Time scales of the Greenland Freshwater Anomaly in the Subpolar North Atlantic

The impact of increasing Greenland freshwater discharge on the subpolar North Atlantic (SPNA) remains unknown as there are uncertainties associated with the time scales of the Greenland freshwater anomaly (GFWA) in the SPNA. Results from numerical simulations tracking GFWA and an analytical approach are employed to estimate the response time suggesting a decadal time scale (13 years) required for the SPNA to adjust for increasing GFWA. Analytical solutions obtained for a long-lasting increase of freshwater discharge show a non-steady state response of the SPNA with increasing content of the GFWA. In contrast, solutions for a short-lived pulse of freshwater demonstrate different responses of the SPNA with a rapid increase of freshwater in the domain followed by an exponential decay after the pulse has passed. Derived theoretical relation between time scales show that residence time scales are time-dependent for a non-steady state case and asymptote the response time scale with time. Residence time of the GFWA deduced from Lagrangian experiments is close to and smaller than the response time, in agreement with the theory. The Lagrangian analysis shows dependence of the residence time on the entrance route of the GFWA and on the depth. The fraction of the GFWA exported through Davis Strait has limited impact on the interior basins, whereas the fraction entering the SPNA from the southwest Greenland shelf spreads into the interior regions. In both cases, the residence time of the GFWA increases with depth demonstrating long persistence of the freshwater anomaly in the subsurface layers.

Artificial Neural Network (ANN) Modeling Analysis of Algal Blooms in an Estuary with Episodic and Anthropogenic Freshwater Inputs

The Youngsan River estuary, located on the southwest coast of South Korea, has transitioned from a natural to an artificial estuary since dike construction in 1981 separated freshwater and seawater zones. This artificial transition has induced changes in the physical properties and circulation within the estuary, which has led to hypoxia and algal blooms. In this study, an artificial neural network (ANN) model was employed to simulate phytoplankton variations, including algal blooms and size fractions based on chlorophyll a, using data obtained by long-term monitoring (2008–2018) of the seawater zone of the Youngsan River estuary. The model was validated through statistical analyses, and the validated model was used to determine the contribution of the environmental factors on size-fractionated phytoplankton variations. The statistical validation of the model showed extremely low sum square error (SSE ≤ 0.0003) and root mean square error (RMSE ≤ 0.0173) values, with R2 ≥ 0.9952. The accuracy of the model predictions was high, despite the considerable irregularity and wide range of phytoplankton variations in the estuary. With respect to phytoplankton size structure, the contribution of seasonal environmental factors such as water temperature and solar radiation was high for net-sized chlorophyll a, whereas the contribution of factors such as freshwater discharge and salinity was high for nano-sized chlorophyll a, which includes typical harmful algae. Notably, because the Youngsan River estuary is influenced by a monsoon climate—characterized by high precipitation in summer—the contribution of freshwater discharge to harmful algal blooms is predicted to increase during this period. Our results suggest that the ANN model can be an important tool for understanding the influence of freshwater discharge, which is essential for managing algal blooms and maintaining the ecosystem health of altered estuaries.