On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results

Abstract. Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.

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Publication Details

Journal
Biogeosciences
Published
2024-12-12
DOI
https://doi.org/10.5194/bg-21-5517-2024
Citations
12
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
3.39

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article

On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results

Xiangzhong Luo, Kyeungwoo Cho, Samuel Green, David Fairbairn et al.
12 citations
Biogeosciences
Plant Water Relations and Carbon Dynamics
3.39
article

On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results

Xiangzhong Luo, Kyeungwoo Cho, Samuel Green, David Fairbairn, Jon Cranko Page, Heather Rumbold, Craig R. Ferguson, Peter Anthoni, Catherine Ottlé, Anna Ukkola, Gabriele Arduini, Martin G. De Kauwe, Matthias Cuntz, Gab Abramowitz, Jonathan Frame, Souhail Boussetta, Sanaa Hobeichi, Yijian Zeng, Zhongbo Su, Silvia Caldararu, Jérôme Ogée, Martin Best, Sergey Malyshev, Jürgen Knauer, Mathew Lipson, Nicolas Vuichard, Patricia de Rosnay, Grey Nearing, David M. Lawrence, Yeonjoo Kim, Claire Brenner, Tomoko Nitta, Martyn Clark, Anthony P. Walker, Keith W. Oleson, Hyungjun Kim, Xiaoni Wang-Faivre, Yunfei Wang, Phillipe Peylin
article en
12 citations

Abstract

Abstract. Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.

BiogeosciencesVol. 21(23)
Karlsruhe Institute of Technology (DE), NSF National Center for Atmospheric Research (US), Albany State University (US), Bureau of Meteorology (AU), Oak Ridge National Laboratory (US), Google (United States) (US), Commonwealth Scientific and Industrial Research Organisation (AU), European Centre for Medium-Range Weather Forecasts (GB), Korea Advanced Institute of Science and Technology (KR), National University of Singapore (SG), University of Calgary (CA), University of Alabama (US), NOAA Geophysical Fluid Dynamics Laboratory (US), Yonsei University (KR), Trinity College Dublin (IE), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), UNSW Sydney (AU), University of Bristol (GB), University at Albany, State University of New York (US), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Laboratoire des Sciences du Climat et de l'Environnement (FR), CEA Paris-Saclay (FR), Geological Survey of Alabama (US), Max Planck Institute for Biogeochemistry (DE), Western Sydney University (AU), The University of Tokyo (JP), BOKU University (AT), University of Twente (NL)
National Aeronautics and Space Administration, National Center for Atmospheric Research, Netherlands eScience Center, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, National Research Foundation of Korea, National Oceanic and Atmospheric Administration, Australian Research Council
Climate action
Openalex Percentile: Top 7%
Plant Water Relations and Carbon Dynamics
3.39
DOIOpen Access PDFOpenAlex
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