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

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)

NASA (2012)

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)

• 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

6/16

STORM FREQUENCY

JRA-55 SUMMER

CLIMATOLOGY

7/16

8/16

Red: Other dataset < JRA-55, Blue: Other dataset > JRA-55

Black dots indicate significant differences to 95% confidence level

TRACK DENSITY: SUMMER

JRA-55 WINTER

CLIMATOLOGY

9/16

10/16

Red: Other dataset < JRA-55, Blue: Other dataset > JRA-55

Black dots indicate significant differences to 95% confidence level

TRACK DENSITY: WINTER

11/16

INTER-ANNUAL VARIABILITY:

STORM FREQUENCY

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

13/16

STORM INTENSITY

13/16

STORM INTENSITYIntensity of All Mid-Latitude (travel between 30-65DegN) Storms between 1980-2017

14/16

INTER-ANNUAL VARIABILITY IN

STORM INTENSITY

Winter

925hPa

Max. Wind

Winter

Min.

MSLP

STORM MATCHING WITH JRA-55

15/16Matching Criteria: Track separation distance less than 4° and tracks match 50% of the time

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

ANY QUESTIONS?

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.