Some observations from observing

Antarctic Weather and ClimateScott.carpentier@bom.gov.au

Manager Antarctic Meteorology

Co-authors:

Andrew Klekociuk (AAD), Simon Alexander (AAD), Jonathan Willie (Universite Toulouse); Ben

Galton-Fenzi (AAD), Tas Van Ommen (AAD), Alain Protat (BoM), Phil Reid (BoM), Jan Lieser

(BoM), Fiona Smith (BoM), Chris Tingwell (BoM)

Poleward of Latitude 40 deg

Upper Air

Surface Synop

N of 40N S of 40S

Sensor height is an issue

(snow accumulation)

Maintenance is an issue

Remoteness is an Issue

Optical Satellite: Worldview

Radar Satellite: TerraSAR-X

Safe field operations: Calibration (right) tests of

satellite images (left) with helicopter radar (bottom-

right)

Antarctic staffed stations + AWS

Antarctic staffed stations + AWS + POLENET

Cold Temperatures

The lowest measured air temperature (2m) on Earth is −89.2°C (−129 F)

on 23 July 1983, observed at Vostok Station in Antarctica

(Turner et al., 2009, https://doi.org/10.1029/2009JD012104).

Ultralow Surface Temperatures in East Antarctica From Satellite

Thermal Infrared Mapping: The Coldest Places on Earth

Scambos et al 2018 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GL078133

Geophysical Research Letters, Volume: 45, Issue: 12, Pages: 6124-6133, First published:

25 June 2018, DOI: (10.1029/2018GL078133)

Using TIR temps from Landsat and Modis:

The lowest temperatures are found in

small (<200 km2) topographic basins of

~2 m depth above 3,800 m elevation.

Approximately 100 sites have observed

minimum surface temperatures of

~−98°C during the winters of 2004–2016.

Comparisons of surface snow

temperatures with near‐surface air

temperatures at nearby weather stations

indicate that ~−98°C surfaces imply

~−94± 4°C 2‐m air temperatures.

Dome C tall tower measurements

http://polarmet.osu.edu/AMOMFW_2016/0608_0840_Vignon.pdf

Minimum Wind Speed

for Sustainable

Turbulence

2 regimes:

Diffusive (left) and turbulent (right)

http://polarmet.osu.edu/AMOMFW_2016/0608_0840_Vignon.pdf

Note BL depth… how many

vertical layers does NWP resolve?

aerodynamic

adjustment of sastrugi

Sastrugi are a direct manifestation of drifting snow and form the main surface roughness elements.

In turn, sastrugi alter the generation of atmospheric turbulence and thus modify the wind field and

the aeolian snow mass fluxes.

aerodynamic

adjustment of sastrugi

In summary, for friction velocities (wind speeds) around 1 (20)m/s and above, the sastrugi streamlining timescale can

be as fast as 3 h. For a wind flow initially aligned with the sastrugi, a deviation of 20–30 deg from the streamlining direction

has the potential to both increase the drag cooeficient CDN10 by 30–120% and to significantly reduce (up to 80 %) the aeolian

snow mass flux, even under increasing friction velocity.

Tsurface Trends from READER

(1970-2017)

Tmean °C/century

-0.2-0.1

+0.0

-1.5

Mirnyi-0.7

Dumont D’Urvlle

(-0.4)

(+0.5) (+0.8)

(-0.5)

(+0.4)

(-1.5)

Values in brackets 1970-2009 when analysis/homogenisation of temperature completed.

Jones 2016 dataset

(1970-2017)

Rainfall mm/decade

+63.4(+64.6)

Home of the Blizzard

Solid Precipitation Intercomparison Experiment

(SPICE)

http://amrc.ssec.wisc.edu/meetings/meeting2015/presentations/AMOMF-

Day1/BAS%20precipitation%20measurements.pdf

Antarctic Precipitation System

Double Fenced Reference Site

(King et al. 2012)Mass Accumulation 2002-2010

Accumulation at Dronning Maud Land

https://www.mmm.ucar.edu/sites/default/files/terpstra_transport.pdf

Episodic snow events

Atmospheric Rivers

Precipitable water composite

The Dominant Role of Extreme Precipitation Events (defined at the

heaviest 10% of daily precipitation amounts) in Antarctic Snowfall Variability

Geophysical Research Letters, Volume: 46, Issue: 6, Pages: 3502-3511, First published: 19 February 2019, DOI: (10.1029/2018GL081517)

Fig. 1 The contribution of HPEs tothe annual precipitation. Thenumber of days of the highest,ranked precipitation that gives 50%of the annual total. Darker coloursindicate where the HPEs are moreimportant.

(From Turner et al. 2019)

Total sea level rise equivalent per Basin

Fricker et al (2019)

KEY SCIENCE QUESTIONS

▪ What will be the rate and magnitude of

sea level rise from Antarctica,

particularly over the coming decades?

▪ When are the points where abrupt or

large changes in the ice sheet become

committed and what are the key regions

susceptible to rapid change?

▪ How will a warming atmosphere and

oceans drive evolution of the ice sheet,

and what are the feedbacks with other

parts of the climate system?

IPCC. Global average: 20 cm of sea level leads to 100 fold increase in flood frequency. A 1 in 100 year flooding event occurs annually. Regional variability (left)

Sea level monitoring

Global total sea level rise (SLR) estimates exceeding 2m by 2100 now lie within the 90% uncertainty bounds for a high emission scenario according to NOAA study (Structured Expert Judgement)

…We find it plausible that SLR could exceed 2 m by 2100 for our high-

temperature scenario, roughly equivalent to business as usual (ie +5 C by

2100). This could result in land loss of 1.79 M km2, including critical regions of

food production, and displacement of up to 187 million people. A SLR of this

magnitude would clearly have profound consequences for humanity.

KEY UNCERTAINTIES

Monitoring the ice sheet and contributions

to sea level

A range of techniques are used to understand

the vulnerability and behaviour of the Antarctic

Ice Sheet, including field deployments of

instruments (GPS and phase sensitive radar

to measure ocean melting, strain thinning and

firn compaction; right), active seismic and

other on ice traverses (bottom) remote

sensing from satellites and airborne

geophysics surveys.

Numerical weather processes – static sea ice within

NWP

Zhaohui Wang

An increase of 1.4 Million square kms

After 10 days

A decrease of 1.7 Million square kms

After 10 days

Polar Prediction Matters

Antarctic contribution to YOPP-SH

ACCESS-G model performance during YOPP-SH

The importance of upper-air observations

45

Forecast Sensitivity to Observations (FSO) in ACCESS-G Global NWP System

Sondes SYNOPS

Neutral

Mostbenefit

Mostbenefit

Neutral

Detrimental

FSO: a technique which projects error reduction in a +24 hour NWP forecast (when compared to a +30h forecast) onto all assimilated observations. FSO impacts can be aggregated by station, observation type, etc. Forecast error is measured globally in this case.

Negative FSO impacts (yellow-red colours) represent stations whose observations are doing the most to reduce 24 hour forecast error.

Sonde and SYNOP observations from remote stations, and especially Antarctic stations, do most to reduce forecast error.

About 85% of all data used in forecast models are from polar-orbiting satellites and these attribute to nearly 60% of the reduction in forecast error, chiefly via:

o Microwave sounders o Hyperspectral infrared;o AMVs (to a lesser degree)

Usage of channels sensitive to the lower troposphere is limited

because of inability to forward model or interpret data

Surface-sensitive and lower tropospheric channels cannot be used over

ice and snow surfaces at the present time

Upper tropospheric channels have excellent polar coverage

Observations from the ATMS microwave sounding instrument used in an ACCESS-G3 configuration

in a six-hour assimilation cycle

(Rubino et al., 2013)

Carbon cycle reconstruction

Australian rainfall teleconnection: SWWA and snowfall

Australian rainfall teleconnection: Eastern Au & salt (winds)

(Vance et al., 2013; 2015)

(van Ommen & Morgan 2010; van Ommen, in prep)

Past Sea Ice Extent

Sea ice reconstruction

(Curran et al., 2003; Curran in preparation)

CMIP5

Global Mean Cloud Droplet Concentration

4x daily radiosondes all summer = ~600 launches

Aerosol Sampling

Cloud, radiation & precipitation instruments on monkey island

Radiosonde

Radars

Photos all © Doug Thost, Australian Antarctic Division

US DoE ARM MARCUS (Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean) campaign

Summer 2017/18 aboard Aurora Australis – three round-trips Hobart – Antarctica; one Hobart – Macquarie Island round-trip

YOPP-endorsed

W-band radar, radiosondes, lidar, ceilometer, Parsivels, radiometers, aerosol suite etc.

All data publically available

SOCRATES flights

Davis Ozone Measurements (AAD-BoM-CAMS)• Weekly ozonesonde flights since 2003 are used for studies stratospheric

dynamics and trends.

• The 2019 stratospheric warming strongly influenced ozone in early Sep.

• Small but significant positive trends are apparent in Sep-Oct stratospheric ozone.

Sep-Oct linear trends by height 2003-2018 (Tully et al., in prep.)

21-24 km ozone highlighting unusual behaviour in Sep-Oct

2019

Summary of 2019 measurements showing weak effects from the ozone hole and strong overburden in Sep.

A. K

leko

ciu

k

A. K

leko

ciu

k

M. T

ully

Gravity Wave Analysis of Radiosonde Data

• Over Davis, gravity waves propagate close to the horizontal and are strongly advected by the background wind in the wintertime.

• About half of the stratospheric waves between early May and mid-October propagate downward.

• A source due to imbalanced flow that is distributed across the winter lower stratosphere best explains the observations.

Climatological percentage of (a) upward and (b) downward propagating gravity waves above Davis for 2001-2012.

Murphy et al. (2014, JGR 119, doi:10.1002/2014JD022448)

Observation-Model Comparison of Winds

• UM simulations are performed at differing fine horizontal resolutions for a case study of orographic gravity wave (OGW) activity observed with the Davis VHF radar.

• Simulations improve at finer resolution and indicate synoptic winds and coastal topography interact to form OGWs.

• Temperature fluctuations induced by OGW influence local cloud presence.

Case study for 18 Feb 2014: (a) The time-height hourly VHF radar horizontal wind field. (b–d) The same but for UM simulations of differing horizontal resolutions.

Alexander et al. (2017, JGR 122, 122, doi:10.1002/2017JD026615.)

Antarctica is:

• Sparsely observed;

• Hostile to people and instrumentation;

• A complex and unique environment that requires improved

representation in NWP and GCM;

• Fundamental to improved predictive services globally.

Conclusions