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We present a quantitative network design (QND) study of the Arctic seaice–ocean system using a software tool that can evaluate hypotheticalobservational networks in a variational data assimilation system. For ademonstration, we evaluate two idealised flight transects derived from NASA'sOperation IceBridge airborne ice surveys in terms of their potential toimprove 10-day to 5-month sea ice forecasts. As target regions for theforecasts we select the Chukchi Sea, an area particularly relevant formaritime traffic and offshore resource exploration, as well as two areasrelated to the Barnett ice severity index (BSI), a standard measure ofshipping conditions along the Alaskan coast that is routinely issued by iceservices. Our analysis quantifies the benefits of sampling upstream of thetarget area and of reducing the sampling uncertainty. We demonstrate howobservations of sea ice and snow thickness can constrain ice and snowvariables in a target region and quantify the complementarity of combiningtwo flight transects. We further quantify the benefit of improved atmosphericforecasts and a well-calibrated model

Exploring the utility of quantitative network design in evaluating Arctic sea ice thickness sampling strategies