• Beesley, J. A., C. S. Bretherton, C. Jakob, E. L. Andreas, J. M. Intrieri, and T. A. Uttal, 2000: A comparison of cloud and boundary layer variables in the ECMWF forecast model with observations at Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp. J. Geophys. Res., 105, 12 33712 349, https://doi.org/10.1029/2000JD900079.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bekryaev, R. V., I. V. Polyakov, and V. A. Alexeev, 2010: Role of polar amplification in long-term surface air temperature variations and modern Arctic warming. J. Climate, 23, 38883906, https://doi.org/10.1175/2010JCLI3297.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boisvert, L. N., and J. C. Stroeve, 2015: The Arctic is becoming warmer and wetter as revealed by the Atmospheric Infrared Sounder. Geophys. Res. Lett., 42, 44394446, https://doi.org/10.1002/2015GL063775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boisvert, L. N., D. L. Wu, T. Vihma, and J. Susskind, 2015: Verification of air/surface humidity differences from AIRS and ERA-Interim in support of turbulent flux estimation in the Arctic. J. Geophys. Res, Atmos., 120, 945963, https://doi.org/10.1002/2014JD021666.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boisvert, L. N., M. A. Webster, A. A. Petty, T. Markus, D. H. Bromwich, and R. I. Cullather, 2018: Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses. J. Climate, 31, 84418462, https://doi.org/10.1175/JCLI-D-18-0125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., and Coauthors, 2015: MERRA-2 : Initial evaluation of the climate. NASA/TM-2015-104606, Vol. 43, 139 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf.

  • Bromwich, D. H., K. M. Hines, and L.-S. Bai, 2009: Development and testing of Polar Weather Research and Forecasting model: 2. Arctic Ocean. J. Geophys. Res., 114, D08122, https://doi.org/10.1029/2008JD010300.

    • Search Google Scholar
    • Export Citation
  • Bromwich, D. H., A. B. Wilson, L.-S. Bai, G. W. K. Moore, and P. Bauer, 2014: Contrasting the regional Arctic System Reanalysis with the global ERA-Interim Reanalysis for the Arctic. Quart. J. Roy. Meteor. Soc., 142, 644658, https://doi.org/10.1002/qj.2527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bromwich, D. H., A. B. Wilson, L. S. Bai, G. W. K. Moore, and P. Bauer, 2016: A comparison of the regional Arctic System Reanalysis and the global ERA-Interim Reanalysis for the Arctic. Quart. J. Roy. Meteor. Soc., 142, 644658, https://doi.org/10.1002/qj.2527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bromwich, D. H., and Coauthors, 2018: The Arctic System Reanalysis, version 2. Bull. Amer. Meteor. Soc., 99, 805828, https://doi.org/10.1175/BAMS-D-16-0215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, L., S. R. Hudson, V. P. Walden, and M. A. Granskog, 2017: Meteorological conditions in a thinner Arctic sea ice regime from winter through summer during the Norwegian Young Sea Ice expedition (N-ICE2015). J. Geophys. Res. Atmos., 122, 72357259, https://doi.org/10.1002/2016JD026034.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cullather, R. I., and M. G. Bosilovich, 2012: The energy budget of the polar atmosphere in MERRA. J. Climate, 25, 524, https://doi.org/10.1175/2011JCLI4138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cullather, R. I., D. H. Bromwich, and M. C. Serreze, 2000: The atmospheric hydrology cycle over the Arctic basin from reanalyses. Part I: Comparison with observations and previous studies. J. Climate, 13, 923937, https://doi.org/10.1175/1520-0442(2000)013<0923:TAHCOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cullather, R. I., T. M. Hamill, D. Bromwich, X. Wu, and P. Taylor, 2016: Systematic Improvements of Reanalyses in the Arctic (SIRTA): A white paper. 51 pp., http://www.iarpccollaborations.org/uploads/cms/documents/sirta-white-paper-final.pdf.

  • de Boer, G., and Coauthors, 2014: Near-surface meteorology during the Arctic Summer Cloud Ocean Study (ASCOS): Evaluation of reanalyses and global climate models. Atmos. Chem. Phys., 14, 427445, https://doi.org/10.5194/acp-14-427-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dorn, W., K. Dethloff, and A. Rinke, 2009: Improved simulation of feedbacks between atmosphere and sea ice over the Arctic Ocean in a coupled regional climate model. Ocean Modell., 29, 103114, https://doi.org/10.1016/j.ocemod.2009.03.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Engström, A., J. Karlsson, and G. Svensson, 2014: The importance of representing mixed-phase clouds for simulating distinctive atmospheric states in the Arctic. J. Climate, 27, 265272, https://doi.org/10.1175/JCLI-D-13-00271.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fetterer, F., K. Knowles, W. Meier, M. Savoie, and A. K. Windnagel, 2017: Sea Ice Index, version 3 [subset: N_201502_extent_v3.0.tif]. National Snow and Ice Data Center, accessed 15 August 2018, https://doi.org/10.7265/N5K072F8.

    • Crossref
    • Export Citation
  • Finkelstein, P. L., and P. F. Sims, 2001: Sampling error in eddy correlation flux measurements. J. Geophys. Res., 106, 35033509, https://doi.org/10.1029/2000JD900731.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graham, R. M., and Coauthors, 2017a: A comparison of the two Arctic atmospheric winter states observed during N-ICE2015 and SHEBA. J. Geophys. Res. Atmos., 122, 57165737, https://doi.org/10.1002/2016JD025475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graham, R. M., L. Cohen, A. A. Petty, L. N. Boisvert, A. Rinke, S. R. Hudson, M. Nicolaus, and M. A. Granskog, 2017b: Increasing frequency and duration of Arctic winter warming events. Geophys. Res. Lett., 44, 69746983, https://doi.org/10.1002/2017GL073395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Granskog, M. A., P. Assmy, S. Gerland, G. Spreen, H. Steen, and L. H. Smedsrud, 2016: Arctic research on thin ice: Consequences of Arctic sea ice loss. Eos, Trans. Amer. Geophys. Union, 97, 2226, https://doi.org/10.1029/2016EO044097.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Granskog, M. A., I. Fer, A. Rinke, and H. Steen, 2018: Atmosphere–ice–ocean–ecosystem processes in a thinner Arctic sea ice regime: The Norwegian Young Sea ICE (N-ICE2015) expedition. J. Geophys. Res. Oceans, 123, 15861594, https://doi.org/10.1002/2017JC013328.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harada, Y., and Coauthors, 2016: The JRA-55 Reanalysis: Representation of atmospheric circulation and climate variability. J. Meteor. Soc. Japan, 94, 269302, https://doi.org/10.2151/jmsj.2016-015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hines, K. M., and D. H. Bromwich, 2008: Development and testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet meteorology. Mon. Wea. Rev., 136, 19711989, https://doi.org/10.1175/2007MWR2112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hines, K. M., D. H. Bromwich, L. Bai, C. M. Bitz, J. G. Powers, and K. W. Manning, 2015: Sea ice enhancements to Polar WRF. Mon. Wea. Rev., 143, 23632385, https://doi.org/10.1175/MWR-D-14-00344.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hudson, S. R., L. Cohen, and V. Walden, 2015: N-ICE2015 surface meteorology [data set]. Norwegian Polar Institute, accessed 15 January 2018, https://doi.org/10.21334/npolar.2015.056a61d1.

    • Crossref
    • Export Citation
  • Hudson, S. R., L. Cohen, and V. Walden, 2016: N-ICE2015 surface broadband radiation data [data set]. Norwegian Polar Institute, accessed 20 April 2017, https://doi.org/10.21334/npolar.2016.a89cb766.

    • Crossref
    • Export Citation
  • Hudson, S. R., L. Cohen, M. Kayser, M. Maturilli, J. Kim, S. Park, W. Moon, and M. A. Granskog, 2017: N-ICE2015 atmospheric profiles from radiosondes [data set]. Norwegian Polar Institute, accessed 5 March 2019, https://doi.org/10.21334/npolar.2017.216df9b3.

    • Crossref
    • Export Citation
  • Hyland, R. W., and A. Wexler, 1983: Formulations for the thermodynamic properties of the saturated phases of H2O from 173.15 K to 473.15 K. ASHRAE Trans., 89, 500519.

    • Search Google Scholar
    • Export Citation
  • Jakobson, E., T. Vihma, T. Palo, L. Jakobson, H. Keernik, and J. Jaagus, 2012: Validation of atmospheric reanalyses over the central Arctic Ocean. Geophys. Res. Lett., 39, L10802, https://doi.org/10.1029/2012GL051591.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kapsch, M. L., N. Skific, R. G. Graversen, M. Tjernström, and J. A. Francis, 2019: Summers with low Arctic sea ice linked to persistence of spring atmospheric circulation patterns. Climate Dyn., 52, 24972512, https://doi.org/10.1007/s00382-018-4279-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kayser, M., and Coauthors, 2017: Vertical thermodynamic structure of the troposphere during the Norwegian young sea ICE expedition (N-ICE2015). J. Geophys. Res. Atmos., 122, 10 85510 872, https://doi.org/10.1002/2016JD026089.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, J., G. Spreen, S. Gerland, C. Haas, S. Hendricks, L. Kaleschke, and C. Wang, 2017: Sea-ice thickness from field measurements in the northwestern Barents Sea. J. Geophys. Res. Oceans, 122, 14971512, https://doi.org/10.1002/2016JC012199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klaus, D., K. Dethloff, W. Dorn, A. Rinke, and D. L. Wu, 2016: New insight of Arctic cloud parameterization from regional climate model simulations, satellite-based, and drifting station data. Geophys. Res. Lett., 43, 54505459, https://doi.org/10.1002/2015GL067530.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and Coauthors, 2015: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Künsch, H. R., 1989: The jackknife and the bootstrap for general stationary observations. Ann. Stat., 17, 12171241, https://doi.org/10.1214/aos/1176347265.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindsay, R., and A. Schweiger, 2015: Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations. Cryosphere, 9, 269283, https://doi.org/10.5194/tcd-8-4545-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindsay, R., M. Wensnahan, A. Schweiger, and J. Zhang, 2014: Evaluation of seven different atmospheric reanalysis products in the Arctic. J. Climate, 27, 25882606, https://doi.org/10.1175/JCLI-D-13-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, J., Z. Zhang, Y. Y. Hu, L. Chen, Y. Dai, and X. Ren, 2008: Assessment of surface air temperature over the Arctic Ocean in reanalysis and IPCC AR4 model simulations with IABP/POLES observations. J. Geophys. Res., 113, D10105, https://doi.org/10.1029/2007JD009380.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Makshtas, A., D. Atkinson, M. Kulakov, S. Shutilin, R. Krishfield, and A. Proshutinsky, 2007: Atmospheric forcing validation for modeling the central Arctic. Geophys. Res. Lett., 34, L20706, https://doi.org/10.1029/2007GL031378.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marcq, S., and J. Weiss, 2012: Influence of sea ice lead-width distribution on turbulent heat transfer between the ocean and the atmosphere. Cryosphere, 6, 143156, https://doi.org/10.5194/tc-6-143-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maykut, G. A., 1978: Energy exchange over young sea ice in the central Arctic. J. Geophys. Res. Oceans, 83, 36463658, https://doi.org/10.1029/JC083IC07P03646.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. de Boer, G. Feingold, J. Harrington, M. D. Shupe, and K. Sulia, 2011: Resilience of persistent Arctic mixed-phase clouds. Nat. Geosci., 5, 1117, https://doi.org/10.1038/ngeo1332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mortin, J., G. Svensson, R. G. Graversen, M. L. Kapsch, J. C. Stroeve, and L. N. Boisvert, 2016: Melt onset over Arctic sea ice controlled by atmospheric moisture transport. Geophys. Res. Lett., 43, 66366642, https://doi.org/10.1002/2016GL069330.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naakka, T., T. Nygård, and T. Vihma, 2018: Arctic humidity inversions: Climatology and processes. J. Climate, 31, 37653787, https://doi.org/10.1175/JCLI-D-17-0497.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Overland, J. E., and M. Wang, 2016: Recent extreme Arctic temperatures are due to a split polar vortex. J. Climate, 29, 56095616, https://doi.org/10.1175/JCLI-D-16-0320.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, D.-S. R., S. Lee, and S. B. Feldstein, 2015: Attribution of the recent winter sea-ice decline over the Atlantic sector of the Arctic Ocean. J. Climate, 28, 40274033, https://doi.org/10.1175/JCLI-D-15-0042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Patton, A., D. N. Politis, and H. White, 2009: Correction to “Automatic block-length selection for the dependent bootstrap” by D. Politis and H. White. Econometric Rev., 28, 372375, https://doi.org/10.1080/07474930802459016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pithan, F., B. Medeiros, and T. Mauritsen, 2014: Mixed-phase clouds cause climate model biases in Arctic wintertime temperature inversions. Climate Dyn., 43, 289303, https://doi.org/10.1007/s00382-013-1964-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pithan, F., and Coauthors, 2016: Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice: The Larcform1 single column model intercomparison. J. Adv. Model. Earth Syst., 8, 13451357, https://doi.org/10.1002/2016MS000630.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rinke, A., K. Dethloff, W. Dorn, D. Handorf, and J. C. Moore, 2013: Simulated Arctic atmospheric feedbacks associated with late summer sea ice anomalies. J. Geophys. Res. Atmos., 118, 76987714, https://doi.org/10.1002/jgrd.50584.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rinke, A., M. Maturilli, R. M. Graham, H. Matthes, D. Handrof, L. Cohen, S. R. Hudson, and J. C. Moore, 2017: Extreme cyclone events in the Arctic: Wintertime variability and trends. Environ. Res. Lett., 12, 094006, https://doi.org/10.1088/1748-9326/aa7def.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151058, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schweiger, A., R. Lindsay, J. Zhang, M. Steele, H. Stern, and R. Kwok, 2011: Uncertainty in modeled Arctic sea ice volume. J. Geophys. Res., 116, C00D06, https://doi.org/10.1029/2011JC007084.

    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2012: Declining summer snowfall in the Arctic: Causes, impacts and feedbacks. Climate Dyn., 38, 22432256, https://doi.org/10.1007/s00382-011-1105-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., I. Simmonds, C. Deser, and R. Tomas, 2013: The atmospheric response to three decades of observed Arctic sea ice loss. J. Climate, 26, 12301248, https://doi.org/10.1175/JCLI-D-12-00063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and J. A. Francis, 2006: The Arctic amplification debate. Climatic Change, 76, 241264, https://doi.org/10.1007/s10584-005-9017-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., A. P. Barrett, and J. Stroeve, 2012: Recent changes in tropospheric water vapor over the Arctic as assessed from radiosondes and atmospheric reanalyses. J. Geophys. Res., 117, D10104, https://doi.org/10.1029/2011JD017421.

    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., and P. Poli, 2014: Arctic warming in ERA-Interim and other analyses. Quart. J. Roy. Meteor. Soc., 141, 11471162, https://doi.org/10.1002/qj.2422.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 30193032, https://doi.org/10.1175/MWR2830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Solomon, A., M. D. Shupe, O. Persson, H. Morrison, T. Yamaguchi, P. M. Caldwell, and G. de Boer, 2014: The sensitivity of springtime Arctic mixed-phase stratocumulus clouds to surface-layer and cloud-top inversion-layer moisture sources. J. Atmos. Sci., 71, 574595, https://doi.org/10.1175/JAS-D-13-0179.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sotiropoulou, G., J. Sedlar, R. Forbes, and M. Tjernström, 2015: Summer Arctic clouds in the ECMWF forecast model: An evaluation of cloud parametrization schemes. Quart. J. Roy. Meteor. Soc., 142, 387400, https://doi.org/10.1002/qj.2658.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J., and D. Notz, 2018: Changing state of Arctic sea ice across all seasons Changing state of Arctic sea ice across all seasons. Environ. Res. Lett., 13, 103001, https://doi.org/10.1088/1748-9326/aade56.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J., M. C. Serreze, M. M. Holland, J. E. Kay, J. Malanik, and A. P. Barrett, 2012: The Arctic’s rapidly shrinking sea ice cover: A research synthesis. Climatic Change, 110, 10051027, https://doi.org/10.1007/s10584-011-0101-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tastula, E. M., T. Vihma, E. L Andreas, and B. Galperin, 2013: Validation of the diurnal cycles in atmospheric reanalyses over Antarctic sea ice. J. Geophys. Res., 118, 41944204, https://doi.org/10.1002/jgrd.50336.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, P., B. Hegyi, R. Boeke, and L. Boisvert, 2018: On the increasing importance of air–sea exchanges in a thawing Arctic: A review. Atmosphere, 9, 41, https://doi.org/10.3390/atmos9020041.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tjernström, M., and R. G. Graversen, 2009: The vertical structure of the lower Arctic troposphere analysed from observations and the ERA-40 reanalysis. Quart. J. Roy. Meteor. Soc., 135, 431443, https://doi.org/10.1002/qj.380.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walden, V. P., S. R. Hudson, L. Cohen, S. Y. Murphy, and M. A. Granskog, 2017a: Atmospheric components of the surface energy budget over young sea ice : Results from the N-ICE2015 campaign. J. Geophys. Res. Atmos., 122, 84278446, https://doi.org/10.1002/2016JD026091.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walden, V. P., S. Murphy, S. R. Hudson, and L. Cohen, 2017b: N-ICE2015 atmospheric turbulent fluxes [data set]. Norwegian Polar Institute, accessed 2 August 2018, https://doi.org/10.21334/npolar.2017.298013b7.

    • Crossref
    • Export Citation
  • Walsh, J. E., and W. L. Chapman, 1998: Arctic cloud–radiation–temperature associations in observational data and atmospheric reanalyses. J. Climate, 11, 30303045, https://doi.org/10.1175/1520-0442(1998)011<3030:ACRTAI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., W. L. Chapman, and D. H. Portis, 2009: Arctic cloud fraction and radiative fluxes in atmospheric reanalyses. J. Climate, 22, 23162334, https://doi.org/10.1175/2008JCLI2213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wesslén, C., M. Tjernström, D. H. Bromwich, G. de Boer, A. M. L. Ekman, L. S. Bai, and S. H. Wang, 2014: The Arctic summer atmosphere: An evaluation of reanalyses using ASCOS data. Atmos. Chem. Phys., 14, 26052624, https://doi.org/10.5194/acp-14-2605-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyser, K., and Coauthors, 2008: An evaluation of Arctic cloud and radiation processes during the SHEBA year: Simulation results from eight Arctic regional climate models. Climate Dyn., 30, 203223, https://doi.org/10.1007/s00382-007-0286-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluation of Six Atmospheric Reanalyses over Arctic Sea Ice from Winter to Early Summer

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  • 1 Norwegian Polar Institute, Fram Centre, Tromsø, Norway
  • 2 Department of Meteorology, University of Bonn, Bonn, Germany
  • 3 Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
  • 4 Department of Physics and Technology, University of Tromsø, Tromsø, Norway
  • 5 Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington
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Abstract

This study evaluates the performance of six atmospheric reanalyses (ERA-Interim, ERA5, JRA-55, CFSv2, MERRA-2, and ASRv2) over Arctic sea ice from winter to early summer. The reanalyses are evaluated using observations from the Norwegian Young Sea Ice campaign (N-ICE2015), a 5-month ice drift in pack ice north of Svalbard. N-ICE2015 observations include surface meteorology, vertical profiles from radiosondes, as well as radiative and turbulent heat fluxes. The reanalyses simulate surface analysis variables well throughout the campaign, but have difficulties with most forecast variables. Wintertime (January–March) correlation coefficients between the reanalyses and observations are above 0.90 for the surface pressure, 2-m temperature, total column water vapor, and downward longwave flux. However, all reanalyses have a positive wintertime 2-m temperature bias, ranging from 1° to 4°C, and negative (i.e., upward) net longwave bias of 3–19 W m−2. These biases are associated with poorly represented surface inversions and are largest during cold-stable periods. Notably, the recent ERA5 and ASRv2 datasets have some of the largest temperature and net longwave biases, respectively. During spring (April–May), reanalyses fail to simulate observed persistent cloud layers. Therefore they overestimate the net shortwave flux (5–79 W m−2) and underestimate the net longwave flux (8–38 W m−2). Promisingly, ERA5 provides the best estimates of downward radiative fluxes in spring and summer, suggesting improved forecasting of Arctic cloud cover. All reanalyses exhibit large negative (upward) residual heat flux biases during winter, and positive (downward) biases during summer. Turbulent heat fluxes over sea ice are simulated poorly in all seasons.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Stephen R. Hudson, stephen.hudson@npolar.no

Abstract

This study evaluates the performance of six atmospheric reanalyses (ERA-Interim, ERA5, JRA-55, CFSv2, MERRA-2, and ASRv2) over Arctic sea ice from winter to early summer. The reanalyses are evaluated using observations from the Norwegian Young Sea Ice campaign (N-ICE2015), a 5-month ice drift in pack ice north of Svalbard. N-ICE2015 observations include surface meteorology, vertical profiles from radiosondes, as well as radiative and turbulent heat fluxes. The reanalyses simulate surface analysis variables well throughout the campaign, but have difficulties with most forecast variables. Wintertime (January–March) correlation coefficients between the reanalyses and observations are above 0.90 for the surface pressure, 2-m temperature, total column water vapor, and downward longwave flux. However, all reanalyses have a positive wintertime 2-m temperature bias, ranging from 1° to 4°C, and negative (i.e., upward) net longwave bias of 3–19 W m−2. These biases are associated with poorly represented surface inversions and are largest during cold-stable periods. Notably, the recent ERA5 and ASRv2 datasets have some of the largest temperature and net longwave biases, respectively. During spring (April–May), reanalyses fail to simulate observed persistent cloud layers. Therefore they overestimate the net shortwave flux (5–79 W m−2) and underestimate the net longwave flux (8–38 W m−2). Promisingly, ERA5 provides the best estimates of downward radiative fluxes in spring and summer, suggesting improved forecasting of Arctic cloud cover. All reanalyses exhibit large negative (upward) residual heat flux biases during winter, and positive (downward) biases during summer. Turbulent heat fluxes over sea ice are simulated poorly in all seasons.

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Corresponding author: Stephen R. Hudson, stephen.hudson@npolar.no
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