Seasonal Forecasts Based on Coupled Climate Prediction Models

Introduction

Since September 1995 Environment Canada (EC) has produced seasonal forecasts of surface air temperature (SAT) anomalies and precipitation (PCPN) anomalies (1-3 month outlooks) for Canada from the Canadian Meteorological Centre (CMC). Since 1 December 2011 these forecasts and their extensions to longer lead times have been obtained from an ensemble of 20 dynamical model runs, 10 from each of two coupled atmosphere-ocean-land physical climate models:

  • CanCM3, developed at the (Canadian Centre for Climate Modelling and Analysis (CCCma), uses the atmospheric model CanAM3 (also known as AGCM3; Scinocca et al. 2008) with horizontal resolution of 315 km (T63) and 31 vertical levels and the ocean model CanOM4 with approximate horizontal resolution of 100 km and 40 vertical levels.
  • CanCM4 (Arora et al. 2011), also from the CCCma uses the atmospheric model CanAM4 (also known as AGCM4) with horizontal resolution of 315 km (T63) and 35 vertical levels and the ocean model CanOM4 with approximate horizontal resolution of 100 km and 40 vertical levels.

The coupled models are initialized by stepping them forward in time while constraining their atmosphere, sea surface temperature and sea-ice states to be close to observation-based CMC analyses of these quantities. Just prior to the beginning of the forecast period subsurface ocean temperatures from the NCEP Global Ocean Data Assimilation System (GODAS) are incorporated using the methods of Tang et al. 2004 and Troccoli et al. 2002. The initial land state including soil moisture and snow cover is determined by the internal workings of the constrained model. At the initial forecast time the constraints are released and the forecast begins.

Because the forecast models include a simulated ocean, future sea surface temperature or SST anomalies and their climate influences are determined by the model as part of the forecast. This is in contrast to the previous system, where SST anomalies observed just prior the beginning of the forecast (preceding 30 days) were used throughout the forecast period. This meant that the useful forecast range was limited to four months. Another important consequence is that the current system can potentially predict a future El Nino or La Nina event, a capability that the previous system did not have.

Model biases are adjusted statistically (see below for further explanation) based on 30 years (1981-2010) of hindcasts from phase two of the Coupled Historical Forecasting Project (CHFP2). The CHFP2 hindcasts also provide the basis for estimating the expected forecast skill of the models (see discussion in Kharin and Zwiers, 2001; Kharin et al., 2001; Kharin et al., 2009; Merryfield et al. 2010). The CHFP2 forecast data are available on line from the CCCma Web site.

Surface Air Temperature Forecast Methodology

The surface air temperature forecasts are made in doing first an average of the daily temperature as predicted by the models. The climatologies of the models are then subtracted from the mean forecast seasonal temperatures to derived the forecast anomalies of each model. The anomalies of the two models are then normalized and combined using an arithmetic average. The surface air temperature forecast anomalies are the anomalies of the mean daily temperature measured at the Stevenson screen height (2 metres). Finally the anomalies are divided in three categories (above, near and below the normal).

Precipitation Forecast Methodology

The precipitation forecasts are made using the total accumulated water precipitation over the season. The precipitation predicted by the models is the total liquid and includes all types: snow, rain, ice pellets, etc. The climatology of the models is subtracted from the total precipitation forecast to derive the anomalies. The anomalies of the two models are then combined using a simple normalized average as described for the surface air temperature. Finally the precipitation anomalies are divided in three categories (above, near and below the normal) as is done for the temperature anomaly forecast.

References

Arora, V., J. Scinocca, G. Boer, J. Christian, K. L. Denman, G. Flato, V. Kharin, W. Lee, W. Merryfield, 2011: Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys. Res. Lett., 38, L05805, doi:10.1029/2010GL046270.

Kharin, V. V. et F. W. Zwiers, 2001: Skill as a function of time scale in ensemble of seasonal hindcasts. Climate Dynamics, 17, 127-141.

Kharin, V.V ., F. W. Zwiers et N. Gagnon, 2001: Skill of seasonal hindcasts as a function of the ensemble size. Climate Dynamics, 17, 835-843.

Kharin, V. V., Q. Teng, F. W. Zwiers, G. J. Boer, J. Derome, J. S. Fontecilla, 2009: Skill assessment of seasonal hindcasts from the Canadian Historical Forecast Project. Atmos. Ocean., 47, 204-223.

Merryfield, W. J., W.-S. Lee, G. J. Boer, V. V. Kharin, B. Pal, J. F. Scinocca and G. M. Flato, 2010: The first Coupled Historical Forecasting Project (CHFP1). Atmos. Ocean, 48, 263-283.

Merryfield, W. J., W.-S. Lee, G. J. Boer, V. V. Kharin, J. F. Scinocca, G. M. Flato, R. S. Ajayamohan, J. C. Fyfe, Y. Tang, and S. Polavarapu, 2013. The Canadian Seasonal to Interannual Prediction System. Part I: Models and initialization, Monthly Weather Review, in press, doi:10.1175/MWR-D-12-00216.1

Scinocca, J.F., N.A McFarlane, M. Lazare, J. Li, 2008: The CCCma Third Generation AGCM and its Extension into the Middle Atmosphere. Atmospheric Chemistry and Physics, 8, 7055-7074.

Tang, Y, R. Kleeman, A. M. Moore, J. Vialard, and A. Weaver, 2004: An off-line, numerically efficient initialization scheme in an oceanic general circulation model for El Nino-Southern Oscillation prediction. J. Geophys. Res., 109, C05014, doi:10.1029/2003JC002159.

Troccoli, A., M. A. Balmaseda, J. Segschneider, J. Vialard, D. L. T. Anderson, K. Haines, T. Stockdale, F. Vitart, and A. D. Fox, 2002: Salinity adjustments in the presence of temperature data assimilation. Mon. Wea. Rev., 130, 89-102.