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Simona Ecaterina Ştefănescu, Loïk Berre, and Margarida Belo Pereira


An ensemble of limited-area forecasts has been obtained by integrating the Aire Limitée Adaptation Dynamique Développement International (ALADIN) limited-area model, in cold-starting mode, from an ensemble of Action de Recherche Petite Echelle Grande Echelle (ARPEGE) global analyses and forecasts. This permits error covariances of the ALADIN 6-h forecast and of the ARPEGE analysis to be estimated. These two fields may be combined in a future ALADIN analysis.

The evolution of dispersion spectra is first studied in a perfect model framework. The ARPEGE analysis reduces the large-scale dispersion of the ARPEGE background by extracting some information from observations. Then, the digital filter initialization reduces the small-scale dispersion by removing the noise caused by interpolation of the ARPEGE analysis onto the ALADIN grid. Finally, the ALADIN 6-h forecast strongly increases the small-scale dispersion, in accordance with its ability to represent small-scale processes.

Some model error contributions are then studied. The variances of the differences between the ALADIN and ARPEGE forecasts, which are started from the same ARPEGE analysis, are of smaller scale than are the ALADIN and ARPEGE perfect model dispersions. The small-scale part of these ARPEGE–ALADIN model differences is shown to correspond to structures that are represented by ALADIN and not by ARPEGE. Therefore, this part may be added to the ARPEGE analysis dispersion. The residual large-scale part is more ambiguous, but it may be added to the ALADIN dispersion; this may reflect some effects of the coupling inaccuracies, and strengthen (in a future ALADIN analysis) the use of the large-scale information from the ARPEGE analysis.

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Martina Weiss, Paul A. Miller, Bart J. J. M. van den Hurk, Twan van Noije, Simona Ştefănescu, Reindert Haarsma, Lambertus H. van Ulft, Wilco Hazeleger, Philippe Le Sager, Benjamin Smith, and Guy Schurgers


In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere–land–ocean–sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift. A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected.

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A Seamless Earth-System Prediction Approach in Action

Wilco Hazeleger, Camiel Severijns, Tido Semmler, Simona Ştefănescu, Shuting Yang, Xueli Wang, Klaus Wyser, Emanuel Dutra, José M. Baldasano, Richard Bintanja, Philippe Bougeault, Rodrigo Caballero, Annica M. L. Ekman, Jens H. Christensen, Bart van den Hurk, Pedro Jimenez, Colin Jones, Per Kållberg, Torben Koenigk, Ray McGrath, Pedro Miranda, Twan van Noije, Tim Palmer, José A. Parodi, Torben Schmith, Frank Selten, Trude Storelvmo, Andreas Sterl, Honoré Tapamo, Martin Vancoppenolle, Pedro Viterbo, and Ulrika Willén
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