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Towards automatic finite-element methods for geodynamics via Firedrake

Firedrake is a state-of-the-art system that automatically generates highly optimised code for simulating finite-element (FE) problems in geophysical fluid dynamics. It creates a separation of concerns between employing the FE method and implementing it. Here, we demonstrate the applicability and benefits of Firedrake for simulating geodynamical flows, with a focus on the slow creeping motion of Earth’s mantle over geological timescales, which is ultimately the engine driving our dynamic Earth.

Currents generated by the sea breeze in the southern Caspian Sea

The smaller thermal heat capacity of land relative to the sea results in land–sea thermal gradients with a daily cycle, called sea breeze systems, with the same daily periodicity. Since tides in the Caspian, as the largest enclosed basin with a persistent sea breeze system through the year, are very weak we found that most of the higher-frequency variations in coastal currents are a response to the sea breeze system.

Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO – v.1

In this study, we present a novel formulation to build a dynamical combination of models, the so-called supermodel, which needs to be trained based on data. Previously, we assumed complete and noise-free observations. Here, we move towards a realistic scenario and develop adaptations to the training methods in order to cope with sparse and noisy observations. The results are very promising and shed light on how to apply the method with state of the art general circulation models.

Marine heatwaves in the Arabian Sea

Marine heatwaves refer to discrete, prolonged warm ocean conditions known to cause severe destruction in marine ecosystems. We find that coastal waters off the west coast of India have experienced a rapid multifold increase in heatwave days since the early 80s. This resulted in more frequent and longer marine heatwave events in the last decade. We show that the rapid warming in the Arabian Sea in the last decade is the primary cause of the observed enhanced heatwave events in this basin.

Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

A large amount of marine litter, such as plastics, is located on or around beaches. Both the total amount of this litter and its transport are poorly understood. We investigate this by training a machine learning model with data of clean-up efforts on Dutch beaches between 2014 and 2019, obtained by about 14 000 volunteers. We find that Dutch beaches contain up to 30 000 kg of litter, largely depending on tides, oceanic transport, and how exposed the beaches are.

Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models

It can be challenging to understand why Earth system models (ESMs) produce specific results because one can arrive at the same result simply by changing the values of the parameters. In our paper, we demonstrate that it is possible to use machine learning to figure out how and why particular components of an ESM (such as biology or ocean circulations) affect the output. This work could be applied to observations to improve the accuracy of the formulations used in ESMs.

Autonomous methane seep site monitoring offshore western Svalbard: hourly to seasonal variability and associated oceanographic parameters

Natural sources of atmospheric methane need to be better described and quantified. We present time series from ocean observatories monitoring two seabed methane seep sites in the Arctic. Methane concentration varied considerably on short timescales and seasonal scales. Seeps persisted throughout the year, with increased potential for atmospheric release in winter due to water mixing. The results highlight and constrain uncertainties in current methane estimates from seabed methane seepage.

The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation

Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. The model will ultimately help to improve projections of sea level rise and climate change.

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.

Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

This study explores the Arctic sea level variability depending on different timescales and the relation to temperature, salinity and mass changes, identifying key parameters and regions that need to be observed co-ordinately. The decadal sea level variability reflects salinity changes. But it can only reflect salinity change at periods of greater than 1 year, highlighting the requirement for enhancing in situ hydrographic observations and complicated interpolation methods.

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