Nonlinear boosting during extreme El Niño through local air-sea interaction

<p>The delayed negative feedback is a key process for the turnabout between El Niño and La Niña. Since the intensity of this dynamical negative feedback is determined by itself, the stronger event is supposed be strongly damped during the decaying phase. However, the extreme El Niño actually lived longer than the normal El Niño. Here, we propose that the far-eastward extension of the warm pool promotes the positive SST tendency during the decaying phase of El Niño so disrupting a strong decay. The warm pool expansion accompanies by the expansion of the convective threshold region toward the eastern Pacific. During and after the mature phase of the extreme El Niño, therefore the rainfall band and the enhanced westerly anomalies over the eastern Pacific move to the east, which enhances the upwelling. This eastward migration of surface winds also plays a role of out-of-phase relationship between SST anomaly and subsurface temperature anomaly. All these processes results in the positive SST tendency through the positive nonlinear dynamical heating. This positive SST tendency maintains the warm eastern Pacific until the following summer.</p>

Predictability of the Barents Sea Ice in Early Winter: Remote Effects of Oceanic and Atmospheric Thermal Conditions from the North Atlantic

Predictability of sea ice concentrations (SICs) in the Barents Sea in early winter (November–December) is studied using canonical correlation analysis with atmospheric and ocean anomalies from the NCEP Climate Forecast System Reanalysis (CFSR) data. It is found that the highest prediction skill for a single-predictor model is obtained from the 13-month lead subsurface temperature at 200-m depth (T200) and the in-phase meridional surface wind (Vsfc). T200 skillfully predicts SIC variability in 35% of the Barents Sea, mainly in the eastern side. The T200 for negative sea ice anomalies exhibits warm anomalies in the subsurface ocean temperature downstream of the Norwegian Atlantic Slope Current (NwASC) on a decadal time scale. The diagnostic analysis of NCEP CFSR data suggests that the subsurface temperature anomaly stored below the thermocline during summer reemerges in late autumn by atmospheric cooling and affects the sea ice. The subsurface temperature anomaly of the NwASC is advected from the North Atlantic subpolar gyre over ~3 years. Also, Vsfc skillfully predicts SIC variability in 32% of the Barents Sea, mainly in the western side. The Vsfc for the negative sea ice anomalies exhibits southerly wind anomalies; Vsfc is related to the large-scale atmospheric circulation patterns from the subtropical North Atlantic to the Eurasian continent. This study suggests that both atmospheric and oceanic remote effects have a potential impact on the forecasting accuracy of SIC.

Estimation of Subsurface Temperature Anomaly in the North Atlantic Using a Self-Organizing Map Neural Network

A self-organizing map (SOM) neural network was developed from Argo gridded datasets in order to estimate a subsurface temperature anomaly (STA) from remote sensing data. The SOM maps were trained using anomalies of sea surface temperature (SST), height (SSH), and salinity (SSS) data from Argo gridded monthly anomaly datasets, labeled with Argo STA data from 2005 through 2010, which were then used to estimate the STAs at different depths in the North Atlantic from the sea surface data. The estimated STA maps and time series were compared with Argo STAs including independent datasets for validation. In the Gulf Stream path areas, the STA estimations from the SOM algorithm show good agreement with in situ measurements taken from the surface down to 700-m depth, with a correlation coefficient larger than 0.8. Sensitivity of the SOM, when including salinity, shows that with SSS anomaly data in the SOM training process reveal the importance of SSS information, which can improve the estimation of STA in the subtropical ocean by up to 30%. In subpolar basins, the monthly climatology SST and SSH can also help to improve the estimation by as much as 40%. The STA time series for 1993–2004 in the midlatitude North Atlantic were estimated from remote sensing SST and altimetry time series using the SOM algorithm. Limitations for the SOM algorithm and possible error sources in the estimation are briefly discussed.