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Artificial Intelligence for Climate Action

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Abstract

The research paper provides targeted insights on the challenges and opportunities for the deployment of AI for climate research, including: • In the face of the urgent global challenge of climate change, the integration of artificial intelligence (AI) into climate research has emerged as a powerful ally in understanding, mitigating, and adapting to the impacts of a changing climate. The key findings and contributions of AI in climate research can be summarised as follows: ● Enhanced Climate Models: AI has revolutionised climate modeling by improving model resolution, data assimilation, and parameterization. This has led to more accurate climate predictions, enabling better-informed policy decisions and climate adaptation strategies. ● Data-Driven Insights: AI's ability to process vast and complex datasets has facilitated the identification of climate trends, anomalies, and critical factors. Machine learning and data analytics have unearthed valuable insights from historical and real-time climate data. ● Resilient Infrastructure: AI has played a pivotal role in developing resilient infrastructure. Through predictive maintenance and optimisation, AI minimises downtime and resource consumption, reducing the environmental footprint of critical infrastructure. ● Disaster Response and Preparedness: AI-powered early warning systems, disaster response tools, and adaptive solutions have proven essential in minimising the impact of extreme weather events, safeguarding lives, and reducing economic losses. ● Carbon Removal and Renewable Energy: AI applications in carbon removal technologies and renewable energy systems offer promising solutions to reduce greenhouse gas emissions and transition to a sustainable, low-carbon energy future. As the data points clearly demonstrate, the urgency of addressing climate change cannot be overstated: ● In 2021, the Intergovernmental Panel on Climate Change (IPCC) reported that global temperatures have already risen by approximately 1.1°C above pre-industrial levels. ● The consequences of climate change are evident, with increasing frequency and intensity of extreme weather events, rising sea levels, and loss of biodiversity. ● The World Bank estimates that by 2030, the number of people affected by river flooding alone could increase by 114 million due to climate change. ● The cost of climate-related disasters is substantial, with economic losses exceeding $200 billion annually in the United States alone. ● The Paris Agreement, a landmark international treaty aimed at limiting global warming, underscores the global consensus on the need for climate action. The need for continued research and action is paramount: ● AI in climate research is a dynamic field with emerging trends such as quantum computing, explainable AI, and a growing emphasis on AI ethics. ● Interdisciplinary collaboration among climate scientists, computer scientists, policymakers, and businesses is essential for driving innovation, bridging knowledge gaps, and translating research into actionable climate solutions. ● AI's potential to contribute to climate mitigation and adaptation strategies hinges on responsible AI practices, transparency, and accountability. AI ethics guidelines and responsible AI frameworks should be integrated into climate research and policy. In conclusion, AI is a pivotal tool in our efforts to combat climate change. It enhances our understanding of the complex climate system, enables more effective climate predictions, and empowers adaptive strategies. The urgency of addressing climate change cannot be overstated, and the need for continued research and action is clear. With responsible AI practices, interdisciplinary collaboration, and ethical AI applications, we can work together to mitigate the impacts of climate change and transition to a sustainable and resilient future.
Artificial Intelligence
for
Climate Action
Kishan Changlani
Dr Renuka Thakore
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Abstract
The accelerating impacts of climate change pose a global crisis
that demands immediate and innovative solutions. This paper
explores the pivotal role of artificial intelligence (AI) in addressing
climate change through mitigation and adaptation strategies. AI,
with its data analytics, machine learning, and modelling
capabilities, has emerged as a powerful tool in climate research.
By analysing vast datasets, improving climate models, and
facilitating adaptive measures, AI is revolutionising our capacity
to combat climate change. In this paper, we delve into the
various facets of AI's contributions to climate research and
discuss the challenges, limitations, and prospects of this
interdisciplinary field.
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Acknowledgment
Kishan nominated by the Local government and municipal
authorities (LGMA) and Renuka nominated by the Research and
Independent NGOs (RINGO) attended the 27th Technology
Executive Committee (TEC) meeting on 18-21 September 2023
and the Joint session of the Technology Executive Committee
(TEC) and Climate Technology Centre & Network (CTCN)
Advisory Board Meeting on 22 September 2023, in Bonn,
Germany. The AI for climate theme garnered significant attention
during these meetings, inspiring Kishan and Renuka to contribute
further to the AI for Climate agenda.
They express gratitude to LGMA and RINGO for their support,
and the Global Sustainable Futures Progress through
Partnerships, a global networking platform for capacity
development for leadership in Sustainability and SDGs and also
World Climate Athletes, an initiative mobilizing athletes for a
sustainable World.
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Introduction
Climate change, driven primarily by human activities such as the burning
of fossil fuels, deforestation, and industrial processes, is an existential
threat to the planet. Its repercussions manifest in rising global
temperatures, extreme weather events, sea-level rise, and disruptions to
ecosystems. Mitigating these effects and adapting to an altered climate
landscape is paramount. To effectively combat climate change, we must
harness the capabilities of AI, which has already demonstrated
significant potential in this endeavour.
This paper seeks to elucidate the symbiotic relationship between AI and
climate research. AI encompasses a spectrum of technologies, including
machine learning, deep learning, natural language processing, and data
analytics, all of which can be deployed to analyse climate data, enhance
predictive models, and inform climate policy. By improving our
understanding of the Earth's climate system and providing insights into
mitigation and adaptation strategies, AI presents a path towards a
sustainable and resilient future.
In the following sections, we will explore the foundational concepts and
the state of AI technology in the context of climate research. We will
delve into the methodologies and applications, such as climate modeling,
data analysis, and adaptation strategies that AI offers. Moreover, this
paper addresses the ethical and environmental concerns that arise from
the increased integration of AI in climate research and the importance of
interdisciplinary collaboration to address these challenges. Finally, we
will outline the emerging trends and future directions in AI for climate
research.
As the global community grapples with the consequences of climate
change, this research paper aims to provide a comprehensive overview
of the potential of AI to mitigate and adapt to the challenges posed by a
changing climate. Through this exploration, we hope to inspire further
research and encourage the integration of AI into climate action agendas
worldwide.
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Key Concepts:
AI (Artificial Intelligence): AI refers to the simulation of human intelligence in machines, enabling
them to perform tasks that typically require human intelligence, such as learning from experience,
reasoning, problem-solving, and understanding natural language. In the context of climate change, AI
technologies, including machine learning and data analytics, are utilised to analyse climate data,
improve climate models, and inform mitigation and adaptation strategies.
Climate Change: Climate change refers to long-term alterations in the Earth's average weather
patterns and conditions. It is driven primarily by human activities, such as the release of greenhouse
gases, leading to global warming, which results in various impacts, including rising temperatures,
sea-level rise, extreme weather events, and disruptions to ecosystems.
Mitigation: Mitigation involves efforts to reduce or prevent the emission of greenhouse gases and
other activities that contribute to climate change. It aims to limit the extent of climate change by
reducing its causes. Mitigation strategies include transitioning to renewable energy sources,
improving energy efficiency, and promoting sustainable land use practices.
Adaptation: Adaptation focuses on preparing for and adjusting to the impacts of climate change that
are already occurring or expected to happen in the future. Adaptation strategies aim to increase
resilience to climate change, protect communities and ecosystems, and minimise damage and loss.
Examples of adaptation measures include building resilient infrastructure, implementing early warning
systems, and altering agricultural practices.
Global Consensus on Climate Change:
There is a near-universal consensus among scientists, international organisations, and governments that
climate change is occurring due to human activities. Key points of this consensus include:
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The Intergovernmental Panel on Climate Change (IPCC) and numerous scientific studies provide
strong evidence that the Earth's climate is warming, and this warming is predominantly driven by
human activities, particularly the burning of fossil fuels and deforestation.
The Paris Agreement, adopted in 2015 under the United Nations Framework Convention on Climate
Change (UNFCCC), is a significant global effort to combat climate change. 196 countries had signed
the agreement, committing to limit global warming to well below 2 degrees Celsius above pre-
industrial levels.
Many governments, institutions, and organisations have set targets to reduce greenhouse gas
emissions and transition to sustainable practices to mitigate climate change. These actions highlight
the global consensus on the urgency of addressing climate change.
Urgency of Taking Action:
The urgency of taking action to address climate change arises from several factors:
Irreversible Impacts: Climate change is causing irreversible and often catastrophic impacts, such as
the loss of biodiversity, sea-level rise, and the increased frequency and intensity of extreme weather
events. Delaying action exacerbates these impacts.
Economic and Societal Costs: The economic and societal costs of climate change, including
damage to infrastructure, reduced agricultural yields, and increased health risks, are substantial.
Addressing climate change is more cost-effective than dealing with its consequences.
Global Equity: Vulnerable communities and countries are disproportionately affected by climate
change, even though they often contribute the least to greenhouse gas emissions. Taking action is a
matter of global equity and justice.
Tipping Points: There is a risk of reaching climate tipping points, where small changes can trigger
significant and potentially irreversible consequences, such as the melting of polar ice sheets.
Moral and Ethical Imperative: Many view addressing climate change as a moral and ethical
imperative to safeguard the well-being of future generations and protect the planet's ecosystems.
In summary, the consensus on climate change, backed by scientific evidence and international agreements, underscores
the urgent need for comprehensive and immediate action to mitigate its impacts and adapt to the changes that are already
underway.
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Application of AI in Climate Research:
Artificial Intelligence (AI) has found a multitude of applications in climate research, enhancing our
understanding of climate systems, predicting changes, and informing mitigation and adaptation strategies.
Some key applications include:
Climate Modeling: AI techniques can improve the accuracy and efficiency of climate models.
Machine learning models can simulate complex interactions within the Earth's climate system,
helping researchers make more reliable climate predictions.
Data Analysis: AI can process and analyse large climate datasets, identifying patterns, trends,
and correlations that might be challenging to uncover through traditional methods.
Extreme Weather Prediction: AI can assist in predicting extreme weather events, such as
hurricanes, droughts, and heatwaves. Early warnings based on AI analysis of meteorological data
can save lives and property.
Climate Impact Assessment: AI can be used to assess the impacts of climate change on
various sectors, including agriculture, water resources, and urban planning. This informs
adaptation strategies.
Emission Reduction: AI helps optimise energy production and consumption, reducing
greenhouse gas emissions. Smart grids, for example, leverage AI to manage energy resources
efficiently.
Machine Learning, Deep Learning, and Data Analytics Techniques:
Machine Learning (ML): ML algorithms enable computers to learn from data and make
predictions or decisions without explicit programming. In climate research, ML is used for climate
pattern recognition, predictive modeling, and data analysis. For instance, ML can predict climate-
related phenomena like El Niño and analyse the impact of aerosols on climate.
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Deep Learning (DL): Deep learning is a subset of ML that involves artificial neural networks with
multiple layers (deep neural networks). DL is instrumental in tasks such as image recognition in
climate science (e.g., identifying cloud patterns) and improving the accuracy of climate models.
Data Analytics: Data analytics techniques help researchers make sense of complex climate
datasets. Statistical methods, data visualisation, and tools like clustering and anomaly detection
are used to extract meaningful insights from climate data. Analytics can uncover trends, assess
climate risks, and support decision-making.
Case Studies of AI Applications in Climate Modeling and Analysis:
IBM's GRAF Model: IBM's Global High-Resolution Atmospheric Forecasting (GRAF) model
uses deep learning to improve short-term weather forecasts. By analysing vast datasets from
various sources, GRAF can provide highly localised and accurate weather predictions,
enhancing our ability to respond to extreme weather events.
Google's Machine Learning for Precipitation Nowcasting: Google has developed a
machine learning model for precipitation nowcasting. The model can predict rainfall patterns at
a high spatial and temporal resolution, aiding in flood prediction and management.
Microsoft's AI for Earth: Microsoft's AI for Earth program supports numerous climate-related
projects. One example is the "LandCoverNet" project, which employs AI to map land cover
changes, helping researchers track deforestation and urban expansion.
Carbon Monitoring for Action (CARMA): CARMA utilises data analytics to monitor global
carbon emissions. It tracks the carbon output of thousands of power plants worldwide,
providing valuable information for climate change mitigation efforts.
The European Centre for Medium-Range Weather Forecasts (ECMWF): The ECMWF uses
machine learning to improve weather forecasts, including predicting the tracks of hurricanes
and cyclones more accurately.
Climate Trace: The Climate Trace use satellites, other remote sensing techniques, and
artificial intelligence to deliver a detailed, independent look at global emissions, and make
meaningful climate action faster and easier by mobilising the global tech community to track
greenhouse gas (GHG) emissions with unprecedented detail and speed and provide this data
freely to the public.
These case studies illustrate how AI, through machine learning, deep learning, and data analytics, is being applied
in climate research to enhance modeling and analysis, ultimately aiding in addressing climate change and its
associated challenges.
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Importance of Data in Climate Research:
Climate research relies on vast datasets gathered from various sources, including satellites,
weather stations, ocean buoys, and remote sensors. This data forms the foundation for
understanding past and present climate conditions, making accurate predictions, and informing
climate policy decisions.
Long-term climate data is crucial for detecting trends and patterns. It helps identify changes in
temperature, precipitation, and other climatic variables, enabling researchers to assess the
impacts of climate change.
Data on greenhouse gas emissions, land use, and deforestation are essential for
understanding the drivers of climate change and formulating strategies for mitigation and
adaptation.
Climate models depend on high-quality data for calibration and validation. Accurate data
improves the reliability of model predictions.
Role of AI in Collecting and Processing Climate Data:
AI plays a critical role in data collection through the analysis of satellite imagery. AI algorithms
can identify and track climate-related phenomena such as hurricanes, wildfires, and melting ice
sheets. They also assist in the interpretation of remote sensing data.
Machine learning techniques can enhance data quality by identifying and correcting errors and
outliers. AI can also help in data fusion, combining information from various sources to create
comprehensive datasets.
Natural language processing can extract valuable climate information from textual sources,
such as scientific publications and climate reports. This aids in keeping datasets up-to-date and
relevant.
AI can automate the data collection process, ensuring real-time monitoring and reporting of
climate-related events. For instance, AI-powered sensors and drones can collect data in remote
or hazardous environments.
Challenges and Potential Solutions:
Data Availability:
Challenge: Climate data is limited in terms of the non-financial entities, including firms,
sovereigns, and households, constituting the majority of financial firms’ exposures to
climate related risks. availability and quality continue to undermine the effective and
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efficient financial pricing of climate risk. Climate data is limited in terms of the forecasting
climate and climate risks in general.
Solution: AI to address outstanding issues related to data gaps and ensure more robust
climate scenarios, forecasts, and disclosures, providing a more complete picture of climate-
related financial risk.
Data Quality and Accuracy:
Challenge: Climate data may suffer from errors, inconsistencies, and biases. Low-quality
data can lead to inaccurate predictions and policy decisions.
Solution: AI can be used to identify and correct errors in climate datasets. Machine
learning algorithms can help detect outliers and inconsistencies. Ongoing data quality
assurance is essential.
Data Volume and Complexity:
Challenge: Climate data is vast and complex, making it challenging to manage and
analyse with traditional methods.
Solution: AI can automate data processing and analysis, making it more efficient. High-
performance computing systems can handle the large volume of climate data.
Data Gaps and Remote Areas:
Challenge: Some regions lack comprehensive data coverage due to inaccessibility or
underdevelopment.
Solution: AI-powered remote sensing technologies, such as satellites and drones, can fill
data gaps in these areas. Collaboration with local communities can enhance data
collection.
Interdisciplinary Collaboration:
Challenge: Climate research often requires expertise from diverse fields, including climate
science, computer science, and data analytics.
Solution: Interdisciplinary collaboration is essential. Establishing partnerships between
climate scientists, AI researchers, and data analysts can drive innovation and address
complex challenges.
Ethical and Privacy Concerns:
Challenge: Data collection can raise concerns about privacy and data security.
Solution: Implement data anonymisation and encryption techniques to protect sensitive
information. Develop clear data governance and ethics policies.
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How AI Enhances Climate Models:
Climate models are complex systems that simulate the Earth's climate, helping researchers understand past climate
patterns and make predictions about future conditions. AI can enhance climate models in several ways:
Improved Resolution: AI, particularly deep learning, can increase the resolution of climate
models. This finer detail allows for more accurate simulations of climate processes, including
atmospheric and oceanic circulation.
Data Assimilation: AI is instrumental in data assimilation, which combines observational data
with model outputs. Machine learning algorithms help integrate vast datasets into climate
models, reducing errors and improving model performance.
Parameterization: AI can refine the parameterization of sub-grid scale processes in climate
models. Machine learning models can learn from observational data and provide more accurate
parameterizations for phenomena like clouds and turbulence.
Faster Computation: AI can optimise the performance of climate models. Quantum computing,
for example, has the potential to dramatically accelerate climate simulations, enabling more
comprehensive and accurate predictions.
AI's Role in Improving Climate Prediction Accuracy:
Advanced Pattern Recognition: Machine learning techniques, such as deep learning, excel in
recognising intricate patterns and relationships in data. AI can identify subtle climate patterns
that might be missed by traditional statistical approaches, resulting in more accurate
predictions.
Data-Driven Calibration: AI can calibrate climate models using observed data, making them
more accurate. By learning from real-world data, machine learning models can fine-tune climate
models to better match observations.
Dynamic Ensemble Models: AI can help create dynamic ensemble models that adjust and
update based on real-time data. This adaptability allows for more precise and timely
predictions, especially in rapidly changing situations, such as extreme weather events.
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Examples of AI-Driven Climate Models:
NOAA's Climate Model Emulator (CME): CME uses machine learning to emulate the
Community Earth System Model (CESM), a complex climate model. CME offers faster and
more efficient climate simulations, facilitating extensive model runs.
The European Centre for Medium-Range Weather Forecasts (ECMWF): ECMWF employs
machine learning for weather prediction. By integrating machine learning techniques into their
forecasting systems, ECMWF enhances the accuracy of weather predictions.
Google's DeepMind AI for Weather Forecasting: DeepMind, a subsidiary of Google, is
working on improving weather and climate forecasting using deep learning. Their AI-driven
approach focuses on better predicting complex climate phenomena like rainfall patterns.
National Center for Atmospheric Research (NCAR) AI-Enhanced Climate Models: NCAR
is actively researching the use of AI, particularly deep learning, to improve climate models.
Their work involves developing AI-based parameterizations for climate model components,
aiming to increase model accuracy.
NASA's GEOS:AI Model: NASA's Global Earth Observation System with Artificial Intelligence
(GEOS:AI) incorporates machine learning into its climate models. This AI-driven model is
designed to enhance the accuracy and efficiency of climate simulations.
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How AI Can Aid in Reducing Greenhouse Gas Emissions:
Energy Efficiency: AI can optimise energy consumption in various sectors, reducing
greenhouse gas emissions. Smart grids, for instance, use AI to balance energy supply and
demand, leading to efficient electricity distribution.
Industrial Processes: AI-driven predictive maintenance can help industries reduce energy
waste by identifying potential equipment failures and inefficiencies before they occur, thus
cutting emissions.
Emissions Reduction Strategies: AI assists in developing emissions reduction strategies by
analysing large datasets, identifying emission sources, and recommending targeted actions to
decrease emissions.
AI Applications in Renewable Energy, Transportation, and Agriculture:
Renewable Energy: AI plays a significant role in the renewable energy sector. Wind and solar
farms use AI for predictive maintenance, optimising energy production. Machine learning can
predict fluctuations in energy production, helping balance supply and demand on the grid.
Transportation: Autonomous electric vehicles (EVs) leverage AI for self-driving capabilities,
leading to more efficient transportation and reduced emissions. Additionally, AI is used for
traffic management and optimising routes for fuel efficiency.
Agriculture: AI can improve agricultural practices, enhancing crop yields and reducing
emissions associated with agriculture. Precision agriculture uses AI to optimise the use of
water, fertilizers, and pesticides, reducing the carbon footprint of farming.
Policy and Industry Collaborations:
Paris Agreement: The Paris Agreement is a prominent example of international collaboration
to combat climate change. It sets global targets for reducing greenhouse gas emissions and
encourages countries to develop and implement mitigation strategies. AI and data analytics are
utilised to track progress and enhance policy effectiveness.
AI for Earth Initiative: Initiatives like Microsoft's AI for Earth program collaborate with NGOs
and government agencies to leverage AI technologies for environmental conservation,
emissions reduction, and sustainability.
Industry Collaborations: Many industries are collaborating with AI research institutions and
startups to develop and implement AI-based solutions for reducing emissions. For example,
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tech companies collaborate with energy providers to create smart grids and renewable energy
integration projects.
Carbon Pricing: Some regions have implemented carbon pricing mechanisms, such as carbon
taxes or cap-and-trade systems, to incentivise emission reduction. AI can be used to monitor
and report emissions data to ensure compliance with such policies.
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AI and Adaptive Strategies:
Climate Adaptation: AI can assist in developing adaptive strategies for climate change
impacts, such as sea-level rise and extreme weather events. Machine learning models can
predict these events, helping communities prepare and respond effectively.
Real-Time Monitoring: AI enables real-time monitoring of climate and environmental data,
aiding in adaptive decision-making. For example, AI can monitor river levels and trigger flood
alerts.
Smart Resource Allocation: AI optimises resource allocation during crises by analysing data
on resource availability, demand, and critical needs. This is crucial for adaptive strategies in
disaster response.
AI Applications in Disaster Response, Urban Planning, and Infrastructure:
Disaster Response:
Smart Resource Allocation: AI optimises resource allocation during crises by analysing data
on resource availability, demand, and critical needs. This is crucial for adaptive strategies in
disaster response.
Early Warning Systems: AI-driven early warning systems can predict natural disasters and
provide timely alerts. For instance, AI is used in earthquake prediction and tsunami detection.
Rescue and Relief: Drones with AI-based object detection and navigation capabilities are used
for search and rescue operations. AI can also analyse satellite imagery to assess damage and
prioritise response efforts.
Urban Planning:
Traffic Management: AI-based traffic management systems optimise traffic flow, reducing
congestion and emissions. Urban planners use AI to develop more efficient public
transportation routes.
Smart Infrastructure: Smart cities incorporate AI to manage energy, water, and waste systems
efficiently, contributing to sustainability and resilience.
Infrastructure:
Predictive Maintenance: AI is used in infrastructure maintenance, predicting when critical
infrastructure, such as bridges or power grids, requires repairs. This proactive approach
reduces downtime and costly emergency fixes.
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Energy Grid Management: AI optimises energy grids by balancing supply and demand and
integrating renewable energy sources, enhancing the resilience of energy infrastructure.
Case Studies of AI-Powered Adaptive Solutions:
IBM's Call for Code: IBM's Call for Code Global Challenge encourages developers to create
AI-powered solutions for disaster preparedness and response. Winners have developed
applications for earthquake early warning, wildfire detection, and hurricane response.
One Concern: One Concern uses AI to model and predict the impact of disasters on
communities. Their platform assists emergency services and governments in planning adaptive
responses to disasters, including earthquakes and floods.
Singapore's Smart Nation Initiative: Singapore uses AI and data analytics to create a smart
and sustainable urban environment. AI is employed for traffic management, energy efficiency,
and urban planning, enhancing adaptive capabilities.
Google's Flood Forecasting Initiative: Google is working on a global flood forecasting
initiative that uses AI to predict floods with high accuracy. This project aims to provide timely
information for adaptive responses in flood-prone regions.
Resilience in a Box (RIB): RIB, developed by the Rockefeller Foundation, is an AI-powered
solution that helps cities and communities strengthen their resilience against various shocks
and stresses. It includes tools for urban planning and risk management.
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Ethical, Technical, and Environmental challenges of AI for climate:
Ethical Challenges:
Equity and Justice: The benefits of AI for climate may not be equitably distributed.
Vulnerable communities could be left behind, exacerbating existing inequalities in climate
impacts.
Privacy Concerns: Collecting and sharing data for climate analysis may raise privacy
issues. Striking the right balance between data collection and individual privacy rights is a
challenge.
Algorithmic Bias: AI systems can inherit biases present in training data, which could lead to
unfair or discriminatory outcomes in climate-related decisions.
Technical Challenges:
Data Quality: Climate data is often heterogeneous and incomplete, making it challenging to
develop accurate AI models. Poor data quality can lead to inaccurate predictions and
decisions.
Model Uncertainty: AI models can generate complex outputs, making it difficult to interpret
their reasoning. Ensuring transparency in model predictions is a technical challenge.
Computational Resources: Training sophisticated AI models for climate research can be
computationally expensive, limiting accessibility for some researchers and institutions.
Environmental Challenges:
Energy Consumption: Training large AI models can have a significant carbon footprint due
to the energy consumption of data centers. Balancing AI's environmental impact with its
potential benefits for climate is a challenge.
Electronic Waste: The rapid evolution of AI hardware can lead to electronic waste, posing
environmental concerns.
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Data Bias, Transparency, Inclusivity, and Accountability:
Data Bias: Climate datasets may have biases, either due to historical factors or data
collection methods. AI models trained on biased data can perpetuate these biases in climate
predictions.
Transparency: The opacity of AI algorithms can make it difficult to understand the rationale
behind AI-driven climate decisions. Ensuring transparency and interpretability in AI models is
crucial for accountability.
Inclusivity: The AI technology stack (both hardware and software) needed to build, test,
train, and deploy AI-powered infrastructure and apps. Increasing AI-powered infrastructure is
the most essential priority for inclusivity.
Accountability: Determining responsibility for AI errors or biases in climate predictions can
be challenging. Establishing accountability frameworks is essential to address these issues.
Potential Rebound Effects:
Efficiency vs. Conservation: AI can make existing systems more efficient, but this may lead
to higher resource consumption if not combined with conservation efforts. For instance, AI-
driven improvements in energy efficiency might encourage increased energy consumption.
Risk Compensation: In some cases, the introduction of AI systems may lead to risk
compensation behavior, where individuals or organisations take on more risk because they
believe AI will mitigate the consequences.
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Emerging Trends in AI and Climate Research:
Explainable AI: The need for transparency in AI models is growing. Explainable AI
techniques aim to make climate predictions more understandable and trustworthy, ensuring
that stakeholders can interpret the results.
Quantum Machine Learning: The application of quantum computing to climate research
holds great promise. Quantum computers can process vast climate datasets more efficiently,
accelerating climate simulations and enabling more accurate predictions.
AI in Carbon Removal: AI can assist in developing and optimising carbon removal
technologies, such as direct air capture and carbon sequestration, as a means of addressing
historical emissions.
Climate Finance and Risk Assessment: AI is becoming crucial in assessing climate-related
financial risks, enabling better-informed investment and insurance decisions.
Role of Quantum Computing, AI Ethics, and Interdisciplinary Collaboration:
Quantum Computing: Quantum computing can revolutionise climate research by solving
complex computational problems more efficiently, enabling higher-resolution models and
more accurate predictions. Research in quantum machine learning and quantum annealers is
vital for its application in climate science.
AI Ethics: Ensuring ethical AI practices in climate research is essential. Researchers and
policymakers need to address biases, fairness, and accountability in AI models and data. AI
ethics frameworks and guidelines should be integrated into climate AI initiatives.
Interdisciplinary Collaboration: Climate research demands collaboration among climate
scientists, computer scientists, data analysts, policymakers, and businesses. Interdisciplinary
partnerships can drive innovation, foster a holistic understanding of climate challenges, and
facilitate the translation of research into actionable policies and solutions.
Recommendations:
For Policymakers:
Foster AI climate research by providing funding and supporting interdisciplinary
collaboration.
Develop AI governance frameworks to ensure ethical AI use in climate research and
policy.
Create policies that promote the responsible use of AI for climate adaptation and
mitigation.
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For Researchers:
Continue research into explainable AI, quantum machine learning, and data quality
enhancement for climate research.
Promote open data sharing and interdisciplinary cooperation to accelerate climate
AI innovations.
Prioritise the development of AI solutions for carbon removal technologies.
For Businesses:
Invest in AI-driven climate solutions to reduce emissions, enhance resource
efficiency, and build climate-resilient infrastructure.
Incorporate AI ethics and sustainability principles into business practices and AI-
driven products.
Collaborate with research institutions and governments to develop AI-powered
climate solutions.
For Civil Society:
Advocate for responsible AI practices, transparency, and accountability in climate
research and policy.
Raise awareness about the potential benefits and challenges of AI in addressing
climate change.
Engage with policymakers to ensure equitable access to AI-driven climate
solutions.
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In the face of the urgent global challenge of climate change, the integration of artificial intelligence (AI) into climate
research has emerged as a powerful ally in understanding, mitigating, and adapting to the impacts of a changing
climate. The key findings and contributions of AI in climate research can be summarised as follows:
Enhanced Climate Models: AI has revolutionised climate modeling by improving model resolution, data
assimilation, and parameterization. This has led to more accurate climate predictions, enabling better-informed
policy decisions and climate adaptation strategies.
Data-Driven Insights: AI's ability to process vast and complex datasets has facilitated the identification of climate
trends, anomalies, and critical factors. Machine learning and data analytics have unearthed valuable insights from
historical and real-time climate data.
Resilient Infrastructure: AI has played a pivotal role in developing resilient infrastructure. Through predictive
maintenance and optimisation, AI minimises downtime and resource consumption, reducing the environmental
footprint of critical infrastructure.
Disaster Response and Preparedness: AI-powered early warning systems, disaster response tools, and adaptive
solutions have proven essential in minimising the impact of extreme weather events, safeguarding lives, and
reducing economic losses.
Carbon Removal and Renewable Energy: AI applications in carbon removal technologies and renewable energy
systems offer promising solutions to reduce greenhouse gas emissions and transition to a sustainable, low-carbon
energy future.
As the data points clearly demonstrate, the urgency of addressing climate change cannot be overstated:
In 2021, the Intergovernmental Panel on Climate Change (IPCC) reported that global temperatures have already
risen by approximately 1.1°C above pre-industrial levels.
The consequences of climate change are evident, with increasing frequency and intensity of extreme weather
events, rising sea levels, and loss of biodiversity.
The World Bank estimates that by 2030, the number of people affected by river flooding alone could increase by
114 million due to climate change.
The cost of climate-related disasters is substantial, with economic losses exceeding $200 billion annually in the
United States alone.
The Paris Agreement, a landmark international treaty aimed at limiting global warming, underscores the global
consensus on the need for climate action. The need for continued research and action is paramount:
AI in climate research is a dynamic field with emerging trends such as quantum computing, explainable AI, and a
growing emphasis on AI ethics.
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Ai for Climate Action
Interdisciplinary collaboration among climate scientists, computer scientists, policymakers, and businesses is
essential for driving innovation, bridging knowledge gaps, and translating research into actionable climate solutions.
AI's potential to contribute to climate mitigation and adaptation strategies hinges on responsible AI practices,
transparency, and accountability. AI ethics guidelines and responsible AI frameworks should be integrated into
climate research and policy.
In conclusion, AI is a pivotal tool in our efforts to combat climate change.
It enhances our understanding of the complex climate system, enables
more effective climate predictions, and empowers adaptive strategies.
The urgency of addressing climate change cannot be overstated, and
the need for continued research and action is clear. With responsible AI
practices, interdisciplinary collaboration, and ethical AI applications, we
can work together to mitigate the impacts of climate change and
transition to a sustainable and resilient future.
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Ai for Climate Action
About the Authors
Kishan Changlani
Sustainability Solutions & Services
Executive,
UNFCCC and UNEP
Partner Member
Dr Renuka Thakore
Lecturer, Researcher -
University of Central Lancashire
Founder - GSFN CIC
A visionary climate optimist deeply committed to sustainability, the founder of
World Climate Athletes, and a dedicated advocate for climate action in
finance, technology, and sports. Kishan serves as an advisor to NGOs and
SusTechs, leveraging his expertise in sustainable finance and technology. He
collaborates with UNEP FI and UNFCCC to drive climate innovation, using
his boundless enthusiasm and exceptional networking skills to amplify the
impact of every initiative he engages in.
Kishan embodies the '5 Habits' of Respect, Responsiveness,
Communication, Stewardship, and Trustworthiness, solidifying his status as a
trusted professional in his field. Currently, he works as an ESG Partner with
Tata Consultancy Services Europe Division, focusing on Sustainable Finance
and Technology for their financial services clients.
The author, a climate scientist with a Ph.D. in Environmental Science,
possesses expertise in understanding the impacts of climate change and
developing sustainable solutions. Her research is deeply rooted in addressing
the urgency of mitigating climate effects. With a profound understanding of
AI's potential, she aims to inspire further research and global action, fostering
the integration of AI into climate solutions.
She is the Founder of Global Sustainable Futures Progress through
Partnerships Network and has successfully created a collaborative platform
for innovative and transdisciplinary partnerships and capacity development
for early career researchers joined by senior experienced researchers from
Global South and Global North.
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Ai for Climate Action
References:
1. Intergovernmental Panel on Climate Change (IPCC): https://www.ipcc.ch/
2. Climate Data Guide by the National Center for Atmospheric Research (NCAR):
https://climatedataguide.ucar.edu/
3. World Meteorological Organization (WMO) Climate Data Portal: https://public.wmo.int/en/resources/climate-
data-and-monitoring
4. The World Bank - Climate Data: https://datacatalog.worldbank.org/dataset/climate-data
5. NASA Earthdata: https://earthdata.nasa.gov/ - Offers climate-related datasets and tools for data analysis.
6. World Meteorological Organization (WMO) AI for Climate Initiative: https://public.wmo.int/en/our-mandate/ai -
Information on WMO's AI initiatives in climate research.
7. National Center for Atmospheric Research (NCAR) AI Research: https://ral.ucar.edu/hsrp/blog/artificial-
intelligence-ai-forecasting - Insights into how AI is advancing weather and climate forecasting.
8. United Nations Climate Change (UNFCCC) - AI for Climate Action: https://unfccc.int/news/un-launches-ai-for-
climate-initiative - Information about the UN's AI for Climate Action initiative.
Machine Learning, Deep Learning, and Data Analytics:
9. National Oceanic and Atmospheric Administration (NOAA) - Machine Learning and AI for Climate Research:
https://www.noaa.gov/office-operations/technology/nmfs-artificial-intelligence/machine-learning-ai-for-climate-
research - Information on NOAA's efforts in applying AI to climate research.
10. European Space Agency (ESA) - Deep Learning and Climate Change:
https://www.esa.int/Applications/Observing_the_Earth/Deep_learning_for_climate_change - Details on how deep
learning is being used for climate change studies.
11. European Centre for Medium-Range Weather Forecasts (ECMWF) - Data Assimilation and Climate
Reanalysis: https://www.ecmwf.int/en/about/what-we-do/climate-data-assimilation - Insights into ECMWF's data
assimilation techniques for climate research.
AI in Climate Research Case Studies:
12. IBM GRAF Model: https://newsroom.ibm.com/2020-09-08-IBM-Introduces-GRIB-API-based-on-ECMWF-
Edition-2-to-improve-speed-and-accuracy-of-weather-and-climate-models - Information about IBM's GRAF model
for weather forecasting.
13. Google Precipitation Nowcasting: https://blog.google/technology/ai/a-look-back-at-2020-nowcasting-the-
present-to-forecast-the-weather-of-the-future/ - Google's application of AI for precipitation nowcasting.
14. Microsoft AI for Earth: https://www.microsoft.com/en-us/ai/ai-for-earth - Information about Microsoft's AI for
Earth program and its various climate-related projects.
15. Carbon Monitoring for Action (CARMA): https://carma.org/ - Details about CARMA's use of data analytics to
monitor global carbon emissions.
16. European Centre for Medium-Range Weather Forecasts (ECMWF) AI in Weather Prediction:
https://www.ecmwf.int/en/about/media-centre/news/2020/ecmwf-and-finnish-meteorological-institute-collaborate-
ai-weather - Information on ECMWF's use of AI in weather prediction.
17. NOAA Climate Model Emulator (CME): https://ral.ucar.edu/hsrp/blog/noaa-taps-artificial-intelligence-reduce-
climate-model-uncertainty
18. ECMWF and Machine Learning: https://www.ecmwf.int/en/about/media-centre/news/2019/introducing-ecmwf-
s-machine-learning-strategy
19. DeepMind and AI for Weather Forecasting: https://deepmind.com/research/case-studies/improving-weather-
forecasts-with-ai
20. NCAR AI-Enhanced Climate Models: https://ral.ucar.edu/hsrp/blog/artificial-intelligence-ai-forecasting
21. NASA's GEOS:AI Model: https://appliedsciences.nasa.gov/content/artificial-intelligence-enhanced-earth-
system-modeling
22. Paris Agreement: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement
23. Microsoft AI for Earth: https://www.microsoft.com/en-us/ai/ai-for-earth
24. World Resources Institute - Carbon Pricing: https://www.wri.org/approach/carbon-pricing
25. The European Commission - AI in Energy: https://ec.europa.eu/digital-single-market/en/artificial-
intelligence/energy
26. IBM - AI for Green Energy: https://www.ibm.com/industries/energy/resources/ai-green-energy
27. IBM's Call for Code: https://developer.ibm.com/callforcode/
28. One Concern: https://www.oneconcern.com/
29. Singapore's Smart Nation Initiative: https://www.smartnation.sg/
30. Google Flood Forecasting Initiative: https://blog.google/outreach-initiatives/googleorg/global-flood-forecasting-
initiative/
31. Rockefeller Foundation - Resilience in a Box: https://www.rockefellerfoundation.org/resilience-in-a-box/
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32. AI for Climate: https://www.weforum.org/agenda/2021/02/ai-in-climate-change-everything-you-need-to-know/
33. Algorithmic Bias and Fairness in AI: https://www.brookings.edu/research/algorithmic-bias-detection-and-
mitigation-best-practices-and-policies-to-reduce-consumer-harms/
34. Electronic Waste and AI: https://www.unenvironment.org/interactive/emissions-gap-report/2020/electronic-
waste-and-climate
35. Rebound Effects in Energy Efficiency: https://www.eesi.org/papers/view/rebound-effects-in-energy-efficiency
36. AI and Climate Justice: https://www.cambridge.org/engage/coe/article-details/6087f7ee4b88f200149b6e18
37. Quantum Computing in Climate Research: https://www.nature.com/articles/s41598-019-50630-x
38. AI Ethics Guidelines for Climate Research: https://www.wri.org/blog/2019/05/how-ethical-use-artificial-
intelligence-can-help-tackle-climate-change
39. Interdisciplinary Collaboration in Climate Research: https://www.nature.com/articles/s41559-020-1152-9
40. AI for Climate Solutions: https://ai4all.mit.edu/ai-climate-solutions
41. Guidelines for Responsible AI in Climate Action: https://www.weforum.org/reports/guidelines-for-responsible-
ai-in-climate-action
42. IPCC Special Report on Global Warming of 1.5°C: https://www.ipcc.ch/sr15/
43. World Bank - The Making of a Climate Refugee: https://www.worldbank.org/en/news/feature/2021/11/08/the-
making-of-a-climate-refugee
44. Costs of Climate Change in the U.S.: https://www.nrdc.org/resources/costs-climate-change
45. Paris Agreement: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement
46. AI and Climate Change: https://www.weforum.org/agenda/2021/02/ai-in-climate-change-everything-you-need-
to-know/
47. CLIMATE TRACE: https://climatetrace.org/our-approach
Conference Paper
Full-text available
El cambio climático se presenta como el desafío más grande para la humanidad. Metodología: Sin embargo, las nuevas tecnologías, especialmente la inteligencia artificial (IA), ofrecen herramientas fundamentales para comprender este fenómeno y desarrollar mecanismos que permitan mitigarlo, adaptarse a él e incluso combatirlo. Resultados: A pesar de sus beneficios potenciales, la IA también juega un papel significativo al contribuir a los problemas asociados con el cambio climático, tanto en su proceso de entrenamiento, implementación y mantenimiento, como en su notable consumo de recursos como el agua. Discusión y Conclusión: Por ende, este ensayo busca emplear las herramientas y conceptos de la Teoría de la Actor-Red para analizar críticamente el papel de la IA en el cambio climático: cómo su implementación y gobernanza pueden diseñarse para maximizar los beneficios y minimizar los impactos negativos, así como entender cómo actúa como un agente que puede agravar este fenómeno global.
Chapter
Climate change is a pressing global challenge that demands improved understanding, accurate ‎predictions, and effective mitigation strategies. High-performance computing (HPC) combined ‎with artificial intelligence (AI) presents a transformative opportunity for climate modeling and ‎prediction. HPC and AI for environmental sustainability have the potential to revolutionize our efforts in ‎addressing pressing environmental challenges. By combining the computational power of HPC ‎with the analytical capabilities of AI, we can accelerate research and develop innovative ‎solutions to promote sustainability.
-Offers climate-related datasets and tools for data analysis
  • Nasa Earthdata
NASA Earthdata: https://earthdata.nasa.gov/ -Offers climate-related datasets and tools for data analysis.
Edition-2-to-improve-speed-and-accuracy-of-weather-and-climate-models -Information about IBM's GRAF model for weather forecasting
  • Ibm Graf Model
IBM GRAF Model: https://newsroom.ibm.com/2020-09-08-IBM-Introduces-GRIB-API-based-on-ECMWF-Edition-2-to-improve-speed-and-accuracy-of-weather-and-climate-models -Information about IBM's GRAF model for weather forecasting.
earth -Information about Microsoft's AI for Earth program and its various climate
  • A I Microsoft
  • Earth
Microsoft AI for Earth: https://www.microsoft.com/en-us/ai/ai-for-earth -Information about Microsoft's AI for Earth program and its various climate-related projects.