The northwestern Pacific Ocean (NWPO) is a region sensitive to global climate change and regional sea–air interactions. A number of remote-sensing images from the past three decades were used to define sensitive marine regions, which were then applied to determine the spatiotemporal association patterns of abnormal variations in marine environmental parameters using a quantitative association rule-mining method. The NWPO object 1 (NWPO-obj1) region (130°–150° E, 2°–15° N) and NWPO object 2 (NWPO-obj2) region (170°–180° E, 0°–8° N) showed more pronounced changes than elsewhere, and the monthly anomaly of sea-surface temperature (SSTA), monthly anomaly of sea-surface chlorophyll-a (chl-a), monthly anomaly of sea-level anomaly (SLAA), and El Niño Southern Oscillation (ENSO) events were closely related to one another in these two regions. In NWPO-obj1, the relation between SLAA and chl-a yields a correlation coefficient of −0.79 and the abnormal drop in SLAA was the principal factor controlling the chl-a bloom. In NWPO-obj2, the SSTA is anti-correlated with chl-a (correlation coefficient of −0.83), and the abnormal increase in SSTA might be one of the main factors leading to the extinction of chl-a. Comparing the two regions, abnormal increases in chl-a and decreases in SSTA in NWPO-obj2 were indicators of abnormal increases in SLAA in NWPO-obj1 (positive and negative correlation coefficients of 0.60 and −0.61, respectively), and the abnormal decrease of SLAA in NWPO-obj1 is correlated with the abnormal decrease of SSTA in NWPO-obj2 (correlation coefficient of 0.86), although ahead by one year. In addition, the abnormal decrease of SLAA in NWPO-obj1 was the only factor influenced by El Niño, while La Niña events had an impact on the abnormal increase of SLAA in NWPO-obj1 and the abnormal decrease of SSTA in NWPO-obj2, and also dominated their interrelationships.

Based on widely used remote sensing ocean net primary production (NPP) datasets, the spatiotemporal variability of NPP is first analyzed over the tropical eastern Indian and western Pacific Ocean for the period 1998–2016 using the conventional empirical orthogonal function (EOF), the lead–lag correlation and the ensemble empirical mode decomposition (EEMD) technique. Barnett and Preisendorfer’s improved Canonical Correlation Analysis (BPCCA) is also applied to derive covariability patterns of NPP with major forcing factors of the chlorophyll a concentration (Chla), sea surface temperature (SST), sea level anomaly (SLA), ocean rainfall (Rain), sea surface wind (Wind), and current (CUR) under climate changes of El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). We find that: (1) The first two seasonal EOF modes capture significant temporal and meridional NPP variability differences, as NPP reaches peaks approximately three months later in the western Pacific Ocean than that of in the eastern Indian Ocean. (2) The second and third interannual EOF modes are closely related with ENSO with a two-month lag and synchronous with IOD, respectively, characterized by southwesterly positive anomaly centers during positive IOD years. (3) NPP presents different varying tendencies and similar multiscale oscillation patterns with interannual and interdecadal cycles of 2~3 years, 5~8 years, and 9~19 years in subregions of the Bay of Bengal, the South China Sea, the southeastern Indian Ocean, and the northwestern Pacific Ocean. (4) The NPP variability is strongly coupled with negative SST, SLA, and Rain anomalies, as well as positive Chla, Wind and CUR anomalies in general during El Niño/positive IOD years. The results reveal the diversity and complexity of large-scale biophysical interactions in the key Indo-Pacific Warm Pool region, which improves our understanding of ocean productivity, ecosystems, and carbon budgets.

The daily sea surface temperatures (SSTs) derived from the visible and infrared scanning radiometer (VIRR) and microwave radiation imager (MWRI) aboard the Fengyun-3C (FY-3C) satellite are evaluated against the measurements of in-situ SST and Reynolds optimum interpolation daily 0.25° SST (OISST) products. Statistical results for 2015 reveal a mean bias ± standard deviation of error ( ) for the daytime VIRR SST, night-time VIRR SST, daytime MWRI SST (MWRID), and night-time MWRI SST (MWRIN) of 0.5877 ± 1.3279°C, −0.4801 ± 1.2588°C, 0.6044 ± 3.9064°C, and 0.7653 ± 3.7307°C, respectively. According to the SST bias performance of each product for different periods and latitudes, it is clear that the SST biases of the VIRR products are the largest at low latitudes and are relatively lower at mid-high latitudes and that negative SST biases in the VIRR increase with varying time. Large abnormal SST biases from MWRI products occur in Time III (the 211th to 319th day of 2015), and the average values of the SST biases are 3.6010°C and 3.7822°C for the MWRID and MWRIN, respectively. We expect that our validation results for VIRR and MWRI products can help algorithm developers further enhance the accuracy of SST retrievals while also helping sensor designers improve the performance of sensors for the next generation of FY satellites.

With satellite observed Sea Surface Temperature (SST) accumulated for multiple decades, multi-time scale variabilities of the Indo-Pacific Warm Pool are examined and contrasted in this study by separating it into the Indian Ocean sector and the Pacific Ocean sector. Surface size, zonal center, meridional center, maximum SST and mean SST as the practical warm pool properties are chosen to investigate the warm pool variations for the period 1982–2018. On the seasonal time scale, the oscillation of the Indian Warm Pool is found much more vigorous than the Pacific Warm Pool on size and intensity, yet the interannual variabilities of the Indian Warm Pool and the Pacific Warm Pool are comparable. The Indian Warm Pool has weak interannual variations (3–5 years) and the Pacific Warm Pool has mighty interdecadal variations. The size, zonal movement and mean SST of the Indian Ocean Warm Pool (IW) are more accurate to depict the seasonal variability, and for the Pacific Ocean Warm Pool (PW), the size, zonal and meridional movements and maximum SST are more suitable. On the interannual scale, except for the meridional movements, all the other properties of the same basin have similar interannual variation signals. Following the correlation analysis, it turns out that the Indian Ocean basin-wide index (IOBW) is the most important contributor to the variabilities of both sectors. Lead-lag correlation results show that variation of the Pacific Ocean Warm Pool leads the IOBW and variation of the Indian Ocean Warm Pool is synchronous with the IOBW. This indicates that both sectors of the Indo-Pacific Warm Pool are significantly correlated with basin-wide warming or cooling.

A process-orientated El Niño index (PEI) is constructed to determine the detailed classification of two major types of El Niño events during 1985–2009 from remote-sensing monthly sea surface temperature (SST) anomaly data sets. Four revised Niño regions are defined in the tropical Pacific, and the index uses SST anomalies and their duration in each region. Based on their varying evolution, the index allows eastern Pacific (EP) El Niño events to be either weak or strong EP type, and central Pacific (CP) El Niño events to be either weak CP, strong CP, or mixed EPCP type. The El Niño types identified by this index are compared to those obtained using previously published methods, and the differences are examined and discussed. The results suggest that the PEI is optimal for monitoring El Niño events of weaker amplitude and shorter duration. The PEI is focused on the development process of El Niño events, providing a novel perspective for classifying El Niño types. Analysis of the SST evolution of the El Niño events identified here reveals several misclassifications in previous studies, stressing the disadvantage of relying solely on single areas or short time periods. Existing EP and CP El Niño events in the study period were reclassified by the PEI into weak, strong, and mixed events. The listing of El Niño types produced here can be used for selecting El Niño events for further study of their dynamics and climatic impacts.

A bias correction method was proposed for correcting the significant negative sea surface temperature (SST) biases in Fengyun-3C (FY-3C) visible and infrared radiometer (VIRR) products. The multichannel SST algorithm (MCSST) for the daytime SST and night-time SST of VIRR products are modified for estimating the debiased VIRR SSTs, and the optimal local matchup data are selected for each SST observations based on the distance to each parts of regressors in R-space. Compared with the probability density function matching technique for bias correction, the proposed method can better remove the negative biases and decrease the standard deviations of biases.