Polar processes and
forecast error
Ayrton Zadra
Recherche en prévision
numérique atmosphérique (RPN-A)
Meteorological Research Division
Environment Canada
OUTLINE
• Polar climate
• Polar seasonal forecasting
• Polar monthly forecasting
• Polar NWP
• Polar mesoscale issues
• THORPEX-IPY
• Conclusion
THORPEX Predictability and Dynamical Processes Working Group and
WGNE Workshop “Diagnosis of Model Errors” ETH Zurich – 7 to 9 July 2010
Polar climate
[source: 2007 IPCC Report on Climate Change]
Large range of present-day
and future climate
simulations due to:
(1) sources of uncertainty:
- large natural variability on interannual, decadal and longer time scales
- uncertain trends in tele-connections
(e.g. Northern Annular Mode (NAM),
ENSO)
observed
simulated
Figure 11.18. Top panels: Temperature anomalies (w.r.t. 1901-1950 for the Arctic, and 1951-2000
for Antarctica): observed (black line), simulated (red envelope) by MMD models incorporating
known forcings; and projected by MMD models for the A1B scenario (orange envelope). Also
shown: bars representing range of projected changes for 2091-2100 for the B1 scenario (blue),
the A1B scenario (orange) and the A2 scenario (red). Dashed black line: when observations are
present for less than 50% of the area. [from 2007 IPCC report]
Polar climate
Surface temperature – projected change
1980-1999 to 2080-2099
[source: 2007 IPCC Report on Climate Change]
Large range of present-day
and future climate
simulations due to:
(2) incomplete understanding &
representation of polar processes:
- complex interaction (atmosphere-landcryosphere-ocean-ecosystem)
- processes not well represented:
> clouds
> planetary boundary layer processes
> sea ice
- inadequate resolution for important
processes in the polar seas
Figure 11.21. Annual surface temperature
change between 1980 to 1999 and 2080 to 2099
in the Arctic and Antarctic from the MMD-A1B
projections. [from 2007 IPCC report]
Surface temperature – multi-model bias
Polar climate
[source: 2007 IPCC Report on Climate Change]
Large range of present-day
and future climate
simulations due to:
(3) lack of observations
(particularly over Antarctica)
making it difficult
Surface temperature – multi-model rmse
- to assess models
- to develop process knowledge
Figure 8.2. Annual mean surface temperature
(a) observed (contours) and multi-model bias (shaded)
(b) multi-model root-mean square error (shaded)
Computed over all AOGCM simulations available in the MMD at PCMDI.
[from 2007 IPCC report]
Polar seasonal forecasting
- well captured propagation of
stratospheric polar vortex
anomalies into troposphere
- stratosphere–troposphere link in
‘free’ seasonal integrations →
operational seasonal/monhtly
forecasts may benefit from the
extended-range predictability
Z1000 anomalies
from ECMWF
seasonal forecasts
(21-40 days)
Seasonal integrations of the
TL95L60 ECMWF model:
Z1000 anomalies
from ERA40
[source: Jung & Leutbecher 2007]
Fig. 10: Mean Z1000 anomalies (in dam) following onset of stratospheric polar vortex events:
ERA-40 data (top); seasonal forecasts with the ECMWF model (bottom), 21–40 days after the onset.
Statistically-significant anomalies are hatched. Results from 40 winters of 1962–2001.
Polar predictability studies: monthly scales
[source: Hai Lin, EC]
Skill of CMC monthly forecast
system:
operational vs experimental
Main changes w.r.t. operational
system:
• single model ensemble with
perturbed physics
• more accurate initial conditions
• better representation of SST
anomalies
• improved sea-ice & snow analysis
• higher resolution
Note: increased skill in polar
regions.
Figure 4: Correlation skill of time mean
surface temperature: operational 40member ensemble forecast system
(left); experimental EPS-based 20member MFS (right).
Polar predictability studies: monthly scales
[source: Hai Lin, EC]
GEM forecast
persistence
-The Canadian monthly forecast
system shows skill* in predicting
the NAO index (top figure)
- Results indicate that the
predictability of the NAO index
increases in the presence of a
strong MJO (bottom figure)
* here the skill measured as the correlation
coefficient between observed and forecast
NAO index
Polar NWP performance
[source: Nordeng et al. 2007]
Overall quality of NWP performance in polar regions
not as good as in lower latitudes, probably because:
1. area is data-sparse
(at least for conventional
observations)
Figure 1: SYNOPs (left panels)
and TEMPs (right panels)
received at the Main
telecommunication Network
during the 1-15 October 2005.
[source Nordeng et al. 2007]
Polar NWP performance
[source: Nordeng et al. 2007]
Overall quality of NWP performance in polar regions
not as good as in lower latitudes, probably because:
2. low troposphere, and large
horizontal variability in stability
temperature and surface
characteristics → small-scale
systems with rapid development:
- polar lows
- heavy snow from
convective systems
- low-level fronts and jets
- mountain lee waves trapped
under inversion
Fig. 1: NOAA-9 satellite image UTC 27 Feb
1987 showing a polar low just before
landfall [source: Dept. Electrical
Engineering & Electronics, Univ. Dundee].
Polar NWP performance
[source: Nordeng et al. 2007]
Overall quality of NWP performance in polar regions
not as good as in lower latitudes, probably because:
3. difficulties for cloud and radiative transfer schemes
- in NWP: surprisingly high liquid-water content at low
temperatures
- in satellite data assimilation: Arctic areas commonly
cloud-covered; difficult to distinguish between cold
surfaces (ice and snow) and clouds
Figure: July 2008 average of
cloud fraction (left) and cloud
top pressure (right) over the
Arctic derived from MODIS
data [source: O. Pancrati and
L. Garand, EC]
Polar NWP performance
[source: Jung & Leutbecher 2007]
Evolution of forecast error for polar regions
- evolution similar to that for the N. Hemisphere as a whole
- largest improvement
“jump” in operational
ECMWF D+2 and D+5
forecasts in the NH polar
regions (autumn 2000):
due to increase in
horizontal resolution
analyses
D+5 error
Fig.3: STD of Z500 forecast error from
ECMWF for NH polar region (north of 70N):
FC = operational deterministic forecast ;
CF = EPS control forecast ;
E4 = re-forecast from ERA30
Also shown: STD of the Z500 fields from
operational analyses and ERA40.
[source: Jung & Leutbecher 2007]
D+2 error
Polar NWP performance
[source: Jung & Leutbecher 2007]
Evolution of forecast
error for polar regions
- increase in horizontal resolution
also beneficial for medium-range
forecast skill for stratospheric
warming events
Fig.6: Time-series of ECMWF 10-day forecasts of the
meridional temperature gradient at 50 hPa for 2
winters showing stratospheric warming events:
verifying analysis
high-res deterministic forecast
low-res EPS control forecast
Gradient is computed from zonally averaged
differences between the polar cap (75-90N) and midlatitudes (50-65N). [source: Jung & Leutbecher 2007]
Polar NWP performance
[source: Jung & Leutbecher 2007]
Evolution of forecast error for polar regions
- sensitivity of forecast error
in polar regions to initial
perturbations (e.g. in the
northern Atlantic region) is
highly flow-dependent
CMC ENSEMBLE STD – SURFACE PRESSURE
- use of ensembles in polar
regions is crucial
Figure: 7-day animation of CMC
EPS standard deviation of surface
pressure over N.Hemisphere, for
a single case (UTC 12-Jun-2010).
hPa
Polar NWP performance
[source: Jung & Leutbecher 2007]
Evolution of forecast error for polar regions
- increase in probabilistic predictability increased 2-3 days in
last 10 years
- EPS benefited from
increase in horizontal
resolution, particularly
for synoptic-scale
features
Fig.9: RPSS for Z500 for
operational ECMWF ensemble
forecasts in NH polar regions
(north of 65N), for total wave
numbers between 8 and 63.
[source: Jung & Leutbecher
2007]
Polar NWP: leading model tendencies
case 31-Jan-2010
5-day average of CMC global model tendencies for temperature;
a boreal winter case [source P. Vaillancourt, EC]
- LW and SW radiation
Leading physics parametrizations: - turbulent diffusion (BL)
- explicit microphysics
Polar NWP: leading model tendencies
case 31-Jan-2010
5-day average of CMC global model tendencies for moisture;
a boreal winter case [source P. Vaillancourt, EC]
- explicit microphysics
Leading physics parametrizations: - turbulent diffusion (BL)
- shallow convection
Polar NWP: mesoscale issues
[source: R. Goodson, HAL/EC]
Material provided by forecasters
from the National Laboratory for
Hydrometeorology and Arctic
Meteorology – Environment Canada
Mission:
• provide improved understanding
and prediction high-impact weather
• focus on hydro-meteorological and
northern latitude weather processes
and phenomena
Fig: Domain covered by the CMC
2.5km Arctic LAM.
Relevant processes:
Relevant forecasts:
- winter: wind / blizzards
- summer: low cloud / fog
“all boundary layer
processes (turbulence,
cloud microphysics, etc.)
that feed into low-level
winds and/or visibility”
Polar NWP: mesoscale issues
[source: R. Goodson, HAL/EC]
Winter: winds / blizzards
Quantity to
forecast
wind
Requires accurate
representation of
- topography and surface
roughness
- boundary layer mixing in
stable Arctic winter
precipitation
Topography around
Pangnirtung, Nunavut
- cloud microphysics
adequate for the Arctic
- evaporation of light
precipitation
snow pack
- precipitation amounts
- snow density / pack
dynamics
- blowing-snow processes
Trajectories of 4 balloons,
launched from Pangnisrtung
(time between launches: 3h)
Polar NWP: mesoscale issues
[source: R. Goodson, HAL/EC]
Summer: low cloud / fog
Requires accurate modeling
of:
- evolution of leads / ice
motion
- melting / ponding of ice
- snow over ice
- cloud microphysics and
boundary layer mixing
adequate to large moisture &
heat fluxes appropriate to
Arctic low cloud formation
Arctic fog (image: U. Kaden)
Results from THORPEX-IPY
www.ipy-thorpex.no
THORPEX IPY Cluster
10 IPY projects from
9 countries with
the following main
objectives:
(T.E. Nordeng, coordinator)
ARCMIP
Arctic Regional Climate
Model Intercomparison Project
(K. Detholf, Alfred-Wegener Institute)
STAR
Norwegian IPY-THORPEX
Storm Studies of the Arctic
(J. Hanesiak, U Manitoba)
GFDex
Greenland Flow
Distortion experiment
(I. Renfrew, U. East Anglia)
(J.E. Kristjansson, U Oslo)
TAWEPI
Thorpex Arctic Weather
and Environmental
Prediction Initiative
(Ayrton Zadra,
Environment Canada)
GREENEX
(H. Olafsson, Iceland & DLR)
Impacts of surfaces fluxes
on severe Arctic storms, climate change
and coastal orographic processes
(W. Perrie, BIO Canada))
T-PARC
Concordiasi
THORPEX Pacific Asian
Regional Campaign
(D. Parsons, NCAR)
Use of IASI data
(F. Rabier, Meteo-France)
Greenland Jets
(A. Dombrack, DLR)
• explore satellite data and
optimised observations to
improve high impact
weather forecasts
• better understand
physical & dynamical
process in polar regions
• to utilise TIGGE (Thorpex
Interactive Grand Global
Ensemble) for polar
prediction
TAWEPI
• TAWEPI = THORPEX Arctic Weather and
Environmental Prediction Initiative
http://collaboration.cmc.ec.gc.ca/science/rpn/tawepi/en/index.html
• Goals of the project:
– Implement regional NWP over the Arctic
– Evaluate enhancement of weather and environmental
predictions in the Arctic
– Implement an operational air/sea/ice coupled model
• Subproject components:
– Improving high Arctic parameterizations and coupling for NWP
– Assessing SV sensitivity to interactions with lower latitudes
– Creating high latitude / altitude analyses using satellite obs
TAWEPI:
singular vector studies
Singular vector analysis of
48h forecast sensitivity for
the summer of 2007.
Target region is outlined
with a dashed black line.
Warmer colours denote
stronger response.
• The sensitivity of Arctic forecast errors to initial analysis
•
•
error is quantified using singular vectors.
SV analysis was performed daily during the IPY period.
The combination of SVs that best reproduces the
observed forecast error is used to evaluate Arctic –
midlatitude interactions.
TAWEPI:
singular vector studies
Strong local sensitivity over Northern Russia
Singular vector analysis of
48h forecast sensitivity for
the summer of 2007.
Target region is outlined
with a dashed black line.
Warmer colours denote
stronger response.
Climatology of cyclone
track density (Sinclair
2006).
Reduced SLP anomaly (mb) from NCEP Reanalysis
(CDC) for summer 2007.
Local climatological maximum in summer cyclone frequency
Summer 2007 SLP anomaly suggests an
anomalously active storm track during the SV
calculation period
Project Concordiasi
[source: Rabier et al. 2010]
www.cnrm.meteo.fr/concordiasi/
• Motivation: Reducing uncertainties in diverse – but
complementary - fields in Antarctic science
– Better use of satellite data for analyses, forecasts and reanalyses
– Progress on the understanding of interactions between ozone
depletion, stratospheric clouds and dynamics
• Experimental design
– Surface-based: radiosoundings at Concordia (and Dumont d’Urville)
+ 45-m instrumented tower, snowfall and accumulation
observations at Concordia
– Stratospheric superpressure balloons with meteorological sensors,
ozone sensors, particle counters, GPS receivers, driftsondes
carrying dropsondes
– Modelling: global and fine-scale models, chemical-transport models
Concordiasi
[source: Rabier et al. 2010]
Modeling activities:
• Statistics at Concordia station
and diagnostic of model
performance
• Improvement in the ECMWF
model: Based on interaction with
polar scientists. Change in
albedo over permanent snow
effective in 2008. Decreased
warm bias.
• Work performed on snow
modelling
Figure: Impact of increased albedo on DJF 2m
temperature. [source: G. Balsamo 2010]
Conclusions
Forecast skill in polar regions has improved in the past
decades, thanks to increased resolution, better
representation of physical processes and improved data &
data assimilation.
However:
• forecast performance is not (yet) as good as in lower
latitudes
• in-situ data remain relatively sparse in polar regions
• IPY data and results are only now becoming available
• the representation of various important processes –
notably boundary layer, polar clouds, snow and sea-ice –
needs to be improved in polar regions…
THANK YOU
Acknowledgments
• RPN/EC: Stéphane Bélair, Jocelin Mailhot, Paul Vaillancourt, Michel Roch, Ron
McTaggart-Cowan, Hai Lain
• ARMA/EC: Mark Buehner, Louis Garand, Peter Houtekamer
• CMC/EC: Normand Gagnon, Allan Rahill, Ahmed Mahidjiba
• HAL/EC: Ron Goodson
• U. Manitoba: John Hanesiak
References
• 2007 IPCC Report in Climate Change
• Jung, T and M. Leutbecher, 2007: Performance of the ECMWF forecasting system in
the Arctic during winter. Q.J.R.Meteorol.Soc. 133, 1327-1340
• Lin, H. 2010: Skill of the EPS-based monthly forecasting system (RPN/EC internal
report)
• Nordeng, T.E, G. Brunet and J. Caughey, 2007: Improvement of weather forecast in
polar regions. WMO Bulletin 54(4), 250-256
• Rabier et al., 2010: The Concordiasi project in Antarctica. Bulletin of the American
Meteorological Society, Jan 2010