On the link between mean state biases and prediction skill in the tropics: an atmospheric perspective
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The present study examines how mean state biases in sea-surface temperature (SST), surface wind and precipitation affect model skill in reproducing surface wind and precipitation anomalies in the tropics. This is done using theoretical arguments, atmosphere-only experiments in the Coupled Model Intercomparison Project Phase 5, and customized sensitivity tests with the SINTEX-F general circulation model. Theoretical arguments suggest that under certain conditions the root mean square error (RMSE) of a variable can be related to its variance and its mean, which indicates a direct link between bias and skill. The anomaly correlation coefficient (ACC), on the other hand, is generally not related to either the mean state or its variance, as several examples document. Multi-model atmosphere-only experiments with prescribed SST warming suggest that both ACC and RMSE of surface wind and precipitation are rather insensitive to warming on the order of 4 K. When SST biases from a free-running control simulation are prescribed in SINTEX-F, the ACC of surface wind is almost unaffected in the equatorial Pacific and Atlantic, while that of precipitation decreases noticeably in some regions but also increases in others. The RMSE of both fields shows widespread deterioration. There is a tendency for warm SST biases to increase the signal-to-noise ratio and sometimes ACC as well. The results suggest that, in the context of atmosphere-only simulations, improving SST and precipitation biases does not necessarily improve the skill in reproducing anomalies of surface wind and precipitation.
The authors would like to thank Prof. Shang-Ping Xie for helpful comments on the manuscript. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison which provides coordinating support and led development of software infrastructure for CMIP, and the climate modeling groups for making available their model output. The authors thank the two anonymous reviewers for their constructive comments, which helped to improve the manuscript.
- Edwards PN (2000) A brief history of atmospheric general circulation modeling. In: Randall DA (ed) General circulation model development: past, present, and future. Academic Press, London, pp 67–90Google Scholar
- Gualdi S, Navarra A, Guilyardi E, Delecluse P (2003) Assessment of the tropical Indo-Pacific climate in the SINTEX CGCM. Ann Geophys 46:1–26Google Scholar
- Gualdi S, Alessandri A, Navarra A (2005) Impact of atmospheric horizontal resolution on El Niño Southern Oscillation forecasts. Tellus 57A:357–374Google Scholar
- Madec G, Delecluse P, Imbard M, Levy C (1998) OPA 8.1 ocean general circulation model reference manual. Tech. Rep. Note 11, LODYC/IPSL, Paris, FranceGoogle Scholar
- Richter I, Chang P, Xu Z, Doi T, Kataoka T, Nagura M, Oettli P, de Szoeke S, Tozuka T (2016) An overview of coupled GCM performance in the tropics. In: Yamagata T, Behera SK (eds) Indo-Pacific climate variability and predictability, vol 8. World Scientific, SingaporeGoogle Scholar
- Roeckner E, Arpe K, Bengtsson L, Christoph M, Claussen M, Dümenil L, Esch M, Giorgetta M, Schlese U, Schulzweida U (1996) The atmospheric general circulation model ECHAM-4: model description and simulation of present-day climate. Tech. Rep. No. 218, Max-Planck-Institut für Meteorologie, Hamburg, GermanyGoogle Scholar
- Scaife AA, Arribas A, Blockley E, Brookshaw A, Clark RT, Dunstone N, Eade R, Fereday D, Folland CK, Gordon M, Hermanson L, Knight JR, Lea DJ, MacLachlan C, Maidens A, Martin M, Peterson AK, Smith D, Vellinga M, Wallace E, Waters J, Williams A (2014) Skillful long-range prediction of European and North American winters. Geophys Res Lett 41:2014GL059. doi: 10.1002/2014gl059637 Google Scholar
- Valcke S, Terray L, Piacentini A (2000) The OASIS coupler user guide version 2.4. Tech. Rep. TR/CGMC/00–10, CERFACE, Toulouse, FranceGoogle 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, Jin EK, Kinter J, Kirtman B, Krishnamurti T, Lau NC, Lau W, Liu P, Pegion 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. doi: 10.1007/s00382-008-0460-0 Google Scholar
- Xie S-P, Carton JA (2004) Tropical Atlantic variability: Patterns, mechanisms, and impacts. In: Wang C, Xie S-P, Carton JA (eds) In Earth climate: the ocean-atmosphere interaction, Geophysical Monograph, 147. AGU, Washington D.C., pp 121–142Google Scholar