diverse as detection of climate change, impacts assessment, and component model development. However, in the context of both data scarcity and model bias, the ability of data assimilation techniques to provide a resource for these activities is limited. Even the current generation of reanalysis products reveals large inter-model differences, particularly in surface meteorology, clouds, and radiation (Jakobson et al., 2012). Quality operational weather forecasts are critical for safe operations in the Arctic. Generally these models are adapted from national operational weather prediction models of Arctic nations, but research has demonstrated that these models require substantial modification to reduce bias (e.g., Bromwich et al., 2009; Schroder et al., 2011). Enhancement of the reanalysis process (including specialized Arctic regional reanalyses) and operational weather prediction will rely on the continuing improvement in understanding Arctic atmospheric processes and their interactions with other Arctic systems.
The ongoing development of limited-area climate system models in the Arctic represents a critical gap in our modeling infrastructure (Proshutinsky et al., 2008). These models allow the testing of our simulation understanding in a framework that has high spatial resolution, uses Arctic-specific physical representations, and ensures that lower-latitude biases are minimized. Although this approach enjoyed considerable advances in earlier decades (e.g., Dethloff et al., 1996; Lynch et al., 1995), development slowed until recently (e.g., Cassano et al., 2011; Dorn et al., 2009; Glisan et al., 2013). These models provide an important platform for testing approaches prior to implementation in global models, as well as providing additional infrastructure for impacts assessment, downscaling, and field campaign support.
Building the operational capacity necessary to address emerging research questions requires a mix of approaches, including partnering to leverage resources. With increased accessibility comes increased activity on the part of tourism, shipping, oil and gas, and other extractive industries. Many of these industries operate extensive investigative and infrastructure development programs. Frequently, the information needs for industry have much in common with the needs of regulatory agencies and curiosity-driven science. When industry operates in remote locations, it also tends to establish or create infrastructure to support safe operations, including housing, transportation, communications, and crisis response capabilities (e.g., search and rescue). Establishing partnerships with these organizations could allow for collection of information that would, in turn, facilitate robust decision making and extend capacities for scientific investigations in the Arctic.