Amplitude-frequency fluctuations of the seasonal cycle, temperature anomalies, and long-range persistence of climate records

Scaling Properties of Atmospheric Wind Speed in Mesoscale Range

The scaling properties of turbulent flows are well established in the inertial sub-range. However, those of the synoptic-scale motions are less known, also because of the difficult analysis of data presenting nonstationary and periodic features. Extensive analysis of experimental wind speed data, collected at the Mauna Loa Observatory of Hawaii, is performed using different methods. Empirical Mode Decomposition, interoccurrence times statistics, and arbitrary-order Hilbert spectral analysis allow to eliminate effects of large-scale modulations, and provide scaling properties of the field fluctuations (Hurst exponent, interoccurrence distribution, and intermittency correction). The obtained results suggest that the mesoscale wind dynamics owns features which are typical of the inertial sub-range turbulence, thus extending the validity of the turbulent cascade phenomenology to scales larger than observed before.

The Extratropical Northern Hemisphere Temperature Reconstruction during the Last Millennium Based on a Novel Method

Large-scale climate history of the past millennium reconstructed solely from tree-ring data is prone to underestimate the amplitude of low-frequency variability. In this paper, we aimed at solving this problem by utilizing a novel method termed "MDVM", which was a combination of the ensemble empirical mode decomposition (EEMD) and variance matching techniques. We compiled a set of 211 tree-ring records from the extratropical Northern Hemisphere (30-90°N) in an effort to develop a new reconstruction of the annual mean temperature by the MDVM method. Among these dataset, a number of 126 records were screened out to reconstruct temperature variability longer than decadal scale for the period 850-2000 AD. The MDVM reconstruction depicted significant low-frequency variability in the past millennium with evident Medieval Warm Period (MWP) over the interval 950-1150 AD and pronounced Little Ice Age (LIA) cumulating in 1450-1850 AD. In the context of 1150-year reconstruction, the accelerating warming in 20th century was likely unprecedented, and the coldest decades appeared in the 1640s, 1600s and 1580s, whereas the warmest decades occurred in the 1990s, 1940s and 1930s. Additionally, the MDVM reconstruction covaried broadly with changes in natural radiative forcing, and especially showed distinct footprints of multiple volcanic eruptions in the last millennium. Comparisons of our results with previous reconstructions and model simulations showed the efficiency of the MDVM method on capturing low-frequency variability, particularly much colder signals of the LIA relative to the reference period. Our results demonstrated that the MDVM method has advantages in studying large-scale and low-frequency climate signals using pure tree-ring data.

FUNDAMENTAL MODES OF ATMOSPHERIC CFC-11 FROM EMPIRICAL MODE DECOMPOSITION

Following an initial growth, the concentrations of chlorofluorocarbon-11 (CFC-11) in the atmosphere started to decline in the 1990's due to world-wide legislative control on emissions. The amplitude of the annual cycle of CFC-11 was much larger in the earlier period compared with that in the later period. We apply here the Ensemble Empirical Mode Decomposition (EEMD) analysis to the CFC-11 data obtained by the U.S. National Oceanic and Atmospheric Administration. The sum of the second and third intrinsic mode functions (IMFs) represents the annual cycle, which shows that the annual cycle of CFC-11 has varied by a factor of 2–3 from the mid-1970's to the present over polar regions. The results provide an illustration of the power of the EEMD method in extracting a variable annual cycle from data dominated by increasing and decreasing trends. Finally, we compare the annual cycle obtained by the EEMD analysis to that obtained using conventional methods such as Fourier transforms and running averages.

Long-range persistence of temperature records induced by long-term climatic phenomena

The occurrence of persistence in climatic systems has been investigated by analyzing 1167 surface temperature records, covering 110 years, in the whole United States. Due to the nonlinear and nonstationary character of temperature time series, the seasonal cycle suffers from both phase and amplitude modulations, which are not properly removed by the classical definition of the temperature anomaly. In order to properly filter out the seasonal component and the monotonic trends, we define the temperature anomaly in a different way by using the empirical mode decomposition (EMD). The essence of this method is to empirically identify the intrinsic oscillatory modes from the temperature records according to their characteristic time scale. The original signal is thus decomposed into a collection of a finite small number of intrinsic mode functions (IMFs), having its own time scale and representing oscillations experiencing amplitude and phase modulations, and a residue, describing the mean trend. The sum of all the IMF components as well as the residue reconstructs the original signal. Partial reconstruction can be achieved by selectively choosing IMFs in order to remove trivial trends and noise. The EMD description in terms of time-dependent amplitude and phase functions overcomes one of the major limitation of the Fourier analysis, namely, a correct description of nonlinearities and nonstationarities. By using the EMD definition of temperature anomalies we found persistence of fluctuations with a different degree according to the geographical location, on time scales in the range 3-15 years. The spatial distribution of the detrended fluctuation analysis exponent, used to quantify the degree of memory, indicates that the long-term persistence could be related to to the presence of climatic regions, which are more sensitive to climatic phenomena such as the El Niño southern oscillation.