Weather forecasts are rarely valid more than ten days ahead. This is a consequence of the chaotic nature of the weather system, also known as the "butterfly effect". Our project mainly revolves around using models to predict weather long into the future and using statistical methods to minimise the systematic errors in the models. Our overall aim is to employ innovative approaches to make seasonal forecasts better and more relevant for users in the public and private sectors.
With a basis in our user group, we were granted funding to launch a new Centre for Research-based Innovation under the Research Council of Norway?s SFI programme. Climate Futures is coordinated by NORCE and has about 40 partners. In addition to the research partners in our projects, the Norwegian School of Economics, SNF, Statistics Norway, and the Meteorological Institute of Norway participate. A wide range of companies and public organizations take part as user partners. These mainly come from four innovation areas: Sustainable Food Production, Resilient Societies, Renewable Energy, and Smart Shipping. All of these areas are in need of better predictions from 10 days to several years into the future. This means that we now have a large and relevant user group, with ample opportunities to develop exciting and applied projects along with the partners.
In addition, our project group was granted funding for a new EU (Horizon 2020) project on climate services for Africa, with partners from Kenya, South Africa, United Kingdom and Germany. The CONFER (Co-production of climate services for East Africa) project is coordinated by NORCE and builds on and expands the research done in our project.
Our work has led to 13 peer-reviewed scientific papers. These have investigated, among other things, the influence of the polar stratospheric vortex on weather forecasting busts during the winter, the ability of the numerical forecasting models to predict tropical cyclones between Mozambique and Madagascar, the influence of ocean current on the climate of Norway, on the role of snow as a mediator of persistence in the climate system, on how sea surface temperatures and Arctic sea ice cover can be used in seasonal forecasting, and on what role assimilation of sea surface temperatures and sea ice plays for the predictive skill of our in-house climate prediction model NorCPM.
Our project has been mentioned 67 in the media. In particular, the monthly updates of our seasonal forecasts have led to a large interest. Our web pages were shown more than 10,000 times in 2020. The media?s interest in our project has given us many useful contacts and an excellent platform for outreach activities.
The aim of SFE is to develop a state-of-the-art operational seasonal climate prediction system for Northern Europe and the Arctic. Tailored seasonal predictions can be helpful tools for risk mitigation, and they can guide more efficient use of resources in many sectors of society, including agriculture, energy, water, transportation, and insurance.
To our users, the SFE will be accessible through a flexible interface which can be queried to obtain predictions of relevant climate indices and variables. Under the hood, our "engine" consists of statistical algorithms that merge vast amounts of data into unified forecasts.
As we increasingly understand the mechanisms that drive the enormously complex climate system, both dynamical and empirical models are steadily improving. At the same time, increased computational power, enhanced observations and remote sensing, and advanced statistical methods to blend models and observations, are driving a big data-fuelled revolution in climate prediction. What is urgently needed now is careful, but speedy, transformation of research into innovative practical applications and services.
Our team consists of experts in handling big data, statisticians, climatologists, climate modellers, and climate service practitioners. Working in complementary fashion with the international research community, and guided by an international peer advisory committee, we will both improve our own models and, taking existing seasonal forecast ensembles, employ innovative empirical-statistical approaches to make the forecasts better and more relevant for our users.