David N. Bresch

Adaptation to changing and new environmental conditions is of fundamental importance to sustainability and requires concerted efforts amongst science, policy, and practice to produce solution-oriented knowledge. Joint knowledge production or co- production of knowledge has become increasingly popular terms to describe the process of scientists, policy makers and actors from the civil society coming together to cooperate in the production, dissemination, and application of knowledge to solve wicked problems such as climate change. Networks are particularly suited to produce knowledge in a joint fashion. However, the process of joint knowledge production (JKP) in networks has rarely been examined. In this paper, we present a sketch of the adaptation network landscape and assess how joint knowledge production supports the development of solution-oriented knowledge in climate change adaptation networks. We conclude that the processes of JKP are diverse, complex, and highly dependent on the interests and roles of actors within the network. To keep such processes alive, signposts in form of analysis and intermediary products along the network lifetime should be positioned as means of stocktaking and monitoring for the future.

In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles — parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets.