2019, Zscheischler, Jakob, Fischer, Erich M., Lange, Stefan

Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure. © 2019 Copernicus GmbH. All rights reserved.

2011, Reusser, D.E., Wrobel, M., Nocke, T., Sterzel, T., Förster, H., Kropp, J.P.

Adaptation to climate change is gaining attention and is very challenging because it requires action at a local scale in response to global problems. At the same time, spatial and temporal uncertainty about climate impacts and effects of adaptation projects is large. Data on climate impacts and adaptation is collected and presented in web-based platforms such as ci:grasp, which is unique in its structuredness and by explicitly linking adaptation projects to the addressed climate impacts. The challenge to find an adequate and readable representation of uncertainty in this context is large and research is just in the initial phase to provide solutions to the problem. Our goal is to present the structure required to address spatial and temporal uncertainty within ci:grasp. We compare existing concepts and representations for uncertainty communication with current practices on web-based platforms. From our review we derive an uncertainty framework for climate information going beyond what is currently present in the web. We make use of a multi-step approach in communicating the uncertainty and a typology of uncertainty distinguishing between epistemic, natural stochastic, and human reflexive uncertainty. While our suggestions are a step forward, much remains to be done.

2017, Hewitson, Bruce, Waagsaether, Katinka, Wohland, Jan, Kloppers, Kate, Kara, Teizeen

The climate change agenda is populated by actors and agencies with different objectives, values, and motivations, yet many seek decision scale climate information to inform policy and adaptation responses. A central element of this network of activity is the climate information website (CIW) that has seen a rapid and organic growth, yet with variable content and quality, and unfettered by any code of practice. This builds an ethical–epistemic dilemma that warrants assessment as the presence of CIWs contribute to real-world consequences and commitment. This study considers the context of CIW growth, and reviews a representative sample of CIWs to draw out key issues for consideration in CIW development. We assess content, function, and use-case value through a dual approach of a typology and user experience narratives to evaluate the general efficacy of a CIW. The typology reveals strong contrasts in content, complicated interfaces, and an overload of choice making it difficult to converge on a stable outcome. The narratives capture user experience and highlight barriers that include navigation difficulties, jargon laden content, minimal or opaque guidance, and inferred information without context about uncertainty and limits to skill. This illuminates four concerns: (1) the ethics of information provision in a context of real-world consequences; (2) interfaces that present barriers to achieving robust solutions; (3) weak capacity of both users and providers to identify information of value from the multimodel and multimethod data; and (4) inclusion of data that infer skill. Nonetheless, results provide a positive indication of a community of practice that is still maturing. WIREs Clim Change 2017, 8:e470. doi: 10.1002/wcc.470. For further resources related to this article, please visit the WIREs website.