Hindcast skill and predictability for precipitation and two-meter air temperature anomalies in global circulation models over the Southeast United States
This paper presents an assessment of the seasonal prediction skill of current global circulation models, with a focus on the two-meter air temperature and precipitation over the Southeast United States. The model seasonal hindcasts are analyzed using measures of potential predictability, anomaly correlation, Brier skill score, and Gerrity skill score. The systematic differences in prediction skill of coupled ocean–atmosphere models versus models using prescribed (either observed or predicted) sea surface temperatures (SSTs) are documented. It is found that the predictability and the hindcast skill of the models vary seasonally and spatially. The largest potential predictability (signal-to-noise ratio) of precipitation anywhere in the United States is found in the Southeast in the spring and winter seasons. The maxima in the potential predictability of two-meter air temperature, however, reside outside the Southeast in all seasons. The largest deterministic hindcast skill over the Southeast is found in wintertime precipitation. At the same time, the boreal winter two-meter air temperature hindcasts have the smallest skill. The large wintertime precipitation skill, the lack of corresponding two-meter air temperature hindcast skill, and a lack of precipitation skill in any other season are features common to all three types of models (atmospheric models forced with observed SSTs, atmospheric models forced with predicted SSTs, and coupled ocean–atmosphere models). Atmospheric models with observed SST forcing demonstrate a moderate skill in hindcasting spring-and summertime two-meter air temperature anomalies, whereas coupled models and atmospheric models forced with predicted SSTs lack similar skill. Probabilistic and categorical hindcasts mirror the deterministic findings, i.e., there is very high skill for winter precipitation and none for summer precipitation. When skillful, the models are conservative, such that low-probability hindcasts tend to be overestimates, whereas high-probability hindcasts tend to be underestimates.
KeywordsEnsemble Member Winter Precipitation Forecast Skill Seasonal Forecast Anomaly Correlation
We thank the various model providers for making available, through APEC Climate Center (APCC), the hindcast datasets used in this study. We thank Ms. Kyong Hee An for facilitating the data access and for providing associated documentation and Ms. Kathy Fearon for her careful reading of the manuscript and helpful editorial comments. All model data used in this study are available online from APCC (http://www.apcc21.net). This research was supported by NOAA grant NA07OAR4310221 and USDA grant 2088-38890-19013.
- Cocke S, LaRow TE, Shin DW (2007) Seasonal rainfall predictions over the southeast United States using the Florida State University nested regional spectral model. J Geophys Res 112. doi: 10.1029/2006JD007535
- Gates WL, Boyle J, Cove C, Dease C, Doutriaux C, Drach R, Fiorino M, Gleckler P, Hnilo J, Marlais S, Phillips T, Potter G, Santer BD, Sperber KR, Taylor K, Williams D (1999) An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull Amer Meteorol Soc 80:29–55CrossRefGoogle Scholar
- Kirtman BP, Shukla J, Balmaseda M, Graham N, Penland C, Xue Y, Zebiak S (2002) Current status of ENSO forecast skill: a report to the Climate Variability and Predictability (CLIVAR) Numerical Experimentation Group (NEG). CLIVAR Working group on seasonal to interannual prediction. p 31Google Scholar
- Shneerov BE, Meleshko VP, Matjugin VA, Spryshev PV, Pavlova TV, Vavulin SV, Shkolnik IM, Subov VA, Gavrilina VM, Govorkova VA (2002) The current status of the MGO global atmospheric circulation model (version-MGO-03). MGO Procceeding 550:3–43Google Scholar
- Trosnikov IV, Kaznacheeva VD, Kiktev DB, Tolstikh MA (2005) Assessment of potential predictability of meteorological variables in dynamical seasonal modeling of atmospheric circulation on the basis of semi-Lagrangian model SL-AV. Russian Meteorol Hydrol 12Google Scholar
- Wang G, Alves O, Hudson D, Hendon H, Liu G, Tseitkin F (2008) SST skill assessment from the new POAMA-1.5 system. BMRC Res Lett 8:2–6Google Scholar
- Wang B, Lee J-Y, Kang I-S, Shukla J, Park C-K, Kumar A, Schemm J, Cocke S, Kug J-S, Luo J-J, Zhou T, Wang B, Fu X, Yun W-T, Alves O, Jun EK, Kinter J, Kirtman B, Krishnamurti T, Lau NC, Lau W, Liu P, Peigon P, Rosati T, Schubert S, Stern W, Suarez M, Yamagata T (2009) Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Clim Dyn 33:93–117Google Scholar
- Weng S-P, Tung Y-C, Huang W-H (2005) Predictions of global sea surface temperature anomalies: introduction of CWB/OPGSST1.1 Forecast System. Proceedings, Conference on Weather Analysis and Forecasting, Taipei, Taiwan, pp 341–345Google Scholar
- Winsberg MD (2003) Florida weather. University Press of Florida. p 218Google Scholar