Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data

Statistical climate prediction has sometimes demonstrated higher accuracy than coupled dynamical forecast systems. This study tests the applicability of springtime soil moisture (SM) over Europe and sea surface temperatures (SSTs) of three North Atlantic (NA) regions as statistical predictors of Eur...

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Published in:Weather and Forecasting
Main Authors: Pyrina, M., Nonnenmacher, M., Wagner, S., Zorita, E.
Format: Article in Journal/Newspaper
Language:English
Published: AMS 2021
Subjects:
Online Access:https://publications.hereon.de/id/40030
https://publications.hzg.de/id/40030
https://doi.org/10.1175/WAF-D-20-0235.1
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spelling fthzgzmk:oai:publications.hereon.de:40030 2023-06-11T04:14:51+02:00 Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data Pyrina, M. Nonnenmacher, M. Wagner, S. Zorita, E. 2021 https://publications.hereon.de/id/40030 https://publications.hzg.de/id/40030 https://doi.org/10.1175/WAF-D-20-0235.1 en eng AMS https://dx.doi.org/10.1175/WAF-D-20-0235.1 urn:issn:0882-8156 https://publications.hereon.de/id/40030 https://publications.hzg.de/id/40030 info:eu-repo/semantics/openAccess open_access oa_allgemein_hybrid issn:0882-8156 Pyrina, M.; Nonnenmacher, M.; Wagner, S.; Zorita, E.: Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data. In: Weather and Forecasting. Vol. 36 (2021) 4, 1537 - 1560. (DOI: /10.1175/WAF-D-20-0235.1) info:eu-repo/semantics/article Zeitschrift Artikel 2021 fthzgzmk https://doi.org/10.1175/WAF-D-20-0235.1 2023-05-28T23:25:16Z Statistical climate prediction has sometimes demonstrated higher accuracy than coupled dynamical forecast systems. This study tests the applicability of springtime soil moisture (SM) over Europe and sea surface temperatures (SSTs) of three North Atlantic (NA) regions as statistical predictors of European mean summer temperature (t2m). We set up two statistical-learning (SL) frameworks, based on methods commonly applied in climate research. The SL models are trained with gridded products derived from station, reanalysis, and satellite data (ERA-20C, ERA-Land, CERA, COBE2, CRU, and ESA-CCI). The predictive potential of SM anomalies in statistical forecasting had so far remained elusive. Our statistical models trained with SM achieve high summer t2m prediction skill in terms of Pearson correlation coefficient (r), with r≥0.5 over Central and Eastern Europe. Moreover, we find that the reanalysis and satellite SM data contain similar information that can be extracted by our methods and used in fitting the forecast models. Furthermore, the predictive potential of SSTs within different areas in the NA basin was tested. The predictive power of SSTs might increase, as in our case, when specific areas are selected. Forecasts based on extratropical SSTs achieve high prediction skill over South Europe. The combined prediction, using SM and SST predictor data, results in r≥0.5 over all European regions south of 50°N and east of 5°W. This is a better skill than the one achieved by other prediction schemes based on dynamical models. Our analysis highlights specific NA mid-latitude regions that are more strongly connected to summer mean European temperature. Article in Journal/Newspaper North Atlantic Hereon Publications (Helmholtz-Zentrum) Weather and Forecasting
institution Open Polar
collection Hereon Publications (Helmholtz-Zentrum)
op_collection_id fthzgzmk
language English
description Statistical climate prediction has sometimes demonstrated higher accuracy than coupled dynamical forecast systems. This study tests the applicability of springtime soil moisture (SM) over Europe and sea surface temperatures (SSTs) of three North Atlantic (NA) regions as statistical predictors of European mean summer temperature (t2m). We set up two statistical-learning (SL) frameworks, based on methods commonly applied in climate research. The SL models are trained with gridded products derived from station, reanalysis, and satellite data (ERA-20C, ERA-Land, CERA, COBE2, CRU, and ESA-CCI). The predictive potential of SM anomalies in statistical forecasting had so far remained elusive. Our statistical models trained with SM achieve high summer t2m prediction skill in terms of Pearson correlation coefficient (r), with r≥0.5 over Central and Eastern Europe. Moreover, we find that the reanalysis and satellite SM data contain similar information that can be extracted by our methods and used in fitting the forecast models. Furthermore, the predictive potential of SSTs within different areas in the NA basin was tested. The predictive power of SSTs might increase, as in our case, when specific areas are selected. Forecasts based on extratropical SSTs achieve high prediction skill over South Europe. The combined prediction, using SM and SST predictor data, results in r≥0.5 over all European regions south of 50°N and east of 5°W. This is a better skill than the one achieved by other prediction schemes based on dynamical models. Our analysis highlights specific NA mid-latitude regions that are more strongly connected to summer mean European temperature.
format Article in Journal/Newspaper
author Pyrina, M.
Nonnenmacher, M.
Wagner, S.
Zorita, E.
spellingShingle Pyrina, M.
Nonnenmacher, M.
Wagner, S.
Zorita, E.
Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data
author_facet Pyrina, M.
Nonnenmacher, M.
Wagner, S.
Zorita, E.
author_sort Pyrina, M.
title Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data
title_short Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data
title_full Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data
title_fullStr Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data
title_full_unstemmed Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data
title_sort statistical seasonal prediction of european summer mean temperature using observational, reanalysis, and satellite data
publisher AMS
publishDate 2021
url https://publications.hereon.de/id/40030
https://publications.hzg.de/id/40030
https://doi.org/10.1175/WAF-D-20-0235.1
genre North Atlantic
genre_facet North Atlantic
op_source issn:0882-8156
Pyrina, M.; Nonnenmacher, M.; Wagner, S.; Zorita, E.: Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data. In: Weather and Forecasting. Vol. 36 (2021) 4, 1537 - 1560. (DOI: /10.1175/WAF-D-20-0235.1)
op_relation https://dx.doi.org/10.1175/WAF-D-20-0235.1
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op_doi https://doi.org/10.1175/WAF-D-20-0235.1
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