Peter Bloomfield

Trends in global temperature

Statistical models consisting of a trend plus serially correlated noise may be fitted to observed climate data such as global surface temperature, the trend and noise representing systematic change and other variations, respectively. When such a model is fitted, the estimated character of the noise determines the precision of the estimated trend, and hence the precision of the estimate of the magnitude of the systematic change in the variable considered. The results of fitting such models to global temperature imply that there is uncertainty in the amount of temperature change over the past century of up to ± 0.2 °C, but that the change of around one half of a degree Celsius is significantly different from zero.The statistical models for climate variability also imply that the observed temperature data provide only imprecise information about the climate sensitivity. This is defined here as the equilibrium response of global temperature to a doubling of the atmospheric concentration of carbon dioxide. The temperature changes observed to date are compatible with a wide range of climate sensitivities, from 0.7 °C to 2.2 °C. When data uncertainties are taken into account, the interval widens even further.

Accounting for meteorological effects in measuring urban ozone levels and trends

Abstract Observed ozone concentrations are valuable indicators of possible health and environmental impacts. However, they are also used to monitor changes and trends in the sources of ozone and of its precursors, and for this purpose the influence of meteorological variables is a confounding factor. This paper examines ozone concentrations and meteorology in the Chicago area. The data are described using least absolute deviations and local regression. The key relationships observed in these analyses are then used to construct a nonlinear regression model relating ozone to meteorology. The model can be used to estimate that part of the trend in ozone levels that cannot be accounted for by trends in meteorology, and to ‘adjust’ observed ozone concentrations for anomalous weather conditions.

Climate spectra and detecting climate change

Part of the debate over possible climate changes centers on the possibility that the changes observed over the previous century are natural in origin. This raises the question of how large a change could be expected as a result of natural variability. If the climate measurement of interest is modelled as a stationary (or related) Gaussian time series, this question can be answered in terms of (a) the way in which change is estimated, and (b) the spectrum of the time series. These computations are illustrated for 128 years of global temperature data using some simple measures of change and for a variety of possible temperature spectra. The results highlight the time scales on which it is important to know the magnitude of natural variability. The uncertainties in estimates of trend are most sensitive to fluctuations in the temperature series with periods from approximately 50 to 500 years. For some of the temperature spectra, it was found that the standard error of the least squares trend estimate was 3 times the standard error derived under the naïve assumption that the temperature series was uncorrelated. The observed trend differs from zero by more than 3 times the largest of the calculated standard errors, however, and is therefore highly significant.

Least Absolute Deviations Curve-Fitting

A method is proposed for least absolute deviations curve fitting. It may be used to obtain least absolute deviations fits of general linear regressions. As a special case it includes a minor variant of a method for fitting straight lines by least absolute deviations that was previously thought to possess no generalization. The method has been tested on a computer and was found on a range of problems to execute in as little as

A Time Series Approach To Numerical Differentiation

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The methodology of TSPO imaging with positron emission tomography

the CPU time required by a published algorithm based on linear programming. More important, this advantage appears to increase indefinitely with the number of data points

Climatological Time Series with Periodic Correlation

The problem of obtaining the derivative of a set of data arises naturally in many fields. The usual methods for obtaining derivatives are based on abstract formulations of the problem, which do not take errors of observation explicitly into account. For this reason, their performarice when applied to observational data is unpredictable. By introducing random errors into the model, one may derive methods whose performance may be stated in statistical terms. The theory of time series analysis provides useful tools for discussing such a model. A parametric family of models is introduced, and estimation of the parameters is discussed.

A characterization of the spatiotemporal variability of non-urban ozone concentrations over the eastern United States

The 18-kDA translocator protein (TSPO) is consistently elevated in activated microglia of the central nervous system (CNS) in response to a variety of insults as well as neurodegenerative and psychiatric conditions. It is therefore a target of interest for molecular strategies aimed at imaging neuroinflammation in vivo. For more than 20 years, positron emission tomography (PET) has allowed the imaging of TSPO density in brain using [11C]-(R)-PK11195, a radiolabelled-specific antagonist of the TSPO that has demonstrated microglial activation in a large number pathological cohorts. The significant clinical interest in brain immunity as a primary or comorbid factor in illness has sparked great interest in the TSPO as a biomarker and a surprising number of second generation TSPO radiotracers have been developed aimed at improving the quality of TSPO imaging through novel radioligands with higher affinity. However, such major investment has not yet resulted in the expected improvement in image quality. We here review the main methodological aspects of TSPO PET imaging with particular attention to TSPO genetics, cellular heterogeneity of TSPO in brain tissue and TSPO distribution in blood and plasma that need to be considered in the quantification of PET data to avoid spurious results as well as ineffective development and use of these radiotracers.

A New Approach to Glaciochemical Time Series Analysis

Abstract Many climatological time series display a periodic correlation structure. This paper examines three issues encountered when analyzing such time series: detection of periodic correlation, modeling periodic correlation, and trend estimation under periodic correlation. Time series containing monthly observations of stratospheric ozone concentrations, average temperatures, and carbon dioxide concentrations are tested for periodic correlation and analyzed further in the paper. A frequency domain test to detect periodic correlation is first reviewed. This test shows that the ozone and temperature series analyzed have a periodic autocorrelation structure; the carbon dioxide series shows periodicities only through its seasonal mean. Next, PARMA models (autoregressive moving average models with periodically varying parameters) are introduced as models for periodically correlated series. Algorithms for fitting a parsimonious PARMA model to a periodically correlated series are presented. Finally, trend esti...

Comparing Forecasting Models in Tourism

Abstract The spatial and temporal variability of the daily 1-h maximum O 3 concentrations over non-urban areas of the eastern United States of America was examined for the period 1985–1990 using principal component analysis. Utilization of Kaisers Varimax orthogonal rotation led to the delineation of six contiguous subregions or “influence regimes” which together accounted for 64.02% of the total variance. Each subregion displayed statistically unique O 3 characteristics and corresponded well with the path and frequency of anticyclones. When compared to the entire domain, the mid-Atlantic and south subregions observe higher mean daily 1-h maximum concentrations. Concentrations are near the domain average for the northeast and southwest subregions and are lowest in the Great Lakes and Florida subregions. The percentage of observations exceeding 120 ppb were greates in the mid-Atlantic and southwest subregions, near the domain average in the northeast and south subregions, and lowest in the Great Lakes and Florida subregions. Examination of the time series of the principal component scores associated with the subregions indicated that Great Lakes and mid-Atlantic subregions tend to observe a stronger seasonal cycle, with maximum concentrations occurring during the last week in June and first week in July, respectively. The strength of this seasonality is weakened for the northeast and south subregions and its timing delayed, until the end of July and the first of August, respectively. The southwest subregion experiences a greatly diminished seasonality, with maximum concentrations delayed until the middle of August. The seasonality found in the Florida subregion is unique in both its strength and timing, as the highest concentrations consistently occur during the months of April and May. The time series were then deseasonalized and autocorrelations and spectral density estimates calculated, revealing that persistence is much more prevalent in the Florida (autocorrelation significant to a lag of 4 days), south (3 days) and southwest (3 days) subregions. Conversely, autocorrelations are only significant to a lag of one day in the northeast and two days for the Great Lakes and mid-Atlantic subregions.