Observed increase in local cooling effect of deforestation at higher latitudes

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Deforestation in mid- to high latitudes is hypothesized to have the potential to cool the Earth’s surface by altering biophysical processes1, 2, 3. In climate models of continental-scale land clearing, the cooling is triggered by increases in surface albedo and is reinforced by a land albedo–sea ice feedback4, 5. This feedback is crucial in the model predictions; without it other biophysical processes may overwhelm the albedo effect to generate warming instead5. Ongoing land-use activities, such as land management for climate mitigation, are occurring at local scales (hectares) presumably too small to generate the feedback, and it is not known whether the intrinsic biophysical mechanism on its own can change the surface temperature in a consistent manner6, 7. Nor has the effect of deforestation on climate been demonstrated over large areas from direct observations. Here we show that surface air temperature is lower in open land than in nearby forested land. The effect is 0.85±0.44K (mean±one standard deviation) northwards of 45°N and 0.21±0.53K southwards. Below 35°N there is weak evidence that deforestation leads to warming. Results are based on comparisons of temperature at forested eddy covariance towers in the USA and Canada and, as a proxy for small areas of cleared land, nearby surface weather stations. Night-time temperature changes unrelated to changes in surface albedo are an important contributor to the overall cooling effect. The observed latitudinal dependence is consistent with theoretical expectation of changes in energy loss from convection and radiation across latitudes in both the daytime and night-time phase of the diurnal cycle, the latter of which remains uncertain in climate models8.

At a glance


  1. Annual mean difference (open land minus forest) in surface air temperature.
    Figure 1: Annual mean difference (open land minus forest) in surface air temperature.

    a, Correlation with latitude. b, Correlation with surface net radiation. The inset to a has the same axes as the main panel but also shows tropical FLUXNET site data. Parameter bounds in the linear regression are for the 95% confidence interval. Circles indicate weather station/forest site pairs and crosses indicate FLUXNET site clusters.

  2. Seasonal and diurnal patterns of surface air temperature.
    Figure 2: Seasonal and diurnal patterns of surface air temperature.

    a and b show the mean temperature difference ±1s.d. for the site pairs north and south of 45°N. c, Mean daily maximum and minimum temperatures for the forests (solid lines) and the surface stations (dotted lines) for 28–45°N (blue lines) and 45–56°N (red lines).

  3. Partition of the biophysical effect at six FLUXNET site clusters in four different climate zones.
    Figure 3: Partition of the biophysical effect at six FLUXNET site clusters in four different climate zones.

    a, Boreal: harvested site versus jack pine forests25. b, Boreal: burnt site versus black spruce forests17. c, Temperate: grassland versus pine and oak/hickory forests26. d, Semi-arid: open shrub land versus pinyon juniper27. e, Tropical: pasture versus rainforest14. f, Tropical: farmland versus rainforest28, 29. Temperatures are 24-h means. Error bars are given as 1s.d. for the clusters with multiple site-year observations. No surface temperature measurements are available for b and d. For comparison, observed changes in surface air temperature (ΔT) are also shown.

  4. Comparison of the DTR for the forests, the surface stations and the NARR model result.
    Figure 4: Comparison of the DTR for the forests, the surface stations and the NARR model result.


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Author information


  1. School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut 06511, USA

    • Xuhui Lee &
    • Lei Zhao
  2. Department of Earth System Science, University of California, Irvine, California 92697, USA

    • Michael L. Goulden
  3. USDA Forest Service, Northern Research Station, Durham, New Hampshire 03824, USA

    • David Y. Hollinger
  4. Climate Research Division, Environment Canada, Saskatoon, S7N 3H5, Canada

    • Alan Barr
  5. Faculty of Land and Food Systems, University of British Columbia, Vancouver, V6T 1Z4, Canada

    • T. Andrew Black
  6. Department of Civil and Environmental Engineering and Geodetic Science, Ohio State University, Columbus, Ohio 43210, USA

    • Gil Bohrer
  7. School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611, USA

    • Rosvel Bracho
  8. Smithsonian Environmental Research Center, Edgewater, Maryland 21037, USA

    • Bert Drake
  9. Department of Environmental Science, Policy and Management, University of California, Berkeley, California 94720, USA

    • Allen Goldstein
  10. Environmental Science Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA

    • Lianhong Gu
  11. Nicholas School of the Environment and Earth Science, Duke University, Durham, North Carolina 27708, USA

    • Gabriel Katul &
    • Ram Oren
  12. School of Forestry, Northern Arizona University, Flagstaff, Arizona 86011, USA

    • Thomas Kolb
  13. College of Forestry, Oregon State University, Corvallis, Oregon 97331, USA

    • Beverly E. Law
  14. Centre d’Étude de la Forêt, Faculté de Foresterie, de Géographie et de Géomatique, Université Laval, Québec City, Québec, G1V 0A6, Canada

    • Hank Margolis
  15. NOAA/ARL/ATDD, Oak Ridge, Tennessee 37830, USA

    • Tilden Meyers
  16. Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado 80309, USA

    • Russell Monson
  17. School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA

    • William Munger &
    • Steven Wofsy
  18. Department of Land, Air and Water Resources, University of California, Davis, California 95616, USA

    • Kyaw Tha Paw U
  19. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA

    • Andrew D. Richardson
  20. Institute of Meteorology and Climate Research, Karlsruhe Institute for Technology, 82467 Garmisch-Partenkirchen, Germany

    • Hans Peter Schmid
  21. Processes Research Section, Environment Canada, Toronto, Ontario, M3H 5T4, Canada

    • Ralf Staebler


X.L. developed the energy balance model, carried out the analysis and wrote the manuscript, M.L.G. and D.Y.H. contributed ideas to data analysis, M.L.G., D.Y.H., T.A.B., G.B., L.G., G.K., T.K., B.E.L., H.M., T.M., W.M., R.O., A.D.R., R.S. and S.W. contributed ideas to manuscript development, M.L.G., D.Y.H., A.B., T.A.B., G.B., R.B., B.D., A.G., L.G., G.K., T.K., B.E.L., X.L., H.M., T.M., R.M., W.M., R.O., K.T.P.U, A.D.R., H.P.S., R.S. and S.W. contributed data, and L.Z. performed the NARR data analysis.

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The authors declare no competing financial interests.

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Supplementary information

PDF files

  1. Supplementary Information (445K)

    The file contains Supplementary Text and Data, Supplementary References, Supplementary Tables 1-3 and Supplementary Figures 1-4 with legends.

Excel files

  1. Supplementary Data 1 (277K)

    This file comprises: 1 Comparison between weather station and NARR screen height temperature (annual mean, Jan mean and July mean) in NARR cells in closest proximity to the station lat/long; 2 NARR diurnal temperature range for NARR cells in closest proximity to forest eddy covariance sites; 3 Summary of temperature statistics for forests and the closest matching weather stations & summary of net radiation data for the forest sites; 4 Year-by-year summary of temperature statistics for each forest/station pair; 5 Year-by-year NARR temperature data at NARR grid matching the paired weather station; 6 Data used for energy balance / factor separation analysis; 7 Data used for analysis of sensitivity to heat storage, FLUXNET cluster a.

Text files

  1. Supplementary Data 2 (29K)

    The file contains monthly temperature data for the site pairs including lapse rates derived from NARR.


  1. Report this comment #33957

    Ken Caldeira said:

    Expecting cooling from deforestation

    Lee et al. (Nature 479, 384?387; 2011) concluded that deforestation in the mid- to high-latitudes caused less of a cooling than what might be expected based on idealized global climate simulations. Here, we offer two hypotheses that might explain their conclusion.

    First, the validity of the space-for-time substitution depends on the extent of land cover change. In highly idealized deforestation scenarios, deforestation occurs over a wide region and cooling occurs throughout and beyond that region, even in model grid cells that did not undergo land-cover change (Claussen et al., Geophys. Res. Lett. 28, 1011-1014; 2001; Bala et al., Proc. Natl. Acad. Sci. 104, 6550?6555; 2007). Therefore, the temperature difference between this area and areas that did not undergo land-cover change is smaller in magnitude than is the temperature change at the area of land cover change between before and after the transition.

    Second, lands that were converted historically from forest to agricultural uses tended to be less snowy (and more carbon-rich) than typical forested land at the same latitude; farmers? past preferences would thus lead to a smaller cooling influence of historical deforestation in these areas (Pongratz et al., Geophys. Res. Lett. 38, L15701; 2011) than predicted by idealized modeling studies.

    Thus, we would expect real world observations such as those described by Lee et al. (2011) to show less of a cooling effect than predicted by idealized global climate simulations.

    Ken Caldeira and Julia Pongratz
    Carnegie Institution Dept. of Global Ecology, Stanford, California, USA. kcaldeira@carnegie.stanford.edu

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