More ERC award glory for UiB climate researchers

Professor Noel Keenlyside has been awarded a Consolidator Grant from the European Research Council (ERC). He is the first University of Bergen researcher to be awarded this prestigious grant.


The Ilulissat ice fjord in west Greenland, used to accompany article on the announcement of the ERC Consolidator Grant to Professor Noel Keenlyside.
PRODUCING MORE RELIABLE CLIMATE PROGNOSES: The excellent climate research environment in Bergen has been awarded with several major grants in the last few months. In January 2015, Professor Noel Keenlyside was awarded UiB’s first ever ERC Consolidator Grant for his research on sea-related climate change. The image shows the Ilulissat ice fjord in west Greenland.

Professor Noel Keenlyside has long made his mark in the climate research environment in Bergen, both at the Geophysical Institute at the University of Bergen (UiB) and the Bjerknes Centre for Climate Research, with tropical meteorology as his specialty. Another topic Keenlyside has done research into is in recent years is the weather fronts of the North Atlantic Ocean. In the past year, he has worked specifically on developing climate alerts.

“I am delighted to get this chance to develop research ideas that I truly believe will lead to better climate forecasts,” says Noel Keenlyside about the Consolidator Grant he has just been awarded from the European Research Council (ERC).


Bergen climate researchers making international impact

Keenlyside is the first UiB researcher to receive the Consolidator Grant, for the project Synchronisation to enhance reliability of climate prediction (STERCP). The project proposal is for five years. In his description of the project, Keenlyside suggests that climate prediction is the next frontier in climate research.

STERCP is the result of long-term collaboration with Keenlyside’s colleagues Mao-Lin Shen and Gregory Duane, who both also work at UiB’s Geophysical Institute and the Bjerknes Centre, which is known for its world-leading interdisciplinary work in climate research.

Keenlyside is the second UiB climate researcher to receive an ERC grant in the last few months. In November 2014, geologist Nele Meckler from UiB’s Department of Earth Science received an ERC Starting Grant, which is awarded to young promising researchers, for her work on reconstructing past climate conditions.

In December 2013, Professor Eystein Jansen and Associate Professor Kerim Nisancioglu, both also at UiB and the Bjerknes Centre, were awarded an ERC Synergy Grant, along with colleagues in Copenhagen. Their project is called Ice2Ice and looks at the relationship between melting Arctic sea ice and the ice cap in Greenland.

All these grants are the result of the on-going commitment to climate research at UiB, the Bjerknes Centre and the greater research community in Bergen.


Developing innovative techniques

Besides pointing out that climate prediction is the next frontier in climate research,

Keenlyside writes the following in his ERC proposal for the STERCP project:

“Prediction of climate on timescales from a season to a decade has shown progress, but beyond the ocean skill remains low. And while the historical evolution of climate at global scales can be reasonably simulated, agreement at a regional level is limited and large uncertainties exist in future climate change. These large uncertainties pose a major challenge to those providing climate services and to informing policy makers.

This proposal aims to investigate the potential of an innovative technique to reduce model systematic error, and hence to improve climate prediction skill and reduce uncertainties in future climate projections. The current practice to account for model systematic error, as for example adopted by the Intergovernmental Panel on Climate Change, is to perform simulations with ensembles of different models. This leads to more reliable predictions, and to a better representation of climate. Instead of running models independently, we propose to connect the different models in a manner that they synchronise and errors compensate, thus leading to a model superior to any of the individual models – a super model.

The concept stems from theoretical nonlinear-dynamics and relies on advanced machine learning algorithms. Its application to climate modelling has been rudimentary. Nevertheless, our initial results show it holds great promise for improving climate prediction. To achieve even greater gains, we will extend the approach to allow greater connectivity among multiple complex climate models to create a true super climate model. We will assess the approach’s potential to enhance seasonal-to-decadal prediction, focusing on the Tropical Pacific and North Atlantic, and to reduce uncertainties in climate projections. Importantly, this work will improve our understanding of climate, as well as how systematic model errors impact prediction skill and contribute to climate change uncertainties.”