetation variables such as leaf area index, gross primary productivity, and land surface temperature. In addition to a better representation of land surface processes, the results point to potential applications of this new version of the ISBA model for water resource monitoring and climate change impact studies.

Global Soil Hydraulic Properties dataset based on legacy site observations and robust parameterization

The representation of land surface processes in hydrological and climatic models critically depends on the soil water characteristics curve (SWCC) that defines the plant availability and water storage in the vadose zone. Despite the availability of SWCC datasets in the literature, significant efforts are required to harmonize reported data before SWCC parameters can be determined and implemented in modeling applications. In this work, a total of 15,259 SWCCs from 2,702 sites were assembled from published literature, harmonized, and quality-checked. The assembled SWCC data provide a global soil hydraulic properties (GSHP) database. Parameters of the van Genuchten (vG) SWCC model were estimated from the data using the R package ‘soilhypfit’. In many cases, information on the wet- or dry-end of the SWCC measurements were missing, and we used pedotransfer functions (PTFs) to estimate saturated and residual water contents. The new database quantifies the differences of SWCCs across climatic regions and can be used to create global maps of soil hydraulic properties.

High-Resolution WRF Simulation of Extreme Heat Events in Eastern China: Large Sensitivity to Land Surface Schemes

Regional climate models with high-resolution simulation are particularly useful for providing a detailed representation of land surface processes, and for studying the relationship between land surface processes and heat events. However, large differences and uncertainties exist among different land surface schemes (LSSs). This study comprehensively assesses the sensitivity to different LSSs based on two extreme heat events in eastern China using the Weather Research and Forecasting (WRF) model. Among the five LSSs (i.e., 5TD, CLM4, Noah, Noah-MP and RUC), Noah is closest to observations in reproducing the temperatures and energy fluxes for both two heat events. The modeled warm biases result mainly from the underestimation of evapotranspirative cooling. Our results show that how each LSS partitions the evapotranspiration (ET) and sensible heat largely determines the relationship between the temperature and turbulent fluxes. Although the simulated two extreme heat events manifest similar biases in the temperatures and energy fluxes, the land surface responses (ET and soil moisture) are different.

Evaluating the land-surface energy partitioning in ERA5

Abstract. Climate reanalyses provide a plethora of global atmospheric and surface parameters in a consistent manner over multi-decadal timescales. Hence, they are widely used in many fields, and an in-depth evaluation of the different variables provided by reanalyses is a necessary means to provide feedback on the quality to their users and the operational centres producing these data sets, and to help guide their development. Recently, the European Centre for Medium-Range Weather Forecasts (ECMWF) released the new state-of-the-art climate reanalysis ERA5, following up on its popular predecessor ERA-Interim. Different sets of variables from ERA5 were already evaluated in a handful of studies, but so far, the quality of land-surface energy partitioning has not been assessed. Here, we evaluate the surface energy partitioning over land in ERA5 and concentrate on the appraisal of the surface latent heat flux, surface sensible heat flux, and Bowen ratio against different reference data sets and using different modelling tools. Most of our analyses point towards a better quality of surface energy partitioning in ERA5 than in ERA-Interim, which may be attributed to a better representation of land-surface processes in ERA5 and certainly to the better quality of near-surface meteorological variables. One of the key shortcomings of the reanalyses identified in our study is the overestimation of the surface latent heat flux over land, which – although substantially lower than in ERA-Interim – still remains in ERA5. Overall, our results indicate the high quality of the surface turbulent fluxes from ERA5 and the general improvement upon ERA-Interim, thereby endorsing the efforts of ECMWF to improve their climate reanalysis and to provide useful data to many scientific and operational fields.

Role of vegetation in representing land surface temperature in the CHTESSEL (CY45R1) and SURFEX-ISBA (v8.1) land surface models: a case study over Iberia

Abstract. Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modeling platform (SURFEX-ISBA) for the 2004–2015 period. The results showed that the daily maximum LST simulated by CHTESSEL over Iberia was affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 ∘C over wide portions of central and southwestern Iberia. This error was shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) leaf area index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also included a clumping approach that introduces seasonality to the vegetation cover. The results showed significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guiding the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface–atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve the model's representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis; hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we showed that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth-generation reanalysis (ERA5). Finally, our results provided hints about the interaction between vegetation land–atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrated the added value of using multiple earth observation products for constraining and improving weather and climate simulations.

Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors

The ability of regional climate models (RCMs) to accurately simulate current and future climate is increasingly important for impact assessment. This is the first evaluation of all reanalysis-driven RCMs within the CORDEX Australasia framework [four configurations of the Weather Forecasting and Research (WRF) model, and single configurations of COSMO-CLM (CCLM) and the Conformal-Cubic Atmospheric Model (CCAM)] to simulate the historical climate of Australia (1981–2010) at 50 km resolution. Simulations of near-surface maximum and minimum temperature and precipitation were compared with gridded observations at annual, seasonal, and daily time scales. The spatial extent, sign, and statistical significance of biases varied markedly between the RCMs. However, all RCMs showed widespread, statistically significant cold biases in maximum temperature which were the largest during winter. This bias exceeded − 5 K for some WRF configurations, and was the lowest for CCLM at ± 2 K. Most WRF configurations and CCAM simulated minimum temperatures more accurately than maximum temperatures, with biases in the range of ± 1.5 K. RCMs overestimated precipitation, especially over Australia’s populous eastern seaboard. Strong negative correlations between mean monthly biases in precipitation and maximum temperature suggest that the maximum temperature cold bias is linked to precipitation overestimation. This analysis shows that the CORDEX Australasia ensemble is a valuable dataset for future impact studies, but improving the representation of land surface processes, and subsequently of surface temperatures, will improve RCM performance. The varying RCM capabilities identified here serve as a foundation for the development of future regional climate projections and impact assessments for Australia.

Impact of Climate Change on Vegetation Distribution and Net Primary Productivity of Forests of Himalayan River Basins: Brahmaputra, Koshi and Indus

The impacts of climate change in terms of forest vegetation shifts and Net Primary Productivity (NPP) changes are assessed for Brahmaputra, Koshi and Indus river basins for the mid (2021-2050) and long (2071-2100) terms for RCP4.5 and RCP8.5 scenarios. Two Dynamical Global Vegetation Models (DGVMs), Integrated BIosphere Simulator (IBIS) and (Lund Postdam and Jena (LPJ), have been used for this purpose. The DGVMs are driven by the ensemble mean climate projections from 5 climate models that contributed to the CMIP5 data base. While both DGVMs project vegetation shifts in the forest areas of the basins, there are large differences in vegetation shifts projected by IBIS and LPJ. This may be attributed to differing representation of land surface processes and to differences in the number of vegetation types (Plant Functional Types) defined and simulated in the two models. However, there is some agreement in NPP changes as projected by both IBIS and LPJ, with IBIS mostly projecting a larger increase in NPP for the future scenarios. Despite the uncertainties with respect to climate change projections at river basin level and the differing impact assessments from different DGVMs, it is necessary to assess the “vulnerability” of the forest ecosystems and forest dependent communities to current climate risks and future climate change and to develop and implement resilience or adaptation measures. Assessment of the “vulnerability” and designing of the adaptation strategies could be undertaken for all the forested grids where both IBIS and LPJ project vegetation shifts.

Exploring new topography-based subgrid spatial structures for improving land surface modeling

Abstract. Topography plays an important role in land surface processes through its influence on atmospheric forcing, soil and vegetation properties, and river network topology and drainage area. Land surface models with a spatial structure that captures spatial heterogeneity, which is directly affected by topography, may improve the representation of land surface processes. Previous studies found that land surface modeling, using subbasins instead of structured grids as computational units, improves the scalability of simulated runoff and streamflow processes. In this study, new land surface spatial structures are explored by further dividing subbasins into subgrid structures based on topographic properties, including surface elevation, slope and aspect. Two methods (local and global) of watershed discretization are applied to derive two types of subgrid structures (geo-located and non-geo-located) over the topographically diverse Columbia River basin in the northwestern United States. In the global method, a fixed elevation classification scheme is used to discretize subbasins. The local method utilizes concepts of hypsometric analysis to discretize each subbasin, using different elevation ranges that also naturally account for slope variations. The relative merits of the two methods and subgrid structures are investigated for their ability to capture topographic heterogeneity and the implications of this on representations of atmospheric forcing and land cover spatial patterns. Results showed that the local method reduces the standard deviation (SD) of subgrid surface elevation in the study domain by 17 to 19 % compared to the global method, highlighting the relative advantages of the local method for capturing subgrid topographic variations. The comparison between the two types of subgrid structures showed that the non-geo-located subgrid structures are more consistent across different area threshold values than the geo-located subgrid structures. Overall the local method and non-geo-located subgrid structures effectively and robustly capture topographic, climatic and vegetation variability, which is important for land surface modeling.

Contributions of soil moisture interactions to future precipitation changes in the GLACE-CMIP5 experiment

Changes in soil moisture are likely to contribute to future changes in latent heat flux and various characteristics of daily precipitation. Such contributions during the second half of the twenty-first century are assessed using the simulations from the GLACE-CMIP5 experiment, applying a linear regression analysis to determine the magnitude of these contributions. As characteristics of daily precipitation, mean daily precipitation, the frequency of wet days and the intensity of precipitation on wet days are considered. Also, the frequency and length of extended wet and dry spells are studied. Particular focus is on the regional (for nine selected regions) as well as seasonal variations in the magnitude of the contributions of the projected differences in soil moisture to the future changes in latent heat flux and in the characteristics of daily precipitation. The results reveal the overall tendency that the projected differences in soil moisture contribute to the future changes in response to the anthropogenic climate forcing for all the meteorological variables considered here. These contributions are stronger and more robust (i.e., there are smaller deviations between individual climate models) for the latent heat flux than for the characteristics of daily precipitation. It is also found that the contributions of the differences in soil moisture to the future changes are generally stronger and more robust for the frequency of wet days than for the intensity of daily precipitation. Consistent with the contributions of the projected differences in soil moisture to the future changes in the frequency of wet days, soil moisture generally contributes to the future changes in the characteristics of wet and dry spells. The magnitude of these contributions does not differ systematically between the frequency and the length of such extended spells, but the contributions are generally slightly stronger for dry spells than for wet spells. Distinguishing between the nine selected regions and between the different seasons, it is found that the strength of the contributions of the differences in soil moisture to the future changes in the various meteorological variables varies by region and, in particular, by season. Similarly, the robustness of these contributions varies between the regions and in the course of the year. The importance of soil moisture changes for the future changes in various aspects of daily precipitation and other aspects of the hydrological cycle illustrates the need for a comprehensive and realistic representation of land surface processes and of land surface conditions in climate models.

Is land surface processes representation a possible weak link in current Regional Climate Models?

The representation of land surface processes and fluxes in climate models critically affects the simulation of near-surface climate over land. Here we present an evaluation of COSMO-CLM2, a model which couples the COSMO-CLM Regional Climate Model to the Community Land Model (CLM4.0). CLM4.0 provides a more detailed representation of land processes compared to the native land surface scheme in COSMO-CLM. We perform historical reanalysis-driven simulations over Europe with COSMO-CLM2 following the EURO-CORDEX intercomparison protocol. We then evaluate simulations performed with COSMO-CLM2, the standard COSMO-CLM and other EURO-CORDEX RCMs against various observational datasets of temperature, precipitation and surface fluxes. Overall, the results indicate that COSMO-CLM2 outperforms both the standard COSMO-CLM and the other EURO-CORDEX models in simulating sensible, latent and surface radiative fluxes as well as 2-meter temperature across different seasons and regions. The performance improvement is particularly strong for turbulent fluxes and for daily maximum temperatures and more modest for daily minimum temperature, suggesting that land surface processes affect daytime even more than nighttime conditions. COSMO-CLM2 also alleviates a long-standing issue of overestimation of interannual summer temperature variability present in most EURO-CORDEX RCMs. Finally, we show that several factors contribute to these improvements, including the representation of evapotranspiration, radiative fluxes and ground heat flux. Overall, these results demonstrate that land processes represent a key area of development to tackle current deficiencies in RCMs.

Investigation of PBL schemes combining the WRF model simulations with scanning water vapor differential absorption lidar measurements

AbstractSix simulations with the Weather Research and Forecasting (WRF) model differing in planetary boundary layer (PBL) schemes and land surface models (LSMs) are investigated in a case study in western Germany during clear‐sky weather conditions. The simulations were performed at 2 km resolution with two local and two nonlocal PBL schemes, combined with two LSMs (NOAH and NOAH‐MP). Resulting convective boundary layer (CBL) features are investigated in combination with high‐resolution water vapor differential absorption lidar measurements at an experimental area. Further, the simulated soil‐vegetation‐atmosphere feedback processes are quantified applying a mixing diagram approach. The investigation shows that the nonlocal PBL schemes simulate a deeper and drier CBL than the local schemes. Furthermore, the application of different LSMs reveals that the entrainment of dry air depends on the energy partitioning at the land surface. The study demonstrates that the impact of processes occurring at the land surface is not constrained to the lower CBL but extends up to the interfacial layer and the lower troposphere. With respect to the choice of the LSM, the discrepancies in simulating a diurnal change of the humidity profiles are even more significant at the interfacial layer than close to the land surface. This indicates that the representation of land surface processes has a significant impact on the simulation of mixing properties within the CBL.

Impact of upper ocean processes and air-sea fluxes on seasonal SST biases over the tropical Indian Ocean in the NCEP Climate Forecasting System

The present study aims to examine the role of air–sea interactions and upper ocean processes in determining the tropical Indian Ocean (TIO) seasonal sea surface temperature (SST) bias in Climate Forecast System version 1 (CFSv1) and version 2 (CFSv2) free runs. CFSv1 displayed dipole like east–west SST bias over the equatorial Indian Ocean from boreal summer to winter and is consistent with errors (bias) in surface winds and upper ocean advection. Large zonal gradients in sea level pressure (SLP) bias and the associated surface wind biases are primarily responsible for the upper ocean current bias. However, over the southern Indian Ocean and parts of Arabian Sea, strong bias in heat flux and mixed layer depth (MLD) have mainly contributed for the SST biases in CFSv1. Equatorial current system is better represented in CFSv2 compared to CFSv1. Improvement in the representation of land-surface processes appears to be contributing towards improving atmospheric circulation and SLP gradients in CFSv2, which may be responsible for the improved ocean circulation. Importantly, east–west dipole like SST bias prevalent in CFSv1 is absent in CFSv2. However, there is a prominent systematic basin-wide TIO cold SST bias in CFSv2. Large biases in surface heat flux (net negative bias) and MLD (deeper) are mainly responsible for SST biases in CFSv2. Negative net heat flux bias in CFSv2 is primarily due to specific humidity bias-induced excess latent heat flux (LHF). Deepening of MLD is mainly due to strong convective mixing, a resultant of anomalous LHF release, which in turn leads to negative SST bias. Models comparison reveals that although representation of SST in CFSv2 is better than in CFSv1, it is essential to improve further the equatorial ocean dynamics and off-equatorial thermodynamics in the form of moist processes and radiative parameterization in order to reduce SST bias in CFSv2.

Hadley Circulation Response to Orbital Precession. Part II: Subtropical Continent

Abstract The response of the monsoonal and annual-mean Hadley circulation to orbital precession is examined in an idealized atmospheric general circulation model with a simplified representation of land surface processes in subtropical latitudes. When perihelion occurs in the summer of a hemisphere with a subtropical continent, changes in the top-of-atmosphere energy balance, together with a poleward shift of the monsoonal circulation boundary, lead to a strengthening of the monsoonal circulation. Spatial variations in surface heat capacity determine whether radiative perturbations are balanced by energy storage or by atmospheric energy fluxes. Although orbital precession does not affect annual-mean insolation, the annual-mean Hadley circulation does respond to orbital precession because its sensitivity to radiative changes varies over the course of the year: the monsoonal circulation in summer is near the angular momentum-conserving limit and responds directly to radiative changes; whereas in winter, the circulation is affected by the momentum fluxes of extratropical eddies and is less sensitive to radiative changes.

Validating a regional climate model’s downscaling ability for East Asian summer monsoonal interannual variability

Performance of a regional climate model (RCM), WRF, for downscaling East Asian summer season climate is investigated based on 11-summer integrations associated with different climate conditions with reanalysis data as the lateral boundary conditions. It is found that while the RCM is essentially unable to improve large-scale circulation patterns in the upper troposphere for most years, it is able to simulate better lower-level meridional moisture transport in the East Asian summer monsoon. For precipitation downscaling, the RCM produces more realistic magnitude of the interannual variation in most areas of East Asia than that in the reanalysis. Furthermore, the RCM significantly improves the spatial pattern of summer rainfall over dry inland areas and mountainous areas, such as Mongolia and the Tibetan Plateau. Meanwhile, it reduces the wet bias over southeast China. Over Mongolia, however, the performance of precipitation downscaling strongly depends on the year: the WRF is skillful for normal and wet years, but not for dry years, which suggests that land surface processes play an important role in downscaling ability. Over the dry area of North China, the WRF shows the worst performance. Additional sensitivity experiments testing land effects in downscaling suggest the initial soil moisture condition and representation of land surface processes with different schemes are sources of uncertainty for precipitation downscaling. Correction of initial soil moisture using the climatology dataset from GSWP-2 is a useful approach to robustly reducing wet bias in inland areas as well as to improve spatial distribution of precipitation. Despite the improvement on RCM downscaling, regional analyses reveal that accurate simulation of precipitation over East China, where the precipitation pattern is strongly influenced by the activity of the Meiyu/Baiu rainfall band, is difficult. Since the location of the rainfall band is closely associated with both lower-level meridional moisture transport and upper-level circulation structures, it is necessary to have realistic upper-air circulation patterns in the RCM as well as lower-level moisture transport in order to improve the circulation-associated convective rainfall band in East Asia.

Dynamic downscaling of 22-year CFS winter seasonal hindcasts with the UCLA-ETA regional climate model over the United States

This study evaluates the UCLA-ETA regional model’s dynamic downscaling ability to improve the National Center for Environmental Prediction Climate Forecast System (NCEP CFS), winter season predictions over the contiguous United States (US). Spatial distributions and temporal variations of seasonal and monthly precipitation are the main focus. A multi-member ensemble means of 22 winters from 1982 through 2004 are included in the study. CFS over-predicts the precipitation in eastern and western US by as much as 45 and 90 % on average compared to observations, respectively. Dynamic downscaling improves the precipitation hindcasts across the domain, except in the southern States, by substantially reducing the excessive precipitation produced by the CFS. Average precipitation root-mean-square error for CFS and UCLA-ETA are 1.5 and 0.9 mm day−1, respectively. In addition, downscaling improves the simulation of spatial distribution of snow water equivalent and land surface heat fluxes. Despite these large improvements, the UCLA-ETA’s ability to improve the inter-annual and intra-seasonal precipitation variability is not clear, probably because of the imposed CFS’ lateral boundary conditions. Preliminary analysis of the cause for the large precipitation differences between the models reveals that the CFS appears to underestimate the moisture flux convergence despite producing excessive precipitation amounts. Additionally, the comparison of modeled monthly surface sensible and latent heat fluxes with Global Land Data Assimilation System land data set shows that the CFS incorrectly partitioned most of surface energy into evaporation, unlike the UCLA-ETA. These findings suggest that the downscaling improvements are mostly due to a better representation of land-surface processes by the UCLA-ETA. Sensitivity tests also reveal that higher-resolution topography only played a secondary role in the dynamic downscaling improvement.

Role of land surface processes and diffuse/direct radiation partitioning in simulating the European climate

Abstract. The influence of land processes and in particular of diffuse/direct radiation partitioning on surface fluxes and associated regional-scale climate feedbacks is investigated using ERA-40 driven simulations over Europe performed with the COSMO-CLM2 Regional Climate Model (RCM). Two alternative Land Surface Models (LSMs), a 2nd generation LSM (TERRA_ML) and a more advanced 3rd generation LSM (Community Land Model version 3.5), and two versions of the atmospheric component are tested, as well as a revised coupling procedure allowing for variations in diffuse/direct light partitioning at the surface, and their accounting by the land surface component. Overall, the RCM performance for various variables (e.g., surface fluxes, temperature and precipitation) is improved when using the more advanced 3rd generation LSM. These improvements are of the same order of magnitude as those arising from a new version of the atmospheric component, demonstrating the benefit of using a realistic representation of land surface processes for regional climate simulations. Taking into account the variability in diffuse/direct light partitioning at the surface further improves the model performance in terms of summer temperature variability at the monthly and daily time scales. Comparisons with observations show that the RCM realistically captures temporal variations in diffuse/direct light partitioning as well as the evapotranspiration sensitivity to these variations. Our results suggest that a modest but consistent fraction (up to 3 %) of the overall variability in summer temperature can be explained by variations in the diffuse to direct ratio.

Influence of Land Use Land Cover on Cyclone Track Prediction – A Study During Aila Cyclone

Land-surface processes are one of the important drivers for weather and climate systems over the tropics. Realistic representation of land surface processes in mesoscale models over the region will help accurate simulation of numerical forecasts. The present study examines the influence of Land Use/ Land Cover Change (LULC) on the forecasting of cyclone intensity and track prediction using Mesoscale Model (MM5). Gridded land use/land cover data set over the Indian region compatible with the MM5 model were generated from Indian Remote Sensing Satellite (IRS-P6) Advanced Wide Field Sensor (AWiFS) for the year 2007-2008. A case study of simulation of ‘Aila’ cyclone has been considered to see the impact of these two sets of LULC data with the use of MM5 model. Results of the study indicated that incorporation of current land use/land cover data sets in mesoscale model provides better forecasting of cyclonic track.

Climate Change Studies Using Coupled Model: Land Surface Perspective

Community Climate System Model (CCSM3), a coupled model developed by National Center for Atmospheric Research (NCAR) containing atmosphere, ocean, sea-ice and land processes, simulation have been analysed for suitability in the Indian Monsoon region. A long control run of CCSM3 with constant forcing at every year has been done and the model climatology has been generated using 20 years of simulation. Atmospheric component of the model has been able to capture the large scale phenomena, however, regional monsoon variability not fully captured by the model simulation. A suitable modification in the flux coupler and the convective parameterization process in regional scale certainly improve the atmospheric part of the climate system. Another major component of the climate model is the representation of Land Surface Processes (LSP). A successful inclusion of LSP in climate model must address the issues related to the regional scale variation of the properties of LSP. A proper understanding of land surface processes is very crucial for climate simulations using numerical models. To understand the LSP-monsoon coupling, the offline Community Land Model (CLM), taken from CCSM3 land component, simulation forced with three hourly atmospheric boundary conditions have also been analyzed and compared with the CLM version of coupled CCSM mode. The distribution of surface heat flux in CCSM coupled mode shows some discrepancies compared to the offline CLM. Both the simulation results are compared with existing climatological features and assessment to improve CCSM3 for the regional climate change studies is made.

Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation

Most coupled general circulation models (GCMs) perform poorly in the tropical Atlantic in terms of climatological seasonal cycle and interannual variability. The reasons for this poor performance are investigated in a suite of sensitivity experiments with the Geophysical Fluid Dynamics Laboratory (GFDL) coupled GCM. The experiments show that a significant portion of the equatorial SST biases in the model is due to weaker than observed equatorial easterlies during boreal spring. Due to these weak easterlies, the tilt of the equatorial thermocline is reduced, with shoaling in the west and deepening in the east. The erroneously deep thermocline in the east prevents cold tongue formation in the following season despite vigorous upwelling, thus inhibiting the Bjerknes feedback. It is further shown that the surface wind errors are due, in part, to deficient precipitation over equatorial South America and excessive precipitation over equatorial Africa, which already exist in the uncoupled atmospheric GCM. Additional tests indicate that the precipitation biases are highly sensitive to land surface conditions such as albedo and soil moisture. This suggests that improving the representation of land surface processes in GCMs offers a way of improving their performance in the tropical Atlantic. The weaker than observed equatorial easterlies also contribute remotely, via equatorial and coastal Kelvin waves, to the severe warm SST biases along the southwest African coast. However, the strength of the subtropical anticyclone and along-shore winds also play an important role.