Robust observations of land-to-atmosphere feedbacks using the information flows of FLUXNET

Feedbacks between atmospheric processes like precipitation and land surface fluxes including evapotranspiration are difficult to observe, but critical for understanding the role of the land surface in the Earth System. To quantify global surface-atmosphere feedbacks we use results of a process network (PN) applied to 251 eddy covariance sites from the LaThuile database to train a neural network across the global terrestrial surface. There is a strong land–atmosphere coupling between latent (LE) and sensible heat flux (H) and precipitation (P) during summer months in temperate regions, and between H and P during winter, whereas tropical rainforests show little coupling seasonality. Savanna, shrubland, and other semi-arid ecosystems exhibit strong responses in their coupling behavior based on water availability. Feedback couplings from surface fluxes to P peaks at aridity (P/potential evapotranspiration ETp) values near unity, whereas coupling with respect to clouds, inferred from reduced global radiation, increases as P/ETp approaches zero. Spatial patterns in feedback coupling strength are related to climatic zone and biome type. Information flow statistics highlight hotspots of (1) persistent land–atmosphere coupling in sub-Saharan Africa, (2) boreal summer coupling in the central and southwestern US, Brazil, and the Congo basin and (3) in the southern Andes, South Africa and Australia during austral summer. Our data-driven approach to quantifying land atmosphere coupling strength that leverages the global FLUXNET database and information flow statistics provides a basis for verification of feedback interactions in general circulation models and for predicting locations where land cover change will feedback to climate or weather. “Big data” methods reveal robust hotspots of land–atmosphere coupling. Sparse observations and inadequate analytical tools have hindered our understanding of land–atmosphere feedbacks, including the exchange of energy, water, and CO2. Emergent methods such as machine learning, however, offer new opportunities, as Tobias Gerken from Montana State University, USA, and colleagues, demonstrate. A “big data” approach is adopted to characterise the spatial and temporal variability of land–atmosphere coupling without a priori assumptions: information flows are computed from 251 FLUXNET sites which are subsequently used to train a neural network. Distinct regional differences in the magnitude of land–atmosphere feedbacks are found, related to climatic zone and biome type; coupling in semi-arid ecosystems, for example, are strongly related to seasonal water availability. Complementing model studies with such empirical approaches may assist in quantifying climate change impacts on ecosystem services.