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 Variational Assimilation of Land Surface Temperature within the ORCHIDEE Land Surface Model Version 1.2.6

2016 ◽  
Author(s):  
H. S. Benavides Pinjosovsky ◽  
S. Thiria ◽  
C. Ottlé ◽  
J. Brajard ◽  
F. Badran ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Adjoint Model ◽  
Variational Assimilation ◽  
Internal Parameters

Abstract. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software denoted YAO was used as a framework to implement a 4D-VAR assimilation method. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the eleven most influent internal parameters of SECHIBA or of the initial conditions of the soil water content by observing the land surface temperature measured in situ or as it could be observed by remote sensing as brightness temperature. The paper presents the fundamental principles of the 4D-Var assimilation, the semi-generator software YAO and some experiments showing the accuracy of the adjoint code distributed. In addition, a distributed version is available when only the land surface temperature is observed.

scholarly journals Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6

2017 ◽  
Vol 10 (1) ◽  
pp. 85-104 ◽  
Author(s):  
Hector Simon Benavides Pinjosovsky ◽  
Sylvie Thiria ◽  
Catherine Ottlé ◽  
Julien Brajard ◽  
Fouad Badran ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Remote Sensing Data ◽  
Surface Model ◽  
Adjoint Model ◽  
Variational Assimilation ◽  
Internal Parameters ◽  
Assimilation Scheme

Abstract. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software called YAO was used as a framework to implement a 4D-VAR assimilation scheme of observations in SECHIBA. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the 11 most influential internal parameters of the soil water content, by observing the land surface temperature or remote sensing data such as the brightness temperature. The paper presents the fundamental principles of the 4D-VAR assimilation, the semi-generator software YAO and a large number of experiments showing the accuracy of the adjoint code in different conditions (sites, PFTs, seasons). In addition, a distributed version is available in the case for which only the land surface temperature is observed.

scholarly journals Quantifying Land Surface Temperature Variability for Two Sahelian Mesoscale Regions during the Wet Season

2013 ◽  
Vol 14 (5) ◽  
pp. 1605-1619 ◽  
Author(s):  
Martin G. De Kauwe ◽  
Christopher M. Taylor ◽  
Philip P. Harris ◽  
Graham P. Weedon ◽  
Richard. J. Ellis
Keyword(s):  
Spectral Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Wet Season ◽  
Rainfall Events ◽  
Spectral Variance ◽  
High Level

Abstract Land–atmosphere feedbacks play an important role in the weather and climate of many semiarid regions. These feedbacks are strongly controlled by how the surface responds to precipitation events, which regulate the return of heat and moisture to the atmosphere. Characteristics of the surface can result in both differing amplitudes and rates of warming following rain. Spectral analysis is used to quantify these surface responses to rainfall events using land surface temperature (LST) derived from Earth observations (EOs). The authors analyzed two mesoscale regions in the Sahel and identified distinct differences in the strength of the short-term (<5 days) spectral variance, notably, a shift toward lower-frequency variability in forest pixels relative to nonforest areas and an increase in amplitude with decreasing vegetation cover. Consistent with these spectral signatures, areas of forest and, to a lesser extent, grassland regions were found to warm up more slowly than sparsely vegetated or barren pixels. The authors applied the same spectral analysis method to simulated LST data from the Joint UK Land Environment Simulator (JULES) land surface model. A reasonable level of agreement was found with the EO spectral analysis for two contrasting land surface regions. However, JULES shows a significant underestimate in the magnitude of the observed response to rain compared to EOs. A sensitivity analysis of the JULES model highlights an unrealistically high level of soil water availability as a key deficiency, which dampens the models response to rainfall events.

scholarly journals Advancing land surface model development with satellite-based Earth observations

2017 ◽  
Vol 21 (5) ◽  
pp. 2483-2495 ◽  
Author(s):  
Rene Orth ◽  
Emanuel Dutra ◽  
Isabel F. Trigo ◽  
Gianpaolo Balsamo
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Weather Forecasting ◽  
Model Performance ◽  
Land Surface Model ◽  
Climate System ◽  
Surface Model ◽  
Earth Observations ◽  
The Impact

Abstract. The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts, we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability, and understanding of climate system feedbacks.

scholarly journals Multimodel Analysis of Energy and Water Fluxes: Intercomparisons between Operational Analyses, a Land Surface Model, and Remote Sensing

2012 ◽  
Vol 13 (1) ◽  
pp. 3-26 ◽  
Author(s):  
Raghuveer K. Vinukollu ◽  
Justin Sheffield ◽  
Eric F Wood ◽  
Michael G. Bosilovich ◽  
David Mocko
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Heat Fluxes ◽  
Surface Model ◽  
Water Fluxes ◽  
Net Radiation ◽  
Energy Fluxes ◽  
Ensemble Mean

Abstract Using data from seven global model operational analyses (OA), one land surface model, and various remote sensing retrievals, the energy and water fluxes over global land areas are intercompared for 2003/04. Remote sensing estimates of evapotranspiration (ET) are obtained from three process-based models that use input forcings from multisensor satellites. An ensemble mean (linear average) of the seven operational (mean-OA) models is used primarily to intercompare the fluxes with comparisons performed at both global and basin scales. At the global scale, it is found that all components of the energy budget represented by the ensemble mean of the OA models have a significant bias. Net radiation estimates had a positive bias (global mean) of 234 MJ m−2 yr−1 (7.4 W m−2) as compared to the remote sensing estimates, with the latent and sensible heat fluxes biased by 470 MJ m−2 yr−1 (13.3 W m−2) and −367 MJ m−2 yr−1 (11.7 W m−2), respectively. The bias in the latent heat flux is affected by the bias in the net radiation, which is primarily due to the biases in the incoming shortwave and outgoing longwave radiation and to the nudging process of the operational models. The OA models also suffer from improper partitioning of the surface heat fluxes. Comparison of precipitation (P) analyses from the various OA models, gauge analysis, and remote sensing retrievals showed better agreement than the energy fluxes. Basin-scale comparisons were consistent with the global-scale results, with the results for the Amazon in particular showing disparities between OA and remote sensing estimates of energy fluxes. The biases in the fluxes are attributable to a combination of errors in the forcing from the OA atmospheric models and the flux calculation methods in their land surface schemes. The atmospheric forcing errors are mainly attributable to high shortwave radiation likely due to the underestimation of clouds, but also precipitation errors, especially in water-limited regions.

scholarly journals Advancing land surface model development with satellite-based Earth observations

2016 ◽  
Author(s):  
Rene Orth ◽  
Emanuel Dutra ◽  
Isabel F. Trigo ◽  
Gianpaolo Balsamo
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Weather Forecasting ◽  
Model Performance ◽  
Land Surface Model ◽  
Climate System ◽  
Surface Model ◽  
Earth Observations ◽  
The Impact

Abstract. The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks.

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