Tropical deforestation causes large reductions in observed precipitation.


Journal

Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462

Informations de publication

Date de publication:
03 2023
Historique:
received: 14 04 2022
accepted: 15 12 2022
pubmed: 2 3 2023
medline: 11 3 2023
entrez: 1 3 2023
Statut: ppublish

Résumé

Tropical forests play a critical role in the hydrological cycle and can influence local and regional precipitation

Identifiants

pubmed: 36859548
doi: 10.1038/s41586-022-05690-1
pii: 10.1038/s41586-022-05690-1
pmc: PMC9995269
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

270-275

Informations de copyright

© 2023. The Author(s).

Références

Lawrence, D. & Vandecar, K. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Change 5, 27–36 (2015).
doi: 10.1038/nclimate2430
Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L. & Marsham, J. H. The effects of tropical vegetation on rainfall. Annu. Rev. Environ. Resour. 43, 193–218 (2018).
doi: 10.1146/annurev-environ-102017-030136
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).
pubmed: 18556546 doi: 10.1126/science.1155121
Spracklen, D. V., Arnold, S. R. & Taylor, C. M. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285 (2012).
pubmed: 22951966 doi: 10.1038/nature11390
Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Change 8, 539–543 (2018).
doi: 10.1038/s41558-018-0177-y
Baker, J. C. A. & Spracklen, D. V. Divergent representation of precipitation recycling in the Amazon and the Congo in CMIP6 models. Geophys. Res. Lett. 49, e2021GL095136 (2022).
pubmed: 35859721 pmcid: 9285528 doi: 10.1029/2021GL095136
Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).
doi: 10.1038/ngeo2382
Staal, A. et al. Hysteresis of tropical forests in the 21st century. Nat. Commun. 11, 4978 (2020).
pubmed: 33020475 pmcid: 7536390 doi: 10.1038/s41467-020-18728-7
Zemp, D. C. et al. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8, 14681 (2017).
pubmed: 28287104 pmcid: 5355804 doi: 10.1038/ncomms14681
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–854 (2013).
pubmed: 24233722 doi: 10.1126/science.1244693
Chagnon, F. J. F. & Bras, R. L. Contemporary climate change in the Amazon. Geophys. Res. Lett. 32, L13703 (2005).
doi: 10.1029/2005GL022722
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).
doi: 10.1038/nclimate3226
Garcia-Carreras, L. & Parker, D. J. How does local tropical deforestation affect rainfall? Geophys. Res. Lett. 38, L19802 (2011).
doi: 10.1029/2011GL049099
Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M. & Börner, J. Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nat. Commun. 12, 2591 (2021).
pubmed: 33972530 pmcid: 8110785 doi: 10.1038/s41467-021-22840-7
McAlpine, C. A. et al. Forest loss and Borneo’s climate. Environ. Res. Lett. 13, 044009 (2018).
Chapman, S. et al. Compounding impact of deforestation on Borneo’s climate during El Niño events. Environ. Res. Lett. 15, 084006 (2020).
Spracklen, D. V. & Garcia-Carreras, L. The impact of Amazonian deforestation on Amazon basin rainfall. Geophys. Res. Lett. 42, 9546–9552 (2015).
doi: 10.1002/2015GL066063
Jiang, Y. et al. Modeled response of South American climate to three decades of deforestation. J. Clim. 34, 2189–2203 (2021).
doi: 10.1175/JCLI-D-20-0380.1
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
pubmed: 32246091 pmcid: 7125108 doi: 10.1038/s41597-020-0453-3
Fassoni-Andrade, A. C. et al. Amazon hydrology from space: scientific advances and future challenges. Rev. Geophys. 59, e2020RG000728 (2021).
doi: 10.1029/2020RG000728
Haiden, T., Janousek, M., Vitart, F., Ferranti, L. & Prates, F. Evaluation of ECMWF Forecasts, Including the 2019 Upgrade. ECMWF Technical Memorandum No. 853 (ECMWF, 2019).
Esquivel-Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25, 39–56 (2019).
doi: 10.1111/gcb.14413
Brum, M. et al. ENSO effects on the transpiration of eastern Amazon trees. Philos. Trans. R. Soc. B 373, 20180085 (2018).
doi: 10.1098/rstb.2018.0085
Bagley, J. E., Desai, A. R., Harding, K. J., Snyder, P. K. & Foley, J. A. Drought and deforestation: has land cover change influenced recent precipitation extremes in the Amazon? J. Clim. 27, 345–361 (2014).
doi: 10.1175/JCLI-D-12-00369.1
Wunderling, N. et al. Recurrent droughts increase risk of cascading tipping events by outpacing adaptive capacities in the Amazon rainforest. Proc. Natl Acad. Sci. USA 119, e2120777119 (2022).
pubmed: 35917341 pmcid: 9371734 doi: 10.1073/pnas.2120777119
Fu, R. & Li, W. The influence of the land surface on the transition from dry to wet season in Amazonia. Theor. Appl. Climatol. 78, 97–110 (2004).
doi: 10.1007/s00704-004-0046-7
Leite-Filho, A. T., de Sousa Pontes, V. Y. & Costa, M. H. Effects of deforestation on the onset of the rainy season and the duration of dry spells in southern Amazonia. J. Geophys. Res. Atmos. 124, 5268–5281 (2019).
doi: 10.1029/2018JD029537
Negri, A. J., Adler, R. F., Xu, L. & Surratt, J. The Impact of Amazonian deforestation on dry season rainfall. J. Clim. 17, 1306–1319 (2004).
doi: 10.1175/1520-0442(2004)017<1306:TIOADO>2.0.CO;2
Chagnon, F. J. F., Bras, R. L. & Wang, J. Climatic shift in patterns of shallow clouds over the Amazon. Geophys. Res. Lett. 31, L24212 (2004).
doi: 10.1029/2004GL021188
Chambers, J. Q. & Artaxo, P. Biosphere–atmosphere interactions: deforestation size influences rainfall. Nat. Clim. Change 7, 175–176 (2017).
doi: 10.1038/nclimate3238
Baudena, M., Tuinenburg, O. A., Ferdinand, P. A. & Staal, A. Effects of land-use change in the Amazon on precipitation are likely underestimated. Glob. Change Biol. 27, 5580–5587 (2021).
doi: 10.1111/gcb.15810
Duku, C. & Hein, L. The impact of deforestation on rainfall in Africa: a data-driven assessment. Environ. Res. Lett. 16, 064044 (2021).
Akkermans, T., Thiery, W. & Van Lipzig, N. P. M. The regional climate impact of a realistic future deforestation scenario in the Congo basin. J. Clim. 27, 2714–2734 (2014).
doi: 10.1175/JCLI-D-13-00361.1
Staal, A. et al. Feedback between drought and deforestation in the Amazon. Environ. Res. Lett. 15, 044024 (2020).
Xu, X. et al. Deforestation triggering irreversible transition in Amazon hydrological cycle. Environ. Res. Lett. 17, 034037 (2022).
Kooperman, G. J. et al. Forest response to rising CO
doi: 10.1038/s41558-018-0144-7
Chen, Z. et al. Global land monsoon precipitation changes in CMIP6 projections. Geophys. Res. Lett. 47, e2019GL086902 (2020).
Stickler, C. M. et al. Dependence of hydropower energy generation on forests in the Amazon Basin at local and regional scales. Proc. Natl Acad. Sci. USA 110, 9601–9606 (2013).
pubmed: 23671098 pmcid: 3677497 doi: 10.1073/pnas.1215331110
Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).
doi: 10.1038/nclimate2153
Strand, J. et al. Spatially explicit valuation of the Brazilian Amazon forest’s ecosystem services. Nat. Sustain. 1, 657–664 (2018).
doi: 10.1038/s41893-018-0175-0
Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).
doi: 10.1038/s43016-021-00429-z
Li, Y. et al. Deforestation-induced climate change reduces carbon storage in remaining tropical forests. Nat. Commun. 13, 1964 (2022).
pubmed: 35413947 pmcid: 9005651 doi: 10.1038/s41467-022-29601-0
Aragão, L. E. O. C. et al. Interactions between rainfall, deforestation and fires during recent years in the Brazilian Amazonia. Philos. Trans. R. Soc. B 363, 1779–1785 (2008).
doi: 10.1098/rstb.2007.0026
Marengo, J. A. et al. Changes in climate and land use over the Amazon region: current and future variability and trends. Front. Earth Sci. https://doi.org/10.3389/feart.2018.00228 (2018).
Jiang, Y. et al. Widespread increase of boreal summer dry season length over the Congo rainforest. Nat. Clim. Change https://doi.org/10.1038/s41558-019-0512-y (2019).
Van Der Ent, R. J. & Savenije, H. H. G. Length and time scales of atmospheric moisture recycling. Atmos. Chem. Phys. 11, 1853–1863 (2011).
doi: 10.5194/acp-11-1853-2011
Sorí, R., Nieto, R., Vicente-Serrano, S. M., Drumond, A. & Gimeno, L. A Lagrangian perspective of the hydrological cycle in the Congo River basin. Earth Syst. Dyn. 8, 653–675 (2017).
doi: 10.5194/esd-8-653-2017
van der Ent, R. J., Savenije, H. H. G., Schaefli, B. & Steele-Dunne, S. C. Origin and fate of atmospheric moisture over continents. Water Resour. Res. 46, W09525 (2010).
Feng, Y. et al. Doubling of annual forest carbon loss over the tropics during the early twenty-first century. Nat. Sustain. 4, 441–451 (2022).
Tuinenburg, O. A., Bosmans, J. H. C. & Staal, A. The global potential of forest restoration for drought mitigation. Environ. Res. Lett. 17, 034045 (2022).
Met Office. Cartopy: a cartographic python library with a Matplotlib interface 2010–2015. Met Office https://scitools.org.uk/cartopy (2022).
Hoyer, S. & Hamman, J. xarray: N-D labeled arrays and datasets in Python. J. Open Res. Softw. https://doi.org/10.5334/jors.148 (2017).
Zhuang, J. xESMF. Zenodo https://doi.org/10.5281/zenodo.1134365 (2022).
Baker, J. C. A. & Spracklen, D. V. Climate benefits of intact Amazon forests and the biophysical consequences of disturbance. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2019.00047 (2019).
Schaaf, C. & Wang, Z. MCD43A3 MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global - 500m V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/modis/mcd43a3.006 (2015).
Waskom, M. Seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
doi: 10.21105/joss.03021
Chen, M. et al. Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios. Sci. Data 7, 320 (2020).
pubmed: 33009403 pmcid: 7532189 doi: 10.1038/s41597-020-00669-x
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
pubmed: 26646728 pmcid: 4672685 doi: 10.1038/sdata.2015.66
Xie, P. et al. NOAA Climate Data Record (CDR) of CPC Morphing technique (CMORPH) high resolution global precipitation estimates, version 1. NOAA National Centers for Environmental Information https://doi.org/10.25921/w9va-q159 (2019).
Xie, P. et al. A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeorol. 8, 607–626 (2007).
doi: 10.1175/JHM583.1
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
doi: 10.1002/qj.3803
Elke, R., Hänsel, S., Finger, P., Schneider, U. & Ziese, M. GPCC Climatology Version 2022 at 0.25°: monthly land-surface precipitation climatology for every month and the total year from rain-gauges built on GTS-based and historical data. GPCC https://doi.org/10.5676/DWD_GPCC/CLIM_M_V2022_025 (2022).
Huffman, G. J. A., Behrangi, R. F., Adler, D. T., Bolvin, E. J. & Nelkin, G. G. Introduction to the new version 3 GPCP monthly global precipitation analysis. GPCP https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/GPCP/Release_Notes.GPCPV3.2.pdf (2022).
Hou, A. Y. et al. The global precipitation measurement mission. Bull. Am. Meteorol. Soc. 95, 701–722 (2014).
doi: 10.1175/BAMS-D-13-00164.1
Kobayashi, S. et al. The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Japan 93, 5–48 (2015).
doi: 10.2151/jmsj.2015-001
Gelaro, R. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
doi: 10.1175/JCLI-D-16-0758.1
Chen, M., Xie, P. & Janowiak, J. E. Global land precipitation: a 50-yr monthly analysis based on gauge observations. J. Hydrometeorol. 3, 249–266 (2002).
doi: 10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2
Nguyen, P. et al. The CHRS data portal, an easily accessible public repository for PERSIANN global satellite precipitation data. Sci. Data 6, 1180296 (2019).
doi: 10.1038/sdata.2018.296
Ashouri, H. et al. PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Am. Meteorol. Soc. 96, 69–83 (2015).
doi: 10.1175/BAMS-D-13-00068.1
Nguyen, P. et al. Persiann dynamic infrared–rain rate (PDIR-now): a near-real-time, quasi-global satellite precipitation dataset. J. Hydrometeorol. 21, 2893–2906 (2020).
pubmed: 34158807 pmcid: 8216223 doi: 10.1175/JHM-D-20-0177.1
Sadeghi, M. et al. PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies. Sci. Data 8, 157 (2021).
pubmed: 34162874 pmcid: 8222311 doi: 10.1038/s41597-021-00940-9
Huffman, G. J. et al. The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).
doi: 10.1175/JHM560.1
Matsuura, K. & Willmott, C. J. Terrestrial precipitation: 1900-2017 gridded monthly time series. Global Precipitation Archive http://climate.geog.udel.edu/~climate/html_pages/Global2017/README.GlobalTsP2017.html (2018).

Auteurs

C Smith (C)

School of Earth and Environment, University of Leeds, Leeds, UK. ee13c2s@leeds.ac.uk.

J C A Baker (JCA)

School of Earth and Environment, University of Leeds, Leeds, UK.

D V Spracklen (DV)

School of Earth and Environment, University of Leeds, Leeds, UK.

Articles similaires

India Carbon Sequestration Environmental Monitoring Carbon Biomass
1.00
Iran Environmental Monitoring Seasons Ecosystem Forests
Ethiopia Conservation of Natural Resources Environmental Monitoring Soil Soil Erosion
Cities China Government Conservation of Natural Resources Humans

Classifications MeSH