Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO
Eroteida Sánchez-García
CORRESPONDING AUTHOR
Agencia Estatal de Meteorologí a (AEMET), Madrid, Spain
José Voces-Aboy
Agencia Estatal de Meteorologí a (AEMET), Madrid, Spain
Beatriz Navascués
Agencia Estatal de Meteorologí a (AEMET), Madrid, Spain
Ernesto Rodríguez-Camino
Agencia Estatal de Meteorologí a (AEMET), Madrid, Spain
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Cited articles
Athanasiadis, P. J., Bellucci, A., Scaife, A., Hermanson, L.,
Meteria, S., Sanna, A., Borrelli, A., MacLachlan, C., and Gualdi, S.: A
Multisystem View of Wintertime NAO Seasonal Predictions, J. Climate, 30,
1461–1475, https://doi.org/10.1175/JCLI-D-16-0153.1, 2017.
Butler, A. H., Arribas, A., Athanassiadou, M., Baehr, J., Calvo, N., Charlton-Perez, A., Déqué, A., Domeisen, D. I. V., Fröhlich, K., Hendon, H., Imada, Y., Ishii, M., Iza, M., Karpechko, A. Yu., Kumar, A., MacLachlan, C., Merryfield, W. J., Müller, W. A., O'Neill, A., Scaife, A. A., Scinocca, J., Sigmond, M., Stockdale, T. N., and Yasuda, T.: The Climate-system Historical
Forecast Project: do stratosphere-resolving models make better seasonal
climate predictions in boreal winter?, Q. J. Roy. Meteor. Soc., 142, 1413–1427,
https://doi.org/10.1002/qj.2743, 2016.
Climate Data Store:
https://cds.climate.copernicus.eu,
last access: 8 August 2019.
Coelho, C. A. S. and Pezzulli, S.: Forecast Calibration and
Combination: A Simple Bayesian Approach for ENSO, J. Climate, 17, 1504–1516,
https://doi.org/10.1175/1520-0442(2004)017<1504:FCACAS>2.0.CO;2, 2004.
Cohen, J. and Jones, J.: A new index for more accurate winter
predictions, Geophys. Res. Lett., 38, L21701,
https://doi.org/10.1029/2011GL049626, 2011.
Cornes, R., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Datasets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge‐Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dobrynin, M., Domeisen, D. I. V., Müller, W. A., Bell, L., Brune, S., Bunzel, F., Düsterhus, A., Fröhlich, K., Pohlmann, H., and Baehr, J.: Improved Teleconnection-Based
Dynamical Seasonal Predictions of Boreal Winter, Geophys. Res. Lett., 45,
3605–3614, https://doi.org/10.1002/2018GL077209, 2018.
European Climate Assessment & Dataset project: E-OBS gridded dataset, available at:
https://www.ecad.eu/download/ensembles/download.php, last access: 8 August 2019.
Eyre, J. R.: Observation Bias correction schemes in data
assimilation systems: a theoretical study of some of some of their
properties, Q. J. Roy. Meteor. Soc., 142, 2284–2291,
https://doi.org/10.1002/qj.2819, 2016.
Helfrich, S. R., McNamara, D., Ramsay, B. H., Baldwin, T., and
Kasheta, T.: Enhancements to, and forthcoming developments in the
Interactive Multisensor Snow and Ice Mapping System (IMS), Hydrol. Process.,
21, 1576–1586, https://doi.org/10.1002/hyp.6720, 2007.
Kalnay, E.: Atmospheric modelling, Data Assimilation and
Predictability, Cambridge University Press, New York, 2003.
Kang, D., Lee, M.-I., Im, J., Kim, D., Kim, H.-M., Kang, H.-S., Schubert, S. D., Arribas, A., and MacLachlan, C.: Prediction of the Arctic Oscillation in
boreal winter by dynamical seasonal forecasting systems, Geophys. Res. Lett.,
41, 3577–3585, https://doi.org/10.1002/2014GL060011, 2014.
Ramsay, B. H.: The interactive multisensory snow and ice mapping
system, Hydrol. Process., 12, 1537–1546,
https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1537::AID-HYP679>3.0.CO;2-A, 1998.
Riddle, E. E., Butler, A. H., Furtado, J. C., Cohen, J. L., and Kumar,
A.: CFSv2 ensemble prediction of the wintertime Arctic Oscillation, Clim.
Dynam., 41, 1099–1116, https://doi.org/10.1007/s00382-013-1850-5, 2013.
Rodríguez-Puebla, C., Encinas, A. H., Nieto, S., and Garmendia,
J.: Spatial and temporal patterns of annual precipitation variability over
the Iberian peninsula, Int. J. Climatol., 18, 299–316,
https://doi.org/10.1002/(SICI)1097-0088(19980315)18:3<299::AID-JOC247>3.0.CO;2-L, 1998.
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T., Dunstone, N., Eade, R., Fereday, D., Folland, C. K., Gordon, M., Hermanson, L., Knight, J. R., Lea, D. J., MacLachlan, C., Maidens, A., Martin, M., Peterson, A. K., Smith, D., Vellinga, M., Wallace, E., Waters, J., and Williams, A.: Skillful long-range prediction of
European and North American winters, Geophys. Res. Lett., 41, 2514–2519,
https://doi.org/10.1002/2014GL059637, 2014.
Stockdale, T. N., Molteni, F., and Ferranti, L.: Atmospheric initial
conditions and the predictability of the Arctic Oscillation, Geophys. Res. Lett., 42, 1173–1179, https://doi.org/10.1002/2014GL062681, 2015.
Voces, J., Sánchez-García, E., Navascués, B., Franco,
F., and Rodríguez-Camino, E.: Sistema estadístico de
predicción estacional para la gestión de los embalses en España,
Nota Técnica no. 21 AEMET,
http://hdl.handle.net/20.500.11765/4431 (last access: 6 August 2019), 2016.
Wilks, D. S.: Statistical methods in the atmospheric sciences,
2nd edition, Academic Press, USA, 2006.
Short summary
We have described a methodology for ensemble member’s weighting of operational seasonal forecasting systems based on an enhanced prediction of a driver of climate variability strongly affecting certain climate variables (e.g. temperature, precipitation) over a certain region. We have applied it to the North Atlantic Oscillation influence on the Iberian Peninsula winter precipitation. This approach is fully general and consequently applicable to any other SFS providing a skilful NAO signal.
We have described a methodology for ensemble member’s weighting of operational seasonal...