Christian Seiler

and 3 more

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.

Sian Kou-Giesbrecht

and 1 more

Despite its pivotal feedback to carbon cycling, representing the dynamic response of vegetation to nitrogen limitation is a key challenge for simulating the terrestrial carbon sink in land models. Here, we explore a representation of this dynamic response of vegetation to nitrogen limitation with a novel representation of biological nitrogen fixation and nitrogen cycling in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. First, we assess how incorporating this dynamic response of vegetation to nitrogen limitation via biological nitrogen fixation influences carbon sequestration for CO2 and nitrogen fertilisation experiments, comparing simulations against observation-based estimates from meta-analyses. This evaluates whether underlying mechanisms are realistically represented. Second, we assess how incorporating the dynamic response of vegetation to nitrogen limitation via biological nitrogen fixation affects carbon sequestration over the late 20th and early 21st century, examining the effects of global change drivers (CO2, nitrogen deposition, climate, and land use change) acting both individually and concurrently. Including nitrogen cycling reduces the terrestrial carbon sink driven by elevated atmospheric CO2 concentration over the historical period. Representing the dynamic response of vegetation to nitrogen limitation via biological nitrogen fixation increases the present-day terrestrial carbon sink by 0.2 Pg C yr-1 because the upregulation of biological nitrogen fixation driven by stronger nitrogen limitation under elevated atmospheric CO2 concentration alleviates nitrogen limitation. Our results highlight the importance of the dynamic response of vegetation to nitrogen limitation for realistically projecting the future terrestrial carbon sink under global change with land models.

Christian Seiler

and 17 more

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO2 emissions during the 1959-2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models collectively referred to as the TRENDY ensemble and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well TRENDY models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference datasets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.