Land Surface Scheme Research Articles

An assessment of natural methane fluxes simulated by the CLASS-CTEM model

Abstract. Natural methane emissions from wetlands and fire, and soil uptake of methane, simulated using the Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem (CLASS-CTEM) modelling framework, over the historical 1850–2008 period, are assessed by using a one-box model of atmospheric methane burden. This one-box model also requires anthropogenic emissions and the methane sink in the atmosphere to simulate the historical evolution of global methane burden. For this purpose, global anthropogenic methane emissions for the period 1850–2008 were reconstructed based on the harmonized representative concentration pathway (RCP) and Emission Database for Global Atmospheric Research (EDGAR) data sets. The methane sink in the atmosphere is represented using bias-corrected methane lifetimes from the Canadian Middle Atmosphere Model (CMAM). The resulting evolution of atmospheric methane concentration over the historical period compares reasonably well with observation-based estimates (correlation = 0.99, root mean square error = 35 ppb). The modelled natural emissions are also assessed using an inverse procedure where the methane lifetimes required to reproduce the observed year-to-year increase in atmospheric methane burden are calculated based upon the specified global anthropogenic and modelled natural emissions that we have used here. These calculated methane lifetimes over the historical period fall within the uncertainty range of observation-based estimates. The present-day (2000–2008) values of modelled methane emissions from wetlands (169 Tg CH4 yr−1) and fire (27 Tg CH4 yr−1), methane uptake by soil (29 Tg CH4 yr−1), and the budget terms associated with overall anthropogenic and natural emissions are consistent with estimates reported in a recent global methane budget that is based on top-down approaches constrained by observed atmospheric methane burden. The modelled wetland emissions increase over the historical period in response to both increases in precipitation and in atmospheric CO2 concentration. This increase in wetland emissions over the historical period yields evolution of the atmospheric methane concentration that compares better with observation-based values than the case when wetland emissions are held constant over the historical period.

New Observed Data Sets for the Validation of Hydrology and Land Surface Models in Cold Climates

AbstractIn a recent WRR paper, Yan et al. (2018, https://doi.org/10.1002/2017WR021290) have derived a simple, elegant, and useful way of reanalyzing quality‐controlled SNOTEL data sets to produce quantitative estimates of water reaching the land surface during rain, snowmelt, and rain‐on‐snow events, and use these data to generate “next‐generation” intensity duration, and frequency curves for these quantities at SNOTEL measurement locations. These new data sets may prove useful as direct inputs to design processes in some cases, but I argue in this commentary that they will probably have much more important application to the validation and refinement of off‐line hydrology models and land surface schemes embedded in climate models, which can then be used to extend these data in space and time to create more comprehensive products to guide infrastructure design. If well validated, such tools can be used to extend backward in time to make detailed hindcasts of the historical record with more complete spatial and temporal coverage (which also facilitates more accurate estimation of extremes with longer return intervals) and forward in time to project future conditions that are needed to design long‐lived infrastructure in what will likely be a highly nonstationary environment. In addition, there is a need to extend these data sets to include lower elevation areas in mountain environments, and other areas of the United States and Canada with cold winter climates.

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

AbstractA 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.

On the appropriate definition of soil profile configuration and initial conditions for land surface–hydrology models in cold regions

Abstract. Arctic and subarctic regions are amongst the most susceptible regions on Earth to global warming and climate change. Understanding and predicting the impact of climate change in these regions require a proper process representation of the interactions between climate, carbon cycle, and hydrology in Earth system models. This study focuses on land surface models (LSMs) that represent the lower boundary condition of general circulation models (GCMs) and regional climate models (RCMs), which simulate climate change evolution at the global and regional scales, respectively. LSMs typically utilize a standard soil configuration with a depth of no more than 4 m, whereas for cold, permafrost regions, field experiments show that attention to deep soil profiles is needed to understand and close the water and energy balances, which are tightly coupled through the phase change. To address this gap, we design and run a series of model experiments with a one-dimensional LSM, called CLASS (Canadian Land Surface Scheme), as embedded in the MESH (Modélisation Environmentale Communautaire – Surface and Hydrology) modelling system, to (1) characterize the effect of soil profile depth under different climate conditions and in the presence of parameter uncertainty; (2) assess the effect of including or excluding the geothermal flux in the LSM at the bottom of the soil column; and (3) develop a methodology for temperature profile initialization in permafrost regions, where the system has an extended memory, by the use of paleo-records and bootstrapping. Our study area is in Norman Wells, Northwest Territories of Canada, where measurements of soil temperature profiles and historical reconstructed climate data are available. Our results demonstrate a dominant role for parameter uncertainty, that is often neglected in LSMs. Considering such high sensitivity to parameter values and dependency on the climate condition, we show that a minimum depth of 20 m is essential to adequately represent the temperature dynamics. We further show that our proposed initialization procedure is effective and robust to uncertainty in paleo-climate reconstructions and that more than 300 years of reconstructed climate time series are needed for proper model initialization.

A Heavy Rainfall Event in Autumn over Beijing—Atmospheric Circulation Background and Hindcast Simulation Using WRF

Heavy rainfall events often occur in Beijing during summer but rarely in autumn. However, during 3–5 September 2015, an exceptionally heavy rainfall event occurred in Beijing. Based on the reanalysis data and the Weather Research and Forecasting (WRF) model simulations, the main contributing factors and the predictability of this heavy rainfall event were examined through comprehensive analyses of vorticity advection and water vapor transport/ budget. The results indicate that a “high-in-the-east–low-in-the-west” pattern of 500-hPa geopotential height over the Beijing area played an important role. The 850-hPa low-level jet (LLJ) provided a mechanism for rising motion and transported abundant water vapor into the Beijing area. Two-way nested hindcast experiments using WRF well reproduced the atmospheric circulation and LLJ. Quantitative analysis indicates that the WRF model with the rapid update cycle (RUC) land surface scheme and the single-moment 6-class (WSM6) microphysics scheme exhibited the best skill, and the model performance improved with a higher resolution. Further analysis indicates that the bias in the precipitation forecast was caused by the bias in water vapor transport.

Evaluating CMIP5 Model Agreement for Multiple Drought Metrics

Abstract Global climate models play an important role in quantifying past and projecting future changes in drought. Previous studies have pointed to shortcomings in these models for simulating droughts, but systematic evaluation of their level of agreement has been limited. Here, historical simulations (1950–2004) for 20 models from the latest Coupled Model Intercomparison Project (CMIP5) were analyzed for a variety of drought metrics and thresholds using a standardized drought index. Model agreement was investigated for different types of drought (precipitation, runoff, and soil moisture) and how this varied with drought severity and duration. At the global scale, climate models were shown to agree well on most precipitation drought metrics, but systematically underestimated precipitation drought intensity compared to observations. Conversely, simulated runoff and soil moisture droughts varied significantly across models, particularly for intensity. Differences in precipitation simulations were found to explain model differences in runoff and soil moisture drought metrics over some regions, but predominantly with respect to drought intensity. This suggests it is insufficient to evaluate models for precipitation droughts to increase confidence in model performance for other types of drought. This study shows large but metric-dependent discrepancies in CMIP5 for modeling different types of droughts that relate strongly to the component models (i.e., atmospheric or land surface scheme) used in the coupled modeling systems. Our results point to a need to consider multiple models in drought impact studies to account for high model uncertainties.

Ocean circulation drifts in multi-millennial climate simulations: the role of salinity corrections and climate feedbacks

Low-resolution, complex general circulation models (GCMs) are valuable tools for studying the Earth system on multi-millennial timescales. However, slowly evolving salinity drifts can cause large shifts in climatic and oceanic regimes over thousands of years. We test two different schemes for neutralising unforced salinity drifts in the FAMOUS GCM: surface flux correction and volumetric flux correction. Although both methods successfully maintain a steady global mean salinity, local drifts and subsequent feedbacks promote cooling (≈ 4 °C over 6000 years) and freshening (≈ 2 psu over 6000 years) in the North Atlantic Ocean, and gradual warming (≈ 0.2 °C per millennium) and salinification (≈ 0.15 psu per millennium) in the North Pacific Ocean. Changes in the surface density in these regions affect the meridional overturning circulation (MOC), such that, after several millennia, the Atlantic MOC (AMOC) is in a collapsed state, and there is a strong, deep Pacific MOC (PMOC). Furthermore, the AMOC exhibits a period of metastability, which is only identifiable with run lengths in excess of 1500 years. We also compare simulations with two different land surface schemes, demonstrating that small biases in the surface climate may cause regional salinity drifts and significant shifts in the MOC (weakening of the AMOC and the initiation then invigoration of PMOC), even when the global hydrological cycle has been forcibly closed. Although there is no specific precursor to the simulated AMOC collapse, the northwest North Pacific and northeast North Atlantic are important areas that should be closely monitored for trends arising from such biases.

Globally scalable alpine snow metrics

Horizontal and altitudinal redistribution of snow by wind transport and avalanches can be important controls on small- and large-scale snow accumulation patterns that control meltwater supply in alpine environments. Redistribution processes control the spatial variability of snow accumulation, which not only controls meltwater supply, but also regulates snowmelt timing, duration, and rates, as well as snow-covered area depletion and the variable contributing area for meltwater runoff generation. However, most hydrological models and land surface schemes do not consider snow redistribution processes, and those that do are difficult to verify without spatially distributed snow depth measurements. These are rarely available in both high resolution and covering large scales. As an increased number of hydrological models include snow redistribution processes there is a need for additional snowcover metrics to verify snow redistribution schemes over large areas using readily available data.This study develops novel high-resolution (20 m), snowcover indices from remotely sensed imagery (Landsat-8 and Sentinel-2) to evaluate snow redistribution models over alpine areas without in-situ or airborne snow observations. A snowcover absence (SA) index, calculated from snow-free areas during the winter, identifies areas of wind erosion or avalanche source areas. A snowcover persistence (SP) index, calculated from snow-covered areas during the summer, identifies snow deposition in drifts and avalanche deposits.The snowcover indices captured the relative differences in surface observations of snow presence and absence between exposed and sheltered sites on an intensely instrumented ridge in the Canadian Rockies Hydrological Observatory. Within the Tuolumne River Basin in central California (1100 km2), the SP index captured roughly half of the spatial variability (R2 = 0.49 to 0.56) in peak SWE as estimated from airborne LiDAR-derived snow depths. At the individual mountain ridge scale (~800 m), variability in both ablation and snow redistribution controlled the SP patterns over 7979 ridges. Differences in shortwave irradiance explained 76% of the SP variance across ridges, but could not explain smaller-scale (~100 m) SP peaks that are associated with snowdrifts and avalanche deposits. The snowcover indices can be used to evaluate snow redistribution models of the finer scale impacts of snow redistribution by wind and gravity as long as the larger scale influences of spatially variable solar irradiance effects are also simulated.

Year-round simulated methane emissions from a permafrost ecosystem in Northeast Siberia

Abstract. Wetlands of northern high latitudes are ecosystems highly vulnerable to climate change. Some degradation effects include soil hydrologic changes due to permafrost thaw, formation of deeper active layers, and rising topsoil temperatures that accelerate the degradation of permafrost carbon and increase in CO2 and CH4 emissions. In this work we present 2 years of modeled year-round CH4 emissions into the atmosphere from a Northeast Siberian region in the Russian Far East. We use a revisited version of the process-based JSBACH-methane model that includes four CH4 transport pathways: plant-mediated transport, ebullition and molecular diffusion in the presence or absence of snow. The gas is emitted through wetlands represented by grid cell inundated areas simulated with a TOPMODEL approach. The magnitude of the summertime modeled CH4 emissions is comparable to ground-based CH4 fluxes measured with the eddy covariance technique and flux chambers in the same area of study, whereas wintertime modeled values are underestimated by 1 order of magnitude. In an annual balance, the most important mechanism for transport of methane into the atmosphere is through plants (61 %). This is followed by ebullition ( ∼ 35 %), while summertime molecular diffusion is negligible (0.02 %) compared to the diffusion through the snow during winter ( ∼ 4 %). We investigate the relationship between temporal changes in the CH4 fluxes, soil temperature, and soil moisture content. Our results highlight the heterogeneity in CH4 emissions at landscape scale and suggest that further improvements to the representation of large-scale hydrological conditions in the model will facilitate a more process-oriented land surface scheme and better simulate CH4 emissions under climate change. This is especially necessary at regional scales in Arctic ecosystems influenced by permafrost thaw.

Urban heat island in current and future climates for the island of Montreal

Urban regions with their distinct surface characteristics modify energy and water partitioning, leading to higher temperatures in comparison to the adjoining non-urban regions. This phenomenon, referred to as the Urban Heat Island (UHI), is studied for the island of Montreal situated in central-eastern Canada in the province of Quebec, for current and future climates, for the summer and fall seasons. Projected changes to the UHI for the region are studied using offline high-resolution (250 m) simulations performed with a land surface scheme, the Canadian Land Surface Scheme (CLASS), with and without the urban representation from the Town Energy Balance (TEB) model, for the RCP 8.5 scenario. Projected changes to land surface and 2 m air temperature fields suggest significant increases for both urban and nonurban regions, with small increases in the UHI intensity. The small increases in UHI intensity are due to the slightly higher increase in urban surface temperatures compared with nonurban regions, associated with increase and decrease in sensible and latent heat fluxes, respectively, for the urban regions. Furthermore, analysis of the projected changes to the number of hot days suggests significant increases, with urban regions augmenting the increases by 5–8 days in summer and 2–5 days in fall.

Impact of dynamic vegetation phenology on the simulated pan-Arctic land surface state

The pan-Arctic land surface is undergoing rapid changes in a warming climate, with near-surface permafrost projected to degrade significantly during the twenty-first century. Vegetation-related feedbacks have the potential to influence the rate of degradation of permafrost. In this study, the impact of dynamic phenology on the pan-Arctic land surface state, particularly near-surface permafrost, for the 1961–2100 period, is assessed by comparing two simulations of the Canadian Land Surface Scheme (CLASS)—one with dynamic phenology, modelled using the Canadian Terrestrial Ecosystem Model (CTEM), and the other with prescribed phenology. These simulations are forced by atmospheric data from a transient climate change simulation of the 5th generation Canadian Regional Climate Model (CRCM5) for the Representative Concentration Pathway 8.5 (RCP8.5). Comparison of the CLASS coupled to CTEM simulation to available observational estimates of plant area index, spatial distribution of permafrost and active layer thickness suggests that the model captures reasonably well the overall distribution of vegetation and permafrost. It is shown that the most important impact of dynamic phenology on the land surface occurs through albedo and it is demonstrated for the first time that vegetation control on albedo during late spring and early summer has the highest potential to impact the degradation of permafrost. While both simulations show extensive near-surface permafrost degradation by the end of the twenty-first century, the strong projected response of vegetation to climate warming and increasing CO2 concentrations in the coupled simulation results in accelerated permafrost degradation in the northernmost continuous permafrost regions.

Description and validation of the Simple, Efficient, Dynamic, Global, Ecological Simulator (SEDGES v.1.0)

Abstract. In this paper, we present a simple dynamic global vegetation model whose primary intended use is auxiliary to the land–atmosphere coupling scheme of a climate model, particularly one of intermediate complexity. The model simulates and provides important ecological-only variables but also some hydrological and surface energy variables that are typically either simulated by land surface schemes or else used as boundary data input for these schemes. The model formulations and their derivations are presented here, in detail. The model includes some realistic and useful features for its level of complexity, including a photosynthetic dependency on light, full coupling of photosynthesis and transpiration through an interactive canopy resistance, and a soil organic carbon dependence for bare-soil albedo. We evaluate the model's performance by running it as part of a simple land surface scheme that is driven by reanalysis data. The evaluation against observational data includes net primary productivity, leaf area index, surface albedo, and diagnosed variables relevant for the closure of the hydrological cycle. In this setup, we find that the model gives an adequate to good simulation of basic large-scale ecological and hydrological variables. Of the variables analyzed in this paper, gross primary productivity is particularly well simulated. The results also reveal the current limitations of the model. The most significant deficiency is the excessive simulation of evapotranspiration in mid- to high northern latitudes during their winter to spring transition. The model has a relative advantage in situations that require some combination of computational efficiency, model transparency and tractability, and the simulation of the large-scale vegetation and land surface characteristics under non-present-day conditions.

Microwave implementation of two-source energy balance approach for estimating evapotranspiration

A newly developed microwave (MW) land surface temperature (LST) product is used to substitute thermal infrared (TIR) based LST in the Atmosphere Land Exchange Inverse (ALEXI) modelling framework for estimating ET from space. ALEXI implements a two-source energy balance (TSEB) land surface scheme in a time-differential approach, designed to minimize sensitivity to absolute biases in input records of LST through the analysis of the rate of temperature change in the morning. Thermal infrared (TIR) retrievals of the diurnal LST curve, traditionally from geostationary platforms, are hindered by cloud cover, reducing model coverage on any given day. This study tests the utility of diurnal temperature information retrieved from a constellation of satellites with microwave radiometers that together provide 6-8 observations of Ka-band brightness temperature per location per day. This represents the first ever attempt at a global implementation of ALEXI with MW-based LST and is intended as the first step towards providing all-weather capability to the ALEXI framework. The analysis is based on 9-year long, global records of ALEXI ET generated using both MW and TIR based diurnal LST information as input. In this study, the MW-LST sampling is restricted to the same clear sky days as in the IR-based implementation to be able to analyse the impact of changing the LST dataset separately from the impact of sampling all-sky conditions. The results show that long-term bulk ET estimates from both LST sources agree well, with a spatial correlation of 92% for total ET in the Europe/Africa domain and agreement in seasonal (3-month) totals of 83-97 % depending on the time of year. Most importantly, the ALEXI-MW also matches ALEXI-IR very closely in terms of 3-month inter-annual anomalies, demonstrating its ability to capture the development and extent of drought conditions. Weekly ET output from the two parallel ALEXI implementations is further compared to a common ground measured reference provided by the FLUXNET consortium. Overall, the two model implementations generate similar performance metrics (correlation and RMSE) for all but the most challenging sites in terms of spatial heterogeneity and level of aridity. It is concluded that a constellation of MW satellites can effectively be used to provide LST for estimating ET through ALEXI, which is an important step towards all-sky satellite-based retrieval of ET using an energy balance framework.

Urban surface effects on current and future climate

This study aims to improve the representation of urban surfaces by implementing the Town Energy Balance (TEB) model for urban surface simulation, and the Canadian Land Surface Scheme (CLASS) for the simulation of natural surfaces. The study is conducted over eastern North American continent selected for its higher urban fractions. Seven sets of simulations with different initial and boundary conditions were conducted, and comparisons were made between urban and non-urban fractions using selected meteorological and energy variables such as temperature, surface albedo, and the turbulent latent and sensible heat fluxes. Results indicated that the realistic representation of urban surfaces by TEB resolved urban radiation and turbulent energy partitioning better than the bare soil formulation representation of urban regions by CLASS - an overall improvement of the mean land surface temperature by about 2 °C. The model also produced an annual mean surface temperature of up to a maximum of 4 °C higher over urban than non-urban regions in the current climate. Diurnal cycle of the urban heat island intensity is higher in the second half of the day than early mornings, and seasonally, the urban heat island intensity is higher in summer than in winter pertaining to maximum solar radiation during the day and in summer. Most of the incident solar radiation on urban surfaces are stored during the day and released at night causing thermal discomfort to urban residents. The urban canopy model TEB also simulated the strong correlation between the urban heat island effect and the turbulent energy fluxes relatively well. Furthermore, projections were made in to the future because the urban canopy model TEB has demonstrated sufficient performance over urban regions for the current climate. Projections show that urban land surfaces will get warmer by up to a maximum of 6 °C in the mid-century (1941–1970) and by up to a maximum of 13 °C at the end of 2100 under RCP8.5 emissions scenario. However, the projection of urban-rural heat island contrast is very low, but significant enough to warm urban regions to cause urban thermal discomfort. The differences in temperature and energy partitions between urban and rural regions show that the realistic representation of urban surfaces in climate models would improve the performance of NWP models. Therefore, climate models should take into account the effects of urban surfaces to appropriately investigate the impact of built-in urban environments on weather and climate, and in turn the effect of these weather and climate changes on urban community.

Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb)

Abstract. The Canadian Land Surface Scheme and the Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component in the family of Canadian Earth system models (CanESMs). Here, CLASS-CTEM is coupled to Environment and Climate Change Canada (ECCC)'s weather and greenhouse gas forecast model (GEM-MACH-GHG) to consistently model atmosphere–land exchange of CO2. The coupling between the land and the atmospheric transport model ensures consistency between meteorological forcing of CO2 fluxes and CO2 transport. The procedure used to spin up carbon pools for CLASS-CTEM for multi-decadal simulations needed to be significantly altered to deal with the limited availability of consistent meteorological information from a constantly changing operational environment in the GEM-MACH-GHG model. Despite the limitations in the spin-up procedure, the simulated fluxes obtained by driving the CLASS-CTEM model with meteorological forcing from GEM-MACH-GHG were comparable to those obtained from CLASS-CTEM when it is driven with standard meteorological forcing from the Climate Research Unit (CRU) combined with reanalysis fields from the National Centers for Environmental Prediction (NCEP) to form CRU-NCEP dataset. This is due to the similarity of the two meteorological datasets in terms of temperature and radiation. However, notable discrepancies in the seasonal variation and spatial patterns of precipitation estimates, especially in the tropics, were reflected in the estimated carbon fluxes, as they significantly affected the magnitude of the vegetation productivity and, to a lesser extent, the seasonal variations in carbon fluxes. Nevertheless, the simulated fluxes based on the meteorological forcing from the GEM-MACH-GHG model are consistent to some extent with other estimates from bottom-up or top-down approaches. Indeed, when simulated fluxes obtained by driving the CLASS-CTEM model with meteorological data from the GEM-MACH-GHG model are used as prior estimates for an atmospheric CO2 inversion analysis using the adjoint of the GEOS-Chem model, the retrieved CO2 flux estimates are comparable to those obtained from other systems in terms of the global budget and the total flux estimates for the northern extratropical regions, which have good observational coverage. In data-poor regions, as expected, differences in the retrieved fluxes due to the prior fluxes become apparent. Coupling CLASS-CTEM into the Environment Canada Carbon Assimilation System (EC-CAS) is considered an important step toward understanding how meteorological uncertainties affect both CO2 flux estimates and modeled atmospheric transport. Ultimately, such an approach will provide more direct feedback to the CLASS-CTEM developers and thus help to improve the performance of CLASS-CTEM by identifying the model limitations based on atmospheric constraints.

Regional climate model simulation for temperature and precipitation over South Asia using different physical parameterisation schemes

The ability of ICTP regional climate model version 4 (RegCM4.3) is investigated by using two land surface schemes: the biosphere-atmosphere transfer scheme (BATS) and the community land model version 3.5 (CLM3.5). To attain the best model configuration over the South Asia region, six sensitivity experiments are conducted with three different cumulus convection schemes. RegCM4.3 coupled with CLM3.5 and mixed convection scheme option (MIX-CLM), produced better simulation than BATS. The cold winter bias and the intensities of wet-dry biases over the foothills of Hindukush-Karakorum-Himalaya (HKH) and Central India are substantially reduced with MIX-CLM. In terms of seasonal variability, results suggest that different convection schemes behaved differently over sub-regions of the domain. The annual cycles of precipitation and temperature are better captured over the Bay of Bengal and Western Ghats by MIX-CLM. In spite of some deficiencies, the MIX-CLM scheme improves the model performance over the various parts of the domain.

Regional climate model simulation for temperature and precipitation over South Asia using different physical parameterisation schemes

The ability of ICTP regional climate model version 4 (RegCM4.3) is investigated by using two land surface schemes: the biosphere-atmosphere transfer scheme (BATS) and the community land model version 3.5 (CLM3.5). To attain the best model configuration over the South Asia region, six sensitivity experiments are conducted with three different cumulus convection schemes. RegCM4.3 coupled with CLM3.5 and mixed convection scheme option (MIX-CLM), produced better simulation than BATS. The cold winter bias and the intensities of wet-dry biases over the foothills of Hindukush-Karakorum-Himalaya (HKH) and Central India are substantially reduced with MIX-CLM. In terms of seasonal variability, results suggest that different convection schemes behaved differently over sub-regions of the domain. The annual cycles of precipitation and temperature are better captured over the Bay of Bengal and Western Ghats by MIX-CLM. In spite of some deficiencies, the MIX-CLM scheme improves the model performance over the various parts of the domain.

Simulation of Land Surface Climate over China with RegCM4.5: Verification and Analysis

The ability of RegCM4.5 using land surface scheme CLM4.5 to simulate the physical variables related to land surface state was investigated. The NCEP-NCAR reanalysis data for the period 1964–2003 were used to drive RegCM4.5 to simulate the land surface temperature, precipitation, soil moisture, latent heat flux, and surface evaporation. Based on observations and reanalysis data, a few land surface variables were analyzed over China. The results showed that some seasonal features of land surface temperature in summer and winter as well as its magnitude could be simulated well. The simulation of precipitation was sensitive to region and season. The model could, to a certain degree, simulate the seasonal migration of rainband in East China. The overall spatial distribution of the simulated soil moisture was better in winter than in summer. The simulation of latent heat flux was also better in winter. In summer, the latent heat flux bias mainly arose from surface evaporation bias in Northwest China, and it primarily arose from vegetation evapotranspiration bias in South China. In addition, the large latent heat flux bias in South China during summer was probably due to less precipitation generated in the model and poor representation of vegetation cover in this region.

Physically Accurate Soil Freeze‐Thaw Processes in a Global Land Surface Scheme

AbstractThe model Soil‐Litter‐Iso (SLI) calculates coupled heat and water transport in soil. It was recently implemented into the Australian land surface model CABLE, which is the land component of the Australian Community Climate and Earth System Simulator (ACCESS). Here we extended SLI to include accurate freeze‐thaw processes in the soil and snow. SLI provides thence an implicit solution of the energy and water balances of soil and snow as a standalone model and within CABLE. The enhanced SLI was tested extensively against theoretical formulations, laboratory experiments, field data, and satellite retrievals. The model performed well for all experiments at wide‐ranging temporal and spatial scales. SLI melts snow faster at the end of the cold season compared to observations though because there is no subgrid variability within SLI given by the implicit, coupled solution of energy and water. Combined CABLE‐SLI shows very realistic dynamics and extent of permafrost on the Northern hemisphere. It illustrated, however, also the limits of possible comparisons between large‐scale land surface models and local permafrost observations. CABLE‐SLI exhibits the same patterns of snow depth and snow water equivalent on the Northern hemisphere compared to satellite‐derived observations but quantitative comparisons depend largely on the given meteorological input fields. Further extension of CABLE‐SLI with depth‐dependence of soil carbon will allow realistic projections of the development of permafrost and frozen carbon stocks in a changing climate.

Evaluation of near-surface wind speed simulations over the Tibetan Plateau from three dynamical downscalings based on WRF model

The Tibetan Plateau (TP) has experienced significant climate changes in recent decades. Wind stilling is one of the most evident changes based on observations. This study assesses near-surface wind speed simulations from three WRF dynamical downscalings over the TP. The assessment is conducted by comparing with observations at 83 stations over the TP and ERA-Interim in 1980–2005. Results show that the three dynamical downscalings and ERA-Interim all overestimated the annual and seasonal wind speeds over the TP, especially in winter. Overestimation of the U component is the main contributor to the overestimation of near-surface wind speeds. The difference between grid and station elevations partly contributes to the larger overestimation, particularly in 3000–4000 m. Impacts of the land surface scheme on the simulations of wind speed are also noticed. Observations show significant decreasing wind speed trend and the variability is larger at higher elevations above 4000 m, which are not represented in the ERA-Interim. Affected by forcing, the dynamical downscalings do not capture the observed significantly decreasing trend, but they outperform the forcing in winter. The better performance in winter but summer is mainly because the dynamical downscalings are unable to reproduce the observed direction of the U component in summer. The ability of dynamical downscalings to simulate the near-surface wind speed may be improved by choosing forcing with better linear trends, enhancing the momentum transports in the planetary boundary parameterization scheme, and considering the unresolved topographic features sufficiently.