An empirical approach to the analysis of local and global climate and weather data and to the determination of CO2 sensitivities

Abstract

Abstract Some freely available global temperature data sets which document the weather for a period of over 100 years, e.g. from NASA, from NOAA, additionally also local data e.g. for Germany (DWD) were analyzed in order to derive meaningful empirical long-term trends with suitable multi-annual averages. This is first demonstrated using global climate data with different approaches, whereby the results are to a high degree consistent. Analyzes of the German temperature and weather data and of climate data from other continents are carried out in a similar manner. For reliable forecasts it is important to determine the CO2 sensitivity as precisely as possible. A very simple method is to smooth out temperatures over 20 years at a time. If these values are plotted at intervals of 10 years over the associated (also averaged) CO2 content, the temperature database (since 1961) is condensed to 5 data points and a statement can be made about the quality of the linearity for the respective database. Both the NASA data and the NOAA data show an unusually good linearity with almost identical CO2 sensitivity (approx. 0.0105 K/ppm CO2). This indicates that the long-term trend in global temperature since around 1960 has been largely determined solely by greenhouse gases. If the regional weather data is used as a basis, there is also in many cases strict linearity with increasing CO2 content. The analysis of the regional data allows the conclusion that there is approximately a specific CO2 sensitivity for every region on earth with specific statistical uncertainties: For mean global land, it is 0.017 K, for Germany it is 0.022 K, and for Alaska even 0.028 K per ppm CO2 .