Bottom water export from the western Ross Sea, 2007 through 2010

Bottom water export from the Ross Sea, February 2007 to January 2011, exhibits seasonal and interannual variability. Temperature minima coupled to salinity maxima in late austral summer, into the fall, indicate input from High-Salinity Shelf Water (HSSW). Secondary temperature minima lacking the high-salinity trait, characteristic of Low-Salinity Shelf Water (LSSW), appear in the spring. Warmer bottom water similar to modified Circumpolar Deep Water (mCDW) is observed in winter and in early summer. The LSSW and mCDW may be drawn from the Drygalski Basin, as the HSSW pool retreats poleward from the shelf break in response to increased winter polar easterlies allowing these less dense overlying waters to spill into the deep ocean within the benthic layer. Bottom salinity decreased from 2007 to 2011 by 0.007 year−1 significantly higher than regional decadal trends, which we propose is a result of HSSW retreat induced by strengthening polar easterlies

Differences Among Subtropical Surface Salinity Patterns

The subtropical ocean, exposed to evaporation excess over precipitation, is characterized by regional sea surface salinity maxima (SSS-max). Ocean circulation and mixing processes inject freshwater, establishing a quasi-steady state, though imbalances across the time spectrum result in periods of increasing and decreasing SSS-max. The integrated effect of the array of atmospheric and oceanic forces governing sea surface salinity is shaped by the specific regional ocean basin configuration as well as their coupling to the global ocean system, resulting in SSS-max patterns and locations that display marked differences between the subtropical regimes. We provide a brief description of the SSS-max characteristics of the five subtropical regimes and present aspects of their regional settings that may account for their dissimilarities

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society

Diverging Fates of the Pacific Ocean Oxygen Minimum Zone and Its Core in a Warming World

Abstract Global ocean oxygen loss is projected to persist in the future, but Earth system models (ESMs) have not yet provided a consistent picture of how it will influence the largest oxygen minimum zone (OMZ) in the tropical Pacific. We examine the change in the Pacific OMZ volume in an ensemble of ESMs from the CMIP6 archive, considering a broad range of oxygen (O2) thresholds relevant to biogeochemical cycles and ecosystems (5–160 µmol/kg). Despite OMZ biases in the historical period of the simulations, the ESM ensemble projections consistently fall into three regimes across ESMs: an expansion of low oxygenated waters (+0.8 [0.6, 1.0] × 1016 m3/century for O2 ≤ 120 µmol/kg, ESM median and interquartile range); a slight contraction of the OMZ core although more uncertain across ESMs (−0.1 [−0.5, 0.0] × 1016 m3/century for O2 ≤ 20 µmol/kg); and at the transition from contraction to expansion regimes, a spatial redistribution but near‐zero change in the volume of hypoxic waters (0.0 [−0.3, +0.1] × 1016 m3/century for O2 ≤ 60 µmol/kg). Changes in circulation and biology dictate the shift from expansion to contraction. Specifically, reduced subtropical ventilation controls the expansion of low oxygenated waters, while a combination of circulation and biological changes explains the contraction of the core (likely changes in mixing, reduced intermediate ventilation and oxygen demand). Increased model complexity (e.g., ecosystem dynamics and equatorial circulation) likely stabilize the OMZ response, suggesting that future changes might lie in the lower bound of current projections. The expansion of low oxygenated waters which delimit the optimum habitat of numerous marine species would severely impact ecosystems and ecosystem services

Coastal vegetation and estuaries are collectively a greenhouse gas sink

Coastal ecosystems release or absorb carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), but the net effects of these ecosystems on the radiative balance remain unknown. We compiled a dataset of observations from 738 sites from studies published between 1975 and 2020 to quantify CO2, CH4 and N2O fluxes in estuaries and coastal vegetation in ten global regions. We show that the CO2-equivalent (CO(2)e) uptake by coastal vegetation is decreased by 23-27% due to estuarine CO(2)e outgassing, resulting in a global median net sink of 391 or 444 TgCO(2)e yr(-1) using the 20- or 100-year global warming potentials, respectively. Globally, total coastal CH4 and N2O emissions decrease the coastal CO2 sink by 9-20%. Southeast Asia, North America and Africa are critical regional hotspots of GHG sinks. Understanding these hotspots can guide our efforts to strengthen coastal CO2 uptake while effectively reducing CH4 and N2O emissions. The authors show that estuarine and coastal vegetation are collectively a greenhouse gas (GHG) sink for the atmosphere, but methane and nitrous oxide emissions counteract the carbon dioxide uptake. Critical coastal GHG sink hotspots are identified in Southeast Asia, North America and Africa

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res, https://huggingface.co/datasets/LEAP/ClimSim_low-res, and https://huggingface.co/datasets/LEAP/ClimSim_low-res_aqua-planet) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society