  The increasing frequency of global climate extremes has significantly
impacted the terrestrial carbon cycle. Extreme weather events such as
heatwaves, droughts, and extreme precipitation pose serious threats to
ecosystem carbon sequestration. This study investigated the impacts of these
extreme events on terrestrial carbon sequestration using data from weather
stations in the Northern Hemisphere, by combining weather station observations
and machine learning-based Gross Primary Production (GPP) and ecosystem
respiration (Reco) estimates. Droughts and heatwaves have the most significant
impact, often correlated as compound events. The effects of extreme
precipitation and cold extremes may have been underestimated in the past.
Whether various extreme events occur in spring or summer led to different
mechanisms. We provided a more precise and station-specific analysis compared
to using coarse-resolution climate reanalysis and model simulations. It also
suggests the need for improved methodologies and the integration of data-driven
and process-based models to better predict and understand the effects of
extreme weather on ecosystem carbon cycling.
