scholarly journals Technical Note: Long-term memory effect in the atmospheric CO<sub>2</sub> concentration at Mauna Loa

2006 ◽  
Vol 6 (6) ◽  
pp. 11957-11970 ◽  
Author(s):  
C. Varotsos ◽  
M.-N. Assimakopoulos ◽  
M. Efstathiou

Abstract. The monthly mean values of the atmospheric carbon dioxide concentration derived from in-situ air samples collected at Mauna Loa Observatory, Hawaii, 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.

2007 ◽  
Vol 7 (3) ◽  
pp. 629-634 ◽  
Author(s):  
C. Varotsos ◽  
M.-N. Assimakopoulos ◽  
M. Efstathiou

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.


2021 ◽  
Author(s):  
Georgios Vagenas ◽  
Theano Iliopoulou ◽  
Panayiotis Dimitriadis ◽  
Demetris Koutsoyiannis

&lt;p&gt;Since the pre-industrial era at the end of the 18&lt;sup&gt;th&lt;/sup&gt; century, the atmospheric carbon dioxide concentration (CO&lt;sub&gt;2&lt;/sub&gt;) has increased by 47.46% from the level of 280 ppmv (parts per million volume) to 412.89 ppmv (Mauna Loa &amp;#8211; NOAA Station, November 2020). These increased concentrations caused by natural &amp; anthropogenic activities, interact with the aquatic environment which acts as a safety valve. Nevertheless, the absorbed CO&lt;sub&gt;2 &lt;/sub&gt;amounts undergo chemical transformations, resulting in increasing ionized concentrations that can significantly reduce the water&amp;#8217;s pH, a process described as ocean acidification. Here, we use the HOT (Hawaii-Ocean-Time series) to perform time series analysis for temperature, carbon dioxide partial pressure and pH. More specifically, we analyze their temporal changes in month and annual time lag. Then, we proceed in comparisons with relevant studies on atmospheric data to evaluate the produced results. Finally, we make an effort to disentangle the results with simplified assumptions connected with the observed impact of ocean acidification on the aquatic ecosystems.&lt;/p&gt;


2019 ◽  
Author(s):  
Mikhail Y. Verbitsky ◽  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Dmitry M. Volobuev

Abstract. Detecting the direction and strength of the causality signal in observed time series is becoming a popular tool for exploration of distributed systems such as Earth's climate system. Here we suggest that in addition to reproducing observed time series of climate variables within required accuracy a model should also exhibit the causality relationship between variables found in nature. Specifically, we propose a novel framework for a comprehensive analysis of climate model responses to external natural and anthropogenic forcing based on the method of conditional dispersion. As an illustration, we assess the causal relationship between anthropogenic forcing (i.e., atmospheric carbon dioxide concentration) and surface temperature anomalies. We demonstrate a strong directional causality between global temperatures and carbon dioxide concentrations (meaning that carbon dioxide affects temperature stronger than temperature affects carbon dioxide) in both the observations and in (CMIP5) climate model simulated temperatures.


2019 ◽  
Vol 12 (9) ◽  
pp. 4053-4060 ◽  
Author(s):  
Mikhail Y. Verbitsky ◽  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Dmitry M. Volobuev

Abstract. Detecting the direction and strength of the causality signal in observed time series is becoming a popular tool for exploration of distributed systems such as Earth's climate system. Here, we suggest that in addition to reproducing observed time series of climate variables within required accuracy a model should also exhibit the causality relationship between variables found in nature. Specifically, we propose a novel framework for a comprehensive analysis of climate model responses to external natural and anthropogenic forcing based on the method of conditional dispersion. As an illustration, we assess the causal relationship between anthropogenic forcing (i.e., atmospheric carbon dioxide concentration) and surface temperature anomalies. We demonstrate a strong directional causality between global temperatures and carbon dioxide concentrations (meaning that carbon dioxide affects temperature more than temperature affects carbon dioxide) in both the observations and in (Coupled Model Intercomparison Project phase 5; CMIP5) climate model simulated temperatures.


2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.


Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 659
Author(s):  
Jue Lu ◽  
Ze Wang

Entropy indicates irregularity or randomness of a dynamic system. Over the decades, entropy calculated at different scales of the system through subsampling or coarse graining has been used as a surrogate measure of system complexity. One popular multi-scale entropy analysis is the multi-scale sample entropy (MSE), which calculates entropy through the sample entropy (SampEn) formula at each time scale. SampEn is defined by the “logarithmic likelihood” that a small section (within a window of a length m) of the data “matches” with other sections will still “match” the others if the section window length increases by one. “Match” is defined by a threshold of r times standard deviation of the entire time series. A problem of current MSE algorithm is that SampEn calculations at different scales are based on the same matching threshold defined by the original time series but data standard deviation actually changes with the subsampling scales. Using a fixed threshold will automatically introduce systematic bias to the calculation results. The purpose of this paper is to mathematically present this systematic bias and to provide methods for correcting it. Our work will help the large MSE user community avoiding introducing the bias to their multi-scale SampEn calculation results.


2016 ◽  
Vol 56 (4) ◽  
pp. 533-544 ◽  
Author(s):  
N. V. Vakulenko ◽  
V. M. Kotlyakov ◽  
F. Parrenin ◽  
D. M. Sonechkin

A concept of the anthropogenic origin of the current global climate warming assumes that growth of concentration of the atmospheric carbon dioxide and other greenhouse gases is of great concern in this process. However, all earlier performed analyses of the Antarctic ice cores, covering the time interval of several glacial cycles for about 1 000 000 years, have demonstrated that the carbon dioxide concentration changes had a certain lag relative to the air temperature changes by several hundred years during every beginning of the glacial terminations as well as at endings of interglacials. In contrast to these findings, a recently published careful analysis of Antarctic ice cores (Parrenin et al., 2013) had shown that both, the carbon dioxide concentration and global temperature, varied almost synchronously during the transition from the last glacial maximum to the Holocene. To resolve this dilemma, a special technique for analysis of the paleoclimatic time series, based on the wavelets, had been developed and applied to the same carbon dioxide concentration and temperature time series which were used in the above paper of Parrenin et al., 2013. Specifically, a stack of the Antarctic δ18O time series (designated as ATS) and the deuterium Dome C – EPICA ones (dD) were compared to one another in order to: firstly, to quantitatively estimate differences between time scales of these series; and, secondly, to clear up the lead–lag relationships between different scales variations within these time series. It was found that accuracy of the mutual ATS and dD time series dating lay within the range of 80–160 years. Perhaps, the mutual dating of the temperature and carbon dioxide concentration series was even worse due to the assumed displacement of air bubbles within the ice. It made us to limit our analysis by the time scales of approximately from 800 to 6000 years. But it should be taken into account that any air bubble movement changes the time scale of the carbon dioxide series as a whole. Therefore, if a difference between variations in any temperature and the carbon dioxide time series is found to be longer than 80–160 years, and if these variations are timescale‑dependent, it means that the bubble displacements are not essential, and so these advancing and delays are characteristic of the time series being compared. Our wavelet‑based comparative and different‑scale analysis confirms that the relationships between the carbon dioxide concentration and temperature variations were essentially timescale‑dependent during the transition from the last glacial maximum to the Holocene. The carbon dioxide concentration variations were ahead of the temperature ones during transition from the glacial maximum to the Boelling – Alleroud warming as well as from the Young Drias cooling to the Holocene optimum. However, the temperature variations were ahead during the transition from the Boelling – Alleroud warming to the Young Drias cooling and during the transition from the Holocene optimum to the present‑day climate.


2021 ◽  
Vol 6 (6) ◽  
pp. 212-214
Author(s):  
AA El-Meligi

There is a significant effect of carbon dioxide on the acidification of the ocean. This research focuses on the acidification of the ocean and its effect on the animal life in the ocean. Also, it focuses on the effect of carbon dioxide concentration in the atmosphere on the ocean acidification. The data are collected from the research institutions and laboratories, such as National Snow and Ice Data Center (NSIDC), Japan, National Oceanic and Atmospheric Administration (NOAA), USA, Mauna Loa Observatory in Hawaii, and other sources of research about acidification of ocean. The results show that the acidity increases with increasing the amount of carbon dioxide in the atmosphere. This is because ocean absorbs nearly 50% of carbon dioxide from the atmosphere. Carbonate ions (CO32-) will be used in forming carbonic acid, which will increase the acidity of the water. Increasing the acidity of water will affect building of the animal Skeleton. It is recommended to reduce the amount of carbon dioxide in the atmosphere; therefore the acidity will be decreased in the ocean.


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