Browse > Article
http://dx.doi.org/10.14191/Atmos.2018.28.3.233

Subseasonal-to-Seasonal (S2S) Prediction Skills of GloSea5 Model: Part 1. Geopotential Height in the Northern Hemisphere Extratropics  

Kim, Sang-Wook (School of Earth and Environmental Sciences, Seoul National University)
Kim, Hera (School of Earth and Environmental Sciences, Seoul National University)
Song, Kanghyun (School of Earth and Environmental Sciences, Seoul National University)
Son, Seok-Woo (School of Earth and Environmental Sciences, Seoul National University)
Lim, Yuna (School of Earth and Environmental Sciences, Seoul National University)
Kang, Hyun-Suk (Earth System Research Division, National Institute of Meteorological Sciences)
Hyun, Yu-Kyung (Earth System Research Division, National Institute of Meteorological Sciences)
Publication Information
Atmosphere / v.28, no.3, 2018 , pp. 233-245 More about this Journal
Abstract
This study explores the Subseasonal-to-Seasonal (S2S) prediction skills of the Northern Hemisphere mid-latitude geopotential height in the Global Seasonal forecasting model version 5 (GloSea5) hindcast experiment. The prediction skills are quantitatively verified for the period of 1991~2010 by computing the Anomaly Correlation Coefficient (ACC) and Mean Square Skill Score (MSSS). GloSea5 model shows a higher prediction skill in winter than in summer at most levels regardless of verification methods. Quantitatively, the prediction limit diagnosed with ACC skill of 500 hPa geopotential height, averaged over $30^{\circ}N{\sim}90^{\circ}N$, is 11.0 days in winter, but only 9.1 days in summer. These prediction limits are primarily set by the planetary-scale eddy phase errors. The stratospheric prediction skills are typically higher than the tropospheric skills except in the summer upper-stratosphere where prediction skills are substantially lower than upper-troposphere. The lack of the summer upper-stratospheric prediction skill is caused by zonal mean error, perhaps strongly related to model mean bias in the stratosphere.
Keywords
GloSea5; Subseasonal-to-Seasonal (S2S) prediction; prediction skill;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Choi, J., S.-W. Son, Y.-G. Ham, J.-Y. Lee, and H.-M. Kim, 2016: Seasonal-to-interannual prediction skills of near-surface air temperature in the CMIP5 decadal hindcast experiments. J. Climate, 29, 1511-1527.   DOI
2 Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis:configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553-597, doi:10.1002/qj.828.   DOI
3 Goddard, L., and Coauthors, 2013: A verification framework for interannual-to-decadal predictions experiments. Climate Dyn., 40, 245-272, doi:10.1007/s00382-012-1481-2.   DOI
4 Gupta, A. S., N. C. Jourdain, J. N. Brown, and D. Monselesan, 2013: Climate drift in the CMIP5 models. J. Climate, 26, 8597-8615, doi:10.1175/JCLI-D-12-00521.1.   DOI
5 Jung, M.-I., S.-W. Son, J. Choi, and H.-S. Kang, 2015:Assessment of 6-month lead prediction skill of the GloSea5 hindcast experiment. Atmosphere, 25, 323-337, doi:10.14191/Atmos.2015.25.2.323 (in Korean with English abstract).   DOI
6 Jung, M.-I., S.-W. Son, Y. Lim, K. Song, D. J. Won, and H.-S. Kang, 2016: Assessment of stratospheric prediction skill of the GloSea5 hindcast experiment. Atmosphere, 26, 203-214, doi:10.14191/Atmos.2016.26.1.203 (in Korean with English abstract).   DOI
7 Kobayashi, S., and Coauthors, 2015: The JRA-55 Reanalysis:General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5-48, doi:10.2151/jmsj.2015-001.   DOI
8 Lee, S.-M., H.-S. Kang, Y.-H. Kim, Y.-H. Byun, and C. Cho, 2016: Verification and comparison of forecast skill between global seasonal forecasting system version 5 and unified model during 2014. Atmosphere, 26, 59-72, doi:10.14191/Atmos.2016.26.1.059 (in Korean with English abstract).   DOI
9 MacLachlan, C., and Coaurhors, 2015: Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Quart. J. Roy. Meteor. Soc., 141, 1072-1084, doi:10.1002/qj.2396.   DOI
10 Madec, G., 2008: NEMO ocean engine. IPSL Tech. Rep. 27, 401 pp.
11 Murphy, A. H., 1988: Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Wea. Rev., 116, 2417-2424.   DOI
12 Palmer, T. N., and D. L. T. Anderson, 1994: The prospects for seasonal forecasting-A review paper. Q. J. R. Meteor. Soc., 120, 755-793.
13 Persson, A., 2015: User guide to ECMWF forecast products Ver. 1.2. ECMWF, 129 pp.
14 Tripathi, O. P., and Coauthors, 2015: The predictability of the extratropical stratosphere on monthly time-scales and its impact on the skill of tropospheric forecasts. Quart. J. Roy. Meteor. Soc., 141, 987-1003, doi:10.1002/qj.2432.   DOI
15 Rae, J. G. L., H. T. Hewitt, A. B. Keen, J. K. Ridley, A. E. West, C. M. Harris, E. C. Hunke, and D. N. Walters, 2015: Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model. Geosci. Model Dev., 8, 2221-2230, doi:10.5194/gmd-8-2221-2015.   DOI
16 Song, K., H. Kim, S.-W. Son, S.-W. Kim, H.-S. Kang, and Y.-K. Hyun, 2018: Subseasonal-to-Seasonal (S2S) prediction of GloSea5 model: Part 2. Stratospheric sudden warming. Atmosphere, 28, 123-139, doi:10.14191/Atmos.2018.28.2.123 (in Korean with English abstract).
17 Stan, C., and D. M. Straus, 2009: Stratospheric predictability and sudden stratospheric warming events. J. Geophys. Res., 114, D12103.   DOI
18 Vitart, F., and Coaurhors, 2017: The Subseasonal to Seasonal (S2S) prediction project database. Bull. Amer. Meteor. Soc., 98, 163-173, doi:10.1175/BAMS-D-16-0017.1.   DOI
19 Walters, D., and Coaurhors, 2017: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations. Geosci. Model Dev., 10, 1487-1520, doi:10.5194/gmd-10-1487-2017.   DOI
20 Williams, K. D., and Coauthors, 2015: The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci. Model Dev., 8, 1509-1524, doi:10.5194/gmd-8-1509-2015.   DOI
21 WMO, 2006: Standardised verification system (SVS) for long-range forecasts (LRF). [Available online at https://www.wmo.int/pages/prog/www/DPS/LRF/ATTACHII-8SVSfrom%20WMO_485_Vol_I.pdf ].
22 WMO, 2013: Sub-seasonal to seasonal prediction research implementation plan. [Available online at https://www.wmo.int/pages/prog/arep/wwrp/new/documents/S2S_Implem_plan_en.pdf].