The Correlated Extremes Workshop last month catalyzed many fascinating conversations about how impacts-relevant research should be conducted; the conceptual and logistical hurdles that need to be overcome for successful multidisciplinary collaborations; and the emerging ways in which much of climate science is tending toward an Earth-systems-modeling approach where it is the interaction of innumerable preconditioning and initial-conditions factors that shape the ultimate extreme event. I've written up a full summary of the proceedings and some of their key takeaways here.
Below, I elaborate on some themes from the workshop that are specifically relevant to regional-climate scales.
Need for integration with engineering and land use/urban planning communities
These are the groups who decide what gets built, and how. At a time when there is more and more recognition of the importance of these kinds of decisions for extreme climate events themselves, not to mention their impacts, there are large opportunities for making such decisions as sensibly as possible with existing climate information and tools, and for furthering this knowledge with targeted decision-relevant collaborations going forward.
Broadening of data about demographics, economic networks, and the like
Geography in its most elemental sense -- where things are located -- plays a crucial role in affecting vulnerabilities on intra-urban scales. The types of granular data that are needed to make assessments about the true effects of particular combinations of extreme events, however, is largely uncollected or (at least) not compiled into easily usable forms. This encompasses sociocultural resilience (the 'cohesion' of a neighborhood) and micro-level data on trading networks, among many others.
Broadening of metrics to include more-intangible factors
Metrics used to assess the impacts of extreme events typically involve easily measured variables, with the ultimate example being counts of a binary outcome, e.g. numbers of people admitted to hospitals with a certain weather-related condition, or estimates of economic losses from an event. Even governments fall prey to the simplicity trap, based their preparation decisions on metrics that are primarily monetary. Needless to say, these hardly capture the range of damages that people care about, and which consequently affect their decision-making, whether as a prospect or a past experience. Although difficult, making serious efforts to incorporate impacts on quality of life would likely yield a much more faithful representation of the serious qualitative changes in a societal system that could stem from a correlated extreme event.
Variability in correlated extremes is significant, and highly affected by large-scale conditions
On timescales from seasonal to subdecadal, conditions that favor correlated extremes of all stripes vary a lot, up to an order of magnitude or more depending on location and event type. This is often because the timescale of the controlling conditions (e.g. large-scale zonal flow, or a certain SST pattern) is much longer than the timescale of the hazards, so the former can induce multiple of the latter in close succession, with accompanying severe impacts. This situation, and our greater ability to understand when it is in place, raises the possibility of predicting how the probability of correlated extremes varies for a small geographical area in response to known large-scale modulators, anthropogenic or natural, such as ENSO or reduced high-latitude sea ice or snow cover.
Tipping points exist and are important, but aren't always foreseeable
This awareness of the possibility of unforeseen cascading impact incentivizes a somewhat different approach to regional correlated extremes than has previously been practiced. The interconnectedness of regions in terms of dimensions which we care about (economic, cultural, etc.) means they can be conceptualized as tight networks, for which it is possible to estimate a characteristic sensitivity to climate stressors, including feedback responses. Tracing all such causal linkages is essentially impossible, which is where approaches based more on sampling the possibility space, such as 'storylines' and 'wargames', may provide more actionable realizations about what tipping points exist and where they lie. However, another essential aspect of tipping points is their complex and shifting nature, meaning that frequent updates are necessary as average conditions shift. Wealthy regions in the developed world are of course best-equipped to conduct such studies for themselves, but when valuing by lives or livelihoods instead of dollars, crowded and poor areas in the developing world are most deserving of ascertaining tipping points beyond which quality of life would erode, as these are where the greatest vulnerabilities lie. For example, what level of sea-level rise in low-lying regions could set off intense land-based conflicts for the remaining 'high ground'? Needless to say, such conflicts would by no means be confined to their region of origin. Globalization of economy and culture has also tended to produce globalization of climate-related conflict, whether it is the great-power struggle in Syria, Somalian pirates attacking shipping lanes, or immigration due to unprecedented heat creating poor farming conditions in Central America. Conflict thus represents one of multiple pathways through which regional climate has the ability to cause global problems, and consequently this presents a compelling motivation for studying it closely when climate and societal challenges combine in intricate and unfortunate ways.
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