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Based on univariate correlation and coherence analyses and considering the physical basis of the relationships, a simple multiforced (multiple) statistical concept is used which correlates observational climatic time series simultaneously with volcanic, solar, ENSO, and the anthropogenic greenhouse gases forcing. This is appropriate to remove some natural climate noise in the observed data and to evaluate the components (signals) possibly due to the anthropogenic greenhouse gas forcing (CO2, or equivalent CO2 implying additional gases) during industrial time. In this paper, we apply this technique to 100 global box data time series 1890–1985, of the surface air temperature, using observed data from Hansen and Lebedeff. The results are presented in terms of latitudinal-seasonal and regional trends, where the observed trend patterns are compared with the hypothetical signals (statistical assessments) possibly due to anthropogenic greenhouse forcing. These latter signals can be amplified to enable a comparison with corresponding results from general circulation model (GCM) CO2 doubling experiments. These observed-statistical assessments lead to results which are, at least qualitatively and in respect to the zonal mean temperatures, very similar to some GCM experiments indicating the maximum CO2 doubling signals (statistical assessment > 12 K) in the arctic winter. However, these signals are moderate in the tropics and in the Southern Hemisphere (global average 2.8–4.4 K). As far as the industrial signals are concerned (observed period) these signals are somewhat larger (maximum 7 K, global average 0.5–0.9 K) than the observed trends (maximum 5 K, global average 0.5 K). Phase shifts of cause and effect may amplify these signals but are very uncertain.This paper was presented at the International Conference on Modelling of Global Climate Change and Variability, held in Hamburg 11–15 September 1989 under the auspices of the Meteorological Institute of the University of Hamburg and the Max Planck Institute for Meteorology. Guest Editor for these papers is Dr. L. Dümenil 相似文献
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C. -D. Schönwiese 《Theoretical and Applied Climatology》1986,37(1-2):1-14
Summary The anthropogenic increase of the atmospheric carbon dioxide (CO2) concentration leads to a global warming of the atmospheric surface layer, whereas the stratosphere is cooled. This greenhouse effect postulated by a number of climate models (on a physical basis) can be conditionally verified by statistical multiple regression techniques. In this study the following climatic time series are used (all data yearly averages): northern hemisphere mean temperatures near surface 1781–1980 (alternatively since 1851 or 1881) and corresponding stratospheric data 1958–1983, sea surface temperatures 1856–1980, northern hemisphere or global average, alternatively, and the global mean sea level fluctuations 1881–1980. In order to account for an appropriate part of explained variance, volcanic and solar forcing parameters are implied and the data are low-pass filtered suppressing variations of the period rangeT < 10 years. Based on the recently assessed preindustrial CO2 concentration of c. 280 ppm and the Mauna Loa value of c. 344 ppm in 1984 this industrial CO2 increase reveals a northern hemisphere temperature increase near surface of c. (.7±.1) K (average and standard deviation of all statistical regression runs), statistically significant at the 95% level. A CO2 doubling (300 to 600 ppm) leads to a statistically derived signal of (3.1±.6) K, satisfactorily congruent with the results of most of the (deterministic) climate models: c. (3±1.5)K. A stratospheric cooling trend in recent time may be existent but is highly non-significant. Similarly, the SST data do not allow to evaluate a significant CO2 signal to noise ratio. In contrast to that the observed long-term global mean sea level increase (9.3 cm) can be predominantly attributed to the CO2 effect (99.9% level).
With 7 Figures 相似文献
Zusammenfassung Der anthropogen bedingte Anstieg der atmosphärischen Kohlendioxid-(CO2-)Konzentration führt zu einer Erwärmung der bodennahen Luftschicht, während die Stratosphäre abgekühlt wird. Dieser Glashauseffekt, von zahlreichen Klimamodellierungen (auf physikalischer Basis) postuliert, kann auf statistischem Weg durch multiple Regressionsrechnungen bedingt verifiziert werden. Die vorliegende Studie basiert auf den folgenden Klima-Zeitreihen (alle Daten in Form von Jahresmittelwerten): nordhemisphärische Mitteltemperatur in Bodennähe 1781–1980 (alternativ seit 1851 bzw. 1881) und in der Stratosphäre 1958–1983, Meeresoberflächentemperatur 1956–1980, nordhemisphärisch bzw. global gemittelt, und mittlere globale Meeresspiegelschwankungen 1881–1980. Um einen angemessenen Teil erklärter Varianz zu erfassen, wurden vulkanische und solare Parameter mit einbezogen und eine Tiefpaßfilterung mit Unterdrückung des PeriodenbereichsT < 10 Jahre zugrunde gelegt. Auf der Basis des kürzlich abgeschätzten vorindustriellen CO2-Konzentrationswertes von ca. 280 ppm und dem Mauna Loa Wert des Jahres 1984 von ca. 344 ppm entspricht dieser industrielle CO2-Anstieg einer Erhöhung der bodennahen nordhemisphärischen Mitteltemperatur von ca. (0.7±0.1) K (Mittelwert und Standardabweichung aller Regressionsrechnungen), was einen auf dem 95%-Niveau signifikanten Temperaturanstieg darstellt. Eine CO2-Verdoppelung (300 auf 600 ppm) führt, ebenfalls auf statistischem Weg, zu einem Temperatursignal von (3.1±0.6) K, in befriedigender Übereinstimmung mit den meisten der (deterministischen) Klimamodelle: ca. (3±1.5) K. In der Stratosphäre könnte in letzter Zeit ein Abkühlungstrend aufgetreten sein, der aber höchst insignifikant ist. Auch die SST-Daten erlauben keine signifikanten Schätzungen des Signal-Rausch-Verhältnisses. Im Gegensatz dazu kann der langfristige Trend des globalen Meeresspiegelanstiegs (9.3 cm) weitgehend dem CO2-Effekt zugeschrieben werden (99.9%-Signifikanzniveau).
With 7 Figures 相似文献
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Statistical time series decomposition into significant components and application to European temperature 总被引：1，自引：0，他引：1
Summary
Time series of observed monthly mean temperatures of European stations and at grid points are decomposed into different kinds
of trends (linear, progressive, degressive), constant or significantly changing annual cycles, episodic and harmonic components,
extreme events and noise. A stepwise regression is used to test whether the components are significant. Special emphasis is
given to extreme events which we distinguish from extreme values. While extreme values may likely occur by chance, it is very
unlikely that extreme events would be in accordance with the features of the time series. On one hand, extreme events alter
the estimates (and test results) of trends and other components. On the other hand, such components have to be known to recognize
extreme events. To deal with this problem, an iterative procedure is introduced that converges fast to robust estimates of
all the components.
Applying this procedure to the last 100 years of European temperatures reveals that the phase of the annual cycle is shifted
backward within the year in western Europe but foreward in eastern Europe. In the latter region, the amplitude of the annual
cycle has also increased significantly. Most of the trend components found in the time series are positive and linear. Nearly
all detected extreme events are cold events which occurred in winter. Their number has significantly grown. Significant harmonic
components with a period of 92.3 months (about 7.7 years) are found mainly in northern and western Europe.
Received August 15, 2000 Revised June 20, 2001 相似文献
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