Top 10: Climate Modelling Frameworks

Climate modelling frameworks are essential tools for understanding and predicting Earth’s complex climate system. 

Ranging from simple energy balance calculations to sophisticated Earth system simulations, climate modelling frameworks can allow organisations to predict, maintain and mitigate risks both for the benefits of business as well as the planet.

According to the World Climate Research Programme, more than 40 major climate models are currently in use worldwide. 

Here are 10 examples of climate modelling frameworks and how some of the world’s largest companies are leveraging the insights they provide.

10. Earth system modelling

Used by: IBM

IBM leverages AI into weather and climate applications, such as storm tracking, forecasting and historical analysis. By using four decades of NASA weather data, IBM has developed a new AI foundation model called Prithvi-weather-climate, named as such as Prithvi is the Sanskrit name for Earth.

“This model is part of our overall strategy to openly and collaboratively develop a family of AI foundation models to support NASA’s science mission goals,” said IMPACT Manager Dr Rahul Ramachandran. “These models will augment our capabilities to draw insights from our vast archives of Earth observations.”

9. Regional climate modelling

Used by: Microsoft

Bringing things back down to earth (a bit), Microsoft’s ClimaX is the first foundation model for weather and climate. Trained using several heterogeneous datasets spanning a variety of weather variables at multiple spatio-temporal resolutions, ClimatX shows that foundational models can be fine-tuned to address a wide variety of climate and weather tasks. These applications include scenarios that involve atmospheric variables and spatio-temporal granularities unseen during pretraining. 

Microsoft has listed the foundation model on its Open Source platform, making it readily available and easy to apply elsewhere.

“We are excited to release ClimaX with the aim of furthering data-driven weather and climate modelling,” Microsoft said. “Our goal is to allow anyone to easily use the latest Machine Learning methods to address multitude of problems, ranging from near-term prediction at a local scale to modelling long-term processes that involve weather and climate variables.

“ClimaX takes a big step forward towards the idea of a single starting point for a variety of such tasks. We can’t wait to see what the future holds for this emerging field.”

8. Urban climate modelling

Used by: Siemens

Siemens utilises climate modelling and digital twin technology to help design more sustainable cities. It is using end-to-end digital twin technology and AI to design a sustainable city district called Siemensstadt Square in Berlin with Siemens Xcelerator.

This solution has been used to integrate all aspects of one district of the development, including campus, buildings and energy, which will include Europe’s largest wastewater heat exchanger of its type, to enable net zero.

“Digital technologies from the entire Siemens Xcelerator platform – from an end-to-end digital twin to artificial intelligence (AI) – will make the district liveable and fit for the future,” Siemens said.

7. Agriculture climate models

Used by: Bayer

FieldView is the flagship product of Climate LLC, Bayer’s digital farming arm.

“We focus on providing a best-in-class digital experience for our customers and their input is what prioritises our work in FieldView,” Brandon Rinkenberger, Chief Customer Officer for Climate LLC and Digital Farming at Bayer said.

Thanks to FieldView’s analytical capabilities, users are able to unlock farm data to drive field and farm performance. 

Bayer says FieldView can:

• Improve crop performance
• Target inputs more precisely
• Identify and resolve problem areas
• Make informed decisions quicker
• Connect, synchronise and share data
• Plan sustainable solutions
• Manage and evaluate agronomic, product and variety trials

6. Ocean modelling framework

Used by: Fugro

Taking climate modelling out to sea, Fugro’s Metocean services unlock insights from geodata for “a safe and liveable world”. Its metocean services provide comprehensive meteorological and oceanographic data collection and analysis for offshore projects, with a focus on supporting renewable energy development and climate resilience efforts.

It does this by gathering high-quality wind, wave and current data which then informs the siting, design and operation of offshore wind farms.

Benefits of metocean predictions from Fugro include ensuring more efficient and sustainable offshore renewable energy projects and accurate site-specific insights on environmental conditions.

5. Renewable energy modelling

Used by: TotalEnergies

TotalEnergies uses renewable energy modelling to support its ambitious transition to a multi-energy company and achieve carbon neutrality ahead of 2050. The company employs advanced modelling techniques to forecast long-term energy demand and supply scenarios, including renewable energy growth. These models inform TotalEnergies’ strategic investments in low-carbon technologies, guiding its expansion into renewable electricity generation, biofuels and other clean energy solutions. 

With Total aiming to produce more than 100TWh of electricity each year by 2030, Total is working to ensure a significant portion comes from renewable sources. This is aided by modelling capabilities, which helps Total optimise the siting and design of renewable energy projects, particularly in offshore wind and solar.

4. Carbon Footprint Modelling

Used by: Amazon/AWS

Amazon’s Customer Carbon Footprint Tool allows customers to track, measure, review and forecast the carbon emissions generated from their AWS usage.

As well as allowing users to measure carbon emissions, advance understanding of what drives carbon footprint and forecasting emissions metrics and goals, the tool helps AWS customers measure and report emissions, as well as track progress and make plans for future carbon emissions reduction.

“As we continue to fulfil our commitment to The Climate Pledge and work toward net-zero carbon emissions, we want to give our customers the data they need to measure their carbon footprint and meet their own carbon reduction goals,” said Nat Sahlstrom, then Director of Amazon Energy said on the tool’s launch.

“The customer carbon footprint tool makes carbon emissions information easy for customers to work with and encourages companies to accelerate their goals, plans and programmes to address the urgency of climate change.”

3. Energy demand modelling

Used by: Google 

Google uses energy demand modelling to optimise its data centre operations and support its ambitious goal of achieving 24/7 carbon-free energy by 2030. The company employs advanced forecasting techniques to predict electricity demand across its global infrastructure, enabling more efficient energy procurement and consumption.

Google’s modelling efforts inform strategic investments in renewable energy projects and guides the development of innovative clean energy solutions. For example, Google’s partnership with NV Energy created a new clean transition rate thanks to this technology.

Overall, Google’s energy demand modelling supports its efforts to improve grid reliability and accelerate the deployment of clean energy technologies.

2. Financial risk modelling

Used by: BlackRock

American multinational investment company BlckRock uses advanced financial risk modelling to assess and manage risks across various asset classes and investment strategies. Its Aladdin Climate model is a prime example of financial risk modeling climate tech.

By combining advanced financial modelling techniques with climate science and data, Aladdin Climate exemplifies how climate tech is being integrated into traditional financial risk modelling to help investors and asset managers understand and mitigate climate-related risks in their portfolios.

Rob Goldstein, BlackRock’s Global COO, said: “There is no single issue that clients ask us more about than the impact of climate risk on their portfolio.”

1. Integrated assessment modelling

Used by: ExxonMobil

ExxonMobil uses integrated assessment modelling (IAM) as a climate tech to analyse energy systems, economic impacts and climate change scenarios.

Exxon’s Outlook for Energy model integrates energy supply and demand projections with climate modelling to forecast long-term energy trends and emissions pathways. This IAM approach allows ExxonMobil to evaluate potential climate policies, technology developments, and market shifts. 

“ExxonMobil is committed to creating sustainable solutions that improve quality of life and meet society’s evolving needs,” the company said. “We intend to do this in ways that help protect people, the environment and the communities where we operate.”

The company's Energy and Carbon Summary reports use IAM outputs to assess climate-related risks and opportunities for their business. ExxonMobil scientists have also contributed to academic IAM studies examining carbon pricing scenarios and emissions reduction pathways.

While providing valuable insights, critics argue ExxonMobil's IAM assumptions may underestimate climate risks and overstate future fossil fuel demand.

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