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Atmosphere-driven cold SST biases over the western North Pacific in the GloSea5 seasonal forecast system

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Abstract

The predictability of the sea surface temperature (SST) in seasonal forecast systems is crucial for accurate seasonal predictions. In this study, we evaluated the prediction of SST in the Global Seasonal forecast system version 5 (GloSea5) hindcast, particularly focusing on the western North Pacific (WNP), where the SST can modify atmospheric convection and the East Asian weather. GloSea5 has a cold SST bias in the WNP that grows over at least 7 months. The bias originates from the surface net heat flux. At the beginning of model integration, the ocean receives excessive heat from the atmosphere because of the predominant positive bias in the downward shortwave radiation (SW), which rapidly decreased within a few days as cloud cover builds. Then, the negative bias in the latent heat (LH) flux increases over time and induces a negative bias in the surface net heat flux. Although the magnitude of the negative bias in LH flux gradually decreases, it remains the most significant contributor to the negative bias in the net heat flux bias for more than 250 days. Uncoupled ocean model experiments showed that the ocean model is unlikely to be the primary source of the SST bias.

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Data Availability

The KMA GloSea5 hindcast was provided by the National Institute of Meteorological Sciences. Part of the hindcast, up to 60 days prediction, can be obtained online (http://apps.ecmwf.int/datasets/data/s2s/). The HadISST data were obtained freely from https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. ERA-Interim data can be obtained freely from http://apps.ecmwf.int/datasets/data/interim_full_daily.

References

  • Ashfaq M, Skinner CB, Diffenbaugh NS (2011) Influence of SST biases on future climate change projections. Clim Dyn 36(7–8):1303–1319. https://doi.org/10.1007/s00382-010-0875-2

    Article  Google Scholar 

  • Bayr T, Domeisen DIV, Wengel C (2019) The effect of the equatorial Pacific cold SST bias on simulated ENSO teleconnections to the North Pacific and California. Clim Dyn 53(7):3771–3789. https://doi.org/10.1007/s00382-019-04746-9

    Article  Google Scholar 

  • Bentamy A, Piollé JF, Grouazel A, Danielson R, Gulev S, Paul F, Azelmat H, Mathieu PP, von Schuckmann K, Sathyendranath S, Evers-King H, Esau I, Johannessen JA, Clayson CA, Pinker RT, Grodsky SA, Bourassa M, Smith SR, Haines K, Valdivieso M, Merchant CJ, Chapron B, Anderson A, Hollmann R, Josey SA (2017) Review and assessment of latent and sensible heat flux accuracy over the global oceans. Remote Sens Environ 201:196–218. https://doi.org/10.1016/j.rse.2017.08.016

    Article  Google Scholar 

  • Bharti V, Schulz E, Fairall CW, Blomquist BW, Huang Y, Protat A, Siems ST, Manton MJ (2019) Assessing Surface Heat Flux Products with In Situ Observations over the Australian Sector of the Southern Ocean. J Atmos Ocean Technol 36(9):1849–1861. https://doi.org/10.1175/JTECH-D-19-0009.1

    Article  Google Scholar 

  • Brunke MA, Wang Z, Zeng X, Bosilovich M, Shie CL (2011) An Assessment of the Uncertainties in Ocean Surface Turbulent Fluxes in 11 Reanalysis, Satellite-Derived, and Combined Global Datasets. J Clim 24(21):5469–5493. https://doi.org/10.1175/2011JCLI4223.1

    Article  Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, Lvd Berg, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, K\(\mathring{a}\)llberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, Rosnay Pd, Tavolato C, Thépaut JN, Vitart F, (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597. https://doi.org/10.1002/qj.828

  • Dippe T, Greatbatch RJ, Ding H (2019) Seasonal prediction of equatorial Atlantic sea surface temperature using simple initialization and bias correction techniques. Atmos Sci Lett 20(5):e898. https://doi.org/10.1002/asl.898

    Article  Google Scholar 

  • Dunxin H, Maochang C (1991) The Western Boundary Current of the pacific and its role in the climate. Chin J Oceanol Limnol 9(1):1–14. https://doi.org/10.1007/BF02849784

    Article  Google Scholar 

  • Dussin R, Barnier B, Brodeau L, Molines J (2016) The making of the drakkar forcing set dfs. Drakkar/myocean report 01–04-16. laboratoire de Glaciologie et de Géophysique de l’Environnement. Université de Grenoble, Grenoble, France

  • Garfinkel CI, White I, Gerber EP, Jucker M (2020) The Impact of SST Biases in the Tropical East Pacific and Agulhas Current Region on Atmospheric Stationary Waves in the Southern Hemisphere. J Clim 33(21):9351–9374. https://doi.org/10.1175/JCLI-D-20-0195.1

    Article  Google Scholar 

  • Hewitt HT, Copsey D, Culverwell ID, Harris CM, Hill RSR, Keen AB, McLaren AJ, Hunke EC (2011) Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system. Geosci Model Dev 4(2):223–253. https://doi.org/10.5194/gmd-4-223-2011

    Article  Google Scholar 

  • Huang R, Zhou L, Chen W (2003) The Progresses of Recent Studies on the Variabilities of the East Asian Monsoon and Their Causes. Adv Atmos Sci 20(1):55–69. https://doi.org/10.1007/BF03342050

    Article  Google Scholar 

  • Jo HS, Yeh SW, Kirtman BP (2014) Role of the western tropical Pacific in the North Pacific regime shift in the winter of 1998/1999. J Geophys Res Oceans 119(9):6161–6170. https://doi.org/10.1002/2013JC009527

    Article  Google Scholar 

  • Jong BT, Ting M, Seager R, Henderson N, Lee DE (2018) Role of Equatorial Pacific SST Forecast Error in the Late Winter California Precipitation Forecast for the 2015/16 El Niño. J Clim 31(2):839–852. https://doi.org/10.1175/JCLI-D-17-0145.1

    Article  Google Scholar 

  • Keeley SPE, Sutton RT, Shaffrey LC (2012) The impact of North Atlantic sea surface temperature errors on the simulation of North Atlantic European region climate. Q J R Meteorol Soc 138(668):1774–1783. https://doi.org/10.1002/qj.1912

    Article  Google Scholar 

  • Li G, Xie SP (2012) Origins of tropical-wide SST biases in CMIP multi-model ensembles. Geophys Res Lett 39(22). https://doi.org/10.1029/2012GL053777

  • MacLachlan C, Arribas A, Peterson KA, Maidens A, Fereday D, Scaife AA, Gordon M, Vellinga M, Williams A, Comer RE, Camp J, Xavier P, Madec G (2015) Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q J R Meteorol Soc 141(689):1072–1084. https://doi.org/10.1002/qj.2396

    Article  Google Scholar 

  • Megann A, Storkey D, Aksenov Y, Alderson S, Calvert D, Graham T, Hyder P, Siddorn J, Sinha B (2014) GO5.0: the joint NERC-Met Office NEMO global ocean model for use in coupled and forced applications. Geosci Model Dev 7(3):1069–1092. https://doi.org/10.5194/gmd-7-1069-2014

    Article  Google Scholar 

  • Mogensen K, Balmaseda MA, Weaver AT, Martin M, Vidard A (2009) Nemovar: A variational data assimilation system for the NEMO ocean model. ECMWF Newsl 120:17–22

    Google Scholar 

  • Mulholland DP, Laloyaux P, Haines K, Balmaseda MA (2015) Origin and Impact of Initialization Shocks in Coupled Atmosphere-Ocean Forecasts. Mon Weather Rev 143(11):4631–4644. https://doi.org/10.1175/MWR-D-15-0076.1

    Article  Google Scholar 

  • Neumann P, Düben P, Adamidis P, Bauer P, Brück M, Kornblueh L, Klocke D, Stevens B, Wedi N, Biercamp J (2019) Assessing the scales in numerical weather and climate predictions: will exascale be the rescue? https://doi.org/10.1098/rsta.2018.0148

  • Nitta T (1987) Convective activities in the tropical western Pacific and their impact on the northern hemisphere summer circulation. J Meteorol Soc Jpn Ser II 65(3):373–390. https://doi.org/10.2151/jmsj1965.65.3_373

  • Park JH, An SI (2014) The impact of tropical western Pacific convection on the North Pacific atmospheric circulation during the boreal winter. Clim Dyn 43(7–8):2227–2238. https://doi.org/10.1007/s00382-013-2047-7

    Article  Google Scholar 

  • Park JH, An SI, Kug JS (2017) Interannual variability of western North Pacific SST anomalies and its impact on North Pacific and North America. Clim Dyn 49(11):3787–3798. https://doi.org/10.1007/s00382-017-3538-8

    Article  Google Scholar 

  • Rae JGL, Hewitt HT, Keen AB, Ridley JK, West AE, Harris CM, Hunke EC, Walters DN (2015) Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model. Geosci Model Dev 8(7):2221–2230. https://doi.org/10.5194/gmd-8-2221-2015

    Article  Google Scholar 

  • Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys 108(D14). https://doi.org/10.1029/2002JD002670

  • Richter I (2015) Climate model biases in the eastern tropical oceans: causes, impacts and ways forward. WIREs Clim Change 6(3):345–358. https://doi.org/10.1002/wcc.338

    Article  Google Scholar 

  • Rosati A, Miyakoda K, Gudgel R (1997) The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon Weather Rev 125:754–772

    Article  Google Scholar 

  • Shukla J, Anderson J, Baumhefner D, Brankovic C, Chang Y, Kalnay E, Marx L, Palmer T, Paolino D, Ploshay J, Schubert S, Straus D, Suarez M, Tribbia J (2000) Dynamical Seasonal Prediction. Bull Am Meteorol Soc81(11):2593–2606. https://doi.org/10.1175/1520-0477(2000)081<2593:DSP>2.3.CO;2

  • Sugiura N, Awaji T, Masuda S, Mochizuki T, Toyoda T, Miyama T, Igarashi H, Ishikawa Y (2008) Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations. J Geophys Res Oceans 113(C10). https://doi.org/10.1029/2008JC004741

  • Tao W, Huang G, Wu R, Hu K, Wang P, Chen D (2017) Asymmetry in summertime atmospheric circulation anomalies over the northwest Pacific during decaying phase of El Niño and La Niña. Clim Dyn 49(5):2007–2023. https://doi.org/10.1007/s00382-016-3432-9

    Article  Google Scholar 

  • Toniazzo T, Woolnough S (2014) Development of warm SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts. Clim Dyn 43(11):2889–2913. https://doi.org/10.1007/s00382-013-1691-2

    Article  Google Scholar 

  • Troccoli A (2010) Seasonal climate forecasting. Meteorol Appl 17(3):251–268. https://doi.org/10.1002/met.184

    Article  Google Scholar 

  • Vannière B, Guilyardi E, Madec G, Doblas-Reyes FJ, Woolnough S (2013) Using seasonal hindcasts to understand the origin of the equatorial cold tongue bias in CGCMs and its impact on ENSO. Clim Dyn 40(3):963–981. https://doi.org/10.1007/s00382-012-1429-6

    Article  Google Scholar 

  • Voldoire A, Exarchou E, Sanchez-Gomez E, Demissie T, Deppenmeier AL, Frauen C, Goubanova K, Hazeleger W, Keenlyside N, Koseki S, Prodhomme C, Shonk J, Toniazzo T, Traoré AK (2019) Role of wind stress in driving SST biases in the Tropical Atlantic. Clim Dyn 53(5):3481–3504. https://doi.org/10.1007/s00382-019-04717-0

    Article  Google Scholar 

  • Walters D, Brooks M, Boutle I, Melvin T, Stratton R, Vosper S, Wells H, Williams K, Wood N, Allen T, Bushell A, Copsey D, Earnshaw P, Edwards J, Gross M, Hardiman S, Harris C, Heming J, Klingaman N, Levine R, Manners J, Martin G, Milton S, Mittermaier M, Morcrette C, Riddick T, Roberts M, Sanchez C, Selwood P, Stirling A, Smith C, Suri D, Tennant W, Vidale PL, Wilkinson J, Willett M, Woolnough S, Xavier P (2017) The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations. Geosci Model Dev 10(4):1487–1520. https://doi.org/10.5194/gmd-10-1487-2017

    Article  Google Scholar 

  • Wang B, Lee JY, Kang IS, Shukla J, Park CK, Kumar A, Schemm J, Cocke S, Kug JS, Luo JJ, Zhou T, Wang B, Fu X, Yun WT, Alves O, Jin EK, Kinter J, Kirtman B, Krishnamurti T, Lau NC, Lau W, Liu P, Pegion P, Rosati T, Schubert S, Stern W, Suarez M, Yamagata T (2009) Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Clim Dyn 33(1):93–117. https://doi.org/10.1007/s00382-008-0460-0

    Article  Google Scholar 

  • Wang B, Wu R, Fu X (2000) Pacific-east asian teleconnection: how does enso affect east asian climate? J Clim13(9):1517–1536. https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2

  • Wang C, Zhang L, Lee SK, Wu L, Mechoso CR (2014) A global perspective on CMIP5 climate model biases. Nature Clim Change 4(3):201–205. https://doi.org/10.1038/nclimate2118

    Article  Google Scholar 

  • Wang C, Zou L, Zhou T (2018) SST biases over the Northwest Pacific and possible causes in CMIP5 models. Sci China Earth Sci 61(6):792–803. https://doi.org/10.1007/s11430-017-9171-8

    Article  Google Scholar 

  • Williams KD, Harris CM, Bodas-Salcedo A, Camp J, Comer RE, Copsey D, Fereday D, Graham T, Hill R, Hinton T, Hyder P, Ineson S, Masato G, Milton SF, Roberts MJ, Rowell DP, Sanchez C, Shelly A, Sinha B, Walters DN, West A, Woollings T, Xavier PK (2015) The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci Model Dev 8(5):1509–1524. https://doi.org/10.5194/gmd-8-1509-2015

    Article  Google Scholar 

  • Yan XH, Ho CR, Zheng Q, Klemas V (1992) Temperature and Size Variabilities of the Western Pacific Warm Pool. Science 258(5088):1643–1645. https://doi.org/10.1126/science.258.5088.1643

    Article  Google Scholar 

  • Yu L (2019) Global air-sea fluxes of heat, fresh water, and momentum: Energy budget closure and unanswered questions. Annu Rev Mar Sci 11(1):227–248. https://doi.org/10.1146/annurev-marine-010816-060704

    Article  Google Scholar 

  • Zhang L, Zhao C (2015) Processes and mechanisms for the model SST biases in the North Atlantic and North Pacific: A link with the Atlantic meridional overturning circulation. J Adv Model Earth Syst 7(2):739–758. https://doi.org/10.1002/2014MS000415

    Article  Google Scholar 

  • Zhang S, Liu Z, Zhang X, Wu X, Han G, Zhao Y, Yu X, Liu C, Liu Y, Wu S, Lu F, Li M, Deng X (2020) Coupled data assimilation and parameter estimation in coupled ocean-atmosphere models: a review. Clim Dyn 54:5127–5144. https://doi.org/10.1007/s00382-020-05275-6

    Article  Google Scholar 

  • Zuidema P, Chang P, Medeiros B, Kirtman BP, Mechoso R, Schneider EK, Toniazzo T, Richter I, Small RJ, Bellomo K, Brandt P, de Szoeke S, Farrar JT, Jung E, Kato S, Li M, Patricola C, Wang Z, Wood R, Xu Z (2016) Challenges and Prospects for Reducing Coupled Climate Model SST Biases in the Eastern Tropical Atlantic and Pacific Oceans: The U.S. CLIVAR Eastern Tropical Oceans Synthesis Working Group. Bull Am Meteorol Soc 97(12):2305–2328. https://doi.org/10.1175/BAMS-D-15-00274.1

    Article  Google Scholar 

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Acknowledgements

The KMA GloSea5 hindcast was provided by the National Institute of Meteorological Sciences. Part of the hindcast, up to 60 days prediction, can be obtained online (http://apps.ecmwf.int/datasets/data/s2s/). The HadISST data were obtained freely from https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. ERA-Interim data can be obtained freely from http://apps.ecmwf.int/datasets/data/interim_full_daily. This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI (2020-01210).

Funding

This study was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI (2020-01210).

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Authors and Affiliations

  1. Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea

    Ajin Cho, Hajoon Song, Yong-Jin Tak & Soon-Il An

  2. Department of Environmental Marine Science, Hanyang University, Ansan, 15588, South Korea

    Sang-Wook Yeh

  3. Operational Systems Development Department, National Institute of Meteorological Sciences, Seogwipo, South Korea

    Sang-Min Lee, Hee-Sook Ji & Yu-Kyung Hyun

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  1. Ajin Cho
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Cho, A., Song, H., Tak, YJ. et al. Atmosphere-driven cold SST biases over the western North Pacific in the GloSea5 seasonal forecast system. Clim Dyn 59, 2571–2584 (2022). https://doi.org/10.1007/s00382-022-06228-x

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