Biogeophysical Effects of Land-Use and Land-Cover Change Not Detectable in Warmest Month

Author:

Grant Luke1ORCID,Gudmundsson Lukas2,Davin Edouard L.345,Lawrence David M.6,Vuichard Nicolas7,Robertson Eddy8,Séférian Roland9,Ribes Aurélien9,Hirsch Annette L.10,Thiery Wim1

Affiliation:

1. a Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, Belgium

2. b Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

3. c Wyss Academy for Nature at the University of Bern, Bern, Switzerland

4. d Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland

5. e Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

6. f National Center for Atmospheric Research, Boulder, Colorado

7. g Laboratoire des Sciences du Climat et de l’Environnement, Gif sur Yvette Cedex, France

8. h Met Office Hadley Centre, Exeter, United Kingdom

9. i CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

10. j Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, New South Wales, Australia

Abstract

Abstract Land-use and land-cover changes (hereafter simply “land use”) alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model-based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (tLU) using regularized optimal fingerprinting for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside historical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved continental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimodel mean. Furthermore, using a principal component analysis, we find that tLU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use.

Funder

U.S. Department of Energy, Office of Biological and Environmental Research

NSF

Horizon 2020 Framework Programme

BELSPO

European Union

Joint UK BEIS/Defra Met Office Hadley Centre Programme

Publisher

American Meteorological Society

Subject

Atmospheric Science

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