The El Niño Southern Oscillation and the salinity of land and water in the United States

The El Niño-Southern Oscillation (ENSO) is a periodic climate phenomenon with important consequences for weather and socioeconomic variables worldwide. This paper investigates the effect of El Niño on the concentration of salts in soils and fresh water. Our analysis shows effects that are statistically significant and large in magnitude. In particular, in response to El Niño events, soil salinity increases by 21% in the northern region of the United States, where average temperatures are positively affected, while it decreases by 29% on average in the southern tiers of the country, where temperatures decline. Similarly, fresh water salinity increases by 9% in warmer counties, while it is 53% lower in cooler counties.

Subsurface Temperature Estimation from Sea Surface Data Using Neural Network Models in the Western Pacific Ocean

Estimating the ocean subsurface thermal structure (OSTS) based on multisource sea surface data in the western Pacific Ocean is of great significance for studying ocean dynamics and El Niño phenomenon, but it is challenging to accurately estimate the OSTS from sea surface parameters in the area. This paper proposed an improved neural network model to estimate the OSTS from 0–2000 m from multisource sea surface data including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). In the model experiment, the rasterized monthly average data from 2005–2015 and 2016 were selected as the training and testing set, respectively. The results showed that the sea surface parameters selected in the paper had a positive effect on the estimation process, and the average RMSE value of the ocean subsurface temperature (OST) estimated by the proposed model was 0.55 °C. Moreover, there were pronounced seasonal variation signals in the upper layers (the upper 200 m), however, this signal gradually diminished with increasing depth. Compared with known estimation models such as the random forest (RF), the multiple linear regression (MLR), and the extreme gradient boosting (XGBoost), the proposed model outperformed these models under the data conditions of the paper. This research can provide an advanced artificial intelligence technique for estimating subsurface thermohaline structure in major sea areas.