Articles | Volume 11, issue 1
https://doi.org/10.5194/gmd-11-195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On the predictability of land surface fluxes from meteorological variables
Climate Change Research Centre, UNSW Australia, Sydney, Australia
Gab Abramowitz
Climate Change Research Centre, UNSW Australia, Sydney, Australia
Andy J. Pitman
Climate Change Research Centre, UNSW Australia, Sydney, Australia
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Cited
17 citations as recorded by crossref.
- Does predictability of fluxes vary between FLUXNET sites? N. Haughton et al. https://doi.org/10.5194/bg-15-4495-2018
- Land surface model underperformance tied to specific meteorological conditions J. Cranko Page et al. https://doi.org/10.5194/bg-23-263-2026
- Are Plant Functional Types Fit for Purpose? J. Cranko Page et al. https://doi.org/10.1029/2023GL104962
- V2Karst V1.1: a parsimonious large-scale integrated vegetation–recharge model to simulate the impact of climate and land cover change in karst regions F. Sarrazin et al. https://doi.org/10.5194/gmd-11-4933-2018
- Theory and the future of land-climate science M. Byrne et al. https://doi.org/10.1038/s41561-024-01553-8
- Does dynamically modeled leaf area improve predictions of land surface water and carbon fluxes? Insights into dynamic vegetation modules S. Westermann et al. https://doi.org/10.5194/bg-21-5277-2024
- TERN, Australia’s land observatory: addressing the global challenge of forecasting ecosystem responses to climate variability and change J. Cleverly et al. https://doi.org/10.1088/1748-9326/ab33cb
- Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands H. Zhou et al. https://doi.org/10.1016/j.agrformet.2021.108506
- Divergence in land surface modeling: linking spread to structure C. Schwalm et al. https://doi.org/10.1088/2515-7620/ab4a8a
- Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle L. Teckentrup et al. https://doi.org/10.5194/esd-14-549-2023
- Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems J. Cranko Page et al. https://doi.org/10.5194/bg-19-1913-2022
- On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results G. Abramowitz et al. https://doi.org/10.5194/bg-21-5517-2024
- A flux tower dataset tailored for land model evaluation A. Ukkola et al. https://doi.org/10.5194/essd-14-449-2022
- Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces R. Wang et al. https://doi.org/10.5194/hess-25-3805-2021
- Non‐Stationary Lags and Legacies in Ecosystem Flux Response to Antecedent Rainfall J. Cranko Page et al. https://doi.org/10.1029/2022JG007144
- Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning R. Wang et al. https://doi.org/10.1175/JHM-D-21-0114.1
- Carbon, water and energy fluxes in agricultural systems of Australia and New Zealand J. Cleverly et al. https://doi.org/10.1016/j.agrformet.2020.107934
17 citations as recorded by crossref.
- Does predictability of fluxes vary between FLUXNET sites? N. Haughton et al. https://doi.org/10.5194/bg-15-4495-2018
- Land surface model underperformance tied to specific meteorological conditions J. Cranko Page et al. https://doi.org/10.5194/bg-23-263-2026
- Are Plant Functional Types Fit for Purpose? J. Cranko Page et al. https://doi.org/10.1029/2023GL104962
- V2Karst V1.1: a parsimonious large-scale integrated vegetation–recharge model to simulate the impact of climate and land cover change in karst regions F. Sarrazin et al. https://doi.org/10.5194/gmd-11-4933-2018
- Theory and the future of land-climate science M. Byrne et al. https://doi.org/10.1038/s41561-024-01553-8
- Does dynamically modeled leaf area improve predictions of land surface water and carbon fluxes? Insights into dynamic vegetation modules S. Westermann et al. https://doi.org/10.5194/bg-21-5277-2024
- TERN, Australia’s land observatory: addressing the global challenge of forecasting ecosystem responses to climate variability and change J. Cleverly et al. https://doi.org/10.1088/1748-9326/ab33cb
- Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands H. Zhou et al. https://doi.org/10.1016/j.agrformet.2021.108506
- Divergence in land surface modeling: linking spread to structure C. Schwalm et al. https://doi.org/10.1088/2515-7620/ab4a8a
- Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle L. Teckentrup et al. https://doi.org/10.5194/esd-14-549-2023
- Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems J. Cranko Page et al. https://doi.org/10.5194/bg-19-1913-2022
- On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results G. Abramowitz et al. https://doi.org/10.5194/bg-21-5517-2024
- A flux tower dataset tailored for land model evaluation A. Ukkola et al. https://doi.org/10.5194/essd-14-449-2022
- Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces R. Wang et al. https://doi.org/10.5194/hess-25-3805-2021
- Non‐Stationary Lags and Legacies in Ecosystem Flux Response to Antecedent Rainfall J. Cranko Page et al. https://doi.org/10.1029/2022JG007144
- Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning R. Wang et al. https://doi.org/10.1175/JHM-D-21-0114.1
- Carbon, water and energy fluxes in agricultural systems of Australia and New Zealand J. Cleverly et al. https://doi.org/10.1016/j.agrformet.2020.107934
Saved (final revised paper)
Latest update: 31 May 2026
Short summary
Previous studies indicate that fluxes of heat, water, and carbon between the land surface and atmosphere are substantially more predictable than the performance of the current crop of land surface models would indicate. This study uses simple empirical models to estimate the amount of useful information in meteorological forcings that is available for predicting land surface fluxes. These models can be used as benchmarks for land surface models and may help identify areas ripe for improvement.
Previous studies indicate that fluxes of heat, water, and carbon between the land surface and...