As a geologically complex mountain system of central Asia, Altai Mountain largely lies in Russia and Kazakhstan, extending into West Mongolia and into North China. Here we use a dendroclimatological method to reconstruct past climate change in Altay based on daily weather records. Located on the Altai Mountain, three sampling sites in China, Russia and Mongolia provide 12, 18 and 3 proxy chronologies respectively; on the basis of daily weather data of Altay City in China, we establish a set of 40 different climate factors. With a hierarchical selection among various climate factors, proxy chronologies and transfer functions, a 400-year summer temperature is eventually reconstructed applying Regression with Correlation for Autocorrelation approach.

The above process demonstrates how weather phenomena affect climate variations. Weather can change from minute-to-minute, hour-to-hour, day-to-day, and season-to season. Climate, however, is the average of weather over time and space. Here we calculate season data based on daily record from hourly meamurements, and then acquire temperature change over long periods using seasonal weather data. Consequently, weather conditions can eventually help us understand long-term climate change.

From the reconstructed temperature series, a mild temperature change with regular fluctuations is shown and warm/cold periods are established. In order to figure out what factors are most responsible for any particular climate change, one must be sure to keep in mind the size of the affected area (local, regional, global, extra-global) and the time over which the change occurred. From this point of view, the central concept of scale must be emphasized here, not only focus on how process operates at different scales but also on how they linked through the boundaries across scales.

From the perspective of time scales, we firstly establish three groups of different scales with continuous grads of 100 years showing the process of scaling down. Based on peak analysis of temperature series, five more new absolute peaks (new peaks appearing between former adjacent peaks) emerge with 100-year scaling down. More detailed variances, high frequency information and noise will be exposed along with scaling down, and the overall series will become fluctuant and non-linear contrast to coarse time scales. That is, climate at fine scales reveals short-period weather patterns.

We always define climate as the weather averaged over a long period of time, typically 30 years. In order to figure out how climate variations affect weather phenomena through scales, we pick out the most optimum scale of 36 applying wavelet transform (Morlet). Fixed on the scale of 36, wavelet coefficients exhibit new temperature variance. In the so called Little Ice Age, more furious fluctuations with extreme cold and warm oscillations in the past four centuries are detected instead of only cold periods. We conclude that in Altay, temperatures vary in alternate warm and cold periods in similar extent, which make an overall mild change over past four centuries. As a result, after this warm period beginning at the end of last century, we can expect a cold one with similar extent.

The results of wavelet transform also show pronounced periodicities and oscillations of 30-40 years and 50-80 years, with more details applying spectrum analysis. We also pick Cosmogenic Nuclide Production (CNP) data for correlation with temperature variance, and both series display almost synchronous change patterns. Because the production rate of cosmogenic isotopes in the atmosphere is proportional to the galactic cosmic ray flux, and therefore it influences the intensity of irradiance arriving in the Earth and inversely correlates with solar activity. These periodicities and the synchronously change patterns indicate solar activities as external forcing of climate change and exhibition of energy distribution in Altay.

In sum, climate is a long period variation affected by external forcing such as solar activities while weather is how the atmosphere behaving during a short time. They can be actively linked across scales along with the process of scaling. When fixed on the most optimum scale of climate change, we can detect short period weather variation and provide prediction accordingly.