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.

A method of estimating tropical cyclone (TC) intensity based on Haiyang-2A (HY-2A) scatterometer, and Special Sensor Microwave Imager and Sounder (SSMIS) observations over the northwestern Pacific Ocean is presented in this paper. Totally, 119 TCs from the 2012 to 2017 typhoon seasons were selected, based on satellite-observed data and China Meteorological Administration (CMA) TC best track data. We investigated the relationship among the TC maximum-sustained wind (MSW), the microwave brightness temperature (TB), and the sea surface wind speed (SSW). Then, a TC intensity estimation model was developed, based on a multivariate linear regression using the training data of 96 TCs. Finally, the proposed method was validated using testing data from 23 other TCs, and its root mean square error (RMSE), mean absolute error (MAE), and bias were 5.94 m/s, 4.62 m/s, and −0.43 m/s, respectively.

Satellite remote sensing is of considerable importance for estimating ground-level PM_2.5 concentrations to support environmental agencies monitoring air quality. However, most current studies have focused mainly on the application of MODIS aerosol optical depth (AOD) to predict PM_2.5 concentrations, while PARASOL AOD, which is sensitive to fine-mode aerosols over land surfaces, has received little attention. In this study, we compared a linear regression model, a quadratic regression model, a power regression model and a logarithmic regression model, which were developed using PARASOL level 2 AOD collected in China from 18 January 2013 to 10 October 2013. We obtained R (correlation coefficient) values of 0.64, 0.63, 0.62, and 0.57 for the four models when they were cross validated with the observed values. Furthermore, after all the data were classified into six levels according to the Air Quality Index (AQI), a low level of statistical significance between the four empirical models was found when the ground-level PM_2.5 concentrations were greater than 75 μg/m³. The maximum R value was 0.44 (for the logarithmic regression model and the power model), and the minimum R value was 0.28 (for the logarithmic regression model and the power model) when the PM_2.5 concentrations were less than 75 μg/m³. We also discussed uncertainty sources and possible improvements.

This paper reveals a study on air pollution process over North China, applying remote sensing technology, using satellite observation and in situ measurements during the twenty-first Asia-Pacific Economic Cooperation (APEC) meeting, which was held in Beijing between 6 and 12 November when the clear weather was called “APEC-Blue”. In the meantime, pollutants concentrations including PM_2.5 and PM₁₀ in Beijing were lower than 100 μg/m³ owing to the effective government measures and policies, as well as meteorological conditions. High aerosol loading (AOD > 1) was observed over south of Beijing and vertical observations showed that the pollutants were prominent near the land surface. Different from the meeting period, high pollutants concentrations with explosive growth (the values of PM_2.5 and PM₁₀ peaking at 291 μg/m³ and 360 μg/m³ respectively) appeared over Beijing after the meeting period, accompanied by strong temperature inversion and high Relative Humidity (RH) values. The pollution particles transferred from Beijing area to south part of North China. Otherwise, fine-mode particles with strong absorption characteristic (UVAI > 1.5, AOD > 1) covered the Beijing sky in the meantime, indicating the existence of black carbon aerosols.

To investigate the main modes of interannual variation of chlorophyll-a (Chla) with seasonal evolution and its variation cycle in the North Indian Ocean based on satellite-derived products during 1998–2016, a season-reliant empirical orthogonal function (S-EOF) analysis and power spectrum analysis based on Fourier transform are applied in the study. The first three dominate modes reveal distinct Chla variability, as the S-EOF1 features by one dipole pattern have a negative anomaly in the central western Indian Ocean and a positive anomaly off the Java–Sumatra coasts, which is mainly synchronously associated with the climate indices of the positive Indian Ocean dipole (IOD) and eastern Pacific El Nino (EP-El Niño). The S-EOF2 indicates a tripolar structure with positive anomalies located in the central Indian Ocean surrounded by two negative anomalies, which is one year behind a positive IOD and EP-El Niño event. The S-EOF3 exhibits a different dipole distribution, with a positive anomaly in the central west and a negative anomaly in the southeast, synchronized or lagging behind the central Pacific El Nino (CP-El Niño). Moreover, regarding the correlation between the main modes of interannual variation and the IOD and El Nino events, the dynamic parameters (such as SST, SLA, rain, and wind) of the tropical Indo-Pacific Ocean are discussed using time-delay correlation and linear regression analysis to explain the key factors and possible influencing mechanism of the joint seasonal and interannual variations of Chla in the northern Indian Ocean.

The greatest warm body in the world exists in the tropical oceans, which stimulates deep convection, resulting in abundant water vapor and precipitation in the tropical atmosphere. Using multiple SST datasets and related precipitation and atmospheric parameter data, this study examines multi-scale variabilities of the Indo-Pacific warm pool (IPWP) as well as the associated rain pool (IPRP). The results show that the IPWP and IPRP are spatially analogous and have significant increasing trends of intensity and coverage. Seasonal variations of the IPWP and IPRP are the strongest and almost coincident with each other. Our results also confirm previous findings that the most important interannual variations of the IPWP and IPRP are associated with various types of ENSO. The composite analysis reveals that the IPWP’s SST structure is linked to the ENSO-induced trade wind anomaly and that SST structural changes cause changes in the position and intensity of the ascending branch of the Walker circulation, which in turn drives changes in the position and intensity of the IPRP.