The impact of interannual climate variations on terrestrial ecosystems is an emergent response, based on the covariation of climate drivers and the independent responses of several ecosystem processes, including productivity, respiration, and disturbance. While these feedbacks are often diagnosed by considering the coincident patterns of climate and carbon cycle variability, ecosystems may also have lagged responses to climate forcing. Such memory in ecosystems may provide a mechanism to damp the impacts of interannual climate stress. Alternatively, ecosystem memory may permit larger cumulative impacts from slow variations in climate.

Here, we use a combination of Earth system model output and satellite observations to understand the emergent lagged responses of terrestrial ecosystems to climate drivers. For example, in the Community Earth System Model (CESM), we see that Amazon rainforest leaf area index (LAI) is highly autocorrelated at seasonal timescales, giving rise to a persistent ecosystem response to temperature at the onset of the wet season. In contrast, satellite observations of LAI from MODIS and Solar Induced Fluorescence (SIF) from GOME-2 do not show this strong autocorrelation, which suggests the annually integrated temperature response in CESM may be too high. We use OCO-2 observations during and after the strong 2015-2016 El Nino to probe for additional examples of ecosystem memory driving interannual variations Our results suggest that careful analysis of autocorrelation within datasets and model output may facilitate development of more predictive models for future climate simulations.