People

TipESM Coordination and Management Team

TipESM is coordinated and managed by the Danish Meteorological Institute (DMI). DMI has established a project office that conducts the administrative, legal, and financial management of the project.

Shuting Yang

Project Coordinator

Danish Meteorological Institute

sy@dmi.dk

Justyna Bekier

Project Manager

Danish Meterological Institute

jabe@dmi.dk

Chiara Bearzotti

Project & Compliance Manager

Danish Meteorological Institute

chb@dmi.dk

TipESM Project Team

TipESM consists of a diverse team of researchers and experts spanning natural and social sciences, as well as communications and project management. The team is made up of consortium and associated partners.

Andy Hartley

Met Office UK

TipESM Involvement

WP7 Leader – Societal and ecosystem consequences of tipping points
WP3 – Robustness and (ir)reversibility of tipping points
WP8 – Mitigating the risk of tipping points
WP9/10 – Clustering, project and data management

Andy Wiltshire

Met Office UK

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP5 – Risk of tipping point cascades and role of extremes
WP7 – Societal and ecosystem consequences of tipping points
WP8 – Mitigating the risk of tipping points
WP9/10 – Clustering, project and data management

Anna Bradley

Met Office UK

TipESM Involvement

WP6 – Climate-driven tipping
WP7 – Societal and ecosystem consequences of tipping points
WP9/10 – Clustering, project and data management

Bo Christensen

Danish Meteorological Institute

TipESM Involvement

WP4 Leader – Early warning indicators of tipping points

Camilla Mathison

Met Office UK, Hadley Centre

TipESM Involvement

WP8 Leader – Mitigating the risk of tipping points
WP9/10 – Clustering, project and data management

Chuncheng Guo

Danish Meteorological Institute

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP2 – Tipping points and their driving processes
WP9/10 – Clustering, project and data management

Colin Jones

University of Leeds

TipESM Involvement

WP1 Leader – ESM protocols, simulations, and data
WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP5 – Risk of tipping point cascades and role of extremes

Desislava Petrova

Barcelona Institute for Global Health (ISGLOBAL)

TipESM Involvement

WP6 – Climate-driven tipping
WP7 – Societal and ecosystem consequences of tipping points

Didier Swingedouw

French National Centre for Scientific Research (CNRS)

TipESM Involvement

WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP4 Leader – Early warning indicators of tipping points
WP7 – Societal and ecosystem consequences of tipping points

Dirk Nikolaus Karger

Swiss Federal Institute for Forest, Snow and Landscape Research WSL

TipESM Involvement

WP6 – Climate-driven tipping

Erika Hayashi

Danish Meteorological Institute

TipESM Involvement

WP9/10 – Clustering, project and data management

Friederike Fröb

University of Bergen

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP5 – Risk of tipping point cascades and role of extremes
WP6 – Climate-driven tipping
WP8 – Mitigating the risk of tipping points

Guillaume Gastineau

French National Centre for Scientific Research (CNRS)

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points

Hazel Jeffrey

University of Leeds

TipESM Involvement

WP9/10 – Clustering, project and data management

Inga Sauer

Potsdam Institute for Climate Impact Research (PIK)

TipESM Involvement

WP6 – Climate-driven tipping

Ivana Cvijanovic

Barcelona Institute for Global Health (ISGLOBAL)

TipESM Involvement

WP7 – Societal and ecosystem consequences of tipping points
WP9/10 – Clustering, project and data management

Jon Robson

NCAS/University of Reading

TipESM Involvement

WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP9/10 – Clustering, project and data management

Juliette Mignot

Institute of Research for Development (IRD)

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP2 Leader – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP4 – Early warning indicators of tipping points
WP5 – Risk of tipping point cascades and role of extremes
WP8 – Mitigating the risk of tipping points
WP9/10 – Clustering, project and data management

Katja Frieler

Potsdam Institute for Climate Impact Research (PIK)

TipESM Involvement

WP6 – Climate-driven tipping of ecological and social systems

Katty Huang

Met Office UK

TipESM Involvement

WP7 – Societal and ecosystem consequences of tipping points
WP9/10 – Clustering, project and data management

Klaus Wyser

Swedish Meteorological and Hydrological Institute

TipESM Involvement

WP1 – ESM protocols, simluations and data

Laura Chica Castells

Barcelona Institute for Global Health (ISGLOBAL)

TipESM Involvement

WP9/10 – Clustering, project and data management

Len Shaffrey

University of Reading

TipESM Involvement

WP3 – Robustness and (ir)reversibility of tipping points
WP5 – Risk of tipping point cascades and role of extremes

Leonardo Lopez

Barcelona Institute for Global Health (ISGLOBAL)

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP2 – Tipping points and their driving processes
WP6 – Climate-driven tipping
WP7 – Societal and ecosystem consequences of tipping points

Lester Kwiatkowski

French National Centre for Scientific Research (CNRS)

TipESM Involvement

WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points

Marion Devilliers

Danish Meteorological Institute

TipESM Involvement

WP4 – Early warning indicators of tipping points

Nikki Brown

Swedish Meteorological and Hydrological Institute

TipESM Involvement

WP9/10 – Clustering, project and data management

Pascale Braconnot

French National Centre for Scientific Research (CNRS)

TipESM Involvement

WP1 Leader – ESM protocols, simulations, and data
WP4 – Early warning indicators of tipping points
WP5 – Risk of tipping point cascades and role of extremes
WP6 – Climate-driven tipping
WP8 – Mitigating the risk of tipping points
WP9/10 – Clustering, project and data management

Robin Smith

University of Reading

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP5 Leader – Risk of tipping point cascades and role of extremes

Ron Kahana

Met Office UK

TipESM Involvement

WP6 – Climate-driven tipping
WP7 – Societal and ecosystem consequences of tipping points
WP8 – Mitigating the risk of tipping points
WP9/10 – Clustering, project and data management

Sandra Zimmermann

Potsdam Institute for Climate Impact Research (PIK)

TipESM Involvement

WP6 – Climate-driven tipping

Shiyu Wang

Swedish Meteorological and Hydrological Institute

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP5 – Risk of tipping point cascades and role of extremes

Stefanie Heinicke

Potsdam Institute for Climate Impact Research (PIK)

TipESM Involvement

WP6 – Climate-driven tipping of ecological and social systems

Steffen Olsen

Danish Meteorological Institute

TipESM Involvement

WP6 – WP6 – Climate-driven tipping of ecological and social systems

Sybren Drijfhout

Royal Netherlands Meteorological Institute (KNMI)

TipESM Involvement

WP2 Leader – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP4 – Early warning indicators of tipping points
WP6 – Climate-driven tipping
WP8 – Mitigating the risk of tipping points
WP9/10 – Clustering, project and data management

Thomas Frölicher

University of Bern

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP2 – Tipping points and their driving processes
WP5 – Risk of tipping point cascades and role of extremes
WP8 Leader – Mitigating the risk of tipping points
WP9/10 – Clustering, project and data management

Tomas Torsvik

University of Bergen

TipESM Involvement

WP1 – ESM protocols, simulations and data

Tony Payne

University of Liverpool

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP4 – Early warning indicators of tipping points
WP5 – Risk of tipping point cascades and role of extremes
WP9/10 – Clustering, project and data management

Torben Koenigk

Swedish Meteorological and Hydrological Institute

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP4 – Early warning indicators of tipping points
WP5 Leader – Risk of tipping point cascades and role of extremes
WP6 – Climate-driven tipping

Ulrika Willén

Swedish Meteorological and Hydrological Institute

TipESM Involvement

WP2 – Tipping points and their driving processes
WP4 – Early warning indicators of tipping points
WP5 – Risk of tipping point cascades and role of extremes

Xavier Rodó

Barcelona Institute for Global Health (ISGLOBAL)

TipESM Involvement

WP3 – Robustness and (ir)reversibility of tipping points
WP6 – Climate-driven tipping
WP7 Leader – Societal and ecosystem consequences of tipping points

Yona Silvy

University of Bern

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP5 – Risk of tipping point cascades and role of extremes

Marine Leyvraz

University of Bern

TipESM Involvement

WP8 – Mitigating the risk of tipping points

Charles Medous

ISGlobal

TipESM Involvement

WP6 – Climate-driven tipping
WP7 – Societal and ecosystem consequences of tipping points

Ben Harvey

University of Reading, NCAS

TipESM Involvement

WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP9/10 – Clustering, project and data management

Dieu Hoang

University of Reading, NCAS

TipESM Involvement

WP2 – Tipping points and their driving processes
WP3 – Robustness and (ir)reversibility of tipping points
WP5 – Risk of tipping point cascades and role of extremes

Arthur Argles

Met Office

TipESM Involvement

WP1 – ESM protocols, simulations, and data
WP3 – Robustness and (ir)reversibility of tipping points