Author: "Decharme, B." - Searchworks@Jio Institute Digital Library Search Results

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248 results on '"Decharme, B."'

1. Imaging permafrost active layer thickness under forest for climate model improvement

Author: Garestier, F., Guillaso, S., Zakharova, E.A., Kouraev, A.V., Decharme, B., Desyatkin, R.V., Desyatkin, A., and Chevalier, T.
Published: 2023
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2. Infiltration from the pedon to global grid scales: An overview and outlook for land surface modeling

Author: Vereecken, H, Weihermüller, L, Assouline, S, Šimůnek, J, Verhoef, A, Herbst, M, Archer, N, Mohanty, B, Montzka, C, Vanderborght, J, Balsamo, G, Bechtold, M, Boone, A, Chadburn, S, Cuntz, M, Decharme, B, Ducharne, A, Ek, M, Garrigues, S, Goergen, K, Ingwersen, J, Kollet, S, Lawrence, DM, Li, Q, Or, D, Swenson, S, de Vrese, P, Walko, R, Wu, Y, and Xue, Y
Subjects: Physical Geography and Environmental Geoscience, Soil Sciences, Crop and Pasture Production, Environmental Engineering
Abstract: Infiltration in soils is a key process that partitions precipitation at the land surface into surface runoff and water that enters the soil profile. We reviewed the basic principles of water infiltration in soils and we analyzed approaches commonly used in land surface models (LSMs) to quantify infiltration as well as its numerical implementation and sensitivity to model parameters. We reviewed methods to upscale infiltration from the point to the field, hillslope, and grid cell scales of LSMs. Despite the progress that has been made, upscaling of local-scale infiltration processes to the grid scale used in LSMs is still far from being treated rigorously. We still lack a consistent theoretical framework to predict effective fluxes and parameters that control infiltration in LSMs. Our analysis shows that there is a large variety of approaches used to estimate soil hydraulic properties. Novel, highly resolved soil information at higher resolutions than the grid scale of LSMs may help in better quantifying subgrid variability of key infiltration parameters. Currently, only a few LSMs consider the impact of soil structure on soil hydraulic properties. Finally, we identified several processes not yet considered in LSMs that are known to strongly influence infiltration. Especially, the impact of soil structure on infiltration requires further research. To tackle these challenges and integrate current knowledge on soil processes affecting infiltration processes into LSMs, we advocate a stronger exchange and scientific interaction between the soil and the land surface modeling communities.
Published: 2019

3. Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region

Author: Xia, J, McGuire, AD, Lawrence, D, Burke, E, Chen, G, Chen, X, Delire, C, Koven, C, MacDougall, A, Peng, S, Rinke, A, Saito, K, Zhang, W, Alkama, R, Bohn, TJ, Ciais, P, Decharme, B, Gouttevin, I, Hajima, T, Hayes, DJ, Huang, K, Ji, D, Krinner, G, Lettenmaier, DP, Miller, PA, Moore, JC, Smith, B, Sueyoshi, T, Shi, Z, Yan, L, Liang, J, Jiang, L, Zhang, Q, and Luo, Y
Subjects: Geophysics
Abstract: Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.
Published: 2017

4. Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region

Author: Wang, W, Rinke, A, Moore, JC, Ji, D, Cui, X, Peng, S, Lawrence, DM, McGuire, AD, Burke, EJ, Chen, X, Decharme, B, Koven, C, MacDougall, A, Saito, K, Zhang, W, Alkama, R, Bohn, TJ, Ciais, P, Delire, C, Gouttevin, I, Hajima, T, Krinner, G, Lettenmaier, DP, Miller, PA, Smith, B, Sueyoshi, T, and Sherstiukov, AB
Subjects: Meteorology & Atmospheric Sciences, Oceanography, Physical Geography and Environmental Geoscience
Abstract: A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C-1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.
Published: 2016

5. Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009

Author: McGuire, AD, Koven, C, Lawrence, DM, Clein, JS, Xia, J, Beer, C, Burke, E, Chen, G, Chen, X, Delire, C, Jafarov, E, MacDougall, AH, Marchenko, S, Nicolsky, D, Peng, S, Rinke, A, Saito, K, Zhang, W, Alkama, R, Bohn, TJ, Ciais, P, Decharme, B, Ekici, A, Gouttevin, I, Hajima, T, Hayes, DJ, Ji, D, Krinner, G, Lettenmaier, DP, Luo, Y, Miller, PA, Moore, JC, Romanovsky, V, Schädel, C, Schaefer, K, Schuur, EAG, Smith, B, Sueyoshi, T, and Zhuang, Q
Subjects: carbon cycle, climate change, permafrost, permafrost carbon feedback, sensitivity, soil carbon, Meteorology & Atmospheric Sciences, Atmospheric Sciences, Geochemistry, Oceanography
Abstract: A significant portion of the large amount of carbon (C) currently stored in soils of the permafrost region in the Northern Hemisphere has the potential to be emitted as the greenhouse gases CO2 and CH4 under a warmer climate. In this study we evaluated the variability in the sensitivity of permafrost and C in recent decades among land surface model simulations over the permafrost region between 1960 and 2009. The 15 model simulations all predict a loss of near-surface permafrost (within 3 m) area over the region, but there are large differences in the magnitude of the simulated rates of loss among the models (0.2 to 58.8 × 103 km2 yr−1). Sensitivity simulations indicated that changes in air temperature largely explained changes in permafrost area, although interactions among changes in other environmental variables also played a role. All of the models indicate that both vegetation and soil C storage together have increased by 156 to 954 Tg C yr−1 between 1960 and 2009 over the permafrost region even though model analyses indicate that warming alone would decrease soil C storage. Increases in gross primary production (GPP) largely explain the simulated increases in vegetation and soil C. The sensitivity of GPP to increases in atmospheric CO2 was the dominant cause of increases in GPP across the models, but comparison of simulated GPP trends across the 1982–2009 period with that of a global GPP data set indicates that all of the models overestimate the trend in GPP. Disturbance also appears to be an important factor affecting C storage, as models that consider disturbance had lower increases in C storage than models that did not consider disturbance. To improve the modeling of C in the permafrost region, there is the need for the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost C feedback and for the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.
Published: 2016

6. Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

Author: Wang, W, Rinke, A, Moore, JC, Cui, X, Ji, D, Li, Q, Zhang, N, Wang, C, Zhang, S, Lawrence, DM, McGuire, AD, Zhang, W, Delire, C, Koven, C, Saito, K, MacDougall, A, Burke, E, and Decharme, B
Subjects: Meteorology & Atmospheric Sciences, Oceanography, Physical Geography and Environmental Geoscience
Abstract: We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135 × 104km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 ×104km2). However the uncertainty (1 to 128 × 104km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0°C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for the Tibetan Plateau.
Published: 2016

7. Simulated high-latitude soil thermal dynamics during the past 4 decades

Author: Peng, S, Ciais, P, Krinner, G, Wang, T, Gouttevin, I, McGuire, AD, Lawrence, D, Burke, E, Chen, X, Decharme, B, Koven, C, MacDougall, A, Rinke, A, Saito, K, Zhang, W, Alkama, R, Bohn, TJ, Delire, C, Hajima, T, Ji, D, Lettenmaier, DP, Miller, PA, Moore, JC, Smith, B, and Sueyoshi, T
Subjects: Earth Sciences, Atmospheric Sciences, Climate Action, Oceanography, Physical Geography and Environmental Geoscience, Meteorology & Atmospheric Sciences, Physical geography and environmental geoscience
Abstract: Soil temperature (Ts/change is a key indicator of the dynamics of permafrost. On seasonal and interannual timescales, the variability of Ts determines the activelayer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing Ts not only drives permafrost thaw/retreat but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960-2000, to characterize the warming rate of Ts in permafrost regions. There is a large spread of Ts trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 °C yr-1. Most models show smaller increase in Ts with increasing depth. Air temperature (Ta/and longwave downward radiation (LWDR) are the main drivers of Ts trends, but their relative contributions differ amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in Ts trends, while trends of Ta only explain 5 % of the differences in Ts trends. Uncertain climate forcing contributes a larger uncertainty in Ts trends (0.021 ± 0.008 °C yr-1, mean ± standard deviation) than the uncertainty of model structure (0.012 ± 0.001 °C yr-1/, diagnosed from the range of response between different models, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active-layer thickness (ALT) is less than 3 m loss rate, is found to be significantly correlated with the magnitude of the trends of Ts at 1 m depth across the models (R D-0:85, P = 0:003), but not with the initial total nearsurface permafrost area (R =-0:30, P = 0:438). The sensitivity of the total boreal near-surface permafrost area to Ts at 1 m is estimated to be of-2.80 ± 0.67 million km2 °C-1. Finally, by using two long-term LWDR data sets and relationships between trends of LWDR and Ts across models, we infer an observation-constrained total boreal near-surface permafrost area decrease comprising between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr-1 from 1960 to 2000. This corresponds to 9-18 % degradation of the current permafrost area.
Published: 2016

8. Water balance in the amazon basin from a land surface model ensemble

Author: Getirana, ACV, Dutra, E, Guimberteau, M, Kam, J, Li, HY, Decharme, B, Zhang, Z, Ducharne, A, Boone, A, Balsamo, G, Rodell, M, Toure, AM, Xue, Y, Peters-Lidard, CD, Kumar, SV, Arsenault, K, Drapeau, G, Leung, LR, Ronchail, J, and Sheffield, J
Subjects: Amazon region, Runoff, Hydrologic models, Land surface model, Meteorology & Atmospheric Sciences, Atmospheric Sciences
Abstract: Despite recent advances in land surfacemodeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-ofthe- art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 18 spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to matchmonthly Global Precipitation Climatology Project (GPCP) andGlobal Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l'Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets andGravity Recovery and ClimateExperiment (GRACE)TWSestimates in two subcatchments of main tributaries (Madeira and Negro Rivers).At the basin scale, simulated ET ranges from 2.39 to 3.26mmday-1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.
Published: 2014

9. Impacts of the Soil Water Transfer Parameterization on the Simulation of Evapotranspiration over a 14-Year Mediterranean Crop Succession

Author: Garrigues, S., Boone, A., Decharme, B., Olioso, A., Albergel, C., Calvet, J.-C., Moulin, S., Buis, S., and Martin, E.
Published: 2018

10. The Plumbing of Land Surface Models : Benchmarking Model Performance

Author: Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z., Haverd, V., van den Hurk, B. J. J., Nearing, G. S., Pak, B., Peters-Lidard, C., Santanello, J. A., Stevens, L., and Vuichard, N.
Published: 2015

11. The Arctic

Author: Thoman, Richard L., primary, Druckenmiller, Matthew L., additional, Moon, Twila A., additional, Andreassen, L. M., additional, Baker, E., additional, Ballinger, Thomas J., additional, Berner, Logan T., additional, Bernhard, Germar H., additional, Bhatt, Uma S., additional, Bjerke, Jarle W., additional, Boisvert, L.N., additional, Box, Jason E., additional, Brettschneider, B., additional, Burgess, D., additional, Butler, Amy H., additional, Cappelen, John, additional, Christiansen, Hanne H., additional, Decharme, B., additional, Derksen, C., additional, Divine, Dmitry, additional, Drozdov, D. S., additional, Elias, Chereque A., additional, Epstein, Howard E., additional, Farrell, Sinead L., additional, Fausto, Robert S., additional, Fettweis, Xavier, additional, Fioletov, Vitali E., additional, Forbes, Bruce C., additional, Frost, Gerald V., additional, Gerland, Sebastian, additional, Goetz, Scott J., additional, Grooß, Jens-Uwe, additional, Haas, Christian, additional, Hanna, Edward, additional, Hanssen, -Bauer Inger, additional, Heijmans, M. M. P. D., additional, Hendricks, Stefan, additional, Ialongo, Iolanda, additional, Isaksen, K., additional, Jensen, C. D., additional, Johnsen, Bjørn, additional, Kaleschke, L., additional, Kholodov, A. L., additional, Kim, Seong-Joong, additional, Kohler, J., additional, Korsgaard, Niels J., additional, Labe, Zachary, additional, Lakkala, Kaisa, additional, Lara, Mark J., additional, Lee, Simon H., additional, Loomis, Bryant, additional, Luks, B., additional, Luojus, K., additional, Macander, Matthew J., additional, Magnússon, R. Í, additional, Malkova, G. V., additional, Mankoff, Kenneth D., additional, Manney, Gloria L., additional, Meier, Walter N., additional, Mote, Thomas, additional, Mudryk, Lawrence, additional, Müller, Rolf, additional, Nyland, K. E., additional, Overland, James E., additional, Pálsson, F., additional, Park, T., additional, Parker, C. L., additional, Perovich, Don, additional, Petty, Alek, additional, Phoenix, Gareth K., additional, Pinzon, J. E., additional, Ricker, Robert, additional, Romanovsky, Vladimir E., additional, Serbin, S. P., additional, Sheffield, G., additional, Shiklomanov, Nikolai I., additional, Smith, Sharon L., additional, Stafford, K. M., additional, Steer, A., additional, Streletskiy, Dimitri A., additional, Svendby, Tove, additional, Tedesco, Marco, additional, Thomson, L., additional, Thorsteinsson, T., additional, Tian-Kunze, X., additional, Timmermans, Mary-Louise, additional, Tømmervik, Hans, additional, Tschudi, Mark, additional, Tucker, C. J., additional, Walker, Donald A., additional, Walsh, John E., additional, Wang, Muyin, additional, Webster, Melinda, additional, Wehrlé, A., additional, Winton, Øyvind, additional, Wolken, G., additional, Wood, K., additional, Wouters, B., additional, and Yang, D., additional
Published: 2022
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12. A Simple Groundwater Scheme for Hydrological and Climate Applications : Description and Offline Evaluation over France

Author: Vergnes, J.-P., Decharme, B., Alkama, R., Martin, E., Habets, F., and Douville, H.
Published: 2012

13. Snow Cover and Spring Flood Flow in the Northern Part of Western Siberia (the Poluy, Nadym, Pur, and Taz Rivers)

Author: Zakharova, E. A., Kouraev, A. V., Biancamaria, S., Kolmakova, M. V., Mognard, N. M., Zemtsov, V. A., Kirpotin, S. N., and Decharme, B.
Published: 2011

14. Trends in Global and Basin-Scale Runoff over the Late Twentieth Century : Methodological Issues and Sources of Uncertainty

Author: Alkama, R., Decharme, B., Douville, H., and Ribes, A.
Published: 2011

15. Global Evaluation of the ISBA-TRIP Continental Hydrological System. : Part I: Comparison to GRACE Terrestrial Water Storage Estimates and In Situ River Discharges

Author: Alkama, R., Decharme, B., Douville, H., Becker, M., Cazenave, A., Sheffield, J., Voldoire, A., Tyteca, S., and Le Moigne, P.
Published: 2010

16. Global Evaluation of the ISBA-TRIP Continental Hydrological System. : Part II: Uncertainties in River Routing Simulation Related to Flow Velocity and Groundwater Storage

Author: Decharme, B., Alkama, R., Douville, H., Becker, M., and Cazenave, A.
Published: 2010

17. Terrestrial waters and sea level variations on interannual time scale

Author: Llovel, W., Becker, M., Cazenave, A., Jevrejeva, S., Alkama, R., Decharme, B., Douville, H., Ablain, M., and Beckley, B.
Published: 2011
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18. Impact of an Exponential Profile of Saturated Hydraulic Conductivity within the ISBA LSM : Simulations over the Rhône Basin

Author: Decharme, B., Douville, H., Boone, A., Habets, F., and Noilhan, J.
Published: 2006

19. The Arctic

Author: Druckenmiller, Matthew L., Moon, Twila A., Thoman, Richard L., Ballinger, Thomas J., Berner, Logan T., Bernhard, Germar H., Bhatt, Uma S., Bjerke, Jarle W., Box, Jason E., Brown, R., Cappelen, John, Christiansen, Hanne H., Decharme, B., Derksen, C., Divine, Dmitry, Drozdov, D. S., Elias Chereque, A., Epstein, Howard E., Farquharson, L. M., Farrell, Sinead L., Fausto, Robert S., Fettweis, Xavier, Fioletov, Vitali E., Forbes, Bruce C., Frost, Gerald V., Gargulinski, Emily, Gerland, Sebastian, Goetz, Scott J., Grabinski, Z., Grooß, Jens-Uwe, Haas, Christian, Hanna, Edward, Hanssen-Bauer, Inger, Hendricks, Stefan, Holmes, Robert M., Ialongo, Iolanda, Isaksen, K., Jain, Piyush, Johnsen, Bjørn, Kaleschke, L., Kholodov, A. L., Kim, Seong-Joong, Korsgaard, Niels J., Labe, Zachary, Lakkala, Kaisa, Lara, Mark J., Loomis, Bryant, Luojus, K., Macander, Matthew J., Malkova, G. V., Mankoff, Kenneth D., Manney, Gloria L., McClelland, James W., Meier, Walter N., Mote, Thomas, Mudryk, L., Müller, Rolf, Nyland, K. E., Overland, James E., Park, T., Pavlova, Olga, Perovich, Don, Petty, Alek, Phoenix, Gareth K., Raynolds, Martha K., Reijmer, C. H., Richter-Menge, Jacqueline, Ricker, Robert, Romanovsky, Vladimir E., Scott, Lindsay, Shapiro, Hazel, Shiklomanov, Alexander I., Shiklomanov, Nikolai I., Smeets, C. J. P. P., Smith, Sharon L., Soja, Amber, Spencer, Robert G. M., Starkweather, Sandy, Streletskiy, Dimitri A., Suslova, Anya, Svendby, Tove, Tank, Suzanne E., Tedesco, Marco, Tian-Kunze, X., Timmermans, Mary-Louise, Tømmervik, Hans, Tretiakov, Mikhail, Tschudi, Mark, Vakhutinsky, Sofia, van As, Dirk, van de Wal, R. S. W., Veraverbeke, Sander, Walker, Donald A., Walsh, John E., Wang, Muyin, Webster, Melinda, Winton, Øyvind, Wood, K., York, Alison, Ziel, Robert, Sub Dynamics Meteorology, Proceskunde, Sub Algemeen Marine & Atmospheric Res, Marine and Atmospheric Research, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)
Subjects: Atmospheric Science, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Greenland ice sheet, 02 engineering and technology, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, 01 natural sciences, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Climatology, Taverne, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Geology, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract: International audience
Published: 2021

20. SEtHyS_Savannah: A multiple source land surface model applied to Sahelian landscapes

Author: Saux-Picart, S., Ottlé, C., Perrier, A., Decharme, B., Coudert, B., Zribi, M., Boulain, N., Cappelaere, B., and Ramier, D.
Published: 2009
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21. Water and energy budgets simulation over the AMMA-Niger super-site spatially constrained with remote sensing data

Author: Saux-Picart, S., Ottlé, C., Decharme, B., André, C., Zribi, M., Perrier, A., Coudert, B., Boulain, N., Cappelaere, B., Descroix, L., and Ramier, D.
Published: 2009
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22. Hydrological modelling and associated microwave emission of a semi-arid region in South-western Niger

Author: Pellarin, T., Laurent, J.P., Cappelaere, B., Decharme, B., Descroix, L., and Ramier, D.
Published: 2009
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23. ERS scatterometer surface soil moisture analysis of two sites in the south and north of the Sahel region of West Africa

Author: Zribi, M., Pardé, M., De Rosnay, P., Baup, F., Boulain, N., Descroix, L., Pellarin, T., Mougin, E., Ottlé, C., and Decharme, B.
Published: 2009
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24. The CNRM-CM5.1 global climate model: description and basic evaluation

Author: Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.
Published: 2013
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25. Tracking Changes in Climate Sensitivity in CNRM Climate Models

Author: Saint‐Martin, D., primary, Geoffroy, O., additional, Voldoire, A., additional, Cattiaux, J., additional, Brient, F., additional, Chauvin, F., additional, Chevallier, M., additional, Colin, J., additional, Decharme, B., additional, Delire, C., additional, Douville, H., additional, Guérémy, J.‐F., additional, Joetzjer, E., additional, Ribes, A., additional, Roehrig, R., additional, Terray, L., additional, and Valcke, S., additional
Published: 2021
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26. Global off-line evaluation of the ISBA-TRIP flood model

Author: Decharme, B., Alkama, R., Papa, F., Faroux, S., Douville, H., and Prigent, C.
Published: 2012
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27. Snow contribution to springtime atmospheric predictability over the second half of the twentieth century

Author: Peings, Yannick, Douville, H., Alkama, R., and Decharme, B.
Published: 2011
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28. Global validation of the ISBA sub-grid hydrology

Author: Decharme, B. and Douville, H.
Published: 2007
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29. Contribution to land surface modelling at the global scale

Author: Decharme, B., Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Université Paul Sabatier - Toulouse III, and Serge Chauzy
Subjects: [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Climate, Climat, Hydrology, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Modelling, Hydrologie, modélisation
Abstract: Mes activités de recherche au sein du groupe de modélisation du climat du CNRM, s’inscrivent dans la thématique générale de la modélisation des surfaces continentales à grande échelle. Mes travaux portent sur la modélisation des processus thermiques, hydrologiques et – dans une moindre mesure – biogéochimiques, de l’échelle locale à l’échelle régionale afin de les généraliser au mieux à l’échelle globale dans les modèles hydrométéorologiques et/ou climatiques avec une attention particulière pour les régions boréales. Ils s’inscrivent dans l’effort collectif entamé au CNRM il y a bien longtemps pour maintenir à l’état de l’art la modélisation des surfaces continentales dans nos modèles de climat. Dans ce manuscrit, je m'arrête sur les processus hydrologiques sur lesquels j’ai travaillé, sur les modèles que j’ai contribué à améliorer et sur certaines études plus appliquées qui m’ont intéressées. Enfin, j’essaye d’entrevoir ce que pourrai être ma contribution à la modélisation des surfaces continentales dans un futur plus ou moins éloigné.
Published: 2020

30. Arctic Report Card 2020: Terrestrial Snow Cover

Author: Mudryk, Lawrence Elias, Chereque, A., Brown, R., Derksen, C., Luojus, K., and Decharme, B.
Abstract: Arctic Report Card
Published: 2020
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31. Uncertainties in the GSWP-2 precipitation forcing and their impacts on regional and global hydrological simulations

Author: Decharme, B. and Douville, H.
Published: 2006
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32. Introduction of a sub-grid hydrology in the ISBA land surface model

Author: Decharme, B. and Douville, H.
Published: 2006
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33. The Arctic

Author: Andersen, J. K., additional, Andreassen, Liss M., additional, Baker, Emily H., additional, Ballinger, Thomas J., additional, Berner, Logan T., additional, Bernhard, Germar H., additional, Bhatt, Uma S., additional, Bjerke, Jarle W., additional, Box, Jason E., additional, Britt, L., additional, Brown, R., additional, Burgess, David, additional, Cappelen, John, additional, Christiansen, Hanne H., additional, Decharme, B., additional, Derksen, C., additional, Drozdov, D. S., additional, Epstein, Howard E., additional, Farquharson, L. M., additional, Farrell, Sinead L., additional, Fausto, Robert S., additional, Fettweis, Xavier, additional, Fioletov, Vitali E., additional, Forbes, Bruce C., additional, Frost, Gerald V., additional, Gerland, Sebastian, additional, Goetz, Scott J., additional, Grooß, Jens-Uwe, additional, Hanna, Edward, additional, Hanssen-Bauer, Inger, additional, Hendricks, Stefan, additional, Ialongo, Iolanda, additional, Isaksen, K., additional, Johnsen, Bjørn, additional, Kaleschke, L., additional, Kholodov, A. L., additional, Kim, Seong-Joong, additional, Kohler, Jack, additional, Labe, Zachary, additional, Ladd, Carol, additional, Lakkala, Kaisa, additional, Lara, Mark J., additional, Loomis, Bryant, additional, Luks, Bartłomiej, additional, Luojus, K., additional, Macander, Matthew J., additional, Malkova, G. V., additional, Mankoff, Kenneth D., additional, Manney, Gloria L., additional, Marsh, J. M., additional, Meier, Walt, additional, Moon, Twila A., additional, Mote, Thomas, additional, Mudryk, L., additional, Mueter, F. J., additional, Müller, Rolf, additional, Nyland, K. E., additional, O’Neel, Shad, additional, Overland, James E., additional, Perovich, Don, additional, Phoenix, Gareth K., additional, Raynolds, Martha K., additional, Reijmer, C. H., additional, Ricker, Robert, additional, Romanovsky, Vladimir E., additional, Schuur, E. A. G., additional, Sharp, Martin, additional, Shiklomanov, Nikolai I., additional, Smeets, C. J. P. P., additional, Smith, Sharon L., additional, Streletskiy, Dimitri A., additional, Tedesco, Marco, additional, Thoman, Richard L., additional, Thorson, J. T., additional, Tian-Kunze, X., additional, Timmermans, Mary-Louise, additional, Tømmervik, Hans, additional, Tschudi, Mark, additional, van As, Dirk, additional, van de Wal, R. S. W., additional, Walker, Donald A., additional, Walsh, John E., additional, Wang, Muyin, additional, Webster, Melinda, additional, Winton, Øyvind, additional, Wolken, Gabriel J., additional, Wood, K., additional, Wouters, Bert, additional, and Zador, S., additional
Published: 2020
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34. Drivers of the enhanced decline of land near-surface relative humidity to abrupt 4xCO2 in CNRM-CM6-1

Author: Douville, Hervé, primary, Decharme, B., additional, Delire, C., additional, Colin, J., additional, Joetzjer, E., additional, Roehrig, R., additional, Saint-Martin, D., additional, Oudar, T., additional, Stchepounoff, R., additional, and Voldoire, A., additional
Published: 2020
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35. Present‐Day and Historical Aerosol and Ozone Characteristics in CNRM CMIP6 Simulations

Author: Michou, M., primary, Nabat, P., additional, Saint‐Martin, D., additional, Bock, J., additional, Decharme, B., additional, Mallet, M., additional, Roehrig, R., additional, Séférian, R., additional, Sénési, S., additional, and Voldoire, A., additional
Published: 2020
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36. State of the Climate in 2018

Author: Ades, M., Adler, R., Aldeco, Laura S., Alejandra, G., Alfaro, Eric J., Aliaga-Nestares, Vannia, Allan, Richard P., Allan, Rob, Alves, Lincoln M., Amador, Jorge A., Andersen, J. K., Anderson, John, Arndt, Derek S., Arosio, C., Arrigo, Kevin, Azorin-Molina, César, Bardin, M. Yu, Barichivich, Jonathan, Barreira, Sandra, Baxter, Stephen, Beck, H. E., Becker, Andreas, Bell, Gerald D., Bellouin, Nicolas, Belmont, M., Benedetti, Angela, Benedict, Imme, Bernhard, G. H., Berrisford, Paul, Berry, David I., Bettio, Lynette, Bhatt, U. S., Biskaborn, B. K., Bissolli, Peter, Bjella, Kevin L., Bjerke, J. K., Blake, Eric S., Blenkinsop, Stephen, Blunden, Jessica, Bock, Olivier, Bosilovich, Michael G., Boucher, Olivier, Box, J. E., Boyer, Tim, Braathen, Geir, Bringas, Francis G., Bromwich, David H., Brown, Alrick, Brown, R., Brown, Timothy J., Buehler, S. A., Cáceres, Luis, Calderón, Blanca, Camargo, Suzana J., Campbell, Jayaka D., Campos Diaz, Diego A., Cappelen, J., Carrea, Laura, Carrier, Seth B., Carter, Brendan R., Castro, Anabel Y., Cetinic, Ivona, Chambers, Don P., Chen, Lin, Cheng, Lijing, Cheng, Vincent Y.S., Christiansen, Hanne H., Christy, John R., Chung, E. S., Claus, Federico, Clem, Kyle R., Coelho, Caio A.S., Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R., Cosca, Cathy, Covey, Curt, Coy, Lawrence, Dávila, Cristina P., Davis, Sean M., de Eyto, Elvira, de Jeu, Richard A.M., De Laat, Jos, Decharme, B., Degasperi, Curtis L., Degenstein, Doug, Demircan, Mesut, Derksen, C., Dhurmea, K. R., Di Girolamo, Larry, Diamond, Howard J., Diaz, Eliecer, Diniz, Fransisco A., Dlugokencky, Ed J., Dohan, Kathleen, Dokulil, Martin T., Dolman, A. Johannes, Domingues, Catia M., Domingues, Ricardo, Donat, Markus G., Dorigo, Wouter A., Drozdov, D. S., Druckenmiller, Matthew L., Dunn, Robert J.H., Durre, Imke, Dutton, Geoff S., Elkharrim, M., Elkins, James W., Epstein, H. E., Espinoza, Jhan C., Famiglietti, James S., Farrell, Sinead L., Fausto, R. S., Feely, Richard A., Feng, Z., Fenimore, Chris, Fettweis, X., Fioletov, Vitali E., Flemming, Johannes, Fogt, Ryan L., Forbes, B. C., Foster, Michael J., Francis, S. D., Franz, Bryan A., Frey, Richard A., Frith, Stacey M., Froidevaux, Lucien, Ganter, Catherine, Garforth, J., Gerland, Sebastian, Gilson, John, Gleason, Karin, Gobron, Nadine, Goetz, S., Goldenberg, Stanley B., Goni, Gustavo, Gray, Alison, Grooß, Jens Uwe, Gruber, Alexander, Gu, Guojun, Guard, Charles Chip P., Gupta, S. K., Gutiérrez, Dimitri, Haas, Christian, Hagos, S., Hahn, Sebastian, Haimberger, Leo, Hall, Brad D., Halpert, Michael S., Hamlington, Benjamin D., Hanna, E., Hanssen-Bauer, I., Harris, Ian, Hazeleger, Wilco, He, Q., Heidinger, Andrew K., Heim, Richard R., Hemming, D. L., Hendricks, Stefan, Hernández, Rafael, Hersbach, H. E., Hidalgo, Hugo G., Ho, Shu Peng Ben, Holmes, R. M., Hu, Chuanmin, Huang, Boyin, Hubbard, Katherine, Hubert, Daan, Hurst, Dale F., Ialongo, Iolanda, Ijampy, J. A., Inness, Antje, Isaac, Victor, Isaksen, K., Ishii, Masayoshi, Jeffries, Martin O., Jevrejeva, Svetlana, Jia, G., Jiménez, C., Jin, Xiangze, John, Viju, Johnsen, Bjørn, Johnson, Gregory C., Johnson, Kenneth S., Johnson, Bryan, Jones, Philip D., Jumaux, Guillaume, Kabidi, Khadija, Kaiser, J. W., Karaköylü, Erdem M., Karlsen, S. R., Karnauskas, Mandy, Kato, Seiji, Kazemi, A. Fazl, Kelble, Christopher, Keller, Linda M., Kennedy, John, Kholodov, A. L., Khoshkam, Mahbobeh, Kidd, R., Killick, Rachel, Kim, Hyungjun, Kim, S. J., King, A. D., King, Brian A., Kipling, Z., Klotzbach, Philip J., Knaff, John A., Korhonen, Johanna, Korshunova, Natalia N., Kramarova, Natalya A., Kratz, D. P., Kruger, Andries, Kruk, Michael C., Krumpen, Thomas, Labbé, L., Ladd, C., Lakatos, Mónika, Lakkala, Kaisa, Lander, Mark A., Landschützer, Peter, Landsea, Chris W., Lareau, Neil P., Lavado-Casimiro, Waldo, Lazzara, Matthew A., Lee, T. C., Leuliette, Eric, L’heureux, Michelle, Li, Bailing, Li, Tim, Lieser, Jan L., Lim, J. Y., Lin, I. I., Liu, Hongxing, Locarnini, Ricardo, Loeb, Norman G., Long, Craig S., López, Luis A., Lorrey, Andrew M., Loyola, Diego, Lumpkin, Rick, Luo, Jing Jia, Luojus, K., Lyman, John M., Malkova, G. V., Manney, Gloria L., Marchenko, S. S., Marengo, José A., Marin, Dora, Marquardt Collow, Allison B., Marra, John J., Marszelewski, Wlodzimierz, Martens, B., Martínez-Güingla, Rodney, Massom, Robert A., May, Linda, Mayer, Michael, Mazloff, Matthew, McBride, Charlotte, McCabe, M., McClelland, J. W., McEvoy, Daniel J., McGree, Simon, McVicar, Tim R., Mears, Carl A., Meier, Walt, Meijers, Andrew, Mekonnen, Ademe, Mengistu Tsidu, G., Menzel, W. Paul, Merchant, Christopher J., Meredith, Michael P., Merrifield, Mark A., Miller, Ben, Miralles, Diego G., Misevicius, Noelia, Mitchum, Gary T., Mochizuki, Y., Monselesan, Didier, Montzka, Stephen A., Mora, Natali, Morice, Colin, Mosquera-Vásquez, Kobi, Mostafa, Awatif E., Mote, T., Mudryk, L., Mühle, Jens, Mullan, A. Brett, Müller, Rolf, Myneni, R., Nash, Eric R., Nauslar, Nicholas J., Nerem, R. Steven, Newman, Paul A., Nicolas, Julien P., Nieto, Juan José, Noetzli, Jeannette, Osborn, Tim J., Osborne, Emily, Overland, J., Oyunjargal, Lamjav, Park, T., Pasch, Richard J., Pascual Ramírez, Reynaldo, Pastor Saavedra, Maria Asuncion, Paterson, Andrew M., Pearce, Petra R., Pelto, Mauri S., Perovich, Don, Petropavlovskikh, Irina, Pezza, Alexandre B., Phillips, C., Phillips, David, Phoenix, G., Pinty, Bernard, Pitts, Michael, Po-Chedley, S., Polashenski, Chris, Preimesberger, W., Purkey, Sarah G., Quispe, Nelson, Rajeevan, Madhavan, Rakotoarimalala, C. L., Ramos, Andrea M., Ramos, Isabel, Randel, W., Raynolds, M. K., Reagan, James, Reid, Phillip, Reimer, Christoph, Rémy, Samuel, Revadekar, Jayashree V., Richardson, A. D., Richter-Menge, Jacqueline, Ricker, Robert, Ripaldi, A., Robinson, David A., Rodell, Matthew, Rodriguez Camino, Ernesto, Romanovsky, Vladimir E., Ronchail, Josyane, Rosenlof, Karen H., Rösner, Benajamin, Roth, Chris, Rozanov, A., Rusak, James A., Rustemeier, Elke, Rutishäuser, T., Sallée, Jean Baptiste, Sánchez-Lugo, Ahira, Santee, Michelle L., Sawaengphokhai, P., Sayouri, Amal, Scambos, Ted A., Scanlon, T., Scardilli, Alvaro S., Schenzinger, Verena, Schladow, S. Geoffey, Schmid, Claudia, Schmid, Martin, Schoeneich, P., Schreck, Carl J., Selkirk, H. B., Sensoy, Serhat, Shi, Lei, Shiklomanov, A. I., Shiklomanov, Nikolai I., Shimpo, A., Shuman, Christopher A., Siegel, David A., Sima, Fatou, Simmons, Adrian J., Smeets, C. J.P.P., Smith, Adam, Smith, Sharon L., Soden, B., Sofieva, Viktoria, Sparks, T. H., Spence, Jacqueline, Spencer, R. G.M., Spillane, Sandra, Srivastava, A. K., Stabeno, P. J., Stackhouse, Paul W., Stammerjohn, Sharon, Stanitski, Diane M., Steinbrecht, Wolfgang, Stella, José L., Stengel, M., Stephenson, Tannecia S., Strahan, Susan E., Streeter, Casey, Streletskiy, Dimitri A., Sun-Mack, Sunny, Suslova, A., Sutton, Adrienne J., Swart, Sebastiann, Sweet, William, Takahashi, Kenneth S., Tank, S. E., Taylor, Michael A., Tedesco, M., Thackeray, S. J., Thompson, Philip R., Timbal, Bertrand, Timmermans, M. L., Tobin, Skie, Tømmervik, H., Tourpali, Kleareti, Trachte, Katja, Tretiakov, M., Trewin, Blair C., Triñanes, Joaquin A., Trotman, Adrian R., Tschudi, Mark, Tye, Mari R., van As, D., van de Wal, R. S.W., van der A, Ronald J., van der Schalie, Robin, van der Schrier, Gerard, van der Werf, Guido R., van Heerwaarden, Chiel, Van Meerbeeck, Cedric J., Verburg, Piet, Vieira, G., Vincent, Lucie A., Vömel, Holger, Vose, Russell S., Walker, D. A., Walsh, J. E., Wang, Bin, Wang, Hui, Wang, Lei, Wang, M., Wang, Mengqiu, Wang, Ray, Wang, Sheng Hung, Wanninkhof, Rik, Watanabe, Shohei, Weber, Mark, Webster, Melinda, Weerts, Albrecht, Weller, Robert A., Westberry, Toby K., Weyhenmeyer, Gesa A., Widlansky, Matthew J., Wijffels, Susan E., Wilber, Anne C., Wild, Jeanette D., Willett, Kate M., Wong, Takmeng, Wood, E. F., Woolway, R. Iestyn, Xue, Yan, Yin, Xungang, Yu, Lisan, Zambrano, Eduardo, Zeyaeyan, Sadegh, Zhang, Huai Min, Zhang, Peiqun, Zhao, Guanguo, Zhao, Lin, Zhou, Xinjia, Zhu, Zhiwei, Ziemke, Jerry R., Ziese, Markus, Andersen, Andrea, Griffin, Jessicca, Hammer, Gregory, Love-Brotak, S. Elizabeth, Misch, Deborah J., Riddle, Deborah B., Veasey, Sara W., Processus et interactions de fine échelle océanique (PROTEO), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Océan et variabilité du climat (VARCLIM), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), Berry, David, Jevrejeva, Svetlana, King, Brian, and Domingues, Catia
Subjects: Surface (mathematics), Atmospheric Science, Materials science, 010504 meteorology & atmospheric sciences, 0207 environmental engineering, Mineralogy, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], 02 engineering and technology, 01 natural sciences, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, SDG 13 - Climate Action, SDG 14 - Life Below Water, 020701 environmental engineering, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract: In 2018, the dominant greenhouse gases released into Earth's atmosphere-carbon dioxide, methane, and nitrous oxide-continued their increase. The annual global average carbon dioxide concentration at Earth's surface was 407.4 ± 0.1 ppm, the highest in the modern instrumental record and in ice core records dating back 800 000 years. Combined, greenhouse gases and several halogenated gases contribute just over 3 W m−2 to radiative forcing and represent a nearly 43% increase since 1990. Carbon dioxide is responsible for about 65% of this radiative forcing. With a weak La Niña in early 2018 transitioning to a weak El Niño by the year's end, the global surface (land and ocean) temperature was the fourth highest on record, with only 2015 through 2017 being warmer. Several European countries reported record high annual temperatures. There were also more high, and fewer low, temperature extremes than in nearly all of the 68-year extremes record. Madagascar recorded a record daily temperature of 40.5°C in Morondava in March, while South Korea set its record high of 41.0°C in August in Hongcheon. Nawabshah, Pakistan, recorded its highest temperature of 50.2°C, which may be a new daily world record for April. Globally, the annual lower troposphere temperature was third to seventh highest, depending on the dataset analyzed. The lower stratospheric temperature was approximately fifth lowest. The 2018 Arctic land surface temperature was 1.2°C above the 1981-2010 average, tying for third highest in the 118-year record, following 2016 and 2017. June's Arctic snow cover extent was almost half of what it was 35 years ago. Across Greenland, however, regional summer temperatures were generally below or near average. Additionally, a satellite survey of 47 glaciers in Greenland indicated a net increase in area for the first time since records began in 1999. Increasing permafrost temperatures were reported at most observation sites in the Arctic, with the overall increase of 0.1°-0.2°C between 2017 and 2018 being comparable to the highest rate of warming ever observed in the region. On 17 March, Arctic sea ice extent marked the second smallest annual maximum in the 38-year record, larger than only 2017. The minimum extent in 2018 was reached on 19 September and again on 23 September, tying 2008 and 2010 for the sixth lowest extent on record. The 23 September date tied 1997 as the latest sea ice minimum date on record. First-year ice now dominates the ice cover, comprising 77% of the March 2018 ice pack compared to 55% during the 1980s. Because thinner, younger ice is more vulnerable to melting out in summer, this shift in sea ice age has contributed to the decreasing trend in minimum ice extent. Regionally, Bering Sea ice extent was at record lows for almost the entire 2017/18 ice season. For the Antarctic continent as a whole, 2018 was warmer than average. On the highest points of the Antarctic Plateau, the automatic weather station Relay (74°S) broke or tied six monthly temperature records throughout the year, with August breaking its record by nearly 8°C. However, cool conditions in the western Bellingshausen Sea and Amundsen Sea sector contributed to a low melt season overall for 2017/18. High SSTs contributed to low summer sea ice extent in the Ross and Weddell Seas in 2018, underpinning the second lowest Antarctic summer minimum sea ice extent on record. Despite conducive conditions for its formation, the ozone hole at its maximum extent in September was near the 2000-18 mean, likely due to an ongoing slow decline in stratospheric chlorine monoxide concentration. Across the oceans, globally averaged SST decreased slightly since the record El Niño year of 2016 but was still far above the climatological mean. On average, SST is increasing at a rate of 0.10° ± 0.01°C decade−1 since 1950. The warming appeared largest in the tropical Indian Ocean and smallest in the North Pacific. The deeper ocean continues to warm year after year. For the seventh consecutive year, global annual mean sea level became the highest in the 26-year record, rising to 81 mm above the 1993 average. As anticipated in a warming climate, the hydrological cycle over the ocean is accelerating: dry regions are becoming drier and wet regions rainier. Closer to the equator, 95 named tropical storms were observed during 2018, well above the 1981-2010 average of 82. Eleven tropical cyclones reached Saffir-Simpson scale Category 5 intensity. North Atlantic Major Hurricane Michael's landfall intensity of 140 kt was the fourth strongest for any continental U.S. hurricane landfall in the 168-year record. Michael caused more than 30 fatalities and $25 billion (U.S. dollars) in damages. In the western North Pacific, Super Typhoon Mangkhut led to 160 fatalities and $6 billion (U.S. dollars) in damages across the Philippines, Hong Kong, Macau, mainland China, Guam, and the Northern Mariana Islands. Tropical Storm Son-Tinh was responsible for 170 fatalities in Vietnam and Laos. Nearly all the islands of Micronesia experienced at least moderate impacts from various tropical cyclones. Across land, many areas around the globe received copious precipitation, notable at different time scales. Rodrigues and Réunion Island near southern Africa each reported their third wettest year on record. In Hawaii, 1262 mm precipitation at Waipā Gardens (Kauai) on 14-15 April set a new U.S. record for 24-h precipitation. In Brazil, the city of Belo Horizonte received nearly 75 mm of rain in just 20 minutes, nearly half its monthly average. Globally, fire activity during 2018 was the lowest since the start of the record in 1997, with a combined burned area of about 500 million hectares. This reinforced the long-term downward trend in fire emissions driven by changes in land use in frequently burning savannas. However, wildfires burned 3.5 million hectares across the United States, well above the 2000-10 average of 2.7 million hectares. Combined, U.S. wildfire damages for the 2017 and 2018 wildfire seasons exceeded $40 billion (U.S. dollars).
Published: 2019

37. Terrestrial Snow Cover

Author: Mudryk, Lawrence, Brown, R., Derksen, C., Luojus, K., Decharme, B., Helfrich, S., Centre national de recherches météorologiques (CNRM), and Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology
Abstract: International audience; Snow accumulation during the 2017/18 winter was well above average across the Eurasian Arctic, consistent with an early start to the snow season . North American Arctic snow accumulation was near normal until May and June when what snow remained was generally deeper than usual for the time of year. Snow cover extent for Eurasia was above average during April, slightly above average for May, and below average by June (relative to the 1981-2010 average). This month-to-month change is consistent with unusually high early spring accumulation combined with rapid late spring snow loss. Despite relatively high spring snow accumulation and snow cover extent over the Arctic during the previous two spring seasons, long-term trends remain negative.https://arctic.noaa.gov/Report-Card/Report-Card-2018/ArtMID/7878/ArticleID/782/Terrestrial-Snow-Cover
Published: 2018

38. Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

Author: Gelati, E., Decharme, B., Calvet, J.-C., Minvielle, M., Polcher, J., Fairbairn, D., Weedon, G. P., Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École des Ponts ParisTech (ENPC)-École polytechnique (X)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Joint Centre for Hydro-Meteorological Research, Met Office Hadley Centre (JCHMR), United Kingdom Met Office [Exeter], Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Subjects: 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, Climate change, 02 engineering and technology, Forcing (mathematics), [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, lcsh:Technology, 01 natural sciences, lcsh:TD1-1066, Precipitation, lcsh:Environmental technology. Sanitary engineering, Water cycle, Leaf area index, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, lcsh:Environmental sciences, 0105 earth and related environmental sciences, General Environmental Science, lcsh:GE1-350, lcsh:T, Discharge, Anomaly (natural sciences), lcsh:Geography. Anthropology. Recreation, 6. Clean water, 020801 environmental engineering, lcsh:G, 13. Climate action, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Climatology, General Earth and Planetary Sciences, Environmental science, Surface runoff
Abstract: Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO2-responsive ISBA-A-gs LSM coupled with the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25–75.5∘ N, 11.5∘ W–62.5∘ E, at 0.5∘ resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them are based on the ERA-Interim reanalysis (ERA-I). The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature and SW and LW radiation. At the monthly timescale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early-autumn LAI. Forcing uncertainty impacts on simulated river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter–spring high flows and underestimate summer–autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely explained by forcing uncertainty, we suggest several research directions. These range from further investigating the discrepancies between LSMs and remote sensing retrievals to developing new model components to represent physical and anthropogenic processes.
Published: 2018

39. Fast‐Forward to Perturbed Equilibrium Climate

Author: Saint‐Martin, D., primary, Geoffroy, O., additional, Watson, L., additional, Douville, H., additional, Bellon, G., additional, Voldoire, A., additional, Cattiaux, J., additional, Decharme, B., additional, and Ribes, A., additional
Published: 2019
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40. Evaluation of CMIP6 DECK Experiments With CNRM‐CM6‐1

Author: Voldoire, A., primary, Saint‐Martin, D., additional, Sénési, S., additional, Decharme, B., additional, Alias, A., additional, Chevallier, M., additional, Colin, J., additional, Guérémy, J.‐F., additional, Michou, M., additional, Moine, M.‐P., additional, Nabat, P., additional, Roehrig, R., additional, Salas y Mélia, D., additional, Séférian, R., additional, Valcke, S., additional, Beau, I., additional, Belamari, S., additional, Berthet, S., additional, Cassou, C., additional, Cattiaux, J., additional, Deshayes, J., additional, Douville, H., additional, Ethé, C., additional, Franchistéguy, L., additional, Geoffroy, O., additional, Lévy, C., additional, Madec, G., additional, Meurdesoif, Y., additional, Msadek, R., additional, Ribes, A., additional, Sanchez‐Gomez, E., additional, Terray, L., additional, and Waldman, R., additional
Published: 2019
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41. A New Process‐Based Soil Methane Scheme: Evaluation Over Arctic Field Sites With the ISBA Land Surface Model

Author: Morel, X., primary, Decharme, B., additional, Delire, C., additional, Krinner, G., additional, Lund, M., additional, Hansen, B. U., additional, and Mastepanov, M., additional
Published: 2019
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42. Assessment of model estimates of land-atmosphere CO2 exchange across Northern Eurasia

Author: Rawlins, M. A., McGuire, A. D., Kimball, J. S., Dass, P., Lawrence, D., Burke, E., Chen, X., Delire, C., Koven, C., MacDougall, A., Peng, S., Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T. J., Ciais, P., Decharme, B., Gouttevin, I., Hajima, T., Ji, D., Krinner, G., Lettenmaier, D. P., Miller, P., Moore, J. C., Smith, B., and Sueyoshi, T.
Subjects: lcsh:Geology, lcsh:QH501-531, lcsh:QH540-549.5, lcsh:QE1-996.5, lcsh:Life, lcsh:Ecology
Abstract: A warming climate is altering land-atmosphere exchanges of carbon, with a potential for increased vegetation productivity as well as the mobilization of permafrost soil carbon stores. Here we investigate land-atmosphere carbon dioxide (CO2) cycling through analysis of net ecosystem productivity (NEP) and its component fluxes of gross primary productivity (GPP) and ecosystem respiration (ER) and soil carbon residence time, simulated by a set of land surface models (LSMs) over a region spanning the drainage basin of Northern Eurasia. The retrospective simulations cover the period 1960–2009 at 0.5° resolution, which is a scale common among many global carbon and climate model simulations. Model performance benchmarks were drawn from comparisons against both observed CO2 fluxes derived from site-based eddy covariance measurements as well as regional-scale GPP estimates based on satellite remote-sensing data. The site-based comparisons depict a tendency for overestimates in GPP and ER for several of the models, particularly at the two sites to the south. For several models the spatial pattern in GPP explains less than half the variance in the MODIS MOD17 GPP product. Across the models NEP increases by as little as 0.01 to as much as 0.79 g C m−2 yr−2, equivalent to 3 to 340 % of the respective model means, over the analysis period. For the multimodel average the increase is 135 % of the mean from the first to last 10 years of record (1960–1969 vs. 2000–2009), with a weakening CO2 sink over the latter decades. Vegetation net primary productivity increased by 8 to 30 % from the first to last 10 years, contributing to soil carbon storage gains. The range in regional mean NEP among the group is twice the multimodel mean, indicative of the uncertainty in CO2 sink strength. The models simulate that inputs to the soil carbon pool exceeded losses, resulting in a net soil carbon gain amid a decrease in residence time. Our analysis points to improvements in model elements controlling vegetation productivity and soil respiration as being needed for reducing uncertainty in land-atmosphere CO2 exchange. These advances will require collection of new field data on vegetation and soil dynamics, the development of benchmarking data sets from measurements and remote-sensing observations, and investments in future model development and intercomparison studies.
Published: 2015

43. Uncertainties in simulated evapotranspiration from land surface models over a 14-year Mediterranean crop succession

Author: Garrigues, Sébastien, Verhoef, A., Vidale, P.L., Sarojini, B, Blyth, E., Olioso, Albert, Boone, A., Albergel, Clément, Decharme, B., Calvet, J.-C., Carrer, Dominique, Moulin, Sophie, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Department of Geography and Environmental Science, University of Reading (UOR), Department of Meteorology [Reading], Centre national de recherches météorologiques (CNRM), Météo France-Centre National de la Recherche Scientifique (CNRS), Météo France, Centre National de la Recherche Scientifique (CNRS), Centre for Ecology and Hydrology, Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), and Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Modèle de bilan énergétique, modèle de transfert hydrique, evapotranspiration, bilan hydraulique, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, plante méditerranéenne, modèle de surface
Abstract: Uncertainties in simulated evapotranspiration from land surface models over a 14-year Mediterranean crop succession. 5. Ileaps Science Conference
Published: 2017

44. L'option multi-energy balance (MEB) du modèle de surface Interaction sol-biosphère-atmosphère (ISBA) dans SURFEX v8 - Partie 2 : Introduction d'une formulation de la litière et évaluation pour des sites de forêt

Author: Napoly, A., Boone, A., Samuelsson, P., Gollvik, S., Martin, E., Seferian, R., Carrer, D., Decharme, B., Jarlan, L., Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Swedish Meteorological and Hydrological Institute (SMHI), Risques, Ecosystèmes, Vulnérabilité, Environnement, Résilience (RECOVER), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Météo France-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), and Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: modelling, TEMPERATURE DE SURFACE, plant litter, LITIERE VEGETALE, [SDE]Environmental Sciences, evapotranspiration, BILAN THERMIQUE, MODELISATION, thermal balance
Abstract: International audience; Land surface models (LSMs) need to balance a complicated trade-off between computational cost and complexity in order to adequately represent the exchanges of energy, water and matter with the atmosphere and the ocean. Some current generation LSMs use a simplified or composite canopy approach that generates recurrent errors in simulated soil temperature and turbulent fluxes. In response to these issues, a new version of the interactions between soil-biosphere-atmosphere (ISBA) land surface model has recently been developed that explicitly solves the transfer of energy and water from the upper canopy and the forest floor, which is characterized as a litter layer. The multi-energy balance (MEB) version of ISBA is first evaluated for three well-instrumented contrasting local-scale sites, and sensitivity tests are performed to explore the behavior of new model parameters. Second, ISBA-MEB is benchmarked against observations from 42 forested sites from the global micro-meteorological network (FLUXNET) for multiple annual cycles.It is shown that ISBA-MEB outperforms the composite version of ISBA in improving the representation of soil temperature, ground, sensible and, to a lesser extent, latent heat fluxes. Both versions of ISBA give comparable results in terms of simulated latent heat flux because of the similar formulations of the water uptake and the stomatal resistance. However, MEB produces a better agreement with the observations of sensible heat flux than the previous version of ISBA for 87.5 % of the simulated years across the 42 forested FLUXNET sites. Most of this improvement arises owing to the improved simulation of the ground conduction flux, which is greatly improved using MEB, especially owing to the forest litter parameterization. It is also shown that certain processes are also modelled more realistically (such as the partitioning of evapotranspiration into transpiration and ground evaporation), even if certain statistical performances are neutral. The analyses demonstrate that the shading effect of the vegetation, the explicit treatment of turbulent transfer for the canopy and ground, and the insulating thermal and hydrological effects of the forest floor litter turn out to be essential for simulating the exchange of energy, water and matter across a large range of forest types and climates.
Published: 2017

45. Impact of climate, vegetation, soil and irrigation on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

Author: Garrigues, Sébastien, Olioso, Albert, Moulin, Sophie, Carrer, Dominique, Decharme, B., Martin E., E., Calvet, J.C., Marloie, Olivier, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Météo France, Centre National de la Recherche Scientifique (CNRS), Risques, Ecosystèmes, Vulnérabilité, Environnement, Résilience (RECOVER), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Ecologie des Forêts Méditerranéennes (URFM), Institut National de la Recherche Agronomique (INRA), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, Météo France-Centre National de la Recherche Scientifique (CNRS), and Ecologie des Forêts Méditerranéennes [Avignon] (URFM 629)
Subjects: influence du climat, végétation méditerranéenne, donnée climatique, [SDE.MCG]Environmental Sciences/Global Changes, ISBA, mediterranean vegetation, évapotranspiration, incertitude de mesure, irrigation, modèle de surface, evapotranspiration, climate, climatological data, bilan hydrique, Milieux et Changements globaux, dynamique de la végétation
Abstract: Impact of climate, vegetation, soil and irrigation on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site. HYMEX Drought and Water Resources workshop
Published: 2016

46. Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009

Author: McGuire, A.D., Koven, C., Lawrence, D.M., Clein, J.S., Xia, J., BEER, C., Burke, E., Chen, G., Chen, X., Delire, C., Jafarov, E., MacDougall, A., Marchenko, S., Nicolsky, D., Peng, Shuang, Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Ciais, Philippe, Decharme, B., Hayes, D.J., Ekici, A., Gouttevin, I., Hajima, T., Ji, D., Krinner, G., Lettenmaier, D.P., Luo, Y., Miller, P.A., Moore, J.C., Romanovsky, V., Schaedel, C., Schaefer, K., Schuur, E.A.G., Smith, Barry, Sueyoshi, T., Zhuang, Q, Geophysical Institute [Fairbanks], University of Alaska [Fairbanks] (UAF), LAWRENCE BERKELEY NATIONAL LABORATORY CALIFORNIA USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), NATIONAL CENTER FOR ATMOSPHERIC RESEARCH BOULDER COLORADO USA, INSTITUTE OF ARCTIC BIOLOGY UNIVERSITY OF ALASKA FAIRBANKS USA, EAST CHINA NORMAL UNIVERSITY SHANGHAI CHN, ACES STOCKHOLM SWE, BOLIN CENTRE FOR CLIMATE RESEARCH STOCKHOLM UNIVERSITY SWE, MET OFFICE HADLEY CENTRE EXETER GBR, OAK RIDGE NATIONAL LABORATORY TENNESSEE USA, UNIVERSITY OF WAHINGTON SEATTLE USA, Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), INSTITUTE OF ARCTIC ALPINE RESEARCH UNIVERSITY OF COLORADO BOULDER USA, SCHOOL OF EARTH AND OCEAN SCIENCES UNIVERSITY OF VICTORIA BRITISH COLUMBIA CAN, Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ALFRED WEGENER INSTITUTE HELMHOLTZ CENTRE FOR POLAR AND MARINE RESEARCH POTSDAM DEU, COLLEGE OF GLOBAL CHANGE AND EARTH SYSTEM SCIENCE BEIJING NORMAL UNIVERSITY CHN CHN, JAPAN AGENCY FOR MARINE EARTH SCIENCE AND TECHNOLOGY YOKOHAMA JPN, DEPARTMENT OF PHYSICAL GEOGRAPHY AND ECOSYSTEM SCIENCE LUND UNIVERSITY SWE, SCHOOL OF EARTH AND SPACE EXPLORATION ARIZONA STATE UNIVERSITY TEMPE USA, ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Hydrologie-Hydraulique (UR HHLY), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), DEPARTMENT OF GEOGRAPHY UNIVERSITY OF CALIFORNIA LOS ANGELES USA, DEPARTMENT OF MICROBIOLOGY AND PLANT BIOLOGY UNIVERSITY OF OKLAHOMA NORMAN USA, NORTHERN ARIZONA UNIVERSITY FLAGSTAFF USA, NATIONAL SNOW AND ICE DATA CENTER UNIVERSITY OF COLORADO BOULDER USA, and PURDUE UNIVERSITY WEST LAFAYETTE INDIANA USA
Subjects: PERMAFROST, PERMAFROST MODELLING, MODEL SENSITIVITY, LAND-SURFACE MODELS, CO2-ENHANCEMENT, CLIMATE CHANGE, [SDE]Environmental Sciences, GROSS PRIMARY PRODUCTION, CARBON DYNAMICS
Abstract: International audience; A significant portion of the large amount of carbon (C) currently stored in soils of the permafrost region in the Northern Hemisphere has the potential to be emitted as the greenhouse gases CO2 and CH4 under a warmer climate. In this study we evaluated the variability in the sensitivity of permafrost and C in recent decades among land surface model simulations over the permafrost region between 1960 and 2009. The 15 model simulations all predict a loss of near-surface permafrost (within 3 m) area over the region, but there are large differences in the magnitude of the simulated rates of loss among the models (0.2 to 58.8 × 103 km2 yr−1). Sensitivity simulations indicated that changes in air temperature largely explained changes in permafrost area, although interactions among changes in other environmental variables also played a role. All of the models indicate that both vegetation and soil C storage together have increased by 156 to 954 Tg C yr−1 between 1960 and 2009 over the permafrost region even though model analyses indicate that warming alone would decrease soil C storage. Increases in gross primary production (GPP) largely explain the simulated increases in vegetation and soil C. The sensitivity of GPP to increases in atmospheric CO2 was the dominant cause of increases in GPP across the models, but comparison of simulated GPP trends across the 1982–2009 period with that of a global GPP data set indicates that all of the models overestimate the trend in GPP. Disturbance also appears to be an important factor affecting C storage, as models that consider disturbance had lower increases in C storage than models that did not consider disturbance. To improve the modeling of C in the permafrost region, there is the need for the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost C feedback and for the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.
Published: 2016

47. Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region

Author: Wang, W., Rinke, A., Moore, J.C., Ji, D., Cui, X., Peng, S., Lawrence, D.M., McGuire, A.D., Burke, E.J., Chen, X., Decharme, B., Koven, C., MacDougall, C., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Ciais, P., Delire, C., Gouttevin, I., Hajima, T., Krinner, G., Lettenmaier, D.P., Miller, P.A., Smith, B., Sueyoshi, T., Sherstiukov, A.B., Beijing Normal University (BNU), Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), National Center for Atmospheric Research [Boulder] (NCAR), University of Alaska [Fairbanks] (UAF), Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], University of Washington [Seattle], Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), School of Earth Sciences [Melbourne], Faculty of Science [Melbourne], University of Melbourne-University of Melbourne, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Lund University [Lund], Arizona State University [Tempe] (ASU), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Hydrologie-Hydraulique (UR HHLY), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), National Institute of Polar Research [Tokyo] (NiPR), World Data Centre, All-Russian Research Institute of Hydrometeorological Information, Beijing Normal University, Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Met Office Hadley Centre (MOHC), and Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA) - Grenoble
Subjects: modelling, [SDE.IE]Environmental Sciences/Environmental Engineering, temperature, [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology, snow, MODELISATION, NEIGE
Abstract: International audience; A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C−1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.
Published: 2016

48. The Plumbing of Land Surface Models: Benchmarking Model Performance

Author: Vuichard, And, Best, M., Abramowitz, G., Johnson, H., Pitman, A., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P., Dong, J., EK, M., Guo, Z., Haverd, V., Van Den Hurk, B., Nearing, G., Pak, B., Peters-Lidard, C., Santanello, J., Stevens, L., Vuichard, N., Department Computational Hydrosystems [UFZ Leipzig], Helmholtz Centre for Environmental Research (UFZ), Joint DECC/Defra Met Office Hadley Centre Climate Programme CA01101Australian Research CouncilCE110001028United States Department of Energy (DOE)DE-FG02-04ER63917DE-FG02-04ER63911CFCAS Natural Sciences and Engineering Research Council of Canada (NSERC) BIOCAP CGIAR Natural Resources Canada European Commission FAO-GTOS-TCO iLEAPS Max Planck Institute for Biogeochemistry National Science Foundation (NSF) Tuscia University Universite Laval and Environment Canada United States Department of Energy (DOE, United Kingdom Met Office [Exeter], University of New South Wales [Sydney] (UNSW), European Centre for Medium-Range Weather Forecasts (ECMWF), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), George Mason University [Fairfax], Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NASA Goddard Space Flight Center (GSFC), GSFC Hydrospheric and Biospheric Sciences Laboratory, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Water and Climate Risk, Amsterdam Global Change Institute, Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Climate Change Research Centre [Sydney] (CCRC), Météo France-Centre National de la Recherche Scientifique (CNRS), and Royal Netherlands Meteorological Institute (KNMI)
Subjects: Surface (mathematics), Atmospheric Science, Meteorology, Climate Models, Parameterization, Project, Sensible heat, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Latent heat, Range (statistics), Energy partitioning, Shortwave radiation, SDG 7 - Affordable and Clean Energy, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Energy, Water, Model comparison, Benchmarking, Model evaluation/performance, Impact, Phase, [SDE]Environmental Sciences, Environmental science, Soil-moisture, Land surface model, Hydrology, Atmosphere Coupling Experiment, Nonlinear regression, Simulation
Abstract: The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.
Published: 2015

49. Assessment of model estimates of land-atmosphere CO 2 exchange across Northern Eurasia

Author: Rawlins, M. A, Mcguire, A. David, Kimball, J., Dass, P, Lawrence, D., Chen, X., Delire, C., Koven, C., Macdougall, A, Peng, S., Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T. J, Ciais, Philippe, Decharme, B., Gouttevin, Isabelle, Hajima, T, Ji, D., Gerhard, Krinner, Lettenmaier, D. P., Miller, P., Moore, J.C., Smith, B., Sueyoshi, T, University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS), Alaska Cooperative Fish and Wildlife Research Unit, United States Geological Survey [Reston] (USGS)-University of Alaska [Fairbanks] (UAF), University of Montana, National Center for Atmospheric Research [Boulder] (NCAR), University of Washington [Seattle], Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institute of Applied Energy (IAE), Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research (UFZ), University of Victoria [Canada] (UVIC), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), College of Global Change and Earth System Science (GCESS), Beijing Normal University (BNU), Helmholtz Zentrum München = German Research Center for Environmental Health, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Lund University [Lund], ASU School of Earth and Space Exploration (SESE), Arizona State University [Tempe] (ASU), ICOS-ATC (ICOS-ATC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Météo-France, Department of Civil and Environmental Engineering, University of Washington, Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Helmholtz Centre for Environmental Research (UFZ), Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Beijing Normal University, Helmholtz-Zentrum München (HZM), Météo-France [Paris], Météo France, Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)
Subjects: LAND-ATMOSPHERE CO2 EXCHANGE, ARCTIC, [SDE]Environmental Sciences
Abstract: [Notes_IRSTEA]European Union 7th Framework Programme under project Page21 (grant 282700) [Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU; International audience; A warming climate is altering land-atmosphere exchanges of carbon, with a potential for increased vegetation productivity as well as the mobilization of permafrost soil carbon stores. Here we investigate land-atmosphere carbon dioxide (CO 2) cycling through analysis of net ecosystem productivity (NEP) and its component fluxes of gross primary productivity (GPP) and ecosystem respiration (ER) and soil carbon residence time, simulated by a set of land surface models (LSMs) over a region spanning the drainage basin of Northern Eurasia. The retrospective simulations cover the period 1960–2009 at 0.5 • resolution, which is a scale common among many global carbon and climate model simulations. Model performance benchmarks were drawn from comparisons against both observed CO 2 fluxes derived from site-based eddy covariance measurements as well as regional-scale GPP estimates based on satellite remote-sensing data. Published by Copernicus Publications on behalf of the European Geosciences Union. 4386 M. A. Rawlins et al.: CO 2 Exchange Across Northern Eurasia The site-based comparisons depict a tendency for overestimates in GPP and ER for several of the models, particularly at the two sites to the south. For several models the spatial pattern in GPP explains less than half the variance in the MODIS MOD17 GPP product. Across the models NEP increases by as little as 0.01 to as much as 0.79 g C m −2 yr −2 , equivalent to 3 to 340 % of the respective model means, over the analysis period. For the multimodel average the increase is 135 % of the mean from the first to last 10 years of record (1960–1969 vs. 2000–2009), with a weakening CO 2 sink over the latter decades. Vegetation net primary productivity increased by 8 to 30 % from the first to last 10 years, contributing to soil carbon storage gains. The range in regional mean NEP among the group is twice the multimodel mean, indicative of the uncertainty in CO 2 sink strength. The models simulate that inputs to the soil carbon pool exceeded losses, resulting in a net soil carbon gain amid a decrease in residence time. Our analysis points to improvements in model elements controlling vegetation productivity and soil respiration as being needed for reducing uncertainty in land-atmosphere CO 2 exchange. These advances will require collection of new field data on vegetation and soil dynamics, the development of benchmarking data sets from measurements and remote-sensing observations, and investments in future model development and intercomparison studies.
Published: 2015

50. Introduction of groundwater capillary rises using subgrid spatial variability of topography into the ISBA land surface model

Author: Vergnes, J.-P., Decharme, B., Habets, Florence, Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), and Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS)
Subjects: continental hydrology, groundwater, evapotranspiration, modeling, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology
Abstract: International audience; This paper presents a simple method for representing upward capillary fluxes from shallow groundwater into the unsaturated soil column of the large-scale hydrological models generally used at low resolution in global climate models. The groundwater scheme implemented in the Total Runoff Integrating Pathways river-routing model in a previous study is coupled with the Interaction between Soil Biosphere Atmosphere (ISBA) land surface model. In this coupling, the simulated water table depth acts as the lower boundary condition for the soil moisture diffusive equation. An original parameterization accounting for the subgrid topography inside each grid cell is proposed in order to compute this fully coupled soil lower boundary condition. The impact of this coupling on the simulated water budget is evaluated over France for the 1989–2009 period. Simulations are performed at high (1/12°) and low (0.5°) resolutions. Upward capillary fluxes induce a decrease in the simulated recharge from ISBA to the aquifers and contributes to an enhancement of the soil moisture memory. The simulated water table depths are then lowered, which induces a slight decrease in the simulated mean annual river discharges. These differences do not affect the comparison with observations. As a consequence, the simulated river discharges and water table heads compare still well with observations for the two soil bottom condition (free drain or fully coupled). It confirms the suitability of the coupling parameterization using subgrid spatial variability of topography. Compared to a free drain experiment, upward capillary fluxes at the bottom of the soil increase the mean annual evapotranspiration simulated over the aquifer domain by 3.12% and 1.54% at high and low resolutions, respectively. This increase can locally reach 50% and 30%, respectively.
Published: 2014

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