Climate and Cryosphere (CliC):
Cryosphere Reanalyses
(Agenda item 7.3)
Jeff Key
NOAA/NESDIS
Cryosphere Variables
Snow
- Snow water equivalent, depth, extent, state, density, snowfall, solid
precipitation, albedo
Lake and River Ice
- Freeze-up/Break-up, thickness, snow on ice
Sea Ice
- extent, concentration, open water, type/age, thickness, motion, icebergs, snow
on ice
Glaciers, Ice Caps, Ice sheets
- mass balance (accumulation/ablation), thickness, area, length (geometry), firn
temperature, snowline/equilibrium line, snow on ice
Frozen Ground/Permafrost
- soil temperature/thermal state, active layer thickness, borehole temperature,
extent, snow cover
Prediction and Use of Cryosphere
Information
• Global models:
– Many GCMs have ice models and predict ice extent and thickness
(see SWIPA comparison of GCMs with regard to ice)
• Regional models:
– Pan-Arctic Ice-Ocean Modeling and Assimilation System
(PIOMAS), 1948 – 2004.
– Arctic System Reanalysis (ASR) - coupled ice-ocean, land surface
and other models. Under development.
– SNODAS at NWS/NOHRSC (an analysis, not a reanalysis)
– Others, e.g., coupled ocean-ice model of the ECCO group
(http://ecco-group.org)
• Product reprocessing
–
–
–
–
Sea ice extent, concentration, motion, thickness, and age
Snow cover
Snow/ice surface temperature and albedo
Winds (not cryosphere, but relevant)
Assimilation of Snow and Ice Data
Snow/ice
• Sea ice motion (sat): PIOMAS (U. Washington), experimental.
• Sea ice extent (sat): PIOMAS, operational NWP and reanalyses,
U.S. Navy PIPs model; Canada; others?
• Sea ice concentration (sat): experimental forecast model
• Snow cover (sat + surf): operational NWP, NOHRSC; Canada;
others?
• Snow water equivalent (sat + surf): operational NWP, NOHRSC
• Ice surface temperature (sat, surf): experimental.
Polar Atmosphere
• Polar winds (sat): operational NWP models (11 worldwide).
Reanalysis use soon (JRA, ERA, others)
PIOMAS, 1948-2004
(Jinlun Zhang, University of Washington)
The Pan-Arctic Ice-Ocean Modeling and Assimilation System is a coupled
ocean-ice capable of assimilating ice concentration and velocity data. It
generates ice thickness, concentration, motion, and snow depth.
Monthly mean model simulated sea ice thickness (m) and satellite observed ice edge (white line) for 9/1979
and 9/2003.
Arctic System Reanalysis
(Dave Bromwich, Ohio State University, and others)
The first dry run of the Arctic
System Renalysis is being run at
reduced resolution, 30 km. It
should be done by the end of
this month (March 2010) for the
period June 2007-September
2008.
It will use sea ice concentration,
thickness, albedo, and snow
cover.
Eos, Vol. 91, No. 2, 12 January 2010
Assimilation of Snow Data
SNODAS at NOHRSC (NOAA/NWS)
•
SNODAS combines all available data, including NWP model output coupled
with meteorological and snow observations, to generate a best estimate of
gridded snow water equivalent in near real-time. SNOWDAS includes:
1.
2.
3.
•
data ingest and downscaling procedures,
a spatially distributed energy-and-mass-balance snow model that is
run once each day, for the previous 24-hour period and for a 12-hour
forecast period, at high spatial (1 km) and temporal (1 hr)
resolutions, and
data assimilation and updating procedures.
The snow model is driven by downscaled analysis and forecast fields from a
mesoscale, NWP model, surface weather observations, satellite-derived
solar radiation data, and radar-derived precipitation data. It is updated with
satellite and surface observations of snow extent, snow depth, and snow
water equivalent.
Climate Model Assessment and Output for SWIPA
(SWIPA = Snow, Water, Ice, and Permafrost of the Arctic, an Arctic Council assessment)
IPCC models vary
considerably in their ability
to hindcast climate patterns
based on location, variable
of interest, and analysis
methods (e.g. means,
variance, trends, etc).
Some models perform well
by some criteria but not by
others.
 Which models are best
for assessments such as
SWIPA?
J. Overland, J. Walsh, V. Kattsov, M. Wang
-- SWIPA ad hoc model liaison team
Primary source of model output for
20th and 21st centuries:
IPCC Fourth Assessment Report archive at PCMDI
(PCMDI: Program for Climate Model Diagnosis and Intercomparison)
-----
~23 global climate models
1-10 ensemble members from each model
A2, A1B, B2 scenarios from most models
simulations span 20th century (prescribed forcing) and
21st century (forcing from scenario)
What is the optimum number of models to include?
Model selection basis: Simulation of seasonal cycle of recent climate
If the N models with the smallest RMSE are selected:
(temperature, 60-90N)
IPCC AR4 models (optimum subset):
Simulated Arctic sea ice extent (September)
Northern Hemisphere September sea ice extent as simulated by IPCC AR4 Models (now CMIP)
models for 20th and 21st century under A1B emission scenario by six models selected for simulating
mean and seasonality of sea ice extent within 20% of the observed values. The thick blue line is the
ensemble mean of the members in the reduced group, and the thick yellow line is the ensemble mean
for all IPCC AR4 models. Thick read line is the observed values based on Hadley Centre sea ice
analysis (HadISST). After Wang and Overland (2009).
Reprocessing
A few current reprocessing efforts:
•Sea ice extent and concentration for SSMR and SSM/I period (NSIDC
and Ocean and Sea Ice group of EUMETSAT's Satellite Application
Facility) – 8 different ice concentration products!
•Sea ice thickness, age, and motion
•Sea Ice Charts of the Russian Arctic, 1933-2006 (AARI and NSIDC)
•Snow “reanalysis” (NSIDC and Rutgers U.)
•Snow/ice surface albedo, surface and TOA radiation
•Greenland climate network (K. Steffen)
•Soil temperature data recovery and access (V. Romanovsky)
•Historical AVHRR polar winds
NOAA Scientific Data Stewardship
• NOAA's National Climatic Data Center (NCDC) initiated the Scientific
Data Stewardship (SDS) Project to lead the Agency's CDR activities and
to coordinate with the partner agencies.
• The SDS Project provides fundamental and geophysical CDRs per the
recommendations of the U.S. Climate Change Science Program (CCSP),
the Global Climate Observing System (GCOS), and the
Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment
Report.
• The Project's goal is to extend proven algorithms to a sufficiently large set
of observations that near-seamless long-term aggregate records are
obtained. Application of same or similar algorithms across different
satellites, when practical, allows development of homogeneous error
structures - a key Project goal.The Project expects rigorous error analysis
in all of its products.
Snow and Ice CDRs
The cryosphere product development team will coordinate the generation,
validation, and archival of fundamental and thematic snow and ice
climate data records.
•Ice/snow surface temperature (AVHRR and MODIS)
•Ice/snow surface broadband albedo (AVHRR and MODIS)
•Sea ice motion (AVHRR and SSM/I or AMSR-E)
•Sea ice concentration and extent (SSM/I and AMSR-E)
•Sea ice thickness/age (AVHRR, MODIS, SSM/I and AMSR-E)
•Snow cover/extent (AVHRR, SSM/I, AMSR-E)
•Surface shortwave and longwave radiation (AVHRR and MODIS)
•Surface ice melt onset and freeze-up (SMMR, SSM/I, AMSR-E)
Sea Ice Thickness/Age
1982-2004+
Sea Ice Thickness
Age from Motion
Ice Motion
1982-2010
Surface Temperature and Albedo
1982-2004
Skin Temperature
Broadband Albedo
Historical Polar Winds from AVHRR
1982-2009
A 28 year dataset of wind vectors (speed, direction, height) in both polar regions
has been generated from AVHRR data.
Daily composite of wind
vectors derived from
NOAA-17 AVHRR GAC
data on March 17, 2003
over Antarctica. The
South Pole is at the center
of the image. The
background is the AVHRR
11 micron brightness
temperature image. Wind
vectors are grouped into
three height categories
(for illustration only):
below 700 hPa (yellow),
from 400 to 700 hPa
(cyan), and above 400
hPa.