We need a data overhaul to help avoid climate disasters, and to help make better and more effective infrastructure investment decisions to protect lives and property, says Marieke Beckmann, from the National Physical Laboratory.
Far from being a far future threat, climate change is a reality that is already impacting the UK. The UK has seen increased rainfall, particularly Scotland, and sea levels have climbed 15.4cm since 1900, and are expected to rise by up to 1.12m by 2100.
As well as being a safety threat, flooding carries a large economic burden for homeowners and the government. It can also cause communities to be ‘decommissioned’, with families becoming climate refugees. At current rates of flooding, and with these set to increase, the Environment Agency announced that £1bn a year needs to be spent on flood management.
Impact assessments used by city planners, when carrying out flood defence decision-making and assessing infrastructure investment, rely on climate change services in order to understand the extent and places likely to be affected.
These services, whether in the form of publicly funded and available forecast reports or private bespoke consultancy, use various degrees of climate modelling that predicts what climate change impacts will occur in 20, 50 or 100 years.
The incredibly complex models amalgamate large amounts of measurements, from surface temperatures to wind patterns and ocean current, to predict the magnitude of impending climate fluctuations. These highly disparate data sources, each bring varying degrees of uncertainty and margin for error. When combined into models, each model will have a given set of uncertainties against them. Thus, it is difficult synthesising this data and modelling it into a set of predictions or scenarios that can be fully guaranteed, understood and acted upon.
If we are to enable effective decision-making and investment in the right places to tackle climate events and effects like increased flood risk, then we need to make sure that the data, models and subsequent information used to inform those decisions is reliable, actionable and fit for purpose. That means ensuring the right level of accuracy of information and acceptable level of risk for the application.
Understanding the uncertainty of climate models is as important as understanding the data and analysed information they provide in guiding investment decisions. Key to achieving this is by developing strategies and methods to more effectively ascertain areas of data uncertainty and degree of error in the climate modelling process, and developing quality assurance systems to reduce uncertainties in climate data.
A newly confirmed satellite mission, The Traceable Radiometry Underpinning Terrestrial - and Helio- Studies (TRUTHS) seeks to do just that. The mission, conceived by the National Physical Laboratory, the UK’s National Measurement Institute, and recently added to the European Space Agency (ESA) proposal programme, will create a climate-quality observatory in space, delivering a ten-fold improvement in measurement uncertainty, also transferable to the wider fleet of satellites that collect climate data.
TRUTHS will enable us to test the climate models against current observations, determine which predicted scenarios are more certain, and weeding out the less realistic models. We therefore improve the intrinsic performance of the model and we improve our risk management ability.
By improving the quality and reliability of climate data in a holistic way and developing new techniques and instruments to reduce uncertainties, we can achieve the data overhaul necessary to make better and more effective infrastructure investment decisions to protect lives and property.
In the future, with more accurate data, such work could even pave the way to the creation of infrastructure that reacts in almost real time to live and reliable data streams, adapting to protect communities against climate events.
Marieke Beckmann is the research and international strategy lead for energy and environment sector at the National Physical Laboratory.