Leveraging observed soil heterotrophic respiration fluxes as a novel constraint on global‐scale models

• Published in: Global Change Biology
• Main Authors: Jian, Jinshi, Bond‐Lamberty, Ben, Hao, Dalei, Sulman, Benjamin N., Patel, Kaizad F., Zheng, Jianqiu, Dorheim, Kalyn, Pennington, Stephanie C., Hartman, Melannie D., Warner, Dan, Wieder, William R.
• Other Authors: U.S. Department of Energy, National Oceanic and Atmospheric Administration
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: Climate change
• Online Access: http://dx.doi.org/10.1111/gcb.15795; https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.15795; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/gcb.15795; https://onlinelibrary.wiley.com/doi/am-pdf/10.1111/gcb.15795

Abstract Microbially explicit models may improve understanding and projections of carbon dynamics in response to future climate change, but their fidelity in simulating global‐scale soil heterotrophic respiration ( R H ), a stringent test for soil biogeochemical models, has never been evaluated. We used statistical global R H products, as well as 7821 daily site‐scale R H measurements, to evaluate the spatiotemporal performance of one first‐order decay model (CASA‐CNP) and two microbially explicit biogeochemical models (CORPSE and MIMICS) that were forced by two different input datasets. CORPSE and MIMICS did not provide any measurable performance improvement; instead, the models were highly sensitive to the input data used to drive them. Spatial variability in R H fluxes was generally well simulated except in the northern middle latitudes (~50°N) and arid regions; models captured the seasonal variability of R H well, but showed more divergence in tropic and arctic regions. Our results demonstrate that the next generation of biogeochemical models shows promise but also needs to be improved for realistic spatiotemporal variability of R H . Finally, we emphasize the importance of net primary production, soil moisture, and soil temperature inputs, and that jointly evaluating soil models for their spatial (global scale) and temporal (site scale) performance provides crucial benchmarks for improving biogeochemical models.