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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) based on dynamical atmospheric models. Since 1 December 2011 these forecasts and their extensions to longer lead times have been obtained from the Canadian Seasonal and Interannual Prediction System (CanSIPS), which consists of two coupled atmosphere-ocean-land physical climate models, CanCM3 and CanCM4. On July the 3rd 2019 CanCM3 was replaced by GEM-NEMO. Since then the seasonal forecasts have been made from an ensemble of 20 dynamical model runs, 10 from each of two atmosphere-ocean-land coupled models:

  • CanCM4 (Arora et al. 2011), developed at the Canadian Centre for Climate Modelling and Analysis (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.
  • GEM-NEMO, developed at the Recherche en Prévision Numérique (RPN), uses the atmospheric model GEM (Côté et al. 1998) with horizontal resolution of about 155 km and 79 vertical levels and the ocean model NEMO (Nucleus for European Modelling of the Ocean, http://www.nemo-ocean.eu) with horizontal resolution of about 110 km and 50 vertical levels.

CanCM4 is initialized by stepping forward in time while constraining its 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. In GEM-NEMO, the atmosphere starts from 10 initial condition members of the Global Ensemble Prediction System (GEPS; Houtekamer et al. 2009; Lin et al. 2016) which are generated from the Ensemble Kalman Filter (EnKF) with observations that are background-checked and bias-corrected by the Global Deterministic Prediction System (GDPS; Buehner et al. 2015). The ocean and sea ice initial conditions come from the CMC GIOPS analysis (Smith et al. 2016). To initialize the land surface fields, the CMC Surface Prediction System (SPS; Carrera et al. 2010) is run offline forced by the near-surface atmospheric and precipitation fields of the CMC analysis.

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 early atmospheric-only 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 for each model. The 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 hindcast 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.

Buehner, M., R. McTaggart-Cowan, A. Beaulne, C. Charette, L. Garand, S. Heilliette, E. Lapalme, S. Laroche, S. R. Macpherson, J. Morneau and A. Zadra, 2015: Implementation of Deterministic Weather Forecasting Systems based on Ensemble-Variational Data Assimilation at Environment Canada. Part I: The Global System. Mon. Wea. Rev .143, 2532-2559.

Carrera, M. L., S. Bélair, V. Fortin, B. Bilodeau, D. Charpentier, and I. Doré (2010), Evaluation of snowpack simulations over the Canadian Rockies with an experimental hydrometeorological modeling system, J. Hydrometeorol., 11, 1123–1140.

Côté, J., S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998: The operational CMC-MRB Global Environmental Multiscale (GEM) model: Part I - Design considerations and formulation. Mon. Wea. Rev., 126, 1373-1395.

Houtekamer, P. L., H. L. Mitchell, and X. Deng, 2009: Model Error representation in an operational ensemble Kalman filter, Mon. Wea. Rev., 137, 2126-2143.

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.

Lin, H., N. Gagnon, S. Beauregard, R. Muncaster, M. Markovic, B. Denis, and M. Charron, 2016: GEPS based monthly prediction at the Canadian Meteorological Centre, Mon. Wea. Rev., 144, 4867-4883. DOI: 10.1175/MWR-D-16-0138.1.

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.

Smith, G.C., F. Roy, M. Reszka, D. Surcel Colan, Z. He, D. Deacu, J.-M. Belanger, S. Skachko, Y. Liu, F. Dupont, J.-F. Lemieux, C. Beaudoin, B. Tranchant, M. Drévillon, G. Garric, C.-E. Testut, J.-M. Lellouche, P. Pellerin, H. Ritchie, Y. Lu, F. Davidson, M. Buehner, M. Lajoie and A. Caya, 2016: Sea ice Forecast Verification in the Canadian Global Ice Ocean Prediction System. Quart. J. Roy. Met. Soc., 142, 659–671, doi: 10.1002/qj.2555.

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.

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