Nutrient Limitations Lead to a Reduced Magnitude of Disequilibrium in the Global Terrestrial Carbon Cycle

Abstract

The terrestrial carbon (C) cycle is shifting to a state of dynamic disequilibrium under a rapid global climate change. However, the magnitude of such disequilibrium is inherently hard to measure directly. Abundant studies have revealed that the availability of nutrients, particularly nitrogen (N) and phosphorus (P), constrains ecosystem productivity and carbon stocks across the globe. However, whether and how nutrient limitation affects the disequilibrium magnitude of the terrestrial C cycle (Xp) has never been evaluated. Here, we developed an approach by combining a process‐based numerical model and an analytical framework to evaluate the role of nutrient limitation on Xp. We found that nutrient limitation did have significant impacts on the Xp. Over the modeled period of 1901–2013, absolute change in Xp was 497.6 PgC under the C‐only run, while it decreased to 155.6 and 124.3 PgC under N and NP limitations, respectively. To understand the underlying reasons, we further disaggregated the changes of Xp into changes in steady‐state C storage and transit C storage with the former being decomposed into a productivity‐driven change, an ecosystem‐C‐residence‐time‐driven (τE‐driven) change, and a change induced by productivity‐τE interactions. We found that nutrient constrained the increase in Xp primarily by dampening the productivity‐driven changes in the steady‐state C storage. Reductions in the productivity‐driven term under N and NP limitations accounted for 94.7% and 94.9%, respectively, of the reductions in the steady‐state C storage. These results indicate that nutrient limitations have profound impacts on future climate‐biosphere feedback by reducing the disequilibrium magnitude of the terrestrial C cycle.

Uncertainty and Emergent Constraints on Enhanced Ecosystem Carbon Stock by Land Greening

“…Second, one critical limiting factor for land carbon uptake is soil moisture (Green et al, 2019;Humphrey et al, 2018Humphrey et al, , 2021, but many ESMs have high degrees of uncertainty in plant hydraulic processes, such as the xylem embolism (Franks et al, 2017;Konings et al, 2017) and drought legacy effect (Anderegg et al, 2015) or lack representation of such processes. Third, most ESMs used in this study did not represent the shifts in plant community structure and composition (Medlyn et al, 2015), which could weaken the positive response of ecosystem productivity and enhance the ecosystem turnover rates to increasing atmospheric CO 2 (Kolby Smith et al, 2016), nitrogen deposition (Bonan & Doney, 2018;Drewniak & Gonzalez-Meler, 2017;Wei, Xia, Wang, et al, 2022), and climate change (Richardson et al, 2018). Lastly, some global models used here did not include the processes of disturbance (e.g., deforestation and fire, Table S3 in Supporting Information S1, Figure S17 in Supporting Information S1), which reduce ECS and increase land C losses (Achat et al, 2015;Walker et al, 2019).…”

Models in Population, Community and Ecosystem Dynamics

“…Several global models not only incorporated the N cycles Zaehle et al, 2014;Meyerholt and Zaehle, 2015) but also implemented the P processes, such as ORCHIDEE-CNP (Goll et al, 2017;Sun et al, 2021), QUINCY v1.0 (Thum et al, 2019), GOLUM-CNP (Wang et al, 2018), JSM (Yu et al, 2020), JULES-CNP (Nakhavali et al, 2022), andE3SM (Zhu et al, 2019). The coupled C-N-P cycle reduced the magnitude of disequilibrium in the terrestrial C cycle (Wei et al, 2022a), but how to accurately represent the nutrient cycles and the effects of N and P limitation in biogeochemical models are still challenges (Hungate et al, 2003;Thomas et al, 2015;Wieder et al, 2015;Sun et al, 2017).…”

Understanding Surface Air Temperature Cold Bias Over China in CMIP6 Models

“…Although cold biases are still commonly present among the state‐of‐the‐art sixth phase of the Coupled Model Intercomparison Project (CMIP6) models, they have been reduced compared to the CMIP3 and CMIP5 models (Hu et al., 2022; Jiang et al., 2020; Y. Zhu & Yang, 2020). This may be related to the expanded Earth system processes (e.g., nutrient limitations in the global terrestrial carbon cycle), and the improvements in model resolutions and cloud microphysics representation (Eyring et al., 2019; Stouffer et al., 2017; Wei et al., 2022).…”