Massonnet, François, Sandra Barreira, Antoine Barthélemy, Roberto Bilbao, Edward Blanchard-Wrigglesworth, Ed Blockley, David H Bromwich, Mitchell Bushuk, Xiaoran Dong, Helge F Goessling, Will Hobbs, Doroteaciro Iovino, Woo-Sung Lee, Cuihua Li, Walter N Meier, William J Merryfield, Eduardo Moreno-Chamarro, and Yushi Morioka, et al., May 2023: SIPN South: Six years of coordinated seasonal Antarctic sea ice predictions. Frontiers in Marine Science, 10, DOI:10.3389/fmars.2023.1148899. Abstract
Antarctic sea ice prediction has garnered increasing attention in recent years, particularly in the context of the recent record lows of February 2022 and 2023. As Antarctica becomes a climate change hotspot, as polar tourism booms, and as scientific expeditions continue to explore this remote continent, the capacity to anticipate sea ice conditions weeks to months in advance is in increasing demand. Spurred by recent studies that uncovered physical mechanisms of Antarctic sea ice predictability and by the intriguing large variations of the observed sea ice extent in recent years, the Sea Ice Prediction Network South (SIPN South) project was initiated in 2017, building upon the Arctic Sea Ice Prediction Network. The SIPN South project annually coordinates spring-to-summer predictions of Antarctic sea ice conditions, to allow robust evaluation and intercomparison, and to guide future development in polar prediction systems. In this paper, we present and discuss the initial SIPN South results collected over six summer seasons (December-February 2017-2018 to 2022-2023). We use data from 22 unique contributors spanning five continents that have together delivered more than 3000 individual forecasts of sea ice area and concentration. The SIPN South median forecast of the circumpolar sea ice area captures the sign of the recent negative anomalies, and the verifying observations are systematically included in the 10-90% range of the forecast distribution. These statements also hold at the regional level except in the Ross Sea where the systematic biases and the ensemble spread are the largest. A notable finding is that the group forecast, constructed by aggregating the data provided by each contributor, outperforms most of the individual forecasts, both at the circumpolar and regional levels. This indicates the value of combining predictions to average out model-specific errors. Finally, we find that dynamical model predictions (i.e., based on process-based general circulation models) generally perform worse than statistical model predictions (i.e., data-driven empirical models including machine learning) in representing the regional variability of sea ice concentration in summer. SIPN South is a collaborative community project that is hosted on a shared public repository. The forecast and verification data used in SIPN South are publicly available in near-real time for further use by the polar research community, and eventually, policymakers.
Using a state-of-the-art coupled general circulation model, physical processes underlying Antarctic sea ice multidecadal variability and predictability are investigated. Our model simulations constrained by atmospheric reanalysis and observed sea surface temperature broadly capture a multidecadal variability in the observed sea ice extent (SIE) with a low sea ice state (late 1970s–1990s) and a high sea ice state (2000s–early 2010s), although the model overestimates the SIE decrease in the Weddell Sea around the 1980s. The low sea ice state is largely due to the deepening of the mixed layer and the associated deep convection that brings subsurface warm water to the surface. During the high sea ice period (post-2000s), the deep convection substantially weakens, so surface wind variability plays a greater role in the SIE variability. Decadal retrospective forecasts started from the above model simulations demonstrate that the Antarctic sea ice multidecadal variability can be skillfully predicted 6–10 years in advance, showing a moderate correlation with the observation. Ensemble members with a deeper mixed layer and stronger deep convection tend to predict a larger sea ice decrease in the 1980s, whereas members with a larger surface wind variability tend to predict a larger sea ice increase after the 2000s. Therefore, skillful simulation and prediction of the Antarctic sea ice multidecadal variability require accurate simulation and prediction of the mixed layer, deep convection, and surface wind variability in the model.
The Model-Analogs technique is used in the present study to assess the decadal sea surface temperature (SST) prediction skill over the Southern Ocean (SO). The Model-Analogs here is based on reanalysis products and model control simulations that have ∼1° ocean/ice (refined to 0.5° at high latitudes) components and 100 km atmosphere/land components. It is found that the model analog hindcasts show comparable skills with the initialized retrospective decadal hindcasts south of 50°S, with even higher skills over the Weddell Sea at longer lead years. The high SST skills primarily arise from the successful capture of SO deep convection states. This deep ocean memory and the associated decadal predictability are also clearly seen when we assess the Model-Analogs technique in a perfect model context. Within 30°S–50°S latitudinal band, the model analog hindcasts show low skills. When we include the externally forced signals estimated from the large ensemble simulations, the model analog hindcasts and initialized decadal hindcasts show identical skills. The Model-Analogs method therefore provides a great baseline for developing future decadal forecast systems. It is unclear whether such analog techniques would also be successful with models that explicitly resolve ocean mesoscale eddies or other small-scale processes. This area of research needs to be explored further.
One of the most puzzling observed features of recent climate has been a multidecadal surface cooling trend over the subpolar Southern Ocean (SO). In this study we use large ensembles of simulations with multiple climate models to study the role of the SO meridional overturning circulation (MOC) in these sea surface temperature (SST) trends. We find that multiple competing processes play prominent roles, consistent with multiple mechanisms proposed in the literature for the observed cooling. Early in the simulations (twentieth century and early twenty-first century) internal variability of the MOC can have a large impact, in part due to substantial simulated multidecadal variability of the MOC. Ensemble members with initially strong convection (and related surface warming due to convective mixing of subsurface warmth to the surface) tend to subsequently cool at the surface as convection associated with internal variability weakens. A second process occurs in the late-twentieth and twenty-first centuries, as weakening of oceanic convection associated with global warming and high-latitude freshening can contribute to the surface cooling trend by suppressing convection and associated vertical mixing of subsurface heat. As the simulations progress, the multidecadal SO variability is suppressed due to forced changes in the mean state and increased oceanic stratification. As a third process, the shallower mixed layers can then rapidly warm due to increasing forcing from greenhouse gas warming. Also, during this period the ensemble spread of SO SST trend partly arises from the spread of the wind-driven Deacon cell strength. Thus, different processes could conceivably have led to the observed cooling trend, consistent with the range of possibilities presented in the literature. To better understand the causes of the observed trend, it is important to better understand the characteristics of internal low-frequency variability in the SO and the response of that variability to global warming.
The low Antarctic sea ice extent following its dramatic decline in late 2016 has persisted over a multiyear period. However, it remains unclear to what extent this low sea ice extent can be attributed to changing ocean conditions. Here, we investigate the causes of this period of low Antarctic sea ice extent using a coupled climate model partially constrained by observations. We find that the subsurface Southern Ocean played a smaller role than the atmosphere in the extreme sea ice extent low in 2016, but was critical for the persistence of negative anomalies over 2016–2021. Prior to 2016, the subsurface Southern Ocean warmed in response to enhanced westerly winds. Decadal hindcasts show that subsurface warming has persisted and gradually destabilized the ocean from below, reducing sea ice extent over several years. The simultaneous variations in the atmosphere and ocean after 2016 have further amplified the decline in Antarctic sea ice extent.
Previous studies have shown the existence of internal multidecadal variability in the Southern Ocean using multiple climate models. This variability, associated with deep ocean convection, can have significant climate impacts. In this work, we use sensitivity studies based on Geophysical Fluid Dynamics Laboratory (GFDL) models to investigate the linkage of this internal variability with the background ocean mean state. We find that mean ocean stratification in the subpolar region that is dominated by mean salinity influences whether this variability occurs, as well as its time scale. The weakening of background stratification favors the occurrence of deep convection. For background stratification states in which the low-frequency variability occurs, weaker ocean stratification corresponds to shorter periods of variability and vice versa. The amplitude of convection variability is largely determined by the amount of heat that can accumulate in the subsurface ocean during periods of the oscillation without deep convection. A larger accumulation of heat in the subsurface reservoir corresponds to a larger amplitude of variability. The subsurface heat buildup is a balance between advection that supplies heat to the reservoir and vertical mixing/convection that depletes it. Subsurface heat accumulation can be intensified both by an enhanced horizontal temperature advection by the Weddell Gyre and by an enhanced ocean stratification leading to reduced vertical mixing and surface heat loss. The paleoclimate records over Antarctica indicate that this multidecadal variability has very likely happened in past climates and that the period of this variability may shift with different climate background mean state.
The 2018 tropical cyclone (TC) season in the North Pacific was very active, with 39 tropical storms including 8 typhoons/hurricanes. This activity was successfully predicted up to 5 months in advance by the Geophysical Fluid Dynamics Laboratory Forecast‐oriented Low Ocean Resolution (FLOR) global coupled model. In this work, a suite of idealized experiments with three dynamical global models (FLOR, NICAM and MRI‐AGCM) was used to show that the active 2018 TC season was primarily caused by warming in the subtropical Pacific, and secondarily by warming in the tropical Pacific. Furthermore, the potential effect of anthropogenic forcing on the active 2018 TC season was investigated using two of the global models (FLOR and MRI‐AGCM). The models projected opposite signs for the changes in TC frequency in the North Pacific by an increase in anthropogenic forcing, thereby highlighting the substantial uncertainty and model dependence in the possible impact of anthropogenic forcing on Pacific TC activity.
