Finale: impact of the ORCHESTRA/ENCORE programmes on Southern Ocean heat and carbon understanding.

The 5-year Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports (ORCHESTRA) programme and its 1-year extension ENCORE (ENCORE is the National Capability ORCHESTRA Extension) was an approximately 11-million-pound programme involving seven UK research centres that finished in March 2022. The project sought to radically improve our ability to measure, understand and predict the exchange, storage and export of heat and carbon by the Southern Ocean. It achieved this through a series of milestone observational campaigns in combination with model development and analysis. Twelve cruises in the Weddell Sea and South Atlantic were undertaken, along with mooring, glider and profiler deployments and aircraft missions, all contributing to measurements of internal ocean and air-sea heat and carbon fluxes. Numerous forward and adjoint numerical experiments were developed and supported by the analysis of coupled climate models. The programme has resulted in over 100 peer-reviewed publications to date as well as significant impacts on climate assessments and policy and science coordination groups. Here, we summarize the research highlights of the programme and assess the progress achieved by ORCHESTRA/ENCORE and the questions it raises for the future. This article is part of a discussion meeting issue 'Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities'.

Harnessing human and machine intelligence for planetary-level climate action.

The ongoing global race for bigger and better artificial intelligence (AI) systems is expected to have a profound societal and environmental impact by altering job markets, disrupting business models, and enabling new governance and societal welfare structures that can affect global consensus for climate action pathways. However, the current AI systems are trained on biased datasets that could destabilize political agencies impacting climate change mitigation and adaptation decisions and compromise social stability, potentially leading to societal tipping events. Thus, the appropriate design of a less biased AI system that reflects both direct and indirect effects on societies and planetary challenges is a question of paramount importance. In this paper, we tackle the question of data-centric knowledge generation for climate action in ways that minimize biased AI. We argue for the need to co-align a less biased AI with an epistemic web on planetary health challenges for more trustworthy decision-making. A human-in-the-loop AI can be designed to align with three goals. First, it can contribute to a planetary epistemic web that supports climate action. Second, it can directly enable mitigation and adaptation interventions through knowledge of social tipping elements. Finally, it can reduce the data injustices associated with AI pretraining datasets.

Regional disparities and seasonal differences in climate risk to rice labour.

The 880 million agricultural workers of the world are especially vulnerable to increasing heat stress due to climate change, affecting the health of individuals and reducing labour productivity. In this study, we focus on rice harvests across Asia and estimate the future impact on labour productivity by considering changes in climate at the time of the annual harvest. During these specific times of the year, heat stress is often high compared to the rest of the year. Examining climate simulations of the Coupled Model Intercomparison Project 6 (CMIP6), we identified that labour productivity metrics for the rice harvest, based on local wet-bulb globe temperature, are strongly correlated with global mean near-surface air temperature in the long term (p ≪ 0.01, R 2 > 0.98 in all models). Limiting global warming to 1.5 °C rather than 2.0 °C prevents a clear reduction in labour capacity of 1% across all Asia and 2% across Southeast Asia, affecting the livelihoods of around 100 million people. Due to differences in mechanization between and within countries, we find that rice labour is especially vulnerable in Indonesia, the Philippines, Bangladesh, and the Indian states of West Bengal and Kerala. Our results highlight the regional disparities and importance in considering seasonal differences in the estimation of the effect of climate change on labour productivity and occupational heat-stress.

Seasonal Arctic sea ice forecasting with probabilistic deep learning.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss.

Oceanographers' contribution to climate modelling and prediction: progress to date and a future perspective.

The ocean plays an essential role in determining aspects of the climate through its influence on coupled processes involving the atmosphere, cyrosphere and biogeochemistry, including budgets of heat and carbon dioxide and sea-level rise. Here, the key developments in ocean modelling over the past 20 years are reviewed and the prospects for the next 20 years are outlined, considering a hierarchy of idealized, conceptual and realistic modelling frameworks. It is emphasized that any long-term modelling strategy needs to be underpinned and complemented by fundamental theoretical and observational research activities. The need to be aware of the societal and technological drivers that will shape future research directions is also articulated.