in

Empirical estimate of forestation-induced precipitation changes in Europe

[adace-ad id="91168"]
  • 1.

    Lee, X. et al. Observed increase in local cooling effect of deforestation at higher latitude. Nature 479, 384–387 (2011).

    Article 

    Google Scholar 

  • 2.

    Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. https://doi.org/10.1038/ncomms7603 (2015).

  • 3.

    Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. https://doi.org/10.5194/essd-2018-24 (2018).

  • 4.

    Jia, G. et al. in Special Report on Climate Change and Land (eds Shukla, P. R. et al.) Ch. 2 (IPCC, 2019).

  • 5.

    Lejeune, Q., Seneviratne, S. I. & Davin, E. L. Historical land-cover change impacts on climate: comparative assessment of LUCID and CMIP5 multimodel experiments. J. Clim. 30, 1439–1459 (2017).

    Article 

    Google Scholar 

  • 6.

    Winckler, J., Reick, C. H. & Pongratz, J. Robust identification of local biogeophysical effects of land-cover change in a global climate model. J. Clim. 30, 1159–1176 (2017).

    Article 

    Google Scholar 

  • 7.

    Duveiller, G. et al. Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations. Earth Syst. Sci. Data 10, 1265–1279 (2018).

    Article 

    Google Scholar 

  • 8.

    Meier, R. et al. Evaluating and improving the Community Land Model’s sensitivity to land cover. Biogeosciences 15, 4731–4757 (2018).

    Article 

    Google Scholar 

  • 9.

    Meier, R., Davin, E. L., Swenson, S. C., Lawrence, D. M. & Schwaab, J. Biomass heat storage dampens diurnal temperature variations in forests. Environ. Res. Lett. 14, 084026 (2019).

    Article 

    Google Scholar 

  • 10.

    Spracklen, D., Arnold, S. & Taylor, C. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285 (2012).

    Article 

    Google Scholar 

  • 11.

    Lejeune, Q., Davin, E. L., Guillod, B. P. & Seneviratne, S. I. Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation. Clim. Dyn. 44, 2769–2786 (2015).

    Article 

    Google Scholar 

  • 12.

    Khanna, J., Medvigy, D., Fueglistaler, S. & Walko, R. Regional dry-season climate changes due to three decades of Amazonian deforestation. Nat. Clim. Change 7, 200–204 (2017).

    Article 

    Google Scholar 

  • 13.

    Yosef, G. et al. Large-scale semi-arid afforestation can enhance precipitation and carbon sequestration potential. Sci. Rep. https://doi.org/10.1038/s41598-018-19265-6 (2018).

  • 14.

    Belušić, D., Fuentes-Franco, R., Strandberg, G. & Jukimenko, A. Afforestation reduces cyclone intensity and precipitation extremes over Europe. Environ. Res. Lett. 14, 074009 (2019).

    Article 

    Google Scholar 

  • 15.

    Perugini, L. et al. Biophysical effects on temperature and precipitation due to land cover change. Environ. Res. Lett. 12, 053002 (2017).

    Article 

    Google Scholar 

  • 16.

    Sandel, B. & Svenning, J. Human impacts drive a global topographic signature in tree cover. Nat. Commun. https://doi.org/10.1038/ncomms3474 (2013).

  • 17.

    Fuchs, R., Herold, M., Verburg, P. H. & Clevers, J. G. P. W. A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe. Biogeosciences 10, 1543–1559 (2013).

    Article 

    Google Scholar 

  • 18.

    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article 

    Google Scholar 

  • 19.

    Fuchs, R., Herold, M., Verburg, P. H., Clevers, J. G. & Eberle, J. Gross changes in reconstructions of historic land cover/use for Europe between 1900 and 2010. Glob. Change Biol. 21, 299–313 (2014).

    Article 

    Google Scholar 

  • 20.

    McGrath, M. J. et al. Reconstructing European forest management from 1600 to 2010. Biogeosciences 12, 4291–4316 (2015).

    Article 

    Google Scholar 

  • 21.

    Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).

    Article 

    Google Scholar 

  • 22.

    Navarro, L. M. & Pereira, H. M. Rewilding Abandoned Landscapes in Europe (Springer, 2015).

  • 23.

    Lewis, E. et al. GSDR: a global sub-daily rainfall dataset. J. Clim. 32, 4715–4729 (2019).

    Article 

    Google Scholar 

  • 24.

    Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An overview of the global historical climatology network-daily database. J. Atmos. Ocean. Technol. 29, 897–910 (2012).

    Article 

    Google Scholar 

  • 25.

    Menne, M. J. et al. Global Historical Climatology Network—Daily (GHCN-Daily) Version 3.20 (NOAA, 2012); https://doi.org/10.7289/V5D21VHZ

  • 26.

    Zhang, M. et al. Response of surface air temperature to small-scale land clearing across latitudes. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/9/3/034002 (2014).

  • 27.

    Liu, H., Randerson, J. T., Lindfors, J. & Chapin, F. S. III Changes in the surface energy budget after fire in boreal ecosystems of interior Alaska: an annual perspective. J. Geophys. Res. https://doi.org/10.1029/2004JD005158 (2005).

  • 28.

    Juang, J.-Y., Katul, G., Siqueira, M., Stoy, P. & Novick, K. Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett. https://doi.org/10.1029/2007GL031296 (2007).

  • 29.

    Vanden Broucke, S., Luyssaert, S., Davin, E. L., Janssens, I. & van Lipzig, N. New insights in the capability of climate models to simulate the impact of LUC based on temperature decomposition of paired site observations. J. Geophys. Res. Atmos. 120, 5417–5436 (2015).

    Article 

    Google Scholar 

  • 30.

    Beck, H. E. et al. MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bull. Am. Meteorol. Soc. 100, 473–500 (2019).

    Article 

    Google Scholar 

  • 31.

    Schwaab, J. et al. Increasing the broad-leaved tree fraction in European forests mitigates hot temperature extremes. Sci. Rep. 10, 14153 (2020).

    Article 

    Google Scholar 

  • 32.

    Cohn, A. S. et al. Forest loss in Brazil increases maximum temperatures within 50 km. Environ. Res. Lett. 14, 084047 (2019).

    Article 

    Google Scholar 

  • 33.

    Houze, R. A. Jr Orographic effects on precipitating clouds. Rev. Geophys. https://doi.org/10.1029/2011RG000365 (2012).

  • 34.

    C3S ERA5-Land Reanalysis (Copernicus Climate Change Service, 2019).

  • 35.

    Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).

    Article 

    Google Scholar 

  • 36.

    Sprenger, M. & Wernli, H. The LAGRANTO Lagrangian analysis tool—version 2.0. Geosci. Model Dev. 8, 2569–2586 (2015).

    Article 

    Google Scholar 

  • 37.

    Kosztra, B., Büttner, G., Hazeu, G. & Arnold, S. Updated CLC Illustrated Nomenclature Guidelines (European Environment Agency, 2019).

  • 38.

    Duveiller, G., Fasbender, D. & Meroni, M. Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci. Rep. 6, 19401 (2016).

    Article 

    Google Scholar 

  • 39.

    Griscom, B. W. et al. Global Reforestation Potential Map (Zenodo, 2017); https://doi.org/10.5281/zenodo.883444

  • 40.

    Sheffield, J. & Wood, E. F. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim. Dyn. 31, 79–105 (2008).

    Article 

    Google Scholar 

  • 41.

    Kotlarski, S. et al. Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci. Model Dev. 7, 1297–1333 (2014).

    Article 

    Google Scholar 

  • 42.

    Prein, A. F. et al. A review on regional convection-permitting climate modeling: demonstrations, prospects, and challenges. Rev. Geophys. 53, 323–361 (2015).

    Article 

    Google Scholar 

  • 43.

    Liu, J. & Niyogi, D. Meta-analysis of urbanization impact on rainfall modification. Sci. Rep. https://doi.org/10.1038/s41598-019-42494-2 (2019).

  • 44.

    Van der Ent, R. J. & Savenije, H. H. G. Length and time scales of atmospheric moisture recycling. Atmos. Chem. Phys. 11, 1853–1863 (2011).

    Article 

    Google Scholar 

  • 45.

    Rüdisühli, S., Sprenger, M., Leutwyler, D., Schär, C. & Wernli, H. Attribution of precipitation to cyclones and fronts over Europe in a kilometer-scale regional climate simulation. Weather Clim. Dyn. 1, 675–699 (2020).

    Article 

    Google Scholar 

  • 46.

    Schultz, N. M., Lawrence, P. J. & Lee, X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. Biogeosci. 122, 903–917 (2017).

    Article 

    Google Scholar 

  • 47.

    Pollock, M. D. et al. Quantifying and mitigating wind-induced undercatch in rainfall measurements. Water Resour. Res. 54, 3863–3875 (2018).

    Article 

    Google Scholar 

  • 48.

    Trabucco, A., Zomer, R. J., Bossio, D. A., Straaten], O. V. & Verchot, L. V. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agr. Ecosyst. Environ. 126, 81–97 (2008).

    Article 

    Google Scholar 

  • 49.

    Padrón, R. S., Gudmundsson, L., Greve, P. & Seneviratne, S. I. Large-scale controls of the surface water balance over land: insights from a systematic review and meta-analysis. Water Resour. Res. 53, 9659–9678 (2017).

    Article 

    Google Scholar 

  • 50.

    Beck, H. E. et al. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci. 21, 589–615 (2017).

    Article 

    Google Scholar 

  • 51.

    Beck, H. E. et al. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol. Earth Syst. Sci. 21, 6201–6217 (2017).

    Article 

    Google Scholar 

  • 52.

    Beck, H. E. et al. Daily evaluation of 26 precipitation datasets using stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci. 23, 207–224 (2019).

    Article 

    Google Scholar 

  • 53.

    Lu, N. Scale effects of topographic ruggedness on precipitation over Qinghai-Tibet Plateau. Atmos. Sci. Lett. 20, e904 (2019).

    Article 

    Google Scholar 

  • 54.

    EU-DEM Statistical Validation (EEA, 2014).

  • 55.

    Siebert, S., Henrich, V., Frenken, K. & Burke, J. Global Map of Irrigation Areas Version 5 (Rheinische Friedrich-Wilhelms-University and FAO, 2013).

  • 56.

    DeAngelis, A. et al. Evidence of enhanced precipitation due to irrigation over the Great Plains of the United States. J. Geophys. Res. Atmos. https://doi.org/10.1029/2010JD013892 (2010).

  • 57.

    Thiery, W. et al. Present-day irrigation mitigates heat extremes. J. Geophys. Res. 122, 1403–1422 (2017).

    Article 

    Google Scholar 

  • 58.

    Wernli, B. H. & Davies, H. C. A Lagrangian-based analysis of extratropical cyclones. I: the method and some applications. Q. J. R. Meteorol. Soc. 123, 467–489 (1997).

    Article 

    Google Scholar 

  • 59.

    Smith, A., Lott, N. & Vose, R. The integrated surface database: recent developments and partnerships. Bull. Am. Meteorol. Soc. 92, 704–708 (2011).

    Article 

    Google Scholar 

  • 60.

    Blenkinsop, S., Lewis, E., Chan, S. C. & Fowler, H. J. Quality-control of an hourly rainfall dataset and climatology of extremes for the UK. Int. J. Climatol. 37, 722–740 (2017).

    Article 

    Google Scholar 

  • 61.

    Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn (CRC Press, 2017).

  • 62.

    Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. 73, 3–36 (2011).

    Article 

    Google Scholar 

  • 63.

    Wood, S. N., Li, Z., Shaddick, G. & Augustin, N. H. Generalized additive models for gigadata: modeling the UK black smoke network daily data. J. Am. Stat. Assoc. 112, 1199–1210 (2017).

    Article 

    Google Scholar 

  • 64.

    Li, Z. & Wood, S. N. Faster model matrix crossproducts for large generalized linear models with discretized covariates. Stat. Comput. 30, 19–25 (2020).

    Article 

    Google Scholar 

  • 65.

    Dormann, C. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).

    Article 

    Google Scholar 

  • 66.

    CH2018. 2018 Climate Scenarios for Switzerland (National Centre for Climate Services, 2018).

  • 67.

    Prein, A. F. et al. Precipitation in the EURO-CORDEX 0.11° and 0.44° simulations: high resolution, high benefits? Clim. Dyn. 46, 383–412 (2016).

    Article 

    Google Scholar 

  • 68.

    Jacob, D. et al. EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg. Environ. Change 14, 563–578 (2014).

    Article 

    Google Scholar 

  • 69.

    Digital Chart of the World (DMA and USGS, 1992).


  • Source: Resources - nature.com

    Microbiome diversity and host immune functions influence survivorship of sponge holobionts under future ocean conditions

    Artificial nighttime lighting impacts visual ecology links between flowers, pollinators and predators