How Companies Are Using Climate Modeling to Improve Risk Decisions

September 20, 2023 6 mins

How Companies Are Using Climate Modeling to Improve Risk Decisions

Climate modeling has been around for decades but mainly used by academic and government scientists. Led by the risk industry, the private sector is adapting these models to broadly assess the physical impacts of climate change.

Key Takeaways

Climate risk models offer a crucial opportunity to go beyond a historical view of risk to adequately assess, quantify and mitigate the impacts of climate change.
They are built to be forward-looking, by incorporating predictive climate science into a risk modeling framework.
Despite their potential, users of climate risk models need to be aware of the possible sources of uncertainty in these tools.

Public awareness and regulatory pressure have created demand for climate modeling to help organizations understand changing risk, build resiliency and respond to disclosure requirements.

Climate modeling has been around for many decades but mainly used by academic and government scientists. Now, the private sector — driven by the risk industry — is adapting these models to broadly assess the physical impacts of climate change.

In this article, we explain what climate modeling is today and how these insights can be applied by organizations.

What Are Climate Risk Models?

Climate risk models have recently emerged to serve the need for relevant and accessible climate information at a level that is appropriate for decision makers across a wide range of industry sectors including financial services, insurance, agriculture, real estate, and others. Consider securing a mortgage for your home as an example. A potential impact of climate change on the mortgage industry is increased credit default rates. Climate risk models can help quantify risks that could materialize over a 30-year mortgage. A coastal home financed today could be untenable in the future due to sea level rise.

Unlike traditional catastrophe models, which often leverage historical data to identify near-term risk, climate risk models are designed to be forward looking, explicitly incorporating predictive climate science into a risk modeling framework. Their goal is to quantify the probability of costly but rare events — and their underling sensitivity to climate change — in a physically consistent way.

Translating Scientific Climate Modeling to Risk Applications

Global climate models, or GCMs, are typically the primary source of climate information that underpins climate risk models. A GCM is a state-of-the-art numerical model that simulates the physical processes and interactions between the atmosphere, ocean, ice and land surfaces that are relevant to the climate system using the latest scientific understanding and technical capabilities.

GCMs are developed in the public sector by large teams of scientists and engineers at major laboratories and universities across the world. Curation and dissemination of GCM data is coordinated under the World Meteorological Organization, World Climate Research Programme and Coupled Model Intercomparison Project. The results help inform the United Nation’s Intergovernmental Panel on Climate Change (IPCC) assessment reports.

A central purpose of GCMs is to understand how the climate system responds to various greenhouse gas (GHG) emissions. Since future GHG emissions are inherently uncertain, a range of trajectories, known as shared socioeconomic pathways, are used to bound the uncertainty surrounding future societal development. This immense undertaking translates into a wealth of climate change information made freely available to the public for download and exploration. While excellent resources, the information provided is not necessarily useful for an end-user yet. At this stage, the data is at a scale far too coarse to adequately inform physical risk assessment for many high-impact phenomena like tropical cyclones. This is where climate risk modeling comes in.

How Are Climate Risk Models Developed?

Downscaling or refining the GCM data is the first step in making climate model projections suitable for quantifying risk. This is the process of taking the large-scale climate models and making them fit-for-purpose at a local level for analysis and planning.

This process typically falls into two broad categories: statistical and dynamical, each with their own tradeoffs:

Statistical downscaling builds relationships between historical observations at small scales and GCM output at coarse scales. It is computationally efficient but requires high quality observations and makes assumptions that may be unrealistic in the future.
Dynamical downscaling uses coarse GCM outputs to drive higher resolution regional climate models. It is physically more realistic but very expensive to implement, limiting its feasibility for widescale usage.

The next step is calculating relevant peril metrics from the downscaled climate model data. This can take many forms depending on the data provider or peril of interest, but generally includes some form of extreme value analysis to estimate the tail behavior of extreme weather phenomena under a changing climate.

Once relevant hazard metrics are calculated from downscaled climate models, the data can be fed into subsequent vulnerability models to explicitly quantify the financial impacts of climate change. In this way, climate risk models can sometimes blend aspects of catastrophe modeling with hazard information derived from GCMs.

Why Are Climate Risk Models So Important Today?

Risk managers need to move beyond a purely historical view of risk to adequately quantify the potential impacts of climate change. This is no small feat and requires bringing public sector climate modeling and private sector catastrophe modeling closer together.

Climate risk models offer significant potential but are not without their own shortcomings. Decision makers need to be cognizant of new sources of uncertainty endemic to this type of modeling. This includes uncertainty around which GCMs and scenarios to use, as well as limitations of the underlying downscaling method. Climate data providers, in turn, should be transparent in quantifying and conveying such information.

How to Approach Climate Modeling: 3 Ways to Start

Effectively leveraging climate risk modeling into an overall risk management framework is a journey. Start by truly understanding and outlining your organization’s climate risk program needs to:

Cultivate a holistic view of physical risk where climate modeling is one part of a broader toolkit.
Identify business use cases to determine the appropriate scope and depth of climate data needed.
Choose data partners with transparent and sound approaches, pairing the latest scientific research with customized business solutions.

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