Cyclone activity in the Arctic from an ensemble of regional climate models (Arctic CORDEX)

M. Akperov1, A. Rinke2, I. Mokhov1, H. Matthes2, D. Handorf2, K. Dethloff2 and Arctic CORDEX team

1A.M. Obukhov Institute of Atmospheric Physics, RAS, Moscow, Russia 2Alfred Wegener Institute, Helmholtz Centre for Polar & Marine Research, Potsdam, Germany

Aim of this work:

To assess an ability of the Arctic RCMs to adequately reproduce the cyclone activity in the Arctic

Data I n  6-hourly mean sea level pressure (MSLP) data from

reanalyses (ASR; ERA-INTERIM; NASA-MERRA2; NCEP-CFSR) and regional climate model (RCMs) simulations driven by ERA-Interim (CORDEX Project) for the Arctic (ca. north of 650).

n  Polar lows characteristics over the Nordic seas from STARS database (Noer et al., 2011).

n  Analysis period 1981-2010.

Type Institution Data Resolution, 0 Nudging Note

Reanalyses

ECMWF ERA-INTERIM 0.75

NASA MERRA2 0.5

NCEP NCEP-CFSR 0.5

PMG ASR 30 km Polar lows analysis

Regional clim

ate models (R

CM

s)

CCCma CanRCM4 0.44 w

AWI HIRHAM5 0.44 w

SMHI RCA4 0.44 w/ and w/o

ULg MAR3.6 0.44 w

UQAM CRCM5 0.44 w/ and w/o

MGO RRCM 0.44 w/o

UNI LUND RCA-GUESS 0.44 w/o

UNI WRF 0.44 w/o

EMUT CCLM 0.12 w/o Polar lows analysis

Data II Reanalyses and RCMs

Methods

Cyclones n  Cyclone identification method (based on MSLP) (Bardin et al.,

2005; Akperov et al., 2015).

Polar lows n  Distance (great-circle) between two points on sphere

(coordinates of real polar lows (PL) and PL from reanalyses or models) doesn’t exceed 40. We look for cyclone with exact timestep or ±6 h from exact time forward or backward and select PL with minimal distance.

§  δP (cyclone depth) = |h-P(So)|, where P(So) – outermost enclosing contour;

Measure of intensity Ek~(δP)2 (Golitsyn et al., 2007) §  R (cyclone radius) = sum(Ri)/N, i=1,N

Cyclone’s identification method 1. Identification of cyclones

(Bardin and Polonsky, 2005; Akperov et al., 2007): - Cyclones are determined as domains that contain the single local minimum

of the MSLP (hPa) enclosed within the maximum closed contour.

2. Cyclone’s tracking: -  nearest neighbour analysis Max distance between two consequent 6-hour steps ≤ 600 km;

Ri

Annual cycle of cyclone frequency over the Arctic from reanalyses data and multi-model RCM ensemble

Frac

tion

of u

nit

Deep cyclones (δp>20 hPa ; 90% percentile) per month

month

Cyclones per month

Spatial distributions of cyclone frequency [cyclone per day] RCMs Reanalyses

Ense

mbl

e m

ean

(col

or s

hadi

ng)

Stde

v. a

cros

s th

e da

ta (i

solin

es)

Taylor diagrams (ref.: ERA-INTERIM) winter

summer

Spatial distributions of cyclone mean depth [hPa]

RCMs Reanalyses

Ense

mbl

e m

ean

(col

or s

hadi

ng)

Stde

v. a

cros

s th

e da

ta (i

solin

es) winter

summer

Spatial distributions of cyclone mean size [km]

RCMs Reanalyses

Ense

mbl

e m

ean

(col

or s

hadi

ng)

Stde

v. a

cros

s th

e da

ta (i

solin

es) winter

summer

Cyclone frequency in dependence of the their depth & size from reanalyses data and multi-model RCM ensemble

size [km]

depth [hPa]

Frac

tion

of u

nit

winter summer

Trends in time series of cyclones (cyclones per year)

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Deep cyclones (δp>20 hPa ; 90% percentile)

All cyclones

datasets

Ratio (Nm/Nr) of number of polar lows from satellite data (Nr=213) to reanalyses and RCMs (Nm) over Norwegian and Barents Seas

1 – ERA-I 2 – ASR 3 –MERRA 4 – NCEP-CFSR 5 – CanRCM4 6 - CCLM 7   – HIRHAM5 8 – RCA4 9 – RCA4-GUESS 10 – RCA4 11 – MAR3.6 (v. 2) 12 – WRF (UNI) 13 – CRCM5 14 – CRCM5

Conclusions

•  Some of the RCMs with nudging show better agreement

in representing cyclone characteristics (including polar lows) compared to other models with/without nudging

•  Strong variations in cyclone frequency across models and reanalyses are observed in winter and for small cyclones, possible due to polar lows

•  State-of-the-art Arctic RCMs can resolve ca. 60% of polar lows