• Anderson, W. G., , A. Gnanadesikan, , R. Hallberg, , J. Dunne, , and B. L. Samuels, 2007: Impact of ocean color on the maintenance of the Pacific cold tongue. Geophys. Res. Lett., 34, L11609, doi:10.1029/2007GL030100.

    • Search Google Scholar
    • Export Citation
  • Ballabrera-Poy, J., , R. Murtugudde, , R. H. Zhang, , and A. J. Busalacchi, 2007: Coupled ocean–atmosphere response to seasonal modulation of ocean color: Impact on interannual climate simulations in the tropical Pacific. J. Climate, 20, 353374, doi:10.1175/JCLI3958.1.

    • Search Google Scholar
    • Export Citation
  • Bishop, J. K., , and W. B. Rossow, 1991: Spatial and temporal variability of global surface solar irradiance. J. Geophys. Res., 96, 16 83916 858, doi:10.1029/91JC01754.

    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., , S. Lee, , and K. L. Swanson, 2002: Storm track dynamics. J. Climate, 15, 21632183, doi:10.1175/1520-0442(2002)015<02163:STD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, S.-J., , Y.-H. Kuo, , P.-Z. Zhang, , and Q.-F. Bai, 1992: Climatology of explosive cyclones off the East Asian coast. Mon. Wea. Rev., 120, 30293035, doi:10.1175/1520-0493(1992)120<3029:COECOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chin, T. M., , J. Vazquez, , E. M. Armstrong, , and A. J. Mariano, 2010: Algorithm theoretical basis document: Multi-scale, motion-compensated analysis of sea surface temperature, version 1.1. NASA Measures Algorithm Theoretical Basis Doc., 17 pp. [Available online at ftp://mariana.jpl.nasa.gov/mur_sst/tmchin/docs/ATBD/atbd_1.1actual.pdf.]

  • Dudhia, J., , D. Gill, , K. Manning, , W. Wang, , and C. Bruyere, cited 2005: PSU/NCAR mesoscale modeling system tutorial class notes and user’s guide (MM5 modeling system version 3). [Available online at http://www.mmm.ucar.edu/mm5/documents/tutorial-v3-notes.html.]

  • Follows, M., , and S. Dutkiewicz, 2001: Meteorological modulation of the North Atlantic spring bloom. Deep-Sea Res. II, 49, 321344, doi:10.1016/S0967-0645(01)00105-9.

    • Search Google Scholar
    • Export Citation
  • Gildor, H., , and N. H. Naik, 2005: Evaluating the effect of interannual variations of surface chlorophyll on upper ocean temperature. J. Geophys. Res., 110, C07012, doi:10.1029/2004JC002779.

    • Search Google Scholar
    • Export Citation
  • Gnanadesikan, A., , and W. G. Anderson, 2009: Ocean water clarity and the ocean general circulation in a coupled climate model. J. Phys. Oceanogr., 39, 314332, doi:10.1175/2008JPO3935.1.

    • Search Google Scholar
    • Export Citation
  • Gnanadesikan, A., , K. Emanuel, , G. A. Vecchi, , W. G. Anderson, , and R. Hallberg, 2010: How ocean color can steer Pacific tropical cyclones. Geophys. Res. Lett., 37, L18802, doi:10.1029/2010GL044514.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., , J. Dudhia, , and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 117 pp, doi:10.5065/D60Z716B.

  • Hirose, N., , K. Takayama, , J.-H. Moon, , T. Watanabe, , and Y. Nishida, 2013: Regional data assimilation system extended to the East Asian marginal seas. Umi to Sora, 89, 19.

    • Search Google Scholar
    • Export Citation
  • Isobe, A., , and S. Kako, 2012: A role of the Yellow and East China Seas in the development of extratropical cyclones in winter. J. Climate, 25, 83288340, doi:10.1175/JCLI-D-11-00391.1.

    • Search Google Scholar
    • Export Citation
  • Jerlov, N. G., 1968: Optical Oceanography. Elsevier, 194 pp.

  • Jochum, M., , S. Yeager, , K. Lindsay, , K. Moore, , and R. Murtugudde, 2010: Quantification of the feedback between phytoplankton and ENSO in the Community Climate System Model. J. Climate, 23, 29162925, doi:10.1175/2010JCLI3254.1.

    • Search Google Scholar
    • Export Citation
  • Kako, S., , and M. Kubota, 2009: Numerical study on the variability of mixed layer temperature in the North Pacific. J. Phys. Oceanogr., 39, 737752, doi:10.1175/2008JPO3979.1.

    • Search Google Scholar
    • Export Citation
  • Kako, S., , A. Isobe, , and M. Kubota, 2011: High resolution ASCAT wind vector dataset gridded by applying an optimum interpolation method to the global ocean. J. Geophys. Res., 116, D23107, doi:10.1029/2010JD015484.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and et al. , 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kobashi, F., , and A. Kubokawa, 2012: Review on North Pacific subtropical countercurrents and subtropical fronts: Role of mode waters in ocean circulation and climate. J. Oceanogr., 68, 2143, doi:10.1007/s10872-011-0083-7.

    • Search Google Scholar
    • Export Citation
  • Kondo, J., 1975: Air–sea bulk transfer coefficients in diabatic conditions. Bound.-Layer Meteor., 9, 91112, doi:10.1007/BF00232256.

    • Search Google Scholar
    • Export Citation
  • Lengaigne, M., , C. Menkes, , O. Aumont, , T. Gorgues, , L. Bopp, , J. M. André, , and G. Madec, 2007: Influence of the oceanic biology on the tropical Pacific climate in a coupled general circulation model. Climate Dyn., 28, 503516, doi:10.1007/s00382-006-0200-2.

    • Search Google Scholar
    • Export Citation
  • Lewis, M. R., , M.-E. Carr, , G. C. Feldman, , W. Esaias, , and C. McClain, 1990: Influence of penetrating solar radiation on the heat budget of the equatorial Pacific Ocean. Nature, 347, 543545, doi:10.1038/347543a0.

    • Search Google Scholar
    • Export Citation
  • Liang, X., , and L. Wu, 2013: Effects of solar penetration on the annual cycle of sea surface temperature in the North Pacific. J. Geophys. Res. Oceans, 118, 27932801, doi:10.1002/jgrc.20208.

    • Search Google Scholar
    • Export Citation
  • Lin, P., , H. Liu, , Y. Yu, , and X. Zhang, 2011: Response of sea surface temperature to chlorophyll-a concentration in the tropical Pacific: Annual mean, seasonal cycle, and interannual variability. Adv. Atmos. Sci.,28, 492510.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130141, doi:10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.

  • Manizza, M., , C. Le Quéré, , A. J. Watson, , and E. T. Buitenhuis, 2005: Bio-optical feedbacks among phytoplankton, upper ocean physics and sea-ice in a global model. Geophys. Res. Lett., 32, L05603, doi:10.1029/2004GL020778.

    • Search Google Scholar
    • Export Citation
  • Manizza, M., , C. Le Quéré, , A. J. Watson, , and E. T. Buitenhuis, 2008: Ocean biogeochemical response to phytoplankton-light feedback in a global model. J. Geophys. Res., 113, C10010, doi:10.1029/2007JC004478.

    • Search Google Scholar
    • Export Citation
  • Marzeion, B., , A. Timmermann, , R. Murtugudde, , and F. F. Jin, 2005: Biophysical feedbacks in the tropical Pacific. J. Climate, 18, 5870, doi:10.1175/JCLI3261.1.

    • Search Google Scholar
    • Export Citation
  • McGillicuddy, D. J., and et al. , 2007: Eddy/wind interactions stimulate extraordinary mid-ocean plankton blooms. Science, 316, 10211026, doi:10.1126/science.1136256.

    • Search Google Scholar
    • Export Citation
  • Moore, J. K., , S. C. Doney, , J. A. Kleypas, , D. M. Glover, , and I. Y. Fung, 2002: An intermediate complexity marine ecosystem model for the global domain. Deep-Sea Res. II, 49, 403462, doi:10.1016/S0967-0645(01)00108-4.

    • Search Google Scholar
    • Export Citation
  • Morel, A., 1988: Optical modeling of the upper ocean in relation to its biogeneous matter content (case I waters). J. Geophys. Res., 93, 10 74910 768, doi:10.1029/JC093iC09p10749.

    • Search Google Scholar
    • Export Citation
  • Murtugudde, R., , J. Beauchamp, , C. R. McClain, , M. Lewis, , and A. J. Busalacchi, 2002: Effects of penetrative radiation on the upper tropical ocean circulation. J. Climate, 15, 470486, doi:10.1175/1520-0442(2002)015<0470:EOPROT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nakamoto, S., , S. Prasanna Kumar, , J. M. Oberhuber, , J. Ishizaka, , K. Muneyama, , and R. Frouin, 2001: Response of the equatorial Pacific to chlorophyll pigment in a mixed layer isopycnal ocean general circulation model. Geophys. Res. Lett., 28, 20212024, doi:10.1029/2000GL012494.

    • Search Google Scholar
    • Export Citation
  • Nakamura, H., 1992: Midwinter suppression of baroclinic wave activity in the Pacific. J. Atmos. Sci., 49, 16291642, doi:10.1175/1520-0469(1992)049<1629:MSOBWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nakamura, H., , T. Izumi, , and T. Sampe, 2002: Interannual and decadal modulations recently observed in the Pacific storm track activity and East Asian winter monsoon. J. Climate, 15, 18551874, doi:10.1175/1520-0442(2002)015<1855:IADMRO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nakamura, H., , A. Nishina, , and S. Minobe, 2012: Response of storm tracks to bimodal Kuroshio path states south of Japan. J. Climate, 25, 77727779, doi:10.1175/JCLI-D-12-00326.1.

    • Search Google Scholar
    • Export Citation
  • Onitsuka, G., , T. Yanagi, , and J.-H. Yoon, 2007: A numerical study on nutrient sources in the surface layer of the Japan Sea using a coupled physical–ecosystem model. J. Geophys. Res., 112, C05042, doi:10.1029/2006JC003981.

    • Search Google Scholar
    • Export Citation
  • Oschlies, A., 2004: Feedbacks of biotically induced radiative heating on upper-ocean heat budget, circulation, and biological production in a coupled ecosystem-circulation model. J. Geophys. Res., 109, C12031, doi:10.1029/2004JC002430.

    • Search Google Scholar
    • Export Citation
  • Patara, L., , M. Vichi, , S. Masina, , P. G. Fogli, , and E. Manzini, 2012: Global response to solar radiation absorbed by phytoplankton in a coupled climate model. Climate Dyn., 39, 19511968, doi:10.1007/s00382-012-1300-9.

    • Search Google Scholar
    • Export Citation
  • Paulson, C. A., , and J. J. Simpson, 1977: Irradiance measurements in the upper ocean. J. Phys. Oceanogr., 7, 952956, doi:10.1175/1520-0485(1977)007<0952:IMITUO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Qiu, B., , and K. A. Kelly, 1993: Upper ocean heat balance in the Kuroshio Extension region. J. Phys. Oceanogr., 23, 20272041, doi:10.1175/1520-0485(1993)023<2027:UOHBIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sathyendranath, S., , A. D. Gouveia, , S. R. Shetye, , P. Ravindran, , and T. Platt, 1991: Biological control of surface temperature in the Arabian Sea. Nature, 349, 5456, doi:10.1038/349054a0.

    • Search Google Scholar
    • Export Citation
  • Schneider, E. K., , and Z. Zhu, 1998: Sensitivity of the simulated annual cycle of sea surface temperature in the equatorial Pacific to sunlight penetration. J. Climate, 11, 19321950, doi:10.1175/1520-0442-11.8.1932.

    • Search Google Scholar
    • Export Citation
  • Shell, K. M., , R. Frouin, , S. Nakamoto, , and R. C. J. Somerville, 2003: Atmospheric response to solar radiation absorbed by phytoplankton. J. Geophys. Res., 108, 4445, doi:10.1029/2003JD003440.

    • Search Google Scholar
    • Export Citation
  • Sonntag, S., , and I. Hence, 2011: Phytoplankton behavior affects ocean mixed layer dynamics through biological-physical feedback mechanisms. Geophys. Res. Lett., 38, L15610, doi:10.1029/2011GL048205.

    • Search Google Scholar
    • Export Citation
  • Strutton, P. G., , and F. P. Chavez, 2004: Biological heating in the equatorial Pacific: Observed variability and potential for real-time calculation. J. Climate, 17, 10971109, doi:10.1175/1520-0442(2004)017<1097:BHITEP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sweeney, C., , A. Gnanadesikan, , S. M. Griffies, , M. J. Harrison, , A. J. Rosati, , and B. L. Samuels, 2005: Impacts of shortwave penetration depth on large-scale ocean circulation and heat transport. J. Phys. Oceanogr., 35, 11031119, doi:10.1175/JPO2740.1.

    • Search Google Scholar
    • Export Citation
  • Taguchi, B., , H. Nakamura, , M. Nonaka, , and S. P. Xie, 2009: Influences of the Kuroshio/Oyashio Extensions on air–sea heat exchanges and storm-track activity as revealed in regional atmospheric model simulations for the 2003/04 cold season. J. Climate, 22, 65366560, doi:10.1175/2009JCLI2910.1.

    • Search Google Scholar
    • Export Citation
  • Timmermann, A., , and F.-F. Jin, 2002: Phytoplankton influences on tropical climate. Geophys. Res. Lett., 29, 2104, doi:10.1029/2002GL015434.

    • Search Google Scholar
    • Export Citation
  • Turner, A. G., , M. Joshi, , E. S. Robertson, , and S. J. Woolnough, 2012: The effect of Arabian Sea optical properties on SST biases and the South Asian summer monsoon in a coupled GCM. Climate Dyn., 39, 811826, doi:10.1007/s00382-011-1254-3.

    • Search Google Scholar
    • Export Citation
  • Wetzel, P., , E. Maier-Reimer, , E. Botzet, , J. Jungclaus, , N. Keenlyside, , and M. Latif, 2006: Effects of ocean biology on the penetrative radiation in a coupled climate model. J. Climate, 19, 39733987, doi:10.1175/JCLI3828.1.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., , J. Hafner, , Y. Tanimoto, , W. T. Liu, , H. Tokinaga, , and H. Xu, 2002: Bathymetric effect on the winter sea surface temperature and climate of the Yellow and East China Seas. Geophys. Res. Lett., 29, 2228, doi:10.1029/2002GL015884.

    • Search Google Scholar
    • Export Citation
  • Yamada, K., , J. Ishizaka, , S. Yoo, , H. C. Kim, , and S. Chiba, 2004: Seasonal and interannual variability of sea surface chlorophyll a concentration in the Japan/East Sea (JES). Prog. Oceanogr., 61, 193211, doi:10.1016/j.pocean.2004.06.001.

    • Search Google Scholar
    • Export Citation
  • Yamamoto, M., , and N. Hirose, 2007: Impact of SST reanalyzed using OGCM on weather simulation: A case of a developing cyclone in the Japan Sea area. Geophys. Res. Lett., 34, L05808, doi:10.1029/2006GL028386.

    • Search Google Scholar
    • Export Citation
  • Yamamoto, M., , and N. Hirose, 2011: Possible modification of atmospheric circulation over the northwestern Pacific induced by a small semi-enclosed ocean. Geophys. Res. Lett., 38, L03804, doi:10.1029/2010GL046214.

    • Search Google Scholar
    • Export Citation
  • Yanagi, T., , G. Onitsuka, , N. Hirose, , and J.-H. Yoon, 2001: A numerical simulation on the mesoscale dynamics of the spring bloom in the Sea of Japan. J. Oceanogr., 57, 617630, doi:10.1023/A:1021691221793.

    • Search Google Scholar
    • Export Citation
  • Yoshiike, S., , and R. Kawamura, 2009: Influence of wintertime large-scale circulation on the explosively developing cyclones over the western North Pacific and their downstream effects. J. Geophys. Res., 114, D13110, doi:10.1029/2009JD011820.

    • Search Google Scholar
    • Export Citation
  • Zhai, L., , C. Tang, , T. Platt, , and S. Sathyendranath, 2011: Ocean response to attenuation of visible light by phytoplankton in the Gulf of St. Lawrence. J. Mar. Syst., 88, 285297, doi:10.1016/j.jmarsys.2011.05.005.

    • Search Google Scholar
    • Export Citation
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    SST map averaged over April and May from 2003 to 2010 (solid lines) using the MURSST dataset. Contour interval is 1°C. Horizontal SST gradient is shown by shading, whose scale is at lower right. Bold line shows domain of the regional atmospheric model used.

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    Sea surface chlorophyll concentration on 15 April, 1 May, and 15 May in the Sea of Japan. These daily maps were built using a Gaussian filter with MODIS dataset from 2003 to 2010. Shading and dense shading, respectively, indicate areas with concentrations higher than 0.8 and 1.0 mg Chl m−3. Contour interval is 0.1 mg Chl m−3. Area depicted is the same as the domain of mixed-layer model described in section 2a.

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    Surface currents (vectors) and mixed-layer depths (shading) on 15 April, 1 May, and 15 May over the Sea of Japan, averaged using the DREAMS product from 2001 to 2010. Current vectors were depicted every second grid cells.

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    Solar radiation absorbed per unit volume within mixed layer (contours) on 15 April, 1 May, and 15 May in the Sea of Japan. Contour interval is 0.5 W m−3. Difference in absorbed solar radiation between mixed-layer models with and without phytoplankton in Eq. (4) is shown by shading. Positive values indicate warming in the model with phytoplankton.

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    Temperature maps (top) computed by the mixed-layer model and (bottom) from MURSST dataset on 15 April, 1 May, and 15 May in the Sea of Japan. Contours in upper panels show temperature in the model with phytoplankton in Eq. (4). Contour interval is 1°C. Shading in upper panels indicates temperature difference between the models with and without phytoplankton. Positive values indicate warming in the model with phytoplankton.

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    Temperature computed in models using climatologically averaged chlorophyll concentration plus its standard deviation, and using the average minus standard deviation. Contours with an interval of 1°C are depicted for temperature in the former case. Temperature difference between the two models is shown by shading. Positive values indicate warming in the model with abundant phytoplankton.

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    SLP (solid lines) and its standard deviation (broken lines) computed in the (left) MM5V3 and (right) NCEP–NCAR reanalysis product. SLPs are averaged over April and May from 2001 and 2010, and standard deviations computed over same period. Contour intervals of SLP and standard deviation are 1 and 2 hPa, respectively.

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    (top) SLPs from the NCEP–NCAR reanalysis product, and those computed by MM5V3 in the (middle) green and (bottom) blue cases, respectively, at 0000 UTC from 17 to 19 May 2006. Contour interval is 2 hPa. Letter “A” in all panels locates typhoon Chanchu. Letter “B” in the lower panels represents a second, simulated typhoon that occurs in the blue case.

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    Difference of averaged SLP (contours in the left panel) and its standard deviation (color shading in right panel) between blue and green cases. Contour intervals are 0.1 hPa for both panels. Positive values indicated that variables computed in the blue case are larger than in the green case. Shading in the left panel indicates areas for which difference of the averaged SLP is statistically significant, as indicated by a t test with 90% confidence level. Color shading in right panel is all statistically significant, as indicated by a 99% F test. Superimposed on the right panel are same contours as in the left panel. The star in the left panel indicates the point at which the SLP difference is depicted in Fig. 10a.

  • View in gallery

    SLP difference and lag correlation. (a) SLP difference between blue and green cases (blue minus green) in spring 2006. Values are spatially averaged over area surrounded by broken line in the −48-h panel of (b), and is normalized by standard deviation computed over entire period. Shading indicates periods when normalized SLP was less than −1 (bold broken line). (b) Lag correlation maps (0, 24, and 48 h prior) of SLP difference at the star in all panels. Correlation was computed using all modeled SLPs over entire period. Contour interval is 0.1.

  • View in gallery

    Difference of modeled results between blue and green cases (blue minus green) during the composite period defined in text. Contours in the left panel show SLP difference at interval 0.5 hPa. Broken lines signify negative values. Color shading indicates difference of SLP standard deviation at interval 0.2 hPa. Contours in right panel are the same as at left. Also shown in the right panel by color shading is difference of poleward eddy heat flux integrated below 850 hPa height, at interval of 1 K m−2 s−1.

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    Wind vectors and speed (shading) averaged over (left) the entire period and (right) the composite period at (top) 500- and (bottom) 925-hPa heights, respectively.

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    Difference of zonal heat (θ; solid line) and vapor (q; broken line) fluxes between blue and green cases (blue minus green) averaged below 850-hPa height. Zonal fluxes were computed at the broken line above the Japan Islands (35°–45°N, 139°E; see inset map and lower right panel of Fig. 12). Eastward fluxes were assigned to be positive in the computations. Ordinate represents the difference divided by the values in the green case (in percent); abscissa indicates surface wind speed (at 925 hPa) averaged over area surrounded by broken line in lower right panel of Fig. 12. For ease of understanding wind magnitude, wind speed on the abscissa denotes anomaly from the mean (〈u〉), and is normalized by standard deviation (σ); both are computed over the entire area and period.

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    Difference of surface wind vectors (925 hPa) between blue and green cases (blue minus green). Vectors in upper (lower) panel are averaged over composite (entire) period.

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    Difference of baroclinicity averaged in lower troposphere (<850 hPa) between blue and green cases (blue minus green) during composite period. Left (right) panel is meridional gradient of equivalent potential temperature (Eady parameter). Contour intervals are 2 × 10−3 K km−1 and 0.1 day−1, respectively, in left and right panels. Bold broken line is same as 0.2-hPa contour of SLP standard deviation in Fig. 11a.

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Synoptic-Scale Atmospheric Motions Modulated by Spring Phytoplankton Bloom in the Sea of Japan

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  • 1 Research Institute for Applied Mechanics, Kyushu University, Kasuga, Japan
  • | 2 Graduate School of Science and Engineering, Kagoshima University, Kagoshima, Japan
  • | 3 Research Institute for Applied Mechanics, Kyushu University, Kasuga, Japan
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Abstract

Atmospheric responses to biological heating caused by the spring phytoplankton bloom in the Sea of Japan are investigated. Sea surface temperature (SST) is first computed using a mixed-layer model with an ocean reanalysis product. Satellite-derived surface chlorophyll concentrations representing phytoplankton population are input to an equation for attenuation coefficients of solar radiation penetrating the mixed layer. Two sets of SST are obtained by this model, using the attenuation coefficients with and without phytoplankton. It is found that the phytoplankton bloom increases SST by up to 0.8°C by mid-May, especially in the northern Sea of Japan. Thereafter, two experiments using a regional atmospheric numerical model are conducted for April and May. One imposes SST synthesized by multiple satellite observations on the lower boundary of the model (the green case). The satellite-derived SST includes influences of biological heating by phytoplankton in the actual ocean. The other uses SST reduced by differences between SSTs computed by the mixed-layer model with and without phytoplankton (the blue case). Under modest wind conditions, extratropical cyclones east and south of the Japan Islands in the blue case develop more rapidly than in the green case. Cyclones are likely initiated by the cool and dry air mass that enhances lower-level baroclinicity above oceanic fronts. This cool and dry air mass is transported from the Sea of Japan, where SST decreases in the absence of phytoplankton. Therefore, incorporating ocean biology is potentially capable of improving regional atmospheric and ocean general circulation models.

Corresponding author address: Atsuhiko Isobe, Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasuga-Koen, Kasuga 8168580, Japan. E-mail: aisobe@riam.kyushu-u.ac.jp

This article is included in the Climate Implications of Frontal Scale Air–Sea Interaction Special Collection.

Abstract

Atmospheric responses to biological heating caused by the spring phytoplankton bloom in the Sea of Japan are investigated. Sea surface temperature (SST) is first computed using a mixed-layer model with an ocean reanalysis product. Satellite-derived surface chlorophyll concentrations representing phytoplankton population are input to an equation for attenuation coefficients of solar radiation penetrating the mixed layer. Two sets of SST are obtained by this model, using the attenuation coefficients with and without phytoplankton. It is found that the phytoplankton bloom increases SST by up to 0.8°C by mid-May, especially in the northern Sea of Japan. Thereafter, two experiments using a regional atmospheric numerical model are conducted for April and May. One imposes SST synthesized by multiple satellite observations on the lower boundary of the model (the green case). The satellite-derived SST includes influences of biological heating by phytoplankton in the actual ocean. The other uses SST reduced by differences between SSTs computed by the mixed-layer model with and without phytoplankton (the blue case). Under modest wind conditions, extratropical cyclones east and south of the Japan Islands in the blue case develop more rapidly than in the green case. Cyclones are likely initiated by the cool and dry air mass that enhances lower-level baroclinicity above oceanic fronts. This cool and dry air mass is transported from the Sea of Japan, where SST decreases in the absence of phytoplankton. Therefore, incorporating ocean biology is potentially capable of improving regional atmospheric and ocean general circulation models.

Corresponding author address: Atsuhiko Isobe, Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasuga-Koen, Kasuga 8168580, Japan. E-mail: aisobe@riam.kyushu-u.ac.jp

This article is included in the Climate Implications of Frontal Scale Air–Sea Interaction Special Collection.

1. Introduction

It is well known that dense phytoplankton populations in the euphotic zone increase the attenuation rate of solar radiation penetrating the sea surface, resulting in increased availability of energy in the upper mixed layer (Jerlov 1968; Paulson and Simpson 1977). Hence, seawater temperature in the upper mixed layer depends partly on concentrations of chlorophyll through such biological heating (or bio-optical heating; e.g., Lewis et al. 1990; Sathyendranath et al. 1991; Strutton and Chavez 2004; Sonntag and Hense 2011). Therefore, studies on biophysical feedback have considered the extent to which biological heating alters sea surface temperature (SST) and its related effect on climate, by comparing results from numerical models with and without phytoplankton. These include ocean general circulation models using atmospheric data in conjunction with satellite-derived chlorophyll data (Nakamoto et al. 2001; Murtugudde et al. 2002; Sweeney et al. 2005; Manizza et al. 2005; Anderson et al. 2007; Zhai et al. 2011), ocean–ecosystem coupled models forced by atmospheric data (Oschlies 2004; Marzeion et al. 2005; Manizza et al. 2008), atmosphere–ocean coupled models using satellite-derived chlorophyll data (Shell et al. 2003; Gildor and Naik 2005; Ballabrera-Poy et al. 2007; Gnanadesikan and Anderson 2009; Gnanadesikan et al. 2010; Lin et al. 2011; Turner et al. 2012; Liang and Wu 2013), and fully coupled models including atmosphere, ocean, and ecosystem (Wetzel et al. 2006; Lengaigne et al. 2007; Jochum et al. 2010; Patara et al. 2012).

However, the coupling of atmosphere, ocean, and ecosystem processes presents a causality dilemma that may obfuscate model results. For instance, the aforementioned studies have given contradictory results in reproducing the cold tongue in the eastern equatorial Pacific. Schneider and Zhu (1998) undertook pioneering work to incorporate sunlight penetration into an atmosphere–ocean coupled model. They demonstrated that long-term average SST in the cold tongue rose in their “no penetration” model relative to a model with deep sunlight penetration, which implies less phytoplankton (their Fig. 8b). Warming of the cold tongue by phytoplankton was revealed in the following numerical model studies: Timmermann and Jin (2002), Marzeion et al. (2005), Wetzel et al. (2006), Lengaigne et al. (2007), and Patara et al. (2012). However, the SST increment due to biological heating was different by these models in the range of 0.5°–2°C (Table 1 in Patara et al. 2012). Meanwhile, annual mean SST in the cold tongue was found to decrease following incorporation of biological heating in other numerical model studies (Nakamoto et al. 2001; Murtugudde et al. 2002; Manizza et al. 2005; Anderson et al. 2007; Manizza et al. 2008; Gnanadesikan and Anderson 2009; Jochum et al. 2010; Lin et al. 2011). This is because SST and related atmospheric properties such as wind and precipitation in the equatorial Pacific are determined by a combination of direct (changes of sunlight attenuation) and indirect (changes of mixed-layer thickness) contributions of biological heating (Marzeion et al. 2005), and probably because this combination is sensitive to the selection of model parameters (Manizza et al. 2005). For instance, if the mixed layer is much deeper, chlorophyll near the surface causes a “shadow” that cools seawater beneath the chlorophyll layer. Thus, subsequent mixing processes make warming in the mixed layer less noticeable (Turner et al. 2012).

The present study deals with the spring phytoplankton bloom in the Sea of Japan (also called the East Sea). This sea is isolated geographically from surrounding waters except for connections via five narrow straits ranging from ~1-km width for Kanmon Strait to ~200-km width for Tsushima Strait (Fig. 1). This geographic isolation may reduce the influence of the causality dilemma in investigating the roles of biological heating on atmospheric and/or oceanic processes. Figure 2 shows average sea surface chlorophyll concentrations on three days, using data between 2003 and 2010 from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites. This dataset is accessible to the public on a website of the Northwest Pacific Region Environmental Cooperation Center (NPEC; http://ocean.nowpap3.go.jp/?page_id=862). To construct maps for these three days, we used a Gaussian filter with e-folding scales of 7 days (15 km) in time (space). As mentioned by Yamada et al. (2004) and Onitsuka et al. (2007), onset of the spring phytoplankton bloom is observed near the subarctic front (a thermohaline front at 38°–39°N) in the Sea of Japan during April, and thereafter the bloom moves northward until mid-May. According to a hydrodynamic and ecosystem coupled model experiment (Yanagi et al. 2001), it is believed that the northward migration of the spring bloom in the Sea of Japan results mainly from a combination of the SST increase and depletion of dissolved inorganic nitrogen. The present study focused on atmospheric and oceanic responses triggered by this spring phytoplankton bloom during April through May. Investigating temporal evolution from onset of the bloom during the seasonal cycle to two months afterward is likely more straightforward than, for instance, ENSO modulation in the tropics, because the beginning of the response is clearly specified [see, e.g., Zhai et al. (2011) for biophysical feedbacks caused by plankton blooms]. In addition, an advantage of dealing with the spring bloom in the Sea of Japan is that the high chlorophyll concentration in the area is likely less “contaminated” by phytoplankton advected from surrounding waters, where the phytoplankton population is partly governed by both atmospheric and oceanic processes. Thus, we are able to demonstrate how the influences of biological heating on atmospheric processes spread from the “source” (the Sea of Japan in the present study) to the surrounding waters. However, our study has a restriction in that we cannot deal with atmospheric and oceanic responses on time scales longer than two months. It is nonetheless interesting to examine whether biological heating plays a measurable role in relatively short-term oceanic and/or atmospheric processes. Such was indicated by Gnanadesikan et al. (2010), who found that biological heating affected the paths of Pacific tropical cyclones.

Fig. 1.
Fig. 1.

SST map averaged over April and May from 2003 to 2010 (solid lines) using the MURSST dataset. Contour interval is 1°C. Horizontal SST gradient is shown by shading, whose scale is at lower right. Bold line shows domain of the regional atmospheric model used.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

Fig. 2.
Fig. 2.

Sea surface chlorophyll concentration on 15 April, 1 May, and 15 May in the Sea of Japan. These daily maps were built using a Gaussian filter with MODIS dataset from 2003 to 2010. Shading and dense shading, respectively, indicate areas with concentrations higher than 0.8 and 1.0 mg Chl m−3. Contour interval is 0.1 mg Chl m−3. Area depicted is the same as the domain of mixed-layer model described in section 2a.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

Geographic properties of the Sea of Japan provide another advantage in exploring the effects of biological heating on regional climate. The sea, which is at the western edge of the North Pacific, is a warm ocean that in spring is first encountered by cool and dry westerly winds from the Asian landmass. Hence, it is anticipated that any SST increase/decrease caused by biological heating might alter atmospheric properties considerably via intense turbulent heat exchange and vapor transfer from the sea. In addition, the lower-level air mass modified by SST efficiently spreads to the North Pacific because of steady westerly winds (Yamamoto and Hirose 2011). Biophysical feedback over the Sea of Japan might thereby modify atmospheric (hence oceanic) processes more strongly than currently envisioned.

As described in section 2, the present study uses a regional atmospheric model to reproduce atmospheric processes, MODIS chlorophyll data to incorporate biological processes, and a mixed-layer model to determine SST via upper oceanic processes [i.e., a spring bloom study of Follows and Dutkiewicz (2001) computing mixed-layer depths instead of SST]. The oceanic mixed-layer model enables interpretation of SST dependence on model parameters more directly than biophysical feedback studies using coupled models. Nonetheless, the simplification ignoring two-way processes altering SST and/or phytoplankton population by atmospheric processes may become less justifiable if biological heating over the Sea of Japan significantly changes the regional climate just above the sea. Highlighted in our study, however, is a “remote modulation” of extratropical cyclones east and south of the Japan Islands (sections 3 and 4). One likely outcome may be that warmer SST from biological heating encourages further phytoplankton growth (hence, a positive feedback to SST). Such a highly complex interaction is however not treated by the present study, and should be investigated using a fully coupled model. Section 5 summarizes detectable influences of biological heating, not only on the planetary-scale climate such as ENSO in the tropics, but also on the regional climate at midlatitudes.

2. Setup of models

a. Mixed-layer model

The mixed-layer model adopted here is the same as that used by Qiu and Kelly (1993) and Kako and Kubota (2009). SST (T) at each ¼° grid cell was computed as
e1
e2
where mixed-layer depth h from an oceanic reanalysis dataset [Data Assimilation Research of the East Asian Marine System (DREAMS); Hirose et al. 2013] was defined as the depth to which seawater density increases by 0.03 σθ from the sea surface. Mixed-layer depths were nearly constant even if we used 0.125 σθ for the definition (not shown), and so the depth is nearly independent of the choice of σθ within the plausible range in the actual ocean. The DREAMS products have a resolution of ° × ° in longitude and latitude, and were averaged within each grid cell of the mixed-layer model. Similarly, horizontal velocities in the uppermost layer (4-m thickness) from DREAMS were used for those in the mixed layer (U), under the assumption that velocities in the surface mixed layer are vertically homogeneous because of intense vertical momentum exchange within this layer. Constants Ah, ρ, c, α, ΔT, and g are, respectively, the horizontal viscosity (103 m2 s−1), seawater density (1028 kg m−3), specific heat of seawater (3930 J kg−1 K−1), thermal expansion coefficient of seawater (2.5 × 10−4 °C −1), temperature difference between the mixed layer and water just below it (0.5°C), and gravitational acceleration. Modeled SSTs were nearly identical, even if we chose 1.0°C for ΔT (not shown). Two adjustable constants m0 and mc were set to 0.5 and 0.83, respectively, in line with Qiu and Kelly (1993). Also, Q denotes net heat flux through the sea surface, and this was computed using the bulk formulas in Kondo (1975) with modeled SST, gridded wind speeds using daily data of the Advanced Scatterometer (ASCAT; Kako et al. 2011; downloaded from http://mepl1.riam.kyushu-u.ac.jp/~kako/ASCAT/NetCDF/), and other atmospheric properties furnished by the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis product (Kalnay et al. 1996). Friction velocity (u*) was computed using ASCAT wind speeds, with air density of 1.2 kg m−3 and drag coefficient of 1.2 × 10−3. Other notations are standard, except where otherwise stated. The entrainment velocity we at the bottom of the mixed layer is given by Eq. (2).
In the present study, biological heating was incorporated into the downward radiative flux [q(z)] in line with Manizza et al. (2005):
e3
where IRED and IBLUE are both 21% of total surface irradiance (I0) provided by the NCEP–NCAR reanalysis product. The remaining 58% of the irradiance (infrared wavelength band) is absorbed in the thin surface layer [<O(10) cm] and is therefore independent of depth in the present application. Attenuation coefficients for the visible wavelength bands with red/yellow (kr) and blue/green (kb) colors in Eq. (3) were evaluated by
e4
where coefficients a, b, and γ are 0.225 (0.0232), 0.037 (0.074), and 0.629 (0.674) for the red/yellow (blue/green) color band, respectively. Also, Chl denotes chlorophyll concentrations from MODIS, under the assumption that concentrations in the surface mixed layer are vertically homogeneous because of the intense vertical mixing within this layer. We examined influences of biological heating on both atmosphere and ocean through experiments with and without chlorophyll concentrations [i.e., Chl = 0 in Eq. (4)]. The sensitivity of modeled SST to formulations of biological heating [i.e., Eqs. (3) and (4)] is also investigated in section 3.

Observations of ocean color are frequently inhibited by clouds and therefore, in contrast to either the reanalysis products or satellite scatterometer measurements, are unable to provide continuous daily data over the full study area. Thus, the mixed-layer model employed daily chlorophyll concentrations averaged for each date from observations in different years. Thereby, that model did not provide SST for a specific year but daily averaged SST with subseasonal variations. Computations with and without chlorophyll were done for the period 1 April through 31 May. Thus, the DREAMS and NCEP–NCAR products and ASCAT data were all converted to daily values for that period by averaging over 10 years from 2001 to 2010. Similarly, daily MODIS chlorophyll data for the same months were computed using the Gaussian filter used to construct Fig. 2. Because the MODIS dataset prior to 2003 is not available on the NPEC website, daily chlorophyll concentrations were computed using a 2003–10 dataset. Nevertheless, general features of the spring bloom (its concentration and location) in Fig. 2 are nearly identical to those observed using the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) from 1997 to 2002 (Fig. 2 of Yamada et al. 2004) and 1997 to 2003 (Fig. 6 of Onitsuka et al. 2007).

The computational procedure was as follows. Equation (1) was solved numerically with the entrainment velocity obtained via Eq. (2) and the downward radiative flux calculated with Eqs. (3) and (4). The model domain was the same as that shown in Fig. 2. The DREAMS product provided daily boundary conditions of ocean currents, seawater temperature, and mixed-layer depths. Initial conditions of these properties on 1 April were also given by the DREAMS product.

b. Regional atmospheric model

The setup of a regional atmospheric numerical model, except for SST conditions, is described below. The wide model domain (Fig. 1) was set to represent regional climate changes that might appear east of the Sea of Japan because of intense westerly winds. The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5V3; Grell et al. 1994) was used with 23 sigma levels and 25-km grid intervals over the entire model domain, because the model with settings given below were optimal for modeling developing extratropical cyclones over the East Asian marginal seas (Yamamoto and Hirose 2007; Isobe and Kako 2012). Data from the NCEP Final Operational Global Data Analysis (http://rda.ucar.edu/datasets/ds083.2/) were used for initial and lateral boundary conditions. Land surface temperature was predicted using a five-layer soil model based on the vertical diffusion equation (ISOIL = 1 in the MM5 user’s guide; Dudhia et al. 2005). The Grell cumulus parameterization (ICUPA = 3), MRF planetary boundary layer (IBLTYP = 5), cloud and rainwater (IMPHYS = 2), and cloud radiation (IFRAD = 2) schemes were also included in the model. The radiative condition (IFUPR = 1) was chosen for the upper boundary (100-hPa level) to absorb upward-propagating wave momentum.

Multiscale ultrahigh-resolution SST (MURSST; Chin et al. 2010) was averaged on the same days from 1 April to 31 May during 2003–10 (as with the MODIS data), and was averaged at each grid cell of MM5V3 to impose the lower boundary condition. This synthesized dataset of multiple satellite observations provides gridded SST at a sufficiently fine resolution to resolve oceanic fronts that frequently initiate extratropical cyclones in the study area (Chen et al. 1992; Nakamura et al. 2002; Xie et al. 2002; Yamamoto and Hirose 2007; Taguchi et al. 2009; Yoshiike and Kawamura 2009; Isobe and Kako 2012; Nakamura et al. 2012). The subarctic front in the Sea of Japan, the Kuroshio front in the East China Sea, the Kuroshio Extension, and the subtropical front between 20° and 30°N in the western North Pacific (Kobashi and Kubokawa 2012) are well identified, even in the SST map averaged over 2 months (Fig. 1). Biological heating was incorporated in the MM5V3 computation, on which daily MURSST was imposed across the model domain, because observed SST is influenced to some extent by phytoplankton. This computation is henceforth referred to as the “green case.”

In the “blue case,” which is the experiment in the absence of phytoplankton, we used the same daily MURSST, but after the removal of temperature differences between the mixed-layer models with and without phytoplankton. The domain of the mixed-layer model only covered the Sea of Japan. Therefore, the difference of SSTs between the green and blue cases could only be evaluated within this limited area. To avoid abrupt SST changes that might occur between the Sea of Japan and surrounding waters because of the simplicity of the mixed-layer model, SST computed in the mixed-layer model was not used directly for the lower boundary condition of the regional atmospheric model.

Computations from 1 April to the end of May were done for 10 spring seasons from 2001 to 2010, with daily SST interpolated at each time step. It should be restated that a single dataset of daily SST was used repeatedly for the modeling, irrespective of the year. Thus, our attention was not on atmospheric properties within a specific year, but on whether statistically significant biases appeared between the green and blue case experiments under various atmospheric conditions during the 10 spring seasons. Modeled properties [e.g., sea level pressure (SLP)] were dumped every 2 h, and thus 7320 data points (12 × 61 days × 10 springs) were analyzed for each property.

3. Results

a. SSTs computed using mixed-layer model

The DREAMS product, which is an oceanic reanalysis validated by observations over the East Asian marginal seas, provides mixed-layer depths and upper-layer currents suitable for computing SST in the mixed-layer model (Fig. 3). Horizontal currents shown in Fig. 3 are those in the uppermost layer of the DREAMS product, and are assumed vertically homogeneous in the surface mixed layer because of the intense vertical mixing of momentum. Horizontal inhomogeneity of the mixed-layer depths is remarkable, and is likely attributable to different upwelling and/or advection features. Those depths are large along the subarctic front extending from southwest to northeast over the Sea of Japan, and are likely maintained by horizontal advection of heat owing to intense northeastward currents along the front. Areas with relatively thin mixed-layer depths (less than 20–30 m) spread gradually in the northwestern Sea of Japan. Biological heating is likely efficient there because seawater temperature is sensitive to solar radiation in such mixed layers of small heat capacity, and because phytoplankton become dense there, especially in early May (Fig. 2). Besides the heat capacity argument, warming of deep mixed layers is unlikely to be efficient in reality, because surface chlorophyll prevents solar radiation from penetrating beneath the chlorophyll layer (Turner et al. 2012).

Fig. 3.
Fig. 3.

Surface currents (vectors) and mixed-layer depths (shading) on 15 April, 1 May, and 15 May over the Sea of Japan, averaged using the DREAMS product from 2001 to 2010. Current vectors were depicted every second grid cells.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

The magnitude of solar radiation absorbed per unit volume in the mixed layer increases significantly via biological heating associated with the spring phytoplankton bloom, especially in the northwestern Sea of Japan (Fig. 4). The solar radiation is almost completely absorbed within the thin uppermost layer [79%, which equals 58% of I0 plus 21% of IRED in Eq. (3)] because the relatively short e-folding depth is shallower than 4.4 m (), irrespective of chlorophyll concentration in Eq. (4). Hence, areas warmed efficiently in Fig. 4 are coincident with those of the thin mixed layer (Fig. 3) because of the small heat capacity. Nonetheless, Fig. 4 demonstrates that solar radiation absorbed per unit volume in the mixed layer increases ~30% in the model with biological heating [i.e., nonzero Chl in Eq. (4)], especially in the northwestern Sea of Japan. This relatively large biological heating might act as negative feedback to the mixed-layer depths in reality because of heat trapping by the surface chlorophyll layer.

Fig. 4.
Fig. 4.

Solar radiation absorbed per unit volume within mixed layer (contours) on 15 April, 1 May, and 15 May in the Sea of Japan. Contour interval is 0.5 W m−3. Difference in absorbed solar radiation between mixed-layer models with and without phytoplankton in Eq. (4) is shown by shading. Positive values indicate warming in the model with phytoplankton.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

Therefore, mixed-layer temperature, which was regarded as SST in our analysis, increased by up to 0.8°C through biological heating at the end of the spring bloom in the Sea of Japan (shading in Fig. 5). In fact, the SST difference was less remarkable on 15 April, but thereafter the area warmed by biological heating prevailed over the sea with temperature increment greater than 0.5°C in mid-May. However, as the computation proceeds, the mixed-layer model underestimates SST in the northern part of the sea; for example, compare SSTs between the mixed-layer model and MURSST (upper and lower panels in Fig. 5, respectively). This excessively cool SST might distort atmospheric responses if used directly for MM5V3 computations. Hence, as mentioned earlier, MURSST is input into the green case of the MM5V3 computations because this satellite-observed SST is partly modified by biological heating. SST in the blue case is the processed MURSST, from which we removed differences between SSTs computed using the mixed-layer model with and without the chlorophyll concentration.

Fig. 5.
Fig. 5.

Temperature maps (top) computed by the mixed-layer model and (bottom) from MURSST dataset on 15 April, 1 May, and 15 May in the Sea of Japan. Contours in upper panels show temperature in the model with phytoplankton in Eq. (4). Contour interval is 1°C. Shading in upper panels indicates temperature difference between the models with and without phytoplankton. Positive values indicate warming in the model with phytoplankton.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

b. SST dependency on attenuation formulas

Although the SST increment in the mixed-layer model depends largely on the choice of attenuation coefficients, Eq. (4) gives a modest SST change among the various formulas. Listed in Table 1 are the differences of SST averaged over the model domain and their standard deviations in mid-May, between the models using different formulas with and without the chlorophyll concentration. The attenuation coefficients, including those used in the present study, are based on irradiance measurements in the actual oceans (Paulson and Simpson 1977; Morel 1988; Bishop and Rossow 1991). The mean SST difference exceeds 1°C for two of these formulas, whereas the mean difference in the present study is about half that. In the next subsection, we demonstrate the nonnegligible role of biological heating on regional climate, even upon imposition of this modest SST increment in the model. Nonetheless, atmospheric responses might be more energetic than demonstrated, if other formulas more sensitive to chlorophyll concentrations were chosen for the experiments. Moreover, the difference of atmospheric responses might be great, even for a small departure from the modest SST. However, our study does not delve into the sensitivity of atmospheric responses above areas with such SST in the Sea of Japan.

Table 1.

Difference of mean temperature and its standard deviation, computed using different mixed-layer models with and without phytoplankton. Values were obtained over the entire model domain for 15 May. Attenuation coefficients listed below were used for each computation. Boldface font denotes the present study.

Table 1.

The modeled SST variation associated with phytoplankton population is an estimate that might appear in reality, likely to occur in accordance with natural fluctuations of phytoplankton in the ocean. Figure 6 is the same as the upper panels of Fig. 5, but for the modeled temperature computed in mixed-layer models using two satellite-observed chlorophyll datasets. One shows daily averaged chlorophyll concentrations plus their standard deviations, which are both calculated on each model grid cell using the 2003–10 dataset. The other is the daily-averaged concentration minus their standard deviations. The SST difference in the northwestern Sea of Japan reaches 0.8°C or more, which is nearly the same as that used for the following blue-case and green-case experiments, although the area warmed by the phytoplankton is somewhat narrower than that in Fig. 5.

Fig. 6.
Fig. 6.

Temperature computed in models using climatologically averaged chlorophyll concentration plus its standard deviation, and using the average minus standard deviation. Contours with an interval of 1°C are depicted for temperature in the former case. Temperature difference between the two models is shown by shading. Positive values indicate warming in the model with abundant phytoplankton.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

c. Comparison of SLPs between the green case, blue case, and observations

Atmospheric properties generated in the green case are anticipated to be similar to observed, and so we first compare the modeled SLP averaged over the computation period (2001–10) with that from the NCEP–NCAR reanalysis product (Fig. 7). The difference between the two maps in Fig. 7 results partly from differences of the SST datasets input to the modeled sea surface. It was nonetheless anticipated that the modeled properties averaged over a long period would be similar to observed, because SLP fluctuations caused by interannual variations must be reduced. In fact, both SLP maps represent well the overall features such that the North Pacific high extends westward around 30°N in spring and SLP fluctuations increase to the northeast.

Fig. 7.
Fig. 7.

SLP (solid lines) and its standard deviation (broken lines) computed in the (left) MM5V3 and (right) NCEP–NCAR reanalysis product. SLPs are averaged over April and May from 2001 and 2010, and standard deviations computed over same period. Contour intervals of SLP and standard deviation are 1 and 2 hPa, respectively.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

In the green case, synoptic motions and the long-term averaged field were also consistent with observed. The upper panels of Fig. 8 present SLP maps from the NCEP–NCAR reanalysis product during 17–19 May 2006, when a typhoon (Chanchu) in the southwestern corner of the domain (letter “A” in upper left panel) moved northeastward until becoming an extratropical cyclone over the Japan Islands on 19 May. Again, daily SST inputs in the green case were not for a specific year, but were climatological averages. Nevertheless, typhoon occurrence in either the observed or modeled SLPs (cf. “A” in the upper and middle panels) suggests that cyclone activities are not always sensitive to interannual variations of the SST field. The typhoon in the green case is more intense than that reproduced in the reanalysis product because the resolution of the present model (25 km) is much higher than that of the reanalysis product (2.5° in latitude/longitude). In fact, the modeled SLP depression associated with the typhoon is consistent with actual SLPs monitored by in situ observatories (not shown).

Fig. 8.
Fig. 8.

(top) SLPs from the NCEP–NCAR reanalysis product, and those computed by MM5V3 in the (middle) green and (bottom) blue cases, respectively, at 0000 UTC from 17 to 19 May 2006. Contour interval is 2 hPa. Letter “A” in all panels locates typhoon Chanchu. Letter “B” in the lower panels represents a second, simulated typhoon that occurs in the blue case.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

The influence of biological heating in the Sea of Japan during the 3 days is surprisingly energetic. SLP maps in the blue case (lower panels in Fig. 8) indicate that another typhoon (letter “B”), which was absent in reality, occurred prior to Chanchu. The two cases differ only in that biological heating is incorporated over the Sea of Japan in the green case. Thus, we suggest the phytoplankton contributed to the absence of the unrealized storm. In addition, it is interesting that the remote modulation of synoptic motion, influenced by biological heating, is found not above the Sea of Japan, but rather south of the Japan Islands.

d. Remote modulation of synoptic motion

When a small perturbation introduced to the models (e.g., SST in the Sea of Japan) grows with time, it has the potential to become a large difference when numerically integrated over a long time (Lorenz 1963). In strict terms, SST in the Sea of Japan was not an initial, but a boundary condition in the models. Nonetheless, a weakly perturbed SST might trigger fluctuations of synoptic motion moving over the Sea of Japan. Hence, the question to address here is not specific to atmospheric phenomena such as typhoon occurrence, but whether statistically significant differences are found in atmospheric properties between the green and blue cases (data number was 7320 at each grid cell for 10 springs). The answer is that this is indeed the case. The left panel of Fig. 9 shows the SLP difference averaged over the entire period between the green and blue cases. The negative areas south and east of the Japan Islands indicate that, on average, SLP in the blue case is lower than in the green case. Positive differences between standard deviations in the two cases also appear within the low-SLP area in the blue case. The implication is that extratropical cyclones with low SLPs and strong SLP fluctuations will develop along the area south and east of the Japan Islands in the blue case. It was also found that this area is sandwiched between areas of reduced SLP fluctuation in the blue case (see the negative differences of standard deviation).

Fig. 9.
Fig. 9.

Difference of averaged SLP (contours in the left panel) and its standard deviation (color shading in right panel) between blue and green cases. Contour intervals are 0.1 hPa for both panels. Positive values indicated that variables computed in the blue case are larger than in the green case. Shading in the left panel indicates areas for which difference of the averaged SLP is statistically significant, as indicated by a t test with 90% confidence level. Color shading in right panel is all statistically significant, as indicated by a 99% F test. Superimposed on the right panel are same contours as in the left panel. The star in the left panel indicates the point at which the SLP difference is depicted in Fig. 10a.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

However, differences of both SLPs and their standard deviations are very small. This suggests that, on average, biological heating over the Sea of Japan has a trivial effect on regional climate. Nonetheless, we next attempted to emphasize the above differences and related atmospheric processes through data processing. This is because, as suggested by the typhoon appearance (disappearance) in the blue (green) case in Fig. 8, the small differences in Fig. 9 likely originate from the relatively rare occasions with biological heating sufficient to meet conditions for a nonnegligible influence on regional climate rather than from trivial influences of biological heating. During the period in which the difference of normalized SLPs was less than −1 (Fig. 10a), it is expected that atmospheric processes generating the low SLP in the blue case are intensified, at least to the east of the Japan Islands. These processes are traceable to the prior 48 h, as shown by the lag-correlation maps of SLP differences between the blue and green cases (Fig. 10b). Correlation was determined at the point where the difference of averaged SLP was maximal (star in left panel of Figs. 9 and 10b). Hence, using the normalized SLP difference within the aforementioned area in all years, we next construct composite maps of modeled properties over the 48 h before the day on which the difference became less than −1 (hereafter, “composite period”). The composite period makes up ~10% of the entire computational period.

Fig. 10.
Fig. 10.

SLP difference and lag correlation. (a) SLP difference between blue and green cases (blue minus green) in spring 2006. Values are spatially averaged over area surrounded by broken line in the −48-h panel of (b), and is normalized by standard deviation computed over entire period. Shading indicates periods when normalized SLP was less than −1 (bold broken line). (b) Lag correlation maps (0, 24, and 48 h prior) of SLP difference at the star in all panels. Correlation was computed using all modeled SLPs over entire period. Contour interval is 0.1.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

Of particular interest is the remote modulation of subweekly-scale fluctuations caused by biological heating in the Sea of Japan. SLP differences between the two cases during the composite period (left panel of Fig. 11) were one order of magnitude higher than those in Fig. 9, and the same order of the standard deviations of SLP itself (Fig. 7). Hence, these SLP differences clearly illustrate a nonnegligible influence of biological heating on regional atmospheric processes. The positive standard deviations to the south and east of the Japan Islands indicate that SLP fluctuations intensified in the blue case. Moreover, in the right panel of Fig. 11, the positive difference is remarkable in the high-pass (<7 days) filtered poleward eddy heat flux, integrated over the lower atmosphere (<850 hPa). A 7-day boxcar filter was used to obtain subweekly-scale properties required for computing the heat flux, because the high-pass filter can capture most of the midlatitude atmospheric variability of period shorter than a week (e.g., Nakamura 1992). This remarkable difference of eddy heat flux means that extratropical cyclones tend to develop to the south and east of the Japan Islands in the absence of biological heating over the Sea of Japan. In other words, phytoplankton in the sea occasionally plays a role in suppressing the development of extratropical cyclones, not directly above those sea areas but above surrounding waters.

Fig. 11.
Fig. 11.

Difference of modeled results between blue and green cases (blue minus green) during the composite period defined in text. Contours in the left panel show SLP difference at interval 0.5 hPa. Broken lines signify negative values. Color shading indicates difference of SLP standard deviation at interval 0.2 hPa. Contours in right panel are the same as at left. Also shown in the right panel by color shading is difference of poleward eddy heat flux integrated below 850 hPa height, at interval of 1 K m−2 s−1.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

4. Discussion

a. Favorable condition for spreading the influence of biological heating

The remote modulation of extratropical cyclones implies that during the composite period, westerly or northwesterly winds might be sufficiently dominant for advecting the air mass modulated above the Sea of Japan into surrounding regions. However, weakness of the background winds in both the mid and lower troposphere during this period was remarkable. Figure 12 shows low-pass filtered wind vectors (>7 days) during the entire (left panels) and composite (right panels) periods, at 500-hPa (upper) and 925-hPa (lower) heights. Commonly observed features, independent of period are intense westerlies at 500 hPa and easterlies (westerlies) in the subtropics (midlatitudes) at 925 hPa. However, during the composite period, the area with wind speeds faster than 20 m s−1 disappeared at 500-hPa height. Weakening of the winds over a broad area in the midtroposphere suggests that it was not caused by regional conditions such as SST, but by change in the wind field on a planetary scale. The weakening of the winds in the lower troposphere is also found during the composite period south and east of the Japan Islands where extratropical cyclones develop as shown in Fig. 11.

Fig. 12.
Fig. 12.

Wind vectors and speed (shading) averaged over (left) the entire period and (right) the composite period at (top) 500- and (bottom) 925-hPa heights, respectively.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

It is likely that modest winds diminish the transport of the air mass altered by biological heating over the Sea of Japan toward surrounding waters. We nonetheless emphasize that stable baroclinicity in the lower atmosphere is maintained above oceanic eddies and fronts under modest wind conditions. For instance, Isobe and Kako (2012) demonstrated that development of extratropical cyclones is suppressed over the Yellow and East China Sea shelves during winter because the air mass with strong baroclinicity, enhanced above oceanic fronts, is shifted southward the south by brisk northerly winds. In fact, numerous oceanic fronts formed over the study area (Fig. 1), where the development of extratropical cyclones has been identified: for example, the Kuroshio Extension (Nakamura et al. 2002; Taguchi et al. 2009), Kuroshio front (Xie et al. 2002; Nakamura et al. 2012), and Sea of Japan (Yamamoto and Hirose 2007; Yoshiike and Kawamura 2009; Isobe and Kako 2012).

In addition, modest winds have another advantage for spreading the influence of biological heating in the Sea of Japan to surrounding waters. It is reasonable to believe that lowered SST in the blue case made the lower troposphere cool and dry over the Sea of Japan, which then acted as a “source” of the air mass favorable for heat and vapor transfers between atmosphere and ocean. In fact, zonal heat and vapor transports averaged over the lower atmosphere demonstrate that both transports in the blue case were smaller (i.e., eastward transport of cool and dry air mass was greater) than in the green case by several percent, regardless of wind speed over the surrounding waters (Fig. 13). A nearly identical difference appeared in both cases by integrating the zonal and heat transports over the lower atmosphere, in lieu of averaging these transports (not shown). Of particular interest is that the heat and vapor transports decreased rapidly as the wind speed anomaly approached −1, at which point the westerly component of the low-pass filtered surface winds (>7 days at 925 hPa) was faster around the Sea of Japan in the blue case than in the green case (Fig. 14). It is therefore believed that under modest wind conditions, the spread of the cool and dry air mass from the Sea of Japan to surrounding waters is reinforced in the absence of phytoplankton, through the modulation of background atmospheric fields such as pressure (hence, winds) over the western North Pacific.

Fig. 13.
Fig. 13.

Difference of zonal heat (θ; solid line) and vapor (q; broken line) fluxes between blue and green cases (blue minus green) averaged below 850-hPa height. Zonal fluxes were computed at the broken line above the Japan Islands (35°–45°N, 139°E; see inset map and lower right panel of Fig. 12). Eastward fluxes were assigned to be positive in the computations. Ordinate represents the difference divided by the values in the green case (in percent); abscissa indicates surface wind speed (at 925 hPa) averaged over area surrounded by broken line in lower right panel of Fig. 12. For ease of understanding wind magnitude, wind speed on the abscissa denotes anomaly from the mean (〈u〉), and is normalized by standard deviation (σ); both are computed over the entire area and period.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

Fig. 14.
Fig. 14.

Difference of surface wind vectors (925 hPa) between blue and green cases (blue minus green). Vectors in upper (lower) panel are averaged over composite (entire) period.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

b. Modulation of extratropical cyclones by phytoplankton in Sea of Japan

In general, the spread of a cool and dry air mass over a warm ocean activates turbulent heat and vapor transfers through the sea surface, such that oceanic fronts in the study area were likely to enhance lower-level baroclinicity in the blue case, especially under modest wind conditions. In reality, it is difficult to show how the cool and dry air mass spreads from the Sea of Japan, even if we construct maps of vertically averaged (or integrated) heat and moisture fluxes over the study area. This is because these fluxes are greatly disturbed by air–sea exchanges under the enhanced extratropical cyclones. Nevertheless, it was found that the meridional gradient of equivalent potential temperature (), averaged vertically below 850 hPa in the blue case, was stronger than in the green case (left panel of Fig. 15) to the east and south of the Japan Islands during the composite period with modest winds. Similarly, the difference of the Eady parameter in the lower atmosphere (<850 hPa) was remarkable east of the Japan Islands during the composite period (right panel of Fig. 15). In this figure, u denotes the zonal wind, f the Coriolis parameter, and N the static stability (e.g., Chang et al. 2002). These areas (shaded red in the figure) are consistent with the Kuroshio front in the East China Sea, Kuroshio Extension, and subtropical front (shaded in Fig. 1).

Fig. 15.
Fig. 15.

Difference of baroclinicity averaged in lower troposphere (<850 hPa) between blue and green cases (blue minus green) during composite period. Left (right) panel is meridional gradient of equivalent potential temperature (Eady parameter). Contour intervals are 2 × 10−3 K km−1 and 0.1 day−1, respectively, in left and right panels. Bold broken line is same as 0.2-hPa contour of SLP standard deviation in Fig. 11a.

Citation: Journal of Climate 27, 20; 10.1175/JCLI-D-14-00277.1

The above baroclinicity maps indicate a scenario in which phytoplankton in the Sea of Japan modulates subweekly-scale atmospheric processes east and south of the Japan Islands. For the sake of understanding, let us consider the situation in the absence of phytoplankton (blue case). Were it not for the spring phytoplankton bloom in the Sea of Japan, SST in mid-May would be reduced by 0.8°C because of the deep penetration of solar radiation below the surface mixed layer. The concentration of phytoplankton is not critical for penetration of both infrared and visible radiation wavelength bands of red/yellow colors, because these are absorbed within the uppermost layer (shallower than a few meters). However, we find that the visible wavelength band with blue/green colors can penetrate to 43-m depths, beyond the mixed-layer depth in spring (Fig. 3), by substituting Chl of 0 mg m−3 into Eq. (4). Penetration for this band is suppressed shallower than 10 m if Chl of 1.0 mg m−3 (Fig. 2) is substituted into Eq. (4). Hence, the lowered SST makes the lower atmosphere cool and dry over the Sea of Japan. This air mass, which is subsequently transported to surrounding waters by westerly winds, activates turbulent heat transfer between atmosphere and ocean. Baroclinicity of the lower atmosphere is thereby enhanced above oceanic fronts along which extratropical cyclones develop. Nevertheless, such development is remarkable only under modest wind conditions, when baroclinicity is steadily maintained in the lower atmosphere above the oceanic fronts. Figure 15 shows that the area favorable for extratropical cyclone development, which is emphasized by an SLP standard deviation greater than 0.2 hPa (bold broken lines; see also Fig. 11a), is between the Kuroshio front (including its extension) and subtropical front. This is reasonable because lower-level baroclinicity above both oceanic fronts can activate synoptic-scale extratropical cyclones moving in between them.

5. Conclusions

The present study demonstrates the influence of biological heating is not only on interannual variability such as ENSO modulation in the tropics but also on subweekly-scale atmospheric motion at midlatitudes. The spring phytoplankton bloom in the Sea of Japan was chosen for the experiments. This was because we could readily follow the time evolution of the influence of biological heating from onset of the spring bloom and straightforwardly demonstrate spatial spread of this influence from the Sea of Japan, which is isolated geographically from surrounding waters. It was found that subweekly-scale extratropical cyclones occasionally developed during the 2 months following onset of the spring bloom, and that this development was not above the Sea of Japan but above oceanic fronts east and south of the Japan Islands. The air mass modified over the Sea of Japan is transported over these fronts. In addition, the development of the extratropical cyclones is not always significant during the entire period of the spring bloom. A weakness of ambient winds is required for cyclone to development, such that the ocean-induced baroclinicity is steadily maintained in the lower atmosphere above the oceanic fronts.

The difficulty in interpreting impacts of the biological heating stems from a very complicated relationship between atmospheric, oceanic, and biological processes. The remote modulation of extratropical cyclone activity highlighted in the present study is likely to increase this difficulty to some extent. Strong spatiotemporal variations are observed ubiquitously in phytoplankton populations throughout the midlatitude oceans (e.g., McGillicuddy et al. 2007) and the Sea of Japan. In this situation, biological heating by phytoplankton abundant in given location might alter subweekly-scale atmospheric motion in other locations. Furthermore, altered atmospheric motion could change SST through both heat exchange and vertical mixing, which further affect phytoplankton populations in the upper mixed layer. If this is the case, phytoplankton at certain location might alter phytoplankton biomass in other locations on a subweekly scale, via a “bridge” composed of physical atmospheric and oceanic processes. Therefore, a coupling of physics and ocean biology is potentially capable of improving oceanic ecosystem models as well as regional atmospheric and ocean general circulation models.

Acknowledgments

The authors express their sincere thanks to members of the hot spot research project (chaired by Hisashi Nakamura) for their fruitful discussions. This work was partially supported by the Ministry of Education, Culture, Sports, Science and Technology of Japan, KAKENHI (22106002).

REFERENCES

  • Anderson, W. G., , A. Gnanadesikan, , R. Hallberg, , J. Dunne, , and B. L. Samuels, 2007: Impact of ocean color on the maintenance of the Pacific cold tongue. Geophys. Res. Lett., 34, L11609, doi:10.1029/2007GL030100.

    • Search Google Scholar
    • Export Citation
  • Ballabrera-Poy, J., , R. Murtugudde, , R. H. Zhang, , and A. J. Busalacchi, 2007: Coupled ocean–atmosphere response to seasonal modulation of ocean color: Impact on interannual climate simulations in the tropical Pacific. J. Climate, 20, 353374, doi:10.1175/JCLI3958.1.

    • Search Google Scholar
    • Export Citation
  • Bishop, J. K., , and W. B. Rossow, 1991: Spatial and temporal variability of global surface solar irradiance. J. Geophys. Res., 96, 16 83916 858, doi:10.1029/91JC01754.

    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., , S. Lee, , and K. L. Swanson, 2002: Storm track dynamics. J. Climate, 15, 21632183, doi:10.1175/1520-0442(2002)015<02163:STD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, S.-J., , Y.-H. Kuo, , P.-Z. Zhang, , and Q.-F. Bai, 1992: Climatology of explosive cyclones off the East Asian coast. Mon. Wea. Rev., 120, 30293035, doi:10.1175/1520-0493(1992)120<3029:COECOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chin, T. M., , J. Vazquez, , E. M. Armstrong, , and A. J. Mariano, 2010: Algorithm theoretical basis document: Multi-scale, motion-compensated analysis of sea surface temperature, version 1.1. NASA Measures Algorithm Theoretical Basis Doc., 17 pp. [Available online at ftp://mariana.jpl.nasa.gov/mur_sst/tmchin/docs/ATBD/atbd_1.1actual.pdf.]

  • Dudhia, J., , D. Gill, , K. Manning, , W. Wang, , and C. Bruyere, cited 2005: PSU/NCAR mesoscale modeling system tutorial class notes and user’s guide (MM5 modeling system version 3). [Available online at http://www.mmm.ucar.edu/mm5/documents/tutorial-v3-notes.html.]

  • Follows, M., , and S. Dutkiewicz, 2001: Meteorological modulation of the North Atlantic spring bloom. Deep-Sea Res. II, 49, 321344, doi:10.1016/S0967-0645(01)00105-9.

    • Search Google Scholar
    • Export Citation
  • Gildor, H., , and N. H. Naik, 2005: Evaluating the effect of interannual variations of surface chlorophyll on upper ocean temperature. J. Geophys. Res., 110, C07012, doi:10.1029/2004JC002779.

    • Search Google Scholar
    • Export Citation
  • Gnanadesikan, A., , and W. G. Anderson, 2009: Ocean water clarity and the ocean general circulation in a coupled climate model. J. Phys. Oceanogr., 39, 314332, doi:10.1175/2008JPO3935.1.

    • Search Google Scholar
    • Export Citation
  • Gnanadesikan, A., , K. Emanuel, , G. A. Vecchi, , W. G. Anderson, , and R. Hallberg, 2010: How ocean color can steer Pacific tropical cyclones. Geophys. Res. Lett., 37, L18802, doi:10.1029/2010GL044514.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., , J. Dudhia, , and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 117 pp, doi:10.5065/D60Z716B.

  • Hirose, N., , K. Takayama, , J.-H. Moon, , T. Watanabe, , and Y. Nishida, 2013: Regional data assimilation system extended to the East Asian marginal seas. Umi to Sora, 89, 19.

    • Search Google Scholar
    • Export Citation
  • Isobe, A., , and S. Kako, 2012: A role of the Yellow and East China Seas in the development of extratropical cyclones in winter. J. Climate, 25, 83288340, doi:10.1175/JCLI-D-11-00391.1.

    • Search Google Scholar
    • Export Citation
  • Jerlov, N. G., 1968: Optical Oceanography. Elsevier, 194 pp.

  • Jochum, M., , S. Yeager, , K. Lindsay, , K. Moore, , and R. Murtugudde, 2010: Quantification of the feedback between phytoplankton and ENSO in the Community Climate System Model. J. Climate, 23, 29162925, doi:10.1175/2010JCLI3254.1.

    • Search Google Scholar
    • Export Citation
  • Kako, S., , and M. Kubota, 2009: Numerical study on the variability of mixed layer temperature in the North Pacific. J. Phys. Oceanogr., 39, 737752, doi:10.1175/2008JPO3979.1.

    • Search Google Scholar
    • Export Citation
  • Kako, S., , A. Isobe, , and M. Kubota, 2011: High resolution ASCAT wind vector dataset gridded by applying an optimum interpolation method to the global ocean. J. Geophys. Res., 116, D23107, doi:10.1029/2010JD015484.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and et al. , 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kobashi, F., , and A. Kubokawa, 2012: Review on North Pacific subtropical countercurrents and subtropical fronts: Role of mode waters in ocean circulation and climate. J. Oceanogr., 68, 2143, doi:10.1007/s10872-011-0083-7.

    • Search Google Scholar
    • Export Citation
  • Kondo, J., 1975: Air–sea bulk transfer coefficients in diabatic conditions. Bound.-Layer Meteor., 9, 91112, doi:10.1007/BF00232256.

    • Search Google Scholar
    • Export Citation
  • Lengaigne, M., , C. Menkes, , O. Aumont, , T. Gorgues, , L. Bopp, , J. M. André, , and G. Madec, 2007: Influence of the oceanic biology on the tropical Pacific climate in a coupled general circulation model. Climate Dyn., 28, 503516, doi:10.1007/s00382-006-0200-2.

    • Search Google Scholar
    • Export Citation
  • Lewis, M. R., , M.-E. Carr, , G. C. Feldman, , W. Esaias, , and C. McClain, 1990: Influence of penetrating solar radiation on the heat budget of the equatorial Pacific Ocean. Nature, 347, 543545, doi:10.1038/347543a0.

    • Search Google Scholar
    • Export Citation
  • Liang, X., , and L. Wu, 2013: Effects of solar penetration on the annual cycle of sea surface temperature in the North Pacific. J. Geophys. Res. Oceans, 118, 27932801, doi:10.1002/jgrc.20208.

    • Search Google Scholar
    • Export Citation
  • Lin, P., , H. Liu, , Y. Yu, , and X. Zhang, 2011: Response of sea surface temperature to chlorophyll-a concentration in the tropical Pacific: Annual mean, seasonal cycle, and interannual variability. Adv. Atmos. Sci.,28, 492510.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130141, doi:10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.

  • Manizza, M., , C. Le Quéré, , A. J. Watson, , and E. T. Buitenhuis, 2005: Bio-optical feedbacks among phytoplankton, upper ocean physics and sea-ice in a global model. Geophys. Res. Lett., 32, L05603, doi:10.1029/2004GL020778.

    • Search Google Scholar
    • Export Citation
  • Manizza, M., , C. Le Quéré, , A. J. Watson, , and E. T. Buitenhuis, 2008: Ocean biogeochemical response to phytoplankton-light feedback in a global model. J. Geophys. Res., 113, C10010, doi:10.1029/2007JC004478.

    • Search Google Scholar
    • Export Citation
  • Marzeion, B., , A. Timmermann, , R. Murtugudde, , and F. F. Jin, 2005: Biophysical feedbacks in the tropical Pacific. J. Climate, 18, 5870, doi:10.1175/JCLI3261.1.

    • Search Google Scholar
    • Export Citation
  • McGillicuddy, D. J., and et al. , 2007: Eddy/wind interactions stimulate extraordinary mid-ocean plankton blooms. Science, 316, 10211026, doi:10.1126/science.1136256.

    • Search Google Scholar
    • Export Citation
  • Moore, J. K., , S. C. Doney, , J. A. Kleypas, , D. M. Glover, , and I. Y. Fung, 2002: An intermediate complexity marine ecosystem model for the global domain. Deep-Sea Res. II, 49, 403462, doi:10.1016/S0967-0645(01)00108-4.

    • Search Google Scholar
    • Export Citation
  • Morel, A., 1988: Optical modeling of the upper ocean in relation to its biogeneous matter content (case I waters). J. Geophys. Res., 93, 10 74910 768, doi:10.1029/JC093iC09p10749.

    • Search Google Scholar
    • Export Citation
  • Murtugudde, R., , J. Beauchamp, , C. R. McClain, , M. Lewis, , and A. J. Busalacchi, 2002: Effects of penetrative radiation on the upper tropical ocean circulation. J. Climate, 15, 470486, doi:10.1175/1520-0442(2002)015<0470:EOPROT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nakamoto, S., , S. Prasanna Kumar, , J. M. Oberhuber, , J. Ishizaka, , K. Muneyama, , and R. Frouin, 2001: Response of the equatorial Pacific to chlorophyll pigment in a mixed layer isopycnal ocean general circulation model. Geophys. Res. Lett., 28, 20212024, doi:10.1029/2000GL012494.

    • Search Google Scholar
    • Export Citation
  • Nakamura, H., 1992: Midwinter suppression of baroclinic wave activity in the Pacific. J. Atmos. Sci., 49, 16291642, doi:10.1175/1520-0469(1992)049<1629:MSOBWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nakamura, H., , T. Izumi, , and T. Sampe, 2002: Interannual and decadal modulations recently observed in the Pacific storm track activity and East Asian winter monsoon. J. Climate, 15, 18551874, doi:10.1175/1520-0442(2002)015<1855:IADMRO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nakamura, H., , A. Nishina, , and S. Minobe, 2012: Response of storm tracks to bimodal Kuroshio path states south of Japan. J. Climate, 25, 77727779, doi:10.1175/JCLI-D-12-00326.1.

    • Search Google Scholar
    • Export Citation
  • Onitsuka, G., , T. Yanagi, , and J.-H. Yoon, 2007: A numerical study on nutrient sources in the surface layer of the Japan Sea using a coupled physical–ecosystem model. J. Geophys. Res., 112, C05042, doi:10.1029/2006JC003981.

    • Search Google Scholar
    • Export Citation
  • Oschlies, A., 2004: Feedbacks of biotically induced radiative heating on upper-ocean heat budget, circulation, and biological production in a coupled ecosystem-circulation model. J. Geophys. Res., 109, C12031, doi:10.1029/2004JC002430.

    • Search Google Scholar
    • Export Citation
  • Patara, L., , M. Vichi, , S. Masina, , P. G. Fogli, , and E. Manzini, 2012: Global response to solar radiation absorbed by phytoplankton in a coupled climate model. Climate Dyn., 39, 19511968, doi:10.1007/s00382-012-1300-9.

    • Search Google Scholar
    • Export Citation
  • Paulson, C. A., , and J. J. Simpson, 1977: Irradiance measurements in the upper ocean. J. Phys. Oceanogr., 7, 952956, doi:10.1175/1520-0485(1977)007<0952:IMITUO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Qiu, B., , and K. A. Kelly, 1993: Upper ocean heat balance in the Kuroshio Extension region. J. Phys. Oceanogr., 23, 20272041, doi:10.1175/1520-0485(1993)023<2027:UOHBIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sathyendranath, S., , A. D. Gouveia, , S. R. Shetye, , P. Ravindran, , and T. Platt, 1991: Biological control of surface temperature in the Arabian Sea. Nature, 349, 5456, doi:10.1038/349054a0.

    • Search Google Scholar
    • Export Citation
  • Schneider, E. K., , and Z. Zhu, 1998: Sensitivity of the simulated annual cycle of sea surface temperature in the equatorial Pacific to sunlight penetration. J. Climate, 11, 19321950, doi:10.1175/1520-0442-11.8.1932.

    • Search Google Scholar
    • Export Citation
  • Shell, K. M., , R. Frouin, , S. Nakamoto, , and R. C. J. Somerville, 2003: Atmospheric response to solar radiation absorbed by phytoplankton. J. Geophys. Res., 108, 4445, doi:10.1029/2003JD003440.

    • Search Google Scholar
    • Export Citation
  • Sonntag, S., , and I. Hence, 2011: Phytoplankton behavior affects ocean mixed layer dynamics through biological-physical feedback mechanisms. Geophys. Res. Lett., 38, L15610, doi:10.1029/2011GL048205.

    • Search Google Scholar
    • Export Citation
  • Strutton, P. G., , and F. P. Chavez, 2004: Biological heating in the equatorial Pacific: Observed variability and potential for real-time calculation. J. Climate, 17, 10971109, doi:10.1175/1520-0442(2004)017<1097:BHITEP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sweeney, C., , A. Gnanadesikan, , S. M. Griffies, , M. J. Harrison, , A. J. Rosati, , and B. L. Samuels, 2005: Impacts of shortwave penetration depth on large-scale ocean circulation and heat transport. J. Phys. Oceanogr., 35, 11031119, doi:10.1175/JPO2740.1.

    • Search Google Scholar
    • Export Citation
  • Taguchi, B., , H. Nakamura, , M. Nonaka, , and S. P. Xie, 2009: Influences of the Kuroshio/Oyashio Extensions on air–sea heat exchanges and storm-track activity as revealed in regional atmospheric model simulations for the 2003/04 cold season. J. Climate, 22, 65366560, doi:10.1175/2009JCLI2910.1.

    • Search Google Scholar
    • Export Citation
  • Timmermann, A., , and F.-F. Jin, 2002: Phytoplankton influences on tropical climate. Geophys. Res. Lett., 29, 2104, doi:10.1029/2002GL015434.

    • Search Google Scholar
    • Export Citation
  • Turner, A. G., , M. Joshi, , E. S. Robertson, , and S. J. Woolnough, 2012: The effect of Arabian Sea optical properties on SST biases and the South Asian summer monsoon in a coupled GCM. Climate Dyn., 39, 811826, doi:10.1007/s00382-011-1254-3.

    • Search Google Scholar
    • Export Citation
  • Wetzel, P., , E. Maier-Reimer, , E. Botzet, , J. Jungclaus, , N. Keenlyside, , and M. Latif, 2006: Effects of ocean biology on the penetrative radiation in a coupled climate model. J. Climate, 19, 39733987, doi:10.1175/JCLI3828.1.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., , J. Hafner, , Y. Tanimoto, , W. T. Liu, , H. Tokinaga, , and H. Xu, 2002: Bathymetric effect on the winter sea surface temperature and climate of the Yellow and East China Seas. Geophys. Res. Lett., 29, 2228, doi:10.1029/2002GL015884.

    • Search Google Scholar
    • Export Citation
  • Yamada, K., , J. Ishizaka, , S. Yoo, , H. C. Kim, , and S. Chiba, 2004: Seasonal and interannual variability of sea surface chlorophyll a concentration in the Japan/East Sea (JES). Prog. Oceanogr., 61, 193211, doi:10.1016/j.pocean.2004.06.001.

    • Search Google Scholar
    • Export Citation
  • Yamamoto, M., , and N. Hirose, 2007: Impact of SST reanalyzed using OGCM on weather simulation: A case of a developing cyclone in the Japan Sea area. Geophys. Res. Lett., 34, L05808, doi:10.1029/2006GL028386.

    • Search Google Scholar
    • Export Citation
  • Yamamoto, M., , and N. Hirose, 2011: Possible modification of atmospheric circulation over the northwestern Pacific induced by a small semi-enclosed ocean. Geophys. Res. Lett., 38, L03804, doi:10.1029/2010GL046214.

    • Search Google Scholar
    • Export Citation
  • Yanagi, T., , G. Onitsuka, , N. Hirose, , and J.-H. Yoon, 2001: A numerical simulation on the mesoscale dynamics of the spring bloom in the Sea of Japan. J. Oceanogr., 57, 617630, doi:10.1023/A:1021691221793.

    • Search Google Scholar
    • Export Citation
  • Yoshiike, S., , and R. Kawamura, 2009: Influence of wintertime large-scale circulation on the explosively developing cyclones over the western North Pacific and their downstream effects. J. Geophys. Res., 114, D13110, doi:10.1029/2009JD011820.

    • Search Google Scholar
    • Export Citation
  • Zhai, L., , C. Tang, , T. Platt, , and S. Sathyendranath, 2011: Ocean response to attenuation of visible light by phytoplankton in the Gulf of St. Lawrence. J. Mar. Syst., 88, 285297, doi:10.1016/j.jmarsys.2011.05.005.

    • Search Google Scholar
    • Export Citation
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