Climate Services最新文献

Cities need to adapt to climate change in an increasingly rapid pace. Data and information on the existing and expected climate impact and the effectiveness of adaptive measures can support the planning and implementation of resilient urban planning. To inform urban climate change adaptation (CCA) in Germany a diverse landscape of climate services exists. However, the literature on usability gaps shows different barriers impeding the use potential of climate services. This study empirically analyzes the needs and barriers of municipal staff of different departments in Constance with regard to utilizing climate data and information. Surveying 72 and interviewing 10 municipal staffers, we found that climate data and information hold great potential for different public services but its handling poses many challenges. Furthermore, we found that a strategic approach mainstreaming climate data and information into cross-departmental work practices on urban CCA is crucial to anchor its usage in complex decision-making systems. The co-development of data-sensitive workflows, decision support tools, and capacity trainings can foster such integration. Based on the survey and interview results we designed a workflow on how to integrate such data and information strategically in municipal work processes.

One of the main instruments of risk management and climate risk reduction in the agricultural sector is the use of climate information services (CIS). Meteorological information can be useful for society by increasing the efficiency of resource consumption and helping to improve decision-making management. However, there is a gap between the production and implementation of weather forecasts, and that is the lack of practical use of weather forecasts in the agricultural decision-making process. In doing so, this research was conducted with the main objective of identifying components influencing the utilize of CIS by farmers in southwest Iran. The research utilized social cognitive theory (SCT) as the underlying framework, employing a survey method and employing Structural Equation Modeling (SEM) for analysis. The research encompassed all farmers residing in Dezful city within the Khuzestan province, located in the southwest of Iran. The results showed that the variables of SCT, namely outcome expectations, perception of others’ behavior, self-efficacy, socio-structural, and behavioral intentions are very powerful in identifying factors affecting farmers’ use of CIS and can account for 76.4% of the variance of farmers’ behavior. Therefore, these research results can provide essential help to authorities to mitigate the negative impacts of climate change on agriculture.

Changes in the frequency of extreme weather events related to climate change potentially pose significant challenges to UK agricultural production. There is a need for improved climate change risk assessments to support adaptation strategies and to ensure security of food production in future.

We describe an innovative and practical framework for spatially explicit modelling of climate change impacts on crop yields, based on the UKCP18 climate projections. Our approach allows the integration of relatively simple crop growth models with high spatial and temporal resolution Earth Observation datasets, describing changes in crop growth parameters within year and over the longer term. We focus on modelling winter wheat, a commercially important crop. We evaluate the results of the model against precision yield data collected from 719 fields. We show that the assimilation of leaf area index data from Sentinel-2 satellite observations improves the agreement of the modelled yields with those observed. Our national-scale results indicate that wheat production initially becomes more favourable under climate change across much of the UK with the projected increase in temperature. From 2050 onwards, yields increase northwards, whilst they decline in South East England as the decrease in precipitation offsets the benefits of rising temperature.

Our framework can readily accommodate growth models for other crops and LAI retrievals from other satellite sensors. The ability to explore impacts of crop yields at fine spatial resolutions is an important part of assessing the potential risks of climate change to UK agriculture and of designing more climate resilient agricultural systems.

This study is the second of a two-part series presenting a novel weather regime-based stochastic weather generator to support bottom-up climate vulnerability assessments of water systems in California. In Part 2 of this series, we present how the model is used to develop an ensemble of climate change scenarios based on both thermodynamic and dynamic signals of climate change. The ensemble includes a suite of 30 climate change scenarios, each consisting of 1000 years of simulated daily climate data (precipitation, maximum temperature, minimum temperature) at a 6 km resolution across the entire state of California. The 30 scenarios represent a range of plausible climate changes to temperature, average precipitation, and precipitation extremes that are reflective of thermodynamic responses of the atmosphere to warming. An additional two scenarios are also created that represent changes in the frequency of weather regimes (e.g., dynamic climate change). Results from these scenarios reveal that when the effects of anthropogenic climate change are combined with plausible realizations of natural climate variability, the severity of extremes in California is amplified significantly. In addition, recent changes in the frequency of large-scale patterns of atmospheric circulation can have impacts of similar magnitude to large (>10%) declines in average precipitation, particularly with respect to drought. The scenarios developed in this work are designed to allow water managers to systematically test the sensitivity of their water system to different combinations of climate change, so that key vulnerabilities can be discovered and then addressed through adaptation planning.

This paper presents the Interactive Digital Climate Atlas of the Canary Islands, a web portal based on geographic information systems for the dissemination of climate information. It provides citizens and public and private administrations with high spatial resolution maps (100 m) of different climate variables such as temperature, rainfall, relative humidity, cloudiness frequency and wind velocity. Also available are Köppen climate classification maps and decadal trends of temperature and precipitation in a context of climate change. This spatial resolution is suitable for the detailed climatic description of small and orographically complex islands. For this purpose, climate series from databases of different public institutions as well as data from remote sensing and reanalysis systems were employed. A statistical process involving data filtering and the detection and correction of inhomogeneous segments was carried out as a preparatory step. Then, a spatial interpolation model was developed using a multiple linear regression method for the generation of the high spatial resolution climate cartography. The results are annual and monthly maps of the climate variables in Canary Islands at 100 m spatial resolution and with different time periods depending on the availability of data on the different variables, but in most cases between 1991 and 2020. Following the European Directive INSPIRE (Directive 2007/2/CE, Infrastructure for Spatial Information in Europe), both map visualisation and downloading in GeoTIFF and NetCDF format are permitted, as well as consultation of the data and metadata of the climatic series used.

Climate services aim to provide climate information to aid decision-making. However, in practice their effectiveness is hampered by factors such as incompatibility with decisions, neglect of user’s context, and limited understanding of the information provided. Enhancing climate services’ user orientation necessitates capacity building to bolster the knowledge and collaborative skills among actors in the climate production chain. This study introduces a learning framework for capacity building in climate services that was developed using iterative systematic cycles for educational design research. The framework contains seven steps, along with relevant guiding questions, derived from the evaluation of case studies and the trainer’s experiences in 21 training events conducted under the User Learning Services (ULS) of the Copernicus Climate Change (C3S). Overall, this study offers a learning framework that can guide the content and design of capacity building in future climate information services. This contributes significantly to the ongoing effort to enhance and understand the impact of climate services on decision making.

Under the pressing warming of climate, interpretable and useful-for-adaptation information has become a need in society and has promoted rapid methodological advances in climate science. One such advance is the development of the dynamical-storyline approach, with which the spread in multi-model scenario projections can be represented as a set of physically plausible scenarios (storylines) defined by (a) a global warming level and (b) changes in large-scale dynamical conditions that arise from climate forcing. Moreover, if changes in regional climate are assessed in such a way that they can clearly inform societal systems or management of natural ecosystems, they can potentially aid decision-making in a practical manner. Such is the aim of the climatic impact-driver (CID) framework, proposed in the Sixth Assessment Report (AR6) from the Intergovernmental Panel on Climate Change. Here, we combine the dynamical-storyline approach with the CID framework and apply them to climate services. We focus on CIDs associated with the viticulture sector and the region of the South American Andes, where currently both Argentina and Chile produce wine. We explain the benefits of this approach from a communication and adaptation perspective. In particular, we found that the CIDs related to seasonally aggregated temperatures are mainly dependent on the global warming level although in some regions, but they can also be sensitive to changes in dynamical conditions. Meanwhile, CIDs related to extreme temperature values and precipitation depend strongly on the dynamical response. We show how adaptation to climate-related compound risks can be informed by a storyline approach, given that they can address compound uncertainty in multiple locations, variables and seasons.

Climate change concerns the energy sector to a high degree because the sector is sensitive both to changing conditions for power and heat production, and to changing demand for electricity, heating and cooling. In this study potential consequences of climate change on different parts of the Swedish energy sector were assessed in a series of workshops, where climate and energy scientists, energy systems experts and analysts met with representatives of the energy sector to assess the vulnerability of the sector and consider what climate indicators could be used to assess impacts of relevance.

The impact of climate change depends on the energy type. Hydropower, for which production is naturally linked to weather and climate, is significantly impacted by climate change. For other forms of production, such as nuclear power, other factors such as e.g. policy and technology development are more important. The series of workshops held in this study, where different aspects of climate change and consequences were discussed, proved very successful and has increased our understanding of climate impacts on the energy system.

Even though the weather-dependent agricultural sector is severely impacted by climate change and requires more adaptive measures, climate change adaptation in the global south is slow. The perception of farmers, who are the final decision-makers, strongly influences the adoption of climate-smart technologies and the (un)willingness to follow recommended measures. Although perception studies have attracted the international community’s interest, these studies have disregarded the heterogeneity within the farming community. Our study uses Q-methodology to address this gap by examining small-scale farmers’ perspectives on climate change and their perceived adaptation capacity in wetland areas of north-eastern Bangladesh. Following post-sort interviews, 36 farmers were invited to participate in the Q-sorting using 34 pre-selected statements. The study revealed three distinct types of perspectives on climate change and adaptive capacity: theists who believe in the act of God and take a “do nothing” approach, realists who believe in climate change but are unaware of climate change trade-offs, and pragmatists who recognize climate change and actively pursue adaptations. The awareness of different climate change perspectives can support policymakers and extension service providers. By replacing their one-size-fits-all approach, they can better assist wetland farmers in developing and implementing tailored adaptation strategies.