Unraveling Climate Change: Harnessing the Power of Software for a Resilient Future

Explore how cutting-edge software, AI, and digital twins are revolutionizing climate science, enabling accurate predictions and informing future-proof decisions to combat climate change impacts.

Model and predict climate change and an image a polar bear with glasses and code in the background

Model and predict the effects of climate change

Prepare yourself. Easier said than done when it concerns a massive, multifaceted issue like climate change. Hotter or colder temperatures, droughts, extreme weather events, floods, wildfires, ... Every location will experience different extreme circumstances, even within a few years. The impact of climate change becomes increasingly visible, firing the growing need to understand and predict its future potential consequences. You need to know what to expect to prepare yourself, to adapt and prevent.

Thanks to rapidly advancing developments in artificial intelligence, machine learning, and geographical information systems, science allows us to make more accurate predictions of what comes next. Both businesses and cities and countries can benefit from this: from urban planning to securing the supply chain, these insights help decision-makers understand and make future-proof decisions.

Types of software tools

Risk and resilience management

Climate change force businesses and cities to prepare their infrastructure and processes. Buildings and factories need to be resistant to extreme weather events and an increase of natural disasters, supply chains need to become more resilient to abrupt though inevitable changes, farmers need to adapt their practices to an evolved climate, ...

An important use of software to predict the impacts of climate change, is the development of scenario-based models and risk analytics. These models use existing climate data and satellite observations combined with calculations about population growth, future emissions, and other factors. This combination of different data sources makes it possible to project the potential impacts of different climate change scenarios. The scenarios allow policymakers to explore the potential risks and benefits of different policy and mitigation options.

Screenshot of a risk analytics tool with a map of the world and parameters such as heat, fire, flood, and wind
An example of a risk analytics tool is the ClimateScore Global of Jupiter Intelligence, assessing the effects of different parameters (Image source: Greenbiz)

Even more targeted analyses are possible with digital twins. Digital twins are virtual representations of physical objects, processes or systems. Combined with AI and the Internet of Things, it's possible to put the digital twin through a variety of conditions and see where improvements need to be made. This is for example useful for new constructions plans to test what the infrastructure will withstand. Digital twins are not only used for climate solutions, but will play a significant role in the climate field. Due to its growing importance, it is estimated that by 2027 it will have a market size of $63.5B.

Image of a digital twin of a train station in a city
Digital twins allow digitizing infrastructure projects and test their viability beforehand the first brick is laid (Image source: Autodesk)

Climate models

Another use of software in this context is through the development of climate models. Climate models are computer programs that simulate weather patterns and the climate in the future. These models use advanced algorithms and data from a variety of sources, such as weather stations, satellites, and ocean buoys, to simulate different conditions of the weather patterns and predict future changes and conditions. They differ from regular daily weather forecasts in the sense that they look in the long-term and can add human-caused conditions in the equation, such as an increase in greenhouse gasses, to predict future development of weather events.

Software can also be used to analyze and interpret data on the current impacts of climate change that we are able to observe already. Wildfire detection tools support us in handling wildfires, through sensors, AI, and satellite imagery. Software can complement satellite imagery to track the retreat of glaciers. Weather data can be analyzed to identify trends and patterns in extreme weather events. These insights are necessary for climate adaptation technologies, which will increase in importance in the climate tech field.

Opportunities for Climate Tech Startups: Insights into Climate Change as a Funding Opportunity
Discover the diverse verticals and solutions that climate change venture capitalists are interested in, from carbon removal methods to energy and electrification. Learn how VCs manage their investments and why sustainable mineral mining and lithium-ion battery recycling matter.


There are of course challenges for the software models, which scientists work to reduce. The accuracy is determined by the input: data sets can be incomplete or inaccurate, architecture requirements not clearly set, or we just don't have enough understanding of the climate system and the interconnections in the natural environment yet.

Another challenge is the computational power. These predictive models can be extremely complex and require a lot of computational power. Due to this limitation, simulations might not take everything into account.

Software has already played a significant role in modeling, understanding and predicting the impacts of climate change. New technological developments will enrich the benefits and opportunities. From developing climate and scenario-based models to analyzing and interpreting data on observed impacts, these calculations allow us to prepare for an inevitable changing planet. It can help governments decide on new regulations, cities to protect their citizens, and businesses to strengthen their operations.