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Real scientific workflows often require working with many heterogeneous but related datasets. Examples in geoscience include: (1) scenario simulations by many different climate models in the same intercomparison project, (2) simulation data at multiple resolutions from a convergence scan or sub-grid-scale study, and (3) observational + simulation data of the same region. There is a need for a general high-level data structure which can organize such data in an accessible way, whilst still being flexible enough to adapt to the user’s mental model of their data. It should also be intuitive, so that simple operations such as calculating average climatologies are still simple to express. It should also serialize to a commonly-used data format, so as not to create backwards compatibility problems. The new xarray-datatree [1] package solves these problems, by providing a tree-like hierarchical data structure that is general enough to be useful in a wide variety of cases. Datatree extends xarray - generalizing xarray.Dataset to build upon an interface that many geoscientists are already familiar with. Analysis operations can be mapped over a whole tree, allowing simple operations to be expressed intuitively, even over complex heterogeneous datasets. Datatree is inspired by netCDF: Xarray’s highest-level object is currently an xarray.Dataset, which stores collections of arrays with a shared coordinate system and corresponds to a single group in a netCDF file. A DataTree object is instead a structured hierarchical collection of Datasets, and would map to multiple netCDF groups. Therefore serialization to and from netCDF files is possible with datatree, so backwards compatibility is maintained. We will explain the model of datatree, its relation to netCDF & Zarr, and how to use the data structure to simplify your own work. We will also give examples of using datatree with real geoscience datasets, such as CMIP6 model data. [2] [1] https://github.com/xarray-contrib/datatree [2] https://medium.com/pangeo/easy-ipcc-part-1-multi-model-datatree-469b87cf9114

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise prediction 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.

We analysed the highest resolution global ocean simulation to date, using xGCM, xhistogram, and dask.

Climate change is one of the most challenging issues of our time. To prevent the worst outcomes, we need to drastically accelerate the creation and distribution of scientific knowledge. But the complex and massive datasets produced by numerical climate models render the common 'download and analyze' workflow inefficient, blocking innovative analysis and fast scientific discoveries. We present python tools and cloud infrastructure developed within the Pangeo community, enabling cutting edge climate science from a web-browser, making it efficient, reproducible, and inclusive. To demonstrate these capabilities we will reproduce a plot from the IPCC report in a live cloud demonstration.

The live version of this SOP is on the Ocean Gliders community in GITHUB. The home repository of this publication is in the Ocean Best Practices Repository. This standard operating procedure (SOP) document for dissolved oxygen (DO) aims to guide the user through the steps necessary to collect good quality dissolved oxygen data using ocean gliders for both real time and post deployment data streams.

The salinity of the ocean is inherently linked to the global hydrological cycle by net evaporation. The surface salinity, however does not just act like a 'rain gauge', ocean dynamics are vital in shaping the sea surface salinity (SSS) distribution. Here I investigate the effect of unsteady motions on scales of several hundred km and smaller - mesoscale eddies - on the water masses in the saltiest regions of the surface oceans. These water masses are eventually subducted equatorward and contribute to the shallow overturning circulation by transporting surface signals from the subtropics to the tropics, making them important components of the variable climate system. Towed CTD measurements in March/April 2013 (a component of the NASA SPURS process study) within the North Atlantic SSS maximum (SSS-max) reveal several relatively fresh and warm anomalies, which deviate strongly from climatological conditions. These features introduce a large amount of freshwater into the subtropical region, exceeding the amount introduced by local rain events. The scales and evolution of the features strongly suggest a connection to mesoscale dynamics. This is supported by high-resolution regional model output, which produces an abundance of features that are similar in scale and structure to those observed, confirming the importance of eddy mixing for the near surface salinity budget of the North Atlantic SSS-max. Observations from the Aquarius satellite and the Argo array in the global SSS-max revealed marked differences in the mean shape and variability of the SSS-maxima. These results motivated an investigation of the role of eddy mixing in setting the regional characteristics of SSS maxima. Observed surface velocities from altimetry are used to stir salinity fields in high-resolution idealized model experiments. Using a water mass framework (salinity coordinates) temporal variability in eddy mixing can be quantified, using diagnostics for the total diffusive flux into the SSS-maxima (transformation rate; TFR) as well as the estimated cross-contour diffusivity(effective diffusivity,$K_{eff}$). Both diagnostics reveal distinct variability in the different ocean basins. In the North Atlantic, both $TFR$ and $K_{eff}$ are dominated by changes in the velocity field while the North Pacific shows high sensitivity of the temporal variability in eddy mixing with respect to the initial conditions used, which represent seasonal/interannual change of the SSS-max shape and position. This implies that temporal variability of eddy mixing and diffusivities must be taken into account when constructing salinity budgets in these regions. Furthermore, the translation of results from one SSS-max region to the other might not be possible, particularly when considering a changing climate, which might influence the mechanisms responsible for temporal variability differently. Lastly evidence is presented for large scale diffusivity variability (particularly in the Pacific), connected to large scale climate fluctuations (ENSO). The evidence presented here suggests a significant modulation of surface diffusivities by climate variability, which represents a feedback mechanism not commonly recognized nor included in modern climate simulations.

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.

17th Ocean Sciences Meeting, 23-28 February 2014, Honolulu, Hawaii USA, The subtropical North Atlantic exhibits the saltiest surface waters of the open ocean, the sea surface salinity maximum (sss-max). Eventually that water is exported towards the Equator as a subsurface salinity maximum (s-max), forming the lower limb of the shallow overturning circulation (SOC). Climatologically the sss-max in the winter, coinciding with the deepest mixed layer, is too salty and cold compared to the s-max. Towed CTD measurements during the field program SPURS (March/April 2013) within the center of the sss-max reveal several fresh and warm anomalies, which deviate strongly from the climatological conditions in the sss-max. Source regions for the anomalies are found in the south and west, collocated to areas of elevated Eddy Kinetic Energy and strong lateral gradients in salinity. Regional model output produces an abundance of fresh features similar in space and time scales. It is hypothesized that a surface layer rich in meso-scale activity, in an otherwise very quiescent mean flow is predominantly responsible for setting mixed layer characteristics in the sss-max, which translate to s-max characteristics, hence influence the variability of the SOC

IV Congress of Marine Sciences, 11-13 June 2014, Las Palmas de Gran Canaria.-- 1 page, SPURS-MIDAS (March-April 2013) on board the Spanish R/V Sarmiento de Gamboa was a contribution to SPURS (Salinity Processes in the Upper ocean Regional Study) focused on the processes responsible for the formation and maintenance of the salinity maximum associated to the North Atlantic subtropical gyre. Scientists from Spain, Ireland, France and US sampled the mesoscale and submesoscale structures in the surface layer (fixed points and towed undulating CTD, underway near surface TSG) and deployed operational and experimental drifters and vertical profilers, plus additional ocean and atmospheric data collection. Validation of salinity maps obtained from the SMOS satellite was one of the objectives of the cruise. The cruise included a joint workplan and coordinated sampling with the US R/V Endeavor, with contribution from SPURS teams on land in real time data and analysis exchange. We present here an overview of the different kinds of measurements made during the cruise, as well as a first comparison between SMOS-derived sea surface salinity products and salinity maps obtained from near-surface sampling in the SPURS-MIDAS area and from surface drifters released during the cruise. The SPURS-MIDAS Team: D. Alcoverro, A. Castellon, L. Centurioni, E. Flo, A. Giraldez, L. Gonzalez, S. Guimbard, O. Hernandez, A. Martinez, M. Maso, B. Moli, J. Olive, M. Pastor, S. Quintana, G. Reverdin, M. Rosell, G. Sutherland, A. ten Doeschate, M. Umbert, X. Vidal, K. Walesby, N. Yarovenko

1 page, figures, 1. SPURS: Salinity Processes in the Upper ocean Regional Study. An international program in 2012-2013 to understand the processes responsible for the formation and maintenance of the salinity maximum associated to the North Atlantic subtropical gyre. http://spurs.jpl.nasa.gov/SPURS

Cruise M79/3 aimed at physical, biogeochemical and biological sampling at and around seamounts in the Cape Verde region. These seamounts are important targets for the local fishery, but nearly nothing is known about their ecology. In order to gain an advanced understanding of the functioning of these ecosystems within their special hydrographic and biogeochemical setting, and in comparison to other seamounts in the NE Atlantic, the following studies were performed on the example of Senghor Seamount and an oceanic reference station: - Identification and description of the physical forcing factors at Senghor Seamounts. - Assessment of the origin, quality and dynamics of particulate organic material in the water column and in the superficial sediments at the seamount. - Description of aspects of biodiversity, zoogeography and ecology of the seamount: communities, faunal connections, trophic relationships, dynamics and productivity. - Adding to the classification of seamounts based on topography and habitat types. In addition, geological samples were obtained at two further seamounts and at a young volcano field south of Santo Antao. Complementing studies performed on cruise M81/1 and on the islands, they will further our understanding of the geological history of the area., METEOR-Berichte