Abstract
<p>This study assesses the representation of Tropical Cyclones (TC) in an ensemble of seasonal forecast models from five different centres (ECMWF, UK Met Office, DWD, CMCC, Météo-France). Northern Hemispheric Tropical Cyclones are identified using a widely applied objective Tropical Cyclone tracking algorithm based on relative vorticity fields. Analyses for three different aspects are carried out: 1) assessment of the skill of the ensemble to predict  the TC frequencies over different ocean basins, 2) analyse the dependency between the model's ability to represent TCs and large-scale biases and 3) assess the impact of stochastic physics and horizontal resolution on TC frequency.</p><p>For the July to October season all seasonal forecast models initialized in June are skilful in predicting the observed inter-annual variability of TC frequency over the North Atlantic (NA). Similarly, the models initialized in May show significant skill over the Western North Pacific (WNP) for the season from June to October. Further to these significant positive correlations over the NA, it is found that most models are also able to discriminate between inactive and active seasons over this region. However, despite these encouraging results, especially  for skill over the NA, most models suffer from large biases. These biases are not only related to biases in the large-scale circulation but also to the representation of intrinsic model uncertainties and the relatively coarse resolution of current seasonal forecasts. At ECMWF model uncertainty is accounted for by the use of stochastic physics, which has been shown to improve forecasts on seasonal time-scales in previous studies. Using a set of simulations conducted with the ECMWF SEAS5 model, the effects of stochastic physics and resolution on the representation of Tropical Cyclones on seasonal time-scales are assessed. Including stochastic physics increases the number of TCs over all ocean basins, but especially over the North Atlantic and Western North Pacific.</p>
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.