Predictability of the Mean Location of Typhoon Formation in a Seasonal Prediction Experiment with a Coupled General Circulation Model

Abstract

We investigate the feasibility of dynamical seasonal predictions of the interannual variability of the mean location of typhoon formation in the western North Pacific (WNP) and its physical mechanisms during the active typhoon season from June to October. We performed seven-month integrations for 28 years starting from late April using the El Niñ o Southern Oscillation (ENSO) prediction system of the Japan Meteorological Agency. Typhoons detected with an objective method using model outputs are verified with best track data from the Regional Specialized Meteorological Center, Tokyo. The good overall deterministic skill in predicting the interannual variability of the mean location of typhoon formation fundamentally stems from the ability to predict the interannual variability of the atmospheric circulation in the WNP influenced by ENSO. The interannual variability of indices representing a latitudinal shift of the atmospheric circulation in the WNP is better predicted than that of indices representing a longitudinal shift in this experiment. This di¤erence in skill among these indices provides a physical basis for the di¤erence in prediction skill between the mean latitude and longitude regarding the interannual variability of typhoon formation. Probabilistic predictions also demonstrate the skillful predictions of the mean location of typhoon formation for tercile-based categories. Therefore, both deterministic and probabilistic predictions using our ENSO prediction system provide useful information about the mean location of typhoon formation.

Assessing the impact of observations on ocean forecasts and reanalyses: Part 1, Global studies

“…The systems used in this study include the 1/4°-resolution global ocean forecast systems developed by Mercator Océan (Lellouche et al 2013), the Met Office (Storkey et al 2010;Blockley et al 2013) and the Canadian consortium (Smith et al 2014), a 1/10°-resolution reanalysis system developed under Bluelink (Oke et al 2013a;Oke et al 2013b), a 1/4°-resolution model-independent analysis system developed at CLS (Guinehut et al 2012), two operational seasonal prediction systems, currently operated at JMA (Takaya et al 2010;Fujii et al 2012) and ECMWF (Balmaseda et al 2013). Several of these systems share a common source code for the model (i.e.…”

“…Several activities for evaluating ocean observations have been performed using MOVE/MRI.COM-G (MOVE-G) by the JMA/MRI group (Takaya et al 2010). MOVE-G is an ocean data assimilation system that underpins the operational seasonal forecasting at JMA.…”

“…Forecasts are started at the end of January, April, July andOctober 2004-2008. Thirteen-month, 11-member ensemble forecasts were performed with each run using the coupled atmosphere-ocean model in the seasonal forecasting system (Takaya et al 2010). The forecast scores for 6-month lead-time are improved for NINO3, NINO3-4 and NINO4 SST when Argo floats or TAO/TRITON buoys data are assimilated (compared to the scores when the data are withheld), and this impact is enhanced for 12-month lead-time ( Figure 10).…”

Evaluating the impacts of the tropical Pacific observing system on the ocean analysis fields in the global ocean data assimilation system for operational seasonal forecasts in JMA

“…COM-G (Usui et al 2006;Fujii et al 2012) (hereafter MOVE-G) is employed in this study. MOVE-G is adopted in the operation of the Japan Meteorological Agency (JMA) in order to provide ocean initial condition for the coupled atmosphere-ocean general circulation model in the seasonal forecast system (Takaya et al 2010). The system adopts nearly global Ocean General Circulation Model (OGCM) (model domain: 75°S-75°N) based on the Meteorological Research Institute Community Ocean Model (MRI.COM).…”

Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting

“…COBE-SST is assimilated in all the assimilation runs. Thirteen-month 11-member ensemble forecasts are then performed with JMA/MRI-CGCM, the CGCM adopted in the forecasting system (Takaya et al, 2010;Yasuda et al, 2007), from 31 January, 26 April, 30 July, and 28 October during the 5 years of [2004][2005][2006][2007][2008]. We thus have 20 forecasts.…”