The German Climate Forecast System: GCFS

Fröhlich, Kristina; Dobrynin, Mikhail; Isensee, Katharina; Gessner, Claudia; Paxian, Andreas; Pohlmann, Holger; Haak, Helmuth; Brune, Sebastian; Früh, Barbara; Baehr, Johanna

doi:10.1002/essoar.10502582.2

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…In this study, hindcast data from six different dynamical seasonal forecast models are used. These are the following six European models, which contribute to the Copernicus Climate Change Service (C3S) multimodel seasonal forecasting system: UK Met Office GloSea5‐GC2 (UKMO) (MacLachlan et al, 2015), German Weather Service GCFS2.0 (DWD; Fröhlich et al, 2020), Euro‐Mediterranean Center on Climate Change SPS3 (CMCC) (Gualdi et al, 2020), European Center for Medium‐Range Weather Forecasts SEAS5 (ECMWF) (Johnson et al, 2019), Météo‐France System 5 (MF5) and Météo‐France System 6 (MF6) (Dorel et al, 2017). Details of the resolution and ensemble size of each of these models are available in Table S1.…”

Section: Methods and Datamentioning

confidence: 99%

The link between North Atlantic tropical cyclones and ENSO in seasonal forecasts

Doane‐Solomon,

Befort,

Camp

et al. 2023

Atmospheric Science Letters

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This study assesses the ability of six European seasonal forecast models to simulate the observed teleconnection between ENSO and tropical cyclones (TCs) over the North Atlantic. While the models generally capture the basin‐wide observed link, its magnitude is overestimated in all forecast models compared to reanalysis. Furthermore, the ENSO‐TC relationship in the Caribbean is poorly simulated. It is shown that incorrect forecasting of wind shear appears to affect the representation of the teleconnection in some models, however it is not a completely sufficient explanation for the overestimation of the link.

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Section: Methods and Datamentioning

confidence: 99%

The link between North Atlantic tropical cyclones and ENSO in seasonal forecasts

Doane‐Solomon,

Befort,

Camp

et al. 2023

Atmospheric Science Letters

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“…We used multi‐model ensemble data of six seasonal prediction systems (specifications in Table S1 in Supporting Information ): the European Centre for Medium‐Range Weather Forecasts fifth‐generation seasonal forecast system (SEAS5, Johnson et al., 2019), the United Kingdom Met Office Global Seasonal Forecast System version 5 (GloSea5, MacLachlan et al., 2015), Météo‐France System 7 (MF‐S7), the German Climate Forecast System 2.0 (GCFS2.0, Fröhlich et al., 2021), the Euro‐Mediterranean Center on Climate Change Seasonal Prediction System 3 (CMCC‐SPS3, Sanna et al., 2017), and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI‐CPS2, Takaya et al., 2018). Focusing mainly on winter conditions (December–February: DJF), we analyzed monthly mean data of 6‐month hindcasts initialized around 1 November between 1993 and 2016 (dates of initialization differ among the models).…”

Section: Methodsmentioning

confidence: 99%

Response of Eurasian Temperature to Barents–Kara Sea Ice: Evaluation by Multi‐Model Seasonal Predictions

Komatsu

Takaya

Toyoda

et al. 2022

Geophysical Research Letters

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In the Arctic, significant warming is occurring and the sea ice extent is rapidly declining (Screen & Simmonds, 2010;Serreze & Francis, 2006), but whether these Arctic sea ice changes are modulating the midlatitude climate is a contentious issue (e.g., Blackport & Screen, 2020;Cohen et al., 2020).Especially controversial is the proposed link between the Arctic and winter Eurasian climate known as the Warm Arctic-Cold Eurasia (WACE) pattern (e.g., Mori et al., 2014) which is characterized by two centers of anomalous temperatures, around the Barents-Kara (BK) Sea and in central and eastern Eurasia (CE) (Figure 1a). The potential influence of BK sea ice variation on the WACE teleconnection pattern seen in model simulations has been intensely debated (

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“…A month then corresponds to the average over 30 consecutive days (300 timesteps) and accordingly we define the season as the average over 90 consecutive days (900 timesteps). For a seasonal prediction (Figure 1b) we leave a gap of 1 month (300 timesteps) between the initialization and the beginning of the season as general practice in some operational seasonal forecasting systems (e.g., Frhlich et al., 2020; Johnson et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

When Does the Lorenz 1963 Model Exhibit the Signal‐To‐Noise Paradox?

Mayer

Düsterhus

Baehr

2021

Geophysical Research Letters

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Seasonal prediction systems based on Earth System Models exhibit a lower proportion of predictable signal to unpredictable noise than the actual world. This puzzling phenomena has been widely referred to as the signal‐to‐noise paradox (SNP). Here, we investigate the SNP in a conceptual framework of a seasonal prediction system based on the Lorenz, 1963 Model (L63). We show that the SNP is not apparent in L63, if the uncertainty assumed for the initialization of the ensemble is equal to the uncertainty in the starting conditions. However, if the uncertainty in the initialization overestimates the uncertainty in the starting conditions, the SNP is apparent. In these experiments the metric used to quantify the SNP also shows a clear lead‐time dependency on subseasonal timescales. We therefore, formulate the alternative hypothesis to previous studies that the SNP could also be related to the magnitude of the initial ensemble spread.

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The German Climate Forecast System: GCFS

Cited by 6 publications

References 17 publications

The link between North Atlantic tropical cyclones and ENSO in seasonal forecasts

The link between North Atlantic tropical cyclones and ENSO in seasonal forecasts

Response of Eurasian Temperature to Barents–Kara Sea Ice: Evaluation by Multi‐Model Seasonal Predictions

When Does the Lorenz 1963 Model Exhibit the Signal‐To‐Noise Paradox?

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