Data is key to managing climate-related risk

When it comes to managing climate-related risk, it is pretty clear that data is the key. As with all risks, in order to manage or mitigate it is necessary to understand them, and that requires data – especially for something so potentially devastating as climate-related risk.

But one of the difficulties that corporates face is gaining access to climate data. And more importantly, how that climate data applies to their own risks, their own operations, and to their supply chain and customers.

So what sort of data is required in terms of managing climate-related risk? Chiara Cagnazzo, sectoral information system manager of the Copernicus Climate Change Service, European Centre for Medium-Range Weather Forecasts, says: “Effective management of climate-related risk needs data to characterise the relevant climate hazards and their anticipated changes in response to climate change scenarios. Data to characterise the hazard needs to be considered together with data to estimate exposure and vulnerability of the affected human or ecological system to the hazards.”

She gives a heatwave as an example. “Heatwaves are generally described as prolonged periods during which conditions are excessively hotter than normal. To anticipate their associated climate change impacts, heatwaves must be characterised in terms of their magnitude, frequency, spatial extent, timing and duration, and how they are expected to change. There are many impacts of heatwaves, within different sectoral domains, such as human health, agriculture, work productivity, wildfire frequency and intensity, infrastructure, etc, and consequently there are numerous ways to define each of those characteristics.”

Holistic risk assessment

Amar Rahman, global head of climate resilience, Zurich Resilience Solutions, says that a holistic risk assessment and quantification methodology should incorporate data specific to the three dimensions of risk: exposures (pain points in the value chain), controls (protection measures in place), and hazard level.

“Climate risks are not only triggered by evolving weather patterns, but by changes to the environment in general such as increased urbanisation, concentration of assets and deterioration of infrastructure (either through poor maintenance, or increased demand due to increase in population). Thus the data incorporated in the assessment process must reflect as many of these aspects as possible,” he says.

Rahman notes that there are multiple internal and third-party data sources that businesses can tap into, such as building sensors and engineering drawings to assess the quality of the controls. “A lot of data can be sourced from publicly accessible sources, including information related to population, critical infrastructure, as well as certain climate and current natural hazard data. And of course, insurers with their natural hazards expertise and rich data can support businesses with these challenges,” he says.

But he adds that it is critical that the background – such as the quality, source, and uncertainties – of the data sets must be very carefully understood before incorporating in any assessment. He explains that there is no ‘perfect’ data, so the quality and uncertainties and reliability of the data must be incorporated in the analysis and communicated accordingly.

Historic data

Historic data is an important part of the assessment process, serving as a baseline for current risk, but more importantly as an indicator for future risk, says Rahman. “Existing tools, based on historical data, whether building codes, or insurance policy pricing models, can still be used, but the temporal evolution of risk with time, whether affecting hazard levels, or quality of controls, e.g. deterioration of infrastructure or building stock, needs to be considered and incorporated into the tool or assessment process,” he adds.

Cagnazzo says past data constitutes the basis to build a past reference period, to be used to evaluate the temporal evolution of climate hazards. When climate hazards are associated with extreme events, data often needs to have high resolution in time and space, and needs to be complemented with local data, she explains.

“At the same time, this local information must be put in a larger context, requiring consistency at least on the regional scale and temporal homogeneity. A long-term time consistency for rare events is needed, but also availability in near-real time together with quality aspects such as completeness of documentation, accessibility, long-term maintenance, overall assessment of past-data fitness for purpose and examples of use.”

She goes on to explain that a dataset of the past that is used in the context of climate risk management is climate re-analysis, “that combines past observations with models to generate consistent time series of multiple climate variables, and to provide hourly 3D reconstructions of the meteorology and the climate of the past and current conditions”.

Managing the risk

So how can climate risk data be used to manage the risk, and what sort of practical steps can be taken? Before starting to collect the data, the scope of the assessment must be carefully defined, says Rahman. He stresses that this should include as many stakeholders as possible, not only within the organisation, but also from the wider community.

“This is a crucial step, which also recognises the interaction and connection of the organisation with the wider community. Once the pain points of each stakeholder are defined, the requisite data needs to be sourced. Data can also be used to monitor the performance of any adaptation or mitigation measures implemented, eg building insulation and sustainable energy generation,” he says.

Cagnazzo says the climate risk assessment enables the assessment of potential hazards and their impacts on various sectors and regions. “Decision makers can therefore identify areas and sectors at high risk, prioritise resources, and develop targeted strategies. Climate risk data also helps in formulating adaptation plans and strategies, allowing decision-makers to develop tailored adaptation measures. This may include infrastructure improvements, land-use planning, ecosystem restoration, and enhanced emergency preparedness. Climate risk data can be integrated into early warning systems to enhance preparedness for extreme weather events and climate-related hazards,” she says.

Climate-related risk can affect supply chains, causing disruptions to transportation networks, infrastructure damage, and disrupt the flow of goods. “To assess and address this risk, supply chain managers can analyse historical climate data and weather & climate forecasts to identify areas susceptible to such events. Mitigation strategies may involve diversifying sourcing locations, enhancing infrastructure resilience, and devising contingency plans for alternative transportation routes,” says Cagnazzo.

And finally, Rahman notes the value of scenario analysis: “From a climate change perspective, scenario analyses model the impact of the various climate change scenarios (global average temperature rise) on a business, whether at group level or at individual sites level. A scenario analysis is also a good way to deal with uncertainties in the data, as the sensitivity of the results to changes to the input data can be compared.”

And he adds: “Scenario analyses should be undertaken not only to test the data, but also as part of a more complete risk assessment to identify the pain points, the vulnerabilities and potential outcomes to the business. This will help determine the requisite adaptation measures.”