Climate risk intelligence to inform location-specific physical risk now and in the future.
For decades, scientists have warned about the increase of climate-related physical hazards. Since March 2020, when the World Health Organization Report declared COVID a pandemic, the world has been hit by more than 100 acute physical risk events, many being climate-related. According to NOAA, 2020 is the sixth consecutive year (2015–2020) in which 10 or more billion-dollar climate disaster events have impacted the United States. The growing awareness and concern around climate change have 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:
- 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 data 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 an 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 serves 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
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 risk intelligence
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 policymaker 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 on 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 require 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.
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 on 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.
If you find this material interesting and care about the changing climate, I would appreciate you forwarding to a friend and leaving a comment. Sust Global makes it easy to stay up to date with the latest analyses and trends in frontier climate science, just subscribe to Sust Insights. If you would like to assess risks to your assets and business from exposure to extreme physical hazard in the years to come, get in touch with us for a demonstration of our Climate Scenario Analysis Platform.