Impacts of Climate Extremes from Mining of Online Texts

Introduction

Climate extremes have multifarious detrimental impacts on human activities and ecosystems. Gaining a detailed understanding of these impacts is essential for disaster risk reduction and for building resilience to extremes in a changing climate. However, current freely-accessible natural hazard databases present limitations in completeness, updateability, validation against climate data, and indirect impact information. This hinders scientific and practical progress.

Project Overview

In ICE-MOT, I aim to build a state-of-the-art impact database for extreme climate events, which overcomes the above key limitations. I will specifically combine text-mining of freely available online sources with the use of state-of-the-art climate data.

Focus Areas

To ensure feasibility, I will initially focus on:

1. English-language texts
2. Wintertime cold spells in North America
3. Windstorms and heavy precipitation in Europe

These extreme events have been studied extensively in my ongoing ERC project.

Methodology

ICE-MOT builds upon the database of climate extremes developed within my ongoing ERC project. It further leverages the experience of my research group in data-driven and machine learning analyses for climate science.

Objectives

I will use this interdisciplinary knowledge base to provide:

• Standardised, complete, and automatically updateable spatio-temporal impact information
• Information on indirect and/or cascading impacts
• Quantification of the climate conditions associated with the recorded impacts

Moreover, the database's automated data extraction and processing pipeline will make it easily scalable to multiple regions and climate extremes.

Timeliness and Relevance

This effort is timely: the recent EU strategy on adaptation to climate change explicitly seeks to gather more and better data on climate-related risks and losses as a key adaptation tool.

Additionally, the climate extremes data gathered in my ongoing ERC project provides a perfect basis to test the innovative idea underlying the ICE-MOT database, an opportunity which should be rapidly exploited.