TY - JOUR
T1 - The canadian seasonal to interannual prediction system. part I
T2 - Models and initialization
AU - Merryfield, William J.
AU - Lee, Woo Sung
AU - Boer, George J.
AU - Kharin, Viatcheslav V.
AU - Scinocca, John F.
AU - Flato, Gregory M.
AU - Ajayamohan, R. S.
AU - Fyfe, John C.
AU - Tang, Youmin
AU - Polavarapu, Saroja
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - The Canadian Seasonal to Interannual Prediction System (CanSIPS) became operational at Environment Canada's Canadian Meteorological Centre (CMC) in December 2011, replacing CMC's previous two-tier system. CanSIPS is a two-model forecasting system that combines ensemble forecasts from the Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled Climate Model, versions 3 and 4 (CanCM3 and CanCM4, respectively). Mean climate as well as climate trends and variability in these models are evaluated in freely running historical simulations. Initial conditions for CanSIPS forecasts are obtained from an ensemble of coupled assimilation runs. These runs assimilate gridded atmospheric analyses bymeans of a procedure that resembles the incremental analysis update technique, but introduces only afraction of the analysis increment in order that differences between ensemble members reflect the magnitude of observational uncertainties. The land surface is initialized through its response to the assimilative meteorology, whereas sea ice concentration and sea surface temperature are relaxed toward gridded observational values. The subsurface ocean is initialized through surface forcing provided bythe assimilation run, together with an offline variational assimilation of gridded observational temperatures followed by an adjustment of the salinity field to preserve static stability. The performance of CanSIPS historical forecasts initialized every month over the period 1981'2010 is documented in a companion paper. The CanCM4 model and the initialization procedures developed for CanSIPS have beenemployed as well for decadal forecasts, including those contributing to phase 5 of the Coupled Model Intercomparison Project.
AB - The Canadian Seasonal to Interannual Prediction System (CanSIPS) became operational at Environment Canada's Canadian Meteorological Centre (CMC) in December 2011, replacing CMC's previous two-tier system. CanSIPS is a two-model forecasting system that combines ensemble forecasts from the Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled Climate Model, versions 3 and 4 (CanCM3 and CanCM4, respectively). Mean climate as well as climate trends and variability in these models are evaluated in freely running historical simulations. Initial conditions for CanSIPS forecasts are obtained from an ensemble of coupled assimilation runs. These runs assimilate gridded atmospheric analyses bymeans of a procedure that resembles the incremental analysis update technique, but introduces only afraction of the analysis increment in order that differences between ensemble members reflect the magnitude of observational uncertainties. The land surface is initialized through its response to the assimilative meteorology, whereas sea ice concentration and sea surface temperature are relaxed toward gridded observational values. The subsurface ocean is initialized through surface forcing provided bythe assimilation run, together with an offline variational assimilation of gridded observational temperatures followed by an adjustment of the salinity field to preserve static stability. The performance of CanSIPS historical forecasts initialized every month over the period 1981'2010 is documented in a companion paper. The CanCM4 model and the initialization procedures developed for CanSIPS have beenemployed as well for decadal forecasts, including those contributing to phase 5 of the Coupled Model Intercomparison Project.
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U2 - 10.1175/MWR-D-12-00216.1
DO - 10.1175/MWR-D-12-00216.1
M3 - Article
AN - SCOPUS:84877985960
VL - 141
SP - 2910
EP - 2945
JO - Monthly Weather Review
JF - Monthly Weather Review
SN - 0027-0644
IS - 8
ER -