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Zhao-Xin Li

Abstract

The climate interannual variability is examined using the general circulation model (GCM) developed at the Laboratoire de Météorologie Dynamique. The model is forced by the observed sea surface temperature for the period 1979–94. An ensemble of eight simulations is realized with different initial conditions. The variability of the Southern Oscillation is studied. The simulated sea level pressure anomalies at both Tahiti and Darwin are realistic compared to observations. It is revealed, however, that the simulated convection activity response to the warm episode of El Niño is too weak over the eastern part of the tropical Pacific. This explains why the simulated Pacific–North American pattern is shifted westward. A global El Niño pattern index is defined and calculated for both the simulation and the National Centers for Environmental Prediction (NCEP) reanalysis data. This serves as a quantitative measure of El Niño’s global impact. A singular value decomposition analysis performed with the tropical Pacific sea surface temperature and the Northern Hemisphere 500-hPa geopotential height shows that the model’s teleconnection between the Tropics and high latitudes is similar to that of the NCEP reanalysis data.

In an exploratory manner, the model’s internal variability versus the external forced variability is studied. It is shown that, except for the equatorial strip, the internal model variability is larger than the external variability. An ensemble mean is thus necessary in order to focus on the model’s response to external sea surface temperature anomalies. An attempt is also made to evaluate statistically the influence of the ensemble’s size on the model’s reproducibility. It is shown that, with this particular GCM, at least five realizations are necessary to correctly assess the teleconnection between the Tropics and the Northern Hemisphere extratropics. This dependency on the number of realizations is less for the tropical circulation.

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Xin Zhao and Pao-Shin Chu

Abstract

A Bayesian framework is developed to detect multiple abrupt shifts in a time series of the annual major hurricanes counts. The hurricane counts are modeled by a Poisson process where the Poisson intensity (i.e., hurricane rate) is codified by a gamma distribution. Here, a triple hypothesis space concerning the annual hurricane rate is considered: “a no change in the rate,” “a single change in the rate,” and “a double change in the rate.” A hierarchical Bayesian approach involving three layers—data, parameter, and hypothesis—is formulated to demonstrate the posterior probability of each possible hypothesis and its relevant model parameters through a Markov chain Monte Carlo (MCMC) method.

Based on sampling from an estimated informative prior for the Poisson rate parameters and the posterior distribution of hypotheses, two simulated examples are illustrated to show the effectiveness of the proposed method. Subsequently, the methodology is applied to the time series of major hurricane counts over the eastern North Pacific (ENP). Results indicate that the hurricane activity over ENP has very likely undergone a decadal variation with two changepoints occurring around 1982 and 1999 with three epochs characterized by the inactive 1972–81 epoch, the active 1982–98 epoch, and the inactive 1999–2003 epoch. The Bayesian method also provides a means for predicting decadal major hurricane variations. A lower number of major hurricanes are predicted for the next decade given the recent inactive period of hurricane activity.

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Xin Zhao and Pao-Shin Chu

Abstract

A hierarchical Bayesian framework is developed to identify multiple abrupt regime shifts in an extreme event series. Specifically, extreme events are modeled as a Poisson process with a gamma-distributed rate. Multiple candidate hypotheses are considered, under each of which there presumably exist a certain number of abrupt shifts of the rate. A Bayesian network involving three layers—data, parameter, and hypothesis—is formulated. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is developed to calculate posterior probability for each hypothesis as well its associated within-hypothesis parameters. Based on the proposed RJMCMC algorithm, a simulated example is designed to illustrate the effectiveness of the method. Subsequently, the algorithm is applied to three real, rare event time series: the annual typhoon counts over the western North Pacific (WNP), the annual extreme heavy rainfall event counts at the Honolulu airport, and the annual heat wave frequency in the Chicago area. Results indicate that the typhoon activity over the WNP is very likely to have undergone a decadal variation, with two change points occurring around 1972 and 1989 characterized by the active 1960–71 epoch, the inactive 1972–88 epoch, and the moderately active 1989–2006 epoch. For the extreme rainfall case, only one shift around 1970 is found and heavy rainfall frequency has remained stationary since then. There is no evidence that the rate of the annual heat wave counts in the Chicago area has had any abrupt change during the past 50 years.

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Pao-Shin Chu and Xin Zhao

Abstract

In this study, a Poisson generalized linear regression model cast in the Bayesian framework is applied to forecast the tropical cyclone (TC) activity in the central North Pacific (CNP) in the peak hurricane season (July–September) using large-scale environmental variables available up to the antecedent May and June. Specifically, five predictor variables are considered: sea surface temperatures, sea level pressures, vertical wind shear, relative vorticity, and precipitable water. The Pearson correlation between the seasonal TC frequency and each of the five potential predictors over the eastern and central North Pacific is computed. The critical region for which the local correlation is statistically significant at the 99% confidence level is determined. To keep the predictor selection process robust, a simple average of the predictor variable over the critical region is then computed. With a noninformative prior assumption for the model parameters, a Bayesian inference for this model is derived in detail. A Gibbs sampler based on the Markov chain Monte Carlo (MCMC) method is designed to integrate the desired posterior predictive distribution. The proposed hierarchical model is physically based and yields a probabilistic prediction for seasonal TC frequency, which would better facilitate decision making. A cross-validation procedure was applied to predict the seasonal TC counts within the period of 1966–2003 and satisfactory results were obtained.

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Pao-Shin Chu and Xin Zhao

Abstract

Bayesian analysis is applied to detect change points in the time series of annual tropical cyclone counts over the central North Pacific. Specifically, a hierarchical Bayesian approach involving three layers—data, parameter, and hypothesis—is formulated to demonstrate the posterior probability of the shifts throughout the time from 1966 to 2002. For the data layer, a Poisson process with gamma distributed intensity is presumed. For the hypothesis layer, a “no change in the intensity” hypothesis and a “single change in the intensity” hypothesis are considered. Results indicate that there is a great likelihood of a change point on tropical cyclone rates around 1982, which is consistent with earlier work based on a simple log-linear regression model. A Bayesian approach also provides a means for predicting decadal tropical cyclone variations. A higher number of tropical cyclones is predicted in the next decade when the possibility of the change point in the early 1980s is taken into account.

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Hervé Le Treut, Michèle Forichon, Olivier Boucher, and Zhao-Xin Li

Abstract

The climate sensitivity to various forcings, and in particular to changes in CO2 and sulfate aerosol concentrations, imposed separately or in a combined manner, is studied with an atmospheric general circulation model coupled to a simple slab oceanic model. The atmospheric model includes a rather detailed treatment of warm cloud microphysics and takes the aerosol indirect effects into account explicitly, although in a simplified manner. The structure of the model response appears to be organized at a global scale, with a partial independence from the geographical structure of the forcing. Atmospheric and surface feedbacks are likely to explain this feature. In particular the cloud feedbacks play a very similar role in the CO2 and aerosol experiments, but with opposite sign. These results strengthen the idea, already apparent from other studies, that, in spite of their different nature and their different geographical and vertical distributions, aerosol may have substantially counteracted the climate effect of greenhouse gases, at least in the Northern Hemisphere, during the twentieth century. When the effects of the two forcings are added, the model response is not symmetric between the two hemispheres. This feature is also consistent with the findings of other modeling groups and has implications for the detection of future climate changes.

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Pao-Shin Chu, Xin Zhao, Ying Ruan, and Melodie Grubbs

Abstract

Heavy rainfall and the associated floods occur frequently in the Hawaiian Islands and have caused huge economic losses as well as social problems. Extreme rainfall events in this study are defined by three different methods based on 1) the mean annual number of days on which 24-h accumulation exceeds a given daily rainfall amount, 2) the value associated with a specific daily rainfall percentile, and 3) the annual maximum daily rainfall values associated with a specific return period. For estimating the statistics of return periods, the three-parameter generalized extreme value distribution is fit using the method of L-moments. Spatial patterns of heavy and very heavy rainfall events across the islands are mapped separately based on the aforementioned three methods. Among all islands, the pattern on the island of Hawaii is most distinguishable, with a high frequency of events along the eastern slopes of Mauna Kea and a low frequency of events on the western portion so that a sharp gradient in extreme events from east to west is prominent. On other islands, extreme rainfall events tend to occur locally, mainly on the windward slopes. A case is presented for estimating return periods given different rainfall intensity for a station in Upper Manoa, Oahu. For the Halloween flood in 2004, the estimated return period is approximately 27 yr, and its true value should be no less than 13 yr with 95% confidence as determined from the adjusted bootstrap resampling technique.

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Pang-Chi Hsu, Pao-Shin Chu, Hiroyuki Murakami, and Xin Zhao

Abstract

In 1995 an abrupt shift in the late-season (October–December) typhoon activity over the western North Pacific (WNP) is detected by a Bayesian changepoint analysis. Interestingly, a similar change also occurs in the late-season sea surface temperature series over the western Pacific, eastern North Pacific, and portions of the Indian Ocean. All of the counts, lifespans, and accumulated cyclone energy of the late-season typhoons during the 1995–2011 epoch decreased significantly, compared with typhoons that occurred during the 1979–94 epoch. The negative vorticity anomaly is found to be the leading contributor to the genesis potential index (GPI) decrease over the southeastern sector of the WNP during 1995–2011. To elucidate the origin of the epochal change in the dynamic environmental conditions, a suite of sensitivity experiments is conducted based on the latest version of the Japan Meteorological Research Institute atmospheric general circulation model (MRI AGCM). The ensemble simulations suggest that the recent change to a La Niña–like state induces an unfavorable dynamic condition for typhoon genesis over the southeastern WNP. Warming in the Indian Ocean, however, contributes insignificantly to the circulation anomaly related to typhoon genesis over the southeastern WNP. The frequency of typhoon occurrence reveals a basinwide decrease over the WNP in the recent epoch, except for a small increase near Taiwan. An empirical statistical analysis shows that the basinwide decrease in the frequency of the typhoon occurrence is primarily attributed to a decrease in typhoon genesis, while the change in track is of less importance.

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Dandan Zhao, Jinyuan Xin, Chongshui Gong, Xin Wang, Yongjing Ma, and Yining Ma

Abstract

The heavy industrial zone of northeastern Asia is dominated by year-round industrial scattering aerosols that undergo hygroscopic growth in summer. With the rapid economic development over the past decade, aerosol optical depth (AOD) has increased (6.35% yr−1) with an annual-mean AOD of 0.61 ± 0.13. Simultaneously, the aerosol particle size and aerosol scattering have increased, with an annual-mean scattering aerosol optical depth (SAOD) reaching 0.58 ± 0.15. However, considering that the annual AOD/gross domestic product (GDP) per capita decreased, the environmental degradation caused by aerosol emission is expected to reach a turning point based on the environmental Kuznets curve (EKC) hypothesis. In addition, annual-mean radiative forcing at the top, bottom, and interior of the atmospheric column reached −2.35 ± 2.33, −54.16 ± 7.26, and 51.81 ± 7.93 W m−2, respectively. The increase in unit SAOD contributes to the growth in net negative top-of-atmosphere (TOA) forcing and surface (SFC) forcing, and unit absorption aerosol optical depth (AAOD) increases together with atmosphere (ATM) forcing. Moreover, the cooling effect of aerosols on the Earth–atmosphere system showed an increase over the most recent 10 years related to the increase in scattering aerosol from development in the old industrial base. Except for local sources, under the western air masses, the circum–Bohai Sea economic zone was the potential source area of anthropogenic aerosols throughout the year with annual daily mean AOD, single-scattering albedo (SSA), TOA forcing, and SFC forcing values of 0.88, 0.93, −8.08, and −63.05 W m−2, respectively. The Mongolian Plateau was the potential natural dust source area under the northeastern air masses.

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Zhongshui Zou, Dongliang Zhao, Jun A. Zhang, Shuiqing Li, Yinhe Cheng, Haibin Lv, and Xin Ma

Abstract

The anomalous phenomena induced by the prevailing swell at low wind speeds prevent a complete understanding of air–sea interaction processes. Many studies have considered this complex problem, but most have focused on near-neutral conditions. In this study, the influence of the swell on the atmospheric boundary under nonneutral conditions was addressed by extending the turbulent closure models of Makin and Kudryavtsev and the Monin–Obukhov similarity theory (MOST; Monin and Yaglom) to the existence of swell and nonneutral conditions. It was shown that wind profiles derived from these models were consistent with each other and both departed from the traditional MOST. At low wind speeds, a supergeostrophic jet appeared on the upper edge of the wave boundary layer, which was also reported in earlier studies. Under nonneutral conditions, the influence of buoyancy was significant. The slope of the wind profile increased under stable conditions and became smoother under unstable conditions. Considering the effects of buoyancy and swell, the wind stress derived from the model agreed quantitatively with the observations.

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