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 water vapor budget (WVB) over the Tibetan Plateau (TP) is closely related to the large-scale atmospheric moisture transportation of the surrounding mainland and oceans, especially for the Indo-Pacific warm pool (IPWP). However, the procession linkage between the WVBs over the TP and its inner basins and IPWP has not been sufficiently elucidated. In this study, the relationship between the summer WVB over the TP and the IPWP was quantitatively investigated using reanalysis datasets and satellite-observed sea surface temperature (SST). The results show that: (1) the mean total summer vapor budget (WVB_t) over the TP in the period of 1979–2018 was 72.5 × 10⁶ kg s⁻¹. Additionally, for the 13 basins within the TP, the summer WVB has decreased from southeast to northwest; the Yarlung Zangbo River Basin had the highest WVB (33.7%), followed by the Upper Yangtze River Basin, Ganges River Basin and Qiangtang Plateau. (2) For the past several decades, the WVB_t over the TP has experienced an increasing trend (3.81 × 10⁶ kg s⁻¹ decade⁻¹), although the southern boundary budget (WVB_s) contributed the most and is most closely related with the WVB_t, while the eastern boundary budget (WVB_e) experienced a decreasing trend (4.21 × 10⁶ kg s⁻¹ decade⁻¹) which was almost equal to the interdecadal variations of the WVB_t. (3) For the IPWP, we defined a new warm pool index of surface latent heat flux (WPI-slhf), and found that an increasing WPI-slhf would cause an anticyclone anomaly in the equatorial western Indian Ocean (near 70° E), resulting in the increased advent of water vapor to the TP. (4) On the interdecadal scale, the correlation coefficients of the variation of the summer WVB_t over the TP with the WPI-slhf and Indian Ocean Dipole (IOD) signal were 0.86 and 0.85, respectively (significant at the 0.05% level). Therefore, the warming and the increasing slhf of the IPWP would significantly contribute to the increasing WVB of the TP in recent decades.

A new method for improving the accuracy of gridded sea surface salinity (SSS) fields is proposed in this paper. The method mainly focuses on dual quality–distance weighting of the Aquarius level 2 along-track SSS data according to quality flags, which represent nonnominal data conditions for measurements. In the weighting progress, 14 data conditions were considered, and their geospatial distributions and influences on the SSS were also visualized and evaluated. Three interpolation methods were employed, and weekly gridded SSS maps were produced for the period from September 2011 to May 2015. These maps were evaluated via comparisons with concurrent Argo buoy measurements. The results show that the proposed method improved the accuracy of the SSS fields by approximately 36% compared to the officially released weekly level 3 products and yielded root mean squared difference (RMSD), correlation and bias values of 0.19 psu, 0.98 and 0.01 psu, respectively. These findings indicate a significant improvement in the accuracy of the SSS fields and provide a better understanding of the influences of different conditions on salinity.