On the Seasonal Forecasting of Regional Tropical Cyclone Activity

Abstract

Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux adjustment.” A suite of 12-month duration retrospective forecasts is performed over the 1981–2012 period, after initializing the climate model to observationally constrained conditions at the start of each forecast period, using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basinwide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally aggregated regional TC activity months in advance are feasible.

Towards predictive understanding of regional climate change

“…Second, when spatial resolution is high (25-50 km), many phenomena are reasonably well simulated in GCMs 97 , including tropical cyclones 63,91 and extratropical weather regimes such as blocking 98,99 . This makes higher resolution desirable.…”

“…Similarly, seasonal prediction 91 and attribution studies of extreme climate events 92 can improve physical understanding and build model confidence. In this context, pacemaker experiments-partial coupling that prescribes observed SST or wind evolution in tropical oceans 19,93,94 -are useful to identify key drivers of regional change.…”

An extreme event of sea-level rise along the Northeast coast of North America in 2009–2010

“…27) contributes to the extreme SLR event both remotely and locally. First, the negative NAO can cause an anomalous heat flux 44 Griffies et al 32 Winton et al 45 CM2.5 0.5°0.25°, z* coordinate Delworth et al 44 Griffies et al 32 Winton et al 45 CM2.5FLORa6 0.5°1°, z* coordinate Vecchi et al 46 Griffies et al 32 Winton et al 45 CM2.5FLOR 0.5°1°, z* coordinate Vecchi et al 46 Griffies et al 32 Winton into the Labrador Sea, thereby influencing the AMOC and SLR along the NE coast of North America ( Supplementary Fig. 5).…”

Explaining Extreme Events of 2014 from a Climate Perspective

“…We used the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-Oriented Low Ocean Resolution model [FLOR; Vecchi et al 2014; see online supplemental material (SM) Section 1] to conduct a suite of initialized and uninitialized numerical simulations. The observational ETSs were derived from the ERA-Interim reanalysis data during 1979-2014 .…”

“…The observed ETSI anomalies for 2013/14 are all in the extreme category with climatological empirical occurrence probability values of ~1.5%, ~3%, and ~10% for Pacific coastal, mid-USA, and mid-Canada, respectively. We first examined the retrospective seasonal forecasts made using FLOR for the period 1990-2014 (Vecchi et al 2014;Jia et al 2015). To estimate the extreme probability, we combined all available hindcasts of 0-1 month ahead to produce 84 samples for each predicted year (see SM Section 2a).…”