Articles | Volume 23, issue 1
https://doi.org/10.5194/bg-23-263-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-263-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jan 2026
Research article |
| 09 Jan 2026
| 09 Jan 2026
Land surface model underperformance tied to specific meteorological conditions
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
ARC Centre of Excellence in Climate Extremes, Sydney, NSW 2052, Australia
current address: Water, Energy and Environmental Engineering Research Unit, Faculty of Technology, University of Oulu, Oulu 90570, Finland
Martin G. De Kauwe
School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK
Andy J. Pitman
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
ARC Centre of Excellence in Climate Extremes, Sydney, NSW 2052, Australia
Isaac R. Towers
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
ARC Centre of Excellence in Climate Extremes, Sydney, NSW 2052, Australia
Ecology and Evolution Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Gabriele Arduini
European Centre for Medium-Range Weather Forecasts, Reading, UK
Martin J. Best
Met Office, Exeter, UK
Craig R. Ferguson
Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA
Jürgen Knauer
School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW 2007, Australia
Hyungjun Kim
Moon Soul Graduate School of Future Strategy, Korea Advanced Institute of Science and Technology, Daejeon, Korea
Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
Graduate School of Green Growth and Sustainability, Korea Advanced Institute of Science and Technology, Daejeon, Korea
Graduate School of Data Science, Korea Advanced Institute of Science and Technology, Daejeon, Korea
David M. Lawrence
NSF National Center for Atmospheric Research, Boulder, CO, USA
Tomoko Nitta
Faculty of Science and Engineering, Chuo University, Japan
Keith W. Oleson
NSF National Center for Atmospheric Research, Boulder, CO, USA
Catherine Ottlé
Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA-CNRS-Université Paris-Saclay, Orme des Merisiers, Gif-sur-Yvette, 91190, France
Anna Ukkola
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
ARC Centre of Excellence in Climate Extremes, Sydney, NSW 2052, Australia
Nicholas Vuichard
Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA-CNRS-Université Paris-Saclay, Orme des Merisiers, Gif-sur-Yvette, 91190, France
Xiaoni Wang-Faivre
Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA-CNRS-Université Paris-Saclay, Orme des Merisiers, Gif-sur-Yvette, 91190, France
Gab Abramowitz
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
ARC Centre of Excellence in Climate Extremes, Sydney, NSW 2052, Australia
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Latest update: 29 Jan 2026
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.
This paper used a large dataset of observations, machine learning predictions, and computer...
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