The activity in the center is organized in short pilot projects and longer strategic projects. At the moment, we have 19 such projects that are divided on the four thematic innovation areas of the centre and some that are cross-cutting across the innovation ares.
Sustainable Food Production
- Improve forecasts based on the dry summer of 2018
- Forecasting of sea surface temperature for fish farms
- User panel for the agricultural sector
- Prediction of the first frost of the fall/winter season
- Modelling potato and onion crop production and demand
- Designing the potato of the future
- The effect of climate change on Norwegian potato production
- Vulnerability analysis for fruit and vegetables to “compound events”
- Political implications of using seasonal forecasts in the agricultural negotiations
- Forecasting of sea ice for the shipping market
- The effect of ocean temperature on hull fouling during port calls and the consequences for later fuel consumption
- Forecasting of fuel consumption on ship tracks based on probability forecast of weather
- Forecasting of water levels in Argentinian rivers
- Use of seasonal forecasts in hydrological models
- Exploring skill in using multi-year climate forecasts in the hydro-power sector
- Use of seasonal forecasts in projections of electricity prices
- Developing better monthly forecasts for the energy sector based on machine learning
- How to understand and consider climate risk?
- Wind forecasts on S2S time scales
- Connecting seasonal forecasts to insurance data
- Handling of climate risk in the finance sector
- Improving forecasts from NorCPM
- Monthly forecasts on Yr.no
- Language and uncertainty – how do we talk about and how do we understand uncertainty?
- Inclusion of monthly forecasts in operational weather services
- Adaptation and accessibility of NorCPM data for end-users.
- Use of observed correlations to improve monthly forecasts
- Precipitation forecasts over South America on interannual to decadal time scale
Seasonal Forecasting Engine
The aim of SFE is to develop a state-of-the-art operational seasonal climate prediction system for Northern Europe and the Arctic. Our motivation is that many companies and public stakeholders face climate-related risks that must be managed to stay competitive and to protect life, property and the environment. 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.
Bjerknes Climate Prediction Unit
The BCPU’s primary objective is to enhance climate prediction to the level where it benefits society, and thus facilitate the needed transition to operational forecasts. The centre focuses on predicting climate in the Atlantic-to-Arctic sector and surrounding continents from a season to a decade and beyond.
CONFER is a multi-national collaboration to bolster resilience to climate impacts and reduce disaster risk in East Africa, potentially reaching 365 million people in eleven countries. Our main objective is to co-develop dedicated climate services for the water, energy and food security sectors with stakeholders and end-users, to enhance their ability to plan for and adapt to seasonal climate fluctuations. The scientific work in CONFER is ambitious and aims to break new ground along three inter-related tracks. First, we will secure end-user engagement by using the Greater Horn of Africa Climate Outlook Fora, which are held three times per year and attract about 200 stakeholders, as platforms for co-production of new and dedicated climate services for our focus sectors. Second, we will improve on the accuracy and local detail of numerical prediction model outputs for East Africa, with a particular focus on seasonal prediction. Third, we will develop statistical and machine learning tools to obtain a new level of seasonal forecast skill based on numerical models and high- resolution satellite data. We will also involve our scientific experts in a large training and capacity development programme designed to enhance climate information uptake in our focus sectors. Read more on the CONFER project’s website.