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Climate exerts both short-term and long-term impacts on the ecosystem carbon assimilation. However, the main climatic drivers for the variability of gross primary productivity (GPP) remain unclear across various timescales and vegetation types. Here, we combine the state-of-the-art machine learning algorithms with a well-established explanatory method to explore the impacts of climatic factors on long-term GPP variability at global FLUXNET sites across four timescales and six plant functional types. Results show that diffuse shortwave radiation (SWdif) dominates GPP variability at the sub-daily (half-hourly to three hourly) timescales especially for the tree species, and acts as the secondary contributor after air temperature at the daily or longer timescales. Attribution analyses further showed that the main effects of SWdif are much higher than their interactive effects with other climatic factors in regulating the GPP variability. By identifying the main climatic drivers, this study improves the understanding of the climate-driven GPP variability and provides important implications for the future projection of ecosystem carbon assimilation under global climate change.

Land ecosystems are important sources and sinks of atmospheric components. In turn, air pollutants affect the exchange rates of carbon and water fluxes between ecosystems and atmosphere. However, these biogeochemical processes are usually not well presented in the Earth system models, limiting the explorations of interactions between land ecosystems and air pollutants from the regional to global scales. Here, we develop and validate the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) by upgrading the Yale Interactive terrestrial Biosphere model with process-based water cycles, fire emissions, wetland methane (CH4) emissions, and the trait-based ozone (O3) damages. Within the iMAPLE, soil moisture and temperature are dynamically calculated based on the water and energy balance in soil layers. Fire emissions are dependent on dryness, lightning, population, and fuel load. Wetland CH4 is produced but consumed through oxidation, ebullition, diffusion, and plant-mediated transport. The trait-based scheme unifies O3 sensitivity of different plant functional types (PFTs) with the leaf mass per area. Validations show correlation coefficients (R) of 0.59-0.86 for gross primary productivity (GPP) and 0.57-0.84 for evapotranspiration (ET) across the six PFTs at 201 flux tower sites, and yield an average R of 0.68 for CH4 emissions at 44 sites. Simulated soil moisture and temperature match reanalysis data with the high R above 0.86 and low normalized mean biases (NMB) within 7%, leading to reasonable simulations of global GPP (R=0.92, NMB=1.3%) and ET (R=0.93, NMB=-10.4%) against satellite-based observations for 2001-2013. The model predicts an annual global area burned of 507.1 Mha, close to the observations of 475.4 Mha with a spatial R of 0.66 for 1997-2016. The wetland CH4 emissions are estimated to be 153.45 Tg [CH4] yr-1 during 2000-2014, close to the multi-model mean of 148 Tg [CH4] yr-1. The model also shows reasonable responses of GPP and ET to the changes in diffuse radiation, and yields a mean O3 damage of 2.9% to global GPP. The iMAPLE provides an advanced tool for studying the interactions between land ecosystem and air pollutants. [ABSTRACT FROM AUTHOR]