Assessing Terrestrial GPP Responses to Climate Variability in CMIP6 Earth System Models: How Do Ecosystems Respond to Large Climate Fluctuations?

Earth System Models (ESMs) play a central role in understanding and projecting the global carbon cycle by explicitly coupling the atmosphere, land, and ocean components. In particular, a large fraction of the interannual variability (IAV) in atmospheric CO₂ growth rate is attributed to variability in terrestrial carbon uptake. Therefore, an adequate representation of interannual variability in terrestrial ecosystem processes, such as gross primary production (GPP), is essential for robust global carbon cycle assessments.

However, in many coupled ESM simulations participating in CMIP, the phase of internal climate variability does not coincide with that of the real world, making direct time-series comparisons of interannual variability fundamentally difficult. This limitation has not yet been fully resolved even with the use of atmosphere–ocean data assimilation, and it has hindered robust evaluation of ecosystem processes governing IAV. As a result, most previous CMIP model evaluations of terrestrial carbon cycle processes have focused on mean states or climatological characteristics, while the representation of variability and ecosystem responses to extreme events has remained insufficiently assessed.

In this study, we propose a phase-insensitive model evaluation framework that is less sensitive to phase mismatches in internal variability. Within this framework, we focus on the general relationships between environmental variability—such as precipitation and temperature—and GPP responses, with particular emphasis on extreme and/or nonlinear variations that strongly contribute to interannual variability. Using multiple CMIP6 models, we evaluate the distributional properties (variability structure) of GPP and climate drivers, as well as their relationships, at monthly and seasonal timescales, through comparison with observational datasets. In addition, we examine whether biases in simulated GPP primarily arise from biases in environmental drivers or from differences in ecosystem response structures.

To capture ecosystem response diversity that is not evident in global-mean analyses, the evaluation is conducted at a regional scale across multiple climate zones. Regions exhibiting pronounced interannual variability in GPP are selected, and model behaviors are systematically compared in terms of seasonality and environmental responses. Because variability at annual timescales is strongly influenced by ecosystem functioning at seasonal scales, monthly and seasonal analyses provide an effective basis for diagnosing terrestrial ecosystem representations in ESMs.

This presentation highlights both common features and inter-model differences in the representation of GPP variability across CMIP models, and discusses implications for evaluating and improving terrestrial ecosystem processes in Earth system models.