scholarly journals Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model

2022 ◽  
Author(s):  
Anthony Bernus ◽  
Catherine Ottlé
Keyword(s):  
Energy Balance ◽  
Land Surface ◽  
Grid Cell ◽  
Systematic Errors ◽  
Global Scale ◽  
Surface Model ◽  
Lake Depth ◽  
Atmospheric Forcing ◽  
Lake Model ◽  
Ice Phenology

Abstract. The freshwater 1-D FLake lake model was coupled to the ORCHIDEE land surface model to simulate lake energy balance at the global scale. A multi-tile approach has been chosen to allow the modelling of various types of lakes within the ORCHIDEE grid cell. The different categories have been defined according to lake depth which is the most influential parameter of FLake, but other properties could be considered in the future. Several depth parameterization strategies have been compared, differing by the way to aggregate the depth of the subgrid lakes, i.e., arithmetical, geometrical, harmonical mean and median. Five atmospheric reanalysis datasets available at 0.5° or 0.25° resolution, have been used to force the model and assess model systematic errors. Simulations have been performed, evaluated and intercompared against observations of lake water temperatures provided by the GloboLakes database over about 1000 lakes and ice phenology derived from the Global Lake and River Ice Phenology database. The results highlighted the large impact of the atmospheric forcing on the lake energy budget simulations and the improvements brought by the highest resolution products (ERA5 and E2OFD). The median of the Root Square Mean Errors (RMSE) calculated at global scale range between 3.2 K and 2.7 K among the forcings, CRUJRA and ERA5 leading respectively to the best and worst results. Depth parameterization strategy appeared to be less influent, with RMSE differences less than 0.1 K for the four aggregation scenarios tested. The simulation of ice phenology presented systematic errors whatever the forcing used and the depth parameterization. Freezing onset was shown to be the less sensitive to forcing and depth parameterization with median of the errors ranging between 10 and 14 days. Larger errors were observed on the simulation of the end of the freezing period significantly influenced by the atmospheric forcing used. Such errors already highlighted in previous works, could be the result of deficiencies in the modeling of snow/ice parameterization processes. Various pathways are drawn to improve the model results, including the use of remote sensing data to better constrain the lake radiative parameters (albedo and extinction coefficient) as well as the lake depth thanks to the recent and forthcoming high resolution satellite missions.

scholarly journals Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1)

2017 ◽  
Author(s):  
Joe R. Melton ◽  
Reinel Sospedra-Alfonso ◽  
Kelly E. McCusker
Keyword(s):  
Soil Texture ◽  
Land Surface ◽  
Clustering Analysis ◽  
Clustering Algorithms ◽  
Terrestrial Ecosystem ◽  
Grid Cell ◽  
Moisture Stress ◽  
Ecosystem Model ◽  
Global Scale ◽  
Terrestrial Ecosystem Model

Abstract. We investigate the application of clustering algorithms to represent sub-grid scale variability in soil texture for use in a global-scale terrestrial ecosystem model. Our model, the coupled Canadian Land Surface Scheme – Canadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented at a coarse spatial resolution (ca. 2.8° × 2.8°) due to its use as the land surface component of the Canadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as a means to represent sub-grid heterogeneity. We first determined that the model was sensitive to tiling of the soil textures via an idealized test case before attempting to cluster soil textures globally. To cluster a high-resolution soil texture dataset onto our coarse model grid, we use two linked algorithms (OPTICS (Ankerst et al., 1999; Daszykowski et al., 2002) and Sander et al. (2003)) to provide tiles of representative soil textures for use as CLASS-CTEM inputs. The clustering process results in, on average, about three tiles per CLASS-CTEM grid cell with most cells having four or less tiles. Results from CLASS-CTEM simulations conducted with the tiled inputs (Cluster) versus those using a simple grid-mean soil texture (Gridmean) show CLASS-CTEM, at least on a global scale, is relatively insensitive to the tiled soil textures, however differences can be large in arid or peatland regions. The Cluster simulation has generally lower soil moisture and lower overall vegetation productivity than the Gridmean simulation except in arid regions where plant productivity increases. In these dry regions, the influence of the tiling is stronger due to the general state of vegetation moisture stress which allows a single tile, whose soil texture retains more plant available water, to yield much higher productivity. Although the use of clustering analysis appears promising as a means to represent sub-grid heterogeneity, soil textures appear to be reasonably represented for global scale simulations using a simple grid-mean value.

scholarly journals Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles

2018 ◽  
Vol 22 (7) ◽  
pp. 3863-3882 ◽  
Author(s):  
Fuxing Wang ◽  
Jan Polcher ◽  
Philippe Peylin ◽  
Vladislav Bastrikov
Keyword(s):  
Iberian Peninsula ◽  
Land Surface ◽  
Measurement Errors ◽  
River Discharge ◽  
Water Footprint ◽  
Human Impacts ◽  
Land Surface Model ◽  
Systematic Errors ◽  
Balance Model ◽  
Surface Model

Abstract. River discharge plays an important role in earth's water cycle, but it is difficult to estimate due to un-gauged rivers, human activities and measurement errors. One approach is based on the observed flux and a simple annual water balance model (ignoring human processes) for un-gauged rivers, but it only provides annual mean values which is insufficient for oceanic modelings. Another way is by forcing a land surface model (LSM) with atmospheric conditions. It provides daily values but with uncertainties associated with the models. We use data assimilation techniques by merging the modeled river discharges by the ORCHIDEE (without human processes currently) LSM and the observations from the Global Runoff Data Centre (GRDC) to obtain optimized discharges over the entire basin. The “model systematic errors” and “human impacts” (dam operation, irrigation, etc.) are taken into account by an optimization parameter x (with annual variation), which is applied to correct model intermediate variable runoff and drainage over each sub-watershed. The method is illustrated over the Iberian Peninsula with 27 GRDC stations over the period 1979–1989. ORCHIDEE represents a realistic discharge over the north of the Iberian Peninsula with small model systematic errors, while the model overestimates discharges by 30–150 % over the south and northeast regions where the blue water footprint is large. The normalized bias has been significantly reduced to less than 30 % after assimilation, and the assimilation result is not sensitive to assimilation strategies. This method also corrects the discharge bias for the basins without observations assimilated by extrapolating the correction from adjacent basins. The “correction” increases the interannual variability in river discharge because of the fluctuation of water usage. The E (P−E) of GLEAM (Global Land Evaporation Amsterdam Model, v3.1a) is lower (higher) than the bias-corrected value, which could be due to the different P forcing and probably the missing processes in the GLEAM model.

scholarly journals Evaluation of ORCHIDEE-MICT-simulated soil moisture over China and impacts of different atmospheric forcing data

2018 ◽  
Vol 22 (10) ◽  
pp. 5463-5484 ◽  
Author(s):  
Zun Yin ◽  
Catherine Ottlé ◽  
Philippe Ciais ◽  
Matthieu Guimberteau ◽  
Xuhui Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Yellow River ◽  
Climatic Research Unit ◽  
Surface Model ◽  
Atmospheric Forcing ◽  
Meteorological Forcing ◽  
Soil Moisture Dynamics ◽  
Moisture Variation ◽  
Moisture Dynamics

Abstract. Soil moisture is a key variable of land surface hydrology, and its correct representation in land surface models is crucial for local to global climate predictions. The errors may come from the model itself (structure and parameterization) but also from the meteorological forcing used. In order to separate the two source of errors, four atmospheric forcing datasets, GSWP3 (Global Soil Wetness Project Phase 3), PGF (Princeton Global meteorological Forcing), CRU-NCEP (Climatic Research Unit-National Center for Environmental Prediction), and WFDEI (WATCH Forcing Data methodology applied to ERA-Interim reanalysis data), were used to drive simulations in China by the land surface model ORCHIDEE-MICT(ORganizing Carbon and Hydrology in Dynamic EcosystEms: aMeliorated Interactions between Carbon and Temperature). Simulated soil moisture was compared with in situ and satellite datasets at different spatial and temporal scales in order to (1) estimate the ability of ORCHIDEE-MICT to represent soil moisture dynamics in China; (2) demonstrate the most suitable forcing dataset for further hydrological studies in Yangtze and Yellow River basins; and (3) understand the discrepancies of simulated soil moisture among simulations. Results showed that ORCHIDEE-MICT can simulate reasonable soil moisture dynamics in China, but the quality varies with forcing data. Simulated soil moisture driven by GSWP3 and WFDEI shows the best performance according to the root mean square error (RMSE) and correlation coefficient, respectively, suggesting that both GSWP3 and WFDEI are good choices for further hydrological studies in the two catchments. The mismatch between simulated and observed soil moisture is mainly explained by the bias of magnitude, suggesting that the parameterization in ORCHIDEE-MICT should be revised for further simulations in China. Underestimated soil moisture in the North China Plain demonstrates possible significant impacts of human activities like irrigation on soil moisture variation, which was not considered in our simulations. Finally, the discrepancies of meteorological variables and simulated soil moisture among the four simulations are analyzed. The result shows that the discrepancy of soil moisture is mainly explained by differences in precipitation frequency and air humidity rather than differences in precipitation amount.

scholarly journals Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products

2006 ◽  
Vol 111 (D18) ◽  
Author(s):  
Anne-Laure Gibelin ◽  
Jean-Christophe Calvet ◽  
Jean-Louis Roujean ◽  
Lionel Jarlan ◽  
Sietse O. Los
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Area Index

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 Modelling river runoff and estimating its weather-related uncertainty for 11 large-scale rivers located in different regions of the globe

2017 ◽  
Vol 49 (4) ◽  
pp. 1072-1087 ◽  
Author(s):  
Yeugeniy M. Gusev ◽  
Olga N. Nasonova ◽  
Evgeny E. Kovalev ◽  
Georgii V. Aizel
Keyword(s):  
River Runoff ◽  
Land Surface ◽  
Input Data ◽  
Large Scale ◽  
Land Surface Model ◽  
Grid Cell ◽  
Surface Model ◽  
Model Parameters ◽  
Data Sets ◽  
Natural Conditions

Abstract In order to study the possibility of reproducing river runoff with making use of the land surface model Soil Water–Atmosphere–Plants (SWAP) and information based on global data sets 11 river basins suggested within the framework of the Inter-Sectoral Impact Model Intercomparison Project and located in various regions of the globe under a wide variety of natural conditions were used. Schematization of each basin as a set of 0.5° × 0.5° computational grid cells connected by a river network was carried out. Input data including atmospheric forcing data and land surface parameters based, respectively, on the global WATCH and ECOCLIMAP data sets were prepared for each grid cell. Simulations of river runoff performed by SWAP with a priori input data showed poor agreement with observations. Optimization of a number of model parameters substantially improved the results. The obtained results confirm the universal character of SWAP. Natural uncertainty of river runoff caused by weather noise was estimated and analysed. It can be treated as the lowest limit of predictability of river runoff. It was shown that differences in runoff uncertainties obtained for different rivers depend greatly on natural conditions of a river basin, in particular, on the ratio of deterministic and random components of the river runoff.

scholarly journals REMOTE SENSING OF GLOBAL MONTHLY EVAPOTRANSPIRATION WITH AN ENERGY BALANCE (EB) MODEL

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1729-1733
Author(s):  
X. Chen ◽  
Z. Su ◽  
Y. Ma
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Land Surface ◽  
Roughness Length ◽  
Meteorological Data ◽  
Global Scale ◽  
Balance Model ◽  
Time Step ◽  
Available Energy ◽  
Hydrological Cycles

<p><strong>Abstract.</strong> A global monthly evapotranspiration (ET) product without spatial-temporal gaps for 2000&amp;ndash;2017 is delivered by using an energy balance (EB) algorithm and MODIS satellite data. It provides us with a moderate resolution estimate of ET without spatial-temporal gaps on a global scale. The model is driven by monthly remote sensing land surface temperature and ERA-Interim meteorological data. A global turbulent exchange parameterization scheme was developed for global momentum and heat roughness length calculation with remote sensing information. The global roughness length was used in the energy balance model, which uses monthly land-air temperature gradient to estimate the turbulent sensible heat, and take the latent heat flux as a residual of the available energy. This study produced an ET product for global landmass, at a monthly time step and 0.05-degree spatial resolution. The performance of ET data has been evaluated in comparison to hundreds flux sites measurements representing a broad range of land covers and climates. The ET product has a mean bias of 3.3&amp;thinsp;mm/month, RMSE value of 36.9&amp;thinsp;mm/month. The monthly ET product can be used to study the global energy and hydrological cycles at either seasonal or inter-annual temporal resolution.</p>

scholarly journals Carbonyl sulfide: comparing a mechanistic representation of the vegetation uptake in a land surface model and the leaf relative uptake approach

2021 ◽  
Vol 18 (9) ◽  
pp. 2917-2955
Author(s):  
Fabienne Maignan ◽  
Camille Abadie ◽  
Marine Remaud ◽  
Linda M. J. Kooijmans ◽  
Kukka-Maaria Kohonen ◽  
...  
Keyword(s):  
Land Surface ◽  
Carbonyl Sulfide ◽  
Land Surface Model ◽  
Global Scale ◽  
C3 Plants ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Co2 Uptake ◽  
Mechanistic Approach ◽  
Relative Uptake

Abstract. Land surface modellers need measurable proxies to constrain the quantity of carbon dioxide (CO2) assimilated by continental plants through photosynthesis, known as gross primary production (GPP). Carbonyl sulfide (COS), which is taken up by leaves through their stomates and then hydrolysed by photosynthetic enzymes, is a candidate GPP proxy. A former study with the ORCHIDEE land surface model used a fixed ratio of COS uptake to CO2 uptake normalised to respective ambient concentrations for each vegetation type (leaf relative uptake, LRU) to compute vegetation COS fluxes from GPP. The LRU approach is known to have limited accuracy since the LRU ratio changes with variables such as photosynthetically active radiation (PAR): while CO2 uptake slows under low light, COS uptake is not light limited. However, the LRU approach has been popular for COS–GPP proxy studies because of its ease of application and apparent low contribution to uncertainty for regional-scale applications. In this study we refined the COS–GPP relationship and implemented in ORCHIDEE a mechanistic model that describes COS uptake by continental vegetation. We compared the simulated COS fluxes against measured hourly COS fluxes at two sites and studied the model behaviour and links with environmental drivers. We performed simulations at a global scale, and we estimated the global COS uptake by vegetation to be −756 Gg S yr−1, in the middle range of former studies (−490 to −1335 Gg S yr−1). Based on monthly mean fluxes simulated by the mechanistic approach in ORCHIDEE, we derived new LRU values for the different vegetation types, ranging between 0.92 and 1.72, close to recently published averages for observed values of 1.21 for C4 and 1.68 for C3 plants. We transported the COS using the monthly vegetation COS fluxes derived from both the mechanistic and the LRU approaches, and we evaluated the simulated COS concentrations at NOAA sites. Although the mechanistic approach was more appropriate when comparing to high-temporal-resolution COS flux measurements, both approaches gave similar results when transporting with monthly COS fluxes and evaluating COS concentrations at stations. In our study, uncertainties between these two approaches are of secondary importance compared to the uncertainties in the COS global budget, which are currently a limiting factor to the potential of COS concentrations to constrain GPP simulated by land surface models on the global scale.

scholarly journals A high resolution global scale groundwater model

2014 ◽  
Vol 11 (5) ◽  
pp. 5217-5250 ◽  
Author(s):  
I. E. M. de Graaf ◽  
E. H. Sutanudjaja ◽  
L. P. H. van Beek ◽  
M. F. P. Bierkens
Keyword(s):  
Groundwater Flow ◽  
Land Surface ◽  
Regional Scale ◽  
Land Surface Model ◽  
Global Scale ◽  
Water Levels ◽  
Natural State ◽  
Surface Model ◽  
Groundwater Model ◽  
Aquifer Systems

Abstract. Groundwater is the world's largest accessible source of fresh water. It plays a vital role in satisfying needs for drinking water, agriculture and industrial activities. During times of drought groundwater sustains baseflow to rivers and wetlands, thereby supporting ecosystems. Most global scale hydrological models (GHMs) do not include a groundwater flow component, mainly due to lack of geohydrological data at the global scale. For the simulation of lateral flow and groundwater head dynamics a realistic physical representation of the groundwater system is needed, especially for GHMs that run at finer resolution. In this study we present a global scale groundwater model (run at 6' as dynamic steady state) using MODFLOW to construct an equilibrium water table at its natural state as the result of long-term climatic forcing. The aquifer schematization and properties were based on available global datasets of lithology and transmissivities combined with estimated aquifer thickness of an upper unconfined aquifer. The model is forced with outputs from the land-surface model PCR-GLOBWB, specifically with net recharge and surface water levels. A sensitivity analysis, in which the model was run with various parameter settings, showed variation in saturated conductivity causes most of the groundwater level variations. Simulated groundwater heads were validated against reported piezometer observations. The validation showed that groundwater depths are reasonably well simulated for many regions of the world, especially for sediment basins (R2 = 0.95). The simulated regional scale groundwater patterns and flowpaths confirm the relevance of taking lateral groundwater flow into account in GHMs. Flowpaths show inter-basin groundwater flow that can be a significant part of a basins water budget and helps to sustain river baseflow, explicitly during times of droughts. Also important aquifer systems are recharged by inter-basin groundwater flows that positively affect water availability.

scholarly journals La représentation des surfaces continentales dans la modélisation du climat à Météo-France

2020 ◽  
pp. 067
Author(s):  
Bertrand Decharme ◽  
Christine Delire ◽  
Aaron Boone
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Climate Modelling ◽  
History Of ◽  
International Climate ◽  
Land Surfaces ◽  
Computational Resources

Les surfaces continentales jouent un rôle non négligeable dans le système climatique de la Terre. Elles occupent d'ailleurs une place majeure dans les cycles globaux de l'eau et du carbone. Elles ont été prises en compte dès les premiers modèles numériques de climat et, avec l'évolution des connaissances, des capacités de calcul et de la demande sociétale, leur représentation s'est aujourd'hui considérablement complexifiée. Nous présentons ici une brève histoire de l'évolution du modèle de surfaces Isba (Interactions sol-biosphère-atmosphère) de Météo-France dans son utilisation à l'échelle du globe en la replaçant dans le contexte international de la modélisation climatique. Land surfaces play a significant role in the Earth climate system, and they are a major component of the global carbon and water cycles. The first numerical climate models took them into account in very simple ways. Through time the complexity of their representation has increased a lot owing to improved knowledge, larger computational resources and changing societal demands. We present here a brief history of the ISBA (Interactions Soil-Biosphere-Atmosphere) land surface model developed at Météo-France when used at the global scale and how it evolved in the context of international climate modelling.

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