| 英文摘要: | Global tropical cyclone climate has been investigated with indicators of frequency, intensity1 and activity2, 3. However, a full understanding of global warming’s influence on tropical cyclone climate remains elusive because of the incomplete nature of these indicators. Here we form a complete three-dimensional variability space of tropical cyclone climate where the variabilities are continuously linked and find that global ocean warmth best explains the out-of-phase relationship between intensity and frequency of global tropical cyclones. In a year with greater ocean warmth, the tropical troposphere is capped by higher pressure anomaly in the middle and upper troposphere even with higher moist static energy anomaly in the lower troposphere, which is thought to inhibit overall tropical cyclone occurrences but lead to greater intensities. A statistical consequence is the trade-off between intensity and frequency. We calculate an average increase in global tropical cyclone intensity of 1.3 m s−1 over the past 30 years of ocean warming occurring at the expense of 6.1 tropical cyclones worldwide. Tropical cyclones (TCs) are perhaps the least welcomed natural phenomena on our planet. Even well-developed and highly complex societies are vulnerable to their destructiveness because of significant exposure4, 5. Ocean warmth is increasing with global warming and a major concern is how TC climate will change. Recent studies from theoretical and numerical projections of future TC climate reveal decreasing frequency and increasing intensity in this warming environment6, 7. Nevertheless, there is little observational support, except for the increasing intensity of the strongest portion of TCs (refs 8, 9). Trend analysis is a popular approach, but results are constrained by a focus on just a few TC climate indicators (for example, accumulated cyclone energy). Is it possible that the influence of global warming on TCs is aligned somewhere in between these indicators? Here we examine a globally consistent TC response to ocean warming by linearly linking frequency, intensity and activity in a continuous variability space10. The linear approach is expected to provide a convenient framework for understanding the TC climate structure. TCs whose lifetime-maximum wind (LMW) speed exceeds 17 m s−1 are selected annually. In this framework, the annual number of TC occurrences and the annual mean LMW are used as indicators for frequency (F) and intensity (I), respectively. These two primary TC indicators are not orthogonal to each other, but the corresponding eigenvectors derived from a principal component analysis create a continuous TC variability space (Supplementary Fig. 1). The principal component of the in-phase relationship between F and I indicates a variability direction close to those of the accumulated cyclone energy (ACE) and power dissipation index (PDI; Supplementary Fig. 6). This variability direction is named A to stand for ‘activity’, and note the other principal component indicates a variability direction orthogonal to A. This variability represents the out-of-phase relationship between F and I, and is named E to stand for ‘efficiency of intensity’, as it shows how much more I contributes to A than does F. Practically, E reveals the degree of trade-off between I and F at a given A. If it were not for E, the TC climate variability space with I, F and A would not span the two-dimensional variability space. The same orthogonal structure is applied to our environmental framework, where the Southern Oscillation Index (SOI) and global sea surface temperature (SST) are chosen as the primary indicators (Supplementary Fig. 3). The former indicates El Niño (N) and the latter global ocean warmth (O). This environmental space suggests two orthogonal variabilities of warm-year El Niño (W) and cold-year El Niño (C), which are indicated by the two principal components. Here, −W and −C point to cold-year La Niña and warm-year La Niña inversely. Note that ‘warm’ and ‘cold’ refer to global mean SST rather than the El Niño phenomenon itself. Figure 1 demonstrates how the two orthogonal spaces, one defined by TC climate and the other by the TC environment, are placed in a three-dimensional variability space. Projection of the TC climate framework onto the environmental framework points to the best explanatory environmental variability for each TC climate variability direction (Supplementary Fig. 4). A projection length (correlation) shows how much the TC climate variability is explained. The contribution of El Niño and global ocean warmth to the projection length is also identified.
| http://www.nature.com/nclimate/journal/v5/n7/full/nclimate2646.html
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