INTER-COMPARISON OF ARCTIC STORMS IN ATMOSPHERIC ... · INTER-COMPARISON OF ARCTIC STORMS IN...
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Copyright University of Reading
INTER-COMPARISON OF ARCTIC STORMS IN ATMOSPHERIC REANALYSIS DATASETS
Alec Vessey (2nd Year PhD Student)
Supervisors: Kevin Hodges (UoR), Len Shaffrey (NCAS/UoR), Jonny Day (ECMWF),
Tom Philp (XL Catlin) 1/16
The 7th European Windstorm Conference
11/10/2018
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MOTIVATION
2/16
Humpert &
Raspotnik
(2012)
Meier et al. (2014)
Less than 1 million km2 Arctic ice extent
in September to occur in 2040s, with the
earliest projections being 2030s.
(Wang and Overland 2012)
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NASA (2012)
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REANALYSIS DATASETS• Reanalysis datasets are widely used in science and business to examine mobile storms
• Assimilate observations into current models to generate a spatially and temporally
coherent dataset of the past
4/16(ERA-Interim: Dee et al. 2011, JRA-55: Kobayashi et al. 2015, MERRA-2: Gelaro et al. 2017, NCEP-CFSR: Saha et al. 2015)
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• Research questions and aims:
• What is the frequency, spatial distribution and intensity of Arctic storms?
• What are the differences in the characteristics of Arctic storms between reanalysis datasets?
• Methodology:
• Compare ERA-Interim, MERRA-2, JRA-55 and NCEP-CFSR, between 1980 – 2017
• Storms identified using Hodges (1994, 1995, 1999) Storm Tracking Algorithm
• Processes 6-hourly data from each dataset
• Identifies storms from maxima in 850hPa vorticity
• T42 spectral filtering used to reduce noise in vorticity field
• Storms have to last more than 2 days, and travel more than 1000km
• Arctic storms are defined as all storms that travel north of 65°N
• Compare frequency, spatial distribution and intensity between reanalysis datasets5/16
CURRENT RESEARCH
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6/16
STORM FREQUENCY
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JRA-55 SUMMER
CLIMATOLOGY
7/16
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8/16
Red: Other dataset < JRA-55, Blue: Other dataset > JRA-55
Black dots indicate significant differences to 95% confidence level
TRACK DENSITY: SUMMER
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JRA-55 WINTER
CLIMATOLOGY
9/16
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10/16
Red: Other dataset < JRA-55, Blue: Other dataset > JRA-55
Black dots indicate significant differences to 95% confidence level
TRACK DENSITY: WINTER
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11/16
INTER-ANNUAL VARIABILITY:
STORM FREQUENCY
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12/16
INTER-ANNUAL VARIABILITY:
CORRELATION COEFFICIENTS
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
ERA-Interim,
JRA-55
ERA-Interim,
MERRA-2
ERA-Interim,
NCEP-CFSR
JRA-55,
MERRA-2
JRA-55,
NCEP-CFSR
MERRA-2,
NCEP-CFSR
Pearsons
Correlation
Coefficient
Winter All Storms Winter Gen Below 65DegN Winter Gen Above 65DegN
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
ERA-Interim,
JRA-55
ERA-Interim,
MERRA-2
ERA-Interim,
NCEP-CFSR
JRA-55,
MERRA-2
JRA-55,
NCEP-CFSR
MERRA-2,
NCEP-CFSR
Summer All Storms Summer Gen Below 65DegN Summer Gen Above 65DegN
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13/16
STORM INTENSITY
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13/16
STORM INTENSITYIntensity of All Mid-Latitude (travel between 30-65DegN) Storms between 1980-2017
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14/16
INTER-ANNUAL VARIABILITY IN
STORM INTENSITY
Winter
925hPa
Max. Wind
Winter
Min.
MSLP
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STORM MATCHING WITH JRA-55
15/16Matching Criteria: Track separation distance less than 4° and tracks match 50% of the time
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CONCLUSIONS
• Arctic storms occur frequently (more than 1 storm occurs per day), and will interfere
with human activity in the Arctic
• There is uncertainty in Arctic storm characteristics between reanalysis datasets
• Higher uncertainty in spatial distributions of storms in winter than in summer - occurring in
the Canadian Archipelago and the North Atlantic storm track leading into north west Siberia
• More uncertainty in the frequency of storms with Arctic genesis in winter time than in
summer time due to differences in genesis density north of Greenland
• Storm tracks generally matching between reanalysis datasets for more intense storms, but
there is high uncertainty in the wind speeds of intense storms
16/16
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ANY QUESTIONS?
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REFERENCES
• Gelaro, R., and Coauthors, 2017: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2)., J. Clim,
30 (14), 5419–5454.
• Dee, D. P., and Coauthors, 2011: The era-interim reanalysis: Configuration and performance of the data assimilation system., Q. J. R.
Meteorol. Soc., 137 (656), 553–597
• Hodges, K., 1994: A general method for tracking analysis and its application to meteorological data. Mon. Weather Rev., 122 (11),
2573–2586.
• Hodges, K., 1995: Feature tracking on the unit sphere. Mon. Weather Rev., 123 (12), 3458–3465.
• Hodges, K., 1999: Adaptive constraints for feature tracking. Mon. Weather Rev., 127 (6), 1362–1373.
• Humpert, M., and Raspotnik, A., 2012, The Future of Arctic Shipping, Available online: https://www.thearcticinstitute.org/future-
arctic-shipping/ (Accessed on 03/09/2018)
• Kobayashi, S., and Coauthors, 2015: The jra-55 reanalysis: General specifications and basic characteristics., J. Meteorol. Soc. Jpn. Ser.
II, 93 (1), 5–48.
• Meier, W. N., and Coauthors, 2014: Arctic sea ice in transformation: A review of recent observed changes and impacts on biology and
human activity. Rev. Geophys., 52 (3), 185–217.
• NASA, 2012, Daily sea ice during Aug and Sept 2012 with winds, (Accessed March 25 2018), https://svs.gsfc.nasa.gov/cgi-
bin/details.cgi?aid=3992
• National Snow and Ice Data Center 2018: Arctic Sea Ice At Minimum Extent: August compared to previous years. Nsidc.org,
(Accessed February 15, 2018)
http://nsidc.org/arcticseaicenews/2017/09/
• Saha, S., and Coauthors, 2010: The ncep climate forecast system reanalysis., Bull. Am. Meteorol. Soc., 91 (8), 1015–1058.