April 25, 2018 – 427 REPORT. Financial institutions, corporations, and governments increasingly strive to identify and respond to risks driven by physical climate impacts. Understanding the risks posed by climate change for facilities or infrastructure assets starts with conducting a risk assessment, which requires an understanding of the physical impacts of climate change. However, climate data in its raw form is difficult to integrate into enterprise risk management, financial risk modelling processes, and capital planning. This primer provides a brief introduction to climate models and data from a business or government perspective.
The first of several reports explaining the data and climate hazards analyzed in Four Twenty Seven’s equity risk scores and portfolio analytics, Using Climate Data unpacks the process through which raw climate data is transformed into usable metrics, such as future temperature projections, to help financial, corporate and government users productively incorporate climate-based analytics into their workflows. Beginning by explaining what a global climate model is, the report explains climate data’s format, computational choices to hedge uncertainty and resources for aggregated climate projections tailored to specific audiences.
- Climate models are simulations of the Earth’s future conditions. Climate projections are based on a compilation of many models and are publicly available.
- Regional climate models and statistical downscaling improve the resolution of data produced by global climate models and are thus valuable options when projections are only needed for one location or several in the same region.
- Climate models can be used to project future trends in temperature and precipitation, but can not project discrete storms or local flooding from sea level rise, which require additional data and analysis.
- Different time horizons of climate projections have different strengths and limitations so it is important to select the data product best suited to a specific project’s goal.
- There are several drivers of uncertainty in climate models and strategies to hedge this uncertainty can help users correctly interpret and use climate projections.