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Bioclimatic atlas of the terrestrial Arctic

  • Box, J. E. et al. Key indicators of Arctic climate change: 1971–2017. Environ. Res. Lett. 14, 045010 (2019).

    ADS 
    CAS 

    Google Scholar 

  • Previdi, M., Smith, K. L. & Polvani, L. M. Arctic amplification of climate change: a review of underlying mechanisms. Environ. Res. Lett. 16, 093003 (2021).

    ADS 
    CAS 

    Google Scholar 

  • Rantanen, M. et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ. 3, 1–10 (2022).

    ADS 

    Google Scholar 

  • Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).

    ADS 

    Google Scholar 

  • Kopec, B. G., Feng, X., Michel, F. A. & Posmentier, E. S. Influence of sea ice on Arctic precipitation. Proc. Natl. Acad. Sci. 113, 46–51 (2016).

    ADS 
    CAS 

    Google Scholar 

  • Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. & Romanovsky, V. E. The changing thermal state of permafrost. Nat. Rev. Earth Environ. 3, 10–23 (2022).

    ADS 

    Google Scholar 

  • Overland, J. et al. The urgency of Arctic change. Polar Sci. 21, 6–13 (2019).

    ADS 

    Google Scholar 

  • Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).

    ADS 
    CAS 

    Google Scholar 

  • Ciavarella, A. et al. Prolonged Siberian heat of 2020 almost impossible without human influence. Clim. Change 166, 9 (2021).

    ADS 

    Google Scholar 

  • Dobricic, S., Russo, S., Pozzoli, L., Wilson, J. & Vignati, E. Increasing occurrence of heat waves in the terrestrial Arctic. Environ. Res. Lett. 15, 024022 (2020).

    ADS 

    Google Scholar 

  • Graham, R. M. et al. Increasing frequency and duration of Arctic winter warming events. Geophys. Res. Lett. 44, 6974–6983 (2017).

    ADS 

    Google Scholar 

  • Knight, J. & Harrison, S. The impacts of climate change on terrestrial Earth surface systems. Nat. Clim. Change 3, 24–29 (2013).

    ADS 

    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 

  • Beck, P. S. A. et al. Changes in forest productivity across Alaska consistent with biome shift. Ecol. Lett. 14, 373–379 (2011).

    Google Scholar 

  • Reichle, L. M., Epstein, H. E., Bhatt, U. S., Raynolds, M. K. & Walker, D. A. Spatial Heterogeneity of the Temporal Dynamics of Arctic Tundra Vegetation. Geophys. Res. Lett. 45, 9206–9215 (2018).

    ADS 

    Google Scholar 

  • Sturm, M., Racine, C. & Tape, K. Increasing shrub abundance in the Arctic. Nature 411, 546–547 (2001).

    ADS 
    CAS 

    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 

  • 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 

  • Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).

    ADS 
    CAS 

    Google Scholar 

  • Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).

    Google Scholar 

  • Virkkala, A.-M. et al. Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties. Glob. Change Biol. 27, 4040–4059 (2021).

    CAS 

    Google Scholar 

  • Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).

    Google Scholar 

  • Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    ADS 

    Google Scholar 

  • Rienecker, M. M. et al. MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Clim. 24, 3624–3648 (2011).

    ADS 

    Google Scholar 

  • Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    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).

    Google Scholar 

  • Karger, D. N., Schmatz, D. R., Dettling, G. & Zimmermann, N. E. High-resolution monthly precipitation and temperature time series from 2006 to 2100. Sci. Data 7, 248 (2020).

    Google Scholar 

  • Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).

    Google Scholar 

  • Niittynen, P., Heikkinen, R. K. & Luoto, M. Snow cover is a neglected driver of Arctic biodiversity loss. Nat. Clim. Change 8, 997–1001 (2018).

    ADS 

    Google Scholar 

  • Slatyer, R. A., Umbers, K. D. L. & Arnold, P. A. Ecological responses to variation in seasonal snow cover. Conserv. Biol. 36, e13727 (2022).

    Google Scholar 

  • Serreze, M. C. et al. Arctic rain on snow events: bridging observations to understand environmental and livelihood impacts. Environ. Res. Lett. 16, 105009 (2021).

    ADS 

    Google Scholar 

  • López, J., Way, D. A. & Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Glob. Change Biol. 27, 1704–1720 (2021).

    ADS 

    Google Scholar 

  • Ennos, A. R. Wind as an ecological factor. Trends Ecol. Evol. 12, 108–111 (1997).

    CAS 

    Google Scholar 

  • Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).

    ADS 

    Google Scholar 

  • Boussetta, S. et al. ECLand: The ECMWF Land Surface Modelling System. Atmosphere 12, 723 (2021).

    ADS 
    CAS 

    Google Scholar 

  • Munõz-Sabater, J. ERA5-Land hourly data from 1981 to present. ECMWF https://doi.org/10.24381/cds.e2161bac (2019). 

  • Munõz-Sabater, J. ERA5-Land hourly data from 1950 to 1980. ECMWF https://doi.org/10.24381/cds.e2161bac (2021).

  • Hoyer, S. & Hamman, J. xarray: N-D labeled Arrays and Datasets in Python. J. Open Res. Softw. 5, 10 (2017).

    Google Scholar 

  • Sen, P. K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).

    MATH 

    Google Scholar 

  • Theil, H. A rank-invariant method of linear and polynomial regression analysis I, II and III. Indag. Math. 173 (1950).

  • Hussain, M. M. & Mahmud, I. pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. J. Open Source Softw. 4, 1556 (2019).

    ADS 

    Google Scholar 

  • Aalto, J. et al. High-resolution analysis of observed thermal growing season variability over northern Europe. Clim. Dyn. 58, 1477–1493 (2022).

    Google Scholar 

  • Zhou, B., Zhai, P., Chen, Y. & Yu, R. Projected changes of thermal growing season over Northern Eurasia in a 1.5 °C and 2 °C warming world. Environ. Res. Lett. 13, 035004 (2018).

    ADS 

    Google Scholar 

  • Barichivich, J., Briffa, K. R., Osborn, T. J., Melvin, T. M. & Caesar, J. Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere. Glob. Biogeochem. Cycles 26 (2012).

  • Wu, F., Jiang, Y., Wen, Y., Zhao, S. & Xu, H. Spatial synchrony in the start and end of the thermal growing season has different trends in the mid-high latitudes of the Northern Hemisphere. Environ. Res. Lett. 16, 124017 (2021).

    ADS 

    Google Scholar 

  • Ruosteenoja, K., Räisänen, J., Venäläinen, A. & Kämäräinen, M. Projections for the duration and degree days of the thermal growing season in Europe derived from CMIP5 model output. Int. J. Climatol. 36, 3039–3055 (2016).

    Google Scholar 

  • Niittynen, P. & Luoto, M. The importance of snow in species distribution models of arctic vegetation. Ecography 41, 1024–1037 (2018).

    Google Scholar 

  • McMaster, G. S. & Wilhelm, W. W. Growing degree-days: one equation, two interpretations. Agric. For. Meteorol. 87, 291–300 (1997).

    ADS 

    Google Scholar 

  • Körner, C. Plant adaptation to cold climates. F1000Research 5, F1000 Faculty Rev-2769 (2016).

  • Niittynen, P. et al. Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions. Nat. Clim. Change 10, 1143–U134 (2020).

    ADS 

    Google Scholar 

  • Cohen, J., Ye, H. & Jones, J. Trends and variability in rain-on-snow events. Geophys. Res. Lett. 42, 7115–7122 (2015).

    ADS 

    Google Scholar 

  • Mooney, P. A. & Li, L. Near future changes to rain-on-snow events in Norway. Environ. Res. Lett. 16, 064039 (2021).

    ADS 

    Google Scholar 

  • Preece, C., Callaghan, T. V. & Phoenix, G. K. Impacts of winter icing events on the growth, phenology and physiology of sub-arctic dwarf shrubs. Physiol. Plant. 146, 460–472 (2012).

    CAS 

    Google Scholar 

  • Putkonen, J. & Roe, G. Rain-on-snow events impact soil temperatures and affect ungulate survival. Geophys. Res. Lett. 30, (2003).

  • Treharne, R., Bjerke, J. W. & Tømmervik, H. & Phoenix, G. K. Development of new metrics to assess and quantify climatic drivers of extreme event driven Arctic browning. Remote Sens. Environ. 243, 111749 (2020).

    ADS 

    Google Scholar 

  • Bokhorst, S. et al. Impacts of extreme winter warming events on plant physiology in a sub-Arctic heath community. Physiol. Plant. 140, 128–140 (2010).

    CAS 

    Google Scholar 

  • Russo, S., Sillmann, J. & Fischer, E. M. Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environ. Res. Lett. 10, 124003 (2015).

    ADS 

    Google Scholar 

  • Alduchov, O. A. & Eskridge, R. E. Improved Magnus Form Approximation of Saturation Vapor Pressure. J. Appl. Meteorol. Climatol. 35, 601–609 (1996).

    ADS 

    Google Scholar 

  • Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).

    Google Scholar 

  • Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).

    ADS 
    CAS 

    Google Scholar 

  • De Frenne, P. et al. Forest microclimates and climate change: Importance, drivers and future research agenda. Glob. Change Biol. 27, 2279–2297 (2021).

    ADS 

    Google Scholar 

  • Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).

    ADS 
    CAS 

    Google Scholar 

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

    ADS 

    Google Scholar 

  • Walker, D. A. 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. Atmospheres 108, (2003).

  • Williams, C. M., Henry, H. A. L. & Sinclair, B. J. Cold truths: how winter drives responses of terrestrial organisms to climate change. Biol. Rev. 90, 214–235 (2015).

    Google Scholar 

  • Peng, S. et al. Change in snow phenology and its potential feedback to temperature in the Northern Hemisphere over the last three decades. Environ. Res. Lett. 8, 014008 (2013).

    ADS 

    Google Scholar 

  • Wheeler, J. A. et al. Increased spring freezing vulnerability for alpine shrubs under early snowmelt. Oecologia 175, 219–229 (2014).

    ADS 
    CAS 

    Google Scholar 

  • Zhu, L., Ives, A. R., Zhang, C., Guo, Y. & Radeloff, V. C. Climate change causes functionally colder winters for snow cover-dependent organisms. Nat. Clim. Change 9, 886–893 (2019).

    ADS 

    Google Scholar 

  • Vitasse, Y. et al. ‘Hearing’ alpine plants growing after snowmelt: ultrasonic snow sensors provide long-term series of alpine plant phenology. Int. J. Biometeorol. 61, 349–361 (2017).

    ADS 

    Google Scholar 

  • Kling, M. M. & Ackerly, D. D. Global wind patterns and the vulnerability of wind-dispersed species to climate change. Nat. Clim. Change 10, 868–875 (2020).

    ADS 

    Google Scholar 

  • Dial, R. J., Maher, C. T., Hewitt, R. E. & Sullivan, P. F. Sufficient conditions for rapid range expansion of a boreal conifer. Nature 608, 546–551 (2022).

    ADS 
    CAS 

    Google Scholar 

  • Nathan, R. et al. Mechanisms of long-distance dispersal of seeds by wind. Nature 418, 409–413 (2002).

    ADS 
    CAS 

    Google Scholar 

  • Sakai, A. Mechanism of Desiccation Damage of Conifers Wintering in Soil-Frozen Areas. Ecology 51, 657–664 (1970).

    Google Scholar 

  • Wilson, J. W. Notes on Wind and its Effects in Arctic-Alpine Vegetation. J. Ecol. 47, 415–427 (1959).

    Google Scholar 

  • Rantanen, M. et al. Bioclimatic atlas of the terrestrial Arctic, figshare, https://doi.org/10.6084/m9.figshare.c.6216368 (2023).

  • Räisänen, J. Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change. The Cryosphere 15, 1677–1696 (2021).

    ADS 

    Google Scholar 

  • Xu, J., Ma, Z., Yan, S. & Peng, J. Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China. J. Hydrol. 605, 127353 (2022).

    Google Scholar 

  • Behrangi, A., Singh, A., Song, Y. & Panahi, M. Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations. Geophys. Res. Lett. 46, 11358–11366 (2019).

    ADS 

    Google Scholar 

  • Menne, M. J., Williams, C. N., Gleason, B. E., Rennie, J. J. & Lawrimore, J. H. The Global Historical Climatology Network Monthly Temperature Dataset, Version 4. J. Clim. 31, 9835–9854 (2018).

    ADS 

    Google Scholar 

  • 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. Atmospheric Ocean. Technol. 29, 897–910 (2012).

    ADS 

    Google Scholar 

  • Atlaskin, E. & Vihma, T. Evaluation of NWP results for wintertime nocturnal boundary-layer temperatures over Europe and Finland. Q. J. R. Meteorol. Soc. 138, 1440–1451 (2012).

    ADS 

    Google Scholar 

  • Lindsay, R., Wensnahan, M., Schweiger, A. & Zhang, J. Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic. J. Clim. 27, 2588–2606 (2014).

    ADS 

    Google Scholar 

  • Wang, C., Graham, R. M., Wang, K., Gerland, S. & Granskog, M. A. Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution. The Cryosphere 13, 1661–1679 (2019).

    ADS 

    Google Scholar 

  • Wesslén, C. et al. The Arctic summer atmosphere: an evaluation of reanalyses using ASCOS data. Atmospheric Chem. Phys. 14, 2605–2624 (2014).

    ADS 

    Google Scholar 


  • Source: Ecology - nature.com

    Preparing to be prepared

    Synapsid tracks with skin impressions illuminate the terrestrial tetrapod diversity in the earliest Permian of equatorial Pangea