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AI-Based Climate Modelling Market Size -By Component, By Deployment Mode, By Technology, By Application Analysis, Share, Growth Forecast, 2025 - 2034
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AI-Based Climate Modelling Market
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Base Year: 2024
Companies covered: 20
Tables & Figures: 200
Countries covered: 21
Pages: 180
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AI-Based Climate Modelling Market Size
The global AI-based climate modelling market size was valued at USD 266.4 million in 2024 and is projected to grow at a CAGR of 23.1% between 2025 and 2034. The need for monitoring climate change effects is enhancing due to climatic variability, which is impacting cross-region resource sharing and increasing prospects for natural disasters. There is development in AI infrastructure, availability of IoT and cloud computing, along with regulatory requirements and AI-based predictive tools.
Governments are investing in climate resilience measures and simultaneously request data-driven strategies within their organizations. Further development in machine learning, deep learning, and availability of IoT devices is enabling more accurate real-time predictions, creating support for AI models. It is foreseen that AI will facilitate better evaluation of big data and enable quicker forecasting, which helps in the integration of drone hardware with climate information systems, thus improving the decision-making processes for sectors such as agriculture, energy, or insurance.
For example, the forward-looking company focused on the proactive AI risk management of climate change, ClimateAI, managed to secure USD 22 million dollars in their Series B funding in April of 2023. The company uses deep learning models in predicting climate change in the long term so that crop yield and supply chain risks can be set in position ahead of the anticipated climate change. This enables their clients to mitigate the consequences advanced on policy. Such innovation stands witness to AI’s immense promise in resolving very sensitive climate change problems.
Most importantly, the development of AI climate modelling tools had sparked interests of disaster risk management AI which aids in predictive analytics with the intention of mitigating disastrous consequences. In today's reality, with the intensity of climate change on the rise, governments and organizations are working around the clock to come up with more effective ways to combat and strategize concerning the harsh weather conditions?.
AI-Based Climate Modelling Market Trends
An ongoing trend in the AI-based climate modeling industry is the adoption of AI alongside sophisticated data ecosystems, including IoT, blockchain, and cloud computing. These enable monitoring and climate analysis at a granular level in real-time, thereby enhancing predictive power.
Emphasis is now being placed on hyper local weather forecasting which can be useful, for example, in agriculture and logistics. Furthermore, AI and deep neural networks are also being applied to the generation of climate scenarios to assess the risk of climate change and its consequences for longer periods. This shift follows the realization that climate resilience is needed for virtually every industry, and it is easier, cheaper and more flexible.
For example, in September 2024, Fermata's AI-powered Croptimus software measures pests and diseases disrupting farming using computer vision and machine learning. The system uses drones, robots, and cameras fixed on greenhouses for continuous monitoring, providing unlimited surveillance as well as vents analytic mapping to facilitate interventions.
Croptimus also increases the sustainability of the practice by minimizing the use of pesticides and maximizing labor efficiency and crop yield. It is trained on high-quality data, and its NVIDIA infrastructure augments rather than replaces traditional farming workflows. This innovation helps farmers increase yields while reducing costs and negative externalities in an industry marked by low margins and high resource consumption.
One issue regarding the climate model-based AI solution is the ambiguity and singularity that regards long-term climate predictions, especially in AI-based modeling. While AI models heavily depend on vast amounts of data, limitation on this scope may hinder their accuracy and robustness in the region under development.
Moreover, the integration of heterogeneous datasets from various sources like satellite images, met reports, weather info, and records from the past is of sensitive nature and carries great technical difficulty. Implementing sophisticated AI models is costly in nature, both monetary and in energy usage – which in turn renders them far from feasible. This makes scaling AI solutions very developed, spinning out leaves many regions without aid.
AI-Based Climate Modelling Market Analysis
AI-Based Climate Modelling Market Share
AI-Based Climate Modelling Market Companies
Major players operating in the AI-based climate modelling industry are:
In the field of AI based climate modelling, new competitors are emerging as proficient machine learning and big data analytics are being employed for the creation of climate prediction tools. They are developing models that integrate satellite images, historical climate records, and present-day environmental conditions to effectively replicate intricate climate systems.
However, the AI-based climate modeling market players are not just developing models but are working closely with regulatory authorities, research bodies, and environmental NGOs. They also target businesses and regulators with the goal of promoting the use of advanced climate predictions in policy making. These systems are designed to have a wide global application as well as cater for regional analysis of climate conditions. In addition, the new players are working to increase computational effectiveness in order to minimize financial costs and improve the environmental friendliness of AI-based modelling methods.
AI-Based Climate Modelling Industry News
The AI-based climate modelling market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($ Mn/Bn) from 2021 to 2034, for the following segments:
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Market, By Component
Market, By Deployment Mode
Market, By Technology
Market, By Application
The above information is provided for the following regions and countries: