TY - JOUR
T1 - Robust observations of land-to-atmosphere feedbacks using the information flows of FLUXNET
AU - Gerken, Tobias
AU - Ruddell, Benjamin L.
AU - Yu, Rong
AU - Stoy, Paul C.
AU - Drewry, Darren T.
N1 - Funding Information:
National Science Foundation awards OIA-1632810, DEB-1552976, and EF-1702029 and the USDA Hatch project 228396. DTD acknowledges the support of the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The authors thank Paul Dirmeyer and two anonymous reviewers for their valuable comments. This work is supported by the National Science Foundation under Grant Nos. EF-1241960, and BCS-1026865, Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER). The opinions are those of the authors, and not necessarily the funding agencies.
Funding Information:
This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval, Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California-Berkeley and the University of Virginia. The GLDAS-2 data are produced by NASA GSFC Hydrological Sciences Laboratory (HSL). The MODIS MCD12C1 data product was retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (lpdaac. usgs.gov/data_access/data_pool). PCS acknowledges contributions from the U. S.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41612-019-0094-4
DO - 10.1038/s41612-019-0094-4
M3 - Article
AN - SCOPUS:85075428354
SN - 2397-3722
VL - 2
JO - npj Climate and Atmospheric Science
JF - npj Climate and Atmospheric Science
IS - 1
M1 - 37
ER -