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SCIENCE CHINA Earth Sciences, Volume 61 , Issue 6 : 792-803(2018) https://doi.org/10.1007/s11430-017-9171-8

SST biases over the Northwest Pacific and possible causes in CMIP5 models

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  • ReceivedMay 19, 2017
  • AcceptedJan 26, 2018
  • PublishedApr 8, 2018

Abstract


Funded by

the National Key Research and Development Program of China(Grant,No.,2017YFA0604004)

by the National Science Foundation of China(Grant,No.,41575105)


Acknowledgment

This work was supported by the National Key Research and Development Program of China (Grant No. 2017YFA 0604004) and the R&D Special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY201506012).


References

[1] Adler R F, Huffman G J, Chang A, Ferraro R, Xie P P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol, 2003, 4: 1147-1167 CrossRef Google Scholar

[2] Cane M A, Zebiak S E. A theory for El Niño and the southern oscillation. Science, 1985, 228: 1085-1087 CrossRef PubMed ADS Google Scholar

[3] Dee D P, Uppala S M, Simmons A J, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M A, Balsamo G, Bauer P, Bechtold P, Beljaars A C M, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer A J, Haimberger L, Healy S B, Hersbach H, Hólm E V, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally A P, Monge-Sanz B M, Morcrette J J, Park B K, Peubey C, de Rosnay P, Tavolato C, Thépaut J N, Vitart F. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q J R Meteorol Soc, 2011, 137: 553-597 CrossRef ADS Google Scholar

[4] de Szoeke S P, Xie S P. The tropical eastern pacific seasonal cycle: Assessment of errors and mechanisms in IPCC AR4 coupled ocean-atmosphere general circulation models. J Clim, 2008, 21: 2573-2590 CrossRef ADS Google Scholar

[5] de Szoeke S P, Xie S P, Miyama T, Richards K J, Small R J O. What maintains the SST front North of the Eastern Pacific equatorial cold tongue?. J Clim, 2007, 20: 2500-2514 CrossRef ADS Google Scholar

[6] de Szoeke S P, Fairall C W, Wolfe D E, Bariteau L, Zuidema P. Surface flux observations on the southeastern tropical Pacific Ocean and attribution of SST errors in coupled ocean-atmosphere models. J Clim, 2010, 23: 4152-4174 CrossRef ADS Google Scholar

[7] Dong L, Zhou T J. The Indian Ocean sea surface temperature warming simulated by CMIP5 models during the Twentieth Century: Competing forcing roles of GHGs and anthropogenic aerosols. J Clim, 2014, 27: 3348-3362 CrossRef ADS Google Scholar

[8] Du Y, Xie S P. Role of atmospheric adjustments in the tropical Indian Ocean warming during the 20th Century in climate models. Geophys Res Lett, 2008, 35: L08712 CrossRef ADS Google Scholar

[9] Fan M, Schneider E K. Observed decadal north Atlantic tripole SST variability. Part I: Weather noise forcing and coupled response. J Atmos Sci, 2012, 69: 35-50 CrossRef ADS Google Scholar

[10] Han Z Y, Zhou T J, Zou L W. Indian Ocean SST biases in a flexible regional ocean atmosphere land system (FROALS) model. Atmos Ocean Sci Lett, 2012, 5: 273-279 CrossRef Google Scholar

[11] Levine R C, Turner A G. Dependence of Indian monsoon rainfall on moisture fluxes across the Arabian Sea and the impact of coupled model sea surface temperature biases. Clim Dyn, 2012, 38: 2167-2190 CrossRef ADS Google Scholar

[12] Levine R C, Turner A G, Marathayil D, Martin G M. The role of northern Arabian Sea surface temperature biases in CMIP5 model simulations and future projections of Indian summer monsoon rainfall. Clim Dyn, 2013, 41: 155-172 CrossRef ADS Google Scholar

[13] Li C. 2008. Coupling mode analysis of summer precipitation in mid-low valley of Yangtze River and sea surface temperature in Northwest Pacific (in Chinese). J Trop Oceanogr, 27: 38–44. Google Scholar

[14] Li G, Xie S P. Origins of tropical-wide SST biases in CMIP multi-model ensembles. Geophys Res Lett, 2012, 39: L22703 CrossRef ADS Google Scholar

[15] Li G, Xie S P. Tropical biases in CMIP5 multimodel ensemble: The excessive equatorial Pacific cold tongue and double ITCZ problems. J Clim, 2014, 27: 1765-1780 CrossRef ADS Google Scholar

[16] Li G, Xie S P, Du Y. Monsoon-induced biases of climate models over the tropical Indian Ocean. J Clim, 2015, 28: 3058-3072 CrossRef ADS Google Scholar

[17] Liu B, Zhou T J, Zou L W, Dong L. 2015. Simulation of northwestern Pacific circulation and its variability in a regional ocean-atmosphere model-FROALS (in Chinese). Acta Oceanolog Sin, 37: 17–28. Google Scholar

[18] Liu H, Lin W, Zhang M. Heat budget of the upper ocean in the South-Central Equatorial Pacific. J Clim, 2010, 23: 1779-1792 CrossRef ADS Google Scholar

[19] Marathayil D, Turner A G, Shaffrey L C, Levine R C. Systematic winter sea-surface temperature biases in the northern Arabian Sea in HiGEM and the CMIP3 models. Environ Res Lett, 2013, 8: 014028 CrossRef ADS Google Scholar

[20] Philander S G H. El Niño southern oscillation phenomena. Nature, 1983, 302: 295-301 CrossRef ADS Google Scholar

[21] Prodhomme C, Terray P, Masson S, Izumo T, Tozuka T, Yamagata T. Impacts of Indian Ocean SST biases on the Indian Monsoon: As simulated in a global coupled model. Clim Dyn, 2014, 42: 271-290 CrossRef ADS Google Scholar

[22] Rayner N A, Parker D E, Horton E B, Folland C K, Alexander L V, Rowell D P, Kent E C, Kaplan A. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res, 2003, 108: 4407 CrossRef ADS Google Scholar

[23] Song C Y, Zhang S W, Jiang H, Wang H, Wang D K, Huang Y Y. 2016. Evaluation and projection of SST in the China seas from CMIP5 (in Chinese). Acta Oceanolog Sin, 38: 1–11. Google Scholar

[24] Song F, Zhou T. The climatology and interannual variability of East Asian Summer Monsoon in CMIP5 coupled models: Does air-sea coupling improve the simulations?. J Clim, 2014, 27: 8761-8777 CrossRef ADS Google Scholar

[25] Taylor K E. Summarizing multiple aspects of model performance in a single diagram. J Geophys Res, 2001, 106: 7183-7192 CrossRef ADS Google Scholar

[26] Wang C Z, Zhang L, Lee S K, Wu L, Mechoso C R. A global perspective on CMIP5 climate model biases. Nat Clim Change, 2014, 4: 201-205 CrossRef ADS Google Scholar

[27] Weisberg R H, Wang C. A western Pacific oscillator paradigm for the El Niño-Southern oscillation. Geophys Res Lett, 1997, 24: 779-782 CrossRef ADS Google Scholar

[28] Xie S P, Deser C, Vecchi G A, Ma J, Teng H, Wittenberg A T. Global warming pattern formation: Sea surface temperature and rainfall. J Clim, 2010, 23: 966-986 CrossRef ADS Google Scholar

[29] Xu J, Chen X, Chen Y, Ding Q, Tian S. The effect of sea surface temperature increase on the potential habitat of Ommastrephes bartramii in the Northwest Pacific Ocean. Acta Oceanol Sin, 2014, 35: 109-116 CrossRef Google Scholar

[30] Yu L, Weller R A. Objectively analyzed air-sea heat fluxes for the global ice-free oceans (1981–2005). Bull Amer Meteorol Soc, 2007, 88: 527-539 CrossRef ADS Google Scholar

[31] Yu L, Jin X, Weller R A. 2008. Multidecade global flux datasets from the Objectively Analyzed Air-Sea Fluxes (OAFlux) Project: Latent and Sensible Heat Fluxes, Ocean Evaporation, and Related Surface Meteorological Variables Lisan Yu. OAFlux Project Tech. Rep. OA-2008-01. Google Scholar

[32] Zhang L, Zhao C. 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, 2015, 7: 739-758 CrossRef ADS Google Scholar

[33] Zhang Y, Rossow W B, Lacis A A, Oinas V, Mishchenko M I. Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. J Geophys Res, 2004, 109: D19105 CrossRef ADS Google Scholar

[34] Zheng Y X, Shinoda T, Lin J L, Kiladis G N. Sea surface temperature biases under the stratus cloud deck in the Southeast Pacific Ocean in 19 IPCC AR4 coupled general circulation models. J Clim, 2011, 24: 4139-4164 CrossRef ADS Google Scholar

[35] Zheng Y, Lin J L, Shinoda T. The equatorial Pacific cold tongue simulated by IPCC AR4 coupled GCMs: Upper ocean heat budget and feedback analysis. J Geophys Res, 2012, 117: C05024 CrossRef ADS Google Scholar

[36] Zou L W, Zhou T J. Development and evaluation of a regional ocean-atmosphere coupled model with focus on the western North Pacific summer monsoon simulation: Impacts of different atmospheric components. Sci China Earth Sci, 2012, 55: 802-815 CrossRef Google Scholar

  • Figure 1

    The spatial distributions of the SST biases simulated by the CMIP5 Multi-Model Ensemble Mean (MME) SST biases in: (a) winter, (b) spring, (c) summer, (d) autumn. The dotted areas are statistically significant at the 5% level by Student’s t test. Units: K.

  • Figure 2

    Annual cycle of SST biases over the Northwest Pacific (10°N–30°N, 120°E–180°) in CMIP5 models. Black line is the MME, colored lines denote the 21 individual CMIP5 models. Units: K.

  • Figure 3

    Taylor diagram of the Northwest Pacific Ocean (10°N–30°N, 120°E–180°) SST simulated in the CMIP5 models. The horizontal and vertical axes show the normalized standard deviations; the values in arc are the spatial correlation; and the distance between each point and REF is the root mean square error (RMSE).

  • Figure 4

    The spatial distributions of the biases in net sea surface heat fluxes due to atmospheric processes ((b), (e), (h), (k)), oceanic processes ((c), (f), (i), (l)) and the sum of both biases ((a), (d), (g), (j)). Positive values indicate heating effects and negative values indicate cooling effects. The first, second, third and last row are winter, spring, summer and autumn respectively. The number shown on the top left corner of each plot are the spatial correlation coefficients between the SST biases and the sum biases of net sea surface heat fluxes due to atmospheric processes and oceanic processes. Units: W m−2.

  • Figure 5

    The scatterplots and correlation coefficients of SST biases (K) and the biases of net sea surface heat fluxes (W m−2) due to atmospheric processes over the Northwest Pacific Ocean (10°N–30°N, 120°E–180°) from individual CMIP5 models during (a) winter, (b) spring, (c) summer, (d) autumn.

  • Figure 6

    The spatial distributions of each component bias of sea surface heat fluxes due to atmospheric processes: surface net shortwave radiation ((a), (e), (i), (m)), surface net longwave radiation ((b), (f), (j), (n)), surface sensible heat flux ((c), (g), (k), (o)), and surface latent heat flux from atmosphere ((d), (h), (l), (p)). Positive values indicate heating effects and negative values indicate cooling effects. The first, second, third and last row are winter, spring, summer and autumn respectively. Units: W m−2.

  • Figure 7

    The scatterplots and correlation coefficients of the biases of latent heat flux associated with atmospheric processes (lh-atm bias, W m−2) and the biases of sea surface net heat fluxes due to atmospheric processes (sw bias, W m−2) over the Northwest Pacific (10°N–30°N, 120°E–180°) from CMIP5 models in (a) winter, (b) spring, (d) autumn. (c) The scatterplot and correlation coefficient of the sum of surface net shortwave radiation biases and latent heat flux biases related to atmospheric processes (W m−2), and the biases of the net sea surface heat fluxes due to atmospheric processes (W m−2) during summer.

  • Figure 8

    (a) The SST biases (black bars) and contributions from atmospheric processes (atm, green bars) and oceanic processes (ocn, blue bars) to SST biases over the Northwest Pacific (10°N–30°N, 120°E–180°) in the MME of CMIP5 models. (b) The contributions of biases of shortwave radiation (sw, red bars), longwave radiation (lw, yellow bars), sensible heat flux (sh, brown bars) and latent heat flux associated with atmospheric processes (lh-atm, gray bars) to the SST biases over the Northwest Pacific Ocean in the MME of CMIP5 models. Units: K.

  • Figure 9

    ((a)–(d)) Spatial distribution of the observed wind (shading, m s−1) at 1000 hPa, rainfall (contour, mm d−1) and SST (line, K) averaged from 1984 to 2005. The first, second, third and last row is winter, spring, summer and autumn respectively. The ((e)–(h)) is similar to the ((a)–(d)), except for the biases between the MME of CMIP5 models and observations. The dotted areas denote that the rainfall biases are statistically significant at the 5% level by Student’s t test. The red box regions show the Northwest Pacific (10°N–30°N, 120°E–180°).

  • Figure 10

    The scatterplots and correlation coefficients of precipitation biases (prbias) over the Maritime Continent region and the biases of wind speed at 1000 hPa (uvbias) over the Northwest Pacific during (a) winter and (b) spring from individual CMIP5 models. The scatterplots and correlation coefficients of ITCZ biases and biases of wind speed over the mid and low latitudes of the North Pacific during (c) summer and (d) autumn.

  • Table 1   A list of the 21 CMIP5 CGCMs used in this study

    Institution

    Model acronym

    HR(lat lon grid points)

    Number

    CSIRO and BoM

    CSIRO and BoM

    CCCma

    NCAR

    NSF DOE and NCAR

    NSF DOE and NCAR

    NSF DOE and NCAR

    LASG and CESS

    IAP

    NOAA/GFDL

    NASA GISS

    NASA GISS

    INM IPSL

    MIROC

    MIROC

    MIROC

    MPI-M

    MPI-M

    MRI

    NCC

    NCC

    ACCESS1.0

    ACCESS1.3

    CanESM2

    CCSM4

    CESM1-BGC

    CESM1-CAM5

    CESM1-CAM5.1-FV2

    FGOASL-g2

    FGOASL-s2

    GFDL-ESM2M

    GISS-E2-H

    GISS-E2-R

    INM-CM4

    MIROC5

    MIROC-ESM

    MIROC-ESM-CHEM

    MPI-ESM-LR

    MPI-ESM-MR

    MRI-CGCM3

    NorESM1-M

    NorESM1-ME

    144×192

    144×192

    64×128

    192×288

    192×288

    192×288

    192×288

    64×128

    108×128

    90×144

    90×144

    90×144

    120×180

    128×256

    64×128

    64×128

    96×192

    96×192

    160×320

    96×144

    96×144

    M1

    M2

    M3

    M4

    M5

    M6

    M7

    M8

    M9

    M10

    M11

    M12

    M13

    M14

    M15

    M16

    M17

    M18

    M19

    M20

    M21

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