29 Jul 2022
29 Jul 2022
Status: this preprint is open for discussion.

Towards an ensemble-based evaluation of land surface models in light of uncertain forcings and observations

Vivek K. Arora1, Christian Seiler2, Libo Wang2, and Sian Kou-Giesbrecht1 Vivek K. Arora et al.
  • 1Canadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment Canada, Victoria, BC, Canada
  • 2Climate Processes Section, Climate Research Division, Environment and Climate Change Canada, Toronto, ON, Canada

Abstract. Quantification of uncertainty in fluxes of energy, water, and CO2 simulated by land surface models (LSMs) remains a challenge. LSMs are typically driven with, and tuned for, a specified meteorological forcing data set and a specified set of geophysical fields. Here, using two data sets each for meteorological forcing and historical land cover reconstruction, as well as two model structures (with and without coupling of carbon and nitrogen cycles), the uncertainty in simulated results over the historical period is quantified for the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. The resulting eight (2 x 2 x 2) equally probable model simulations are evaluated using an in-house model evaluation framework that uses multiple observations-based data sets for a range of quantities. Among the primary global energy, water, and carbon related fluxes and state variables, simulated area burned, fire CO2 emissions, soil carbon mass, vegetation biomass, runoff, heterotrophic respiration, gross primary productivity, and sensible heat flux show the largest spread across the eight simulations relative to their mean. Simulated net atmosphere-land CO2 flux, which is considered a critical determinant of the performance of LSMs, is found to be largely independent of the simulated pre-industrial vegetation and soil carbon mass. This indicates that models can provide reliable estimates of the strength of the land carbon sink despite biases in carbon stocks. Results show that evaluating an ensemble of model results against multiple observations allows to disentangle model deficiencies from uncertainties in model inputs, observation-based data, and model configuration.

Vivek K. Arora et al.

Status: open (until 09 Sep 2022)

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Vivek K. Arora et al.

Vivek K. Arora et al.


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Short summary
The behaviour of natural systems is now very often represented through mathematical models. These models represent our understanding of how the nature works. Of course, the nature doesn't care about our understanding. Since our understanding is not perfect, evaluating models is challenging and there are uncertainties. This manuscript illustrates this uncertainty for land models and argues that evaluating models in the light of uncertainty in various components provides useful information.