Arctic Monitoring and Assessment Programme. Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017 (Arctic Monitoring and Assessment Programme (AMAP), 2017).

Chapin, F. S. 3rd et al. Role of land-surface changes in arctic summer warming. Science 310, 657–660 (2005).

ADS  CAS  PubMed  Google Scholar 

Tape, K. D., Christie, K., Carroll, G. & O’donnell, J. A. Novel wildlife in the Arctic: the influence of changing riparian ecosystems and shrub habitat expansion on snowshoe hares. Glob. Change Biol. 22, 208–219 (2016).

ADS  Google Scholar 

Downing, A. & Cuerrier, A. A synthesis of the impacts of climate change on the First Nations and Inuit of Canada. Indian J. Tradit. Knowl. 10, 57–70 (2011).

Google Scholar 

National Academies of Sciences. Understanding Northern Latitude Vegetation Greening and Browning: Proceedings of a Workshop (The National Academies Press, 2019).

Bjorkman, A. D. et al. Plant functional trait change across a warming tundra biome. Nature 562, 57–62 (2018).

ADS  CAS  PubMed  Google Scholar 

Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nat. Clim. Change 2, 453–457 (2012).

ADS  Google Scholar 

Gauthier, G. et al. Long-term monitoring at multiple trophic levels suggests heterogeneity in responses to climate change in the Canadian Arctic tundra. Philos. Trans. R. Soc. Ser. B 368, 20120482 (2013).

Google Scholar 

Myers-Smith, I. H. et al. Eighteen years of ecological monitoring reveals multiple lines of evidence for tundra vegetation change. Ecol. Monogr. 89, e01351 (2019).

Google Scholar 

Tape, K. D., Hallinger, M., Welker, J. M. & Ruess, R. W. Landscape heterogeneity of shrub expansion in Arctic Alaska. Ecosystems 15, 711–724 (2012).

CAS  Google Scholar 

Pattison, R. R., Jorgenson, J. C., Raynolds, M. K. & Welker, J. M. Trends in NDVI and Tundra Community Composition in the Arctic of NE Alaska Between 1984 and 2009. Ecosystems 18, 707–719 (2015).

Google Scholar 

Gamm, C. M. et al. Declining growth of deciduous shrubs in the warming climate of continental western Greenland. J. Ecol. 106, 640–654 (2018).

CAS  Google Scholar 

Forchhammer M. Sea-ice induced growth decline in Arctic shrubs. Biol. Lett. 13, 20170122 (2017).

Street, L., Shaver, G., Williams, M. & Van Wijk, M. What is the relationship between changes in canopy leaf area and changes in photosynthetic CO2 flux in arctic ecosystems? J. Ecol. 95, 139–150 (2007).

Google Scholar 

Raynolds, M. K., Walker, D. A., Epstein, H. E., Pinzon, J. E. & Tucker, C. J. A new estimate of tundra-biome phytomass from trans-Arctic field data and AVHRR NDVI. Remote Sens. Lett. 3, 403–411 (2012).

Google Scholar 

Berner, L. T., Jantz, P., Tape, K. D. & Goetz, S. J. Tundra plant aboveground biomass and shrub dominance mapped across the North Slope of Alaska. Environ. Res. Lett. 13, 035002 (2018).

ADS  Google Scholar 

Bhatt, U. S. et al. Changing seasonality of panarctic tundra vegetation in relationship to climatic variables. Environ. Res. Lett. 12, 1–18 (2017).

Google Scholar 

Guay, K. C. et al. Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment. Glob. Change Biol. 20, 3147–3158 (2014).

ADS  Google Scholar 

Pinzon, J. & Tucker, C. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).

ADS  Google Scholar 

Ju, J. & Masek, J. G. The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data. Remote Sens. Environ. 176, 1–16 (2016).

ADS  Google Scholar 

Karlsen, S. R., Anderson, H. B., Van der Wal, R. & Hansen, B. B. A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity. Environ. Res. Lett. 13, 025011 (2018).

ADS  Google Scholar 

McManus, kM. et al. Satellite-based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010. Glob. Change Biol. 18, 2313–2323 (2012).

ADS  Google Scholar 

Frost, G. V., Epstein, H. & Walker, D. Regional and landscape-scale variability of Landsat-observed vegetation dynamics in northwest Siberian tundra. Environ. Res. Lett. 9, 025004 (2014).

ADS  Google Scholar 

Raynolds, M. K. & Walker, D. A. Increased wetness confounds Landsat-derived NDVI trends in the central Alaska North Slope region, 1985–2011. Environ. Res. Lett. 11, 085004 (2016).

ADS  Google Scholar 

Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

ADS  Google Scholar 

Zhu, Z., Wang, S. & Woodcock, C. E. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sens. Environ. 159, 269–277 (2015).

ADS  Google Scholar 

Pastick, N. J. et al. Spatiotemporal remote sensing of ecosystem change and causation across Alaska. Glob. Change Biol. 25, 1171–1189 (2019).

ADS  Google Scholar 

Walker, D. et al. Phytomass, LAI, and NDVI in northern Alaska: relationships to summer warmth, soil pH, plant functional types, and extrapolation to the circumpolar Arctic. J. Geophys. Res. 108, 8169 (2003).

Google Scholar 

Lucht, W. et al. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science 296, 1687–1689 (2002).

ADS  CAS  PubMed  Google Scholar 

Fraser, R. H., Lantz, T. C., Olthof, I., Kokelj, S. V. & Sims, R. A. Warming-induced shrub expansion and lichen decline in the Western Canadian. Arct. Ecosyst. 17, 1151–1168 (2014).

Google Scholar 

Bonney, M. T., Danby, R. K. & Treitz, P. M. Landscape variability of vegetation change across the forest to tundra transition of central Canada. Remote Sens. Environ. 217, 18–29 (2018).

ADS  Google Scholar 

Cuerrier, A., Brunet, N. D., Gérin-Lajoie, J., Downing, A. & Lévesque, E. The study of Inuit knowledge of climate change in Nunavik, Quebec: a mixed methods approach. Hum. Ecol. 43, 379–394 (2015).

Google Scholar 

Forbes, B. C. & Stammler, F. Arctic climate change discourse: the contrasting politics of research agendas in the West and Russia. Polar Res. 28, 28–42 (2009).

Google Scholar 

Forbes, B. C., Fauria, M. M. & Zetterberg, P. Russian Arctic warming and ‘greening’ are closely tracked by tundra shrub willows. Glob. Change Biol. 16, 1542–1554 (2010).

ADS  Google Scholar 

Tape, K., Sturm, M. & Racine, C. The evidence for shrub expansion in Northern Alaska and the Pan-Arctic. Glob. Change Biol. 12, 686–702 (2006).

ADS  Google Scholar 

Ropars, P. & Boudreau, S. Shrub expansion at the forest–tundra ecotone: spatial heterogeneity linked to local topography. Environ. Res. Lett. 7, 015501 (2012).

ADS  Google Scholar 

Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).

ADS  Google Scholar 

Park, T. et al. Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Environ. Res. Lett. 11, 084001 (2016).

ADS  Google Scholar 

Riihimäki, H., Heiskanen, J. & Luoto, M. The effect of topography on arctic-alpine aboveground biomass and NDVI patterns. Int. J. Appl. Earth Obs. Geoinf. 56, 44–53 (2017).

ADS  Google Scholar 

Fraser, R. H., Olthof, I., Lantz, T. C. & Schmitt, C. UAV photogrammetry for mapping vegetation in the low-Arctic. Arct. Sci. 2, 79–102 (2016).

Google Scholar 

Berner, L. T., Beck, P. S. A., Bunn, A. G. & Goetz, S. J. Plant response to climate change along the forest-tundra ecotone in northeastern Siberia. Glob. Change Biol. 19, 3449–3462 (2013).

Google Scholar 

Myers-Smith, I. H. et al. Climate sensitivity of shrub growth across the tundra biome. Nat. Clim. Change 5, 887–891 (2015).

ADS  Google Scholar 

Bjorkman, A. D., Vellend, M., Frei, E. R. & Henry, G. H. Climate adaptation is not enough: warming does not facilitate success of southern tundra plant populations in the high Arctic. Glob. Change Biol. 23, 1540–1551 (2017).

ADS  Google Scholar 

Post, E. & Pedersen, C. Opposing plant community responses to warming with and without herbivores. Proc. Natl Acad. Sci. USA 105, 12353–12358 (2008).

ADS  CAS  PubMed  Google Scholar 

Yu, Q., Epstein, H., Engstrom, R. & Walker, D. Circumpolar arctic tundra biomass and productivity dynamics in response to projected climate change and herbivory. Glob. Change Biol. 23, 3895–3907 (2017).

ADS  Google Scholar 

Liljedahl, A. K. et al. Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nat. Geosci. 9, 312–318 (2016).

ADS  CAS  Google Scholar 

Perreault, N., Levesque, E., Fortier, D. & Lamarque, L. J. Thermo-erosion gullies boost the transition from wet to mesic tundra vegetation. Biogeosciences 13, 1237–1253 (2016).

ADS  Google Scholar 

Grant, R. F., Mekonnen, Z. A., Riley, W. J., Arora, B. & Torn, M. S. Mathematical modelling of Arctic Polygonal Tundra with Ecosys: 2. Microtopography determines how CO2 and CH4 exchange responds to changes in temperature and precipitation. J. Geophys. Res. 122, 3174–3187 (2017).

CAS  Google Scholar 

Phoenix, G. K. & Bjerke, J. W. Arctic browning: extreme events and trends reversing arctic greening. Glob. Change Biol. 22, 2960–2962 (2016).

ADS  Google Scholar 

Treharne, R., Bjerke, J. W., Tømmervik, H., Stendardi, L. & Phoenix, G. K. Arctic browning: Impacts of extreme climatic events on heathland ecosystem CO2 fluxes. Glob. Change Biol. 25, 489–503 (2018).

ADS  Google Scholar 

Forbes, B. C. et al. High resilience in the Yamal-Nenets social–ecological system, west Siberian Arctic, Russia. Proc. Natl Acad. Sci. USA 106, 22041–22048 (2009).

ADS  CAS  PubMed  Google Scholar 

Mekonnen, Z. A., Riley, W. J. & Grant, R. F. Accelerated nutrient cycling and increased light competition will lead to 21st century shrub expansion in North American Arctic tundra. J. Geophys. Res. 123, 1683–1701 (2018).

CAS  Google Scholar 

Rocha, A. V. et al. The footprint of Alaskan tundra fires during the past half-century: implications for surface properties and radiative forcing. Environ. Res. Lett. 7, 044039 (2012).

ADS  Google Scholar 

Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L. & Justice, C. O. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ. 217, 72–85 (2018).

ADS  PubMed  PubMed Central  Google Scholar 

Hu, F. S. et al. Arctic tundra fires: natural variability and responses to climate change. Front. Ecol. Environ. 13, 369–377 (2015).

Google Scholar 

Mack, M. C. et al. Carbon loss from an unprecedented Arctic tundra wildfire. Nature 475, 489–492 (2011).

ADS  CAS  PubMed  Google Scholar 

Jones, B. M. et al. Identification of unrecognized tundra fire events on the north slope of Alaska. J. Geophys. Res. 118, 1334–1344 (2013).

Google Scholar 

Loranty, M. M. et al. Siberian tundra ecosystem vegetation and carbon stocks four decades after wildfire. J. Geophys. Res. 119, 2144–2154 (2014).

CAS  Google Scholar 

Natali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Change 9, 852–857 (2019).

ADS  CAS  Google Scholar 

Schuur, E. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).

ADS  CAS  PubMed  Google Scholar 

Pearson, R. G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Change 3, 673–677 (2013).

ADS  Google Scholar 

Loranty, M. M., Goetz, S. J. & Beck, P. S. A. Tundra vegetation effects on pan-Arctic albedo. Environ. Res. Lett. 6, 024014 (2011).

ADS  Google Scholar 

Loranty, M. M. et al. Reviews and syntheses: changing ecosystem influences on soil thermal regimes in northern high-latitude permafrost regions. Biogeosciences 15, 5287–5313 (2018).

ADS  CAS  Google Scholar 

Tape, K. D., Gustine, D. D., Ruess, R. W., Adams, L. G. & Clark, J. A. Range expansion of moose in Arctic Alaska linked to warming and increased shrub habitat. PLoS ONE 11, e0152636 (2016).

PubMed  PubMed Central  Google Scholar 

Tape, K. D., Jones, B. M., Arp, C. D., Nitze, I. & Grosse, G. Tundra be dammed: beaver colonization of the Arctic. Glob. Change Biol. 24, 4478–4488 (2018).

ADS  Google Scholar 

Joly, K., Jandt, R. R. & Klein, D. R. Decrease of lichens in Arctic ecosystems: the role of wildfire, caribou, reindeer, competition and climate in north‐western Alaska. Polar Res. 28, 433–442 (2009).

Google Scholar 

Macias-Fauria, M., Forbes, B. C., Zetterberg, P. & Kumpula, T. Eurasian Arctic greening reveals teleconnections and the potential for structurally novel ecosystems. Nat. Clim. Change 2, 613–618 (2012).

ADS  Google Scholar 

Wesche, S. D. & Chan, H. M. Adapting to the impacts of climate change on food security among Inuit in the Western Canadian Arctic. EcoHealth 7, 361–373 (2010).

PubMed  Google Scholar 

Kuhnlein, H. V. & Chan, H. M. Environment and contaminants in traditional food systems of northern indigenous peoples. Annu. Rev. Nutr. 20, 595–626 (2000).

CAS  PubMed  Google Scholar 

Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).

ADS  Google Scholar 

Virtanen, R. et al. Where do the treeless tundra areas of northern highlands fit in the global biome system: toward an ecologically natural subdivision of the tundra biome. Ecol. Evol. 6, 143–158 (2016).

PubMed  Google Scholar 

Masek, J. G. et al. A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geosci. Remote Sens. Lett. 3, 68–72 (2006).

ADS  Google Scholar 

Vermote, E., Justice, C., Claverie, M. & Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 185, 46–56 (2016).

ADS  PubMed  Google Scholar 

Python Software Foundation. Python Language Software Version 3.7.3. https://www.python.org/ (2020).

Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017).

ADS  Google Scholar 

Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).

ADS  PubMed  Google Scholar 

Sulla-Menashe, D., Friedl, M. A. & Woodcock, C. E. Sources of bias and variability in long-term Landsat time series over Canadian boreal forests. Remote Sens. Environ. 177, 206–219 (2016).

ADS  Google Scholar 

Liaw, A. & Wiener, M. Classification and Regression by randomForest. R News 2, 18–22 (2002).

Google Scholar 

Wright, M. N. & Ziegler, A. Ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77, 1–17 (2017).

Google Scholar 

Melaas, E. K. et al. Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat. Remote Sens. Environ. 186, 452–464 (2016).

ADS  Google Scholar 

Markham, B. L. & Helder, D. L. Forty-year calibrated record of earth-reflected radiance from Landsat: a review. Remote Sens. Environ. 122, 30–40 (2012).

ADS  Google Scholar 

Markham, B. et al. Landsat-8 operational land imager radiometric calibration and stability. Remote Sens. 6, 12275–12308 (2014).

ADS  Google Scholar 

Kendall, M. G. Rank Correlation Methods 4th edn (Charles Griffin, 1975).

Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).

MathSciNet  MATH  Google Scholar 

Bronaugh, D. & Werner, A. zyp: Zhang + Yue-Pilon Trends Package. R Package Version 0.10-1.1. https://CRAN.R-project.org/package=zyp (2012).

R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).

Rohde, R. et al. A new estimate of the average Earth surface land temperature spanning 1753 to 2011. Geoinform. Geostat. 7, https://doi.org/10.4172/2327-4581.1000101 (2013).

Hansen, J., Ruedy, R., Sato, M. & Lo, K. Global surface temperature change. Rev. Geophys. 48, RG4004 (2010).

ADS  Google Scholar 

Cowtan, K. & Way, R. G. Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q. J. R. Meteorol. Soc. 140, 1935–1944 (2014).

ADS  Google Scholar 

Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

Google Scholar 

Willmott, C. J. & Matsuura, K. Terrestrial Air Temperature and Precipitation: Monthly Time Series (1900–2017) v. 5.01. http://climate.geog.udel.edu/~climate (University of Deleware, 2018).

Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).

MATH  Google Scholar 

Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).

PubMed  PubMed Central  Google Scholar 

Obu, J. et al. ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Extent for the Northern Hemisphere, v1.0. https://doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc (Centre for Environmental Data Analysis, 2019).

Obu, J. et al. ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v1.0. https://doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc (Centre for Environmental Data Analysis, 2019).

Obu, J. et al. ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Active Layer Thickness for the Northern Hemisphere, v1.0. https://doi.org/10.5285/1ee56c42cf6c4ef698693e00a63795f4 (Centre for Environmental Data Analysis, 2019).

Olefeldt, D. et al. Arctic Circumpolar Distribution and Soil Carbon of Thermokarst Landscapes. https://doi.org/10.3334/ORNLDAAC/1332 (ORNL DAAC, 2015).

Defourny, P. et al. Land Cover Climate Change Initiative—Product User Guide Version v2. http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (European Space Agency, 2017).

Rizzoli, P. et al. Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS J. Photogramm. Remote Sens. 132, 119–139 (2017).

ADS  Google Scholar 

Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).

Google Scholar 

Greenwell, B. M. pdp: an R package for constructing partial dependence plots. R. J. 9, 421–436 (2017).

Google Scholar 

Le Moullec, M., Buchwal, A., Wal, R., Sandal, L. & Hansen, B. B. Annual ring growth of a widespread high arctic shrub reflects past fluctuations in community-level plant biomass. J. Ecol. 107, 436–451 (2019).

Google Scholar 

Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).

Google Scholar 

Euskirchen, E., Bret-Harte, M. S., Scott, G., Edgar, C. & Shaver G. R. Seasonal patterns of carbon dioxide and water fluxes in three representative tundra ecosystems in northern Alaska. Ecosphere 3, https://doi.org/10.1890/ES1811-00202.00201 (2012).

Euskirchen, E. S. et al. Interannual and seasonal patterns of carbon dioxide, water, and energy fluxes from ecotonal and thermokarst-impacted ecosystems on carbon-rich permafrost soils in Northeastern Siberia. J. Geophys. Res. 122, 2651–2668 (2017).

CAS  Google Scholar 

Baldocchi, D. et al. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82, 2415–2434 (2001).

ADS  Google Scholar 

Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439 (2005).

ADS  Google Scholar 

Hijmans, R. J. raster: Geographic Analysis and Modeling. R package version 3.0-12. http://CRAN.R-project.org/package=raster (2019).

Bivand, R., Keitt, T. & Rowlingson B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R Package Version 1.4-8. https://CRAN.R-project.org/package=rgdal (2019).

Bivand, R. & Lewin-Koh, N. maptools: Tools for Handling Spatial Objects. R Package Version 0.9.9. https://CRAN.R-project.org/package=maptools (2019).

Dawle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’. R Package Version 1.12.8. https://CRAN.R-project.org/package=data.table (2019).

Wickham, H. & Francois, R. dplyr: A Grammar of Data Manipulation. R Package Version 0.8.5. https://CRAN.R-project.org/package=dplyr (2015).

Wickham, H. & Henry, L. tidyr: Tidy Messy Data. R Package Version 1.0.2. https://CRAN.R-project.org/package=tidyr (2020).

Sarkar, D. Lattice: Multivariate Data Visualization with R (Springer, 2008).

Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).

Kassambara, A. ggpubr: ‘ggplot2’ Basde Publication Ready Plots. R Package Version 0.2.5. https://CRAN.R-project.org/package=ggpubr (2020).