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Future Planning
Implementation Workshop
Science Writing Workshop
Science Planning Workshop
Potsdam Workshop
Prague Workshop 2017
Implementation WS 2017 St. Petersburg

Expedition

A Constellation of Observations

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MOSAiC observations will be specifically designed to characterize the important processes within the atmosphere-ice-ocean system that impact the sea-ice mass and energy budgets.  These include heat, moisture, and momentum fluxes in the atmosphere and ocean, water vapor, clouds and aerosols, biogeochemical cycles in the ocean and ice, and many others.  The centerpiece of the observatory will be a manned, ship-based ice camp with comprehensive instrument suites to thoroughly observe processes within the atmosphere, ice, and ocean.  This central, intensive observatory will be embedded within a constellation of distributed measurements made by buoys, ice-tethered profilers, remote stations, underwater drifters, unmanned aerial systems, aircraft, additional ships, and satellites.  These distributed observations will provide critical information on the spatial context and variability of key parameters, and allow for limited measurements in environments with sea-ice of differing age, thickness, and concentration.

Preliminary Drift Trajectory Plan

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Potential drift trajectory of the Polarstern for the selected
starting position at 120° E and 84° N. Colors represent
the month of the drift starting in October 2019 and ending
in October 2020. The small color bar illustrates the sea ice
concentration on October 15th 2014.
The MOSAiC Observatory will be deployed in, and drift with, the Arctic sea-ice pack for at least a full annual cycle, starting in fall 2019.  Initial plans are for the drift to start in the newly forming autumn sea-ice in, or near, the East Siberian Sea.  The specific location will be selected to allow for the observatory to follow the Transpolar Drift towards the North Pole and on to the Fram Strait.  By drifting with the ice in a "sea-ice Lagrangian" mode, MOSAiC will observe the atmospheric and oceanic processes that impact the sea-ice as it evolves from new first year ice to multi-year ice and eventually towards the decay stage as the ice approaches the North Atlantic sector - in effect observing the full life cycle of sea-ice. 

A Model Testbed

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To most effectively understand, explain, and project changes in the Arctic climate system, models must be used synergistically with observations.  While observations are critical for evaluating and developing model representations, models are ultimately needed to integrate the complex collections of physical processes into a consistent framework that can be used to forecast weather, predict sea-ice concentrations, and project future climate conditions.  Models provide perspectives on scale, teleconnections, and feedbacks that are difficult to gain from observations alone. 
MOSAiC observations will serve as a testbed for models at many scales, providing the constraints that are needed to improve process-based, sub-grid-scale model parameterizations for sea-ice environments.  This testbed will facilitate detailed studies using high-resolution process models, while serving as a centerpiece for Regional Climate Model activities, both of which will contribute towards the development of Global Climate Models that are better able to represent Arctic processes.  Model guidance is critical for the basic design of MOSAiC, by identifying specific processes that contribute to model uncertainty and guiding the deployment of observational assets.  Additionally, regional modeling activities will aim to address important questions regarding the role of the Arctic as a global energy sink, the manner in which global teleconnection patterns are affected by a changing Arctic ice pack, and the impacts that these changes may have on lower-latitude circulation and weather.  MOSAiC modeling and observational activities will be closely linked with international modeling efforts organized by the World Weather Research Program and World Climate Research Program, such as the Year of Polar Prediction.