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- Hanssen-Bauer, I., et al.
(författare)
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Statistical downscaling of climate scenarios over Scandinavia
- 2005
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Ingår i: Climate Research. - : Inter-Research Science Center. - 0936-577X .- 1616-1572. ; 29:3, s. 255-268
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Tidskriftsartikel (refereegranskat)abstract
- Studies from recent years involving development and application of statistical downscaling models for Scandinavia (mainly Norway and Sweden) are reviewed. In most of the studies linear techniques were applied. Local temperature and/or precipitation were predictands in a majority of the studies. Large-scale temperature fields, either from 2 m or 850 hPa, were found to be the best predictors for local temperature, while a combination of atmospheric circulation indices and tropospheric humidity information were the best predictors for local precipitation. Statistically downscaled temperature scenarios for Scandinavia differ depending on climate model, emission scenario and downscaling strategy. There are nevertheless several common features in the temperature scenarios. The warming rates during the 21st century are projected to increase with distance from the coast and with latitude. In most of Scandinavia higher warming rates are projected in winter than in summer. For precipitation, the spread between different scenarios is larger than for temperature. A substantial part of the projected precipitation change is connected to projected changes in atmospheric circulation, which differ considerably from one model integration to another. A tendency for increased large-scale humidity over Scandinavia still implies that projections for the 21st century typically indicate increased annual precipitation. This tendency is most significant during winter. In northern Scandinavia the projections tend to show increased precipitation also during summer, but several scenarios show reduced summer precipitation in parts of southern Scandinavia. Comparisons with results from global and regional climate models indicate that both regional modeling and statistical downscaling add value to the results from the global models.
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| 4. |
- Benestad, R.E, et al.
(författare)
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On using principal components to represent stations in empirical-statistical downscaling
- 2015
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Ingår i: Tellus. Series A, Dynamic meteorology and oceanography. - : Stockholm University Press. - 0280-6495 .- 1600-0870. ; 67:28326
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Tidskriftsartikel (refereegranskat)abstract
- We test a strategy for downscaling seasonal mean temperature for many locations within a region, based on principal component analysis (PCA), and assess potential benefits of this strategy which include an enhancement of the signal-to-noise ratio, more efficient computations, and reduced sensitivity to the choice of predictor domain. These conditions are tested in some case studies for parts of Europe (northern and central) and northern China. Results show that the downscaled results were not highly sensitive to whether a PCA-basis or a more traditional strategy was used. However, the results based on a PCA were associated with marginally and systematically higher correlation scores as well as lower root-mean-squared errors. The results were also consistent with the notion that PCA emphasises the large-scale dependency in the station data and an enhancement of the signal-to-noise ratio. Furthermore, the computations were more efficient when the predictands were represented in terms of principal components.
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| 5. |
- Moe, S. Jannicke, et al.
(författare)
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Integrating climate model projections into environmental risk assessment : A probabilistic modeling approach
- 2024
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Ingår i: Integrated Environmental Assessment and Management. - 1551-3777 .- 1551-3793.
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Tidskriftsartikel (refereegranskat)abstract
- The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;00:1-17. (c) 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). A SETAC workshop was organized in 2022 to address the integration of future projections from global climate models (GCMs) into environmental risk assessment models.The modeling approach presented is based on deriving on robust climate information with relevance for the assessment: future climate projections from ensembles of GCMs, regionally downscaled, and summarized by statistical properties.Three case studies in Norway, Australia, and the United States were used to show examples of quantification of climate change impacts on traditional risk assessment components such as chemical exposure and hazard, as well as on the vulnerability of assessment endpoints to other stressors.The case studies also demonstrated that probabilistic modeling methods such as Bayesian networks can be useful for integrating all quantified climate change impacts on risk components, together with estimated uncertainty, into a probabilistic risk characterization.
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