Search Results

You are looking at 1 - 10 of 51 items for

  • Author or Editor: Xuebin Zhang x
  • All content x
Clear All Modify Search
Jiafeng Wang and Xuebin Zhang

Abstract

Large-scale atmospheric variables have been statistically downscaled to derive winter (December–March) maximum daily precipitation at stations over North America using the generalized extreme value distribution (GEV). Here, the leading principal components of the sea level pressure field and local specific humidity are covariates of the distribution parameters. The GEV parameters are estimated using data from 1949 to 1999 and the r-largest method. This statistical downscaling procedure is found to yield skill over the southern and northern West Coast, central United States, and areas of western and eastern Canada when tested with independent data.

The projected changes in covariates or predictors are obtained from transient climate change simulations conducted with the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 (CGCM3.1) forced by the Intergovernmental Panel on Climate Change (IPCC) A2 forcing scenario. They are then used to derive the GEV distribution parameters for the period 2050–99. The projected frequency of the current 20-yr return maximum daily precipitation for that period suggests that extreme precipitation risk will increase heavily over the south and central United States but decrease over the Canadian prairies. The difference between the statistical downscaling results and those estimated using GCM simulation is also discussed.

Full access
Cheng Qian and Xuebin Zhang

Abstract

Temperature seasonality, the difference between summer and winter temperatures in mid–high latitudes, is an important component of the climate. Whether humans have had detectable influences on changing surface temperature seasonality at scales smaller than the subcontinental scale, where humans are directly impacted, is not clear. In this study, the first detection and attribution analysis of changes in temperature seasonality in China has been carried out. Detection and attribution of both summer and winter temperatures were also conducted, with careful consideration of observational uncertainty and the inconsistency between observation and model simulations induced by the long coastline and country border in China. The results show that the response to external forcings is robustly detectable in the spatiotemporal pattern of weakening seasonality and in that of warming winter temperature, although models may have underestimated the observed changes. The response to external forcings is detectable and consistent with the observed change in summer temperature averaged over China. Human influences are detectable in changes in seasonality and summer and winter temperatures, most robustly in winter, and these influences can be separated from those of natural forcing when averaged over China. The recent increase in summer temperature was found to be due to external forcings, and the warming hiatus in winter temperature from 1998 to 2013 was due to a statistically significant cooling trend induced by internal variability. These results will give insights into the understanding of the warming hiatus in China, as well as the hot summers and cold winters in recent years.

Full access
Cheng Qian and Xuebin Zhang

Abstract

The annual cycle is the largest variability for many climate variables outside the tropics. Whether human activities have affected the annual cycle at the regional scale is unclear. In this study, long-term changes in the amplitude of surface air temperature annual cycle in the observations are compared with those simulated by the climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Different spatial domains ranging from hemispheric to subcontinental scales in mid- to high-latitude land areas for the period 1950–2005 are considered. Both the optimal fingerprinting and a nonoptimal detection and attribution technique are used. The results show that the space–time pattern of model-simulated responses to the combined effect of anthropogenic and natural forcings is consistent with the observed changes. In particular, models capture not only the decrease in the temperature seasonality in the northern high latitudes and East Asia, but also the increase in the Mediterranean region. A human influence on the weakening in the temperature seasonality in the Northern Hemisphere is detected, particularly in the high latitudes (50°–70°N) where the influence of the anthropogenic forcing can be separated from that of the natural forcing.

Full access
Xuebin Zhang and Michael J. McPhaden

Abstract

Vertical advection of temperature is the primary mechanism by which El Niño–Southern Oscillation (ENSO) time-scale sea surface temperature (SST) anomalies are generated in the eastern equatorial Pacific. Variations in vertical advection are mediated primarily by remote wind-forced thermocline displacements, which control the temperature of water upwelled to the surface. However, during some ENSO events, large wind stress variations occur in the eastern Pacific that in principle should affect local upwelling rates, the depth of the thermocline, and SST. In this study, the impact of these wind stress variations on the eastern equatorial Pacific is addressed using multiple linear regression analysis and a linear equatorial wave model. The regression analysis indicates that a zonal wind stress anomaly of 0.01 N m−2 leads to approximately a 1°C SST anomaly over the Niño-3 region (5°N–5°S, 90°–150°W) due to changes in local upwelling rates. Wind stress variations of this magnitude occurred in the eastern Pacific during the 1982/83 and 1997/98 El Niños, accounting for about 1/3 of the maximum SST anomaly during these events. The linear equatorial wave model also indicates that depending on the period in question, zonal wind stress variations in the eastern Pacific can work either with or against remote wind stress forcing from the central and western Pacific to determine the thermocline depth in the eastern Pacific. Thus, zonal wind stress variations in the eastern Pacific contribute to the generation of interannual SST anomalies through both changes in local upwelling rates and changes in thermocline depth. Positive feedbacks between the ocean and atmosphere in the eastern Pacific are shown to influence the evolution of the surface wind field, especially during strong El Niño events, emphasizing the coupled nature of variability in the region. Implications of these results for understanding the character of event-to-event differences in El Niño and La Niña are discussed.

Full access
Xuebin Zhang and Michael J. McPhaden

Abstract

Previous studies have described the impacts of wind stress variations in the eastern Pacific on sea surface temperature (SST) anomalies associated with the El Niño–Southern Oscillation (ENSO) phenomenon. However, these studies have usually focused on individual El Niño events and typically have not considered impacts on La Niña—the cold phase of the ENSO cycle. This paper examines effects of wind stress and heat flux forcing on interannual SST variations in the eastern equatorial Pacific from sensitivity tests using an ocean general circulation model over the period 1980–2002. Results indicate that in the Niño-3 region (5°N–5°S, 90°–150°W) a zonal wind stress anomaly of 0.01 N m−2 leads to about 1°C SST anomaly and that air–sea heat fluxes tend to damp interannual SST anomalies generated by other physical processes at a rate of about 40 W m−2 (°C)−1. These results systematically quantify expectations from previous event specific numerical model studies that local forcing in the eastern Pacific can significantly affect the evolution of both warm and cold phases of the ENSO cycle. The results are also consistent with a strictly empirical analysis that indicates that a wind stress anomaly of 0.01 N m−2 leads to ∼1°C SST anomaly in the Niño-3 region.

Full access
Xuebin Zhang and Michael J. McPhaden

Abstract

The authors use a new and novel heat balance formalism for the upper 50 m of the Niño-3 region (5°N–5°S, 90°–150°W) to investigate the oceanographic processes underlying interannual sea surface temperature (SST) variations in the eastern equatorial Pacific. The focus is on a better understanding of the relationship between local and remote atmospheric forcing in generating SST anomalies associated with El Niño–Southern Oscillation (ENSO) events. The heat balance analysis indicates that heat advection across 50-m depth and across 150°W are the important oceanic mechanisms responsible for temperature variations with the former being dominant. On the other hand, net surface heat flux adjusted for penetrative radiation damps SST. Jointly, these processes can explain most of interannual variations in temperature tendency averaged over the Niño-3 region. Decomposition of vertical advection across the bottom indicates that the mean seasonal advection of anomalous temperature (the so-called thermocline feedback) dominates and is highly correlated with 20°C isotherm depth variations, which are mainly forced by remote winds in the western and central equatorial Pacific. Temperature advection by anomalous vertical velocity (the “Ekman feedback”), which is highly correlated with local zonal wind stress variations, is smaller with an amplitude of about 40% on average of remotely forced vertical heat advection. These results support those of recent empirical and modeling studies in which local atmospheric forcing, while not dominant, significantly affects ENSO SST variations in the eastern equatorial Pacific.

Full access
Francis W. Zwiers and Xuebin Zhang

Abstract

Using an optimal detection technique, the extent to which the combined effect of changes in greenhouse gases and sulfate aerosols (GS) may be detected in observed surface temperatures is assessed in six spatial domains decreasing in size from the globe to Eurasia and North America, separately. The GS signal is detected in the annual mean near-surface temperatures of the past 50 yr in all domains. It is also detected in some seasonal mean temperatures of the past 50 yr, with detection in more seasons in larger domains.

Full access
Francis W. Zwiers, Xuebin Zhang, and Yang Feng

Abstract

Observed 1961–2000 annual extreme temperatures, namely annual maximum daily maximum (TXx) and minimum (TNx) temperatures and annual minimum daily maximum (TXn) and minimum (TNn) temperatures, are compared with those from climate simulations of multiple model ensembles with historical anthropogenic (ANT) forcing and with combined anthropogenic and natural external forcings (ALL) at both global and regional scales using a technique that allows changes in long return period extreme temperatures to be inferred. Generalized extreme value (GEV) distributions are fitted to the observed extreme temperatures using a time-evolving pattern of location parameters obtained from model-simulated extreme temperatures under ANT or ALL forcing. Evaluation of the parameters of the fitted GEV distributions shows that both ANT and ALL influence can be detected in TNx, TNn, TXn, and TXx at the global scale over the land areas for which there are observations, and also regionally over many large land areas, with detection in more regions in TNx. Therefore, it is concluded that the influence of anthropogenic forcing has had a detectable influence on extreme temperatures that have impacts on human society and natural systems at global and regional scales. External influence is estimated to have resulted in large changes in the likelihood of extreme annual maximum and minimum daily temperatures. Globally, waiting times for extreme annual minimum daily minimum and daily maximum temperature events that were expected to recur once every 20 yr in the 1960s are now estimated to exceed 35 and 30 yr, respectively. In contrast, waiting times for circa 1960s 20-yr extremes of annual maximum daily minimum and daily maximum temperatures are estimated to have decreased to fewer than 10 and 15 yr, respectively.

Full access
Xuebin Zhang, Bruce Cornuelle, and Dean Roemmich

Abstract

The bifurcation of the North Equatorial Current (NEC) plays an important role in the heat and water mass exchanges between the tropical and subtropical gyres in the Pacific Ocean. The variability of western boundary transport (WBT) east of the Philippine coast at the mean NEC bifurcation latitude (12°N) is examined here. A tropical Pacific regional model is set up based on the Massachusetts Institute of Technology general circulation model and its adjoint, which calculates the sensitivities of a defined meridional transport to atmospheric forcing fields and ocean state going backward in time. The adjoint-derived sensitivity of the WBT at the mean NEC bifurcation latitude to surface wind stress is dominated by curl-like patterns that are located farther eastward and southward with increasing time lag. The temporal evolution of the adjoint sensitivity of the WBT to wind stress resembles wind-forced Rossby wave dynamics but propagating with speeds determined by the background stratification and current, suggesting that wind-forced Rossby waves are the underlying mechanism. Interannual-to-decadal variations of the WBT can be hindcast well by multiplying the adjoint sensitivity and the time-lagged wind stress over the whole model domain and summing over time lags. The analysis agrees with previous findings that surface wind stress (especially zonal wind stress in the western subtropical Pacific) largely determines the WBT east of the Philippines, and with a time lag based on Rossby wave propagation. This adjoint sensitivity study quantifies the contribution of wind stress at all latitudes and longitudes and provides a novel perspective to understand the relationship between the WBT and wind forcing over the Pacific Ocean.

Full access
Xuebin Zhang, Francis W. Zwiers, and Guilong Li

Abstract

Using Monte Carlo simulations, several methods for detecting a trend in the magnitude of extreme values are compared. Ordinary least squares regression is found to be the least reliable method. A Kendall's tau–based method provides some improvement. The advantage of this method over that of least squares diminishes when the sample size is moderate to small. Explicit consideration of the extreme value distribution when computing trend always outperforms the above two methods. The use of the r largest values as extremes enhances the power of detection for moderate values of r; the use of larger values of r may lead to bias in the magnitude of the estimated trend.

Full access