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SDG: 13 Climate action

Climate action

The challenge

Earth System Models (ESMs) are central to understanding the climate system and predicting its future evolution. They underpin IPCC assessments and inform major international policy processes, including the Global Stocktake. 

However, these models are extremely computationally expensive to run. This creates a significant constraint on the timeliness and scope of the evidence available to policymakers. 

Before simulations can be used, ESMs must first be “spun up” to a stable pre-industrial state. This process requires simulating thousands of years of climate dynamics to allow slow-moving components, such as the deep ocean and terrestrial carbon cycle, to equilibrate. Even on the most powerful supercomputers, a single spin-up can take more than two years. 

This limitation has direct consequences for both science and policy. In practice, it is often only feasible to run a single spin-up. This restricts the ability to explore the substantial uncertainties inherent in these models and narrows the range of future climate projections available to decision-makers. 

As a result, policymakers must rely on a constrained evidence base when making decisions on critical issues such as carbon budgets, climate risks, and long-term adaptation planning. Despite its significance, the spin-up problem remained unresolved for decades. 

Previous research by Khatiwala in 2023 demonstrated a promising numerical algorithm that was more than ten times faster than conventional approaches, but further work was needed to test and demonstrate its robustness in operational settings. The scale of the scientific and policy challenge, and the potential to unlock more reliable projections, created a clear demand for a practical solution, bringing the Met Office into the Sprint as a key partner to assess how the approach could be applied within UKESM and inform future IPCC-relevant modelling.

The Solutions

The Sprint delivered a scalable and operational solution to the long-standing spin-up problem by developing, testing and implementing a fast numerical algorithm across multiple components of Earth System Models.  

The team successfully: 

  •  interfaced the algorithm with MEDUSA and JULES, the marine biogeochemical and land vegetation components of UKESM 
  • adapted the code to run within the Met Office’s workflow software and supercomputing environment 
  • demonstrated substantial acceleration in spin-up times, achieving performance improvements of approximately 10 to 12 times faster for NEMO-MEDUSA and between 10 and 50 times faster for JULES.  
  • generated multiple equilibrium realisations of MEDUSA using parameter sets identified by the Met Office as key sources of bias in previous IPCC simulations, creating a pathway for improved representation of uncertainty in future projections 

They not only improved computational efficiency but also revealed important differences in model complexity, particularly highlighting the greater challenges associated with land system models. 

The pathway

The initial focus was on accelerating the spin-up of MEDUSA and JULES, the marine biogeochemical and land vegetation components of UKESM, which represent key rate-limiting processes in Earth System Models. Once this acceleration was successfully demonstrated, the algorithm and associated code were made available to the Met Office and adapted for deployment within their supercomputing environment for production-scale simulations. This transition from methodological innovation to operational application use was critical to ensuring that the approach could directly inform CMIP simulations and, by extension, future IPCC assessments. 

Together, these advances provided the Met Office and the wider international modelling community with a practical, transferable solution that could increase the range of scenarios explored in CMIP simulations and strengthen the evidence base underpinning climate policy decisions. 

A peer reviewed publication in Science Advances and an accompanying article in The Conversation established the credibility and visibility of the approach within the scientific and policy relevant modelling community.

What happened next?

The algorithm was consolidated into a transferable, open-source tool and successfully deployed on the Met Office supercomputer for production calculations. 

It was successfully tested beyond UKESM components, showing that the method generalises across a wide range of leading Earth System Models and components. This included the National Center for Atmospheric Research Community Land Model in the United States, the Norwegian Earth System Model, the PISCES biogeochemical model used within NEMO by multiple European and international modelling centres, and the Model of Oceanic Pelagic Stoichiometry at GEOMAR in Germany.  

The code was made openly available via Zenodo, enabling uptake and adaptation by the international modelling community, with early take up reported across modelling centres in Spain, the USA, Norway, Japan and Germany, reinforcing its potential as a shared methodological advance for reducing the time and cost of producing the climate projections policymakers rely on. An andacc user guide was developed to help practitioners implement the algorithm in their own workflows. 

  “Policymakers rely on climate projections to inform negotiations as the world tries to meet the Paris Agreement. This work is a step towards reducing the time it takes to produce those critical climate projections.” (Professor Helene Hewitt OBE, Co chair of the CMIP Panel) 

the challenge

Ecological restoration is central to achieving climate targets. However, progress is constrained by major uncertainties in how land use and management practices affect greenhouse gas fluxes. For many UK habitats, it is unclear whether a site is a net source or sink of emissions. 

This creates a significant barrier for decision-makers. Stakeholders such as DAERA and The Wildlife Trusts must develop climate strategies and emissions inventories despite limited and inconsistent evidence. Uncertainty around emissions factors makes it difficult to justify choices, assess trade-offs, and meet policy requirements, including those related to biogenic methane. 

These gaps also affect public confidence. Communities may question land-use changes where local impacts are clear, but climate benefits remain uncertain. 

the Solutions

The Sprint delivered a set of practical outputs to reduce uncertainty and strengthen the evidence base for decision-making. The work focused on producing robust, policy-relevant analysis that could be directly used by DAERA and The Wildlife Trusts. 

The team delivered the following: 

  • A set of methane emissions scenarios for Northern Ireland, based on historical National Inventory data aligned with DAERA projections, to support development of the Climate Action Plan.  
  • Updated emissions estimates and uncertainty ranges for key habitats, including managed peatlands, to support The Wildlife Trusts’ greenhouse gas accounting.  
  • A methodological framework enabling The Wildlife Trusts to update emissions estimates using a consistent and transparent protocol.  
  • Updated analysis using newly available land-use emissions data to ensure outputs remained robust and policy-ready.  
  • Two academic papers demonstrating the importance of explicitly accounting for uncertainty in emissions estimates.  

These outputs improved the quality, transparency, and usability of emissions data for policy and practice. They also ensured that the evidence base remained current and aligned with evolving scientific understanding.

the pathways

The Sprint was designed to strengthen the evidence base underpinning decisions on ecological restoration and land management. It focused on improving how uncertainty is quantified, communicated, and used in decision-making. 

The work applied robust statistical modelling to key emission sources across grassland, peatland, and agricultural systems. This enabled stakeholders to better understand what is known, what remains uncertain, and how those uncertainties affect practical choices. 

Close collaboration with DAERA and The Wildlife Trusts ensured that outputs were directly usable and aligned with policy needs. The work was shaped by requirements under the Climate Change Act (Northern Ireland), including the need to account for biogenic methane and emissions uncertainty within Climate Action Plans. 

The Sprint also remained responsive to a changing policy context. As priorities evolved, particularly in Northern Ireland, the work adapted to focus more directly on agricultural emissions and their role within Climate Action planning. This ensured that the research remained timely, relevant, and grounded in real decision-making processes.

What happened next

Following the Sprint, the research was translated into accessible  outputs for immediate use by policymakers and practitioners. Using Northern Ireland as a case study, the work provided robust evidence on emissions across key land-use systems and clarified the implications of using different greenhouse gas metrics. 

The outputs were designed to feed directly into policy processes. They supported the development of DAERA’s Draft Climate Action Plan and The Wildlife Trusts’ Net Zero strategy. 

The Sprint also led to wider engagement and uptake. DAERA used the analysis to support its first assessment of agricultural mitigation using multiple greenhouse gas metrics, helping to meet statutory requirements under the Climate Change Act (Northern Ireland) 2022. The Wildlife Trusts used the findings to strengthen emissions estimates across large areas of peatland and to inform engagement with UK policymakers.