Seasonal and interannual variability of sea surface chlorophyll a concentration in the Japan/East Sea (JES)

Yamada, Keiko; Ishizaka, Joji; Yoo, Sinjae; Kim, Hyun Cheol; Chiba, Sanae

doi:10.1016/j.pocean.2004.06.001

Cited by 137 publications

(103 citation statements)

References 20 publications

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“…In contrast, that of ocean AOD increases up to 0.1 in spring (April-May) and to ∼ 0.05 in late fall and early winter (November-December), which can likely be attributed to monthly variations in Chl concentration over East Asia. The climatological Chl concentration reported by Yamada et al (2004) is highest during spring (1.2-2.7 µg L −1 ), lower during late fall (0.8-1.2), and 0.2-0.4 µg L −1 during other seasons. Thus, the change in monthly bias for ocean AOD is likely affected by Chl concentrations in the current GOCI ocean AOD algorithm.…”

Section: Bias As a Function Of Hour Month And Yearmentioning

confidence: 88%

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

et al. 2018

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Abstract. The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleighcorrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τ G is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τ G has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τ A from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τ G − τ A ) is within −0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τ G are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

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Section: Bias As a Function Of Hour Month And Yearmentioning

confidence: 88%

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

et al. 2018

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“…It is consistent to the Chl-a in situ observations, Chl-a peaks are observed in the Bohai Bay and Laizhou Bay (Bohai Sea) (e.g., Wang and Kang, 1998), in the northern Yellow Sea and Jiaozhou Bay (southern Yellow Sea) (e.g., Gao, 2009;Liu, 2004), in the southwest of the Korea, East of Jeju Island and in Japan coastal waters (e.g., Yamada et al, 2004). Chl-a spatial pattern matches the spatial distribution of nutrients in the Bohai and Yellow Seas, with higher nutrient concentrations near shore but lower offshore (Lin et al, 2005;Wang et al, 2003;Wei et al, 2004;Yu et al, 2000).…”

Section: The Spatial Difference Of Chl-a Seasonal Patterns In the Bohmentioning

confidence: 99%

Trends of satellite derived chlorophyll-a (1997–2011) in the Bohai and Yellow Seas, China: Effects of bathymetry on seasonal and inter-annual patterns

Chen

Wang

2013

Progress in Oceanography

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a b s t r a c tThe spatial and temporal variability of sea surface chlorophyll-a (Chl-a) concentrations in the Bohai and Yellow Seas were analyzed, using satellite-derived Chl-a products from SeaWiFS and MODIS sensors over the period of September 1997-September 2011. A set of monthly and cloud-free Chl-a data was produced by the Data Interpolating Empirical Orthogonal Function (DINEOF) method. The results indicate that there are different Chl-a seasonal patterns existing in the Yangtze River mouth, coastal and offshore waters, respectively. In the Yangtze River mouth, a long-lasting Chl-a peak (May-September) is seen in summer. In coastal waters, two significant Chl-a maxima occur in winter-spring and late summer, respectively. In offshore waters, only one significant spring (March-April) Chl-a maximum is evident with a time lag of 1-3 months to coastal waters and the signal of autumn maximum is very weak. In coastal waters, wind-tide-thermohaline circulations and East Asia summer rainy monsoon may important physical factors to impact the seasonal pattern of Chl-a, but increased human activity (e.g., eutrophication, dam) could significantly enhance this process. In offshore waters, the impact on the circulation of the YSWC in winter and YSCW in summer in the central Yellow Sea could be important physical factor in explaining the variability of Chl-a in seasonal patterns. The decadal trends of Chl-a and sea surface temperature are decreasing in coastal waters, with a significantly positive correlation. In offshore waters, the decadal trends of Chl-a is increasing but a slight decreasing sea surface temperature trend is seen, and they indicate a negative correlation.

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“…For instance, cold-air outbreaks can cause sufficient deepening of the surface mixed layer that may delay the onset of the spring phytoplankton bloom in the Japan Sea (Yamada et al 2004;Onitsuka and Yanagi 2005). It is therefore reasonable to consider that interannual variations in the frequency and intensity of cold-air outbreaks may contribute significantly to biological variability in the Japan Sea.…”

Section: Introductionmentioning

confidence: 99%

Atmosphere and Marginal-Sea Interaction Leading to an Interannual Variation in Cold-Air Outbreak Activity over the Japan Sea

Isobe

Beardsley

2007

Journal of Climate

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The interannual variation in cold-air outbreak activity over the Japan Sea is investigated using Japan Meteorological Agency buoy 21002 and Quick Scatterometer (QuikSCAT) wind data, Japan Oceanographic Data Center sea surface temperature (SST) data, NCEP-NCAR reanalysis surface wind and sea level pressure (SLP) data, and the winter Arctic Oscillation (AO) index of Thompson and Wallace. Cold-air outbreaks occur during the "winter" November-March period, and wind data for this season for the 19-winter period 1981-2000 were analyzed. Wavelet spectra averaged between 5-and 15-day periods were used to evaluate the intensity of cold-air outbreaks quantitatively. The winter mean wavelet spectra exhibited a clear interannual variation and a significant positive correlation with the AO index, indicating that intensive cold-air outbreaks frequently occur during relatively warm winters caused by a quasi-decadal AO. Based on the SST and SLP data, the low atmospheric surface pressure disturbances tend to develop over the warm East China Sea in warm winters in the positive AO phase. As these low SLP disturbances advance toward the northern Japan islands during the positive AO phase, they intensify more, leading to stronger cold-air outbreaks over the Japan Sea and increased sea surface cooling over the northern Japan Sea.

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Seasonal and interannual variability of sea surface chlorophyll a concentration in the Japan/East Sea (JES)

Cited by 137 publications

References 20 publications

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

Trends of satellite derived chlorophyll-a (1997–2011) in the Bohai and Yellow Seas, China: Effects of bathymetry on seasonal and inter-annual patterns

Atmosphere and Marginal-Sea Interaction Leading to an Interannual Variation in Cold-Air Outbreak Activity over the Japan Sea

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