Outline This is the Southern Ocean Monthly Climatology of Yamazaki et al. "Unlocking Southern Ocean Under-ice Seasonality with a New Monthly Climatology". The interpolation method follows Barth et al. (2014) available via DIVAnd Julia package (https://github.com/gher-uliege/DIVAnd.jl). CTD data sourced from Argo, MEOP, and World Ocean Database (including low resolution ocean station data). The dataset covers south of 40S and above 2000 dbar (above 1000 dbar for "_minimal"). The horizontal grid is 1/4 and 1/2 degrees in latitude and longitude, and the vertical grid is the 66 WOA layers. Mixed layer depth, temperature, salinity, crudely derived from max("Δσθ_10m=0.03kg/m³", "Holte&Talley"), are also provided in "_MLD". The following variables are included (* are excluded in "_minimal"): In-situ temperature (°C) in ITS-90 Practical salinity (psu) *Standard deviation of temperature (°C), inferred by the spread of observations *Standard deviation of practical salinity (psu), inferred by the spread of observations *Interpolation error of temperature (°C), inferred by the sparsity of observations *Interpolation error of practical salinity (psu), inferred by the sparsity of observations *Cabbeling correction for temperature (°C) *Cabbeling correction for practical salinity (psu) *Density stabilization factor for temperature (°C) *Density stabilization factor for practical salinity (psu) Project Description The advent of under-ice profiling float and biologging techniques has enabled year-round observation of the Southern Ocean and its Antarctic margin. These under-ice data are often overlooked in widely used oceanographic datasets, despite their importance in understanding seasonality and its role in sea ice changes, water mass formation, and glacial melt. We develop a monthly climatology of the Southern using Data Interpolating Variational Analysis, which excels in multi-dimensional interpolation and consistent handling of topography and horizontal advection. The dataset will be instrumental in investigating the seasonality and improving ocean models, thereby making valuable under-ice observations more accessible.

Internal climate variability encompasses processes ranging from daily weather fluctuations to multidecadal interactions within the climate system. Understanding these processes is crucial for distinguishing natural variability from human-induced climate change. A large component of internal variability on sub-seasonal to multi-decadal time scales are associated with recurring patterns or ‘climate modes’. Using pre-industrial control (piControl) simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigate eight critical climate modes: Eastern Pacific El Niño (EP-El Niño), Central Pacific El Niño (CP-El Niño), Interdecadal Pacific Oscillation (IPO), Indian Ocean Dipole (IOD), Subsurface Dipole Mode (SDM), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and Southern Annular Mode (SAM). These modes were derived from 23 CMIP6 models, each with over 500 years of simulation data, ensuring robust statistical insights into their spatial and temporal structures. The datasets were validated against observational data, revealing broad-scale consistency and highlighting biases in regional features and amplitudes. For example, the models effectively capture spatial patterns such as the tripolar SST anomaly of the IPO and the equatorial Pacific warming of EP-El Niño. However, regional discrepancies, like exaggerated warming or cooling in specific areas, were observed. Despite these biases, the datasets provide critical tools for understanding climate variability, conducting detection and attribution studies, and improving climate projections. Details regarding the generated NetCDF files are provided in the accompanying README file. All datasets are publicly accessible (https://doi.org/10.5281/zenodo.17274477, and additionally linked to this record), supporting future research and policy development to address climate variability and its implications for climate change adaptation and mitigation.

The ocean absorbs >90% of anthropogenic heat in the Earth system, moderating global atmospheric warming. However, it remains unclear how this heat uptake is distributed by basin and across water masses. Here we analyze historical and recent observations to show that ocean heat uptake has accelerated dramatically since the 1990s, nearly doubling during 2010–2020 relative to 1990–2000. Of the total ocean heat uptake over the Argo era 2005–2020, about 89% can be found in global mode and intermediate water layers, spanning both hemispheres and both subtropical and subpolar mode waters. Due to anthropogenic warming, there are significant changes in the volume of these water-mass layers as they warm and freshen. After factoring out volumetric changes, the combined warming of these layers accounts for ~76% of global ocean warming. We further decompose these water-mass layers into regional water masses over the subtropical Pacific and Atlantic Oceans and in the Southern Ocean. This shows that regional mode and intermediate waters are responsible for a disproportionate fraction of total heat uptake compared to their volume, with important implications for understanding ongoing ocean warming, sea-level rise, and climate impacts. The study titled “Recent acceleration in global ocean heat accumulation by mode and intermediate waters” was published in Nature Communications in October 2023. All datasets are publicly accessible (https://doi.org/10.5281/zenodo.17274477) and are additionally linked to this record.

The East Antarctic Ice Sheet (EAIS) is the largest source of potential sea-level rise, containing some 19 m of sea-level equivalent. One of the well-investigated regions in East Antarctica is Law Dome, which is a small independent ice cap situated to the west of Totten Ice Shelf. The ice cap is slow-moving, has a low melt-rate at the surface and moderate wind speeds, making it a useful study site for our investigations. Radar data from Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project has good coverage over this area. A new method is proposed for the estimation of attenuation rate from radar data which is mathematically modeled as a constrained regularised l2 minimization problem. In the proposed method, only radar data is required and the englacial reflectors are automatically detected from the radar data itself. A final product of 3D attenuation rates and 3D samples count is provided for the research community in this data set.