Gross primary productivity responses to meteorological drivers: insights from observations and multi-model ensembles

Abstract. Climate change has a substantial impact on ecosystem gross primary productivity (GPP), but the specific roles of different meteorological factors across various vegetation types remain unclear. This study investigates GPP responses to variations in temperature, precipitation, and drought, using data from three observational products and 17 dynamic vegetation models. Observed GPP showed a positive response to temperature in boreal regions, with sensitivities ranging from 0.01 to 0.05 g C m² day^-1 K^-1. In contrast, GPP responded negatively to temperature in the tropics, with sensitivities of -0.07±0.15 g C m² day^-1 K^-1 for evergreen broadleaf forests and -0.25±0.11 g C m² day^-1 K^-1 for C₄ grasslands. Precipitation had a relatively low impact on GPP in deciduous and evergreen forests, while non-tree species, such as grasslands and croplands, showed a positive response. GPP sensitivity to drought index (scPDSI) was similar to that of precipitation, except that observed GPP in evergreen forests negatively responded to scPDSI. The models generally reproduced these observed patterns but tended to overestimate the effect of precipitation on GPP. As a result, they predicted higher sensitivity in tropical grasslands to drought stress but lower resilience in trees. Both observations and simulations exhibited negative GPP responses to extreme warming and drought on a global scale, though models tended to overestimate the magnitude of these negative effects. This study distinguished GPP responses to key meteorological factors across vegetation types and numerical models, providing critical insights for improving the prediction of terrestrial carbon sinks and promoting the climatic resilience of ecosystems.