Articles | Volume 15, issue 10
https://doi.org/10.5194/gmd-15-4275-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-4275-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
01 Jun 2022
Development and technical paper |
| 01 Jun 2022
| 01 Jun 2022
Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model
Anthony Bernus
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l’Environnement, Institut Pierre-Simon Laplace (IPSL), CEA-CNRS-Université Paris-Saclay, Orme des Merisiers, 91190 Gif-sur-Yvette, France
Catherine Ottlé
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l’Environnement, Institut Pierre-Simon Laplace (IPSL), CEA-CNRS-Université Paris-Saclay, Orme des Merisiers, 91190 Gif-sur-Yvette, France
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Jon Cranko Page, Martin G. De Kauwe, Andy J. Pitman, Isaac R. Towers, Gabriele Arduini, Martin J. Best, Craig Ferguson, Jürgen Knauer, Hyungjun Kim, David M. Lawrence, Tomoko Nitta, Keith W. Oleson, Catherine Ottlé, Anna Ukkola, Nicholas Vuichard, and Gab Abramowitz
EGUsphere, https://doi.org/10.5194/egusphere-2025-4149, https://doi.org/10.5194/egusphere-2025-4149, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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This paper used a large dataset of observations, machine learning predictions, and computer model simulations to test how well land surface models represent the water, energy, and carbon cycles. We found that the models work well under "normal" weather but do not meet performance expectations during coinciding extreme conditions. Since these extremes are relatively rare, targeted model improvements could deliver major performance gains.
Rodrigo San Martin, Catherine Ottlé, Anna Sorenssön, Pradeebane Vattinada Ayar, Florent Mouillot, and Marielle Malfante
EGUsphere, https://doi.org/10.5194/egusphere-2025-3484, https://doi.org/10.5194/egusphere-2025-3484, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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We studied wildfires in the Gran Chaco, one of the world's largest dry forests, to understand why some fires grow larger than others. By analyzing fire size and weather conditions during burning, we found that strong winds and low humidity were key drivers of fire expansion. This work helps improve our understanding of extreme fire events and supports better fire risk management in dry ecosystems.
Zacharie Titus, Amélie Cuynet, Elodie Salmon, and Catherine Ottlé
The Cryosphere, 19, 2105–2114, https://doi.org/10.5194/tc-19-2105-2025, https://doi.org/10.5194/tc-19-2105-2025, 2025
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The representation of lake ice dynamics is key to model water–atmosphere energy and mass transfers in cold environments. The use of albedo satellite products to constrain the modeling of ice coverage appears to be very suitable and valuable. In this work, we show how the modeling of lake albedo and ice phenology in the land surface model ORCHIDEE was improved by accounting for fractional ice cover calibrated against lake surface albedo data.
Luis-Enrique Olivera-Guerra, Catherine Ottlé, Nina Raoult, and Philippe Peylin
Hydrol. Earth Syst. Sci., 29, 261–290, https://doi.org/10.5194/hess-29-261-2025, https://doi.org/10.5194/hess-29-261-2025, 2025
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We assimilate the recent ESA-CCI land surface temperature (LST) product to optimize parameters of a land surface model (ORCHIDEE). We test different assimilation strategies to evaluate the best strategy over various in situ stations across Europe. We also provide advice on how to assimilate this LST product to better simulate LST and surface energy fluxes. Finally, we demonstrate the effectiveness of this optimization, which is essential to better simulate future projections.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
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This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Sylvie Charbit, Christophe Dumas, Fabienne Maignan, Catherine Ottlé, Nina Raoult, Xavier Fettweis, and Philippe Conesa
The Cryosphere, 18, 5067–5099, https://doi.org/10.5194/tc-18-5067-2024, https://doi.org/10.5194/tc-18-5067-2024, 2024
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The evolution of the Greenland ice sheet is highly dependent on surface melting and therefore on the processes operating at the snow–atmosphere interface and within the snow cover. Here we present new developments to apply a snow model to the Greenland ice sheet. The performance of this model is analysed in terms of its ability to simulate ablation processes. Our analysis shows that the model performs well when compared with the MAR regional polar atmospheric model.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Mickaël Lalande, Martin Ménégoz, Gerhard Krinner, Catherine Ottlé, and Frédérique Cheruy
The Cryosphere, 17, 5095–5130, https://doi.org/10.5194/tc-17-5095-2023, https://doi.org/10.5194/tc-17-5095-2023, 2023
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This study investigates the impact of topography on snow cover parameterizations using models and observations. Parameterizations without topography-based considerations overestimate snow cover. Incorporating topography reduces snow overestimation by 5–10 % in mountains, in turn reducing cold biases. However, some biases remain, requiring further calibration and more data. Assessing snow cover parameterizations is challenging due to limited and uncertain data in mountainous regions.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
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As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Nina Raoult, Sylvie Charbit, Christophe Dumas, Fabienne Maignan, Catherine Ottlé, and Vladislav Bastrikov
The Cryosphere, 17, 2705–2724, https://doi.org/10.5194/tc-17-2705-2023, https://doi.org/10.5194/tc-17-2705-2023, 2023
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Greenland ice sheet melting due to global warming could significantly impact global sea-level rise. The ice sheet's albedo, i.e. how reflective the surface is, affects the melting speed. The ORCHIDEE computer model is used to simulate albedo and snowmelt to make predictions. However, the albedo in ORCHIDEE is lower than that observed using satellites. To correct this, we change model parameters (e.g. the rate of snow decay) to reduce the difference between simulated and observed values.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
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The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
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We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Zun Yin, Catherine Ottlé, Philippe Ciais, Feng Zhou, Xuhui Wang, Polcher Jan, Patrice Dumas, Shushi Peng, Laurent Li, Xudong Zhou, Yan Bo, Yi Xi, and Shilong Piao
Hydrol. Earth Syst. Sci., 25, 1133–1150, https://doi.org/10.5194/hess-25-1133-2021, https://doi.org/10.5194/hess-25-1133-2021, 2021
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We improved the irrigation module in a land surface model ORCHIDEE and developed a dam operation model with the aim to investigate how irrigation and dams affect the streamflow fluctuations of the Yellow River. Results show that irrigation mainly reduces the annual river flow. The dam operation, however, mainly affects streamflow variation. By considering two generic operation rules, flood control and base flow guarantee, our dam model can sustainably improve the simulation accuracy.
Natasha MacBean, Russell L. Scott, Joel A. Biederman, Catherine Ottlé, Nicolas Vuichard, Agnès Ducharne, Thomas Kolb, Sabina Dore, Marcy Litvak, and David J. P. Moore
Hydrol. Earth Syst. Sci., 24, 5203–5230, https://doi.org/10.5194/hess-24-5203-2020, https://doi.org/10.5194/hess-24-5203-2020, 2020
Cited articles
Ahmadzadeh Kokya, T., Pejman, A. H., Mahin Abdollahzadeh, E., Ahmadzadeh Kokya,
B., and Nazariha, N.: Evaluation of salt effects on some thermodynamic
properties of Urmia Lake water. I, 5, 343–348, Int.
J. Environ. Res., 5, 343–348, 2011. a
Balsamo, G., Salgado, R., Dutra, E., Bousseta, S., Stockdale, T., and Potes,
M.: On the contribution of lakes in predicting near-surface temperature in a
global weather forecasting model, Tellus A, 64, 15829, https://doi.org/10.3402/tellusa.v64i0.15829, 2012. a
Bastviken, D., Cole, J., Pace, M., and Tranvik, L.: Methane emissions from
lakes: Dependence of lake characteristics, two regional assessments, and a
global estimate: Lake Methane Emissions, Global Biogeochem. Cy.,
18, GB4009, https://doi.org/10.1029/2004GB002238, 2004. a, b
Beaulieu, J. J., DelSontro, T., and Downing, J. A.: Eutrophication will
increase methane emissions from lakes and impoundments during the 21st
century, Nat. Commun., 10, 1375, https://doi.org/10.1038/s41467-019-09100-5,
2019. a
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25∘ global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/hess-21-589-2017, 2017. a
Bennett, N. D., Croke, B. F., Guariso, G., Guillaume, J. H., Hamilton, S. H.,
Jakeman, A. J., Marsili-Libelli, S., Newham, L. T., Norton, J. P., Perrin,
C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B. D., and
Andreassian, V.: Characterising performance of environmental models,
Environ. Modell. Softw., 40, 1–20,
https://doi.org/10.1016/j.envsoft.2012.09.011, 2013. a
Benson, B., Magnuson, J., and Sharma, S.: Global Lake and River Ice Phenology Database, Version 1, NSIDC: National Snow and Ice Data Center, Boulder, Colorado USA [data set], https://doi.org/10.7265/N5W66HP8, 2000. a, b
Bernus, A. and Ottlé, C.: ORCHIDEE-FLAKE code, Zenodo [code], https://doi.org/10.5281/zenodo.6383273, 2022. a
Bernus, A., Ottle, C., and Raoult, N.: Variance based sensitivity analysis of
FLake lake model for global land surface modeling, J. Geophys.
Res.-Atmos., 126, e2019JD031928, https://doi.org/10.1029/2019JD031928, 2021. a, b, c, d
Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT Mission
and Its Capabilities for Land Hydrology, Surv. Geophys., 37,
307–337, https://doi.org/10.1007/s10712-015-9346-y, 2016. a
Bonan, G. B.: Sensitivity of a GCM Simulation to Inclusion of Inland
Water Surfaces, J. Climate, 8, 2691–2704,
https://doi.org/10.1175/1520-0442(1995)008<2691:SOAGST>2.0.CO;2, 1995. a
Bontemps, S., Boettcher, M., Brockmann, C., Kirches, G., Lamarche, C., Radoux, J., Santoro, M., Vanbogaert, E., Wegmüller, U., Herold, M., Achard, F., Ramoino, F., Arino, O., and Defourny, P.: Multi-year global land cover mapping at 300 m and characterization for climate modelling: achievements of the Land Cover component of the ESA Climate Change Initiative, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 323–328, https://doi.org/10.5194/isprsarchives-XL-7-W3-323-2015, 2015. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P.,
Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., D'Andrea, F., Davini, P., Lavergne, C., Denvil, S., Deshayes, J.,
Devilliers, M., Ducharne, A., Dufresne, J., Dupont, E., Éthé, C., Fairhead,
L., Falletti, L., Flavoni, S., Foujols, M., Gardoll, S., Gastineau, G.,
Ghattas, J., Grandpeix, J., Guenet, B., Guez, E., L., Guilyardi, E.,
Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S.,
Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C.,
Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J.,
Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J.,
Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C.,
Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D.,
Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J.,
Viovy, N., and Vuichard, N.: Presentation and Evaluation of the
IPSL‐CM6A‐LR Climate Model, J. Adv. Model.
Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020. a
Bowling, L. C. and Lettenmaier, D. P.: Modeling the Effects of Lakes and
Wetlands on the Water Balance of Arctic Environments, J.
Hydrometeorol., 11, 276–295, https://doi.org/10.1175/2009JHM1084.1, 2010. a
Carrea, L. and Merchant, C. J.: GloboLakes: Lake Surface Water Temperature (LSWT) v4.0 (1995–2016), Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/76a29c5b55204b66a40308fc2ba9cdb3, 2019. a, b, c, d
Cheruy, F., Ducharne, A., Hourdin, F., Musat, I., Vignon, E., Gastineau, G.,
Bastrikov, V., Vuichard, N., Diallo, B., Dufresne, J., Ghattas, J.,
Grandpeix, J., Idelkadi, A., Mellul, L., Maignan, F., Ménégoz, M., Ottlé,
C., Peylin, P., Servonnat, J., Wang, F., and Zhao, Y.: Improved
Near‐Surface Continental Climate in IPSL‐CM6A‐LR by
Combined Evolutions of Atmospheric and Land Surface Physics,
J. Adv. Model. Earth Sy., 12, e2019MS002005,
https://doi.org/10.1029/2019MS002005, 2020. a, b
Choulga, M., Kourzeneva, E., Balsamo, G., Boussetta, S., and Wedi, N.: Upgraded global mapping information for earth system modelling: an application to surface water depth at the ECMWF, Hydrol. Earth Syst. Sci., 23, 4051–4076, https://doi.org/10.5194/hess-23-4051-2019, 2019. a, b
Cole, J. J., Prairie, Y. T., Caraco, N. F., McDowell, W. H., Tranvik, L. J.,
Striegl, R. G., Duarte, C. M., Kortelainen, P., Downing, J. A., Middelburg,
J. J., and Melack, J.: Plumbing the Global Carbon Cycle: Integrating
Inland Waters into the Terrestrial Carbon Budget, Ecosystems, 10,
172–185, https://doi.org/10.1007/s10021-006-9013-8, 2007. a
de Rosnay, P.: Integrated parameterization of irrigation in the land surface
model ORCHIDEE. Validation over Indian Peninsula, Geophys.
Res. Lett., 30, 1986, https://doi.org/10.1029/2003GL018024, 2003. a
Downing, J. A., Prairie, Y. T., Cole, J. J., Duarte, C. M., Tranvik, L. J.,
Striegl, R. G., McDowell, W. H., Kortelainen, P., Caraco, N. F., Melack,
J. M., and Middelburg, J. J.: The global abundance and size distribution of
lakes, ponds, and impoundments, Limnol. Oceanogr., 51, 2388–2397,
https://doi.org/10.4319/lo.2006.51.5.2388, 2006. a
European Space Agency (ESA): Land cover CCI product user guide version 2, Tech. Rep., ESA,
https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 19 May 2022),
2017. a
Garnaud, C., MacKay, M., and Fortin, V.: A One‐Dimensional Lake Model
in ECCC's Land Surface Prediction System, J. Adv.
Model. Earth Sy., 14, e2021MS002861, https://doi.org/10.1029/2021MS002861, 2022. a, b
Golaz, J., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay‐Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke,
M. A., Brus, S. R., Burrows, S. M., Cameron‐Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar,
J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman,
M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones,
P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H., Lin,
W., Lipscomb, W. H., Ma, P., Mahajan, S., Maltrud, M. E., Mametjanov, A.,
McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch,
P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F.,
Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J.,
Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang,
H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang,
Y., Yoon, J., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K.,
Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled
Model Version 1: Overview and Evaluation at Standard Resolution,
J. Adv. Model. Earth Sy., 11, 2089–2129,
https://doi.org/10.1029/2018MS001603, 2019. a
Guinaldo, T., Munier, S., Le Moigne, P., Boone, A., Decharme, B., Choulga, M., and Leroux, D. J.: Parametrization of a lake water dynamics model MLake in the ISBA-CTRIP land surface system (SURFEX v8.1), Geosci. Model Dev., 14, 1309–1344, https://doi.org/10.5194/gmd-14-1309-2021, 2021. a
Harris, I., Jones, P., Osborn, T., and Lister, D.: Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset, Int. J. Climatol., 34, 623–642,
https://doi.org/10.1002/joc.3711, 2014. a, b
Hartley, A., MacBean, N., Georgievski, G., and Bontemps, S.: Uncertainty in
plant functional type distributions and its impact on land surface models,
Remote Sens. Environ., 203, 71–89, https://doi.org/10.1016/j.rse.2017.07.037,
2017. a
Heiskanen, J. J., Mammarella, I., Ojala, A., Stepanenko, V., Erkkila, K.-M.,
Miettinen, H., Sandström, H., Eugster, W., Lepparanta, M., Jarvinen, H.,
Vesala, T., and Nordbo, A.: Effects of water clarity on lake stratification
and lake-atmosphere heat exchange, J. Geophys. Res.-Atmos., 120, 7412–7428, https://doi.org/10.1002/2014JD022938, 2015. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteorol. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hostetler, S. W. and Bartlein, P. J.: Simulation of lake evaporation with
application to modeling lake level variations of Harney-Malheur Lake,
Oregon, Water Resour. Res., 26, 2603–2612,
https://doi.org/10.1029/WR026i010p02603, 1990. a
Huziy, O. and Sushama, L.: Impact of lake–river connectivity and interflow on
the Canadian RCM simulated regional climate and hydrology for Northeast
Canada, Clim. Dynam., 48, 709–725, https://doi.org/10.1007/s00382-016-3104-9,
2017. a
Jacob, D. and Podzun, R.: Sensitivity studies with the regional climate model
REMO, Meteorol. Atmos. Phys., 63, 119–129,
https://doi.org/10.1007/BF01025368, 1997. a
Kobayashi, K. and Salam, M. U.: Comparing Simulated and Measured Values
Using Mean Squared Deviation and its Components, Agron. J.,
92, 345–352, https://doi.org/10.2134/agronj2000.922345x, 2000. a, b
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 Reanalysis: General Specifications and Basic
Characteristics, J. Meteorol. Soc. Jpn. Ser. II,
93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015. a
Krinner, G.: Impact of lakes and wetlands on boreal climate, J.
Geophys. Res., 108, 4520, https://doi.org/10.1029/2002JD002597, 2003. a
Le Moigne, P., Colin, J., and Decharme, B.: Impact of lake surface temperatures
simulated by the FLake scheme in the CNRM-CM5 climate model, Tellus A, 68, 31274,
https://doi.org/10.3402/tellusa.v68.31274, 2016. a, b
Lurton, T., Balkanski, Y., Bastrikov, V., Bekki, S., Bopp, L., Braconnot, P.,
Brockmann, P., Cadule, P., Contoux, C., Cozic, A., Cugnet, D., Dufresne, J.,
Éthé, C., Foujols, M., Ghattas, J., Hauglustaine, D., Hu, R., Kageyama, M.,
Khodri, M., Lebas, N., Levavasseur, G., Marchand, M., Ottlé, C., Peylin, P.,
Sima, A., Szopa, S., Thiéblemont, R., Vuichard, N., and Boucher, O.:
Implementation of the CMIP6 Forcing Data in the IPSL‐CM6A‐LR
Model, J. Adv. Model. Earth Sy., 12, e2019MS001940,
https://doi.org/10.1029/2019MS001940, 2020. a
MacCallum, S. N. and Merchant, C. J.: Surface water temperature observations of
large lakes by optimal estimation, Can. J. Remote Sens., 38,
25–45, https://doi.org/10.5589/m12-010, 2012. a
MacKay, M. D.: A Process-Oriented Small Lake Scheme for Coupled
Climate Modeling Applications, J. Hydrometeorol., 13,
1911–1924, https://doi.org/10.1175/JHM-D-11-0116.1, 2012. a
Madec, G., Bourdallé-Badie, R., Pierre-Antoine Bouttier, Bricaud, C.,
Bruciaferri, D., Calvert, D., Chanut, J., Clementi, E., Coward, A., Delrosso,
D., Ethé, C., Flavoni, S., Graham, T., Harle, J., Iovino, D., Lea, D.,
Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Paul, J.,
Rousset, C., Storkey, D., Storto, A., and Vancoppenolle, M.: NEMO ocean
engine, Zenodo [code], https://doi.org/10.5281/zenodo.1464816, 2008. a
Malkki, P. and Tamsalu, R. E.: Physical Features of the Baltic Sea, 252,
Finnish Marine Research, 1985. a
Martynov, A., Sushama, L., Laprise, R., Winger, K., and Dugas, B.: Interactive
lakes in the Canadian Regional Climate Model, version 5: the role of
lakes in the regional climate of North America, Tellus A, 64, 16226, https://doi.org/10.3402/tellusa.v64i0.16226,
2012. a
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating
the volume and age of water stored in global lakes using a geo-statistical
approach, Nat. Commun., 7, 13603, https://doi.org/10.1038/ncomms13603, 2016 (data available at: https://hydrosheds.org/page/hydrolakes, last access: 19 May 2022). a, b, c, d
Milly, P. C. D., Malyshev, S. L., Shevliakova, E., Dunne, K. A., Findell,
K. L., Gleeson, T., Liang, Z., Phillipps, P., Stouffer, R. J., and Swenson,
S.: An Enhanced Model of Land Water and Energy for Global
Hydrologic and Earth-System Studies, J. Hydrometeorol., 15,
1739–1761, https://doi.org/10.1175/JHM-D-13-0162.1, 2014. a
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis,
S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, P., Bozbiyik, A.,
Fisher, R., Heald, C., Kluzek, E., Lamarque, J.-F., Lawrence, P., Leung, L.,
Lipscomb, W., Muszala, S., Ricciuto, D., Sacks, W., Sun, Y., Tang, J., and
Yang, Z.-L.: Technical description of version 4.5 of the Community Land
Model (CLM), Tech. Rep., UCAR/NCAR, https://doi.org/10.5065/D6RR1W7M, 2013. a
Pietikäinen, J.-P., Markkanen, T., Sieck, K., Jacob, D., Korhonen, J., Räisänen, P., Gao, Y., Ahola, J., Korhonen, H., Laaksonen, A., and Kaurola, J.: The regional climate model REMO (v2015) coupled with the 1-D freshwater lake model FLake (v1): Fenno-Scandinavian climate and lakes, Geosci. Model Dev., 11, 1321–1342, https://doi.org/10.5194/gmd-11-1321-2018, 2018. a, b, c
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R., Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S., Herold, M., Kirches, G., Lamarche, C., Lederer, D., Ottlé, C., Peters, M., and Peylin, P.: Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative, Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015. a
Rooney, G. G. and Bornemann, F. J.: The performance of FLake in the Met
Office Unified Model, Tellus A,
65, 21363, https://doi.org/10.3402/tellusa.v65i0.21363, 2013. a
Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., Castaldi, S., Chandra, N., Crevoisier, C., Crill, P. M., Covey, K., Curry, C. L., Etiope, G., Frankenberg, C., Gedney, N., Hegglin, M. I., Höglund-Isaksson, L., Hugelius, G., Ishizawa, M., Ito, A., Janssens-Maenhout, G., Jensen, K. M., Joos, F., Kleinen, T., Krummel, P. B., Langenfelds, R. L., Laruelle, G. G., Liu, L., Machida, T., Maksyutov, S., McDonald, K. C., McNorton, J., Miller, P. A., Melton, J. R., Morino, I., Müller, J., Murguia-Flores, F., Naik, V., Niwa, Y., Noce, S., O'Doherty, S., Parker, R. J., Peng, C., Peng, S., Peters, G. P., Prigent, C., Prinn, R., Ramonet, M., Regnier, P., Riley, W. J., Rosentreter, J. A., Segers, A., Simpson, I. J., Shi, H., Smith, S. J., Steele, L. P., Thornton, B. F., Tian, H., Tohjima, Y., Tubiello, F. N., Tsuruta, A., Viovy, N., Voulgarakis, A., Weber, T. S., van Weele, M., van der Werf, G. R., Weiss, R. F., Worthy, D., Wunch, D., Yin, Y., Yoshida, Y., Zhang, W., Zhang, Z., Zhao, Y., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: The Global Methane Budget 2000–2017, Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, 2020. a
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020. a
Semmler, T., Cheng, B., Yang, Y., and Rontu, L.: Snow and ice on Bear Lake
(Alaska) – sensitivity experiments with two lake ice models, Tellus A, 64, 17339,
https://doi.org/10.3402/tellusa.v64i0.17339, 2012. a, b, c, d
Sima, S., Ahmadalipour, A., and Tajrishy, M.: Mapping surface temperature in a
hyper-saline lake and investigating the effect of temperature distribution on
the lake evaporation, Remote Sens. Environ., 136, 374–385,
https://doi.org/10.1016/j.rse.2013.05.014, 2013. a
Stepanenko, V., Goyette, S., Martinov, A., Perroud, M., Fang, X., and Mironov,
D.: First steps of a lake model intercomparison project: LakeMIP, Boreal
Environment Research Publishing Board, 15, 191–202, 2010. a
Stepanenko, V. M., Martynov, A., Jöhnk, K. D., Subin, Z. M., Perroud, M., Fang, X., Beyrich, F., Mironov, D., and Goyette, S.: A one-dimensional model intercomparison study of thermal regime of a shallow, turbid midlatitude lake, Geosci. Model Dev., 6, 1337–1352, https://doi.org/10.5194/gmd-6-1337-2013, 2013. a
Subin, Z. M., Riley, W. J., and Mironov, D.: An improved lake model for climate
simulations: Model structure, evaluation, and sensitivity analyses in
CESM1, J. Adv. Model. Earth Sy., 4, M02001,
https://doi.org/10.1029/2011MS000072, 2012. a
Tebbs, E., Remedios, J., Avery, S., and Harper, D.: Remote sensing the
hydrological variability of Tanzania's Lake Natron, a vital Lesser
Flamingo breeding site under threat, Ecohydrology & Hydrobiology, 13,
148–158, https://doi.org/10.1016/j.ecohyd.2013.02.002, 2013. a
Van de Walle, J., Thiery, W., Brousse, O., Souverijns, N., Demuzere, M., and
van Lipzig, N. P. M.: A convection-permitting model for the Lake Victoria
Basin: evaluation and insight into the mesoscale versus synoptic
atmospheric dynamics, Clim. Dynam., 54, 1779–1799,
https://doi.org/10.1007/s00382-019-05088-2, 2020. a
Verpoorter, C., Kutser, T., Seekell, D. A., and Tranvik, L. J.: A global
inventory of lakes based on high-resolution satellite imagery, Geophys.
Res. Lett., 41, 6396–6402, https://doi.org/10.1002/2014GL060641, 2014. a
Viovy, N.: CRUNCEP Version 7 – Atmospheric Forcing Data for the
Community Land Model, NCAR/UCAR [data set], https://doi.org/10.5065/PZ8F-F017, 2018. a
Vuichard, N., Messina, P., Luyssaert, S., Guenet, B., Zaehle, S., Ghattas, J., Bastrikov, V., and Peylin, P.: Accounting for carbon and nitrogen interactions in the global terrestrial ecosystem model ORCHIDEE (trunk version, rev 4999): multi-scale evaluation of gross primary production, Geosci. Model Dev., 12, 4751–4779, https://doi.org/10.5194/gmd-12-4751-2019, 2019. a, b
Wang, S., Li, J., Zhang, B., Lee, Z., Spyrakos, E., Feng, L., Liu, C., Zhao,
H., Wu, Y., Zhu, L., Jia, L., Wan, W., Zhang, F., Shen, Q., Tyler, A. N., and
Zhang, X.: Changes of water clarity in large lakes and reservoirs across
China observed from long-term MODIS, Remote Sens. Environ., 247,
111949, https://doi.org/10.1016/j.rse.2020.111949, 2020. a
Wang, T., Ottlé, C., Boone, A., Ciais, P., Brun, E., Morin, S., Krinner, G.,
Piao, S., and Peng, S.: Evaluation of an improved intermediate complexity
snow scheme in the ORCHIDEE land surface model: ORCHIDEE SNOW MODEL
EVALUATION, J. Geophys. Res.-Atmos., 118, 6064–6079,
https://doi.org/10.1002/jgrd.50395, 2013. a
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, A., 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., and Sherstiukov, A. B.: Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region, The Cryosphere, 10, 1721–1737, https://doi.org/10.5194/tc-10-1721-2016, 2016. a
Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E.,
Österle, H., Adam, J. C., Bellouin, N., Boucher, O., and Best, M.: Creation
of the WATCH Forcing Data and Its Use to Assess Global and
Regional Reference Crop Evaporation over Land during the
Twentieth Century, J. Hydrometeorol., 12, 823–848,
https://doi.org/10.1175/2011JHM1369.1, 2011. a
Wei, Y., Liu, S., Huntzinger, D. N., Michalak, A. M., Viovy, N., Post, W. M., Schwalm, C. R., Schaefer, K., Jacobson, A. R., Lu, C., Tian, H., Ricciuto, D. M., Cook, R. B., Mao, J., and Shi, X.: The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project – Part 2: Environmental driver data, Geosci. Model Dev., 7, 2875–2893, https://doi.org/10.5194/gmd-7-2875-2014, 2014. a
West, W. E., Creamer, K. P., and Jones, S. E.: Productivity and depth regulate
lake contributions to atmospheric methane: Lake productivity fuels methane
emissions, Limnol. Oceanogr., 61, S51–S61, https://doi.org/10.1002/lno.10247,
2016. a
Zavialov, P. O., Izhitskiy, A. S., Kirillin, G. B., Khan, V. M., Konovalov, B. V., Makkaveev, P. N., Pelevin, V. V., Rimskiy-Korsakov, N. A., Alymkulov, S. A., and Zhumaliev, K. M.: New profiling and mooring records help to assess variability of Lake Issyk-Kul and reveal unknown features of its thermohaline structure, Hydrol. Earth Syst. Sci., 22, 6279–6295, https://doi.org/10.5194/hess-22-6279-2018, 2018.
a
Zolfaghari, K., Duguay, C. R., and Kheyrollah Pour, H.: Satellite-derived light extinction coefficient and its impact on thermal structure simulations in a 1-D lake model, Hydrol. Earth Syst. Sci., 21, 377–391, https://doi.org/10.5194/hess-21-377-2017, 2017. a, b
Short summary
The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy...
