Advertisement

Current Climate Change Reports

, Volume 5, Issue 4, pp 334–344 | Cite as

Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction

  • François MassonnetEmail author
Climate Change and Snow/Sea Ice (PJ Kushner, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Climate Change and Snow/Sea Ice

Abstract

Purpose of review

The Arctic and Antarctic are among the regions most exposed to climate change, but ironically, they are also the ones for which the least observations are available. Climate models have been instrumental in completing the big picture. It is generally accepted that observations feed the development of climate models: parameterizations are designed based on empirically observed relationships, climate model predictions are initialized using observational products, and numerical simulations are evaluated given matching observational datasets.

Recent findings

Recent research suggests that the opposite also holds: climate models can feed the development of polar observational networks by indicating the type, location, frequency, and timing of measurements that would be most useful for answering a specific scientific question.

Summary

Here, we review the foundations of this emerging notion with five cases borrowed from the field of polar prediction with a focus on sea ice (sub-seasonal to centennial time scales). We suggest that climate models, besides their usual purposes, can be used to objectively prioritize future observational needs – if, of course, the limitations of the realism of these models have been recognized. This idea, which has been already extensively exploited in the context of Numerical Weather Prediction, reinforces the notion that observations and models are two sides of the same coin rather than distinct conceptual entities.

Keywords

polar regions observing system design climate modeling environmental prediction emergent constraints data assimilation observing system experiments satellite simulators 

Notes

Acknowledgments

The research leading to these results has received funding from the Belgian Fonds National de la Recherche Scientifique (F.R.S.-FNRS), and the European Commission’s Horizon 2020 projects APPLICATE (GA 727862) and PRIMAVERA (GA 641727).

We acknowledge two anonymous reviewers, as well as Peter Bauer, Irina Sandu, Dirk Notz and Leandro Ponsoni for useful insights and feedback on the manuscript.

Compliance with Ethical Standards

Conflict of Interest

The corresponding author states that there is no conflict of interest.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Stevens B, Schwartz SE. Observing and modeling Earth’s energy flows. Surv Geophys. 2012;33:779–816.Google Scholar
  2. 2.
    Anav A, Friedlingstein P, Kidston M, Bopp L, Ciais P, Cox P, et al. Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models. J Clim. 2013;26(18):6801–43.Google Scholar
  3. 3.
    Gent PR. Coupled Models and Climate Projections. 2nd ed. Vol. 103, Ocean Circulation and Climate: A 21st Century Perspective. Elsevier Ltd.; 2013. 609–623 p.Google Scholar
  4. 4.
    Hegerl G, Zwiers F. Use of models in detection and attribution of climate change. Wires Clim Chang. 2011;2:570–91.Google Scholar
  5. 5.
    Stott PA, Christidis N, Otto FEL, Sun Y, Vanderlinden J, van Oldenborgh GJ, et al. Attribution of extreme weather and climate-related events. Wires Clim Chang. 2016;7:23–41.Google Scholar
  6. 6.
    Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, et al. Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change. 2018.Google Scholar
  7. 7.
    Kravitz B, Caldeira K, Boucher O, Robock A, Rasch PJ, Alterskjær K, et al. Climate model response from the Geoengineering model Intercomparison project (GeoMIP). J Geophys Res Atmos. 2013;118:8320–32.Google Scholar
  8. 8.
    Lazzara MA, Weidner GA, Keller LM, Thom JE, Cassano JJ. Antarctic automatic Weather Station program: 30 years of polar observation. Bull Am Meteorol Soc. 2012 Mar 23;93(10):1519–37.Google Scholar
  9. 9.
    IABP. International Arctic Buoy Programme [Internet]. Available from: http://iabp.apl.washington.edu
  10. 10.
    IPAB. International Programme for Antarctic Buoys 2001 [Internet]. 2001. Available from: https://www.ipab.aq/
  11. 11.
    Woodgate RA. Increases in the Pacific inflow to the Arctic from 1990 to 2015 , and insights into seasonal trends and driving mechanisms from year-round Bering Strait mooring data. Prog Oceanogr 2018;160(June 2017):124–154.Google Scholar
  12. 12.
    Worby AP, Geiger CA, Paget MJ, Van Woert ML, Ackley SF, DeLiberty TL. Thickness distribution of Antarctic Sea ice. J Geophys Res Ocean. 2008;113(5):1–14.Google Scholar
  13. 13.
    Tomasi C, Petkov B, Benedetti E, Valenziano L, Vitale V. Analysis of a 4 year radiosonde data set at dome C for characterizing temperature and moisture conditions of the Antarctic atmosphere. J Geophys Res. 2011;116(D15304):1–18.Google Scholar
  14. 14.
    Ehrlich A, Wendisch M, Lüpkes C, Buschmann M, Bozem H, Chechin D, et al. A comprehensive in situ and remote sensing data set from the Arctic CLoud observations using airborne measurements during polar Day (ACLOUD) campaign. Earth Syst Sci Data Discuss [Internet]. 2019;2019:1–42 Available from: https://www.earth-syst-sci-data-discuss.net/essd-2019-96/.
  15. 15.
    Kurtz NT, Farrell SL, Studinger M, Galin N, Harbeck JP, Lindsay R, et al. Sea ice thickness, freeboard, and snow depth products from operation IceBridge airborne data. Cryosphere. 2013;7(4):1035–56.Google Scholar
  16. 16.
    Kwok R, Rothrock DA. Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008. Geophys Res Lett. 2009;36(15).Google Scholar
  17. 17.
    de Boer G, Argrow B, Cassano J, Cione J, Frew E, Lawrence D, et al. Advancing unmanned aerial capabilities for atmospheric research. Bull am Meteorol Soc [Internet]. 2018;100(3):ES105-ES108. Available from.  https://doi.org/10.1175/BAMS-D-18-0254.1.Google Scholar
  18. 18.
    Williams G, Maksym T, Wilkinson J, Kunz C, Murphy C, Kimball P, et al. Thick and deformed Antarctic Sea ice mapped with autonomous underwater vehicles. Nat Geosci. 2015;8:61–7.Google Scholar
  19. 19.
    Lavergne T, Sørensen AM, Kern S, Tonboe R, Notz D, Aaboe S, et al. Version 2 of the EUMETSAT OSI SAF and ESA CCI Sea-ice concentration climate data records. Cryosphere. 2019;13:49–78.Google Scholar
  20. 20.
    Kaleschke L, Maaß N, Mäkynen M, Drusch M. Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period. Geophys Res Lett. 2012;39(L05501):1–5.Google Scholar
  21. 21.
    Hori M, Sugiura K, Kobayashi K, Aoki T, Tanikawa T, Kuchiki K, et al. Remote Sensing of Environment A 38-year ( 1978–2015 ) Northern Hemisphere daily snow cover extent product derived using consistent objective criteria from satellite-borne optical sensors. Remote Sens Environ [Internet]. 2017;191:402–18. Available from:  https://doi.org/10.1016/j.rse.2017.01.023 Google Scholar
  22. 22.
    Zwally HJ, Yi D, Kwok R, Zhao Y. ICESat measurements of sea ice freeboard and estimates of sea ice thickness in the Weddell Sea. J Geophys Res Ocean. 2008;113(2):1–17.Google Scholar
  23. 23.
    Csatho BM, Schenk AF, Veen CJ Van Der, Babonis G, Duncan K. Laser altimetry reveals complex pattern of Greenland Ice Sheet dynamics. Proc Natl Acad Sci. 2014;111(52).Google Scholar
  24. 24.
    Quartly GD, Rinne E, Passaro M, Andersen OB, Dinardo S, Fleury S, et al. Review of radar altimetry techniques over the Arctic Ocean: recent Progress and future opportunities for sea level and sea ice research. Cryosph Discuss [Internet]. 2018;2018:1–51 Available from: https://www.the-cryosphere-discuss.net/tc-2018-148/.
  25. 25.
    Meier WN, Hovelsrud GK, Van Oort BEH, Key JR, Kovacs KM, Michel C, et al. Arctic Sea ice in transformation: a review of recent observed changes and impacts on biology and human activity. Rev Geophys. 2014;52(3):185–217.Google Scholar
  26. 26.
    Brown RD, Robinson DA. Northern hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. Cryosph [Internet]. 2011;5(1):219–29 Available from: https://www.the-cryosphere.net/5/219/2011/.Google Scholar
  27. 27.
    Kjeldsen KK, Korsgaard NJ, Bjørk AA, Khan SA, Box JE, Funder S, et al. Spatial and temporal distribution of mass loss from the Greenland ice sheet since AD 1900. Nature [Internet]. 2015;528:396–400 Available from:  https://doi.org/10.1038/nature16183.Google Scholar
  28. 28.
    Shepherd A, Ivins E, Rignot E, Smith B, van den Broeke M, Velicogna I, et al. Mass balance of the Antarctic ice sheet from 1992 to 2017. Nature [Internet]. 2018;558(7709):219–22. Available from.  https://doi.org/10.1038/s41586-018-0179-y.
  29. 29.
    Bauer P, Bradley A, Bromwich D, Casati B, Chen P, Chevallier M, et al. WWRP Polar Prediction Project: Implementation Plan for the Year of Polar Prediction (YOPP) [Internet]. Available from: https://www.polarprediction.net/fileadmin/user_upload/www.polarprediction.net/Home/YOPP/YOPP_Documents/FINAL_WWRP_PPP_YOPP_Plan_28_July_2016_web-1.pdf
  30. 30.
    Jung T, Gordon N, Bauer P, Bromwich DH, Chevallier M, Day JJ, et al. Advancing polar prediction capabilities on daily to seasonal time scales. Bull Am Meteorol Soc. 2016;(September):1631–48.Google Scholar
  31. 31.
    Bauer P. Observing system experiments (OSE) to estimate the impact of observations in NWP [Internet]. 2009. Available from: https://doi.org/www.ecmwf.int/sites/default/files/elibrary/2009/7978-observing-systemexperiments-ose-estimate-impact-observations-nwp.pdf Google Scholar
  32. 32.
    Arnold CP, Dey CH. Observing-systems simulation experiments : past, present, and future. Bull Am Meteorol Soc. 1986;67(6):687–95.Google Scholar
  33. 33.
    Atlas R. Atmospheric observations and experiments to assess their usefulness in data assimilation. J Meteorol Soc Japan. 1997;75(1B):111–30.Google Scholar
  34. 34.
    Zapotocny TH, Jung JA, Le Marshall JF, Treadon RE. A two-season impact study of four satellite data types and Rawinsonde data in the NCEP global data assimilation system. Weather Forecast. 2008;23:80–100.Google Scholar
  35. 35.
    Randriamampianina R, Schyberg H. Mile M. Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model. 2019;11(981):1–23.Google Scholar
  36. 36.
    Yamazaki A, Inoue J, Dethloff K, Maturilli M, König-Langlo G. Impact of radiosonde observations on forecasting summertime Arctic cyclone formation. J Geophys Res. 2015;120:3249–73.Google Scholar
  37. 37.
    Lawrence H, Farnan J, Bormann N, Bauer P. An assessment of the use of observations in the Arctic at ECMWF [Internet]. 2019. Available from: https://www.ecmwf.int/sites/default/files/elibrary/2019/18925-assessment-use-observations-arctic-ecmwf.pdf Google Scholar
  38. 38.
    Inoue J, Yamazaki A, Ono J, Dethloff K, Maturilli M. Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea route. Sci Rep [Internet]. 2015;5(16868):1–8 Available from:  https://doi.org/10.1038/srep16868.Google Scholar
  39. 39.
    Masutani M, Woollen JS, Lord SJ, Emmitt GD, Kleespies TJ, Wood SA, et al. Observing system simulation experiments at the National Centers for environmental prediction. J Geophys Res. 2010;115(D07101):1–15.Google Scholar
  40. 40.
    Masutani M, Garand L, Lahoz W, Andersson E, Rochon Y, Riishojgaard L, et al. Observing System Simulation Experiments: Justifying new Arctic Observation Capabilities [Internet]. Available from: https://repository.library.noaa.gov/view/noaa/6965
  41. 41.
    Hardt, Matthias, and Frank Scherbaum. “The Design of Optimum Networks for Aftershock Recordings.”Geophysical Journal International 117, no. 3 (June 1994): 716–26.  https://doi.org/10.1111/j.1365-246X.1994.tb02464.x.Google Scholar
  42. 42.
    Kaminski T, Rayner PJ. Assimilation and network design. In: ecological studies [Internet]. Springer New York; 2008. p. 33–52. Available from. 2008.  https://doi.org/10.1007/978-0-387-76570-9_3.Google Scholar
  43. 43.
    Day JJ, Hawkins E, Tietsche S. Will Arctic Sea ice thickness initialization improve seasonal forecast skill? Geophys Res Lett. 2014;41(21):7566–75.Google Scholar
  44. 44.
    Blanchard-Wrigglesworth E, Armour KC, Bitz CM, Deweaver E. Persistence and inherent predictability of Arctic Sea ice in a GCM ensemble and observations. J Clim. 2011;24(1):231–50.Google Scholar
  45. 45.
    Chevallier M, Salas-Mélia D. The role of sea ice thickness distribution in the arctic sea ice potential predictability: a diagnostic approach with a coupled GCM. J Clim. 2012;25(8):3025–38.Google Scholar
  46. 46.
    Zhang YF, Bitz CM, Anderson JL, Collins N, Hendricks J, Hoar T, et al. Insights on sea ice data assimilation from perfect model observing system simulation experiments. J Clim. 2018;31(15):5911–26.Google Scholar
  47. 47.
    Kaminski T, Kauker F, Eicken H, Karcher M. Exploring the utility of quantitative network design in evaluating Arctic sea ice thickness sampling strategies. [cited 2019 Oct 8];9(4):1721–33. Available from: https://www.the-cryosphere.net/9/1721/2015/ Google Scholar
  48. 48.
    Kaminski T, Kauker F, Toudal Pedersen L, Voßbeck M, Haak H, Niederdrenk L, et al. Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance. [cited 2019 Oct 8];12(8):2569–94. Available from: https://www.the-cryosphere.net/12/2569/2018/
  49. 49.
    Newman L, Schofield O, Wahlin A, Constable A, Swart S, Williams M, et al. Understanding the Southern Ocean through sustained observations. Bull Aust Meteorol Oceanogr Soc. 2016;28(January):170.Google Scholar
  50. 50.
    Privé NC, Errico RM, Tai K. The influence of observation errors on analysis error and forecast skill investigated with an observing system simulation experiment. J Geophys Res. 2013;118:5332–46.Google Scholar
  51. 51.
    Zygmuntowska M, Rampal P, Ivanova N, Smedsrud LH. Uncertainties in Arctic Sea ice thickness and volume: new estimates and implications for trends. Cryosphere. 2014;8(2):705–20.Google Scholar
  52. 52.
    Bunzel F, Notz D, Pedersen LT. Retrievals of Arctic Sea-ice volume and its trend significantly affected by interannual snow variability. Geophys Res Lett. 2018;45(11):11751–9.Google Scholar
  53. 53.
    Notz D. How well must climate models agree with observations? Philos Trans R Soc A Math Phys Eng Sci. 2015;373(2052).Google Scholar
  54. 54.
    Massonnet F, Vancoppenolle M, Goosse H, Docquier D, Fichefet T, Blanchard-Wrigglesworth E. Arctic Sea-ice change tied to its mean state through thermodynamic processes. Nat Clim Chang. 2018;8(7):599–603.Google Scholar
  55. 55.
    Warren SG, Rigor IG, Untersteiner N, Radionov VF, Bryazgin NN, Aleksandrov YI, et al. Snow depth on Arctic Sea ice. J Clim [Internet]. 1999;12(6):1814–29. Available from:  https://doi.org/10.1175/1520-0442(1999)012%3C1814:SDOASI%3E2.0.CO.
  56. 56.
    Laxon SW, Giles KA, Ridout AL, Wingham DJ, Willatt R, Cullen R, et al. CryoSat-2 estimates of Arctic Sea ice thickness and volume. Geophys Res Lett. 2013;40(4):732–7.Google Scholar
  57. 57.
    Kern S, Lavergne T, Notz D, Pedersen LT, Tonboe RT, Sørensen AM. Satellite passive Microwave Sea-ice concentration data set Intercomparison : closed ice and ship-based observations. Cryosph discuss [Internet]. 2019;1–55. Available from.  https://doi.org/10.5194/tc-2019-120.
  58. 58.
    Ivanova N, Pedersen LT, Tonboe RT, Kern S, Heygster G, Lavergne T, et al. Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations. Cryosphere. 2015;9(5):1797–817.Google Scholar
  59. 59.
    Andersen S, Tonboe R, Kaleschke L, Heygster G, Pedersen LT. Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice. J Geophys Res. 2007;112(C08004).Google Scholar
  60. 60.
    Sallila H, Farrell SL, Mccurry J, Rinne E. Assessment of contemporary satellite sea ice thickness products for Arctic Sea ice. Cryosph discuss [Internet]. 2019;1187–213. Available from.  https://doi.org/10.5194/tc-13-1187-2019.Google Scholar
  61. 61.
    Wang X, Key J, Kwok R, Zhang J. Comparison of Arctic Sea Ice Thickness from Satellites , Aircraft , and PIOMAS Data. Remote Sens 2016;8(713):1–17.Google Scholar
  62. 62.
    Bodas-Salcedo A, Webb MJ, Bony S, Chepfer H, Dufresne J, Zhang Y, et al. COSP satellite simulation software for model assessment. Bull Am Meteorol Soc. 2015;92(8):1023–43.Google Scholar
  63. 63.
    Klein SA, Jakob C. Validation and Sensitivities of Frontal Clouds Simulated by the ECMWF Model. Mon Weather Rev [Internet]. 1999 Oct 1;127(10):2514–31. Available from:  https://doi.org/10.1175/1520-0493(1999)127%3C2514:VASOFC%3E2.0.CO.
  64. 64.
    Kay JE, Hillman BR, Klein SA, Y. Z, Medeiros B, Pincus R, et al. Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using Satellite Observations and Their Corresponding Instrument Simulators. J Clim 2012;25:5190–5207.Google Scholar
  65. 65.
    Kay JE, Bourdages L, Miller NB, Morrison A, Yettella V, Chepfer H, et al. Evaluating and improving cloud phase in the community atmosphere model version 5 using spaceborne lidar observations. J Geophys Res. 2016;121(8):4162–76.Google Scholar
  66. 66.
    Chepfer H, Bony S, Winker D, Chiriaco M, Dufresne J-L, Sèze G. Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model. Geophys res Lett [Internet]. 2008;35(15). Available from.  https://doi.org/10.1029/2008GL034207.
  67. 67.
    Roberts AF. A Variational method for sea ice ridging in earth system models. J Adv Model Earth Syst. 2019;11:771–805.Google Scholar
  68. 68.
    Rampal P, Bouillon S, Ólason E, Morlighem M. NeXtSIM: a new Lagrangian Sea ice model. Cryosphere. 2016;10(3):1055–73.Google Scholar
  69. 69.
    Kay JE, Ecuyer TL, Chepfer H, Loeb N, Morrison A, Cesana G. Recent advances in Arctic cloud and climate research. Curr Clim Chang Reports. 2016;2:159–69.Google Scholar
  70. 70.
    Lavergne T. A step back is a move forward.  https://doi.org/10.6084/m9.figshare.5501536.v1. 2017.
  71. 71.
    Stroeve JC, Kattsov V, Barrett A, Serreze M, Pavlova T, Holland M, et al. Trends in Arctic Sea ice extent from CMIP5, CMIP3 and observations. Geophys Res Lett. 2012;39(16):1–7.Google Scholar
  72. 72.
    Smith DM, Screen JA, Deser C, Cohen J, Fyfe JC, García-Serrano J, et al. The polar amplification model Intercomparison project (PAMIP) contribution to CMIP6: investigating the causes and consequences of polar amplification. Geosci Model Dev [Internet]. 2019;12(3):1139–64 Available from: https://www.geosci-model-dev.net/12/1139/2019/.Google Scholar
  73. 73.
    Vihma T, Screen J, Tjernström M, Newton B, Zhang X, Popova V, et al. The atmospheric role in the Arctic water cycle: a review on processes, past and future changes, and their impacts. J Geophys Res Biogeosci. 2015:586–620.Google Scholar
  74. 74.
    Rawlins MA, Steele M, Holland MM, Adam JC, Cherry JE, Francis JA, et al. 18;23(21):5715–37. Available from; 2010 Jun.  https://doi.org/10.1175/2010JCLI3421.1.Google Scholar
  75. 75.
    Eyring V, Cox PM, Flato GM, Gleckler PJ, Abramowitz G, Caldwell P, et al. Taking climate model evaluation to the next level. Nat Clim Chang [Internet]. 2019; Available from:  https://doi.org/10.1038/s41558-018-0355-y Google Scholar
  76. 76.
    Bracegirdle TJ, Stephenson DB. On the robustness of emergent constraints used in multimodel climate change projections of arctic warming. J Clim. 2013;26(2):669–78.Google Scholar
  77. 77.
    Boé J, Hall A, Qu X. September Sea-ice cover in the Arctic Ocean projected to vanish by 2100. Nat Geosci [Internet]. 2009;2(5):341–3 Available from:  https://doi.org/10.1038/ngeo467.Google Scholar
  78. 78.
    Collins M, Chandler RE, Cox PM, Huthnance JM, Rougier J, Stephenson DB. Quantifying future climate change. Nat Clim Chang [Internet]. 2012;2(6):403–9 Available from:  https://doi.org/10.1038/nclimate1414.Google Scholar
  79. 79.
    Massonnet F, Fichefet T, Goosse H, Bitz CM, Philippon-Berthier G, Holland MM, et al. Constraining projections of summer Arctic Sea ice. Cryosphere. 2012;6(6):1383–94.Google Scholar
  80. 80.
    Borodina A, Fischer EM, Knutti R. Emergent constraints in climate projections : a case study of changes in high-latitude temperature variability. J Clim. 2017;30:3655–70.Google Scholar
  81. 81.
    Bitz CM, Roe GH. A Mechanism for the High rate of Sea Ice Thinning in the Arctic Ocean. J Clim. 2004;i:1–6.Google Scholar
  82. 82.
    van der Linden EC, Bintanja R, Hazeleger W, Katsman CA. The role of the mean state of Arctic Sea ice on near-surface temperature trends. J Clim. 2014;27(8):2819–41.Google Scholar
  83. 83.
    Bracegirdle TJ, Stephenson DB, Turner J, Phillips T. The importance of sea ice area biases in 21st century multimodel projections of Antarctic temperature and precipitation. 2015;832–9.Google Scholar
  84. 84.
    Klein SA, Hall A. Emergent constraints for cloud feedbacks. Curr Clim Chang Reports. 2015;1:276–87.Google Scholar
  85. 85.
    Mahlstein I, Knutti R. Ocean heat transport as a cause for model uncertainty in projected Arctic warming. J Clim. 2011;24(5):1451–60.Google Scholar
  86. 86.
    Hall A, Qu X. Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys Res Lett. 2006;33(3):1–4.Google Scholar
  87. 87.
    Bowler NE. Accounting for the effect of observation errors on verification of MOGREPS. Meteorol Appl. 2008;15:199–205.Google Scholar
  88. 88.
    Ferro C. Measuring forecast performance in the presence of observation error. Q J R Meteorol Soc. 2017;2:2665–76.Google Scholar
  89. 89.
    Bunzel F, Notz D, Baehr J, Müller WA, Fröhlich K. Seasonal climate forecasts significantly affected by observational uncertainty of Arctic Sea ice concentration. Geophys Res Lett. 2016;43(2):852–9.Google Scholar
  90. 90.
    Massonnet F, Bellprat O, Guemas V, Doblas-Reyes FJ. Using climate models to estimate the quality of global observational data sets. Science (80- ). 2016;354(6311).Google Scholar
  91. 91.
    Kushner PJ, Mudryk LR, Merryfield W, Ambadan JT, Berg A, Bichet A, et al. Canadian snow and sea ice : assessment of snow , sea ice , and related climate processes in Canada’s Earth system model and climate-prediction system. Cryosphere. 2018;12:1137–1156.Google Scholar
  92. 92.
    Sospedra-Alfonso R, Merryfield WJ, Kharin V V. Representation of Snow in the Canadian Seasonal to Interannual Prediction System . Part II : Potential Predictability and Hindcast Skill. J Hydrometeorol 2016;17:2511–2535.Google Scholar
  93. 93.
    Reichle R. The MERRA-Land Data Product [Internet]. Vol. 3. 2012. Available from: http://gmao.gsfc.nasa.gov/pubs/office_notes
  94. 94.
    Hardin JW, North GR, Shen SS. Minimum error estimates of global mean temperature through optimal arrangement of gauges. Environmetrics. 1992;3:15–27.Google Scholar
  95. 95.
    North GR, Shen SS, Hardin JW. Estimation of the global mean temperature with point gauges. Environmetrics. 1992:1–14.Google Scholar
  96. 96.
    Rayner PJ, Enting IG, Trudinger CM. Optimizing the CO2 observing network for constraining sources and sinks. Tellus B Chem Phys Meteorol [Internet]. 1;48(4):433–44. Available from; 1996 Jan.  https://doi.org/10.3402/tellusb.v48i4.15924.Google Scholar
  97. 97.
    Lique C, Steele M. Seasonal to decadal variability of Arctic Ocean heat content: A model-based analysis and implications for autonomous observing systems. J Geophys Res Ocean [Internet]. 1;118(4):1673–95. Available from; 2013 Apr.  https://doi.org/10.1002/jgrc.20127.Google Scholar
  98. 98.
    Blanchard-Wrigglesworth E, Bitz CM. Characteristics of Arctic Sea-ice thickness variability in GCMs. J Clim. 2014;27(21):8244–58.Google Scholar
  99. 99.
    Chevallier M, Massonnet F, Goessling H, Guemas V, Jung T. The role of sea ice in sub-seasonal predictability. In: Sub-seasonal to seasonal prediction The gap between weather and climate forecasting; 2019. p. 201–21 Google Scholar
  100. 100.
    Ponsoni L, Massonnet F, Fichefet T, Chevallier M, Docquier D. On the timescales and length scales of the Arctic Sea ice thickness anomalies : a study based on 14 reanalyses. Cryosphere. 2019;13:521–43.Google Scholar
  101. 101.
    Lindsay RW, Zhang J. Arctic Ocean ice thickness: modes of variability and the best locations from which to monitor them. J Phys Oceanogr. 2006;36:496–506.Google Scholar
  102. 102.
    Ponsoni, L., Massonnet, F., Docquier, D., Van Achter, G., and Fichefet, T. Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations. Submitted to The Cryosphere (2019).Google Scholar
  103. 103.
    Labe Z, Magnusdottir G, Stern H. Variability of Arctic Sea ice thickness using PIOMAS and the CESM large ensemble. J Clim. 2018;31:3233–47.Google Scholar
  104. 104.
    Bintanja R, Van Der Linden EC. The changing seasonal climate in the Arctic. Sci Rep. 2013;3:1–8.Google Scholar
  105. 105.
    Holland MM, Stroeve J. Changing seasonal sea ice predictor relationships in a changing Arctic climate. Geophys Res Lett. 2011;38(18):1–6.Google Scholar
  106. 106.
    Weatherhead EC, Wielicki BA, Ramaswamy V, Abbott M, Ackerman TP, Atlas R, et al. Designing the climate observing Systemof the future Elizabeth. Earth’s Futur. 2017;6:80–102.Google Scholar
  107. 107.
    Brunet G, Shapiro M, Hoskins B, Moncrieff M, Dole R, Kiladis GN, et al. 4;91(10):1397–406. Available from; 2010 May.  https://doi.org/10.1175/2010BAMS3013.1.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Georges Lemaître Centre for Earth and Climate Research, Earth and Life InstituteUniversité catholique de LouvainLouvain-la-NeuveBelgium

Personalised recommendations