CanSIPS development plans CanSISE Workshop - 30 Oct 2013 Bill Merryfield CCCma.
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Transcript of CanSIPS development plans CanSISE Workshop - 30 Oct 2013 Bill Merryfield CCCma.
CanSIPS development plans
CanSISE Workshop - 30 Oct 2013
Bill Merryfield
CCCma
Avenues for CanSIPS development
• Initialization improvements: sea ice, land, …
• Model improvements : all physical components + ESM
• System improvements: larger ensemble size, new
products, …
Initialization improvements
1 M
ar 1
981
1 M
ar 2
010
1 S
ep 1
981
1 S
ep 2
010
• Based on relaxation to (not very realistic) model seasonal thickness climatology
• Unlikely to accurately capture thinning trend
Sea ice thickness on first day of forecasts (~initial values)
meters
CanSIPS sea ice thickness initialization
1 M
ar 1
981
1 M
ar 2
010
CanSIPS sea ice thickness initialization
1 S
ep 1
981
1 S
ep 2
010
Sea ice thickness on first day of forecasts (~initial values)
meters
Ice extent trends:
HadISST1.1 NASA Bootstrap
= 0.55
CanSIPS fcsts NASA Bootstrap
= 0.36 !
• Based on relaxation to (not very realistic) model seasonal thickness climatology
• Unlikely to accurately capture thinning trend
CanSISE sub-project A2.3: Improved CanSIPS sea ice initialization
Pre
dic
tor:
Sea
Ice
Vo
lum
e (P
IOM
AS
S)
Predictor: Sea Ice Extent (NSIDC)
correlation
M. Chevallier, G. Smith
• Approach 1: find empirical relationships between ice thickness and observables (e.g. September and current-month ice concentration), based on models that validate best with observations
• Approach 2: allow thickness to set itself in assimilating models, possibly with corrections to reduce bias
Example of empirical relationships:lagged correlations between volume(as predictor) and extent (as predictand)
CanSIPS Land initialization (current)
www.eoearth.org/view/article/152990
Direct atmospheric initialization through assimilation of 6-hourly T, q, u, v
Indirect land initialization through response to model atmosphere
Data Sources: Hindcasts vs Operational
**pending availability of CMCNEMOVAR analysis
Change in atmospheric data source:Effect on soil moisture
• Plots below compare soil moisture in first forecast month for ERA vs CMC-based initialization
• VFSM = volume fraction of soil moisture (%)
• Anomalies are relative to 1981-2010 hindcast climatology
CanCM3CanCM4
Global mean VFSM anomaly Canada mean VFSM anomaly
ERA assimilation
CMC assimilation
CMC assimilation began 1 Jan 2010
Effects of soil moisture biases on forecasts
Mean differences in JJA forecasts for 2010-12 (lead 0)
Cmm day-1
2m temperature precipitation
Dots indicate statistical significance of CMC – ERA diffs according to t test
plots by Slava Kharin
data constraint on land variables would eliminate such drifts
Problem solved using bias correction methodology of Kharin & Scinocca (GRL 2012), but
New approach: Constrain land variables with CaLDAS?
• CaLDAS = Canadian Land Data Assimilation System
• Will be global, available in real time
• Variable sets, ranges differ from CLASS will need to map CaLDAS
variables into CLASS variables
• Would need to extend CaLDAS to cover hindcast period, ideally back to
1981 (proposed under CanSISE)
Model improvements
Atmospheric/land/earth-system model development
• CLASS2.7 3.6: improved snow physics with liquid water component
• Additional snow model improvements (K. von Salzen talk)
• Interactive vegetation (CTEM = Canadian Terrestrial Ecosystem Model)
• Ocean ecosystem model (CMOC = Canadian Model of Ocean Carbon)
• Atmospheric and ocean physics improvements
Next CanSIPS target model: CanESM4.2?
Drought reduced leaf area index reduced evapotranspiration persisted drought (positive feedback)
CanSIPSOGCM
CanSIPSAGCM
Model resolution
CanSIPSAGCM
CanSIPSOGCM
NCEP
UK Met
NCEPUK Met
Model resolution
CanSIPSAGCM
CanSIPSOGCM
NCEP
UK Met
NCEPUK Met
CanSIPSv3?
or downscale using Canadian Regional Climate Model?
Model resolution
CanCM3/4 ice model resolution
• Sea ice and coastlines represented at AGCM resolution (128x64)
• “Pole problem” due to convergence of meridians
CanCM3/4 ice model resolution
OPA/NEMOORCA1resolution
CanCM3/4 ice model resolution
OPA/NEMOORCA1resolution
OPA/NEMOORCA025resolution
CanCM3/4 ice model resolution
OPA/NEMOORCA1resolution
OPA/NEMOORCA025resolution
Summary• Near-term CanSIPS initialization improvement will focus on
land and sea ice thickness
• Near-term model development will focus on snow + ecosystem components (+ atmosphere/ocean physics improvements)
• Longer-term model development will include coupling to OPA/NEMO, which will vastly better resolve Arctic coastlines & sea ice and eliminate pole problem
• Coupled GEM to be applied to seasonal prediction
CanSIPSv2 = CanESM4.2 + coupled GEM?
• RCM downscaling for Canadian regions a possibility
Solution: Modify CMC-based assimilation runs using bias correction method of Kharin & Scinocca (GRL 2012)
1. Extend ERA-based assimilation runs to mid-2012
2. From these runs make 6-hourly soil moisture time series from 1 Jan 2010
3. Repeat CMC-based assimilation runs, assimilating soil moisture from ERA-based runs from step 2 using:
4. Construct cyclostationary bias correcting forcing (“G”) from soil moisture assimilation term:
The bias correcting term “G” is not a relaxation term. For a given grid point, it only depends on the day of the year.
usual model equations assimilation terms
model soilmoisture
assimilated ERA-basedsoil moisture
mean annual cycle