Computer models that use artificial intelligence (AI) cannot forecast record-breaking weather as well as traditional climate models, according to a new study.
It is well established that AI climate models have surpassed traditional, physics-based climate models for some aspects of weather forecasting.
However, new research published in Science Advances finds that AI models still “underperform” in forecasting record-breaking extreme weather events.
The authors tested how well both AI and traditional weather models could simulate thousands of record-breaking hot, cold and windy events that were recorded in 2018 and 2020.
They find that AI models underestimate both the frequency and intensity of record-breaking events.
A study author tells Carbon Brief that the analysis is a “warning shot” against replacing traditional models with AI models for weather forecasting “too quickly”.
AI weather forecasts
Extreme weather events, such as floods, heatwaves and storms, drive hundreds of billions of dollars in damages every year through the destruction of cropland, impacts on infrastructure and the loss of human life.
Many governments have developed early warning systems to prepare the general public and mobilise disaster response teams for imminent extreme weather events. These systems have been shown to minimise damages and save lives.
For decades, scientists have used numerical weather prediction models to simulate the weather days, or weeks, in advance.
These models rely on a series of complex equations that reproduce processes in the atmosphere and ocean. The equations are rooted in fundamental laws of physics, based on decades of research by climate scientists. As a result, these models are referred to as “physics-based” models.
However, AI-based climate models are gaining popularity as an alternative for weather forecasting.
Instead of using physics, these models use a statistical approach. Scientists present AI models with a large batch of historical weather data, known as training data, which teaches the model to recognise patterns and make predictions.
To produce a new forecast, the AI model draws on this bank of knowledge and follows the patterns that it knows.
There are many advantages to AI weather forecasts. For example, they use less computing power than physics-based models, because they do not have to run thousands of mathematical equations.
Furthermore, many AI models have been found to perform better than traditional physics-based models at weather forecasts.
However, these models also have drawbacks.
Study author Prof Sebastian Engelke, a professor at the research institute for statistics and information science at the University of Geneva, tells Carbon Brief that AI models “depend strongly on the training data” and are “relatively constrained to the range of this dataset”.
In other words, AI models struggle to simulate brand new weather patterns, instead tending forecast events of a similar strength to those seen before. As a result, it is unclear whether AI models can simulate unprecedented, record-breaking extreme events that, by definition, have never been seen before.
Record-breaking extremes
Extreme weather events are becoming more intense and frequent as the climate warms. Record-shattering extremes – those that break existing records by large margins – are also becoming more regular.
For example, during a 2021 heatwave in north-western US and Canada, local temperature records were broken by up to 5C. According to one study, the heatwave would have been “impossible” without human-caused climate change.
The new study explores how accurately AI and physics-based models can forecast such record-breaking extremes.
First, the authors identified every heat, cold and wind event in 2018 and 2020 that broke a record previously set between 1979 and 2017. (They chose these years due to data availability.) The authors use ERA5 reanalysis data to identify these records.
This produced a large sample size of record-breaking events. For the year 2020, the authors identified around 160,000 heat, 33,000 cold and 53,000 wind records, spread across different seasons and world regions.
For their traditional, physics-based model, the authors selected the High RESolution forecast model from the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts. This is “widely considered as the leading physics-based numerical weather prediction model”, according to the paper.
They also selected three “leading” AI weather models – the GraphCast model from Google Deepmind, Pangu-Weather developed by Huawei Cloud and the Fuxi model, developed by a team from Shanghai.
The authors then assessed how accurately each model could forecast the extremes observed in the year 2020.
Dr Zhongwei Zhang is the lead author on the study and a researcher at Karlsruhe Institute of Technology. He tells Carbon Brief that many AI weather forecast models were built for “general weather conditions”, as they use all historical weather data to train the models. Meanwhile, forecasting extremes is considered a “secondary task” by the models.
The authors explored a range of different “lead times” – in other words, how far into the future the model is forecasting. For example, a lead time of two days could mean the model uses the weather conditions at midnight on 1 January to simulate weather conditions at midnight on 3 January.
The plot below shows how accurately the models forecasted all extreme events (left) and heat extremes (right) under different lead times. This is measured using “root mean square error” – a metric of how accurate a model is, where a lower value indicates lower error and higher accuracy.
The chart on the left shows how two of the AI models (blue and green) performed better than the physics-based model (black) when forecasting all weather across the year 2020.
However, the chart on the right illustrates how the physics-based model (black) performed better than all three AI models (blue, red and green) when it came to forecasting heat extremes.
The authors note that the performance gap between AI and physics-based models is widest for lower lead times, indicating that AI models have greater difficulty making predictions in the near future.
They find similar results for cold and wind records.
In addition, the authors find that AI models generally “underpredict” temperature during heat records and “overpredict” during cold records.
The study finds that the larger the margin that the record is broken by, the less well the AI model predicts the intensity of the event.
‘Warning shot’
Study author Prof Erich Fischer is a climate scientist at ETH Zurich and a Carbon Brief contributing editor. He tells Carbon Brief that the result is “not unexpected”.
He adds that the analysis is a “warning shot” against replacing traditional models with AI models for weather forecasting “too quickly”.
The analysis, he continues, is a “warning shot” against replacing traditional models with AI models for weather forecasting “too quickly”.
AI models are likely to continue to improve, but scientists should “not yet” fully replace traditional forecasting models with AI ones, according to Fischer.
He explains that accurate forecasts are “most needed” in the runup to potential record-breaking extremes, because they are the trigger for early warning systems that help minimise damages caused by extreme weather.
Leonardo Olivetti is a PhD student at Uppsala University, who has published work on AI weather forecasting and was not involved in the study.
He tells Carbon Brief that “many other studies” have identified issues with using AI models for “extremes”, but this paper is novel for its specific focus on extremes.
Olivetti notes that AI models are already used alongside physics-based models at “some of the major weather forecasting centres around the world”. However, the study results suggest “caution against relying too heavily on these [AI] models”, he says.
Prof Martin Schultz, a professor in computational earth system science at the University of Cologne who was not involved in the study, tells Carbon Brief that the results of the analysis are “very interesting, but not too surprising”.
He adds that the study “justifies the continued use of classical numerical weather models in operational forecasts, in spite of their tremendous computational costs”.
Advances in forecasting
The field of AI weather forecasting is evolving rapidly.
Olivetti notes that the three AI models tested in the study are an “older generation” of AI models. In the last two years, newer “probabilistic” forecast models have emerged that “claim to better capture extremes”, he explains.
The three AI models used in the analysis are “deterministic”, meaning that they only simulate one possible future outcome.
In contrast, study author Engelke tells Carbon Brief that probabilistic models “create several possible future states of the weather” and are therefore more likely to capture record-breaking extremes.
Engelke says it is “important” to evaluate the newer generation of models for their ability to forecast weather extremes.
He adds that this paper has set out a “protocol” for testing the ability of AI models to predict unprecedented extreme events, which he hopes other researchers will go on to use.
The study says that another “promising direction” for future research is to develop models that combine aspects of traditional, physics-based weather forecasts with AI models.
Engelke says this approach would be “best of both worlds”, as it would combine the ability of physics-based models to simulate record-breaking weather with the computational efficiency of AI models.
Dr Kyle Hilburn, a research scientist at Colorado State University, notes that the study does not address extreme rainfall, which he says “presents challenges for both modelling and observing”. This, he says, is an “important” area for future research.
The post Traditional models still ‘outperform AI’ for extreme weather forecasts appeared first on Carbon Brief.
Traditional models still ‘outperform AI’ for extreme weather forecasts
The UK is pioneering the use of “super batteries” that can store energy for long periods, smoothing the output from wind and solar power as the country moves towards net-zero.
It is aiming to build “long-duration energy storage” (LDES) that fills up when supplies are plentiful, to help cover the gaps when the wind does not blow and the sun does not shine.
These periods can last for days or even weeks – sometimes referred to as “dunkelflaute”, a German word meaning “dark doldrums” – whereas the current batteries on the electricity system only last a matter of hours.
The nation’s energy regulator Ofgem has now identified 16 LDES projects that it is “minded to” support under a new “cap-and-floor scheme”.
The technologies selected can be used to store energy for long periods in the form of gravity, chemical processes or electrical charge.
These include pumped hydro, which has dominated long-term storage in the past, through to large lithium-ion batteries, “flow batteries” with novel chemistry and compressed-air storage.
The use of these technologies is expected to cut energy system costs in the UK by more than £24bn between 2030 and 2050.
This Q&A looks at what LDES means and where it can come from, why it is needed and what the UK and others are doing to support its use.
What is LDES?
LDES is a broad category of technologies, with some variation in definition.
The UK government defines it as technologies that can store energy for anywhere from four hours up to years. Ofgem uses a slightly different threshold of eight hours and upwards.
Sir Chris Llewellyn Smith, emeritus professor of physics at the University of Oxford and lead author of a Royal Society report on large-scale electricity storage, tells Carbon Brief:
“[The Department of Energy Storage and Net-Zero] (DESNZ) seems to describe it as including things which we would regard as some short duration or medium duration [storage]. It’s a big confusion…For us, long duration is stuff that can last not just into seasons, but into years and into decades.”
LDES can be used to support several different aspects of the electricity system, including the integration of variable renewable energy.
Currently in the UK, there is 2.8 gigawatts (GW) of LDES, made up of four pumped-hydro energy storage assets in Scotland and Wales.
(This article refers to the UK throughout, but strictly relates to the island of Great Britain made up of England, Scotland and Wales. Northern Ireland is part of the separate all-Ireland electricity system.)
The largest of these existing sites is the Dinorwig power station in North Wales, sometimes referred to as the “electric mountain”. This is a 1,728 megawatt (MW) station opened in the 1980s, which is used to manage short-term surges in electricity demand.
For example, during England’s football World Cup match against the Democratic Republic of Congo on 1 July 2026, electricity demand rose by around 1.2GW at half-time and 1.7GW at full-time. This is equivalent to the total electricity demand for the cities of Glasgow and Leeds, combined.
Pumped storage, alongside batteries, has been used to keep the electricity system balanced during such moments by providing enough electricity to keep the system secure very quickly.
As the UK’s electricity system becomes increasingly dominated by variable renewables, however, the need for LDES to manage peaks and troughs of generation is growing.
George Martin, principal for power system modelling at analytics company LCP Delta, tells Carbon Brief that wind power creates a particular need for LDES. He says:
“[LDES is] really important for the system, particularly in a wind-driven system. You get more peaks and troughs in your renewable output and, [while] short duration [storage] can obviously help with that, with things like ‘dunkelflaute’, long-duration storage is what is needed.”
As such, the UK is working to expand the capacity and duration of storage available through LDES, as well as the range of technologies this system is based on.
For example, in May 2026 the UK’s largest vanadium “flow battery” site opened, co-located with a 3MW solar farm in Uckfield, East Sussex. (A flow battery stores energy in liquid chemical mixtures that are pumped between tanks, via an electrochemical cell.)
The Uckfield site consists of 90 vanadium flow batteries, which can be used to store 21 megawatt-hours (MWh) of electricity. This is equivalent to seven hours of peak output from the attached solar farm and is roughly enough electricity to power 3,000 homes for a day.
The batteries can be used to store surplus daytime solar generation, which can then be used in the evening and overnight.
Other LDES technologies with a longer storage capacity could be used to similarly help manage power supply and demand, but over weeks, months or seasons. This could include compressed-air energy storage, hydrogen storage and others.
The diversity of LDES technologies reflects the range of roles it is expected to play in the electricity system in the UK. This could be meeting short-term surges, helping to utilise surplus renewable energy generation or providing longer-term flexibility.
What types of LDES are available?
There are numerous types of energy storage technology, although most fall into four main categories: mechanical; thermal; chemical; and electrochemical.
For example, a pumped-hydro project uses surplus energy to pump water uphill to a reservoir. The mechanical energy is released when the water flows down through a turbine.
Thermal storage could be a tank of gravel that is heated up, then later used to warm up water. Electrochemical storage is familiar in the form of batteries.
Finally, chemical storage relates to energy stored in molecular bonds, for example, making hydrogen from water. (Similarly, the energy in fossil fuels, which is ultimately derived from the sun, is a form of chemical storage.)
A key consideration for each LDES technology is the amount of energy it can store, measured in watt-hours (Wh). For example, a 1MW battery with four hours of storage contains 4MWh of electricity. It can therefore be used to deliver 1MW continuously for up to four hours.
Another consideration is whether the energy can be stored for long periods before use – and whether it is economic to do so.
In recent years in the UK, battery energy storage – predominantly lithium-ion batteries with a duration of one to four hours – has dominated the storage sector. The lithium battery sector in the UK has grown from almost nothing in 2015 to more than 6GW today.
However, as lithium-ion batteries have only tended to hold a few hours of storage, they cannot help support the grid during longer periods of low renewable energy generation.
Technologies such as vanadium-redox flow batteries, compressed-air energy storage or hydrogen salt-cavern storage could potentially help manage supply and demand over days, weeks or even years.
A range of LDES technology options are shown in the table below.
| Technology | Type | Duration | How does it work? |
| Gravity storage | Mechanical | Hours | A heavy object is lifted, storing kinetic energy that can be turned back into electrical energy by a generator. |
| Lithium-ion batteries | Electrochemical | Hours | Lithium ions move between a negative anode and a positive cathode through an electrolyte within the battery. |
| Liquid air | Mechanical | Hours to days | Air is compressed and cooled until it becomes a liquid. When the air becomes a gas again, it drives a turbine. |
| Vanadium flow | Electrochemical | Hours to days | Liquid chemical mixtures are pumped between tanks, via an electrochemical cell. |
| Compressed air | Mechanical | Hours to days | Air is compressed to a high pressure and stored in underground geological formations, such as salt caverns or disused oil and gas wells. |
| Pumped hydro | Mechanical | Hours to days | Water is pumped up a hill to a reservoir and then released to drive a turbine. |
| Hydrogen salt cavern storage | Chemical | Seasons | Surplus energy is used to make hydrogen from water. The hydrogen is then stored in underground salt caverns, before being burned as fuel. |
| Thermal energy storage | Thermal | Seasons | A material such as gravel is heated with surplus energy and kept in an insulated store, before being used to warm water. |
Each option has specific advantages and disadvantages; for example, while pumped hydro storage has a high upfront cost, it has a long lifespan of over 50 years. As such, its capital cost per kilowatt hour (kWh) is lower than many other storage options over time.
(Pumped hydro is the most established LDES technology in the world, but no new projects have been built in the UK since the 1980s.)
While it has historically been a short-duration form of storage, some lithium-ion batteries can now store power for much longer chunks of time.
Lithium-based grid batteries now often offer 8-12 hours of storage and – as shown in the table above – even longer durations are possible
As Ed Porter, director for Europe at data company Modo Energy, quipped on LinkedIn following the cap-and-floor scheme results:
“Lithium [is] going far beyond 8 hours; that debate must surely be dead now.”
While even 12 hours is of limited use for gaps in generation of days, weeks or seasons, there are numerous benefits to lithium-ion batteries in comparison to other LDES technologies. For example, the cost of these batteries has fallen by an average of 20% per year over the last decade.
Given the variation in technologies – including scale, lifespan, commercial readiness and aspects such as necessary geography – comparing the costs of each technology is challenging.
However, utilising a diverse set of storage technologies is expected to be particularly beneficial for electricity systems, according to experts.
Julia Souder, CEO of industry group the LDES Council, tells Carbon Brief:
“The UK is leading the charge on technology diversity. We’re witnessing matching different LDES solutions to the real differences in market structure and country needs.
“But make no mistake: a handful of LDES technologies will do the heavy lifting over the next decade. We’re seeing that play out in which technologies are winning through the UK government’s new cap-and-floor mechanism for long duration storage.”
How much LDES will the UK need?
LDES is expected to be a key component of the UK’s electricity system in the future, particularly as it moves away from easily stored and dispatched fossil fuels such as gas.
The government has set a target of “clean power by 2030”, in the lead-up to the wider net-zero by 2050 goal.
In 2024, the Labour administration set out an “action plan” for reaching the 2030 target, which included substantial increases to electricity generation technologies.
This included setting widely discussed targets to double offshore wind, triple onshore wind and quadruple solar capacity by 2030, alongside rebuilding the UK’s nuclear fleet.
But the action plan also set a less well-known target for 4-6GW of LDES, to help balance this renewables-dominated electricity mix. This is in addition to 23-27GW of short-duration battery energy storage, new interconnectors and a big push to develop consumer-led flexibility.
There is also a major expansion of LDES to 3.8-5.3GW by 2030 in the most recent “future energy scenarios” report from the National Electricity System Operator (Neso), as shown in the chart below.
Neso’s pathways show LDES rising to between 16.6GW and 13.2GW by 2050, mainly dependent on how hydrogen is used in the electricity system.
The Neso report notes that few LDES schemes are likely to come online before 2030, due to the long project development and planning times, as well as high capital expenditures.
Which types of LDES is the UK planning to use?
While the UK is pursuing a diverse range of LDES, certain technologies are likely to make up the bulk of LDES in the next decade or so.
This is evident in the technologies that have bid successfully into the UK government’s new “cap-and-floor” mechanism for LDES.
The scheme was first announced in 2024 and is designed to guarantee a minimum level of revenue for energy storage operators – the “floor” – as well as to put a limit on profits via the “cap”.
(The mechanism will be funded through electricity bills. However, Ofgem expects it to be broadly cost-neutral over time.)
Similar mechanisms have been used to support the development of other technologies in the UK, in particular those with high upfront costs, such as interconnectors. Ultimately, it minimises the risk for developers by guaranteeing a certain level of future revenue.
In 2025, 171 LDES projects with a total capacity of 52.6GW applied to enter the cap and floor scheme, which is administered by Ofgem. Of these, 77 projects (28.7GW) were deemed eligible to enter a second “assessment” phase.
These were made up of nine different technologies, as shown in the figure below. However, lithium-ion batteries dominated the process, making up more than 20GW of the 29GW total.
No pure vanadium-flow batteries, liquid-air energy storage, iron-air batteries, sodium-sulphur batteries or hydrogen batteries were deemed eligible for the second phase.
(Conventional hydrogen storage was not eligible to bid into the process either, but could be supported through other means. The government is expected to release an updated hydrogen strategy later in 2026.)
Ultimately, Ofgem announced in June 2026 that it was “minded to” support 7.6GW of LDES capacity, spread across 16 projects. Of this total, 4GW is expected to be online by the end of the decade, at the bottom end of the range said to be required for the clean power 2030 target.
The 16 projects are listed in the table below. They comprise four technologies: pumped storage hydro (3.9GW); lithium batteries (3.6GW); one vanadium-zinc flow battery (65MW); and one compressed- air energy storage site (50MW).
| Name | Technology | Region | Capacity (MW) | Duration (hours) | Storage capacity (MWh) |
| Earba PSH | Pumped storage hydro | North Scotland | 1,800 | 15 | 27,000 |
| Coire Glas | Pumped storage hydro | North Scotland | 1,440 | 32 | 46,100 |
| Loch Kemp Storage | Pumped storage hydro | North Scotland | 660 | 22 | 14,500 |
| East Claydon Storage | Lithium battery | East England | 500 | 12 | 6,000 |
| Sundon Storage | Lithium battery | East England | 500 | 8 | 4,000 |
| Field Netherton | Lithium battery | North Scotland | 400 | 16 | 6,400 |
| Field New Deer | Lithium battery | North Scotland | 400 | 18 | 7,200 |
| Field Lond Stratton | Lithium battery | East England | 400 | 16 | 6,400 |
| Springwell | Lithium battery | East Midlands | 400 | 11 | 4,400 |
| Drakelow (Innova) | Lithium battery | West Midlands | 385 | 9 | 3,500 |
| Field Rigifa | Lithium battery | North Scotland | 200 | 18 | 3,600 |
| Field Fyrish | Lithium battery | North Scotland | 200 | 17 | 3,400 |
| Ocker Hill BESS | Lithium battery | West Midlands | 145 | 8 | 1,200 |
| Thornton BESS 2 | Lithium battery | East Midlands | 100 | 11 | 1,100 |
| Frontier Legacy | Vanadium-zinc flow battery | North Wales | 65 | 8 | 500 |
| TeesCAES | Compressed air | North-east England | 50 | 30 | 1,500 |
Welcoming Ofgem’s initial decision on the cap-and-floor mechanism, energy minister Michael Shanks said in a statement:
“Forty years after the country’s last pumped storage facility, this government is getting Britain building again…
“We are [going] further and faster in delivering the clean-power mission by rolling out a new generation of pumped-hydro storage and state-of-the-art batteries – making more of the clean, homegrown power we already produce, cutting waste, lowering bills and strengthening our energy security.”
Collectively, the provisionally successful projects can provide between eight and 32 hours’ worth of electricity storage. The top ten projects in terms of duration that applied for the mechanism – those with at least 12 hours’ worth of storage – all moved forward.
Following Ofgem’s “minded-to” decision, the regulator launched a consultation that ended on 7 August 2026. It will now make a final decision on the projects that will be supported through the “cap and floor” mechanism.
Martin tells Carbon Brief that “it’s not over” yet, with Ofgem likely to face scrutiny over the methodology it used to determine these final results. He adds:
“There’s going to be a lot of activity and a lot of responses to that consultation. I don’t expect the overall amount of capacity that’s been awarded to change, although they could increase it – it could only go up, probably.
“But there might be some change in what projects end up getting approved as a result, or maybe they end up making some changes for the next window [of applications for LDES support].”
Alongside the cap-and-floor process being run by Ofgem, the government introduced legislation via the Planning and Infrastructure Act to support the introduction of the scheme.
Additionally, in August 2026, Innovate UK – the UK’s national innovation agency – announced new funding for “ultra-long” duration battery energy storage.
Up to £3m will be invested in demonstration projects as part of the first phase of the funding, with £10m available in the sector to support the development of technologies capable of storing and discharging at least 100 continuous hours of electricity.
In a statement responding to the new funding, Dr Jamie Speirs of the University of Strathclyde and co-director of the UK Energy Research Centre, said achieving the UK’s low-carbon ambitions will rely on “unlocking” LDES to support a highly renewable system. He added:
“By providing flexibility across hours, days and even seasons, LDES could enable a resilient, low-carbon electricity system – reducing curtailment, strengthening security of supply and ensuring that intermittent renewables can maximise their contribution to the grid in all conditions.
“Investing in innovation opportunities such as this call to support market deployment of LDES technologies is a key way to support these technologies to market, giving us the best chance to meet our net zero targets.”
Phase one of the funding is open for applications until 30 September, with grants of between £350,000 and £700,000 available for the successful projects.
Seamus Garvey, professor of dynamics at the University of Nottingham, welcomes the new funding. However, he cautions that more needs to be done to ensure the future markets for medium- and long- duration storage are not compromised by early commitments to storage at shorter timescales. He tells Carbon Brief:
“Energy storage will be required over many timescales and as we decarbonise further and further, the requirements for longer durations grow and grow.
“One key problem in my opinion is that because we are tending to buy into lots of short-duration stores now, we are actually removing pieces of market that could be accessible by longer duration stores and that is making the (already-difficult) problem of financing these stores ever more difficult.”
How could LDES impact energy bills?
The rollout of LDES technologies is widely expected to help reduce energy bills as the UK transitions to a clean-energy system.
There is still a significant amount of uncertainty over the development of LDES, due to the wide range of options, nascent stages of development and lack of market maturity. Nevertheless, most research agrees that it will cut electricity system costs by the middle of the century, relative to a world where LDES is not used.
For example, adding 20GW of LDES could reduce electricity system costs by £16-51bn between 2030 and 2050, compared with a scenario that has limited flexible capacity, according to analysis for the Department for Energy Security and Net Zero (DESNZ), by thinktank Regen and LCP Delta. The analysis, published in 2023, found that 20GW of LDES could reduce costs by around £26bn.
Analysis by LCP Delta in 2025 found that building 20GW of established medium-sized LDES technologies – pumped hydro with a capacity of 8-12 hours – by 2050 would have a system benefit of more than £10bn.
LDES could reduce total UK electricity system costs by £7-13bn annually by 2040-2050, according to a report from the Transition Finance Council – a public-private body launched by the City of London Corporation and the UK government – citing a range of other studies.
The council says this would predominantly be by avoiding “curtailment”, where some generators are paid to switch off because the electricity grid cannot accommodate their output. It says that LDES would defer the need for additional grid investment and would reduce balancing costs, including curtailment.
(In the financial year 2024-25, balancing costs reached £2.7bn, adding around £40 to the average household electricity bill. Some £1.9bn of this – £28 per household – related to constraints, where wind is “curtailed” and gas plants are switched on elsewhere.)
Curtailment is a particular issue in Scotland, where much of the UK’s wind capacity sits behind congested sections of the national electricity network. Porter notes on LinkedIn that this helps explain why 79% of the LDES projects by storage capacity are located in northern Scotland.
Martin says LDES will allow the UK to “use our renewable fleet more efficiently”. He adds:
“[LDES] is able to increase renewable energy and then decrease gas generation during high-demand periods, and that brings all sorts of benefits to the system.
“It reduces emissions, it reduces the overall cost of the system, it can help reduce bills for consumers. So those are the types of benefits that we’ll see as a result of [more] LDES being [on the system].”
The Transition Finance Council report adds that despite the upfront cost, LDES quickly pays for itself. It estimates that each gigawatt of long-duration flexibility on the system requires around £2-2.5bn in investment, but yields annual system savings of £0.5-1bn once operational.
As such, even accounting for the upfront cost of developing LDES, the technologies would provide £30-60bn of electricity system savings over 25 years, the council says. It adds that this means LDES “will repay itself several times over”.
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The post Q&A: What is ‘long-duration energy storage’ – and why does the UK need it? appeared first on Carbon Brief.
Q&A: What is ‘long-duration energy storage’ – and why does the UK need it?
Claver Gatete is Executive Secretary of the UN Economic Commission for Africa. Jason Veysey is Energy Modeling Program Director and Senior Scientist at the Stockholm Environment Institute. Lisa Sachs is Director of the Columbia Center on Sustainable Investment at Columbia University.
The case for global energy transition has rarely been clearer. The closure of the Strait of Hormuz earlier this year exposed the cost of unplanned, fossil-dependent systems, while the falling cost of renewables, the rising penetration of electric vehicles, and the growing value of demand flexibility have made the direction of travel obvious. The benefits of a clean, secure, integrated system are no longer in dispute. What remains unclear is how to build it.
Countries around the world have called for faster renewable energy deployment and alternative energy arrangements. A secure, affordable, resilient, decarbonised system requires specific investments in specific places in a specific sequence, optimised across sectors and borders. But very few governments have the analytical foundation to translate those imperatives into investment.
The two instruments that are supposed to determine investment priorities for decarbonisation – Nationally Determined Contributions (NDCs) and country platforms – cannot answer the most basic question facing any country undertaking an energy transition: what should the energy system look like?
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New coal mine openings slow as East Asian demand plateaus
China and Australia drove global decline in new capacity additions in 2025, but planned production kept growing
To close this gap, every country needs a bankable, economy-wide optimisation model for its energy system. A model is not a plan, but it can help answer the critical question of what the future energy system should look like. It shows how optimal scenarios vary as assumptions and policies are adjusted, calculates investment requirements and sequencing, and quantifies how system costs are affected by assumptions, policies, and exogenous variables like trade policy and financing terms.
Tool for efficient investment
Optimisation is a simplified way of simulating an energy system, but it can be an extremely powerful tool for moving energy planning from reactive (how do we manage the disparate actions in the energy system?) to intentional (what energy system underpins our national objectives?). A model can show how optimal scenarios vary as assumptions and policies are adjusted, and how investment requirements are quantified and sequenced.
Optimisation models can treat the energy system and the sectors it serves as an integrated whole, optimising across sectors and projects in ways that can be mutually reinforcing. If considered independently, growth in industrial demand, transport electrification, and digital infrastructure can add stress to the energy system. But an optimised plan can arrange these and other changes in an efficient, synergistic way.
Two to tango: How governments can unlock private investment for national climate goals
New load can be added where low-cost power is available; industrial customers can ensure the viability of investments in energy supply; electric vehicle charging policy can smooth load curves and reduce costs for all consumers.
Additionally, optimisation modeling can also change the financeability of investments. Taken alone, each project faces uncertainty about the rest of the system, which raises the cost of capital and causes projects to stall or unwind after contracts are signed. A coherent, optimised plan makes visible the coordination that private capital would otherwise have to bet on: identified offtake, sequenced and committed transmission, contracted power supply, and so on.
What COP31 and COP32 should do
The upcoming COPs in Turkey and Ethiopia can shift the center of gravity of international climate cooperation from fragmented commitments to planning. Three moves are urgently needed.
First, optimised, economy-wide, long-term energy system planning must be the foundation on which any meaningful NDC, country platform, or finance commitment rests. NDCs are typically drafted by environment or single-line ministries, with limited cross-sectoral input from ministries of energy, finance, and planning. They contain targets, derived from sectoral strategies or national commitments, not from an analytically grounded picture of what the energy system should look like and what investments would make it work. Country platforms are generally a portfolio of investments assembled from existing project pipelines, rather than derived from a system-level analysis of what an optimised, decarbonised energy system would require.
Second, recognise regions as a key planning unit. Modern integrated energy systems are inherently regional. Renewable endowments are unevenly distributed; balancing variable supply across borders lowers aggregate cost, reduces redundant backup capacity, and unlocks economies of scale no individual nation can achieve. Many energy investments in Southeast Asia, East Africa, Southern Africa and Central Asia may only be financeable in a regional context. Assessing domestic infrastructure without regional optimisation perpetuates the perception that decarbonisation is more expensive than it is.
COP31 leaders unveil global targets, with spotlight on electrification
Third, finance the planning capacity. A coordinated commitment by multilateral development banks, bilateral donors, and philanthropic partners to help every region and its constituent countries develop and maintain their own modelling capability, with open-source tools and regional analytical hubs, would close the most consequential gap in the current architecture. The cost is small relative to current spending on country platforms, failed project preparation, and misallocated infrastructure investment.
This includes supporting regional institutions such as the ASEAN Centre for Energy, the African Energy Commission, regional power pools, and the Latin American and Caribbean Energy Organization to determine what optimised regional systems require. Country-by-country pledging, repeated at every COP, will not deliver what meaningfully integrated systems can.
The 2026 energy crisis made the cost of unplanned, fossil-dependent systems newly visible. That window of clarity will close. The international community should seize the moment to build the planning foundation that has been missing for thirty years, rather than commissioning another round of NDCs or pledges, striving for outcomes neither was designed to deliver.
The post Every country needs a model to help optimise its energy transition appeared first on Climate Home News.
Every country needs a model to help optimise its energy transition
The world is currently experiencing what is expected to become the strongest El Niño on record – dubbed a “super El Niño” by many.
El Niño is the warm phase of a recurring climate pattern in the tropical Pacific that releases heat from the ocean into the atmosphere.
This temporarily raises global temperatures and reshapes rainfall and extreme weather around the world – impacting the lives of billions of people.
The current El Niño event began in June and is expected to last into 2027.
El Niño is part of a wider climate pattern called the El Niño-Southern Oscillation (ENSO) cycle.
The ENSO cycle also has a cool phase, known as La Niña, as well as a “neutral” phase. El Niño and La Niña events typically last between nine and 12 months, but can go on longer.
Below, Carbon Brief explains how the ENSO cycle works, its impacts on extreme weather and global temperatures and why this El Niño event is projected to be the most intense since records began.
The post Explainer: How the ‘super El Niño’ will reshape the world’s weather appeared first on Carbon Brief.
https://interactive.carbonbrief.org/el-nino-explainer/index.html
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