EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

The Extreme Weather Event Real-time Attribution Machine (EWERAM) – An Overview

Jordis Tradowsky1, Greg Bodeker1, Leroy Bird1, Stefanie Kremser1, Peter Kreft2, Iman Soltanzadeh2, Johannes Rausch2, Sapna Rana2, Graham Rye2, Andy Ziegler2, Suzanne Rosier3, Daithi Stone3, Sam Dean3, James Renwick4, David Frame4, and Adrian McDonald5
Jordis Tradowsky et al.
  • 1Bodeker Scientific, Alexandra, New Zealand
  • 2Meteorological Service of New Zealand Limited, Wellington, New Zealand
  • 3National Institute of Water and Atmospheric Research, Wellington, New Zealand
  • 4Victoria University of Wellington, Wellington, New Zealand
  • 5University of Canterbury, Christchurch, New Zealand

As greenhouse gases continue to accumulate in Earth’s atmosphere, the nature of extreme weather events (EWEs) has been changing and is expected to change in the future. EWEs have contributions from anthropogenic climate change as well as from natural variability, which complicates attribution statements. EWERAM is a project that has been funded through the New Zealand Ministry of Business, Innovation and Employment Smart Ideas programme to develop the capability to provide, within days of an EWE having occurred over New Zealand, and while public interest is still high, scientifically defensible statements about the role of climate change in both the severity and frequency of that event. This is expected to raise public awareness and understanding of the effects of climate change on EWEs.

A team of researchers from five institutions across New Zealand are participating in EWERAM. EWE attribution is a multi-faceted problem and different approaches are required to address different research aims. Although robustly assessing the contribution of changes in the thermodynamic state to an observed event can be more tractable than including changes in the dynamics of weather systems, for New Zealand, changes in dynamics have had a large impact on the frequency and location of EWEs. As such, we have initiated several lines of research to deliver metrics on EWE attribution, tailored to meet the needs of various stakeholders, that encompass the effects of both dynamical and thermodynamical changes in the atmosphere. This presentation will give an overview of EWERAM and present the methodologies and tools used in the project.

How to cite: Tradowsky, J., Bodeker, G., Bird, L., Kremser, S., Kreft, P., Soltanzadeh, I., Rausch, J., Rana, S., Rye, G., Ziegler, A., Rosier, S., Stone, D., Dean, S., Renwick, J., Frame, D., and McDonald, A.: The Extreme Weather Event Real-time Attribution Machine (EWERAM) – An Overview, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11715,, 2020

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