A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
Abstract. 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 more »
Authors:
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Award ID(s):
Publication Date:
NSF-PAR ID:
10233245
Journal Name:
Advances in Statistical Climatology, Meteorology and Oceanography
Volume:
6
Issue:
2
Page Range or eLocation-ID:
223 to 244
ISSN:
2364-3587
2. Abstract The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, R S ), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and R S are irreconcilable. When we estimate GPP based on R S measurements and some assumptions about R S :GPP ratios, we found the resulted global GPP values (bootstrap mean $${149}_{-23}^{+29}$$ 149 − 23 + 29 Pg C yr −1 ) are significantly higher than most GPP estimates reported in the literature ( $${113}_{-18}^{+18}$$ 113 − 18 + 18 Pg C yr −1 ). Similarly, historical GPP estimates imply a soil respiration flux (Rs GPP , bootstrap mean of $${68}_{-8}^{+10}$$ 68 − 8 + 10 Pg C yr −1 ) statistically inconsistent with most published R S values ( $${87}_{-8}^{+9}$$ 87 − 8 + 9 Pg C yr −1 ), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global R S :GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well asmore »