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Suzana J. Camargo

Abstract

Tropical cyclone (TC) activity is analyzed in 14 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The global TC activity in the historical runs is compared with observations. The simulation of TC activity in the CMIP5 models is not as good as in higher-resolution simulations. The CMIP5 global TC frequency is much lower than observed, and there is significant deficiency in the geographical patterns of TC tracks and formation. Although all of the models underestimate the global frequency of TCs, the models present a wide range of global TC frequency. The models with the highest horizontal resolution have the highest level of global TC activity, though resolution is not the only factor that determines model TC activity. A cold SST bias could potentially contribute to the low number of TCs in the models. The models show no consensus regarding the difference of TC activity in two warming scenarios [representative concentration pathway 4.5 (RCP4.5) and RCP8.5] and the historical simulation. The author examined in more detail North Atlantic and eastern North Pacific TC activity in a subset of models and found no robust changes across models in TC frequency. Therefore, there is no robust signal across the CMIP5 models in global and regional TC changes in activity for future scenarios. The future changes in various large-scale environmental fields associated with TC activity were also examined globally: genesis potential index, potential intensity, vertical wind shear, and sea level pressure. The multimodel mean changes of these variables in the CMIP5 models are consistent with the changes obtained in the CMIP3 models.

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Suzana J. Camargo and Adam H. Sobel

Abstract

The statistical relationship between the quasi-biennial oscillation (QBO) and tropical cyclone (TC) activity is explored, with a focus on the North Atlantic. Although there is a statistically significant relationship between the QBO and TCs in the Atlantic from the 1950s to the 1980s, as found by previous studies, that relationship is no longer present in later years. Several possibilities for this change are explored, including the interaction with ENSO, volcanoes, QBO decadal variability, and interactions with solar forcing. None provides a completely satisfying explanation. In the other basins, the relationship is weaker than in the Atlantic, even in the early record.

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Adam H. Sobel and Suzana J. Camargo

Abstract

The authors investigate the influence of western North Pacific (WNP) tropical cyclones (TCs) on their large-scale environment by lag regressing various large-scale climate variables [atmospheric temperature, winds, relative vorticity, outgoing longwave radiation (OLR), column water vapor, and sea surface temperature (SST)] on an index of TC activity [accumulated cyclone energy (ACE)] on a weekly time scale. At all leads and lags out to several months, persistent, slowly evolving signals indicative of the El Niño–Southern Oscillation (ENSO) phenomenon are seen in all the variables, reflecting the known seasonal relationship of TCs in the WNP to ENSO. Superimposed on this are more rapidly evolving signals, at leads and lags of one or two weeks, directly associated with the TCs themselves. These include anomalies of positive low-level vorticity, negative OLR, and high column water vapor associated with anomalously positive ACE, found in the region where TCs most commonly form and develop. In the same region, lagging ACE by a week or two and so presumably reflecting the influence of TCs on the local environment, signals are found that might be expected to negatively influence the environment for later cyclogenesis. These signals include an SST reduction in the primary region of TC activity, and a reduction in column water vapor and increase in OLR that may or may not be a result of the SST reduction.

On the same short time scale, an increase in equatorial SST near and east of the date line is seen, presumably associated with equatorial surface westerly anomalies that are also found. This, combined with the correlation between ACE and ENSO indices on the seasonal time scale, suggests the possibility that TCs may play an active role in ENSO dynamics.

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Adam H. Sobel and Suzana J. Camargo

Abstract

The authors analyze changes in the tropical sea surface temperature (SST), surface wind, and other fields from the twentieth to the twenty-first century in climate projections using the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel ensemble, focusing on the seasons January–March (JFM) and July–September (JAS). When the annual mean change is subtracted, the remaining “seasonal changes” have robust, coherent structures. The JFM and JAS changes resemble each other very closely after either a change of sign or reflection about the equator. The seasonal changes include an increase in the summer hemisphere SST and a decrease in the winter hemisphere SST. These appear to be thermodynamic consequences of easterly trade winds strengthening in the winter subtropics and weakening in the summer subtropics. These in turn are associated with the weakening and expansion of the Hadley circulation, documented by previous studies, which themselves are likely consequences of changes in extratropical eddies. The seasonal SST changes influence the environment for deep convection: peak precipitation in the summer hemisphere increases by around 10% and convective available potential energy (CAPE) increases by as much as 25%. Comparable fractions of these changes are attributable to the annual mean change and the seasonal changes, though the two have very different spatial structures. Since the annual mean change is marked by relative warming in the Northern Hemisphere compared to the Southern Hemisphere, the seasonal changes oppose the annual mean change in JFM and enhance it in JAS.

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Amato T. Evan and Suzana J. Camargo

Abstract

On average 1–2 tropical cyclones form over the Arabian Sea each year, and few of these storms are intense enough to be classified as very severe or super cyclonic storms. As such, few studies have explicitly identified the seasonal to interannual changes in environmental conditions that are associated with Arabian Sea tropical cyclogenesis. However, over the last 30 yr several intense Arabian storms did form and make landfall, with large impacts, which motivates this new study of the basin. The conclusions of earlier studies are visited by utilizing modern observational and reanalysis data to identify the large-scale features associated with Arabian tropical cyclone variability on seasonal time scales. Then year-to-year changes in environmental conditions that are related to interannual variability in Arabian storms during the pre- and postmonsoon periods are elucidated. The analysis of the relationship between large-scale environmental variables and seasonal storm frequency supports conclusions from work completed more than 40 yr prior. Investigation of the year-to-year changes in premonsoon storm frequency suggests that May (June) storms are associated with an early (late) onset of the southwest monsoon. The findings also demonstrate that November cyclones (the month when the majority of postmonsoon cyclogenesis occurs) primarily form during periods when the Bay of Bengal experiences a broad region of high sea level pressure, implying that November storms form in either the Arabian Sea or the Bay of Bengal but not in both during the same year. Finally, the analysis of changes in a genesis potential index suggests that long-term variability in the potential for a storm to form is dictated by changes in midlevel moisture.

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Suzana J. Camargo and Stephen E. Zebiak

Abstract

Dynamical seasonal forecasts of tropical storm frequency require robust and efficient algorithms for detection and tracking of tropical storms in atmospheric general circulation models (AGCMs). Tropical storms are generally detected when dynamic and thermodynamic variables meet specified criteria. Here, it is shown that objectively defined model- and basin-dependent detection criteria improve simulations of tropical storm climatology and interannual variability in low-resolution AGCMs. An improved tracking method provides more realistic tracking and accurate counting of storms.

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Suzana J. Camargo and Anthony G. Barnston

Abstract

The International Research Institute for Climate and Society (IRI) has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper the method used to obtain these forecasts is described and the forecast performance is evaluated. The forecasts are based on tropical cyclone–like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the previous month’s observations, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors recently observed SSTs. For the recent 6-yr period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued.

Despite variations from one basin to another, the levels of hindcast skill for the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent at fairly modest but statistically significant levels. The dynamical forecasts require statistical postprossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Skill levels decrease only slowly with increasing lead time up to 2–3 months. During the recent period of real-time forecasts, the issued forecasts have had higher probabilistic skill than the raw model output, due to the forecasters’ subjective elimination of the “overconfidence” bias in the model’s forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered.

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Yujia You, Mingfang Ting, and Suzana J. Camargo

Abstract

The synoptic low-pressure systems (LPSs) formed over the downwind side of the Tibetan Plateau explain a substantial portion of summer rainfall extremes along their paths. Recent studies have found that the total extreme rainfall trend over the East Asian landmass, which features the “south flood-north drought” pattern, can be understood to a great extent by the changes in terrestrial LPSs. Yet, the energy sources fueling these storms and the environmental drivers of their long-term trends remain unclear. Utilizing a probabilistic clustering method, three clusters of terrestrial LPS tracks for the period 1979-2018 are identified. Besides the differences in trajectories that distinguish the clusters into northeastward-migrating and quasi-stationary types, prominent inter-cluster differences are found in the LPS evolution, energetics, and trends. The Lorenz energetics suggest that while condensational heating is indispensable for all three clusters, the migratory type, which has greater intensity and faster development, is more closely tied to baroclinicity. Nonetheless, the summer baroclinicity alone is not enough to sustain these LPSs as these storms dissipate quickly after propagating out of the humid monsoon region and into the drier extratropics. Over time, the occurrences of migratory LPSs decreases, and that of quasi-stationary LPSs increases. Using a Poisson model that links the LPS genesis to local environmental conditions, the decreasing occurrence of migratory LPSs is shown to result from the weakened baroclinicity, whereas the increasing occurrence of quasi-stationary LPSs is primarily driven by enhanced relative humidity and reduced steering flow in the mid-to-lower troposphere over East Asia.

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Suzana J. Camargo and Adam H. Sobel

Abstract

The influence of the El Niño–Southern Oscillation (ENSO) on tropical cyclone intensity in the western North Pacific basin is examined. Accumulated cyclone energy (ACE), constructed from the best-track dataset for the region for the period 1950–2002, and other related variables are analyzed. ACE is positively correlated with ENSO indices. This and other statistics of the interannually varying tropical cyclone distribution are used to show that there is a tendency in El Niño years toward tropical cyclones that are both more intense and longer-lived than in La Niña years. ACE leads ENSO indices: during the peak season (northern summer and fall), ACE is correlated approximately as strongly with ENSO indices up to six months later (northern winter), as well as simultaneously. It appears that not all of this lead–lag relationship is easily explained by the autocorrelation of the ENSO indices, though much of it is. Interannual variations in the annual mean lifetime, intensity, and number of tropical cyclones all contribute to the ENSO signal in ACE, though the lifetime effect appears to be the most important of the three.

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Louis-Philippe Caron, Mathieu Boudreault, and Suzana J. Camargo

Abstract

Variability in tropical cyclone activity in the eastern Pacific basin has been linked to a wide range of climate factors, yet the dominant factors driving this variability have yet to be identified. Using Poisson regressions and a track clustering method, the authors analyze and compare the climate influence on cyclone activity in this region. The authors show that local sea surface temperature and upper-ocean heat content as well as large-scale conditions in the northern Atlantic are the dominant influence in modulating eastern North Pacific tropical cyclone activity. The results also support previous findings suggesting that the influence of the Atlantic Ocean occurs through changes in dynamical conditions over the eastern Pacific. Using model selection algorithms, the authors then proceed to construct a statistical model of eastern Pacific tropical cyclone activity. The various model selection techniques used agree in selecting one predictor from the Atlantic (northern North Atlantic sea surface temperature) and one predictor from the Pacific (relative sea surface temperature) to represent the best possible model. Finally, we show that this simple model could have predicted the anomalously high level of activity observed in 2014.

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