Volcanic forcing improves Atmosphere-Ocean Coupled General Circulation Model scaling performance

Recent Atmosphere-Ocean Coupled General Circulation Model (AOGCM) simulations of the twentieth century climate, which account for anthropogenic and natural forcings, make it possible to study the origin of long-term temperature correlations found in the observed records. We study ensemble experiments performed with the NCAR PCM for 10 different historical scenarios, including no forcings, greenhouse gas, sulfate aerosol, ozone, solar, volcanic forcing and various combinations, such as it natural, anthropogenic and all forcings. We compare the scaling exponents characterizing the long-term correlations of the observed and simulated model data for 16 representative land stations and 16 sites in the Atlantic Ocean for these scenarios. We find that inclusion of volcanic forcing in the AOGCM considerably improves the PCM scaling behavior. The scenarios containing volcanic forcing are able to reproduce quite well the observed scaling exponents for the land with exponents around 0.65 independent of the station distance from the ocean. For the Atlantic Ocean, scenarios with the volcanic forcing slightly underestimate the observed persistence exhibiting an average exponent 0.74 instead of 0.85 for reconstructed data.Comment: 4 figure

Long-term power-law fluctuation in Internet traffic

Power-law fluctuation in observed Internet packet flow are discussed. The data is obtained by a multi router traffic grapher (MRTG) system for 9 months. The internet packet flow is analyzed using the detrended fluctuation analysis. By extracting the average daily trend, the data shows clear power-law fluctuations. The exponents of the fluctuation for the incoming and outgoing flow are almost unity. Internet traffic can be understood as a daily periodic flow with power-law fluctuations.Comment: 10 pages, 8 figure

Detrended fluctuation analysis as a statistical tool to monitor the climate

Detrended fluctuation analysis is used to investigate power law relationship between the monthly averages of the maximum daily temperatures for different locations in the western US. On the map created by the power law exponents, we can distinguish different geographical regions with different power law exponents. When the power law exponents obtained from the detrended fluctuation analysis are plotted versus the standard deviation of the temperature fluctuations, we observe different data points belonging to the different climates, hence indicating that by observing the long-time trends in the fluctuations of temperature we can distinguish between different climates.Comment: 8 pages, 4 figures, submitted to JSTA