[1] Statistical analysis is carried out for satellite-based global daily tropospheric and stratospheric temperature anomaly and solar irradiance data sets. Behavior of the series appears to be nonstationary with stationary daily increments. Estimating long-range dependence between the increments reveals a remarkable difference between the two temperature series. Global average tropospheric temperature anomaly behaves similarly to the solar irradiance anomaly. Their daily increments show antipersistency for scales longer than 2 months. The property points at a cumulative negative feedback in the Earth climate system governing the tropospheric variability during the last 22 years. The result emphasizes a dominating role of the solar irradiance variability in variations of the tropospheric temperature and gives no support to the theory of anthropogenic climate change. The global average stratospheric temperature anomaly proceeds like a 1-dim random walk at least up to 11 years, allowing good presentation by means of the autoregressive integrated moving average (ARIMA) models for monthly series.

No abstract

Temporal variability of daily time series for total solar irradiance at the top of the atmosphere, the Microwave Sounding Unit (MSU) based global, hemispherical and zonal average temperature for the lower troposphere and stratosphere together with 5 surface air temperature data, measured at various meteorological stations have been studied by means of the structure function. From the growth rate of the structure function in the time interval between 32 and 4096 days it follows that the variability of the series represents an anti-persistent (AP) behavior. This property in turn shows a domination of negative feedback in the physical system generating the lower tropospheric temperature variability. Distribution of the increments over various ranges and correlations between them are calculated in order to determine the quantitative characteristics describing temporal variability.

No abstract

Customarily, climate studies of long-range temperature variability have been carried out using annual or monthly averages. The approach mixes the details of short-and long-range variability that are different for air temperature series. This work shows that a useful method for eliminating short-range variability on long-range variability is to apply a sufficiently long (about 2 months) time step to the daily series. An autoregressive integrated moving average model is fitted to daily maximum and minimum temperature anomalies from the mean seasonal cycle, using data from a number of Australian and New Zealand weather stations. The fitted model can be considered as a sum of random walk plus white noise. This enables us to obtain a quantitative long-term description of the temperature variability.