Updated respiration routines alter spatio-temporal patterns of carbon cycling in a global land surface model
Year: 2021
Type: article
Abstract: We updated the routines used to estimate leaf maintenance respiration (MR) in the Energy Land Model (ELM) using a comprehensive global respiration data base. The updated algorithm includes a temperature acclimating base rate, an updated instantaneous temperature response, and new plant functional type specific parameters. The updated MR algorithm resulted in a very large increase in global MR of 16.1 Pg (38%), but the signal was not geographically uniform. The increase was concentrated in the tropics and humid warm-temperate forests. The increase in MR led to large but proportionally smaller decreases in global net primary production (19%) and in average global leaf area index (15%). The effect on global gross primary production (GPP) was a more modest 5.7 Pg (4%). A detailed site level analysis also demonstrated a wide range of effects the updated algorithm can have on the seasonal cycle of GPP. Output from the updated and old models did not differ markedly in how closely they matched a suite of benchmarks. Given the substantial impact on the land surface carbon cycle, a neutral influence on model benchmarks, and better alignment with empirical evidence, an MR algorithm similar to the one presented here should be adopted into ELM.
Source: Environmental Research Letters
Institutions: University of Minnesota, Johns Hopkins University, Oak Ridge National Laboratory, Australian National University, Australian Research Council, German Centre for Integrative Biodiversity Research, Max Planck Institute for Biogeochemistry, University of Illinois Urbana-Champaign, University of Michigan, Western Sydney University
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,255
Awards: Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting, III: Small: Stochastic Algorithms for Large Scale Data Analysis, BII-Implementation: The causes and consequences of plant biodiversity across scales in a rapidly changing world, G4850210401, G659584030
OpenAlex ID: W3200064360