Global sea surface salinity (SSS) has been obtained from space since 2009 by the Soil Moisture and Ocean Salinity (SMOS) mission and has been further enhanced by Aquarius in 2011 and Soil Moisture Active‐Passive (SMAP) missions in 2015. Due to the differences between SMOS, Aquarius, and SMAP in the instruments used, retrieval algorithms, and error correction strategies, the quality of their gridded products are different. In this paper, we have assessed the accuracy of three satellite products using in situ gridded data and buoy data. Compared with gridded in situ salinity measurements, the monthly Aquarius data are of the best quality, reaching the mission target accuracy (0.2 PSU) in the open ocean. SMOS and SMAP agree well with in situ data in the open ocean between 40°S and 40°N (root‐mean‐square deviation [RMSD]: SMOS 0.211 PSU, SMAP 0.233 PSU). The RMSD of SMAP is lower than that of SMOS at high latitudes, which may due to the fact that the roughness correction of SMAP is based on the Aquarius geophysical model function. Meanwhile, time series comparison of salinity measured at 1 m by moored buoys indicates that satellite SSS captures variability of SSS at weekly time scales with reasonably good accuracy (RMSD: SMOS 0.25 PSU, SMAP 0.26 PSU), when excluding suspicious buoy data. Synergetic analysis of satellite SSS and Argo data indicates that satellite SSS can be applied as real‐time quality control of buoy 1‐m salinity data.

How to extract the causal relations in climate–cyclone interactions is an important problem in atmospheric science. Traditionally, the most commonly used research methodology in this field is time-delayed correlation analysis. This may be not appropriate, since a correlation cannot imply causality, as it lacks the needed asymmetry or directedness between dynamical events. This study introduces a recently developed and very concise but rigorous formula—that is, a formula for information flow (IF)—to fulfill the purpose. A new way to normalize the IF is proposed and then the normalized IF (NIF) is used to detect the causal relation between the tropical cyclone (TC) genesis over the western North Pacific (WNP) and a variety of climate modes. It is shown that El Niño–Southern Oscillation and Pacific decadal oscillation are the dominant factors that modulate the WNP TC genesis. The western Pacific subtropical high and the monsoon trough are also playing important roles in affecting the TCs in the western and eastern regions of the WNP, respectively. With these selected climate indices as predictors, a method of fuzzy graph evolved from a nonparametric Bayesian process (BNP-FG), which is capable of handling situations with insufficient samples, is employed to perform a seasonal TC forecast. A forecast with the classic Poisson regression is also conducted for comparison. The BNP-FG model and the causality analysis are found to provide a satisfactory estimation of the number of TC genesis observed in recent years. Considering its generality, it is expected to be applicable in other climate-related predictions.

The navigability potential of the Northeast Passage has gradually emerged with the melting of Arctic sea ice. For the purpose of navigation safety in the Arctic area, a reliable daily sea ice concentration (SIC) prediction result is required. As the mature application of deep learning technique in short-term prediction of other fields (atmosphere, ocean, and hurricane, etc.), a new model was proposed for daily SIC prediction by selecting multiple factors, adopting gradient loss function (Grad-loss) and incorporating an improved predictive recurrent neural network (PredRNN++). Three control experiments are designed to test the impact of these three improvements for model performance with multiple indicators. Results show that the proposed model has best prediction skill in our experiments by taking physical process and local SIC variation into consideration, which can continuously predict daily SIC for up to 9 days.

This paper presents a new route planning system for the purpose of evaluating the strategic prospects for future Arctic routes. The route planning problem can be regarded as a multi criteria decision making problem with large uncertainties originating from multi-climate models and experts’ knowledge and can be solved by a modified A* algorithm where the hesitant fuzzy set theory is incorporated. Compared to the traditional A* algorithm, the navigability of the Arctic route is firstly analyzed as a measure to determine the obstacle nodes and three key factors to the vessel navigation including sailing time, economic cost and risk are overall considered in the HFS-A* algorithm. A numerical experiment is presented to test the performance of the proposed algorithm.