Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

A Machine Learning Approach to Emulation and Biophysical Parameter Estimation with the Community Land Model, Version 5

TitleA Machine Learning Approach to Emulation and Biophysical Parameter Estimation with the Community Land Model, Version 5
Publication TypeJournal Article
Year of Publication2020
JournalAdvances in Statistical Climatology, Meteorology and Oceanography
Volume6
Number2
Pages223-244
Abstract / Summary

Land models are essential tools for understanding and predicting terrestrial processes and climate-carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work, we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand-tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to the overall uncertainty in land model projections.

URLhttp://dx.doi.org/10.5194/ascmo-6-223-2020
DOI10.5194/ascmo-6-223-2020
Journal: Advances in Statistical Climatology, Meteorology and Oceanography
Year of Publication: 2020
Volume: 6
Number: 2
Pages: 223-244
Publication Date: 12/2020

Land models are essential tools for understanding and predicting terrestrial processes and climate-carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work, we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand-tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to the overall uncertainty in land model projections.

DOI: 10.5194/ascmo-6-223-2020
Citation:
Dagon, K, B Sanderson, R Fisher, and D Lawrence.  2020.  "A Machine Learning Approach to Emulation and Biophysical Parameter Estimation with the Community Land Model, Version 5."  Advances in Statistical Climatology, Meteorology and Oceanography 6(2): 223-244.  https://doi.org/10.5194/ascmo-6-223-2020.