IPCC_Managing Risks of Extreme Events.pdf - Climate Access

Chapter 3Changes in Climate Extremes and their Impacts on the Natural Physical Environmentalone. That is, when the SST changes due to greenhouse warming aredeconvolved from the background natural variability, that part of the SSTvariability, by itself, has no manifest effect on tropical cyclogenesis. Inthis case, the simple observed relationship between tropical cyclogenesisand SST, while robust, does not adequately capture the relevant physicalmechanisms of tropical cyclogenesis in a warming world.Another challenge to identifying causes behind observed changes intropical cyclone activity is introduced by uncertainties in the reanalysisdata used to identify environmental changes in regions where tropicalcyclones develop and evolve (Bister and Emanuel, 2002; Emanuel,2010). In particular, heterogeneity in upper-tropospheric kinematic andthermodynamic metrics complicates the interpretation of long-termchanges in vertical wind shear and potential intensity, both of which areimportant environmental controls on tropical cyclones.Based on a variety of model simulations, the expected long-termchanges in global tropical cyclone characteristics under greenhousewarming is a decrease or little change in frequency concurrent with anincrease in mean intensity. One of the challenges for identifying thesechanges in the existing data records is that the expected changespredicted by the models are generally small when compared withchanges associated with observed short-term natural variability. Basedon changes in tropical cyclone intensity predicted by idealized numericalsimulations with CO 2 -induced tropical SST warming, Knutson and Tuleya(2004) suggested that clearly detectable increases may not be manifestfor decades to come. Their argument was based on a comparison of theamplitude of the modeled upward trend (i.e., the signal) in storm intensitywith the amplitude of the interannual variability (i.e., the noise). Therecent high-resolution dynamical downscaling study of Bender et al. (2010)supports this argument and suggests that the predicted increases in thefrequency of the strongest Atlantic storms may not emerge as a clearstatistically significant signal until the latter half of the 21st centuryunder the SRES A1B warming scenario. Still, it should be noted thatwhile these model projections suggest that a statistically significant signalmay not emerge until some future time, the likelihood of more intensetropical cyclones is projected to continually increase throughout the21st century.With the exception of the North Atlantic, much of the global tropicalcyclone data is confined to the period from the mid-20th century topresent. In addition to the limited period of record, the uncertainties inthe historical tropical cyclone data (Section 3.2.1 and this section) andthe extent of tropical cyclone variability due to random processes andlinkages with various climate modes such as El Niño, do not presentlyallow for the detection of any clear trends in tropical cyclone activitythat can be attributed to greenhouse warming. As such, it remainsunclear to what degree the causal phenomena described here havemodulated post-industrial tropical cyclone activity.The AR4 concluded that it is more likely than not that anthropogenicinfluence has contributed to increases in the frequency of the mostintense tropical cyclones (Hegerl et al., 2007). Based on subsequentresearch that further elucidated the scope of uncertainties in both thehistorical tropical cyclone data as well as the physical mechanismsunderpinning the observed relationships, no such attribution conclusionwas drawn in the recent WMO assessment (Knutson et al., 2010). Thepresent assessment regarding detection and attribution of trends intropical cyclone activity is similar to the WMO assessment (Knutson etal., 2010): the uncertainties in the historical tropical cyclone records, theincomplete understanding of the physical mechanisms linking tropicalcyclone metrics to climate change, and the degree of tropical cyclonevariability – comprising random processes and linkages to variousnatural climate modes such as El Niño – provide only low confidence forthe attribution of any detectable changes in tropical cyclone activity toanthropogenic influences.Projected Changes and UncertaintiesThe AR4 concluded (Meehl et al., 2007b) that a broad range of modelingstudies project a likely increase in peak wind intensity and near-stormprecipitation in future tropical cyclones. A reduction of the overallnumber of storms was also projected (but with lower confidence), with agreater reduction in weaker storms in most basins and an increase in thefrequency of the most intense storms. Knutson et al. (2010) concludedthat it is likely that the mean maximum wind speed and near-stormrainfall rates of tropical cyclones will increase with projected 21stcenturywarming, and it is more likely than not that the frequency of themost intense storms will increase substantially in some basins, but it islikely that overall global tropical cyclone frequency will decrease orremain essentially unchanged. The conclusions here are similar to thoseof the AR4 and Knutson et al. (2010).The spatial resolution of some models such as the CMIP3 coupledocean-atmosphere models used in the AR4 is generally not high enoughto accurately resolve tropical cyclones, and especially to simulate theirintensity (Randall et al., 2007). Higher-resolution global models havehad some success in reproducing tropical cyclone-like vortices (e.g.,Chauvin et al., 2006; Oouchi et al., 2006; Zhao et al., 2009), but onlytheir coarse characteristics. Significant progress has been recentlymade, however, using downscaling techniques whereby high-resolutionmodels capable of reproducing more realistic tropical cyclones are runusing boundary conditions provided by either reanalysis data sets oroutput fields from lower-resolution climate models such as those usedin the AR4 (e.g., Knutson et al., 2007; Emanuel et al., 2008; Knutson etal., 2008; Emanuel, 2010). A recent study by Bender et al. (2010) appliesa cascading technique that downscales first from global to regionalscale, and then uses the simulated storms from the regional model toinitialize a very high-resolution hurricane forecasting model. Thesedownscaling studies have been increasingly successful at reproducingobserved tropical cyclone characteristics, which provides increasedconfidence in their projections, and it is expected that more progresswill be made as computing resources improve. Still, awareness thatlimitations exist in the models used for tropical cyclone projections,particularly the ability to accurately reproduce natural climate phenomena161