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 Mar Mar Sep Sep 2010 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 Mar Mar 2010 CanSIPS sea ice thickness initialization 1 Sep Sep 2010 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 Predictor: Sea Ice Volume (PIOMASS) 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) 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 CMC NEMOVAR 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 hindcast climatology CanCM3 CanCM4 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 (lead 0) CC mm 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)

CanSIPS OGCM CanSIPS AGCM Model resolution

CanSIPS AGCM CanSIPS OGCM NCEP UK Met NCEP UK Met Model resolution

CanSIPS AGCM CanSIPS OGCM NCEP UK Met NCEP UK 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/NEMO ORCA1 resolution

CanCM3/4 ice model resolution OPA/NEMO ORCA1 resolution OPA/NEMO ORCA025 resolution

CanCM3/4 ice model resolution OPA/NEMO ORCA1 resolution OPA/NEMO ORCA025 resolution

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 From these runs make 6-hourly soil moisture time series from 1 Jan 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 equationsassimilation terms model soil moisture assimilated ERA-based soil moisture mean annual cycle