Beyond a deterministic representation of the temperature dependence of soil CO2 efflux

Abstract Soil CO2 efflux involves complex biological and physical processes that contribute to the production and transport of CO2 from soils to the atmosphere. Temperature is widely used in deterministic empirical models, but these approaches cannot fully capture the complexity of the temperature-soil CO2 efflux relationship due to environmental drivers' confounding and interacting effects beyond temperature. We introduce the Bernstein copula-based cosimulation (BCC) as a data-driven probabilistic approach for modeling the temperature-soil CO2 efflux relationship. The BCC considers the joint probability distribution and temporal dependence of soil CO2 efflux, often ignored in deterministic models. Our results show that this probabilistic approach accurately reproduced the original probability distribution, temporal dependency, and temperature-soil CO2 efflux relationship. We propose that probabilistic approaches hold promise for accurately representing dependency relationships for modeling soil CO2 efflux across various environmental conditions and predicting the effects of climate change.