The Impact of Climate Forcing Biases and the Nitrogen Cycle on Land Carbon Balance Projections

Abstract

Earth System Models (ESMs) project that the terrestrial carbon sink will continue to grow as atmospheric CO$_2$ increases, but this projection is uncertain due to biases in the simulated climate and how ESMs represent ecosystem processes. In particular, the strength of the CO$_2$ fertilization effect, which is modulated by nutrient cycles, varies substantially across models. This study evaluates land carbon balance uncertainties for the Canadian Earth System Model (CanESM) by conducting simulations where the latest version of CanESM’s land surface component is driven offline with raw and bias-adjusted CanESM5 climate forcing data. To quantify the impact of nutrient limitation, we complete simulations where the nitrogen cycle is enabled or disabled. Results show that bias adjustment improves model performance across most ecosystem variables, primarily due to reduced biases in precipitation. Turning the nitrogen cycle on increases the global land carbon sink during the historical period (1995-2014) due to enhanced nitrogen deposition, placing it within the Global Carbon Budget uncertainty range. During the future period (2080-2099), the simulated land carbon sink increases in response to bias adjustment and decreases in response to the dynamic carbon-nitrogen interaction, leading to a net decrease when both factors are acting together. The dominating impact of the nitrogen cycle demonstrates the importance of representing nutrient limitation in ESMs. Such efforts may produce more robust carbon balance projections in support of global climate change mitigation policies such as the 2015 Paris Agreement.

The Impact of Climate Forcing Biases and the Nitrogen Cycle on Land Carbon Balance Projections

“…The AMBER code, analysis scripts, analysis outputs, and the observation-based reference data used for model evaluation in this study can be downloaded from https://doi.org/10.5281/zenodo.7799563 (Seiler, 2023). The CLASSIC code is available at https://doi.org/10.5281/zenodo.3522407 (Melton, Arora, et al, 2019).…”