Abstract
Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost of running just one simulation at high resolution can hinder proper analysis. Statistical emulation is an increasingly common technique for developing fast approximate models in a way that maintains accuracy but also provides comprehensive uncertainty bounds for the approximation. In this work, we develop a statistical emulation framework for land surface models which acknowledges the forcing data fed into the model, providing predictions at a high resolution. We use The Joint UK Land Environment Simulator (JULES) as a case study for this strategy, and perform initial sensitivity analysis and parameter tuning to showcase its capabilities. JULES is perhaps one of the most complex land surface models, and so our success here suggests incredible gains can be made for all types of land surface model.
Highlights
Land surface models (LSMs) represent the terrestrial biosphere within weather and climate models, focusing on hydrometeorology and biogeophysical coupling with the atmosphere
We develop a statistical emulation framework for land surface models which acknowledges the forcing data fed into the model, providing predictions at a high resolution
We investigate only 13, 10 of which are different for each plant functional types (PFTs), chosen based on previous sensitivity studies of Joint UK Land Environment Simulator (JULES) (Booth et al (2012); Raoult et al (2016)), and experience working with JULES
Summary
Land surface models (LSMs) represent the terrestrial biosphere within weather and climate models, focusing on hydrometeorology and biogeophysical coupling with the atmosphere. The latter includes nutrient flows between vegetation and soils, and 15 the turbulent exchange of CO2, heat, moisture, and momentum between the land surface and the atmosphere. LSMs can be used to further scientific understanding of land surface processes and to inform policy decisions. For both applications, increased confidence in simulated results and knowledge of model uncertainty is needed, which typically involves running the 20 model many times with varied forcings and parameters (Booth et al, 2012; Murphy et al, 2004). The computational cost of running these models limits the number of runs that can be obtained, constraining the resulting analysis
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