Taking bold action to address climate change involves more than accelerating deep decarbonization, it requires adaptations that reduce vulnerability and the risks of climate change in ways that support increased social cohesion, civic participation, and community stewardship—all markers of community resilience. Much can be done to manage maladaptation to current climate conditions without the need for detailed future climate projections. But, for most infrastructure and long-term investments, a more specific understanding of future climate change conditions is useful for driving investment and disinvestment.
Currently, there is a Climate Intelligence Arms Race underway in the private sector that seeks to advance investments and acquisitions of intellectual property and climate services technology. These technologies integrate climate data with observations related to financial assets, supply chains, and a range of financial risk models to increase empirical understanding of climate impacts and to support financial investment decision making.
A recent article in Nature Climate Change by Tanya Fiedler and her Australian colleagues consider the emerging analytic demands of the private sector for reliable climate information driving standardization in disclosure, governance and strategy. The article evaluates the uses and limitations of information currently available from different types of applied climate models and climate services providers, and what they find is worrisome.
The article notes that financial risk analyses consider timeframes from milliseconds to 40 or more years at spatial scales from individual buildings to global supply chains. A wide range of climate attributes (e.g., temperature, precipitation, extreme events, etc.) can indicate risks, and information demands across variables and scales are quite diverse. The authors carefully consider the data available from different types of model experiments (e.g., greenhouse gas forced and observationally initialized simulations) and model types, including various approaches to downscaling.
Their analysis produces a nuanced set of conclusions about the variables and time and spatial scales at which different types of modeling provide useful inputs for financial risk analysis. For some current applications they reach “an uncomfortable assessment” that the use of climate models for assessing climate risk is not valid. They worry that business are inappropriately using data available from a variety of public sources and from climate risk analytics firms with potential serious consequences such as “maladaptation and heightened vulnerability of business to climate change, an overconfidence in assessments of risk, material misstatement of risk in financial reports, and the creation of greenwash.” By extension, an underlying thesis of the Climate Intelligence Arms Race, which Fiedler, et al. cite, is that the risks of maladaptation are even more extreme for public sector stakeholders who often lack the wherewithal to externally validate blackbox models and data to inform long-term capital investments in public infrastructure. When it comes to private sector climate services technology, the need for proprietary investment in R&D needs to be balanced with the public interest in transparent applied climate science that can be externally validated.
Justifiably in our view, these perspectives have received widespread attention in the financial and mainstream media. And that was before the Commodity Futures Trading Commission established a new unit to evaluate climate-related risk, joining other federal financial entities in efforts to understand and price these risks, which only increases the salience of this analysis. It is essential that the scholarly community gives the article similar attention and debate, especially since several of Fiedler et al.’s conclusions call into question current practices of publicly serving diverse types of climate data for application without underlying detailed assessments and guidance for specific applications.
Several of the recommendations made in the article align with the goals of the Science for Climate Action Network (SCAN), including, (i) use-case analysis of “where and when climate projections provide useful information” for applications, (ii) the assessment of various types of climate data and projections using “expert judgment sourced from within the weather and climate sciences,” and, (iii) the “co-evolution of efforts ... to improve the information exchange between risk assessment in industry, finance and climate science.” The Independent Advisory Committee report that led to the launch of SCAN calls for creation of sustained communities of practice including users, climate intermediaries, and Earth, environmental, and social scientists to develop currently missing guidance on application of climate science to design durable solutions to recurring high priority climate challenges.
The risks of climate change are serious and demand rapid and effective action. Fiedler et al. provide important analysis and ideas that will avoid compounding the risks of climate change with the heightened risks of faulty analytics. The point isn’t to slow down implementation but rather to speed up the process of effective implementation that integrates climate science into climate action, whether that is private investment in renewable energy or public infrastructure supporting broader measures of community resilience.
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