The growing awareness and concern around climate change has spurred research, development and measurements. Today’s climate data and tools focus on many different physical aspects but we can broadly classify climate risk data as the following:

An abstraction of climate risk data categories

• Historic event data catalogs: Past extreme physical climate risk events including their spatial footprints. These data sets from public and commercial sources have global coverage and precise spatial footprints as they represent historic events.

• Early warning data streams: Detection and monitoring systems that enable tracking, projecting and warning on near term impact from imminent extreme weather events like wildfires, floods and cyclones. These datas streams have variable latency, often effective temporal accuracy but poor spatial resolution, as in it is hard for these systems to precisely define where exactly a hazard will have impact, so the warnings/alerts span large areas.

• Forward looking climate projections: Derived from simulations of integrated assessment models (IAMs) and global climate models (GCMs) like those that form part of the CMIP6, these project climate risk across multiple scenarios. The datasets are often available over a range of spatial and temporal resolutions. Their performance on backtesting benchmarks vary and they are expected to match up with future observations in the aggregate i.e. over longer temporal and large spatial scales.

Each of these data streams serve a specific community of users. Historic catalogs serve researchers, early warning systems serve civil governments and disaster response teams and forward looking climate projections serve planning related activities. Together these datasets have the potential to help our global community assess social, environmental and financial impacts of climate risk.

However, one pressing issue is that these data collections reside at disparate data sources, have varying cadence and are difficult for someone who isn’t a domain expert in climate, to access, aggregate and interpret.

Because the data isn’t integrated or harmonized, the different collections cannot easily be compared against one another. Most importantly, the data is collected on a global scale, yet the asset ownership and business decision-making have a strong local bias.

Local effects from global phenomena

Local misunderstandings on global phenomena can occur when we mistake gradual weather changes over a long time horizon for normalcy (Credit: xkcd)

While most of us tune into news and pretend to have a keen interest in global affairs, most of the decisions we make in our day-to-day lives are driven by local conditions, those within our close proximity. There is no better example of this local sensitivity when it comes to our actions and behaviors than our concern with weather and our local environment during times of an emergency such as a fire, heatwave, flood or cyclone.

When you live next to a water source (river/lake) that gets contaminated by toxic mining waste, or when we live next to a severe flood zone that affects our friends and family, we begin to perk up and take notice of phenomena that have been unfolding in the past years. While such events directly impact our day-to-day, it also makes us conscious of climate risk and environmental hazards that impact larger populations.

Acute physical hazards are driven by globally distributed imbalances to the earth’s subsystems. Because of this, climate models are natively global. Yet the impact on us is on a local asset level. To plan for climate-related risks and opportunities, that can be seen from geospatial analysis, we need to bridge the gap between global climate models, near real time environmental sensing and disparate physical assets.

Introducing climate intelligence

Climate Intelligence bridges the gap between disparate climate data with global footprints and business decision making at the asset level

The explosion of new data sources, observations, platforms, apps, monitoring satellites, data, and the cloud, coupled with the velocity of change… has made cutting through the noise and making sense of climate data almost impossible. In order to make climate data actionable, in other words, data that we can use to plan and manage for future disasters, we need to integrate and drive synergies between data collections from the above distinct categories.

Climate data becomes intelligent when it can help achieve goals such as quantifying and managing financial and environmental risk. This would involve the ability to:

• Drive Understanding: This would require harmonizing the discrete data collections in such a way that risk analysts can easily interrogate and understand climate patterns and projections as described in a previous post.

• Augment Planning: To deliver insights in a way such that an analyst can easily and quickly assess how different future scenarios would affect their tangible assets based on their locations across the globe. It can also enable a policy maker to assess current and future physical risks and protect new infrastructure and growing human settlements.

• Enable Action: Transforming these disparate climate data sources to the asset level allows for custom grouping, sorting and aggregating risk across scenarios. This enables risk professionals to quantify financial impacts to investment portfolios and smartly manage their assets.

As previously noted in mental models around climate tech, technologies around clean energy transition enables us to get on a path towards green climate scenarios. Most emergency response systems are focused on the near term impact of physical hazards and help ‘act on’ near/mid-term manifestations of climate change. However, understanding and planning for the physical manifestations of climate change requires a deeper dive into climate scenarios and a closer inspection of the impact of the changing climate across longer time horizons and larger spatial regions.

Key takeaways

Think global, act local: Climate science describes global phenomena but decision making around climate happens at the local scale. Climate risk intelligence can bridge the gap between global climate data and physical impact to local assets.

Data transformation is essential: Climate risk intelligence brings the ability to make frontier climate science and the massive amounts of geospatial data intelligible through data transformations.

Stay tuned: Next week, we’ll go deeper on one specific example of climate risk intelligence, describing how flood impact simulations can assist the mining and commodities industry, and local communities.

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This Blog was originally posted on the Data Driven Investor Medium page