Abstract. Oceanographic fronts are transitions between thermohaline structures with different characteristics. Such transitions are ubiquitous, and their locations and properties affect how the ocean operates as part of the global climate system. In the Southern Ocean, fronts have classically been defined using a small number of continuous, circumpolar features in sea surface height or dynamic height. Modern observational and theoretical developments are challenging and expanding this traditional framework to accommodate a more complex view of fronts. Here, we present a complementary new approach for calculating fronts using an unsupervised classification method called Gaussian mixture modelling (GMM) and a novel inter-class parameter called the I-metric. The I-metric approach produces a probabilistic view of front location, emphasising the fact that the boundaries between water masses are not uniformly sharp across the entire Southern Ocean. The I-metric approach uses thermohaline information from a range of depth levels, making it more general than approaches that only use near-surface properties. We train the GMM using an observationally constrained state estimate in order to have more uniform spatial and temporal data coverage. The probabilistic boundaries defined by the I-metric roughly coincide with several classically defined fronts, offering a novel view of this structure. The I-metric fronts appear to be relatively sharp in the open ocean and somewhat diffuse near large topographic features, possibly highlighting the importance of topographically induced mixing. For comparison with a more localised method, we also use an edge detection approach for identifying fronts. We find a strong correlation between the edge field of the leading principal component and the zonal velocity; the edge detection method highlights the presence of jets, which are supported by thermal wind balance. This more localised method highlights the complex, multiscale structure of Southern Ocean fronts, complementing and contrasting with the more domain-wide view offered by the I-metric. The Sobel edge detection method may be useful for defining and tracking smaller-scale fronts and jets in model or reanalysis data. The I-metric approach may prove to be a useful method for inter-model comparison, as it uses the thermohaline structure of those models instead of tracking somewhat ad hoc values of sea surface height and/or dynamic height, which can vary considerably between models. In addition, the general I-metric approach allows front definitions to shift with changing temperature and salinity structures, which may be useful for characterising fronts in a changing climate.
Most studies into the effects of climate change have headline results in the form of a global change in mean temperature. More useful for businesses and governments, however, are measures of the localized impact, and also of extremes rather than averages. We have addressed this by examining the change in frequency of exceeding a daily mean temperature threshold, defined as ‘disruption days’, as it is often this exceedance which has the most dramatic impacts on personal or economic behaviour. Our exceedance analysis tackles the resolution of climate change both geographically and temporally, the latter specifically to address the 5‐ to 20‐year time horizon which can be recognized in business planning. We apply bias correction with quantile mapping to meteorological reanalysis data from ECMWF ERA5 and output from CMIP5 climate model simulations. By determining the daily frequency at which a mean temperature threshold is exceeded in this bias‐corrected dataset, we can compare predicted and historic frequencies to estimate the change in the number of disruption days. Furthermore, by combining results from 18 different climate models, we can estimate the likelihood of more extreme events, taking into account model variations. This is useful for worst‐case scenario planning. Taking the city of Chicago as an example, the expected frequency of years with 40 or more disruption days above the 25°C threshold rises by a factor of four for a time period centred on 2040, compared with a period centred on 2000. Alternately, looking at the change in the number of days at a given likelihood, an example is Shenzhen, where the number of disruption days in a once‐per‐decade event exceeding the 25°C or 30°C threshold is expected to rise by a factor of four. In a future stage, superimposing these results onto maps of, for instance, GDP sensitivity or production days lost, will provide more accurate and targeted conclusions for future impacts of climate change. This method of quantifying costs on business‐relevant timescales will enable businesses and governments properly include risks associated with facilities, plan mitigating actions and make accurate provisions. It can also, for example, inform their disclosure of physical risks under the framework of the Task Force on Climate‐related Financial Disclosures. This approach is equally applicable to other weather‐related, localized phenomena likely to be impacted by climate change.
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