Articles | Volume 29, issue 1
https://doi.org/10.5194/hess-29-261-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-261-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assimilating ESA CCI land surface temperature into the ORCHIDEE land surface model: insights from a multi-site study across Europe
Luis-Enrique Olivera-Guerra
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
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
Nina Raoult
Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Laver Building, North Park Road, Exeter, EX4 4QE, UK
Philippe Peylin
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
Related authors
Pierre Laluet, Luis Olivera-Guerra, Víctor Altés, Vincent Rivalland, Alexis Jeantet, Julien Tournebize, Omar Cenobio-Cruz, Anaïs Barella-Ortiz, Pere Quintana-Seguí, Josep Maria Villar, and Olivier Merlin
Hydrol. Earth Syst. Sci., 28, 3695–3716, https://doi.org/10.5194/hess-28-3695-2024, https://doi.org/10.5194/hess-28-3695-2024, 2024
Short summary
Short summary
Monitoring agricultural drainage flow in irrigated areas is key to water and soil management. In this paper, four simple drainage models are evaluated on two irrigated sub-basins where drainage flow is measured daily. The evaluation of their precision shows that they simulate drainage very well when calibrated with drainage data and that one of them is slightly better. The evaluation of their accuracy shows that only one model can provide rough drainage estimates without calibration data.
Rémi Gaillard, Patricia Cadule, Philippe Peylin, Nicolas Vuichard, and Bertrand Guenet
EGUsphere, https://doi.org/10.5194/egusphere-2025-3656, https://doi.org/10.5194/egusphere-2025-3656, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The release of carbon from thawing permafrost soils could amplify future climate warming. However, this feedback is highly uncertain because most Earth system models (ESM) do not represent permafrost carbon. We have improved the Institut Pierre-Simon Laplace ESM by including permafrost physical, carbon and nitrogen processes to better represent Arctic ecosystems. The model more accurately represents past and present permafrost physics and biogeochemistry, paving the way for future projections.
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).
Short summary
Short summary
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).
Short summary
Short summary
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
Short summary
Short summary
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.
Simon Beylat, Nina Raoult, Cédric Bacour, Natalie Douglas, Tristan Quaife, Vladislav Bastrikov, Peter Julien Rayner, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2025-109, https://doi.org/10.5194/egusphere-2025-109, 2025
Short summary
Short summary
Land surface models are important tools for understanding and predicting the land components of the carbon cycle. Atmospheric CO2 concentration data is a valuable source of information that can be used to improve the accuracy of these models. In this study, we present a statistical method named 4DEnVar to calibrate parameters of a land surface model using this data. We show that this method is easy to implement and more efficient and accurate than traditional methods.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Stijn N. C. Dellaert, Hugo Denier van der Gon, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, and John R. Worden
Earth Syst. Sci. Data, 16, 4325–4350, https://doi.org/10.5194/essd-16-4325-2024, https://doi.org/10.5194/essd-16-4325-2024, 2024
Short summary
Short summary
This study provides an overview of data availability from observation- and inventory-based CH4 emission estimates. It systematically compares them and provides recommendations for robust comparisons, aiming to steadily engage more parties in using observational methods to complement their UNFCCC submissions. Anticipating improvements in atmospheric modelling and observations, future developments need to resolve knowledge gaps in both approaches and to better quantify remaining uncertainty.
Pierre Laluet, Luis Olivera-Guerra, Víctor Altés, Vincent Rivalland, Alexis Jeantet, Julien Tournebize, Omar Cenobio-Cruz, Anaïs Barella-Ortiz, Pere Quintana-Seguí, Josep Maria Villar, and Olivier Merlin
Hydrol. Earth Syst. Sci., 28, 3695–3716, https://doi.org/10.5194/hess-28-3695-2024, https://doi.org/10.5194/hess-28-3695-2024, 2024
Short summary
Short summary
Monitoring agricultural drainage flow in irrigated areas is key to water and soil management. In this paper, four simple drainage models are evaluated on two irrigated sub-basins where drainage flow is measured daily. The evaluation of their precision shows that they simulate drainage very well when calibrated with drainage data and that one of them is slightly better. The evaluation of their accuracy shows that only one model can provide rough drainage estimates without calibration data.
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
Short summary
Short summary
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.
Nina Raoult, Louis-Axel Edouard-Rambaut, Nicolas Vuichard, Vladislav Bastrikov, Anne Sofie Lansø, Bertrand Guenet, and Philippe Peylin
Biogeosciences, 21, 1017–1036, https://doi.org/10.5194/bg-21-1017-2024, https://doi.org/10.5194/bg-21-1017-2024, 2024
Short summary
Short summary
Observations are used to reduce uncertainty in land surface models (LSMs) by optimising poorly constraining parameters. However, optimising against current conditions does not necessarily ensure that the parameters treated as invariant will be robust in a changing climate. Manipulation experiments offer us a unique chance to optimise our models under different (here atmospheric CO2) conditions. By using these data in optimisations, we gain confidence in the future projections of LSMs.
Mounia Mostefaoui, Philippe Ciais, Matthew J. McGrath, Philippe Peylin, Prabir K. Patra, and Yolandi Ernst
Earth Syst. Sci. Data, 16, 245–275, https://doi.org/10.5194/essd-16-245-2024, https://doi.org/10.5194/essd-16-245-2024, 2024
Short summary
Short summary
Our aim is to assess African anthropogenic greenhouse gas emissions and removals by using different data products, including inventories and process-based models, and to compare their relative merits with inversion data coming from satellites. We show a good match among the various estimates in terms of overall trends at a regional level and on a decadal basis, but large differences exist even among similar data types, which is a limit to the possibility of verification of country-reported data.
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
Short summary
Short summary
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
Short summary
Short summary
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, Tim Jupp, Ben Booth, and Peter Cox
Earth Syst. Dynam., 14, 723–731, https://doi.org/10.5194/esd-14-723-2023, https://doi.org/10.5194/esd-14-723-2023, 2023
Short summary
Short summary
Climate models are used to predict the impact of climate change. However, poorly constrained parameters used in the physics of the models mean that we simulate a large spread of possible future outcomes. We can use real-world observations to reduce the uncertainty of parameter values, but we do not have observations to reduce the spread of possible future outcomes directly. We present a method for translating the reduction in parameter uncertainty into a reduction in possible model projections.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cédric Bacour, Natasha MacBean, Frédéric Chevallier, Sébastien Léonard, Ernest N. Koffi, and Philippe Peylin
Biogeosciences, 20, 1089–1111, https://doi.org/10.5194/bg-20-1089-2023, https://doi.org/10.5194/bg-20-1089-2023, 2023
Short summary
Short summary
The impact of assimilating different dataset combinations on regional to global-scale C budgets is explored with the ORCHIDEE model. Assimilating simultaneously multiple datasets is preferable to optimize the values of the model parameters and avoid model overfitting. The challenges in constraining soil C disequilibrium using atmospheric CO2 data are highlighted for an accurate prediction of the land sink distribution.
Zun Yin, Kirsten L. Findell, Paul Dirmeyer, Elena Shevliakova, Sergey Malyshev, Khaled Ghannam, Nina Raoult, and Zhihong Tan
Hydrol. Earth Syst. Sci., 27, 861–872, https://doi.org/10.5194/hess-27-861-2023, https://doi.org/10.5194/hess-27-861-2023, 2023
Short summary
Short summary
Land–atmosphere (L–A) interactions typically focus on daytime processes connecting the land state with the overlying atmospheric boundary layer. However, much prior L–A work used monthly or daily means due to the lack of daytime-only data products. Here we show that monthly smoothing can significantly obscure the L–A coupling signal, and including nighttime information can mute or mask the daytime processes of interest. We propose diagnosing L–A coupling within models or archiving subdaily data.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
Short summary
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.
Camille Abadie, Fabienne Maignan, Marine Remaud, Jérôme Ogée, J. Elliott Campbell, Mary E. Whelan, Florian Kitz, Felix M. Spielmann, Georg Wohlfahrt, Richard Wehr, Wu Sun, Nina Raoult, Ulli Seibt, Didier Hauglustaine, Sinikka T. Lennartz, Sauveur Belviso, David Montagne, and Philippe Peylin
Biogeosciences, 19, 2427–2463, https://doi.org/10.5194/bg-19-2427-2022, https://doi.org/10.5194/bg-19-2427-2022, 2022
Short summary
Short summary
A better constraint of the components of the carbonyl sulfide (COS) global budget is needed to exploit its potential as a proxy of gross primary productivity. In this study, we compare two representations of oxic soil COS fluxes, and we develop an approach to represent anoxic soil COS fluxes in a land surface model. We show the importance of atmospheric COS concentration variations on oxic soil COS fluxes and provide new estimates for oxic and anoxic soil contributions to the COS global budget.
Guillaume Marie, B. Sebastiaan Luyssaert, Cecile Dardel, Thuy Le Toan, Alexandre Bouvet, Stéphane Mermoz, Ludovic Villard, Vladislav Bastrikov, and Philippe Peylin
Geosci. Model Dev., 15, 2599–2617, https://doi.org/10.5194/gmd-15-2599-2022, https://doi.org/10.5194/gmd-15-2599-2022, 2022
Short summary
Short summary
Most Earth system models make use of vegetation maps to initialize a simulation at global scale. Satellite-based biomass map estimates for Africa were used to estimate cover fractions for the 15 land cover classes. This study successfully demonstrates that satellite-based biomass maps can be used to better constrain vegetation maps. Applying this approach at the global scale would increase confidence in assessments of present-day biomass stocks.
Marine Remaud, Frédéric Chevallier, Fabienne Maignan, Sauveur Belviso, Antoine Berchet, Alexandra Parouffe, Camille Abadie, Cédric Bacour, Sinikka Lennartz, and Philippe Peylin
Atmos. Chem. Phys., 22, 2525–2552, https://doi.org/10.5194/acp-22-2525-2022, https://doi.org/10.5194/acp-22-2525-2022, 2022
Short summary
Short summary
Carbonyl sulfide (COS) has been recognized as a promising indicator of the plant gross primary production (GPP). Here, we assimilate both COS and CO2 measurements into an atmospheric transport model to obtain information on GPP, plant respiration and COS budget. A possible scenario for the period 2008–2019 leads to a global COS biospheric sink of 800 GgS yr−1 and higher oceanic emissions between 400 and 600 GgS yr−1.
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, https://doi.org/10.5194/gmd-14-5331-2021, 2021
Short summary
Short summary
We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
Fabienne Maignan, Camille Abadie, Marine Remaud, Linda M. J. Kooijmans, Kukka-Maaria Kohonen, Róisín Commane, Richard Wehr, J. Elliott Campbell, Sauveur Belviso, Stephen A. Montzka, Nina Raoult, Ulli Seibt, Yoichi P. Shiga, Nicolas Vuichard, Mary E. Whelan, and Philippe Peylin
Biogeosciences, 18, 2917–2955, https://doi.org/10.5194/bg-18-2917-2021, https://doi.org/10.5194/bg-18-2917-2021, 2021
Short summary
Short summary
The assimilation of carbonyl sulfide (COS) by continental vegetation has been proposed as a proxy for gross primary production (GPP). Using a land surface and a transport model, we compare a mechanistic representation of the plant COS uptake (Berry et al., 2013) to the classical leaf relative uptake (LRU) approach linking GPP and vegetation COS fluxes. We show that at high temporal resolutions a mechanistic approach is mandatory, but at large scales the LRU approach compares similarly.
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
Short summary
Short summary
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
Abadie, C., Maignan, F., Remaud, M., Ogée, J., Campbell, J. E., Whelan, M. E., Kitz, F., Spielmann, F. M., Wohlfahrt, G., Wehr, R., Sun, W., Raoult, N., Seibt, U., Hauglustaine, D., Lennartz, S. T., Belviso, S., Montagne, D., and Peylin, P.: Global modelling of soil carbonyl sulfide exchanges, Biogeosciences, 19, 2427–2463, https://doi.org/10.5194/bg-19-2427-2022, 2022.
Bacour, C., Peylin, P., MacBean, N., Rayner, P. J., Delage, F., Chevallier, F., Weiss, M., Demarty, J., Santaren, D., Baret, F., Berveiller, D., Dufrêne, E., and Prunet, P.: Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process-oriented biosphere model, J. Geophys. Res.-Biogeo., 120, 1839–1857, https://doi.org/10.1002/2015JG002966, 2015.
Bacour, C., MacBean, N., Chevallier, F., Léonard, S., Koffi, E. N., and Peylin, P.: Assimilation of multiple datasets results in large differences in regional- to global-scale NEE and GPP budgets simulated by a terrestrial biosphere model, Biogeosciences, 20, 1089–1111, https://doi.org/10.5194/bg-20-1089-2023, 2023.
Bastrikov, V., Macbean, N., Bacour, C., Santaren, D., Kuppel, S., and Peylin, P.: Land surface model parameter optimisation using in situ flux data: Comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2), Geosci. Model Dev., 11, 4739–4754, https://doi.org/10.5194/gmd-11-4739-2018, 2018.
Bateni, S. M. and Entekhabi, D.: Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters, Water Ressour. Reas., 48, 8521, https://doi.org/10.1029/2011WR011542, 2012.
Bateni, S. M., Entekhabi, D., and Jeng, D. S.: Variational assimilation of land surface temperature and the estimation of surface energy balance components, J. Hydrol., 481, 143–156, https://doi.org/10.1016/j.jhydrol.2012.12.039, 2013.
Benavides Pinjosovsky, H. S., Thiria, S., Ottlé, C., Brajard, J., Badran, F., and Maugis, P.: Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6, Geosci. Model Dev., 10, 85–104, https://doi.org/10.5194/gmd-10-85-2017, 2017.
Boni, G., Entekhabi, D., and Castelli, F.: Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation, Water Resour. Res., 37, 1713–1722, https://doi.org/10.1029/2001WR900020, 2001.
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., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J. L., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M. A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J. Y., Guenet, B., Guez, L. E., 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. B., 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 Syst., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020.
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A Limited Memory Algorithm for Bound Constrained Optimization, SIAM J. Sci. Comput., 16, 1190–1208, https://doi.org/10.1137/0916069, 1995.
Campolongo, F., Cariboni, J., and Saltelli, A.: An effective screening design for sensitivity analysis of large models, Environ. Model. Softw., 22, 1509–1518, https://doi.org/10.1016/j.envsoft.2006.10.004, 2007.
Caparrini, F., Castelli, F., and Entekhabi, D.: Mapping of land-atmosphere heat fluxes and surface parameters with remote sensing data, Bound.-Lay. Meteorol., 107, 605–633, https://doi.org/10.1023/A:1022821718791, 2003.
Caparrini, F., Castelli, F., and Entekhabi, D.: Estimation of surface turbulent fluxes through assimilation of radiometric surface temperature sequences, J. Hydrometeorol., 5(, 145–159, https://doi.org/10.1175/1525-7541(2004)005<0145:EOSTFT>2.0.CO;2, 2004.
Coudert, B., Ottlé, C., Boudevillain, B., Demarty, J., and Guillevic, P.: Contribution of thermal infrared remote sensing data in multiobjective calibration of a dual-source SVAT model, J. Hydrometeorol., 7, 404–420, https://doi.org/10.1175/JHM503.1, 2006.
Coudert, B., Ottlé, C., and Briottet, X.: Monitoring land surface processes with thermal infrared data: Calibration of SVAT parameters based on the optimisation of diurnal surface temperature cycling features, Remote Sens. Environ., 112, 872–887, https://doi.org/10.1016/j.rse.2007.06.024, 2008.
Crow, W. T., Wood, E. F., and Pan, M.: Multiobjective calibration of land surface model evapotranspiration predictions using streamflow observations and spaceborne surface radiometric temperature retrievals, J. Geophys. Res.-Atmos., 108, 4725, https://doi.org/10.1029/2002jd003292, 2003.
Demarty, J., Ottlé, C., Braud, I., Olioso, A., Frangi, J. P., Gupta, H. V., and Bastidas, L. A.: Constraining a physically based Soil-Vegetation-Atmosphere Transfer model with surface water content and thermal infrared brightness temperature measurements using a multiobjective approach, Water Resour. Res., 41, W01011, https://doi.org/10.1029/2004WR003695, 2005.
De Rosnay, P., Polcher, J. D., Bruen, M., and Laval, K.: Impact of a physically based soil water flow and soil‐plant interaction representation for modeling large‐scale land surface processes, J. Geophys. Res.-Atmos., 107, 4118, https://doi.org/10.1029/2001JD000634, 2002.
FLUXNET: La Thuile Synth. Dataset, FLUXNET, https://fluxnet.org/data/la-thuile-dataset/ (last access: 6 January 2025), 2016.
Fu, Z., Ciais, P., Feldman, A. F., Gentine, P., Makowski, D., Prentice, I. C., Stoy, P. C., Bastos, A., and Wigneron, J.-P.: Critical soil moisture thresholds of plant water stress in terrestrial ecosystems, Sci. Adv., 8, eabq7827, https://doi.org/10.1126/sciadv.abq7827, 2022.
Fu, Z., Ciais, P., Wigneron, J.-P., Gentine, P., Feldman, A. F., Makowski, D., Viovy, N., Kemanian, A. R., Goll, D. S., Stoy, P. C., Prentice, I. C., Yakir, D., Liu, L., Ma, H., Li, X., Huang, Y., Yu, K., Zhu, P., Li, X., Zhu, Z., Lian, J., and Smith, W. K.: Global critical soil moisture thresholds of plant water stress, Nat. Commun., 15, 4826. https://doi.org/10.1038/s41467-024-49244-7, 2024.
Ghent, D., Kaduk, J., Remedios, J., Ardö, J., and Balzter, H.: Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter, J. Geophys. Res., 115, 19112, https://doi.org/10.1029/2010JD014392, 2010.
Goldberg, D. E.: Genetic algorithms in search, optimization, and machine learning, Addion Wesley, p. 36., ISBN 0201157675, 1989.
Haupt, R. L. and Haupt, S. E.: Practical genetic algorithms, Wiley, ISBN 9780471671749, https://doi.org/10.1002/0471671746, 2004.
Hollmann, R., Merchant, C. J., Saunders, R., Downy, C., Buchwitz, M., Cazenave, A., Chuvieco, E., Defourny, P., De Leeuw, G., Forsberg, R., Holzer-Popp, T., Paul, F., Sandven, S., Sathyendranath, S., Van Roozendael, M., and Wagner, W.: The ESA climate change initiative: Satellite data records for essential climate variables, B. Am. Meteorol. Soc., 94, 1541–1552, https://doi.org/10.1175/BAMS-D-11-00254.1, 2013.
Kato, T., Knorr, W., Scholze, M., Veenendaal, E., Kaminski, T., Kattge, J., and Gobron, N.: Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana, Biogeosciences, 10, 789–802, https://doi.org/10.5194/bg-10-789-2013, 2013.
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.
Koetz, B., Bastiaanssen, W., Berger, M., Defourney, P., Del Bello, U., Drusch, M., Drinkwater, M., Duca, R., Fernandez, V., Ghent, D., Guzinski, R., Hoogeveen, J., Hook, S., Lagouarde, J.-P., Lemoine, G., Manolis, I., Martimort, P., Masek, J., Massart, M., Notarnicola, C., Sobrino, J., and Udelhoven, T.: High Spatio-Temporal Resolution Land Surface Temperature Mission- – A Copernicus candidate mission in support of agricultural monitoring, in: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 22–27 July 2018, Valencia, Spain, https://doi.org/10.1109/IGARSS.2018.8517433, 2018.
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005.
Kuppel, S., Peylin, P., Chevallier, F., Bacour, C., Maignan, F., and Richardson, A. D.: Constraining a global ecosystem model with multi-site eddy-covariance data, Biogeosciences, 9, 3757–3776, https://doi.org/10.5194/bg-9-3757-2012, 2012.
Lagouarde, J.-P., Bhattacharya, B. K., Crébassol, P., Gamet, P., Adlakha, D., Murthy, C. S., Singh, S. K., Mishra, M., Nigam, R., Raju, P. V., Babu, S. S., Shukla, M. V., Pandya, M. R., Boulet, G., Briottet, X., Dadou, I., Dedieu, G., Gouhier, M., Hagolle, O., Irvine, M., Jacob, F., Kumar, K. K., Laignel, B., Maisongrande, P., Mallick, K., Olioso, A., Ottlé, C., Roujean, J.-L., Sobrino, J., Ramakrishnan, R., Sekhar, M., and Sarkar, S. S.: Indo-French high-resolution thermal nfrared space mission for Earth natural resources assessment and monitoring – concept and definition of Trishna, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 403–407, https://doi.org/10.5194/isprs-archives-XLII-3-W6-403-2019, 2019.
Lu, Y., Steele-Dunne, S. C., Farhadi, L., and Van De Giesen, N.: Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data, Water Resour. Res., 53, 10858–10877, https://doi.org/10.1002/2017WR021415, 2017.
Lurton, T., Balkanski, Y., Bastrikov, V., Bekki, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Contoux, C., Cozic, A., Cugnet, D., Dufresne, J. L., Éthé, C., Foujols, M. A., Ghattas, J., Hauglustaine, D., Hu, R. M., 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 Syst., 12, e2019MS001940, https://doi.org/10.1029/2019MS001940, 2020.
MacBean, N., Peylin, P., Chevallier, F., Scholze, M., and Schürmann, G.: Consistent assimilation of multiple data streams in a carbon cycle data assimilation system, Geosci. Model Dev., 9, 3569–3588, https://doi.org/10.5194/gmd-9-3569-2016, 2016.
Margulis, S. A. and Entekhabi, D.: Variational assimilation of radiometric surface temperature and reference-level micrometeorology into a model of the atmospheric boundary layer and land surface, Mon. Weather Rev., 131, 1272–1288, https://doi.org/10.1175/1520-0493(2003)131<1272:VAORST>2.0.CO;2, 2003.
Meng, C. L., Li, Z. L., Zhan, X., Shi, J. C., and Liu, C. Y.: Land surface temperature data assimilation and its impact on evapotranspiration estimates from the common land model, Water Resour. Res., 45, W02421, https://doi.org/10.1029/2008WR006971, 2009.
Moradkhani, H., Hsu, K. L., Gupta, H., and Sorooshian, S.: Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter, Water Resour. Res., 41, 1–17, https://doi.org/10.1029/2004WR003604, 2005.
Morris, M. D.: Factorial sampling plans for preliminary computational experiments, Technometrics, 33, 161–174, https://doi.org/10.1080/00401706.1991.10484804, 1991.
Olioso, A., Chauki, H., Courault, D., and Wigneron, J. P.: Estimation of evapotranspiration and photosynthesis by assimilation of remote sensing data into SVAT models, Remote Sens. Environ., 68, 341–356, https://doi.org/10.1016/S0034-4257(98)00121-7, 1999.
ORCHIDAS: ORCHIDEE Data Assimilation Systems, https://orchidas.lsce.ipsl.fr (last access: 6 January 2025), 2025.
ORCHIDEE: Wiki of ORCHIDEE model, ORCHIDEE [code], http://forge.ipsl.jussieu.fr/orchidee (last access: 6 January 2025), 2025.
Peng, C., Guiot, J., Wu, H., Jiang, H., and Luo, Y.: Integrating models with data in ecology and palaeoecology: Advances towards a model-data fusion approach, Ecol. Lett., 14, 522–536, https://doi.org/10.1111/j.1461-0248.2011.01603.x, 2011.
Peylin, P., Bacour, C., MacBean, N., Leonard, S., Rayner, P., Kuppel, S., Koffi, E., Kane, A., Maignan, F., Chevallier, F., Ciais, P., and Prunet, P.: A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle, Geosci. Model Dev., 9, 3321–3346, https://doi.org/10.5194/gmd-9-3321-2016, 2016.
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.
Raoult, N., Ottlé, C., Peylin, P., Bastrikov, V., and Maugis, P.: Evaluating and optimizing surface soil moisture drydowns in the orchidee land surface model at in situ locations, J. Hydrometeorol., 22, 1025–1043, https://doi.org/10.1175/JHM-D-20-0115.1, 2021.
Raoult, N., Charbit, S., Dumas, C., Maignan, F., Ottlé, C., and Bastrikov, V.: Improving modelled albedo over the Greenland ice sheet through parameter optimisation and MODIS snow albedo retrievals, The Cryosphere, 17, 2705–2724, https://doi.org/10.5194/tc-17-2705-2023, 2023.
Raoult, N. M., Jupp, T. E., Cox, P. M., and Luke, C. M.: Land-surface parameter optimisation using data assimilation techniques: The adJULES system V1.0, Geosci. Model Dev., 9, 2833–2852, https://doi.org/10.5194/gmd-9-2833-2016, 2016.
Rayner, P. J., Scholze, M., Knorr, W., Kaminski, T., Giering, R., and Widmann, H.: Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS), Global Biogeochem. Cy., 19, GB2026, https://doi.org/10.1029/2004GB002254, 2005.
Rayner, P. J., Michalak, A. M., and Chevallier, F.: Fundamentals of data assimilation applied to biogeochemistry, Atmos. Chem. Phys., 19, 13911–13932, https://doi.org/10.5194/acp-19-13911-2019, 2019.
Ridler, M. E., Sandholt, I., Butts, M., Lerer, S., Mougin, E., Timouk, F., Kergoat, L., and Madsen, H.: Calibrating a soil–vegetation–atmosphere transfer model with remote sensing estimates of surface temperature and soil surface moisture in a semi arid environment, J. Hydrol., 436–437, 1–12, https://doi.org/10.1016/j.jhydrol.2012.01.047, 2012.
Ruano, M. V., Ribes, J., Seco, A., and Ferrer, J.: An improved sampling strategy based on trajectory design for application of the Morris method to systems with many input factors, Environ. Model. Softw., 37, 103–109, https://doi.org/10.1016/J.ENVSOFT.2012.03.008, 2012.
Santaren, D., Peylin, P., Viovy, N., and Ciais, P.: Optimizing a process-based ecosystem model with eddy-covariance flux measurements: A pine forest in southern France, Global Biogeochem. Cy., 21, GB2013, https://doi.org/10.1029/2006GB002834, 2007.
Santaren, D., Peylin, P., Bacour, C., Ciais, P., and Longdoz, B.: Ecosystem model optimization using in situ flux observations: Benefit of Monte Carlo versus variational schemes and analyses of the year-to-year model performances, Biogeosciences, 11, 7137–7158, https://doi.org/10.5194/bg-11-7137-2014, 2014.
Sini, F., Boni, G., Caparrini, F., and Entekhabi, D.: Estimation of large-scale evaporation fields based on assimilation of remotely sensed land temperature, Water Resour. Res., 44, W06410, https://doi.org/10.1029/2006WR005574, 2008.
Sobol, I. M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simul., 55, 271–280, https://doi.org/10.1016/S0378-4754(00)00270-6, 2001.
Tarantola, A.: Inverse Problem Theory and Methods for Model Parameter Estimation, Society for Industrialand Applied Mathematics, Philadelphia, https://doi.org/10.1137/1.9780898717921, 2005.
Veal, K., Ermida, S., Ghent, D., Perry, M., and Trigo, I.: Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product (2009–2020), version 1.00, NERC EDS Cent. Environ. Data Anal. [data set], https://catalogue.ceda.ac.uk/uuid/6775e27575124407afeebb4bb1dfaaf5 (last access: 6 January 2025), 2022.
Warm Winter 2020 Team and ICOS Ecosystem Thematic Centre: Warm Winter 2020 ecosystem eddy covariance flux product for 73 stations in FLUXNET-Archive format-release 2022-1 (version 1.0), ICOS Carbon Portal [data set], https://www.icos-cp.eu/data-products/2G60-ZHAK (last access: 6 January 2025), 2022.
Zobler, L.: Global Soil Types, 1-Degree Grid (Zobler), Oak Ridge National Laboratory Distributed Active Archive Center [data set], https://doi.org/10.3334/ORNLDAAC/418, 1999.
Short summary
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.
We assimilate the recent ESA-CCI land surface temperature (LST) product to optimize parameters...