What Can Models Tell us About Tropical Cyclones?

Dr. James Done and Dr. Greg Holland

National Center for Atmospheric Research Earth System Laboratory, Boulder CO

Summary:

Global Climate Model (GCM)-based Tropical Cyclone (TC) data hold considerable

promise as an adjunct to the historical TC record. Multiple GCM simulations commonly

extend for over one hundred years thereby substantially complementing the shorter

observational record.

GCMs can capture TC frequency remarkably well but the quality of the geographic

distribution of TC tracks varies across the different GCMs. Some degree of bias

correction or GCM improvement is usually necessary prior to using TC track data in a

risk assessment capacity. GCMs alone do not capture major hurricanes but when

combined with a regional climate model or statistical model can produce major hurricane

activity.

GCMs are the only way to adequately assess future TC activity and there is useful

information in the future changes if not in the absolute values. Improvements in both

GCMs and bias correction techniques are moving the field rapidly towards using GCM-

based TC data for risk assessment. The Willis Research Network is engaged in leading

research into determining TC activity from GCM simulations that aims not only to

reassess TC risk in vulnerable regions but also connects to industry impacts.

Details:

Making statistically robust statements about Tropical Cyclone (TC) risk has been a

longstanding challenge largely due to the short historical record length, changes in

observation systems and unknown errors. Catastrophe modeling has traditionally

enhanced the record through statistical techniques, but has recently begun to explore the

role of Global Climate Models (GCMs) and related techniques.

GCMs are physically based tools to simulate and analyze the global climate system.

Historically, GCMs were developed to reproduce climate features such as continental-

scale annual temperatures. More recently, the availability of powerful computers

together with advances in techniques has made it possible to generate climate simulations

that begin to include day-to-day weather such as winter storms (e.g. Catto et al. 2011) and

TCs (e.g. Daloz et al. 2012). Assessing the ability of GCMs to capture TC activity is an

active area of research and here we present an overview of what the current generation of

GCMs can tell us about TCs.

It is well known in the forecasting community that computer generated TC forecasts need

to include scales as small as a few miles across to accurately represent their essential

elements (Davis et al. 2008; Davis et al. 2010) such as formation from clusters of

thunderstorms, intensification to major hurricane status, eye-wall structure, outer spiral

rainbands, and the surface wind field at landfall. GCM simulations are not yet able to

include these scales and therefore miss these features, yet remarkably they can generate

TC-like vortices that, although very weak, may be counted and tracked (e.g. Bengtsson et

al. 1982). Identification of these TC-like vortices is an art in itself. That TCs exist in an

atmosphere containing circulations on many scales means an arbitrary circulation

threshold needs to be set to define a TC. This threshold is nearly always set such that the

simulated TC frequency compares well with the historical TC record (as discussed in

Suzuki-Parker 2012). In this sense, GCMs can capture TC frequency remarkably well.

Rather than relying on the poor TC intensity and structure information in climate models

alternative techniques have been developed to determine these from GCM simulations.

One such technique employs a weather forecast model embedded in a GCM simulation as

a Regional Climate Model (RCM) that has the resolution to adequately simulate

hurricanes (e.g. Knutson et al. 2007). This RCM method has the added advantage of

providing information on hurricane intensity and surface wind fields but the additional

computational cost means it can cover just a few decades of simulation. An example

hurricane generated using the NCAR RCM is shown in Fig. 1. Another technique applies

empirical relationships between, say, ocean temperatures and TC frequency to infer TC

frequency from GCM or RCM simulation data (e.g. Bruyre et al. 2012). This technique

requires little computational power and can therefore be run on hundreds of years of

GCM simulations.

Figure 1: An example hurricane generated by embedding a weather model into a GCM

simulation.

These new simulated TC datasets hold considerable promise as an adjunct to historical

data and they are the only way that we can adequately assess future changes as

historical observations can only accommodate past changes and variability.

Nevertheless, there are significant challenges to overcome before application to risk

assessment. GCMs contain error and this often includes a bias that can adversely affect

the hurricane climatology. For example, African summers may be too dry, the

Indonesian Maritime Continent too wet or the eastern oceanic regions too warm. Such

biases are known to impact TC activity, sometimes severely (Done et al. 2013). GCMs

can also have difficulty simulating the El Nio Southern Oscillation with repercussions

for North Atlantic TC activity. Some GCM errors are due to known missing processes

such as the cooling of the ocean under intense TCs or the role that intense TCs are

thought to play in maintaining the climate system. Further errors arise due to the coarse

nature of GCMs. For example, it is well known that GCMs are lacking in their

representation of easterly waves, pulses of energy that track East to West across the

tropical oceans, which are a major source of seeds for TC formation, particularly in the

North Atlantic. Bias correcting GCMs is an active area of research and has shown

considerable promise. This, combined with improving GCMs themselves, is moving the

field rapidly towards using GCM-based TC data for risk assessment.

A number of recent developments are anticipated to improve the ability of GCMs to

represent TC activity. Cutting edge GCMs now include: the ability to zoom in on regions

of interest while retaining a global view of climate (e.g. Skamarock et al. 2012); multiple

Earth system components such as interactive vegetation and soil; and, fine detailed ocean

models to capture features such as the loop current in the Gulf of Mexico (as discussed in

Taylor et al. 2012), all of which have anticipated benefits for the simulation of TC

activity. The Willis Research Network plays a leading role in developing this new

technology specifically for TC risk assessment, and further details are provided in Done

et al (2013).

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