• Atkinson, D. E., and K. Gajewski, 2002: High-resolution estimation of summer surface air temperature in the Canadian Arctic Archipelago. J. Climate, 15, 36013614, https://doi.org/10.1175/1520-0442(2002)015<3601:HREOSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Baker, N. L., and R. Daley, 2000: Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Quart. J. Roy. Meteor. Soc., 126, 14311454, https://doi.org/10.1002/qj.49712656511.

    • Search Google Scholar
    • Export Citation
  • 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. Oceanic Technol., 37, 341349, https://doi.org/10.1175/JTECH-D-19-0177.1.

    • Search Google Scholar
    • Export Citation
  • Barton, N., and Coauthors, 2020: The Navy’s Earth System Prediction Capability: A new global coupled atmosphere‐ocean‐sea ice prediction system designed for daily to subseasonal forecasting. Earth Space Sci., 7, e2020EA001199, https://doi.org/10.1029/2020EA001199.

    • Search Google Scholar
    • Export Citation
  • Bauer, P., L. Magnusson, J. N. Thépaut, and T. M. Hamill, 2016: Aspects of ECMWF model performance in polar areas. Quart. J. Roy. Meteor. Soc., 142, 583596, https://doi.org/10.1002/qj.2449.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Bentamy, A., K. B. Katsaros, A. M. Mestas-Nuñez, W. M. Drennan, E. B. Forde, and H. Roquet, 2003: Satellite estimates of wind speed and latent heat flux over the global oceans. J. Climate, 16, 637656, https://doi.org/10.1175/1520-0442(2003)016<0637:SEOWSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bhatt, U. S., and Coauthors, 2017: Changing seasonality of panarctic tundra vegetation in relationship to climatic variables. Environ. Res. Lett., 12, 055003, https://doi.org/10.1088/1748-9326/aa6b0b.

    • Search Google Scholar
    • Export Citation
  • Boutin, G., C. Lique, F. Ardhuin, C. Rousset, C. Talandier, M. Accensi, and F. Girard-Ardhuin, 2020: Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone. Cryosphere, 14, 709735, https://doi.org/10.5194/tc-14-709-2020.

    • Search Google Scholar
    • Export Citation
  • Cardinali, C., 2009: Monitoring the observation impact on the short-range forecast. Quart. J. Roy. Meteor. Soc., 135, 239250, https://doi.org/10.1002/qj.366.

    • Search Google Scholar
    • Export Citation
  • Castro, S. L., G. A. Wick, and M. Steele, 2016: Validation of satellite sea surface temperature analyses in the Beaufort Sea using UpTempO buoys. Remote Sens. Environ., 187, 458475, https://doi.org/10.1016/j.rse.2016.10.035.

    • Search Google Scholar
    • Export Citation
  • Chiodi, A. M., and Coauthors, 2021: Exploring the Pacific Arctic seasonal ice zone with saildrone USVs. Front. Mar. Sci., 8, 640690, https://doi.org/10.3389/fmars.2021.640697.

    • Search Google Scholar
    • Export Citation
  • Chu, D., S. Parker-Stetter, L. C. Hufnagle, R. Thomas, J. Getsiv-Clemons, S. Gauthier, and C. Stanley, 2019: 2018 Unmanned Surface Vehicle (Saildrone) acoustic survey off the west coasts of the United States and Canada. OCEANS 2019 MTS/IEEE SEATTLE, Seattle, WA, Institute of Electrical and Electronics Engineers, https://doi.org/10.23919/OCEANS40490.2019.8962778.

    • Search Google Scholar
    • Export Citation
  • CMC, 2019: Global Ensemble Prediction System (GEPS) update from version 5.0.0 to version 6.0.0. Environment and Climate Change Canada, Tech Doc., 72 pp., https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/technote_geps-600_20190703_e.pdf.

    • Search Google Scholar
    • Export Citation
  • Cokelet, E. D., C. Meinig, N. Lawrence-Slavas, P. J. Stabeno, C. W. Mordy, H. M. Tabisola, R. Jenkins, and J. N. Cross, 2015: The use of Saildrones to examine spring conditions in the Bering Sea. OCEANS 2015-MTS/IEEE Washington, Washington, DC, Institute of Electrical and Electronics Engineers, https://doi.org/10.23919/OCEANS.2015.7404357.

    • Search Google Scholar
    • Export Citation
  • Danielson, S. L., and Coauthors, 2020: Manifestation and consequences of warming and altered heat fluxes over the Bering and Chukchi Sea continental shelves. Deep-Sea Res. II, 177, 104781, https://doi.org/10.1016/j.dsr2.2020.104781.

    • Search Google Scholar
    • Export Citation
  • De Robertis, A., and Coauthors, 2019: Long-term measurements of fish backscatter from Saildrone unmanned surface vehicles and comparison with observations from a noise-reduced research vessel. ICES J. Mar. Sci., 76, 24592470, https://doi.org/10.1093/icesjms/fsz124.

    • Search Google Scholar
    • Export Citation
  • DeGrandpre, M., W. Evans, M. L. Timmermans, R. Krisheld, B. Williams, and M. Steele, 2020: Changes in the Arctic Ocean carbon cycle with diminishing ice cover. Geophys. Res. Lett., 47, e2020GL088051, https://doi.org/10.1029/2020GL088051.

    • Search Google Scholar
    • Export Citation
  • Donlon, C. J., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, 2012: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sens. Environ., 116, 140158, https://doi.org/10.1016/j.rse.2010.10.017.

    • Search Google Scholar
    • Export Citation
  • ECMWF, 2018: IFS documentation CY45R1—Part I: Observations. ECMWF, https://www.ecmwf.int/node/18711.

  • ECMWF, 2019: IFS documentation CY46R1—Part I: Observations. ECMWF, https://www.ecmwf.int/node/19305.

  • Errico, R. M., 2007: Interpretations of an adjoint-derived observational impact measure. Tellus, 59A, 273276, https://doi.org/10.1111/j.1600-0870.2006.00217.x.

    • Search Google Scholar
    • Export Citation
  • Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson, 2003: Bulk parameterization of air–sea fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571591, https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Figueroa, S. N., and Coauthors, 2016: The Brazilian Global Atmospheric Model (BAM): Performance for tropical rainfall forecasting and sensitivity to convective scheme and horizontal resolution. Wea. Forecasting, 31, 15471572, https://doi.org/10.1175/WAF-D-16-0062.1.

    • Search Google Scholar
    • Export Citation
  • Francis, J. A., 2002: Validation of reanalysis upper‐level winds in the Arctic with independent rawinsonde data. Geophys. Res. Lett., 29, 1315, https://doi.org/10.1029/2001GL014578.

    • Search Google Scholar
    • Export Citation
  • Frolov, S., W. Campbell, B. Ruston, C. H. Bishop, D. Kuhl, M. Flatau, and J. McLay, 2020: Assimilation of low-peaking satellite observations using the coupled interface framework. Mon. Wea. Rev., 148, 637665, https://doi.org/10.1175/MWR-D-19-0029.1.

    • Search Google Scholar
    • Export Citation
  • Gentemann, C. L., and Coauthors, 2020: Saildrone: Adaptively sampling the marine environment. Bull. Amer. Meteor. Soc., 101, E744E762, https://doi.org/10.1175/BAMS-D-19-0015.1.

    • Search Google Scholar
    • Export Citation
  • Goessling, H. F., and Coauthors, 2016: Paving the way for the year of polar prediction. Bull. Amer. Meteor. Soc., 97, ES85ES88, https://doi.org/10.1175/BAMS-D-15-00270.1.

    • Search Google Scholar
    • Export Citation
  • Good, S., and Coauthors, 2020: The current configuration of the OSTIA system for operational production of foundation sea surface temperature and ice concentration analyses. Remote Sens., 12, 720, https://doi.org/10.3390/rs12040720.

    • Search Google Scholar
    • Export Citation
  • Gordon, N., T. Jung, and S. Klebe, 2014: The polar prediction project. WMO Bull., 63, 4244.

  • Grebmeier, J. M., S. E. Moore, L. W. Cooper, and K. E. Frey, 2019: The distributed biological observatory: A change detection array in the Pacific Arctic—An introduction. Deep-Sea Res. II, 162, 17, https://doi.org/10.1016/j.dsr2.2019.05.005.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Hunke, E. C., D. Notz, A. K. Turner, and M. Vancoppenolle, 2011: The multiphase physics of sea ice: A review for model developers. Cryosphere, 5, 9891009, https://doi.org/10.5194/tc-5-989-2011.

    • Search Google Scholar
    • Export Citation
  • Inoue, J., 2020: Review of forecast skills for weather and sea ice in supporting Arctic navigation. Polar Sci., 27, 100523, https://doi.org/10.1016/j.polar.2020.100523.

    • Search Google Scholar
    • Export Citation
  • Judt, F., 2018: Insights into atmospheric predictability through global convection-permitting model simulations. J. Atmos. Sci., 75, 14771497, https://doi.org/10.1175/JAS-D-17-0343.1.

    • Search Google Scholar
    • Export Citation
  • Judt, F., 2020: Atmospheric predictability of the tropics, middle latitudes, and polar regions explored through global storm-resolving simulations. J. Atmos. Sci., 77, 257276, https://doi.org/10.1175/JAS-D-19-0116.1.

    • Search Google Scholar
    • Export Citation
  • Jung, T., and M. Matsueda, 2016: Verification of global numerical weather forecasting systems in polar regions using TIGGE data. Quart. J. Roy. Meteor. Soc., 142, 574582, https://doi.org/10.1002/qj.2437.

    • Search Google Scholar
    • Export Citation
  • Jung, T., and Coauthors, 2016: Advancing polar prediction capabilities on daily to seasonal time scales. Bull. Amer. Meteor. Soc., 97, 16311647, https://doi.org/10.1175/BAMS-D-14-00246.1.

    • Search Google Scholar
    • Export Citation
  • Køltzow, M., B. Casati, E. Bazile, T. Haiden, and T. Valkonen, 2019: An NWP model intercomparison of surface weather parameters in the European Arctic during the year of polar prediction special observing period Northern Hemisphere 1. Wea. Forecasting, 34, 959983, https://doi.org/10.1175/WAF-D-19-0003.1.

    • Search Google Scholar
    • Export Citation
  • Krishnamurthy, V., 2019: Predictability of weather and climate. Earth Space Sci., 6, 10431056, https://doi.org/10.1029/2019EA000586.

  • Kuhn, C. E., and Coauthors, 2020: Test of unmanned surface vehicles to conduct remote focal follow studies of a marine predator. Mar. Ecol. Prog. Ser., 635, 17, https://doi.org/10.3354/meps13224.

    • Search Google Scholar
    • Export Citation
  • Lawrence, H., N. Bormann, I. Sandu, J. Day, J. Farnan, and P. Bauer, 2019: Use and impact of Arctic observations in the ECMWF numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 145, 34323454, https://doi.org/10.1002/qj.3628.

    • Search Google Scholar
    • Export Citation
  • Leutbecher, M., 2019: Ensemble size: How suboptimal is less than infinity? Quart. J. Roy. Meteor. Soc., 145, 107128, https://doi.org/10.1002/qj.3387.

    • Search Google Scholar
    • Export Citation
  • Lewis, K. M., G. L. van Dijken, and K. R. Arrigo, 2020: Changes in phytoplankton concentration now drive increased Arctic Ocean primary production. Science, 369, 198202, https://doi.org/10.1126/science.aay8380.

    • Search Google Scholar
    • Export Citation
  • Li, M., and Coauthors, 2019: Circulation of the Chukchi Sea shelfbreak and slope from moored timeseries. Prog. Oceanogr., 172, 1433, https://doi.org/10.1016/j.pocean.2019.01.002.

    • Search Google Scholar
    • Export Citation
  • Lindsay, R., 1998: Temporal variability of the energy balance of thick Arctic pack ice. J. Climate, 11, 313333, https://doi.org/10.1175/1520-0442(1998)011<0313:TVOTEB>2.0.CO;2.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., and J. R. Key, 2016: Assessment of Arctic cloud cover anomalies in atmospheric reanalysis products using satellite data. J. Climate, 29, 60656083, https://doi.org/10.1175/JCLI-D-15-0861.1.

    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., and R. T. Marriot, 2014: Forecast sensitivity to observations in the Met Office Global numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 140, 209224, https://doi.org/10.1002/qj.2122.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1982: Atmospheric predictability experiments with a large numerical model. Tellus, 34, 505513, https://doi.org/10.3402/tellusa.v34i6.10836.

    • Search Google Scholar
    • Export Citation
  • Lüpkes, C., T. Vihma, E. Jakobson, G. König‐Langlo, and A. Tetzlaff, 2010: Meteorological observations from ship cruises during summer to the central Arctic: A comparison with reanalysis data. Geophys. Res. Lett., 37, L09810, https://doi.org/10.1029/2010GL042724.

    • Search Google Scholar
    • Export Citation
  • Mears, C. A., K. Deborah, and F. J. Wentz, 2001: Comparison of special sensor microwave imager and buoy measured wind speeds from 1987 to 1997. J. Geophys. Res., 106, 11 71911 729, https://doi.org/10.1029/1999JC000097.

    • Search Google Scholar
    • Export Citation
  • Meier, W. N., and Coauthors, 2019: Sea ice. NOAA Arctic Report Card, NOAA, https://doi.org/10.25923/y2wd-fn85.

  • Meinig, C., N. Lawrence-Slavas, R. Jenkins, and H. M. Tabisola, 2015: The use of Saildrones to examine spring conditions in the Bering Sea: Vehicle specification and mission performance. OCEANS 2015-MTS/IEEE Washington, Washington, DC, Institute of Electrical and Electronics Engineers, https://doi.org/10.23919/OCEANS.2015.7404348.

    • Search Google Scholar
    • Export Citation
  • Meinig, C., and Coauthors, 2019: Public–private partnerships to advance regional ocean observing capabilities: A Saildrone and NOAA-PMEL case study and future considerations to expand to global scale observing. Front. Mar. Sci., 6, 448, https://doi.org/10.3389/fmars.2019.00448.

    • Search Google Scholar
    • Export Citation
  • Mordy, C. W., and Coauthors, 2017: Advances in ecosystem research: Saildrone surveys of oceanography, fish, and marine mammals in the Bering Sea. Oceanography, 30, 113115, https://doi.org/10.5670/oceanog.2017.230.

    • Search Google Scholar
    • Export Citation
  • Mulholland, D. P., P. Laloyaux, K. Haines, and M. A. Balmaseda, 2015: Origin and impact of initialization shocks in coupled atmosphere–ocean forecasts. Mon. Wea. Rev., 143, 46314644, https://doi.org/10.1175/MWR-D-15-0076.1.

    • Search Google Scholar
    • Export Citation
  • Naakka, T., T. Nygård, M. Tjernstrom, T. Vihma, R. Pirazzini, and I. M. Brooks, 2019: The impact of radiosounding observations on numerical weather prediction analyses in the Arctic. Geophys. Res. Lett., 46, 85278535, https://doi.org/10.1029/2019GL083332.

    • Search Google Scholar
    • Export Citation
  • Overland, J. E., and Coauthors, 2019: Surface air temperature. NOAA Arctic Report Card 2020, NOAA, 7 pp., https://doi.org/10.25923/gcw8-2z06.

    • Search Google Scholar
    • Export Citation
  • Peixoto, J. P., and A. H. Oort, 1992: Physics of Climate. American Institute of Physics, 520 pp.

  • Sallila, H., S. L. Farrell, J. McCurry, and E. Rinne, 2019: Assessment of contemporary satellite sea ice thickness products for Arctic sea ice. Cryosphere, 13, 11871213, https://doi.org/10.5194/tc-13-1187-2019.

    • Search Google Scholar
    • Export Citation
  • Schellekens, J., A. H. Weerts, R. J. Moore, C. E. Pierce, and S. Hildon, 2011: The use of MOGREPS ensemble rainfall forecasts in operational flood forecasting systems across England and Wales. Adv. Geosci., 29, 7784, https://doi.org/10.5194/adgeo-29-77-2011.

    • Search Google Scholar
    • Export Citation
  • Scott, J. P., S. Crooke, I. Cetinić, C. E. Del Castillo, and C. L. Gentemann, 2020: Correcting non-photochemical quenching of Saildrone chlorophyll-a fluorescence for evaluation of satellite ocean color retrievals. Opt. Express, 28, 42744285, https://doi.org/10.1364/OE.382029.

    • Search Google Scholar
    • Export Citation
  • Sedlar, J., and M. Tjernstrom, 2019: A climatological process-based evaluation of AIRS tropospheric thermodynamics over the high-latitude Arctic. J. Appl. Meteor. Climatol., 58, 18671886, https://doi.org/10.1175/JAMC-D-18-0306.1.

    • Search Google Scholar
    • Export Citation
  • Sedlar, J., and Coauthors, 2020: Confronting Arctic troposphere, clouds, and surface energy budget representations in regional climate models with observations. J. Geophys. Res. Atmos., 125, e2019JD031783, https://doi.org/10.1029/2019JD031783.

    • Search Google Scholar
    • Export Citation
  • Shen, B.-W., R. A. Pielke Sr., X. Zeng, J.-J. Baik, S. Faghih-Naini, J. Cui, and R. Atlas, 2021: Is weather chaotic? Coexistence of chaos and order within a generalized Lorenz model. Bull. Amer. Meteor. Soc., 102, E148E158, https://doi.org/10.1175/BAMS-D-19-0165.1.

    • Search Google Scholar
    • Export Citation
  • Shen, X. S., and Coauthors, 2020: Research and operational development of numerical weather prediction in China. J. Meteor. Res., 34, 675698, https://doi.org/10.1007/s13351-020-9847-6.

    • Search Google Scholar
    • Export Citation
  • Smith, G. C., and Coauthors, 2019: Polar ocean observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. Front. Mar. Sci., 6, 429, https://doi.org/10.3389/fmars.2019.00429.

    • Search Google Scholar
    • Export Citation
  • Steele, M., W. Ermold, and J. Zhang, 2008: Arctic Ocean surface warming trends over the past 100 years. Geophys. Res. Lett., 35, L02614, https://doi.org/10.1029/2007GL031651.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., J. Duan, J. C. McWilliams, M. Münnich, and J. D. Neelin, 2002: Entrainment, Rayleigh friction, and boundary layer winds over the tropical Pacific. J. Climate, 15, 3044, https://doi.org/10.1175/1520-0442(2002)015<0030:ERFABL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sutton, A. J., N. L. Williams, and B. Tilbrook, 2021: Constraining Southern Ocean CO2 flux uncertainty using uncrewed surface vehicle observations. Geophys. Res. Lett., 48, e2020GL091748, https://doi.org/10.1029/2020GL091748.

    • Search Google Scholar
    • Export Citation
  • Swinbank, R., and Coauthors, 2016: The TIGGE project and its achievements. Bull. Amer. Meteor. Soc., 97, 4967, https://doi.org/10.1175/BAMS-D-13-00191.1.

    • Search Google Scholar
    • Export Citation
  • Timmermans, M.-L., and C. Ladd, 2019: Sea surface temperature. NOAA Arctic Report Card, https://arctic.noaa.gov/Report-Card/Report-Card-2019/ArtMID/7916/ArticleID/840/Sea-Surface-Temperature.

    • Search Google Scholar
    • Export Citation
  • Tjernström, M., G. Svensson, L. Magnusson, I. M. Brooks, J. Prytherch, J. Vüllers, and G. Young, 2021: Central Arctic weather forecasting: Confronting the ECMWF IFS with observations from the Arctic Ocean 2018 expedition. Quart. J. Roy. Meteor. Soc., 147, 12781299, https://doi.org/10.1002/qj.3971.

    • Search Google Scholar
    • Export Citation
  • Tomita, H., T. Hihara, S. I. Kako, M. Kubota, and K. Kutsuwada, 2019: An introduction to J-OFURO3, a third-generation Japanese ocean flux data set using remote-sensing observations. J. Oceanogr., 75, 171194, https://doi.org/10.1007/s10872-018-0493-x.

    • Search Google Scholar
    • Export Citation
  • Vazquez-Cuervo, J., J. Gomez-Valdes, M. Bouali, L. E. Miranda, T. Van der Stocken, W. Tang, and C. Gentemann, 2019: Using saildrones to validate satellite-derived sea surface salinity and sea surface temperature along the California/Baja Coast. Remote Sens., 11, 1964, https://doi.org/10.3390/rs11171964.

    • Search Google Scholar
    • Export Citation
  • Vihma, T., 2014: Effects of Arctic sea ice decline on weather and climate: A review. Surv. Geophys., 35, 11751214, https://doi.org/10.1007/s10712-014-9284-0.

    • Search Google Scholar
    • Export Citation
  • Wang, M., and J. E. Overland, 2009: A sea ice free summer arctic within 30 years? Geophys. Res. Lett., 36, L07502, https://doi.org/10.1029/2009GL037820.

    • Search Google Scholar
    • Export Citation
  • Yadav, J., A. Kumar, and R. Mohan, 2020: Dramatic decline of Arctic sea ice linked to global warming. Nat. Hazards, 103, 26172621, https://doi.org/10.1007/s11069-020-04064-y.

    • Search Google Scholar
    • Export Citation
  • Yamagami, A., M. Matsueda, and H. L. Tanaka, 2019: Skill of medium-range reforecast for summertime extraordinary Arctic cyclones in 1986–2016. Pol. Sci., 20, 107116, https://doi.org/10.1016/j.polar.2019.02.003.

    • Search Google Scholar
    • Export Citation
  • Yang, F., and V. Tallapragada, 2018: Evaluation of retrospective and real-time NGGPS FV3GFS experiments for Q3FY18 beta implementation. 25th Conf. on Numerical Weather Prediction, Denver, CO, Amer. Meteor. Soc., 5B.3, https://ams.confex.com/ams/29WAF25NWP/webprogram/Paper345231.html.

    • Search Google Scholar
    • Export Citation
  • Yonehara, H., and Coauthors, 2018: Upgrade to JMA’s operational NWP high-resolution global model. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, RSMC Tech Rev. 23, Tokyo, Japan, RSMC, 6 pp., https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/techrev/text23-1.pdf.

    • Search Google Scholar
    • Export Citation
  • Yu, L., and R. A. Weller, 2007: Objectively analyzed air–sea heat fluxes for the global ice-free oceans (1981–2005). Bull. Amer. Meteor. Soc., 88, 527540, https://doi.org/10.1175/BAMS-88-4-527.

    • Search Google Scholar
    • Export Citation
  • Zhang, D., and Coauthors, 2019: Comparing air-sea flux measurements from a new unmanned surface vehicle and proven platforms during the SPURS-2 field campaign. Oceanography, 32, 122133, https://doi.org/10.5670/oceanog.2019.220.

    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and R. Gelaro, 2008: Observation sensitivity calculations using the adjoint of the Gridpoint Statistical Interpolation (GSI) analysis system. Mon. Wea. Rev., 136, 335351, https://doi.org/10.1175/MWR3525.1.

    • Search Google Scholar
    • Export Citation
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Evaluation of Surface Conditions from Operational Forecasts Using In Situ Saildrone Observations in the Pacific Arctic

Chidong ZhangaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington

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Aaron F. LevinebUniversity of Washington, Seattle, Washington

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Muyin WangaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington
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Chelle GentemanncFarallon Institute, Petaluma, California

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Calvin W. MordyaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington
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Edward D. CokeletaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington

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Philip A. BrownedEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Qiong YangaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington
bUniversity of Washington, Seattle, Washington

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Noah Lawrence-SlavasaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington

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Christian MeinigaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington

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Gregory SmitheEnvironment and Climate Change Canada, Montreal, California

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Andy ChiodiaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington
bUniversity of Washington, Seattle, Washington

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Dongxiao ZhangaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington
bUniversity of Washington, Seattle, Washington

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Phyllis StabenoaNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington

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Wanqiu WangfNOAA/National Centers for Environmental Prediction, College Park, Maryland

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Hong-Li RengChinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, China

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K. Andrew PetersoneEnvironment and Climate Change Canada, Montreal, California

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Silvio N. FigueroahCenter for Weather Forecasting and Climate Studies, National Institute for Space Research, São Paulo, Brazil

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Michael SteeleiPolar Science Center, Applied Physics Lab, University of Washington, Seattle, Washington

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Neil P. BartonjNaval Research Laboratory, Monterey, California

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Andrew HuangkScience Applications International Corporation, Monterey, California

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Hyun-Cheol ShinlKorea Meteorological Administration, Seoul, South Korea

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Abstract

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June–September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Yang’s current affiliation: The Climate Corporation, Seattle, Washington.

Corresponding author: Chidong Zhang, chidong.zhang@noaa.gov

Abstract

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June–September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Yang’s current affiliation: The Climate Corporation, Seattle, Washington.

Corresponding author: Chidong Zhang, chidong.zhang@noaa.gov
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