Long-memory processes in ozone and temperature variations at the region 60° S–60° N

Multiple serial correlations in global air temperature anomaly time series

Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth's climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Niño-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Δσ, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences.

Lockdown during COVID-19 pandemic: A case study from Indian cities shows insignificant effects on persistent property of urban air quality

The influence of reduction in emissions on the inherent temporal characteristics of PM_2.5 and NO₂ concentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis (DFA) during the lockdown period (March 24–April 20, 2020) and the corresponding period during the previous two years (i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PM_2.5 and NO₂ concentration in urban cities, causing low concentrations. It is observed that the original PM_2.5 and NO₂ concentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PM_2.5 and NO₂ concentrations. Hurst exponent of the PM_2.5 and NO₂ concentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations (APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.

A review on methodology in O3-NOx-VOC sensitivity study

Gaining insight into the response of surface ozone (O₃) formation to its precursors plays an important role in the policy-making of O₃ pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O₃ and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O₃ and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O₃ prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O₃ and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O₃ concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O₃ formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O₃ formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O₃ and its precursors could be used to test and improve the simulation performance of modern models.

Atmospheric pollution in the ten most populated US cities. Evidence of persistence

The degree of persistence in daily PM₂₅ and O₃ in the ten most populated US cities, namely New York, Los Angeles, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, Dallas and San Jose is examined in this work. We employ a methodology based on fractional integration, using the order of integration as a measure of the degree of persistence. Using data for the time period from January 1, 2019 to December 31, 2020, our results indicate that fractional integration and long memory features are both present in all the examined cases, with the integration order of the series being constrained in the (0, 1) interval. Based on this, the estimation of the coefficients for the time trend produces results which are substantially different from those obtained under the I (0) assumption.

Comparative analysis of contributions of wet deposition and photodegradation to the removal of atmospheric BaP by MFDCCA

Benzo [a] pyrene (BaP) in the atmosphere possess great carcinogenic potential to human health, and the understanding of its scavenging mechanisms has attracted considerable attention. In this work, a new quantitative method is proposed to make a comparative analysis of the long-term contributions of wet deposition and photodegradation to BaP removal based on multi-fractal detrended cross-correlation analysis (MFDCCA). According to the precipitation and global solar radiation (GSR) observations from 1998 to 2016 for two urban sites (Central/Western District and TsuenWan) in Hong Kong, the wet deposition and photodegradation of BaP are analyzed. Using MFDCCA method, long-term cross-correlation between precipitation/GSR and BaP are investigated. Moreover, the differences of multifractal features in cross-correlations of precipitation-BaP and GSR-BaP system are analyzed. Strong long-term persistence is observed in the cross-correlations for precipitation-BaP system in a one-year cycle; while cross-correlations between GSR and BaP show weak persistence over the whole timescale. Based on the meteorology in Hong Kong, this difference has been discussed. Then, contributions of wet deposition and photodegradation to atmospheric BaP removal are quantified based on MFDCCA method, which are further compared between summer and winter. The comparative analysis suggests that wet deposition plays a more significant role in the removal of atmospheric BaP. Specifically, in summer, the contributions of wet deposition are twice as much as that of photodegradation for both two sites; while in winter, the contribution of photodegradation is a little higher than that of wet deposition to BaP removal. Meanwhile, for wet deposition, the contributions in summer are about ten times greater than that in winter; while for photodegradation, the difference in contributions between summer and winter are relatively smaller. Furthermore, based on sliding window technique, the temporal evolutions in the contributions of wet deposition/photodegradation to BaP removal have been presented for both two sites. On this basis, it is discovered that the comprehensive contributions of wet deposition and photodegradation peak in June, and reach their lowest levels in December for both two sites. Quantifying the contribution of meteorological factors to the removal of atmospheric BaP is help for understanding its geochemical cycle.

Characterizing variability and predictability for air pollutants with stochastic models

We investigate the dynamics of particulate matter, nitrogen oxides, and ozone concentrations in Hong Kong. Using fluctuation functions as a measure for their variability, we develop several simple data models and test their predictive power. We discuss two relevant dynamical properties, namely, the scaling of fluctuations, which is associated with long memory, and the deviations from the Gaussian distribution. While the scaling of fluctuations can be shown to be an artifact of a relatively regular seasonal cycle, the process does not follow a normal distribution even when corrected for correlations and non-stationarity due to random (Poissonian) spikes. We compare predictability and other fitted model parameters between stations and pollutants.

Role of PM2.5 in the photodegradation of the atmospheric benzene

The presence of PM_2.5 may affect the photodegradation of benzene in the natural atmosphere. On one hand, the photodegradation of benzene may be promoted with the increase in PM_2.5 concentrations, owing to adsorption and catalysis effect of PM_2.5 surface; On the other hand, PM_2.5 can scatter or block ultraviolet light and lead to weakening the photochemical reactions in the atmospheric system. It is very difficult to prove which process is dominant in the real atmosphere due to the complexity of the atmosphere. Based on coupling detrended fluctuation analysis, the goal of this work is to reveal the role of PM_2.5 in the photodegradation of benzene in real atmosphere over long time scales. The 9 years regular monitoring data from 2007 to 2016 in Puzi of Taiwan are analyzed. A new nonlinear parameter (PDB) is established to characterize the photodegradation degree of atmospheric benzene. Based on sliding window technique, the correlations between the temporal variation of PDB and PM_2.5 are analyzed. The results show that there is a positive correlation between PDB and PM_2.5 in daytime and little correlation between them in nighttime. It indicates that PM_2.5 mainly plays the promoting effect on the photodegradation of atmospheric benzene. This is the first study to directly determine the role of PM_2.5 in the photochemical behavior of atmospheric benzene based on long term field observation data. Moreover, the results suggest that the regional transport of PM_2.5 could seriously affect the geochemistry cycle of some VOCs. This research provides a new analysis method to directly quantify the effect of PM_2.5 on the photodegradation of VOCs in the real atmosphere. It is helpful for evaluating the role of PM_2.5 in the complex photochemical system.

Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece

This work examines if chaos and long memory exist in PM10 concentrations recorded in Athens, Greece. The algorithms of Katz, Higuchi, and Sevcik were employed for the calculation of fractal dimensions and Rescaled Range (R/S) analysis for the calculation of the Hurst exponent. Windows of approximately two months’ duration were employed, sliding one sample forward until the end of each utilized signal. Analysis was applied to three long PM10 time series recorded by three different stations located around Athens. Analysis identified numerous dynamical complex fractal time-series segments with patterns of long memory. All these windows exhibited Hurst exponents above 0.8 and fractal dimensions below 1.5 for the Katz and Higuchi algorithms, and 1.2 for the Sevcik algorithm. The paper discusses the importance of threshold values for the postanalysis of the discrimination of fractal and long-memory windows. After setting thresholds, computational calculations were performed on all possible combinations of two or more techniques for the data of all or two stations under study. When all techniques were combined, several common dates were found for the data of the two combinations of two stations. When the three techniques were combined, more common dates were found if the Katz algorithm was not included in the meta-analysis. Excluding Katz’s algorithm, 12 common dates were found for the data from all stations. This is the first time that the results from sliding-window chaos and long-memory techniques in PM10 time series were combined in this manner.

Attenuation of long-range temporal correlations of neuronal oscillations in young children with autism spectrum disorder

Although autism spectrum disorder (ASD) was previously found to be associated with aberrant brain structure, neuronal amplitudes and spatial neuronal interactions, surprisingly little is known about the temporal dynamics of neuronal oscillations in this disease. Here, the hemoglobin concentration signals (i.e., oxy-Hb and deoxy-Hb) of young children with ASD and typically developing (TD) children were recorded via functional near infrared spectroscopy (fNIRS) when they were watching a cartoon. The long-range temporal correlations (LRTCs) of hemoglobin concentration signals were quantified using detrended fluctuation analysis (DFA). Compared with TD group, the DFA exponents of young children with ASD were significantly smaller over left temporal region for oxy-Hb signal, and over bilateral temporo-occipital regions for deoxy-Hb signals, indicating a shift-to-randomness of brain oscillations in the children with ASD. Testing the relationship between age and DFA exponents revealed that this association could be modulated by autism. The correlation coefficients between age and DFA exponents were significantly more positive in TD group, compared to those in ASD group over several brain regions. Furthermore, the DFA exponents of oxy-Hb in left temporal region were negatively correlated with autistic symptom severity. These results suggest that the decreased DFA exponent of hemoglobin concentration signals may be one of the pathologic changes in ASD, and studying the temporal structure of brain activity via fNIRS technique may provide physiological indicators for autism.

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The LRTCs of fNIRS signals are attenuated in young children with ASD.

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Opposite relationships between age and LRTCs of fNIRS signals are revealed in young children with ASD and TD.

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The LRTCs of oxy-Hb in left temporal region are negatively correlated with autistic symptom severity.

Records in fractal stochastic processes

The record statistics in stationary and non-stationary fractal time series is studied extensively. By calculating various concepts in record dynamics, we find some interesting results. In stationary fractional Gaussian noises, we observe a universal behavior for the whole range of Hurst exponents. However, for non-stationary fractional Brownian motions, the record dynamics is crucially dependent on the memory, which plays the role of a non-stationarity index, here. Indeed, the deviation from the results of the stationary case increases by increasing the Hurst exponent in fractional Brownian motions. We demonstrate that the memory governs the dynamics of the records as long as it causes non-stationarity in fractal stochastic processes; otherwise, it has no impact on the record statistics.

On the long-range dependence of fractional Poisson and negative binomial processes

We discuss the short-range dependence (SRD) property of the increments of the fractional Poisson process, called the fractional Poissonian noise. We also establish that the fractional negative binomial process (FNBP) has the long-range dependence (LRD) property, while the increments of the FNBP have the SRD property. Our definitions of the SRD/LRD properties are similar to those for a stationary process and different from those recently used in Biard and Saussereau (2014).

Novel approach to analysing large data sets of personal sun exposure measurements

Personal sun exposure measurements provide important information to guide the development of sun awareness and disease prevention campaigns. We assess the scaling properties of personal ultraviolet radiation (pUVR) sun exposure measurements using the wavelet transform (WT) spectral analysis to process long-range, high-frequency personal recordings collected by electronic UVR dosimeters designed to measure erythemal UVR exposure. We analysed the sun exposure recordings of school children, farmers, marathon runners and outdoor workers in South Africa, and construction workers and work site supervisors in New Zealand. We found scaling behaviour in all the analysed pUVR data sets. We found that the observed scaling changes from uncorrelated to long-range correlated with increasing duration of sun exposure. Peaks in the WT spectra that we found suggest the existence of characteristic times in sun exposure behaviour that were to some extent universal across our data set. Our study also showed that WT measures enable group classification, as well as distinction between individual UVR exposures, otherwise unattainable by conventional statistical methods.

A wavelet lifting approach to long-memory estimation

Reliable estimation of long-range dependence parameters is vital in time series. For example, in environmental and climate science such estimation is often key to understanding climate dynamics, variability and often prediction. The challenge of data collection in such disciplines means that, in practice, the sampling pattern is either irregular or blighted by missing observations. Unfortunately, virtually all existing Hurst parameter estimation methods assume regularly sampled time series and require modification to cope with irregularity or missing data. However, such interventions come at the price of inducing higher estimator bias and variation, often worryingly ignored. This article proposes a new Hurst exponent estimation method which naturally copes with data sampling irregularity. The new method is based on a multiscale lifting transform exploiting its ability to produce wavelet-like coefficients on irregular data and, simultaneously, to effect a necessary powerful decorrelation of those coefficients. Simulations show that our method is accurate and effective, performing well against competitors even in regular data settings. Armed with this evidence our method sheds new light on long-memory intensity results in environmental and climate science applications, sometimes suggesting that different scientific conclusions may need to be drawn.

Marcinkiewicz law of large numbers for outer products of heavy-tailed, long-range dependent data

The Marcinkiewicz strong law, limn→∞(1 / n1/p)∑k=1n(Dk - D) = 0 almost surely with p ∈ (1, 2), is studied for outer products Dk = {XkX̅kT}, where {Xk} and {X̅k} are both two-sided (multivariate) linear processes (with coefficient matrices (Cl), (C̅l) and independent and identically distributed zero-mean innovations {Ξ} and {Ξ̅}). Matrix sequences Cl and C ̅l can decay slowly enough (as |l| → ∞) that {Xk,X ̅k} have long-range dependence, while {Dk} can have heavy tails. In particular, the heavy-tail and long-range-dependence phenomena for {Dk} are handled simultaneously and a new decoupling property is proved that shows the convergence rate is determined by the worst of the heavy tails or the long-range dependence, but not the combination. The main result is applied to obtain a Marcinkiewicz strong law of large numbers for stochastic approximation, nonlinear function forms, and autocovariances.

Complex network approach to fractional time series

In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.

Plausible reasons for the inconsistencies between the modeled and observed temperatures in the tropical troposphere

We hereby attempt to detect plausible reasons for the discrepancies between the measured and modeled tropospheric temperature anomalies in the tropics. For this purpose, we calculate the trends of the upper‐minus‐lower tropospheric temperature anomaly differences (TAD) for both the measured and modeled time series during 1979–2010. The modeled TAD trend is significantly higher than that of the measured ones, confirming that the vertical amplification of warming is exaggerated in models. To investigate the cause of this exaggeration, we compare the intrinsic properties of the measured and modeled TAD by employing detrended fluctuation analysis (DFA). The DFA exponent obtained for the measured values reveals white noise behavior, while the exponent for the modeled ones shows that they exhibit long‐range power law correlations. We suggest that the vertical amplification of warming derived from modeled simulations is weighted with a persistent signal, which should be removed in order to achieve better agreement with observations.

On the scaling effect in global surface air temperature anomalies

Abstract. The annual and the monthly mean values of the land-surface air temperature anomalies from 1880–2011, over both hemispheres, are used to investigate the existence of long-range correlations in their temporal evolution. The analytical tool employed is the detrended fluctuation analysis, which eliminates the noise of the non-stationarities that characterize the land-surface air temperature anomalies in both hemispheres. The reliability of the results obtained from this tool (e.g., power-law scaling) is investigated, especially for large scales, by using error bounds statistics, the autocorrelation function (e.g., rejection of its exponential decay) and the method of local slopes (e.g., their constancy in a sufficient range). The main finding is that deviations of one sign of the land-surface air temperature anomalies in both hemispheres are generally followed by deviations with the same sign at different time intervals. In other words, the land-surface air temperature anomalies exhibit persistent behaviour, i.e., deviations tend to keep the same sign. Taking into account our earlier study, according to which the land and sea surface temperature anomalies exhibit scaling behaviour in the Northern and Southern Hemisphere, we conclude that the difference between the scaling exponents mainly stems from the sea surface temperature, which exhibits a stronger memory in the Southern than in the Northern Hemisphere. Moreover, the variability of the scaling exponents of the annual mean values of the land-surface air temperature anomalies versus latitude shows an increasing trend from the low latitudes to polar regions, starting from the classical random walk (white noise) over the tropics. There is a gradual increase of the scaling exponent from low to high latitudes (which is stronger over the Southern Hemisphere).

On the 11 year solar cycle signature in global total ozone dynamics

The main aim of the present study is to investigate further the association between total ozone (TOZ) and the 11 year solar cycle (SC) during the period 1979–2010, by employing satellite observations of TOZ made by Nimbus‐7, Meteor‐3, Earth Probe Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) instrumentation. A statistically significant correlation between the annual mean TOZ over both hemispheres and sunspot number (SN) is found. On the contrary, focusing on the January and February mean monthly TOZ fluctuations from the equator to the high latitudes of the Northern Hemisphere, no association between TOZ and SN is derived. This is attributed to the existence of the quasi‐biennial‐oscillation (QBO) and the El Niño‐Southern oscillation (ENSO) in the TOZ time series. The latter oscillation is herewith expressed by the recently introduced Ozone ENSO Index (OEI). However, when considering the TOZ zonal means centred at 17.5 and 27.5°N during the east phase years of the QBO in the equatorial zonal wind at 50 hPa, a statistically significant correlation between TOZ and SN is revealed. It is an indication that the quasi‐periodic fluctuations (i.e. QBO, ENSO) strongly contaminate the relationship between TOZ and solar activity. Plausible mechanisms are discussed, exploring the momentum flux (MF) measurements between 45 and 75°N, in the periods of increased dynamical variability. The findings obtained point to the conclusion that the 11 year solar cycle response in TOZ is caused by dynamical changes which are caused by solar activity. These are of crucial importance because solar radiation is a major driving force of the climate system. Copyright © 2012 Royal Meteorological Society

Studying the time scale dependence of environmental variables predictability using fractal analysis

Prediction of meteorological and air quality variables motivates a lot of research in the atmospheric sciences and exposure assessment communities. An interesting related issue regards the relative predictive power that can be expected at different time scales, and whether it vanishes altogether at certain ranges. An improved understanding of our predictive powers enables better environmental management and more efficient decision making processes. Fractal analysis is commonly used to characterize the self-affinity of time series. This work introduces the Continuous Wavelet Transform (CWT) fractal analysis method as a tool for assessing environmental time series predictability. The high temporal scale resolution of the CWT enables detailed information about the Hurst parameter, a common temporal fractality measure, and thus about time scale variations in predictability. We analyzed a few years records of half-hourly air pollution and meteorological time series from which the trivial seasonal and daily cycles were removed. We encountered a general trend of decreasing Hurst values from about 1.4 (good autocorrelation and predictability), in the sub-daily time scale to 0.5 (which implies complete randomness) in the monthly to seasonal scales. The air pollutants predictability follows that of the meteorological variables in the short time scales but is better at longer scales.

Nitric acid and particulate matter measurements at Athens, Greece, in connection with corrosion studies

Abstract. For a long time, scientists have been concerned about the effects of air pollution on materials and especially on the monuments of the cultural heritage. The EU funded a project, entitled MULTI-ASSESS, to determine these effects and to develop dose-response functions appropriate for the new multi-pollutant environment. The University of Athens participated in this effort as a targeted field exposure test site. In the present paper, the measurements of the passive samplers, which were exposed during the same period with the samples for corrosion studies, at the Athens station, are presented. The results have shown that only 16.5% of the deposited mass was water soluble. The vertical distribution of passive particle collectors has led to the conclusion that the height of maximum deposition of each ion is different. In addition, a variation of the water-soluble mass to total deposited mass between 8% and 31% was observed.

Scaling behaviour of the global tropopause

Abstract. Detrended fluctuation analysis is applied to the time series of the global tropopause height derived from the 1980–2004 daily radiosonde data, in order to detect long-range correlations in its time evolution. Global tropopause height fluctuations in small time-intervals are found to be positively correlated to those in larger time intervals in a power-law fashion. The exponent of this dependence is larger in the tropics than in the middle and high latitudes in both hemispheres. Greater persistence is observed in the tropopause of the Northern than in the Southern Hemisphere. A plausible physical explanation of the fact that long-range correlations in tropopause variability decreases with increasing latitude is that the column ozone fluctuations (that are closely related with the tropopause ones) exhibit long range correlations, which are larger in tropics than in the middle and high latitudes at long time scales. This finding for the tropopause height variability should reduce the existing uncertainties in assessing the climatic characteristics. More specifically the reliably modelled values of a climatic variable (i.e. past and future simulations) must exhibit the same scaling behaviour with that possibly existing in the real observations of the variable under consideration. An effort has been made to this end by applying the detrended fluctuation analysis to the global mean monthly land and sea surface temperature anomalies during the period January 1850–August 2008. The result obtained supports the findings presented above, notably: the correlations between the fluctuations in the global mean monthly land and sea surface temperature display scaling behaviour which must characterizes any projection.

Development of a nonlocal convective mixing scheme with varying upward mixing rates for use in air quality and chemical transport models

BACKGROUND, AIM, AND SCOPE

Asymmetrical convective non-local scheme (CON) with varying upward mixing rates is developed for simulation of vertical turbulent mixing in the convective boundary layer in air quality and chemical transport models.

MATERIALS AND METHODS

The upward mixing rate form the surface layer is parameterized using the sensible heat flux and the friction and convective velocities. Upward mixing rates varying with height are scaled with an amount of turbulent kinetic energy in layer, while the downward mixing rates are derived from mass conservation.

RESULTS

This scheme provides a less rapid mass transport out of surface layer into other layers than other asymmetrical convective mixing schemes.

DISCUSSION

In this paper, we studied the performance of a nonlocal convective mixing scheme with varying upward mixing in the atmospheric boundary layer and its impact on the concentration of pollutants calculated with chemical and air-quality models. This scheme was additionally compared versus a local eddy-diffusivity scheme (KSC). Simulated concentrations of NO(2) and the nitrate wet deposition by the CON scheme are closer to the observations when compared to those obtained from using the KSC scheme.

CONCLUSIONS

Concentrations calculated with the CON scheme are in general higher and closer to the observations than those obtained by the KSC scheme (of the order of 15-20%). Nitrate wet deposition calculated with the CON scheme are in general higher and closer to the observations than those obtained by the KSC scheme.

RECOMMENDATIONS AND PERSPECTIVES

To examine the performance of the scheme, simulated and measured concentrations of a pollutant (NO(2)) and nitrate wet deposition was compared for the year 2002. The comparison was made for the whole domain used in simulations performed by the chemical European Monitoring and Evaluation Programme Unified model (version UNI-ACID, rv2.0) where schemes were incorporated.

Impacts of the solar eclipse of 29 March 2006 on the surface ozone concentration, the solar ultraviolet radiation and the meteorological parameters at Athens, Greece

Abstract. In this study the variations in the surface ozone concentration, the solar ultraviolet radiation and the meteorological parameters at the ground before, during and after the total solar eclipse of 29 March 2006 have been examined. This analysis is based on the measurements performed at four stations located in the greater Athens basin in Greece. The experimental data demonstrated that the solar eclipse phenomenon affects the surface ozone concentration as well as the temperature, the relative humidity and the wind speed near the ground. The decrease in the surface ozone concentration that observed after the beginning of the eclipse event lasted almost two hours, probably due to the decreased efficiency of the photochemical ozone formation. The reduction of the solar ultraviolet radiation at 312 and 365 nm reached 97% and 93% respectively, while the air temperature dropped, the relative humidity increased and the wind speed decreased.

Technical Note: Long-term memory effect in the atmospheric CO2concentration at Mauna Loa

Abstract. The monthly mean values of the atmospheric carbon dioxide concentration derived from in-situ air samples collected at Mauna Loa Observatory, Hawaii, USA during 1958–2004 (the longest continuous record available in the world) are analyzed by employing the detrended fluctuation analysis to detect scaling behavior in this time series. The main result is that the fluctuations of carbon dioxide concentrations exhibit long-range power-law correlations (long memory) with lag times ranging from four months to eleven years, which correspond to 1/f noise. This result indicates that random perturbations in the carbon dioxide concentrations give rise to noise, characterized by a frequency spectrum following a power-law with exponent that approaches to one; the latter shows that the correlation times grow strongly. This feature is pointing out that a correctly rescaled subset of the original time series of the carbon dioxide concentrations resembles the original time series. Finally, the power-law relationship derived from the real measurements of the carbon dioxide concentrations could also serve as a tool to improve the confidence of the atmospheric chemistry-transport and global climate models.