Due to increasing temperatures, the growing season is expanding in Norway, except on glaciers and in high mountain areas. It now starts earlier and ends later in the year, when night–time temperatures may still be low. Therefore, despite a decreasing number of frost days in a generally warmer climate, a longer growing season may lead to an increased risk of frost early or late in the growing season. Frost early in the growing season can pose a risk for several plant species. In Norway, there have been incidents where fruit crops were destroyed after an unusually warm period followed by a cold spell. In this study, we show that frost in the beginning of the growing season is already a risk factor in lowland areas of South Norway, with the highest risk in the southeast. Changes between the periods 1961–1990 and 1991–2020 show the largest increase in the number of days along the west coast of South Norway, which is also where the growing season has increased the most. Future scenarios indicate an even higher risk of frost days in the beginning of the growing season along the coast of Norway, except in the northernmost areas. The increase in risk is highest in the same areas where the growing season is projected to increase the most. In contrast, the risk decreases in many inland areas of South Norway.
Successful application of numerical weather prediction (NWP) and emerging data-driven forecast models depends not only on their objective performance but also on how forecasters interpret, trust and communicate their outputs and uncertainties. This study presents insights from a series of verification workshops conducted with operational duty forecasters at the Norwegian Meteorological Institute. The workshops aimed to foster dialogue between model developers and forecasters, improve verification systems, and create evaluation procedures that serve both model development and operational forecasting. The workshops explored how forecasters evaluate model quality, identified parameters and products that are challenging to forecast or evaluate, and explored necessary tools for building trust in model products, including data-driven forecasts. Forecasters emphasized the importance of personal operational experience, peer interaction, and case-based verification in building knowledge about model quality. The findings indicate that forecasters' experience-based verification complements quantitative objective verification and plays an important role in forecasters' decision-making. Forecasters expressed a need for verification systems tailored to operational situations, such as specific events or similar past cases, and highlighted the importance of practical tools that can be used during shifts. They also stressed the need for reliable verification that includes comparisons with traditional NWP models for building trust in data-driven models, particularly for extreme events. Overall, the study highlights the importance of the communication of model strengths and weaknesses and the co-development of tools to support operational forecasting. It provides a basis for further development of verification practices and enhancing collaboration between forecasters and model development.
Weather forecasts are widely used for day-to-day decision making, and the recipients of the forecasts rely on accurate information to be able to make the best possible decisions. However, despite advances in forecasting, errors can still occur such an incorrect prediction of timing, location, intensity or persistence of a weather event. Based on the results from two surveys, this work aims to investigate how important the accuracy of each of these forecast attributes is to the general public by asking them to rate forecasts where one of the attributes is not correct. We show that even though all attributes are considered important for a satisfactory and useful forecast, forecasts that underestimate the intensity of an event or assume intermittent rainfall while the event turns out to be continuous receive the lowest ratings. Notably, we also found that survey participants rate forecasts worse if they perceive them as missed events rather than false alarms. Understanding the public's priorities regarding forecast attributes may contribute to forecasters' decisions about which attributes to prioritize or help the development of additional verification methods that take these preferences into account when evaluating the usefulness of forecasts for their users.
Human endeavours are becoming ever more sensitive to the weather in a changing climate. Accurate and timely forecasts and warnings of weather and its impacts provide valuable intelligence to take actions and to avoid harm. Furthermore, weather intelligence increases economic productivity enabling businesses and other enterprises make more efficient decisions. The quality and richness of weather intelligence is increasing substantially because of scientific and technological advances in areas such as remote sensing, modelling and simulation, data science, behavioural science and artificial intelligence. These advances are happening alongside the digital transformation of economies and societies, presenting a unique opportunity to integrate weather intelligence with the digital transformation. Realising the full value of weather intelligence in this context needs diverse data resources to be brought together and it requires collaboration among private, public and academic sector actors in the global weather enterprise including National Meteorological and Hydrological Services (NMHSs). Here we examine how the opportunities arising from innovative science and technology and the digital transformation of economies can be exploited to enhance decision-making for societal benefit. Whilst these general considerations apply to many countries and regions, here we discuss their application within South Asia.
The exchange of adaptation-relevant climate information between scientists, stakeholders and the general public is marked by a gap between user needs and provided information. This multidimensional gap can be described in terms of temporal and spatial scales, variable selection, specificity of needs, and consideration of uncertainty. To bridge this gap, we argue for a multi-way format of co-creating (a) a viable form of information exchange and (b) the relevant information itself, while recognising the needs of users and capabilities of providers. This is to ensure that relevant information can be provided to users who are motivated to apply them. We here describe the offer-need gap in the Main River catchment (central Germany), which is increasingly characterized by climate change and user-induced water scarcity, and present a framework for bridging the gap in stakeholder dialogues.
