Hyun-Jin Yang

Sea Ice Concentration (SIC) plays an important role in the Antarctic polynyas, especially where biological interactions through changes in chlorophyll‐a concentration (CHL) phenology are concerned. To specifically understand how much SIC change affects CHL in the Ross Sea polynya (RSP), the regime shift index (RSI) was applied to the SIC anomaly, as per Rudionov (2004), https://doi.org/10.1029/2004gl019448 . This resulted in three principal temporal regimes: Regime 1 (R1), 1997–2004; Regime 2 (R2), 2004–2016; and Regime 3 (R3), 2016–2019. Based on reconstructed daily CHL observations from remote sensing and application of a random forest algorithm with 10 physical predictors, our analysis revealed that (a) earlier sea ice melt onset corresponds to an earlier CHL peak across all regimes; (b) SIC changes explain approximately 10%–12.5% of CHL variation in the RSP: a first‐ever estimation of SIC influence in the RSP; and (c) although CHL is significantly influenced by SIC changes, the relative contributions of the 10 geophysical predictors vary across the three regimes, with photosynthetically active radiation consistently emerging as the dominant driver. This result may explain why CHL has been observed to decrease despite the unprecedented decrease in Antarctic SIC since 2016.

As insolation increases during the Antarctic summer, sea ice melts, and the iron in the sea ice is released into the ocean Iron leads to phytoplankton blooms at sea ice margins and polynya in Antarctica, a high nutrient and low chlorophyll region. However, it is difficult to investigate the direct effect of sea ice on chlorophyll a changes because the observed regions are physically difficult to access. Therefore, this study intends to investigate the sea ice effect on chlorophyll a variation by using the decreasing salinity when sea ice melts in the Ross Sea, Antarctica. Using Random Forest, an ensemble bagging tree method of machine learning, the relationship was analyzed by 11 variables, including physical variables (sea surface temperature, photosynthetically available radiation, atmospheric temperature, wind, and salinity) as input data and chlorophyll a data observed from satellites as output data. As a result, the square of the correlation coefficient (R2) of the test data set was 0.97, and the root mean square error (RMSE) was 0.41 mg m-3, showing high accuracy. In addition, the importance of salinity was identified by calculating the variable importance in the model. These results provide the importance of salinity in predicted future chlorophyll a changes in the Antarctic Ocean due to climate change.