How well does the Arctic System Reanalysis (ASR) reproduce the climatic extremes over North-American-Arctic Region?

Alvaro Avila, PhD student in applied meteorology, Universidade Federal de Viçosa (Brazil)
David Howard Bromwich, faculty mentor

Background

• Hometown: Puerto Tejada, Colombia
• Degrees received: Bachelor of Science in agricultural engineering, Universidad del Valle, Colombia; Master of Science and PhD in applied meteorology, Universidade Federal de Viçosa, Brazil

What is the issue or problem addressed in your research?

Weather observations and climate data are sparse across the Arctic compared to the mid-latitudes and tropics. However, available data clearly indicate rapid changes occurring to the physical environment of the Arctic. To overcome this data limitation, retrospective analyses (i.e. reanalyses) synthesize a wide variety of surface and atmospheric datasets. Also, understanding extreme climate patterns is essential to the development of public policies and the mitigation of impacts on human activity.

What methodology did you use in your research?

This study evaluates the ability of the Arctic System Reanalysis (ASR), a product developed here at The Ohio State University by the Polar Meteorology Group (PMG) at the Byrd Polar and Climate Research Center, to represent observed extreme events of temperature and precipitation over the North-American-Arctic. This guidance will help PMG researchers evaluate the performance of ASR compared to other contemporary reanalyses, proving its value in the study of Arctic climate change and variability.

What are the purpose/rationale and implications of your research?

This study highlights a novel contribution of ASR, a critical asset to the scientific community, in simulating the Earth system in terms of climatic extremes across the North-American-Arctic region. The results of this work will provide a breakthrough in understanding and decreasing the uncertainty about the patterns of extreme temperature and precipitation events over the Arctic where observational information is sparse.