Satellite-derived SST validation based on in-situ data during summer in the East China Sea and western North Pacific

Satellite-derived sea surface temperature (SST) is validated based on in-situ data from the East China Sea (ECS) and western North Pacific where most typhoons, which make landfall on the Korean peninsula, are formed and pass. While forecasting typhoons in terms of intensity and track, coupled ocean-typhoon models are significantly influenced by initial ocean condition. Potentially, satellite-derived SST is a very useful dataset to obtain initial ocean field because of its wide spatial coverage and high temporal resolution. In this study, satellite-derived SST from various sources such as Tropical Rainfall Measuring Mission Microwave Imager (TMI), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and New Generation Sea Surface Temperature for Open Ocean (NGSST-O) datasets from merged SSTs were compared with in-situ observation data using an indirect method which is using near surface temperature for validation of satellite derived SST. In-situ observation data included shipboard measurements such as Expendable Bathythermograph (XBT), and Conductivity, Temperature, Depth (CTD), and Argo buoy data. This study shows that in-situ data can be used for microwave derived SST validation because homogeneous features of seawater prevail at water depths of 2 m to 10 m under favorable wind conditions during the summer season in the East China Sea. As a result of validation, root-mean-square errors (RMSEs) are shown to be 0.55 °C between microwave SST and XBT/CTD data mostly under weak wind conditions, and 0.7 °C between XBT/CTD measurement and NGSST-O data. Microwave SST RMSE of 0.55 °C is a potentially valuable data source for general application. Change of SST before and after typhoon passing may imply strength of ocean mixing due to upwelling and turbulent mixing driven by the typhoon. Based on SST change, ocean mixing, driven by Typhoon Nari, was examined. Satellite-derived SST reveals a significant SST drop around the track immediately following the passing of Typhoon Nari in October, 2007.     

Spatial classification of La Niña events

Thirty La Niña events have been selected from monthly mean sea surface temperature (SST) data of the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) and Centennial Observation-Based Estimates (COBE SST2) datasets from 1870 to 2013 based on a criterion of –0.5°С for a cold anomaly in the Niño 3.4 region (5° N–5° S, 170°–120° W) and its minimum duration of 5 months. The selected events are classified by hierarchical clustering analysis according to two characteristics: geographic coordinates and SST anomalies during the mature phase of La Niña. The objective classification method identifies two types of La Niña differing by the evolution of negative SST anomalies in the equatorial Pacific and by the Southern Oscillation Index.     

Coral extension rates in the NW Indian Ocean I: reconstruction of 20th century SST variability and monsoon current strength

To date, only a few coral proxy studies have investigated coral growth as an indicator of climate variability. This study presents the first extension-rate record (Porites lutea) from the Maldives (NW Indian Ocean), inferred from skeletal δ¹⁸O chronology for the lagoon of Rasdhoo Atoll (4°N/73°W) in the central area of the Maldives, influenced by the Indian monsoon. The record spans 90 years over the period 1917–2007. The mean annual extension over this period was 9.9 mm/year, and an increase of annual extension rates until 1990 by 3 mm/year can be explained by a rise of 0.7°C in sea surface temperature (SST) in this region. After 1990, the extension rates do no continue increasing, possibly due to ecological stress caused by progressive ocean warming and acidification. The correlation between annual extension rates and SSTs is thus significant and strong in the lower part of the record until 1955 (r = +0.69, p < 0.0001), but weaker thereafter (r = +0.44, p < 0.001). The extension rates yield a distinct interannual variability of 3–4 years, caused by interannual SST fluctuations driven by the El Ni?o-Southern Oscillation. A variability of 8–9 years is likely driven by SST variations endemic to the Indian Ocean. Spectral peaks between 18–19 years and 6–7 years cannot be explained by SST fluctuations, but by variations in the strength of the SW monsoon currents. It is suggested that during phases of stronger monsoon activity, the coral sacrificed coral extension in favor of a denser, more robust skeleton. The geomorphology of the atoll may strengthen the potential of this new coral archive to track climate variability.     

Variability of sea surface temperature and warm pool area in the South China Sea and its relationship to the western Pacific warm pool

Sea surface temperature (SST) data derived from satellite and in situ measurements are used to study the thermal variability in the South China Sea (SCS). Time–frequency–energy distributions, periods of variability, and trends are computed by the Hilbert–Huang transform method. The SST trend from 1982 to 2005 is 0.276°C per decade in the SCS which is higher than 0.144°C per decade in the western Pacific warm pool (WPWP). The warm pool (SST ≥ 28°C) area in the SCS has increased by 0.20 × 10⁶ km² per decade. The SST and area of the warm pool in the SCS are strongly correlated, respectively, with the SST and area of the WPWP with a time lag of 1 month, suggestive of a strong connection between these two warm pools. Once the annual cycle is eliminated, decadal oscillations dominate the variability of SST and warm pool area in the SCS.     

Mixed layer variability in Northern Arabian Sea as detected by an Argo float

Seasonal evolution of surface mixed layer in the Northern Arabian Sea (NAS) between 17° N–20.5° N and 59° E-69° E was observed by using Argo float daily data for about 9 months, from April 2002 through December 2002. Results showed that during April - May mixed layer shoaled due to light winds, clear sky and intense solar insolation. Sea surface temperature (SST) rose by 2.3 °C and ocean gained an average of 99.8 Wm⁻². Mixed layer reached maximum depth of about 71 m during June - September owing to strong winds and cloudy skies. Ocean gained abnormally low ∼18 Wm⁻² and SST dropped by 3.4 °C. During the inter monsoon period, October, mixed layer shoaled and maintained a depth of 20 to 30 m. November - December was accompanied by moderate winds, dropping of SST by 1.5 °C and ocean lost an average of 52.5 Wm⁻². Mixed layer deepened gradually reaching a maximum of 62 m in December. Analysis of surface fluxes and winds suggested that winds and fluxes are the dominating factors causing deepening of mixed layer during summer and winter monsoon periods respectively. Relatively high correlation between MLD, net heat flux and wind speed revealed that short term variability of MLD coincided well with short term variability of surface forcing.     

Improvement of New Generation Sea Surface Temperature for Open ocean (NGSST-O): a new sub-sampling method of blending microwave observations

An improved version (version 1.6) of the blended optimum interpolation sea surface temperature (SST), obtained by use of infrared (IR) and microwave (MW) data of the New Generation SST for Open ocean (NGSST-O) product, has been developed. A major improvement is the introduction of a sub-sampling scheme for MW-SST with a finer grid; this has resulted in reduction of the blocky patterning occasionally found in blended SST products with finer grids. Spectral comparison of along-track sea surface height and NGSST-O suggests that mesoscale turbulence was reproduced in the updated NGSST-O in a wide wavelength range.     

集合同化方法在黄海海域海表面温度同化中的应用

The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.     

Eddy Activities around the Nansei Shoto (Okinawa Islands) Revealed by TRMM

Sea surface temperature (SST) variation around the Nansei Shoto (Okinawa Islands), Japan from March 1998 to February 1999 is investigated using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) images. Root mean squared (RMS) error of SST by TMI from the in situ observed SST is 0.9°C. The results of statistical analysis of SST by TMI show that a 14–16 days period variation dominates around the main Okinawa Island, while a 9–11 days period variation dominates along the shelf edge of the East China Sea. This revised version was published online in July 2006 with corrections to the Cover Date.     

亚印太交汇区海表温度的分布和变化特征及其对中国降水的影响

利用1967-2009年的逐月海表温度(Sea Surface Temperature,SST)资料和降水资料,以及经验正交函数(Empirical Orthogonal Function,EOF)和相关分析方法,探讨了亚印太交汇区(Joining Area of Asia and Indian-Pacific Oce...     

In-situ diurnal sea surface temperature variations and near-surface thermal structure in the tropical hot event of the Indo-Pacific warm pool

Diurnal Sea Surface Temperature (SST) variations and the near-surface thermal structure of the tropical hot event (HE) have been investigated using advanced in-situ equatorial observations with hourly temporal resolution. The information on the HE area defined by the satellite cloud-free SSTs is used to sample the in-situ observations. The in-situ SSTs sampled for the HE conditions show that a maximum (minimum) SST has a histogram mode at 30.8°C (29.0°C), and frequently appears at 15:00 (07:00) local time. The amplitude of the diurnal SST variation (DSST) is defined by the difference between the maximum and minimum SSTs. The mean DSST during HEs is greater than 0.5°C, and has a maximum of about 0.75°C at the HE peak. The time series of mean DSST gradually increases (rapidly decreases) before (after) the peak. The satellite SST has a systematic positive bias against the corresponding daytime SST measured by the Triangle Trans-Ocean buoy Network. This bias is enhanced under conditions of large in-situ DSST. One-dimensional numerical model simulation suggests that the systematic bias is caused by the sharp vertical temperature gradient in the surface layer of HE. The near-surface thermal structure is generated by conditions of high insolation and low wind speed, which is the typical HE condition.     

Interannual SST variability in the Japan/East Sea and relationship with environmental variables

Interannual variability of the Japan/East Sea (JES) sea surface temperature (SST) is investigated from the reconstructed NOAA/AVHRR Oceans Pathfinder best SST data (1985–2002) using the complex empirical function (CEOF) analysis. The iterative empirical function analysis is used for the SST data reconstruction. The first two leading CEOFs account for 86.0% of total variance with 66.4% for the first mode and 19.6% for the second mode. The first CEOF mode represents a standing oscillation and a maximum belt in the central JES. There are two near-7-year events and one 2–3-year event during the period of 1985–2002. The first mode oscillates by adjacent atmospheric systems such as the Aleutian Low, the North Pacific High, the Siberian High, and the East Asian jet stream. Positive correlation in a zonal belt between the first mode JES SST anomaly and the background surface air temperature/SST anomaly reveals intensive ocean-atmosphere interaction near the Polar Front in the North Pacific. The second CEOF mode represents two features: standing oscillation and propagating signal. The standing oscillation occurs in the northern (north of 44°N) and southern (south of 39°N and west of 136°E) JES with around 180° phase difference. A weak southwestward propagating signal is detected between the two regions. The eastward propagating signal is detected from the East Korean Bay to near 135°E. The second mode contains 4–5-year periodicity before 1998 and 2–3-year periodicity thereafter. It is associated with the Arctic Oscillation, which leads it by 1–5-year. Furthermore, a strong correlation with the background surface air temperature/SST anomaly is detected in the tropical to subtropical western Pacific.     

利用西北印度洋船测数据评估基于卫星的海表面温度

本文描述了一次夏季在西北印度洋进行的调查船水文测量,用船测数据评估卫星海面表温度,并寻找影响海表面温度误差的主要因素。我们考虑了两种卫星数据,第一种是微波遥感产品——热带降雨测量任务微波成像仪TMI数据,另外一种是融合了微波,红外线,以及少部分观测数据的融合数据产品——可处理海表温度和海冰分析OSTIA数据。结果表明融合数据的日平均海表面温度的平均误差和均方根误差都比微波遥感小。这一结果证明了融合红外线遥感,微波遥感以及观测数据来提高海表面温度数据质量的必要性。此外,我们分析了海表面温度误差与各项水文参数之间的相关关系,包括风速,大气温度,想对湿度,大气压力,能见度。结果表明风速与TMI海表面温度误差的相关系数最大。而大气温度是影响OSTIA海表面温度误差最重要的因素;与此同时,想对湿度与海表面温度误差的相关系数也很高。     

Habitat suitability index of Chub mackerel (<Emphasis Type="Italic">Scomber japonicus</Emphasis>) from July to September in the East China Sea

The habitat quality of Chub mackerel (Scomber japonicus) in the East China Sea has been a subject of concern in the last 10 years due to large fluctuations in annual catches of this stock. For example, the Chinese light-purse seine fishery recorded 84000 tons in 1999 compared to 17000 tons in 2006. The fluctuations have been attributed to variability in habitat quality. The habitat suitability Index (HSI) has been widely used to describe fish habitat quality and in fishing ground forecasting. In this paper we use catch data and satellite derived environmental variables to determine habitat suitability indices for Chub mackerel during July to September in the East China Sea. More than 90% of the total catch was found to come from the areas with sea surface temperature of 28.0°–29.4°C, sea surface salinity of 33.6–34.2 psu, chlorophyll-a concentration of 0.15–0.50 mg/m³ and sea surface height anomaly of −0.1–1.1 m. Of the four conventional models of HSI, the Arithmetic Mean Model (AMM) was found to be most suitable according to Akaike Information Criterion analysis. Based on the estimation of AMM in 2004, the monthly HSIs in the waters of 123°–125°E and 27°30′–28°00′ N were more than 0.6 during July to September, which coincides with the catch distribution in the same time period. This implies that AMM can yield a reliable prediction of the Chub mackerel’s habitat in the East China Sea.     

Observation of Algal Bloom in the Northwest Arabian Sea Using Multisensor Remote Sensing Satellite Data

Algal bloom observed using Indian Remote Sensing Satellite IRS-P4 Ocean Color Monitor (OCM) derived chlorophyll images during December 23, 2003–January 8, 2004, off the Oman coast. High chlorophyll concentration (～20 mg m^?3) patches were observed. MODIS-Aqua data of January 1, 2004, were analyzed to generate normalized water leaving radiance (nLw) images for seven visible channels: 412, 443, 488, 531, 551, 667 and 678 nm. The channels 667 and 678 nm showed interesting algal bloom features. The bloom features were detected in OCM image of January 2, 2004, using Subramanian's Trichodesmium detection Protocol. MODIS-Aqua retrieved Sea Surface Temperature (SST) around the bloom patches was observed to be >240°C. The OCM chlorophyll mean observed to be very high (>10.0 mg m^?3) in two bloom pockets. Quickscat scatterometer derived wind speed was found to be optimum in the range of 3–5 m/sec.     

Advanced sea surface temperature retrieval using the Japanese geostationary satellite,Himawari-6

Producing high-quality match-ups coupling the Japanese geostationary satellite, Himawari-6 (H6), and buoy SST observations, we have developed the new SST retrieval method. Kawamura et al. (2010) developed the previous version of SST product called MTSAT SST, which left several scientific/technical questions. For solving them, 6,711 algorithm tuning match-ups with precise navigation and 240,476 validation match-ups are generated for covering all seasons and wide ocean coverage. For discriminating the previous MTSAT SST, we call the new version of SST H6 SST. It is found that the SZA dependences of MTSAT SST algorithm are different from area to area of SZA > 40–50° N/S. The regionally different SZA dependences are treated by dividing the H6 disk coverage into five areas by the latitude lines of 40° N/S first and the longitude lines of 100° K and 180° K. Using the algorithm tuning match-ups, Nonlinear SST (NLSST) equations are derived for all of the five areas. Though the sun zenith angle dependency correction term is also examined, there is no significant regional difference. Therefore, this term is used in the H6 SST algorithm again. The new H6 SST equation is formed by the areal NLSST and the sun zenith angle dependency term for each area. The statistical evaluation of H6 SST using the validation match-ups show the small negative biases and the RMS errors of about 0.74° K for each area. For the full H6 disk, the bias is −0.1° K and the RMS error 0.74° K. The histogram of H6 SST minus the in situ SST for each area has a similar Gaussian shape with small negative skewness, and the monthly validation of H6 SST for each area is consistent with those for the whole period and the histograms     

利用BP神经网络标准化东南太平洋茎柔鱼(Dosidicus gigas)资源丰度

东南太平洋茎柔鱼(Dosidicus gigas)是短生命周期大洋性经济鱼类,其资源量受环境因素变化的影响较大。根据我国鱿钓船队2013~2017年在东南太平洋的生产统计数据,结合海洋环境数据包括海表面温度(SST)、海表面盐度(SSS)、叶绿素a浓度(chl a),运用BP神经网络(back propagation network)模型来标准化单位捕捞努力量渔获量(catch per unit effort, CPUE,也称名义CPUE)。以均方误差(mean square errors, MSE)和平均相对变动值(average relative variances, ARV)为最优模型判断依据,比较隐含层节点3-10的神经网络模型,发现6-9-1结构为最优模型。用Garson算法解释模型结果,发现各输入层因子对东南太平洋茎柔鱼资源丰度影响重要度排序为chl a、SST、经度(Lon)、SSS、纬度(Lat)、月份(Month)。并作名义CPUE和标准化CPUE资源丰度对比分布图,结果显示CPUE与标准化CPUE总体分布状况基本一致,但局部区域存在明显差异, 80°~85°W及10°~20°S海域适宜鱿钓生产,表明BP神经网络模型可以适用于东南太平洋茎柔鱼的CPUE标准化,从而为鱿钓渔业生产提供一定参考依据。     

Subsurface Water Masses in the Central North Pacific Transition Region: The Repeat Section along the 18° Meridian

A repeat hydrographic section has been maintained over two decades along the 180° meridian across the subarctic-subtropical transition region. The section is naturally divided into at least three distinct zones. In the Subarctic Zone north of 46°N, the permanent halocline dominates the density stratification, supporting a subsurface temperature minimum (STM). The Subarctic Frontal Zone (SFZ) between 42°–46°N is the region where the subarctic halocline outcrops. To the south is the Subtropical Zone, where the permanent thermocline dominates the density stratification, containing a pycnostad of North Pacific Central Mode Water (CMW). The STM water colder than 4°C in the Subarctic Zone is originated in the winter mixed layer of the Bering Sea. The temporal variation of its core temperature lags 12–16 months behind the variations of both the winter sea surface temperature (SST) and the summer STM temperature in the Bering Sea, suggesting that the thermal anomalies imposed on the STM water by wintertime air-sea interaction in the Bering Sea spread over the western subarctic gyre, reaching the 180° meridian within a year or so. The CMW in this section originates in the winter mixed layer near the northern edge of the Subtropical Zone between 160°E and 180°. The CMW properties changed abruptly from 1988 to 1989; its temperature and salinity increased and its potential density decreased. It is argued that these changes were caused by the climate regime shift in 1988/1989 characterized by weakening of the Aleutian Low and the westerlies and increase in the SST in the subarctic-subtropical transition region. This revised version was published online in August 2006 with corrections to the Cover Date.     

Numerical Simulation of North Indian Ocean State Prior to the Onset of SW Monsoon Using SSM/I Winds

The Sea Surface Temperatures (SST) and currents are simulated over the north Indian Ocean, during the onset phase of southwest monsoon for the three years 1994, 1995, and 1996, using daily Special Sensor Microwave/Imager (SSM/I) winds and National Center for Environmental Prediction (NCEP) heat fluxes as forcings in the 2½ layer thermodynamic numerical ocean model. The results are discussed for the 30-day period from 16 May to 13 June for all the three years, to determine the ocean state during the onset phase of SW monsoon. The maximum variability in the simulated SST is found along the Somali coast, Indian coasts, and equatorial regions. The maximum SST in the North Arabian Sea is found to be greater than 30°C and minimum SST in the west equatorial region is 25°C during the onset phase of all three years. Model SSTs are in agreement with Reynolds SST. SST gradients in the north-south as well as in the east-west directions, west of 80°E are found to change significantly prior to the onset. It can be inferred from the study that the SST gradient of 2.5°C/2000 km is seen due north and due west of the region 2° - 7°S, 60° - 65°E, about 8 to 10 days prior to the arrival of SW monsoon near Kerala coast. Upper and lower layer circulation fields do not show prominent interannual variability.     

热带三大洋海表温度和云水关系的初探

热带太平洋、大西洋和热带印度洋是全球海洋-大气相互作用最显著的海域。为了探讨热带三大洋局地海洋-大气相互作用中的某些特征,利用新的卫星遥感资料对热带三大洋的云水和海表温度(SST)异常变化进行了统计相关分析。研究发现:在赤道太平洋和赤道大西洋云水和SST二者在超前或滞后5周内都呈现明显的正相关关系,表明3个赤道海域云水和SST之间具有明显的相互作用正反馈机制;在热带西印度洋SST异常超前CLW异常一周时,正相关系数最大,SST的变化导致云水的变化;而在以较冷海水为下垫面的赤道外东北太平洋,东南太平洋以及赤道外的南大西洋海域,二者呈明显的负相关关系,云水的变化会引起SST的变化。位于菲律宾以东的热带西北太平洋区,云水量的变化引起SST的变化;而赤道东印度洋暖池区即存在云水量的增加(减少)引起SST降低(升高)的现象,也存在SST降低(升高)引起云水量的减少(增加)的现象。以上结果将为进一步理解海洋-大气相互作用,在数值模式中正确设计海洋-大气边界层参数化方案提供参考。     

Interannual variability of the Kuroshio intrusion in the South China Sea

The interannual variability of intrusions of the Kuroshio into the South China Sea (SCS) is investigated using satellite remote sensing data supported by in-situ measurements. The mesoscale circulation of the SCS is predominantly wind-forced by the northeast winter and southwest summer monsoons. Although the region has been studied extensively, considerable uncertainty remains about the annual and interannual mesoscale nature of the circulation. The frequency and characteristics of Kuroshio intrusions and their effect on circulation patterns in the northeast SCS are also not well understood. Satellite observations of Sea Surface Temperature (SST) from the Tropical Rainfall Measuring Mission (TRMM) and the Advanced Very High Resolution Radiometer (AVHRR) and Sea Surface Height Anomalies (SSHA) from TOPEX/ Poseidon for the period 1997–2005 are used here to analyze the annual and interannual variability in Kuroshio intrusions and their effects on the region. Analysis of SST and SSHA shows the formation and characteristics of intrusions vary considerably each year. Typically, the intrusion occurs in the central region of Luzon Strait and results in an anticyclonic circulation in the northeastern SCS. However, in some years, the intrusion is located in the northern portion of Luzon Strait and a cyclonic intrusion results. Wind stress and wind stress curl derived from the National Aeronautics and Space Administration (NASA) QuikSCAT satellite scatterometer are used to evaluate the relationship between wind stress or wind stress curl and the presence of winter Kuroshio intrusions into the SCS.