Uncertainties in projecting climate-change impacts in marine ecosystems

Abstract

Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal variability uncertainty, (iii) parametric uncertainty, and (iv) scenario uncertainty. For each uncertainty type, we then examine the current state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gains on the seasonal-to-decadal time-scale. We conclude that all parts of marine science could benefit from a greater exchange of ideas, particularly concerning such a universal problem such as the treatment of uncertainty. Finally, marine science should strive to reach the point where scenario uncertainty is the dominant uncertainty in our projections.

Uses of Innovative Modeling Tools within the Implementation of the Marine Strategy Framework Directive

“…There is a risk that the pressure to implement and fulfill legislative requirements could affect the entire process of assessment. Acknowledgment of uncertainty (in both data and models; Carstensen and Lindegarth, 2016;Payne et al, 2016;Peck et al, 2016), recognition of coupled socialecological systems and that decisions reflect societal choice, and the acknowledgment of these trade-offs are therefore needed in GES indicator development (Long et al, 2015). Models can support these aims, as exemplified below.…”

“…The scientific community is now addressing this issue, for instance, through the use of lay language, more accessible to policy makers, in the expression of confidence attributed to modeling results (Pörtner et al, 2014). Further to this, the partitioning of sources of uncertainty in climate change impact projections, and the explicit assessment of their contributions, are paramount to improve the perception of confidence in modeling results in research-policy communication (Payne et al, 2016). For instance, though explicit recognition and quantification of how physical and biogeochemical model structure, initialization, internal variability, parametric, and scenario uncertainties are carried forward into fish distribution models (Gårdmark et al, 2013;Cheung et al, 2016;Payne et al, 2016) used to derive GES indicators such as those described here.…”

Progress in designing and delivering effective fishing industry-science data collection in the UK

“…Fisheries are also increasingly recognized as an integrated system with ecological, economic, social, and institutional aspects that require interdisciplinary approaches and a more participatory governance structure (Stephenson et al, 2016). Further, there is increasing uncertainty in resource management resulting from the impact of climate change on many marine ecosystem components (Littell et al, 2011;Payne et al, 2016). These challenges, and the expanding objectives for sustainability need to be supported by diverse types of information and methods to provide tactical and strategic decisions across multiple spatial and temporal scales.…”

A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0

“…Yet the pace and magnitude of projected climate change over the coming century, in combination with fisheries exploitation and a raft of other human impacts, suggests that marine ecosystems will remain under considerable pressure in the mid-to long-term (Pörtner et al, 2014;UN, 2016). Identification of the potential future effects of these pressures, even with high uncertainty (Payne et al, 2016), is required to anticipate the impacts of environmental change on ecosystem resilience (Bernhardt and Leslie, 2013), biodiversity conservation (Cheung et al, 2016a;5 Queirós et al, 2016), socio-economics (Fernandes et al, 2017) and food security (Barange et al, 2014;Merino et al, 2012).…”

“…CMIP and other efforts have highlighted differences among models, provided ranges of potential climate change responses and ensemble projections for end-users, and allowed the outputs of individual analyses to be interpreted in a broader context. They have also provided a 15 quantification of the relative contributions of different sources of uncertainty to projected uncertainties in climate responses (Hawkins and Sutton, 2009;Payne et al, 2016). In addition to model intercomparison experiments for the climate and ocean system, a systematic intercomparison and assessment of impact models -including the marine realm -is similarly essential for understanding the impacts of (and associated uncertainty around) climate change on important biological and human systems (Barange et al, 2014).…”