Abstract
AbstractStatistical 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 midlatitude regions that are more strongly connected to summer mean European temperature.
Highlights
Seasonal climate prediction focuses on producing forecasts at lead times on the order of a few months
The results regarding the predictions utilizing tropical North Atlantic (TNA) sea surface temperatures (SSTs) are shown in the appendix (Fig. A10), as no statistically significant prediction skill is found for the datasets and model setups used
The gridded summer t2m prediction skill in terms of r values is given for the best temperature predictors in Fig. 1 for the models based on the Canonical correlation analysis (CCA) method, and in Fig. 2 for the models based on the principal component analysis (PCA) method
Summary
Seasonal climate prediction focuses on producing forecasts at lead times on the order of a few months. Forecasting at these time scales can help sectors such as health, energy, and agriculture to anticipate changes in service requests, energy demand, and weather related risks (Curtis et al 2017; Maracchi et al 2005; Allegrini et al 2012). Sources of predictability at seasonal time scales for both dynamical and statistical models are the slowly varying components of the climate system. These components can act as boundary forcings for the troposphere and subsequently affect local weather and climate after some time lag. We design statistical models to forecast European summer temperatures one season in advance, based on the springtime predictors soil moisture and North Atlantic sea surface temperature
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