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The Polar Atmosphere:
Forecasts from weather and climate
models
David H. Bromwich
Byrd Polar Research Center, The Ohio State University
Research Urgencies in the Polar Regions
Siena, 23 Sept 2011
Atmospheric changes in the Arctic
• Widespread warming of the Arctic:
– Record temperature and melt extent
in Greenland in 2010
[Box et al., Arctic Report Card, 2010]
– Lowest (or 2nd-lowest) Arctic sea-ice
extent reached on 8-9 Sept. 2011
Greenland melt extent in 2005
http://cires.colorado.edu/science/groups/steffen/greenland/melt2005/
Ice concentration, 8 Sept. 2011
University of Bremen (http://www.iup.uni-bremen.de:8084/amsr)
Atmospheric changes in the Antarctic
• More regional changes:
– Warming in the Antarctic
Peninsula and West Antarctica(?)
– But lowest Antarctic melt extent
in 2008 & 2009 since 1987
[Tedesco & Monaghan, 2009]
[Steig et al., 2009]
Melt ponds
Breakup of the Larsen B Ice Shelf in 2002
Arctic vs Antarctic
[Bromwich and Wang, 2008]
• Despite being both “polar”, the Arctic and
Antarctic exhibit important differences in their
climate and exposure to climate warming
Numerical models
• Models are used:
– to monitor/predict/
understand climate change
– for weather guidance
Source: NOAA
• The reliability of these models in polar regions
(and elsewhere) depends on:
– our understanding of the physical processes
– our ability to represent them numerically
– the availability of observations to constrain the
model
Numerical Models & time scales
Shorter time scales • Numerical Weather Prediction
(NWP) models
(e.g., days)
• Reanalyses (global, regional)
Longer time scales
(e.g., centuries)
• Global Climate Models (GCMs)
MODELING THE POLAR ATMOSPHERE
Physical processes that need to be
optimized for polar regions
• Surface energy balance and heat transfer over
sea ice and permanent ice surfaces
• Variable sea ice thickness and snow thickness
over sea ice
• Seasonally-varying sea ice albedo
• Cloud / radiation interaction
• Ice phase microphysics
• Turbulence (boundary layer) parameterization
Example: Sea-ice albedo variability
May
June
August
Sept.
July
Images: Courtesy from D. Perovich
Example: Sea-ice albedo variability
Observations from the SHEBA experiment (Arctic)
1.0
0.8
Snow covered ice
Bare ice
Albedo
0.6
0.4
Ponds
0.2
Leads
0.0
Apr 1
May 1
Jun 1
Jul 1
Aug 1
Sep 1
Oct 1
[Perovich et al., 2007]
NUMERICAL WEATHER PREDICTION
IN POLAR REGIONS
Challenges for NWP in Polar Regions
• NWP models generate short-term forecasts
based on a known initial state of the
atmosphere
• The accuracy of the forecasts depends on
– the proper representation of physical processes
– the amount/distribution of observations used to
initialize the forecasts (data assimilation)
• Challenge: the sparsity of observations in high
latitudes (esp. in Antarctica)
Tropospheric observations for NWP
• Limited number of radiosonde observations in high latitudes (the Arctic
Ocean and Southern Ocean are virtual data voids)
• However, the growing volume of satellite observations used for data
assimilation has greatly improved the performance of NWP models in high
latitudes
• Satellite data assimilation remains challenging over ice-covered surfaces
Arctic and Antarctic stations reporting daily radiosoundings in July 2006
Andersson et al., ECMWF Newsletter, 2007
Antarctic AWS network - 2011
• Expansion of the AWS network
in Antarctica since the 1990s
• Provide observations used for:
– data assimilation into NWP
models
– model evaluation
• Challenges:
Map from AMRC/UW-Madison
(http://amrc.ssec.wisc.edu/)
– maintenance of the network
– Surface observations only
NWP in the Arctic
• NAM: North American Mesoscale Model
• 12-km grid, 4-day forecasts
• Model: Weather Research and Forecasting (WRF) model
• Twice-daily forecasts by the Polar Meteorology Group
• 45-km grid, 4-day forecasts; Model: Polar WRF
• NWP in the Arctic becoming increasingly important as (if?)
economic activities develop in this part of the world
NAM
“Unfreezing Arctic Assets”, WSJ, 18 Sep. 2010
NWP in the Arctic
by the Polar Meteorology Group
2m temperature and sea-level pressure
6h total precipitation
Italy
24h forecast for 12 UTC 14 Sept 2011
Website: http://polarmet.osu.edu/nwp/?model=arctic_wrf
USA
NWP in the Antarctic
• A dedicated effort:
The Antarctic Mesoscale
Prediction Sytem (AMPS)
• Support operations of the US
Antarctic Program
• Model: A polar-optimized
version of WRF (Polar WRF)
• Grids with resolutions ranging
15km (Antarctica) to 1.6km
(McMurdo area)
• Forecasts out to 5 days
AMPS grids
McMurdo
www.mmm.ucar.edu/rt/wrf/amps/
AMPS 24-h forecast (00 UTC 15 Sep 2011)
Ant. Peninsula domain (resolution: 5km)
Antarctic domain (resolution: 20km)
Website: www.mmm.ucar.edu/rt/wrf/amps/
REANALYSES IN POLAR REGIONS
Challenges for reanalyses in Polar Regions
• A reanalysis uses a state-of-the-art NWP
model to retrospectively analyze historic
observations (e.g., from 1979 onward)
• Challenges:
– Global models not “tuned” for high latitudes,
(hence the benefits of regional reanalyses)
– Sparsity of observations
– Impact of changes in the observations (e.g., from
satellites)
The Antarctic surface mass balance
from global reanalyses
mm/yr
Bromwich et al.,
J. Climate, 2011
• Figure: Mean annual Antarctic Precip-minus-Evap (P-E) during 1989-2009
from 5 global reanalyses and one observation-based dataset.
• The reanalyses show various skills at representing the mean Antarctic
climate, which itself is known with great uncertainties
Trends in global reanalyses
The Arctic warming
ERA-40
NCEP-NCAR
JRA-25
DJF
MAM
JJA
SON
[Graversen et al., Nature, 2008]
•
•
•
The magnitude of tropospheric temperature trends in the Arctic varies greatly
from one reanalysis to the other.
Challenge: produce temporally consistent datasets not affected by changes in the
observing system (e.g., satellite observations) and suitable for climate change
assessment
Some improvements in the most recent reanalyses thanks to more effective bias
correction of satellite radiances
Trends in global reanalyses
The Arctic warming
ERA-40 minus ERA-Interim
Figures from Screen and Simmonds, J. Climate, 2011
Regional reanalysis:
The Arctic System Reanalysis (ASR)
Regional reanalysis:
The Arctic System Reanalysis (ASR)
• A physically-consistent integration of Arctic and other
Northern Hemisphere data.
• Mesoscale model: Polar WRF
• High resolution in space (10 km) and time (3 hours),
convenient for synoptic and mesoscale studies
• Begins with years 2000-2010 (Earth Observing System)
• Assimilation of a wide range of conventional and satellite
observations
• Participants:
– Ohio State University - Byrd Polar Research Center (BPRC) and
Ohio Supercomputer Center (OSC)
– National Center for Atmospheric Research (NCAR)
– Universities of Colorado and Illinois.
Precipitation yearly total 2007
ASR vs ERA-Interim
ASR
ERA-Int
ERA
(cm)
GLOBAL CLIMATE MODELS (GCMs)
IN POLAR REGIONS
Challenges for GCMs in Polar Regions
• Benchmarking of GCMs in polar regions
– Sparse observations, esp. on multi-decadal scales
• Model grid resolution:
– IPCC AR4 models: typically 250km horiz.
– Ant. Peninsula, Ant. steep coastal slopes: scales < 100km
• Parameterizations of subgrid-scale processes
– Optimized for lower latitudes
– E.g., the atmospheric boundary layer (very stable over
snow/ice)
• Representation of atmosphere-ocean interactions
– Annual cycle of sea-ice cover
– Climate modes of variability (e.g., SAM, ENSO)
Benchmarking of IPCC AR4 GCMs
Example 1: Antarctic temperatures & P-E
Observation-based
Ensemble
Five AR4 GCMS
Monaghan et al.,
GRL, 2008
• 20th century annual Antarctic temperature trends in the five GCMs are
about 2.5-to-5 times larger than observed
• Better agreement between the GCMs and observations for snowfall,
although the GCMs differ in their ability to reproduce the
magnitude/distribution of snowfall
• Uncertainties in the observations themselves (reconstructed fields)
Benchmarking of AR4 GCMs
Example 2: Polar clouds
Mean annual cloud fraction (%)
• Clouds play an important in
the moisture/precipitation and
energy budget of the Antarctic
Ice Sheet
• They are simulated with
various skills by GCMs (figure)
• Substantial progress has been
made recently in our
knowledge of the climatology
of Antarctic clouds thanks to
observations from active
satellite sensors (CloudSatCALIPSO)
[Bromwich et al., submitted]
Atmospheric modes of variability
(Southern Hemisphere)
• The reliability of GCM simulations
also depends upon their ability to
reproduce the observed modes of
atmospheric variability
• These modes influence the
temperature and moisture advection
onto Antarctica
Southern Annular Mode
(aka Antarctic Oscillation)
ENSO teleconnection
El Niño
La Niña
SST composites for El Niño and La
Niña conditions [X. Yuan, 2004]
Impact of stratospheric ozone
Figure: Multi-model mean of the regression of the leading EOF of ensemble mean Southern Hemisphere sea level
pressure for models with (red) and without (blue) ozone forcing. The thick red line is a 10-year low-pass filtered
version of the mean. The grey shading represents the intermodel spread at the 95% confi dence level. [IPCC, 2007]
•
•
The projected changes of the SAM depend, in part, on whether the GCMs include
ozone forcing (the SAM is also influenced by greenhouse-gas concentrations)
The projected changes in ENSO variability are strongly influenced by the coupling
between the atmospheric and ocean models, and highly model-dependent.
Atmosphere-ocean interface: Sea ice
• AR4 GCMs exhibit a wide
range of sea ice extents
• Excessive sea-ice cover in
CCSM4 is due to anomalously
strong zonal winds over the
Southern Ocean
Sea-ice extent in IPCC AR4 GCMs
March
September
Antarctic sea-ice extent in CCSM4
observations
[Landrum et al., submitted]
Figure: 1980–1999 sea ice distribution simulated by 14
AOGCMs. For each pixel, the figure indicates the number of
models that simulate at least 15% of the area covered by sea ice.
The red line shows the observed 15% concentration boundaries.
[IPCC, 2007]
Projected temperature and precipitation
changes (IPCC, 2007)
Future climate
change simulated by
GCMs must be
viewed bearing in
mind the limitations
of these models
Annual surface temperature and precipitation changes between 198099 and 2080-99 from the multi-model ensemble A1B projections
[IPCC, 2007]
Concluding remarks
• Despite substantial progress made in the
MODELING of the polar atmosphere, further
improvements are still needed in current models,
esp. in GCMs.
• This effort would/will benefit
from the development of an
integrated and robust OBSERVING
network in both polar regions to:
– monitor ongoing changes
– help enhance our understanding of
the physical processes at play
– and provide input for numerical
models
Leverett Glacier, Transantarctic Mountains. Photo by Paul Thur.