Cities are under pressure to minimise the impacts of climate change on urban life. From local councils to utility and transport companies - many different organisations are responsible for managing roads, preparing infrastructure for extreme weather and responding to the other localised impacts caused by the changing climate. But navigating the scientific evidence and the evolving data landscape on climate projections to understand their practical impacts isn’t easy. Organisations often turn to expert climate services providers to help them develop climate adaptation plans.

A climate service is the provision of climate information to help end users make climate-smart decisions. Climate information can range from seasonal forecasts (eg, forest fire outlooks) to long-term projections (eg, sea-level rise).

Climate service providers (such as the Met Office) face four main challenges that impact their ability to support public organisations with climate adaptation.

• Incorporating the latest climate information – increasing amounts of information and continuous technological advancements mean climate services providers need to frequently update their knowledge base on climate projections and impacts and exercise best judgement on how to incorporate this in their services.
• Managing uncertainty – three factors contribute to large uncertainties in projecting the future climate: future emissions (not knowing whether we will be able to reduce emissions in future and by how much; our evolving understanding of the climate system; natural variability in the climate system.
• Multiple approaches for producing climate services – there are different approaches for selection, analysis and communication of climate information which increases complexity and can often make it difficult to achieve consensus;
• Distributed climate knowledge - multiple experts hold climate knowledge, and they are often not connected.

HACID is a European project to develop novel tools that leverage collective human and machine intelligence to support decision-making in open-ended domains. One key application domain focuses on climate adaptation and is being developed in close collaboration with our project partners Met Office, CNR and MPG. Our approach combines three key methodological innovations:

• We will crowdsource key insights from diverse groups of experts to combine different approaches when using climate data and connect the distributed knowledge.
• We will use artificial intelligence to streamline the processing of different sources of data to support climate services providers to keep up with the exponential increase in climate information.
• Finally, we will use knowledge graph technology to aggregate knowledge and expert insights into solutions.

Knowledge graphs (KGs) organise data from multiple sources. Unlike typical databases, they capture information about data points of interest in a given domain or task (like people, places or events), and the connections between them. They are also “smart” in the sense they can infer new connections between data points that weren’t originally connected. AstraZeneca, for example, built a biomedical knowledge graph with heterogeneous biomedical data such as genes, proteins, compounds, and diseases. The knowledge graph supports them in finding connections in the data and predicting new disease targets for drug discovery.

Through our approach, we hope to combine crowdsourced input and other sources of data to uncover new solutions for climate questions and accelerate climate adaptation.

We spoke to three public organisations to understand their needs (and their partner’s needs) when using climate data and the role that the data plays in their climate adaptation decision-making. We found that climate decisions are important for business operations and asset management. Within these areas of focus, public organisations are facing three main challenges.

The process of developing a climate service typically involves three high-level phases: planning, creation of content, and final delivery. Research with internal Met Office employees on the high-level process of delivering climate services (data provision, briefing documents and training) revealed three main challenges in current practice.

This is one of the three user journeys created as an output of a Met Office workshop. The workshop focused on capturing the end-to-end process of delivering climate services and the challenges at each step.

We set out to create a decision-support tool that centralises knowledge, supports climate scientists dealing with data uncertainty and promotes explainability. The HACID tool will support climate scientists by allowing them to:

1. crowdsource solutions for climate questions from other climate scientists and
2. surface climate information from different data sources (e.g., papers, reports, climate models, case studies)

We hope that, through the creation of this expert community and centralised knowledge base, in future, we can open the platform to public organisations. In HACID’s future vision, public organisations would be able to ask questions directly and receive valuable information to support their climate decision-making.

We developed a concept design for a new tool and outlined the envisioned user journey.

The service blueprint documents the interactions between actors, actions and functionality within the HACID-DSS concept. It also includes wireframes from the prototype.

1. The climate service provider’s planning team creates a ‘case’ in the HACID tool with information on the climate service request (ie, name of the organisation, specific climate question).
2. A knowledge graph is created, with information relevant to the case by scraping existing data from the web and or surfacing relevant information previously contributed by experts.
3. The climate services provider analyses the case and the existing data. Then they decide where they are lacking information or would benefit from expert input and post it as a question (eg, what is the most authoritative source with information for extreme rainfall events in the UK 2025-2050?)
4. A pool of experts from academia, government agencies and the private sector provide their recommendations for information sources and explain how to analyse them.
5. The contributions are added to the knowledge graph and aggregated by an algorithm into a ranked list.
6. The climate services provider sees the final ranking of information sources and is able to interact with the knowledge graph to explore all the different contributions and make their final decision on what information or data sources to use.
7. Finally, the climate services provider provides feedback on the usefulness of the crowdsourced solution (eg, rating the depth or the novelty of the solution) so the system can learn and the algorithms used for aggregating the solutions are continuously improved.

Our partner the National Research Council of Italy (CNR) will lead the technical development of a HACID prototype for user testing. The prototype will be based on the user research and concept design described above.

During this phase of the project, our team will be working on designing and delivering the participatory evaluation process and helping partners to adapt the tool development process to be more participatory (eg, testing new methods for eliciting inputs from experts during the development of the knowledge graph)

This prototype development and the evaluation of the process will help with further iteration of the participatory AI methodology first developed by Nesta in the context of humanitarian innovation.

We’ll be sharing what we learn along the way, so look out for more project updates. In the meantime, if you’d like to learn more about this project, get in touch at [email protected] using the subject line HACID.