Abstract. Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.

We demonstrate and validate a Bayesian approach to model calibration applicable to computationally expensive General Circulation Models (GCMs) that includes a posterior estimate of the intrinsic structural error of the model. Bayesian artificial neural networks (BANNs) are trained with output from a GCM and used as emulators of the full model to allow a computationally efficient Markov Chain Monte Carlo (MCMC) sampling of the posterior for the GCM parameters calibrated against seasonal climatologies of temperature, pressure, and humidity. We validate the methodology by calibrating to targets produced by a model run with added noise. We then demonstrate a calibration of five GCM parameters against an observational data set. The approach accounts for both parametric and structural uncertainties of the model as well as uncertainties associated with the observational calibration data. This enables the generation of statistically rigorous probabilistic forecasts for future climate states. All calibration experiments are performed with emulators trained using a maximum of one hundred model runs, in accord with typical resource restrictions imposed by computationally expensive models. We conclude by summarizing remaining issues to address in order to create a complete and validated operational methodology for objective calibration of computationally expensive models.

Climate change will alter ecosystems and impose hardships on marine resource users as fish assemblages redistribute to habitats that meet their physiological requirements. Marine gadids represent some of the most ecologically and socio-economically important species in the North Atlantic, but face an uncertain future in the wake of rising ocean temperatures. We applied CMIP5 ocean temperature projections to egg survival and juvenile growth models of three northwest Atlantic coastal species of gadids (Atlantic cod, Polar cod, and Greenland cod), each with different thermal affinities and life histories. We illustrate how physiologically based species distribution models (SDMs) can be used to predict habitat distribution shifts and compare vulnerabilities of species and life stages with changing ocean conditions. We also derived an integrated habitat suitability index from the combined surfaces of each metric to predict areas and periods where thermal conditions were suitable for both life stages. Suitable thermal habitat shifted poleward for the juvenile life stages of all three species, but the area remaining differed across species and life stages through time. Arctic specialists like Polar cod are predicted to experience reductions in suitable juvenile habitat based on metrics of egg survival and growth potential. In contrast, habitat loss in boreal and subarctic species like Atlantic cod and Greenland cod may be dampened due to increases in suitable egg survival habitats as suitable juvenile growth potential habitats decrease. These results emphasize the need for mechanistic SDMs that can account for the combined effects of changing seasonal thermal requirements under varying climate change scenarios.

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Fog in northern climates and Arctic environs can be a risk to helicopter operations and shipping interests, as are high seas from severe storms that frequent these regions. Visibility conditions and forecasts determine whether helicopters can safely land on offshore facilities, or if personnel will need to be transferred by ship. High sea state conditions can affect offshore oil and gas exploration and production operations, including drilling, logistics, crane operations and emergency response. A workshop on Metocean Monitoring and Forecasting for the Newfoundland & Labrador Offshore, held 22-24 September 2014, identified the need of improving the visibility and severe sea state forecasting for Grand Banks which can have a positive contribution to safety and operations in the harsh North Atlantic Canada offshore environment. This has led to an open and collaborative multi-year Metocean Research and Development Project that is presently in its third year. Some twenty government, academic, and industry agencies are participating in this project. Detailed buoy and offshore installations-based scientific measurements have been collected over the past three years where previously there has been a lack of good quality observations. A climatology of low visibility (less than 1km) events shows a high frequency (about 55% of the time) during summer months. A "conceptual model" of Grand Banks fog has been developed, that defines the physical conditions under which fog develops, is maintained, moves and dissipates. The conceptual model will be the basis for the development of new visibility prediction systems which currently are not well established or verified. High seas, with wave heights over 6m, occur more frequently during winter. Sea state prediction systems are being evaluated for severe ocean wave conditions where they have reduced predictive skill. Currently, work is underway to establish the accuracy and consistency of several existing visibility and sea state prediction systems. This paper will illustrate results from the climatological studies and some of the unique metocean monitoring data being collected. The forecasting techniques (e.g. numerical atmospheric and oceanic prediction models, satellite-based schemes, and rules based systems) being evaluated, are outlined.