scholarly journals ISBA-MEB (SURFEX v8.1): model snow evaluation for local-scale forest sites

2020 ◽  
Author(s):  
Adrien Napoly ◽  
Aaron Boone ◽  
Théo Welfringer
Keyword(s):  
Energy Budget ◽  
Land Surface ◽  
Positive Impact ◽  
Soil Depth ◽  
Large Degree ◽  
Heat Fluxes ◽  
Close Coupling ◽  
Forest Sites ◽  
Soil Temperatures ◽  
The Impact

Abstract. An accurate modelling of the effect of snow cover on the surface energy and mass fluxes is required from land surface models. The Interactions between Soil–Biosphere–Atmosphere (ISBA) model adopts a default configuration using a composite soil-vegetation energy budget approach which is shown to have certain limitations for representing snow and soil phase change processes in areas of high vegetation cover since it does not explicitly represent the snow pack lying on the ground below the canopy. In particular, previous studies using ISBA have pointed out that the snowpack ablation tends to occur to early in the season in forest regions in the northern hemisphere. The multi-energy balance (MEB) version of ISBA has been developed recently, to a large degree, to address this issue. A vegetation layer, which is distinct from the soil, has been added to ISBA and new processes are now explicitly represented such as snow interception and an under-story litter layer. To evaluate the behavior of this new scheme in a cold forested region, long-term offline simulations have been performed for the three Berms forest sites located in Saskatchewan, Canada. It is shown that the new scheme leads to an improved energy budget representation, especially in terms of the ground and sensible heat fluxes, with decreases in RMSE of 77 and 18 %, respectively. A consistent positive impact for soil temperatures is obtained, particularly in terms of bias which is reduced from −6.2 to −0.1 K at a 10 cm soil depth on average for the three sites and 12 studied years. The impact of using MEB on the snowpack simulation is in a better agreement with observations during the snow season, especially in terms of the time of ablation: errors are on the order of 1 day averaged over the 3 sites and all of the years using MEB, which represents a reduction in error of 20 days compared to the composite scheme. The analysis shows that this improvement is mostly cause by the ability of MEB to represent a snowpack that nearly completely covers the soil below the canopy decouples the soil from the atmosphere while keeping a close coupling between the vegetation and the atmosphere.

scholarly journals ISBA-MEB (SURFEX v8.1): model snow evaluation for local-scale forest sites

2020 ◽  
Vol 13 (12) ◽  
pp. 6523-6545
Author(s):  
Adrien Napoly ◽  
Aaron Boone ◽  
Théo Welfringer
Keyword(s):  
Land Surface ◽  
Positive Impact ◽  
Soil Depth ◽  
Large Degree ◽  
Heat Fluxes ◽  
Close Coupling ◽  
Forest Sites ◽  
Ground Heat Flux ◽  
Soil Temperatures ◽  
The Impact

Abstract. Accurate modeling of the effect of snow cover on the surface energy and mass fluxes is required from land surface models. The Interactions between Soil–Biosphere–Atmosphere (ISBA) model uses a composite soil–vegetation approach that has limitations when representing snow and soil phase change processes in areas of high vegetation cover since it does not explicitly represent the snowpack lying on the ground below the canopy. In particular, previous studies using ISBA have pointed out that the snowpack ablation tends to occur to early in the season in forest regions in the Northern Hemisphere. The multi-energy balance (MEB) version of ISBA has been developed recently, to a large degree, to address this issue. A vegetation layer, which is distinct from the soil, has been added to ISBA and new processes are now explicitly represented, such as snow interception and an understory litter layer. To evaluate the behavior of this new scheme in a cold forested region, long-term offline simulations have been performed for the three BERMS forest sites located in Saskatchewan, Canada. It is shown that the new scheme leads to an improved energy budget representation, especially in terms of the ground and sensible heat fluxes, with decreases in root-mean-square error (RMSE) of 77 % and 18 %, respectively. A positive impact for soil temperatures, consistent with the improvement of the ground heat flux, is obtained, particularly in terms of bias, which is reduced from −6.2 to −0.1 K at a 10 cm soil depth on average for the three sites and 12 studied years. The impact of using MEB on the snowpack simulation is a better agreement with observations during the snow season, especially concerning the last day of snow in the season: errors are on the order of 1 d averaged over the three sites and all of the years using MEB, which represents a reduction in error of 20 d compared to the composite scheme. The analysis shows that this improvement is mostly caused by the ability of MEB to represent a snowpack that nearly completely covers the soil below the canopy and that decouples the soil from the atmosphere, while keeping a close coupling between the vegetation and the atmosphere.

scholarly journals Improvement of modelling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements

2020 ◽  
Author(s):  
Anna B. Harper ◽  
Karina E. Williams ◽  
Patrick C. McGuire ◽  
Maria Carolina Duran Rojas ◽  
Debbie Hemming ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Deciduous Forest ◽  
Soil Depth ◽  
Regional Climate ◽  
Moisture Stress ◽  
Plant Responses ◽  
Soil Moisture Stress ◽  
Forest Sites ◽  
The Impact

Abstract. Drought is predicted to increase in the future due to climate change, bringing with it a myriad of impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance, in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local/regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales, and evaluated ten different representations of stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high latitudes/cold region sites, while LE was best simulated in temperate and high latitude/cold sites. Errors not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savannah and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14, and the soil depth from 3m to 10.8m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation, when the onset of stress was delayed, and when roots extended deeper into the soil. For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and made the simulation worse. Further evaluation into the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress.

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

scholarly journals Simulation of semi-arid biomass plantations and irrigation using the WRF-NOAH model – a comparison with observations from Israel

2014 ◽  
Vol 18 (5) ◽  
pp. 1761-1783 ◽  
Author(s):  
O. Branch ◽  
K. Warrach-Sagi ◽  
V. Wulfmeyer ◽  
S. Cohen
Keyword(s):  
Land Surface ◽  
High Surface ◽  
Land Surface Model ◽  
Vapour Pressure Deficit ◽  
Soil Heat Flux ◽  
Heat Fluxes ◽  
Surface Radiation ◽  
Surface Model ◽  
Maximum Deviation ◽  
Soil Temperatures

Abstract. A 10 × 10 km irrigated biomass plantation was simulated in an arid region of Israel to simulate diurnal energy balances during the summer of 2012 (JJA). The goal is to examine daytime horizontal flux gradients between plantation and desert. Simulations were carried out within the coupled WRF-NOAH atmosphere/land surface model. MODIS land surface data was adjusted by prescribing tailored land surface and soil/plant parameters, and by adding a controllable sub-surface irrigation scheme to NOAH. Two model cases studies were compared – Impact and Control. Impact simulates the irrigated plantation. Control simulates the existing land surface, where the predominant land surface is bare desert soil. Central to the study is parameter validation against land surface observations from a desert site and from a 400 ha Simmondsia chinensis (jojoba) plantation. Control was validated with desert observations, and Impact with Jojoba observations. Model evapotranspiration was validated with two Penman–Monteith estimates based on the observations. Control simulates daytime desert conditions with a maximum deviation for surface 2 m air temperatures (T2) of 0.2 °C, vapour pressure deficit (VPD) of 0.25 hPa, wind speed (U) of 0.5 m s−1, surface radiation (Rn) of 25 W m−2, soil heat flux (G) of 30 W m−2 and 5 cm soil temperatures (ST5) of 1.5 °C. Impact simulates irrigated vegetation conditions with a maximum deviation for T2 of 1–1.5 °C, VPD of 0.5 hPa, U of 0.5 m s−1, Rn of 50 W m−5, G of 40 W m−2 and ST5 of 2 °C. Latent heat curves in Impact correspond closely with Penman–Monteith estimates, and magnitudes of 160 W m−2 over the plantation are usual. Sensible heat fluxes, are around 450 W m−2 and are at least 100–110 W m−2 higher than the surrounding desert. This surplus is driven by reduced albedo and high surface resistance, and demonstrates that high evaporation rates may not occur over Jojoba if irrigation is optimized. Furthermore, increased daytime T2 over plantations highlight the need for hourly as well as daily mean statistics. Daily mean statistics alone may imply an overall cooling effect due to surplus nocturnal cooling, when in fact a daytime warming effect is observed.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

Irrigation in JULES Land-Only Simulations over South and East Asia

2021 ◽  
Author(s):  
Markus Todt ◽  
Pier Luigi Vidale ◽  
Patrick C. McGuire ◽  
Omar V. Müller
Keyword(s):  
Soil Moisture ◽  
East Asia ◽  
Land Surface ◽  
River Flow ◽  
Heat Fluxes ◽  
Global Climate Models ◽  
Irrigated Agriculture ◽  
Irrigation Season ◽  
Irrigation Scheme ◽  
The Impact

<p>Capturing soil moisture-atmosphere feedbacks in a weather or climate model requires realistic simulation of various land surface processes. However, irrigation and other water management methods are still missing in most global climate models today, despite irrigated agriculture being the dominant land use in parts of Asia. In this study, we test the irrigation scheme available in the land model JULES (Joint UK Land Environment Simulator) by running land-only simulations over South and East Asia driven by WFDEI (WATCH Forcing Data ERA-Interim) forcing data. Irrigation in JULES is applied on a daily basis by replenishing soil moisture in the upper soil layers to field capacity, and we use a version of the irrigation scheme that extracts water for irrigation from groundwater and rivers, which physically limits the amount of irrigation that can be applied. We prescribe irrigation for C3 grasses in order to simulate the effects of agriculture, albeit retaining the simpler, widely used 5-PFT (plant functional type) configuration in JULES. Irrigation generally increases soil moisture and evapotranspiration, which results in increasing latent heat fluxes and decreasing sensible heat fluxes. Comparison with combined observational/machine-learning products for turbulent fluxes shows that while irrigation can reduce biases, other biases in JULES, unrelated to irrigation, are larger than improvements due to the inclusion of irrigation. Irrigation also affects water fluxes within the soil, e.g. runoff and drainage into the groundwater level, as well as soil moisture outside of the irrigation season. We find that the irrigation scheme, at least in the uncoupled land-atmosphere setting, can rapidly deplete groundwater to the point that river flow becomes the main source of irrigation (over the North China Plain and the Indus region) and can have the counterintuitive effect of decreasing annual average soil moisture (over the Ganges plain). Subsequently, we will explore the impact of irrigation on regional climate by conducting coupled land-atmosphere simulations.</p>

Contribution of lakes in the ORCHIDEE land surface model

2020 ◽  
Author(s):  
Anthony Bernus ◽  
Catherine Ottle ◽  
Nina Raoult
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Energy Budget ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Heat Fluxes ◽  
Energy Budgets ◽  
Surface Model ◽  
Local Climate

<p>Lakes play a major role on local climate and boundary layer stratification. At global scale, they have been shown to have an impact on the energy budget, (see for example Le Moigne et al., 2016 or Bonan, 1995 ) . To represent the energy budget of lakes at a global scale, the FLake (Mironov et al, 2008) lake model has been coupled to the ORCHIDEE land surface model - the continental part of the IPSL earth system model. By including Flake in ORCHIDEE, we aim to improve the representation of land surface temperature and heat fluxes. Using the standard CMIP6 configuration of ORCHIDEE,  two 40-year simulations were generated (one coupled with FLake and one without) using the CRUJRA meteorological forcing data at a spatial resolution of 0.5°. We compare land surface temperatures and heat fluxes from the two ORCHIDEE simulations and assess the impacts of lakes on surface energy budgets. MODIS satellite land surface temperature products will be used to validate the simulations. We expect a better fit between the simulated land surface temperature and the MODIS data when the FLake configuration is used. The preliminary results of the comparison will be presented.</p>

scholarly journals Multidataset Study of Optimal Parameter and Uncertainty Estimation of a Land Surface Model with Bayesian Stochastic Inversion and Multicriteria Method

2004 ◽  
Vol 43 (10) ◽  
pp. 1477-1497 ◽  
Author(s):  
Youlong Xia ◽  
Mrinal K. Sen ◽  
Charles S. Jackson ◽  
Paul L. Stoffa
Keyword(s):  
Land Surface ◽  
Root Zone ◽  
Model Simulation ◽  
Optimal Parameter ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Model ◽  
Optimal Parameters ◽  
Stochastic Inversion ◽  
The Impact

Abstract This study evaluates the ability of Bayesian stochastic inversion (BSI) and multicriteria (MC) methods to search for the optimal parameter sets of the Chameleon Surface Model (CHASM) using prescribed forcing to simulate observed sensible and latent heat fluxes from seven measurement sites representative of six biomes including temperate coniferous forests, tropical forests, temperate and tropical grasslands, temperate crops, and semiarid grasslands. Calibration results with the BSI and MC show that estimated optimal values are very similar for the important parameters that are specific to the CHASM model. The model simulations based on estimated optimal parameter sets perform much better than the default parameter sets. Cross-validations for two tropical forest sites show that the calibrated parameters for one site can be transferred to another site within the same biome. The uncertainties of optimal parameters are obtained through BSI, which estimates a multidimensional posterior probability density function (PPD). Marginal PPD analyses show that nonoptimal choices of stomatal resistance would contribute most to model simulation errors at all sites, followed by ground and vegetation roughness length at six of seven sites. The impact of initial root-zone soil moisture and nonmosaic approach on estimation of optimal parameters and their uncertainties is discussed.

scholarly journals JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator

2014 ◽  
Vol 7 (5) ◽  
pp. 6773-6809
Author(s):  
T. Osborne ◽  
J. Gornall ◽  
J. Hooker ◽  
K. Williams ◽  
A. Wiltshire ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Land Surface Model ◽  
Water Security ◽  
Climate Change Impacts ◽  
Heat Fluxes ◽  
Surface Model ◽  
Yield Model ◽  
Area Index ◽  
The Impact

Abstract. Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soy bean, maize and rice is presented. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soy bean at the global level, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index and canopy height better than in standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an earth system and crop yield model perspective is encouraging however, more effort is needed to develop the parameterisation of the model for specific applications. Key future model developments identified include the specification of the yield gap to enable better representation of the spatial variability in yield.

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