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Analysis to evaluate and improve model performance in the Central Arctic: Unique perspectives from autonomous platforms during MOSAiC

General

Project start
01.01.2018
Project end
31.12.2022
Type of project
ARMAP/NSF
Project theme
Ocean & fiord systems
Project topic
Oceanography

Fieldwork / Study

Fieldwork country
Arctic Oceans and various regions
Fieldwork region
Arctic (entire region)
Fieldwork location

Geolocation is 84, -120

Fieldwork start
16.02.2020
Fieldwork end
15.04.2020

SAR information

Fieldwork / Study

Fieldwork country
Arctic Oceans and various regions
Fieldwork region
Arctic (entire region)
Fieldwork location

Geolocation is 84, -120

Fieldwork start
16.04.2020
Fieldwork end
15.06.2020

SAR information

Fieldwork / Study

Fieldwork country
Arctic Oceans and various regions
Fieldwork region
Arctic (entire region)
Fieldwork location

Geolocation is 84, -120

Fieldwork start
16.06.2020
Fieldwork end
15.08.2020

SAR information

Project details

02.09.2019
Science / project summary

This study will use an emerging technology, unmanned aircraft systems, to collect measurements with the goal of improving weather and climate models of the Arctic system. It is part of the international MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) program, an extensive field effort to freeze an icebreaker into sea ice for an entire year to serve as a research platform for a comprehensive study of the atmosphere, ocean and ice system in the high Arctic. The unique and potentially transformative aspect of this project is that unmanned aircraft collect data at small spatial and temporal scales, providing new information about variability in temperature, humidity, and winds. In addition, direct measurements of these variables over breaks in the sea ice have been very limited to date. Therefore, this study will address a significant source of error in our current ability to forecast how energy is transferred between the atmosphere and underlying ice and sea surface. Together with information from collaborating scientists participating in the MOSAiC field effort, the investigators will evaluate a series of hypotheses related to the performance of model simulations of key processes over the central Arctic Ocean. The investigators will also give pubic lectures at schools and other venues, capitalizing on interest and excitement in use of new technology though use of videos and photos of the unmanned aircraft systems. They will support training for early career scientists by involving graduate students and postdoctoral scientists. The investigators will deploy an unmanned aircraft system to measure atmospheric temperature, winds, and humidity, as well as surface albedo. Flights will take place from mid-winter (February) through late summer (August) to capture variable conditions in both the atmosphere and sea ice surface and will include routine profiling of the lower atmosphere, spatial mapping of thermodynamic quantities and surface albedo, and mapping of the lower atmospheric structure over leads. This data will be evaluated with measurements of the atmosphere, ocean and ice collected by other scientists as part of the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) project to address hypotheses related to the performance of modeling tools in simulating key processes over the central Arctic Ocean. These include questions about sub-grid scale variability of atmospheric and surface parameters and its influence on model-simulated surface energy budget; the influence of leads in the sea ice on energy transfer from the ocean to the atmosphere and how models represent this transfer; and the importance of vertical resolution in simulation of the Arctic atmosphere and its impact on the simulation of clouds and the surface energy budget. The investigators will compare observations from unmanned aerial systems to a variety of simulations, ranging from global products to fully-coupled regional simulations completed using the Regional Arctic System Model (RASM) to detailed single-column and 2D modeling at high resolution.

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