Articles | Volume 21, issue 23
https://doi.org/10.5194/bg-21-5517-2024
© Author(s) 2024. 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-21-5517-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Dec 2024
Research article |
| 12 Dec 2024
| 12 Dec 2024
On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Anna Ukkola
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Sanaa Hobeichi
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Jon Cranko Page
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Mathew Lipson
Bureau of Meteorology, Melbourne, Australia
Martin G. De Kauwe
School of Biological Sciences, University of Bristol, Bristol, BS8 1TQ, UK
Samuel Green
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Claire Brenner
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
Jonathan Frame
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
Grey Nearing
Google Research, Mountain View, CA, USA
Martyn Clark
Department of Civil Engineering, University of Calgary, Calgary, Canada
Martin Best
UKMO, Exeter, UK
Peter Anthoni
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany
Gabriele Arduini
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Souhail Boussetta
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Silvia Caldararu
Max Planck Institute for Biogeochemistry, Jena, Germany
Discipline of Botany, School of Natural Sciences, trinity College Dublin, Dublin, Ireland
Kyeungwoo Cho
Department of Civil and Environmental Engineering, Yonsei University, Seoul, South Korea
Matthias Cuntz
INRAE, Nancy, France
David Fairbairn
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Craig R. Ferguson
Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA
Hyungjun Kim
HydroKlima Lab, KAIST, Daejeon, South Korea
Yeonjoo Kim
Department of Civil and Environmental Engineering, Yonsei University, Seoul, South Korea
Jürgen Knauer
CSIRO Environment, Canberra, Australia
Hawkesbury Institute for the Environment, Western Sydney University, Sydney, Australia
David Lawrence
NCAR, Boulder, CO, USA
Xiangzhong Luo
Department of Geography, National University of Singapore, Singapore
Sergey Malyshev
NOAA GFDL, Princeton, NJ, USA
Tomoko Nitta
Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
Jerome Ogee
INRAE, Nancy, France
Keith Oleson
NCAR, Boulder, CO, USA
Catherine Ottlé
LSCE, Paris, France
Phillipe Peylin
LSCE, Paris, France
Patricia de Rosnay
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Heather Rumbold
UKMO, Exeter, UK
Bob Su
Faculty of Geo-Information Science and Earth Observation, University of Twente, the Netherlands
Nicolas Vuichard
LSCE, Paris, France
Anthony P. Walker
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Xiaoni Wang-Faivre
LSCE, Paris, France
Yunfei Wang
Faculty of Geo-Information Science and Earth Observation, University of Twente, the Netherlands
Yijian Zeng
Faculty of Geo-Information Science and Earth Observation, University of Twente, the Netherlands
Data sets
PLUMBER2: forcing and evaluation datasets for a model inter-comparison project for land surface models v1.0 A. M. Ukkola et al. https://doi.org/10.25914/5fdb0902607e1
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.
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land...
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