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