Surface Air Temperature
Ballinger, T. J., and Coauthors, 2023: Surface air temperature. Arctic Report Card 2023, R. L. Thoman, T. A. Moon, and M. L. Druckenmiller, Eds., https://doi.org/10.25923/x3ta-6e63.
Cohen, J., and Coauthors, 2020: Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather. Nat. Climate Change, 10, 20-29, https://doi.org/10.1038/s41558-019-0662-y.
ECCC, 2024: Past weather and climate: Historical data. Environment and Climate Change Canada, Station climate IDs 2400800 and 2400802, accessed 4 September 2024, https://climate.weather.gc.ca/historical_data/search_historic_data_e.html.
Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, https://doi.org/10.1029/2010RG000345.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999-2049, https://doi.org/10.1002/qj.3803.
Lenssen, N. J. L., G. A. Schmidt, J. E. Hansen, M. J. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res.-Atmos., 124, 6307-6326, https://doi.org/10.1029/2018JD029522.
Overland, J. E., 2024: Emergence of Arctic extremes. Climate, 12, 109, https://doi.org/10.3390/cli12080109.
Polyakov, I. V., T. J. Ballinger, R. Lader, and X. Zhang, 2024: Modulated trends in Arctic surface air temperature extremes as a fingerprint of climate change. J. Climate, 37, 2381-2404, https://doi.org/10.1175/JCLI-D-23-0266.1.
Schoen, E. R., K. G. Howard, J. M. Murphy, D. E. Schindler, P. A. H. Westley, and V. R. von Biela, 2023: Divergent responses of western Alaska salmon to a changing climate. Arctic Report Card 2023, R. L. Thoman, T. A. Moon, and M. L. Druckenmiller, Eds., https://doi.org/10.25923/f2hv-5581.
Sweeney, A. J., Q. Fu, S. Po-Chedley, H. Wang, and M. Wang, 2023: Internal variability increased Arctic amplification during 1980-2022. Geophys. Res. Lett., 50, e2023GL106060, https://doi.org/10.1029/2023GL106060.
Taylor, P. C., and Coauthors, 2022: Process drivers, inter-model spread, and the path forward: A review of amplified Arctic warming. Front. Earth Sci., 9, 758361, https://doi.org/10.3389/feart.2021.758361.
Thoman, R., 2024: Update: August 2024 Arctic Heatwave. Alaska and Arctic Climate Newsletter, https://alaskaclimate.substack.com/p/update-august-2024-arctic-heatwave.
Timmermans, M. -L., and Z. Labe, 2023: Sea surface temperature. Arctic Report Card 2023, R. L. Thoman, T. A. Moon, and M. L. Druckenmiller, Eds., https://doi.org/10.25923/e8jc-f342.
Zhou, W., L. R. Leung, and J. Lu, 2024: Steady threefold Arctic amplification of externally forced warming masked by natural variability. Nat. Geosci., 17, 508-515, https://doi.org/10.1038/s41561-024-01441-1.
Precipitation
Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf , K. Schamm, U. Schneider, and M. Ziese, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present. Earth Sys. Sci. Data, 5, 71-99, https://doi.org/10.5194/essd-5-71-2013.
Bigalke, S., and J. E. Walsh, 2022: Future changes of snow in Alaska under stabilized global warming scenarios. Atmosphere, 13, 541, https://doi.org/10.3390/atmos13040541.
Box, J. E., and Coauthors, 2021: Recent developments in Arctic climate observation indicators. AMAP Arctic Climate Change Update 2021: Key Trends and Impacts, Arctic Monitoring and Assessment Programme (AMAP), Tromso, Norway, 7-29, https://www.amap.no/documents/doc/amap-arctic-climate-change-update-2021-key-trends-and-impacts/3594.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999-2049, https://doi.org/10.1002/qj.3803.
Loeb, N. A., A. Crawford, J. C. Stroeve, and J. Hanesiak, 2022: Extreme precipitation in the eastern Canadian Arctic and Greenland: An evaluation of atmospheric reanalyses. Front. Env. Sci., 10, 866929, https://doi.org/10.3389/fenvs.2022.866929.
McCrystall, M. R., J. Stroeve, M. C. Serreze, B. C. Forbes, and J. A. Screen, 2021: New climate models reveal faster and larger increases in Arctic precipitation than previously projected. Nat. Commun., 12(1), 6765, https://doi.org/10.1038/s41467-021-27031-y.
Schneider, U., P. Finger, E. Rustemeier, M. Ziese, and S. Hänsel, 2022: Global precipitation analysis products of the GPCC, Global Precipitation Climatology Centre, https://opendata.dwd.de/climate_environment/GPCC/PDF/GPCC_intro_products_v2022.pdf.
Walsh, J. E., S. Bigalke, S. A. McAfee, R. Lader, M. C. Serreze, and T. J. Ballinger, 2022: Precipitation. Arctic Report Card 2022, M. L. Druckenmiller, R. L. Thoman, and T. A. Moon, Eds., https://doi.org/10.25923/n07s-3s69.
Ye, H., D. Yang, A. Behrangi, S. L. Stuefer, X. Pan, E. Mekis, Y. Dibike, and J. E. Walsh, 2021: Precipitation Characteristics and Changes. Arctic Hydrology, Permafrost and Ecosystems (D. Yang and D. L. Kane, Eds.), Springer Nature Switzerland, https://doi.org/10.1007/978-3-030-50930-9_2.
Yu, L., and S. Zhong, 2021: Trends in Arctic seasonal and extreme precipitation in recent decades. Theor. Appl. Climatol., 145, 1541-1559, https://doi.org/10.1007/s00704-021-03717-7.
Terrestrial Snow Cover
Brown, R., and Coauthors, 2017: Arctic terrestrial snow cover. Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017. pp. 25-64. Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway.
Decharme, B., 2024: Crocus-ERA5 daily snow product over the Northern Hemisphere at 0.25° resolution (Version 2023), Zenodo, accessed 3 September 2024, https://doi.org/10.5281/zenodo.10943718.
GMAO (Global Modeling and Assimilation Office), 2015: MERRA-2tavg1_2d_lnd_Nx:2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Land Surface Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GESDISC), accessed: 3 August 2024, https://doi.org/10.5067/RKPHT8KC1Y1T.
Luojus, K., and Coauthors, 2022: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979-2020), version 2.0. NERC EDS Centre for Environmental Data Analysis, accessed: 3 September 2024, https://doi.org/10.5285/4647cc9ad3c044439d6c643208d3c494.
Meredith, M., and Coauthors, 2019: Polar Regions. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, H.-O. Pörtner, and co-editors, Cambridge University Press, Cambridge, UK and New York, NY, USA, 203-320, https://doi.org/10.1017/9781009157964.005.
Mortimer, C., L. Mudryk, C. Derksen, K. Luojus, R. Brown, R. Kelly, and M. Tedesco, 2020: Evaluation of long-term Northern Hemisphere snow water equivalent products. Cryosphere, 14, 1579-1594, https://doi.org/10.5194/tc-14-1579-2020.
Muñoz Sabater, J., 2019: ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), accessed 3 September 2024, https://doi.org/10.24381/cds.e2161bac.
Robinson, D. A., T. W. Estilow, and NOAA CDR Program, 2012: NOAA Climate Data Record (CDR) of Northern Hemisphere (NH) Snow Cover Extent (SCE), Version 1 [r01]. NOAA National Centers for Environmental Information, accessed: 3 September 2024, https://doi.org/10.7289/V5N014G9.
U.S. National Ice Center, 2008: IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1. Boulder, Colorado, USA. NSIDC: National Snow and Ice Data Center, accessed: 3 August 2024, https://doi.org/10.7265/N52R3PMC.
Greenland Ice Sheet
Colgan, W., and Coauthors, 2015: Hybrid glacier Inventory, Gravimetry and Altimetry (HIGA) mass balance product for Greenland and the Canadian Arctic. Remote Sens. Environ., 168, 24-39, https://doi.org/10.1016/j.rse.2015.06.016.
Loomis, B. D., S. B. Luthcke, and T. J. Sabaka, 2019: Regularization and error characterization of GRACE mascons. J. Geodesy, 93, 1381-1398, https://doi.org/10.1007/s00190-019-01252-y.
Mankoff, K. D., A. Solgaard, W. Colgan, A. P. Ahlstrøm, S. A. Khan, and R. S. Fausto, 2020: Greenland ice sheet solid ice discharge from 1986 through March 2020. Earth Syst. Sci. Data, 12, 1367-1383, https://doi.org/10.5194/essd-12-1367-2020.
Medley, B., T. A. Neumann, H. J. Zwally, B. E. Smith, and C. M. Stevens, 2022: Simulations of firn processes over the Greenland and Antarctic ice sheets: 1980-2021. Cryosphere, 16, 3971-4011, https://doi.org/10.5194/tc-16-3971-2022.
Morlighem, M., and Coauthors, 2017: BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multibeam echo sounding combined with mass conservation. Geophys. Res. Lett., 44(21), 11051-11061, https://doi.org/10.1002/2017GL074954.
Mote, T. L., 2007: Greenland surface melt trends 1973-2007: Evidence of a large increase in 2007. Geophys. Res. Lett., 34(22), L22507, https://doi.org/10.1029/2007GL031976.
Poinar, K., and Coauthors, 2023: Greenland Ice Sheet. Arctic Report Card 2023, R. L. Thoman, T. A. Moon, and M. L. Druckenmiller, Eds., https://doi.org/10.25923/yetx-rs76.
Smith, B., 2023: Algorithm Theoretical Basis Document (ATBD) for Land-ice DEM (ATL14) and Land-ice height change (ATL15). NASA Goddard Space Flight Center, https://nsidc.org/sites/default/files/documents/technical-reference/icesat2_atl14_atl15_atbd_v003.pdf.
Smith, B., S. Dickinson, B. P. Jelley, T. A. Neumann, D. Hancock, J. Lee, and K. Harbeck, 2023: ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series, Version 6 [Data Set]. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, CO, USA, accessed: 25 August 2024, https://doi.org/10.5067/ATLAS/ATL11.006.
van As, D., R. S. Fausto, J. Cappelen, R. S. W. van de Wal, R. J. Braithwaite, H. Machguth, and PROMICE project team, 2016: Placing Greenland ice sheet ablation measurements in a multi-decadal context. GEUS Bull., 35, 71-74, https://doi.org/10.34194/geusb.v35.4942.
Vandecrux, B., and Coauthors, 2023: The historical Greenland Climate Network (GC-Net) curated and augmented level-1 dataset. Earth Syst. Sci. Data, 15, 5467-5489, https://doi.org/10.5194/essd-15-5467-2023.
Wahr, J., M. Molenaar, and F. Bryan, 1998: Time variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE. J. Geophys. Res., 103(B12), 30205-30229, https://doi.org/10.1029/98JB02844.
Watkins, M. M., D. N. Wiese, D. N. Yuan, C. Boening, and F. W. Landerer, 2015: Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. J. Geophys. Res.-Sol. Ea., 120(4), 2648–2671, https://doi.org/10.1002/2014JB011547.
Zemp, M., and Coauthors, 2019: Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature, 568, 382-386, https://doi.org/10.1038/s41586-019-1071-0.
Sea Ice
Bliss, A. C., 2023: Passive microwave Arctic ice melt onset dates from the advanced horizontal range algorithm 1979-2022. Sci. Data, 10, 857, https://doi.org/10.1038/s41597-023-02760-5.
Cavalieri, D. J., C. L. Parkinson, P. Gloersen, and H. J. Zwally, 1996 (updated yearly): Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, CO, USA, accessed 12 September 2023, https://doi.org/10.5067/8GQ8LZQVL0VL.
European Space Agency, 2023: SMOS-CryoSat L4 Sea Ice Thickness, Version 206. https://doi.org/10.57780/sm1-4f787c3.
Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel, 2017 (updated daily): Sea Ice Index, Version 3. NSIDC: National Snow and Ice Data Center, Boulder, CO, USA, accessed 12 September 2023, https://doi.org/10.7265/N5K072F8.
Lavergne, T., and Coauthors, 2019: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records. Cryosphere, 13, 49-78, https://doi.org/10.5194/tc-13-49-2019.
Maslanik, J., and J. Stroeve, 1999: Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations, Version 1. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, CO, USA, accessed 12 September 2023, https://doi.org/10.5067/U8C09DWVX9LM.
Petty, A. A., N. Kurtz, R. Kwok, T. Markus, T. A. Neumann, and N. Keeney, 2023a: ICESat-2 L4 Monthly Gridded Sea Ice Thickness, Version 3 [Data Set]. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, CO, USA, accessed 13 August 2023, https://doi.org/10.5067/ZCSU8Y5U1BQW.
Petty A. A., N. Keeney, A. Cabaj, P. Kushner, and M. Bagnardi, 2023b: Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection.. Cryosphere, 17, 127-156, https://doi.org/10.5194/tc-17-127-2023.
Ricker, R., S. Hendricks, L. Kaleschke, X. Tian-Kunze, J. King, and C. Haas, 2017. A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data. Cryosphere, 11, 1607-1623, https://doi.org/10.5194/tc-11-1607-2017.
Sea Ice Today, 2024: The waning of Arctic summer. National Snow and Ice Data Center, Northwest Passage image and analysis from Stephen Howell, Environment and Climate Change Canada, accessed 19 August 2024, https://nsidc.org/sea-ice-today/analyses/waning-arctic-summer.
Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik, 2019a: EASE-Grid Sea Ice Age, Version 4. [March, 1984-2020]. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, CO, USA, accessed 5 September 2023, https://doi.org/10.5067/UTAV7490FEPB.
Tschudi, M., W. N. Meier, and J. S. Stewart, 2019b: Quicklook Arctic Weekly EASE-Grid Sea Ice Age, Version 1. [March, 2021]. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, CO, USA, accessed 5 September 2023, https://doi.org/10.5067/2XXGZY3DUGNQ.
Sea Surface Temperature
Banzon, V., T. M. Smith, M. Steele, B. Huang, and H. -M. Zhang, 2020: Improved estimation of proxy sea surface temperature in the Arctic. J. Atmos. Ocean. Tech., 37, 341-349, https://doi.org/10.1175/JTECH-D-19-0177.1.
Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1. J. Climate, 34(8), 2923-2939, https://doi.org/10.1175/JCLI-D-20-0166.1.
Meier, W. N., F. Fetterer, A. K. Windnagel, and J. S. Stewart, 2021a: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. [1982-2021]. NSIDC: National Snow and Ice Data Center, Boulder, CO, USA, accessed 3 September 2024, https://doi.org/10.7265/efmz-2t65.
Meier, W. N., F. Fetterer, A. K. Windnagel, and J. S. Stewart, 2021b: Near-Real-Time NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 2. [1982-2021], accessed 3 September 2024, https://doi.org/10.7265/tgam-yv28.
Nielsen-Englyst, P., J. L. Høyer, W. M. Kolbe, G. Dybkjær, T. Lavergne, R. T. Tonboe, S. Skarpalezos, and I. Karagali, 2023: A combined sea and sea-ice surface temperature climate dataset of the Arctic, 1982-2021. Remote Sens. Environ., 284, 113331, https://doi.org/10.1016/j.rse.2022.113331.
NOAA, 2024: Optimum Interpolation Sea Surface Temperature (OISST) high resolution dataset, version 2.1. NOAA/PSL, accessed 3 September 2024, https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html.
Oldenburg, D., Y. Kwon, C. Frankignoul, G. Danabasoglu, S. Yeager, and W. M. Kim, 2024: The respective roles of ocean heat transport and surface heat fluxes in driving Arctic Ocean warming and sea ice decline. J. Climate, 37, 1431-1448, https://doi.org/10.1175/JCLI-D-23-0399.1.
Peng, G., W. N. Meier, D. J. Scott, and M. H. Savoie, 2013: A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring. Earth Syst. Sci. Data, 5, 311-318, https://doi.org/10.5194/essd-5-311-2013.
Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2.
Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473-5496, https://doi.org/10.1175/2007JCLI1824.1.
Timmermans, M. -L., and Z. M. Labe, 2020: Sea surface temperature. Arctic Report Card 2020, R. L. Thoman, J. Richter-Menge, and M. L. Druckenmiller, Eds., https://doi.org/10.25923/v0fs-m920.
Timmermans, M. -L., and Z. M. Labe, 2023: Sea surface temperature. Arctic Report Card 2023, M. L. Druckenmiller, R. L. Thoman, and T. A. Moon, Eds., https://doi.org/10.25923/e8jc-f342.
Arctic Ocean Primary Productivity: The Response of Marine Algae to Climate Warming and Sea Ice Decline
Amargant-Arumí, M., and Coauthors, 2024: Interannual differences in sea ice regime in the north-western Barents Sea cause major changes in summer pelagic production and export mechanisms. Prog. Oceanogr., 220, 103178, https://doi.org/10.1016/j.pocean.2023.103178.
Ardyna, M., M. Babin, M. Gosselin, E. Devred, S. Bélanger, A. Matsuoka, and J. -É. Tremblay, 2013: Parameterization of vertical chlorophyll a in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates. Biogeosciences, 10(6), 4383-4404, https://doi.org/10.5194/bg-10-4383-2013.
Ardyna, M., and Coauthors, 2020: Under-ice phytoplankton blooms: Shedding light on the “invisible” part of Arctic primary production. Front. Mar. Sci., 7, 608032, https://doi.org/10.3389/fmars.2020.608032.
Behrenfeld, M. J., and P. G. Falkowski, 1997: Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr., 42(1), 1-20, https://doi.org/10.4319/lo.1997.42.1.0001.
Castagno, A. P., T. J. W. Wagner, M. R. Cape, C. W. Lester, E. Bailey, C. Alves-de-Souza, R.A. York, and A. H. Fleming, 2023: Increased sea ice melt as a driver of enhanced Arctic phytoplankton blooming. Glob. Change Biol., 29(17), 5087-5098, https://doi.org/10.1111/gcb.16815.
Castro de la Guardia, L., and Coauthors, 2023: Assessing net primary production in the northwestern Barents Sea using in situ, remote sensing and modelling approaches. Prog. Oceanogr., 219, 103160, https://doi.org/10.1016/j.pocean.2023.103160.
Comiso, J. C., 2015: Variability and trends of the global sea ice cover and sea level: Effects on physicochemical parameters. Climate Change and Marine and Freshwater Toxins, L. M. Botana, M. C. Lauzao, and N. Vilarino, Eds., De Gruyter, Berlin, Germany, https://doi.org/10.1515/9783110333596-003.
Comiso, J. C., W. N. Meier, and R. Gersten, 2017: Variability and trends in the Arctic Sea ice cover: Results from different techniques. J. Geophys. Res.-Oceans, 122, 6883-6900, https://doi.org/10.1002/2017JC012768.
Frey, K. E., J. C. Comiso, L. V. Stock, L. N. C. Young, L. W. Cooper, and J. M. Grebmeier, 2023a: A comprehensive satellite-based assessment across the Pacific Arctic Distributed Biological Observatory shows widespread late-season sea surface warming and sea ice declines with significant influences on primary productivity. PLoS ONE, 18(7), e0287960, https://doi.org/10.1371/journal.pone.0287960.
Frey, K. E., J. C. Comiso, L. W. Cooper, C. Garcia, J. M. Grebmeier, and L. V. Stock, 2023b: Arctic ocean primary productivity: The response of marine algae to climate warming and sea ice decline. Arctic Report Card 2023, R. L. Thoman, T. A. Moon, and M. L. Druckenmiller, Eds., https://doi.org/10.25923/nb05-8w13.
Fujiwara, A., and Coauthors, 2018: Changes in phytoplankton community structure during wind-induced fall bloom on the central Chukchi shelf. Polar Biol., 41, 1279-1295, https://doi.org/10.1007/s00300-018-2284-7.
Koch, C. W., and Coauthors, 2023: Year-round utilization of sea ice-associated carbon in Arctic ecosystems. Nat. Commun., 14, 1964, https://doi.org/10.1038/s41467-023-37612-8.
Lalande, C., J. M. Grebmeier, R. R. Hopcroft, and S. L. Danielson, 2020: Annual cycle of export fluxes of biogenic matter near Hanna Shoal in the northeast Chukchi Sea. Deep-Sea Res. Pt. II, 177, 104730, https://doi.org/10.1016/j.dsr2.2020.104730.
Lewis, K. M., and K. R. Arrigo, 2020: Ocean color algorithms for estimating chlorophyll a, CDOM absorption, and particle backscattering in the Arctic Ocean. J. Geophys. Res.- Oceans, 125, e2019JC015706, https://doi.org/10.1029/2019JC015706.
Li, W. K., F. A. McLaughlin, C. Lovejoy, and E. C. Carmack, 2009: Smallest algae thrive as the Arctic Ocean freshens. Science, 326, 539-539, https://doi.org/10.1126/science.1179798.
Rantanen, M., A. Y. Karpechko, A. Lipponen, K. Nordling, O. Hyvärinen, K. Ruosteenoja, T. Vihma, and A. Laaksonen, 2022: The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ., 3, 168, https://doi.org/10.1038/s43247-022-00498-3.
Terhaar, J., R. Lauerwald, P. Regnier, N. Gruber, and L. Bopp, 2021: Around one third of current Arctic Ocean primary production sustained by rivers and coastal erosion. Nat. Commun., 12, 169, https://doi.org/10.1038/s41467-020-20470-z.
Tundra Greenness
Bennett, K. E., and Coauthors, 2022: Spatial patterns of snow distribution in the sub-Arctic. Cryosphere, 16, 3269-3293, https://doi.org/10.5194/tc-16-3269-2022.
Berner, L. T., and S. J. Goetz, 2022: Satellite observations document trends consistent with a boreal forest biome shift. Global Change Biol., 28(10), 3275-3292, https://doi.org/10.1111/gcb.16121.
Conservation of Arctic Flora and Fauna (CAFF), 2024: Extreme events monitoring tool, accessed 10 September 2024, https://www.caff.is/work/approach/extreme-events/.
Crawford, C. J., and Coauthors, 2023: The 50-year Landsat collection 2 archive. Sci. Remote Sens., 8, 100103, https://doi.org/10.1016/j.srs.2023.100103.
Didan, K., 2021a: MODIS/Terra Vegetation Indices 16-Day L3 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center, https://doi.org/10.5067/MODIS/MOD13A1.061.
Didan, K., 2021b: MODIS/Aqua Vegetation Indices 16-Day L3 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center, https://doi.org/10.5067/MODIS/MYD13A1.061.
Heijmans, M. M. P. D., and Coauthors, 2022: Tundra vegetation change and impacts on permafrost. Nat. Rev. Earth Environ., 3, 68-84, https://doi.org/10.1038/s43017-021-00233-0.
Karlsen, S. R., A. Elvebakk, L. Stendardi, K. A. Høgda, and M. Macias-Fauria, 2024: Greening of Svalbard. Sci. Total Environ., 945, 174130, https://doi.org/10.1016/j.scitotenv.2024.174130.
Kim, J. E., J. A. Wang, Y. Li, C. I. Czimczik, and J. T. Randerson, 2024: Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America. Glob. Change Biol., 30(1), e17151, https://doi.org/10.1111/gcb.17151.
Pinzon, J. E., E. W. Pak, C. J. Tucker, U. S. Bhatt, G. V. Frost, and M. J. Macander, 2023: Global Vegetation Greenness (NDVI) from AVHRR GIMMS-3G+, 1981-2022 [Data set]. ORNL DAAC, Oak Ridge, TN, USA, https://doi.org/10.3334/ORNLDAAC/2187.
Ramirez, J. I., and Coauthors, 2024: Reindeer grazing reduces climate-driven vegetation changes and shifts trophic interactions in the Fennoscandian tundra. Oikos, 2024(11), e10595, https://doi.org/10.1111/oik.10595.
Raynolds, M. K., and Coauthors, 2019: A raster version of the Circumpolar Arctic Vegetation Map (CAVM). Remote Sens. Environ., 232, 111297, https://doi.org/10.1016/j.rse.2019.111297.
Tape, K. D., J. A. Clark, B. M. Jones, S. Kantner, B. V. Gaglioti, G. Grosse, and I. Nitze, 2022: Expanding beaver pond distribution in Arctic Alaska, 1949 to 2019. Sci. Rep., 12, 7123, https://doi.org/10.1038/s41598-022-09330-6.
Tassone, M. S., H. E. Epstein, A. H. Armstrong, U. S. Bhatt, G. V. Frost, B. Heim, M. K. Raynolds, and D. A. Walker, 2024: Drivers of heterogeneity in tundra vegetation productivity on the Yamal Peninsula, Siberia, Russia. Environ. Res.: Ecology, 3, 015003, https://doi.org/10.1088/2752-664X/ad220f.
Wong, R. E., L. T. Berner, P. F. Sullivan, C. S. Potter, and R. J. Dial, 2024: Pixel walking along the boreal forest-Arctic tundra ecotone: Large scale ground-truthing of satellite-derived greenness (NDVI). Glob. Change Biol., 30(6), e17374, https://doi.org/10.1111/gcb.17374.
Migratory Tundra Caribou in a Warmer Climate
Callaghan, T. V., R. Cazzolla Gatti, and G. Phoenix, 2022: The need to understand the stability of arctic vegetation during rapid climate change: An assessment of imbalance in the literature. Ambio, 51, 1034-1044, https://doi.org/10.1007/s13280-021-01607-w.
Cameron, M. D., K. Joly, G. A. Breed, C. P. H. Mulder, and K. Kielland, 2020: Pronounced fidelity and selection for average conditions of calving area suggestive of spatial memory in a highly migratory ungulate. Front. Ecol. Evol., 8, 564567, https://doi.org/10.3389/fevo.2020.564567.
Gunn, A., 2003: Voles, lemmings and caribou – population cycles revisited? Rangifer, 23(5), 105–111, https://doi.org/10.7557/2.23.5.1689.
Gunn, A., 2016: Rangifer tarandus. The IUCN Red List of Threatened Species 2016: e.T29742A22167140, accessed September 2024, https://dx.doi.org/10.2305/IUCN.UK.2016-1.RLTS.T29742A22167140.en.
Landauer, M., S. Rasmus, and B. Forbes, 2021: What drives reindeer management in Finland towards social and ecological tipping points? Reg. Environ. Change, 21, 32, https://doi.org/10.1007/s10113-021-01757-3.
Mallory, C. D., S. N. Williamson, M. W. Campbell, and M. S. Boyce, 2020: Response of barren-ground caribou to advancing spring phenology. Oecologia, 192, 837-852, https://doi.org/10.1007/s00442-020-04604-0.
Nunavut Impact Review Board (NIRB), 2023: Reconsideration report and recommendations for the Meliadine Extension Proposal Related to Agnico Eagle Mines Limited’s Meliadine Gold Mine Project Certificate No. 006 NIRB File No. 11MN034. Accessed September 2024, https://www.nirb.ca/portal/pdash.php?appid=125684.
Russell, D., and A. Gunn, 2024: Arctic Conservation Forecast project (ARCON4): assessing vulnerability of migratory tundra caribou to climate change. Report prepared for World Wildlife Fund Global Arctic Programme (available on request to the authors and WWF).
Russell, D. E., A. Gunn, and S. Kutz, 2018: Migratory tundra caribou and wild reindeer. Arctic Report Card 2018, E. Osborne, J. Richter-Menge, and M. Jeffries, Eds., https://www.arctic.noaa.gov/report-card.
Russell, D., A. Gunn, and R. White, 2021: A decision support tool for assessing cumulative effects on an Arctic migratory tundra caribou population. Ecol. Soc., 26(1), 4, https://doi.org/10.5751/ES-12105-260104.
Russell D., R. White, and A. Gunn, 2024: Understanding productivity of North American Migratory tundra caribou (Rangifer tarandus): role of vital rates and climate. Government of Northwest Territories, Department of Environment and Natural Resources, Manuscript Report 318. Yellowknife, Northwest Territories, Canada.
Russell, D. E., P. H. Whitfield, J. Cai, A. Gunn, R. G. White, and K. Poole, 2013: CARMA’s MERRA-based caribou range climate database. Rangifer, 33(2), 145-152, https://doi.org/10.7557/2.33.2.2535.
Tłı̨chǫ Government, 2022: Ekwǫ Nàxoèhdee K’è 2022 Results. Tłı̨chǫ Research and Training Institute 2023, accessed September 2024, https://research.tlicho.ca/research/bootsontheground.
White, R. G., D. E. Russell, and C. J. Daniel, 2014: Simulation of maintenance, growth and reproduction of caribou and reindeer as influenced by ecological aspects of nutrition, climate change and industrial development using an energy-protein model. Rangifer, 34(2), 1-126, https://doi.org/10.7557/2.34.2.3269.
Arctic Terrestrial Carbon Cycling
Alaska Interagency Coordination Center, Wildland Fire Predictive Services Maps. Alaska Fire Service Bureau of Land Management, accessed 22 October 2024, https://fire.ak.blm.gov/predsvcs/maps.php.
Brown, J., O. Ferrians, J. A. Heginbottom, and E. Melnikov, 2002: Circum-Arctic Map of Permafrost and Ground-Ice Conditions, Version 2. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, CO, USA, accessed 20 January 2017, https://doi.org/10.7265/skbg-kf16.
Canadian Forest Service, 2021: Canadian National Fire Database. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, accessed 22 October 2024, https://cwfis.cfs.nrcan.gc.ca/ha/nfdb.
Copernicus Atmosphere Monitoring Service, 2022: CAMS global biomass burning emissions based on fire radiative power (GFAS). Atmosphere Data Store, accessed 22 October 2024, https://ads.atmosphere.copernicus.eu/datasets/cams-global-fire-emissions-gfas?tab=overview.
Euskirchen, E. S., and Coauthors, 2024: Persistent net release of carbon dioxide and methane from an Alaskan lowland boreal peatland complex. Glob. Change Biol., 30(1), e17139, https://doi.org/10.1111/gcb.17139.
Giglio, L., L. Boschetti, D. P. Roy, M. L. Humber, and C. O. Justice, 2018: The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ., 217, 72-85, https://doi.org/10.1016/j.rse.2018.08.005.
Hugelius, G., and Coauthors, 2014: Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences, 11, 6573-6593, https://doi.org/10.5194/bg-11-6573-2014.
Hugelius, G. and Coauthors, 2024: Permafrost region greenhouse gas budgets suggest a weak CO2 sink and CH4 and N2O sources, but magnitudes differ between top-down and bottom-up methods. Global Biogeochem. Cy., 38(10), e2023GB007969, https://doi.org/10.1029/2023GB007969.
Kasischke, E. S., D. Williams, and D. Barry, 2002: Analysis of the patterns of large fires in the boreal forest region of Alaska. Int. J. Wildland Fire, 11(2), 131-144, https://doi.org/10.1071/WF02023.
Lundin, E., P. Crill, H. Grudd, J. Holst, A. Kristoffersson, A. Meire, M. Mölder, and N. Rakos, 2024: ETC L2 Fluxes, Abisko-Stordalen Palsa Bog, 2021-12-31-2024-08-31. ICOS RI, https://hdl.handle.net/11676/pPnlUbSqmjVMEf0SjkOpoUCX.
NEON (National Ecological Observatory Network), 2024: Bundled data products – eddy covariance (DP4.00200.001), provisional data, accessed 12 September 2024, https://data.neonscience.org/data-products/DP4.00200.001.
Ramage, J., and Coauthors, 2024: The net GHG balance and budget of the permafrost region (2000-2020) from ecosystem flux upscaling. Global Biogeochem. Cy., 38(4), e2023GB007953, https://doi.org/10.1029/2023GB007953.
Scholten, R. C., S. Veraverbeke, Y. Chen, and J. T. Randerson, 2024: Spatial variability in Arctic-boreal fire regimes influenced by environmental and human factors. Nat. Geosci., 17, 866-873, https://doi.org/10.1038/s41561-024-01505-2.
Schuur, E. A. G., and Coauthors, 2021: Tundra underlain by thawing permafrost persistently emits carbon to the atmosphere over 15 years of measurements. J. Geophys. Res.:-Biogeo., 126(6), e2020JG006044, https://doi.org/10.1029/2020JG006044.
Schuur, E. A. G., and Coauthors, 2022: Permafrost and climate change: Carbon cycle feedbacks from the warming Arctic. Annu. Rev. Env. Resour., 47, 343-371, https://doi.org/10.1146/annurev-environ-012220-011847.
See, C. R., and Coauthors, 2024: Decadal increases in carbon uptake offset by respiratory losses across northern permafrost ecosystems. Nat. Climate Change, 14, 853-862, https://doi.org/10.1038/s41558-024-02057-4.
Smith, S. L., V. E. Romanovsky, K. Isaksen, K. E. Nyland, N. I. Shiklomanov, D. A. Streletskiy, and H. H. Christiansen, 2024: Permafrost. State of the Climate in 2023. Bull. Amer. Meteor. Soc., 105(8), S314-S317, https://doi.org/10.1175/BAMS-D-24-0101.1.
Stocks, B. J., and Coauthors, 2002: Large forest fires in Canada, 1959-1997. J. Geophys. Res.-Atmos., 107, 8149, https://doi.org/10.1029/2001JD000484.
Virkkala, A. -M., and Coauthors, 2024a: An increasing Arctic-boreal CO2 sink offset by wildfires and source regions. bioRxiv, https://doi.org/10.1101/2024.02.09.579581.
Virkkala, A. -M., B. M. Rogers, J. D. Watts, K. Arndt, S. Potter, I. Wargowsky, and S. Natali, 2024b: Machine learning-based Arctic-boreal terrestrial ecosystem CO2 fluxes, 2001-2020. ORNL DAAC, Oak Ridge, TN, USA, https://doi.org/10.3334/ORNLDAAC/2377.
Ice Seals of Alaska
Crawford J. A., L. T. Quakenbush, and J. J. Citta, 2015: A comparison of ringed and bearded seal diet, condition and productivity between historical (1975-1984) and recent (2003-2012) periods in the Alaskan Bering and Chukchi seas. Prog. Oceanogr., 136, 133-150, https://doi.org/10.1016/j.pocean.2015.05.011.
Deary, A. L., C. D. Vestfals, F. J. Mueter, E. A. Logerwell, E. D. Goldstein, P. J. Stabeno, S. L. Danielson, R. R. Hopcroft, and J. T. Duffy-Anderson, 2021: Seasonal abundance, distribution, and growth of the early life stages of polar cod (Boreogadus saida) and saffron cod (Eleginus gracilis) in the US Arctic. Polar Biol., 44, 2055-2076, https://doi.org/10.1007/s00300-021-02940-2.
DeMaster, D. P., 1978: Calculation of the average age of sexual maturity in marine mammals. J. Fish. Res. Board Can., 35(6), 912-915, https://doi.org/10.1139/f78-148.
Florko, K. R. N., T. C. Tai, W. W. L. Cheung, S. H. Ferguson, U. R. Sumaila, D. J. Yurkowski, and M. Auger-Méthé, 2021: Predicting how climate change threatens the prey base of Arctic marine predators. Ecol. Lett., 24(12), 2563–2575, https://doi.org/10.1111/ele.13866.
Johnson, M. L., C. H. Fiscus, B. T. Ostenson, and M. L. Barbour, 1966: Marine mammals. Environment of the Cape Thompson Region, Alaska, N. J. Wilimovsky and J. N. Wolfe, Eds., U.S. Atomic Energy Commission, Oak Ridge, TN, 877-924.
Laws, R. M., 1956: Growth and sexual maturity in aquatic mammals. Nature, 178, 193-194, https://doi.org/10.1038/178193a0.
Marsh, J. M., and F. J. Mueter, 2020: Influences of temperature, predators, and competitors on polar cod (Boreogadus saida) at the southern margin of their distribution. Polar Biol., 43, 995-1014, https://doi.org/10.1007/s00300-019-02575-4.
Nelson, M. A., L. T. Quakenbush, B. D. Taras, and Ice Seal Committee, 2019: Subsistence harvest of ringed, bearded, spotted, and ribbon seals in Alaska is sustainable. Endangered Species Res., 40, 1-16, https://doi.org/10.3354/ESR00973.
NOAA Fisheries, 2024a: Diseased ice seals and unusual mortality events: UMEs for ice seals in the Bering and Chukchi Seas of Alaska. Last updated by Alaska Regional Office on 08/28/2024, https://www.fisheries.noaa.gov/alaska/marine-life-distress/diseased-ice-seals-and-unusual-mortality-events#2018%E2%80%932020-unusual-mortality-event.
NOAA Fisheries, 2024b: 2018-2019 Ice Seal Unusual Mortality Event in Alaska (CLOSED): NOAA Fisheries closed the investigation into the Unusual Mortality Event affecting stranded bearded, ringed, and spotted seals in Alaska. Last updated by Alaska Regional Office on 08/28/2024, https://www.fisheries.noaa.gov/alaska/marine-life-distress/2018-2019-ice-seal-unusual-mortality-event-alaska-closed.
Quakenbush, L., 2020: Biological monitoring of ice seals in Alaska to determine health and status of populations-diet, disease, contaminants, reproduction, body condition, growth, and age at maturity. Alaska Department of Fish and Game, final report to NOAA, award number NA16NMF4390029. 47 pp + appendices, https://www.adfg.alaska.gov/static/research/programs/marinemammals/pdfs/biomonitoring_biology _adfg_2020_pinniped_research_ice_seals_report.pdf.
Riedman, M., 1990: The Pinnipeds: Seals, Sea Lions, and Walruses. University of California Press. 439 pp.
Stevenson, D. E., and R. R. Lauth, 2019: Bottom trawl surveys in the northern Bering Sea indicate recent shifts in the distribution of marine species. Polar Biol., 42, 407-421, https://doi.org/10.1007/s00300-018-2431-1.
U.S. Federal Register, 2012a: Threatened status for the Arctic, Okhotsk, and Baltic subspecies of the ringed seal and endangered status for the Ladoga subspecies of the ringed seal; Final Rule. FR 77 (249):76706-76738 (28 December 2012). National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Department of Commerce, Washington, DC.
U.S. Federal Register, 2012b: Threatened status for the Beringia and Okhotsk distinct population segments of the Erignathus barbatus nauticus subspecies of the bearded seal; Final Rule. FR 77 (249): 76740-76768 (28 December 2012). National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Department of Commerce, Washington, DC.
The Original Researchers: Hunters are Scientists Deserving Sustained Support
ACIA, 2005: Arctic Climate Impact Assessment. ACIA Overview report, Cambridge University Press, 1020 pp, accessed 8 September 2024, https://www.amap.no/documents/doc/arctic-arctic-climate-impact-assessment/796.
Ellam Yua, J. Raymond-Yakoubian, R. A. Daniel, and C. Behe, 2022: A framework for co-production of knowledge in the context of Arctic research. Ecol. Soc., 27(1), 34, https://doi.org/10.5751/ES-12960-270134.
ITK, 2018: National Inuit strategy on research. Inuit Tapiriit Kanatami (ITK), Ottawa, accessed 4 September 2024, https://www.itk.ca/wp-content/uploads/2018/04/ITK_NISR-Report_English_low_res.pdf.
Ljubicic, G. J., R. Mearns, S. Okpakok, and S. Robertson, 2022: Nunami iliharniq (Learning from the land): Reflecting on relational accountability in land-based learning and cross-cultural research in Uqšuqtuuq (Gjoa Haven, Nunavut). Arctic Sci., 8(1), 252-291, https://doi.org/10.1139/as-2020-0059.
Pfeifer, P., 2018: From the credibility gap to capacity building: An Inuit critique of Canadian Arctic research. Northern Public Affairs, 6(1), 29-34.
Reed, G., and Coauthors, 2024: For Our Future: Indigenous Resilience Report. Canada National Climate Assessment, Ottawa, Ontario, accessed 8 September 2024, https://changingclimate.ca/site/assets/uploads/sites/7/2024/03/Indigenous-Resilience-Report_Final_EN.pdf.
Simonee, N., J. Alooloo, N. A. Carter, G. Ljubicic, and J. Dawson, 2021: Sila Qanuippa? (How’s the weather?): integrating Inuit Qaujimajatuqangit and environmental forecasting products to support travel safety around Pond Inlet, Nunavut, in a changing climate. Weather Climate Soc., 13, 933-962, https://doi.org/10.1175/WCAS-D-20-0174.1.
Wilson, K. J., T. Bell, A. Arreak, B. Koonoo, D. Angnatsiak, and G. J. Ljubicic, 2020: The Sikumiut Model: Changing the role of non-Indigenous research partners in practice to support Inuit self-determination in research. Arctic Sci., 6(3), 127-153, https://doi.org/10.1139/as-2019-0021.