Xiaogu Zheng

Mixture Model For Overdispersion of Precipitation

Abstract Stochastic models fit to time series of daily precipitation amount generally ignore any year-to-year (i.e., low frequency) source of random variation, and such models are known to underestimate the interannual variance of monthly or seasonal total precipitation. To explicitly account for this “overdispersion” phenomenon, a mixture model is proposed. A hidden index, taking on one of two possible states, is assumed to exist (perhaps representing different modes of atmospheric circulation). To represent the intermittency of precipitation and the tendency of wet or dry spells to persist, a stochastic model known as a chain-dependent process is applied. The parameters of this stochastic model are permitted to vary conditionally on the hidden index. Data for one location in California (whose previous study motivated the present approach), as well as for another location in New Zealand, are analyzed. To estimate the parameters of a mixture of two conditional chain-dependent processes by maximum likeliho...

Structural Time Series Models and Trend Detection in Global and Regional Temperature Series

A unified statistical approach to identify suitable structural time series models for annual mean temperature is proposed. This includes a generalized model that can represent all the commonly used structural time series models for trend detection, the use of differenced series (successive year-to-year differences), and explicit methods for comparing the validity of no-trend nonstationary residuals models relative to trend models. Its application to Intergovernmental Panel on Climate Change global and latitude-belt temperature series reveals that a linear trend model (starting in 1890, with Southern Oscillation index signal removal and a red noise residuals process) is the optimal model for much of the globe, from the Northern Hemisphere Tropics to the Southern Hemisphere midlatitudes, but that a random stationary increment process (with no deterministic trend) is preferred for the northern part of the Northern Hemisphere. The result for the higher northern latitudes appears to be related to the greater climate variability there and does not exclude the possibility of a trend being present. The hemispheric and global series will contain a mixture of the two processes but are dominated by and best represented by the linear trend model. The latitudinal detectability of trends is oppositely matched to where GCMs indicate greatest anthropogenic trend, that is, it is best for the Tropics rather than for the high latitudes. The results reinforce the view that the global temperatures are affected by a long-term trend that is not of natural origin.

Potential Predictability of Seasonal Means Based on Monthly Time Series of Meteorological Variables

Abstract Based only on monthly mean data, an analysis of variance method is proposed for decomposing the interannual atmospheric variability in seasonal-mean time series into components related to “weather noise” and to slowly varying boundary forcing and low-frequency internal dynamics. The “potential predictability” is then defined as the fraction of the total interannual variance accounted for by the latter two components. A study using synthetic data showed that the method proposed here is comparable in performance to conventional methods requiring daily data. The technique was applied to gridded global data of monthly surface temperature, 500-hPa height, and 300-hPa wind in order to examine the geographical and seasonal dependencies of their potential predictability. For all the variables, the highest potential predictability tends to be found in the Tropics, where seasonal anomalies in the atmosphere are strongly coupled with the underlying sea surface temperature anomalies and the weather noise com...

Trend Detection in Regional-Mean Temperature Series: Maximum, Minimum, Mean, Diurnal Range, and SST

Abstract Regional climate trends are of interest both for understanding natural climate processes and as tests of anthropogenic climate change signatures. Relative to global trends, however, their detection is hampered by smaller datasets and the influence of regional climate processes such as the Southern Oscillation. Regional trends are often presented by authors without demonstration of statistical significance. In this paper, regional-average annual series of air temperature and sea surface temperature for the New Zealand region are analyzed using a systematic statistical approach that selects the optimum statistical model (with respect to serial correlation, linearity, etc.), explicitly models the interannual variability associated with observable regional climate processes, and tests significance. It is found that the residuals are stationary and are a red noise process [ARMA(1,0)] for all the series examined. The SOI and a meridional circulation anomaly index (both high-pass filtered) are “explanat...

Mapping Frost Occurrence Using Satellite Data

Abstract Detailed maps of the date of the first and last frost, the length of the frost-free period, and the number of days of frost during spring have been produced for the Otago region of New Zealand. These frost occurrence variables are estimated using a combination of channel-5 (11.5–12.5 μm) infrared brightness temperature data and the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) satellite instrument. It is shown that a generalized linear model with quasi-likelihood estimation is best suited to the estimation of these frost occurrence variables. The maps show that frosts occur earlier in the autumn and later in the spring with increasing distance away from the coast and from major rivers and lakes. Frost is most prevalent on the top of high mountain ranges where the air temperature is coldest and is shown to be more common near the bottom of inland basins as compared with the sides of the surrounding hills because of cold-air drainage and pon...

Validating Interannual Variability in an Ensemble of AGCM Simulations

This paper is an effort to explore general statistical procedures for validating the interannual variability in ensembles of atmospheric general circulation model simulations. The authors propose an estimation method for the correlation coefficient between the SST-forced components of simulated and observed seasonal means. The proposed methodology has been applied to study the interannual variability of New Zealand surface temperature as simulated by the Australian Bureau of Meteorology Research Centre nine-level atmospheric general circulation model. Compared with observations, it is found that the model simulates this variability well in most cases, except for summer maximum temperatures. It is suggested that deficiencies in the simulated storm tracks during summer may account for some apparent deficiency in the model. The failure in simulating the summer maximum temperatures also indicates the need for model validation.

A Study of Predictable Patterns for Seasonal Forecasting of New Zealand Rainfall

Abstract A recently developed variance decomposition approach is applied to study the causes of the predictability of New Zealand seasonal mean rainfall. In terms of predictability, the Southern Oscillation is identified as being the most important cause of variability for both the winter and summer New Zealand rainfall, especially for the North Island. Indian Ocean sea surface temperature variability and the Southern Hemisphere annular mode are the second most important causes of variability for winter and summer rainfall, respectively. Based on this study, a statistical prediction scheme has been developed. May Nino-3 (5°N–5°S, 150°–90°W) SSTs and March–May (MAM) central Indian Ocean SSTs are identified as being the most important predictors for the winter rainfall, while September–November (SON) Nino-3 SSTs, November local New Zealand SSTs, and the SON Southern Hemisphere annular mode index are the most important predictors for the summer rainfall. The predictive skill, in term of the percentage explai...

Mapping near-surface air temperature, pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal resolution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km×1 km. The approach comprises two steps: (1) fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; (2) apply a simple kriging procedure to the residual for trend surface correction.The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.

Interannual variability of autumn to spring seasonal precipitation in eastern China

The interannual variability of seasonal precipitation in eastern China from fall to following spring is examined for the period of 1951–2004 based on observations at 106 stations. The temporal variability of seasonal mean values is decomposed into intraseasonal (fast) and slow (potentially predictable) components. EOF analysis is then applied to both the fast and predictable components. We find that (1) the most predictable signal migrates in a north–south direction along with the annual cycle of the monsoon in east China, while spatial patterns of the leading fast modes does not change much; (2) the predictable signal of precipitation in eastern China is associated with anomalous atmospheric circulation patterns having more zonally symmetric structures while the fast time-varying precipitation components are accompanied by wavy anomalous atmospheric circulation patterns; (3) the most predictable signal has an apparent 1-season lagged correlation with the interannual variation of sea surface temperature associated with El Niño/Southern Oscillation; (4) The fast rainfall component is largely attributed to the intraseasonal variabilities of the Siberian High over the Eurasian continent and the subtropical high associated with the Western-Pacific-Oscillation-like variabilities over the North Pacific; and (5) The ENSO signal in the fall seasonal precipitation persisted throughout the entire 54-year period while the signal in winter intensified significantly after the mid-1970s. This is attributed to the weaker/stronger intensification of ENSO anomalies in the tropical Pacific during the fall/winter.

COHERENT PATTERNS OF INTERANNUAL VARIABILITY OF THE ATMOSPHERIC CIRCULATION: THE ROLE OF INTRASEASONAL VARIABILITY

In this chapter, methods, using both daily and monthly data, are proposed for studying coherent patterns of interannual variability in seasonal means of the atmospheric circulation that arise from sub-seasonal, or intraseasonal, variability. Meteorological phenomena that vary significantly within a season include atmospheric blocking and intraseasonal oscillations such as, for example, the Madden-Julian oscillation. Such phenomena may be regarded as essentially comprising a random process. By removing the influence of this intraseasonal variability, a slow and more potentially predictable component can be derived that is more associated with very slowly varying external forcing and internal dynamics. The efficacy of the methodology is shown by testing it on synthetic data where the intraseasonal and slow components are known a priori. For both the Northern Hemisphere and Southern Hemisphere observed wintertime atmospheric circulation, coherent patterns of variability in the intraseasonal component are shown to be related to atmospheric blocking and intraseasonal dynamics. Similarly, coherent patterns associated with the slow component are more related to external forcings, such as sea surface temperature variability, El Nino/Southern Oscillation and very slow internal dynamics.