Short-term Variability Research Articles

Physical and Biological Controls on Short-Term Variations in Dissolved Oxygen in Shallow Waters of a Large Temperate Estuary

Physical and Biological Controls on Short-Term Variations in Dissolved Oxygen in Shallow Waters of a Large Temperate Estuary

Occurrence of sudden storm commencement in interplanetary space

Sudden storm commencement (SSC) events detected in interplanetary space from 1869 to 2023, are subjectedto wavelet and Lomb-Scargle periodogram analyses to determine the potential frequency of their recurrence. This period includes SSC events from the preceding fourteen solar cycles. Using Lomb-Scargle periodogram analysis, we have identified SSC occurrences independently during the odd and even solar activity cycles. We also used the wavelet analysis approach throughout the positive (A > 0) and negative (A < 0) polarity states of the heliospheric magnetic fields, in addition to solar activity cycles. This study reveals some notable short-term periodic variations in the SSC time series. The findings indicate that SSC occurrences have a conspicuous and important ∼ 44.0-day period. We have also observed solar and extended solar rotation periods in the time series, along with some intermittent variations (e.g., ∼22.0-, ∼19.0-days). The observed periodicities in the SSC time series are discussed and compared with the previously detected periodicities of solar wind and plasma, geomagnetic activity, and cosmic rays’ intensity indicators.

Investigating Arctic Cyclone–Tropopause Polar Vortex Interactions with Idealized Observing System Simulation Experiments

Abstract Arctic cyclones (ACs) are a primary driver of surface weather in the Arctic, contributing to heat and moisture transport and forcing short-term sea ice variability. Still, our understanding of the processes that drive ACs, particularly their large scales and long lifetimes, is limited. ACs are commonly associated with one or more cyclonic tropopause polar vortices (TPVs), potential vorticity (PV) anomalies in the upper troposphere and lower stratosphere that may spur baroclinic development in the surface system, though the exact processes that link the two have yet to be fully explored. In this study, we investigate physical links between TPVs, especially their mesoscale structure and moisture profiles, and ACs with idealized observing system simulation experiments (OSSEs). Starting with a nature run, we simulate different types of dropsonde observations over a TPV during the nascent phase of a nearby AC. The Model for Prediction Across Scales (MPAS) and the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter are then used to run experiments to test the impact of these detailed TPV observations. In addition to a control, five main experiments are conducted, assimilating new observations of temperature and humidity. All experiments reduce forecast errors at the surface and throughout the troposphere. Additional humidity observations alter vertical PV distributions, which in turn impact the development of the AC. Experiments with additional temperature observations exhibit improvements in TPV structure and surrounding PV features and produce stronger surface cyclones with skillful TPV forecasts for up to 36 h longer than the control. Significance Statement Arctic cyclones (ACs) are a weather feature that can produce high winds, precipitation, and changes to sea ice cover in the Arctic. As a result, forecasting these storms accurately is important for human and economic interests in the region; however, there are currently gaps in our understanding of how ACs strengthen and persist. In this study, we explore potential links between ACs and weather features higher up in the atmosphere called tropopause polar vortices (TPVs) using computer modeling experiments. This study shows that there are important connections between the characteristics of TPVs and the development of ACs. These findings will be useful for making more accurate forecasts of future events and advancing our knowledge of how sea ice changes relate to the atmosphere.

Comparison of probabilistic structural reliability methods for ultimate limit state assessment of wind turbines

The probabilistic design of offshore wind turbines aims to ensure structural safety in a cost-effective way. This involves conducting structural reliability assessments for different design options and considering different structural responses. There are several structural reliability methods, and this paper will apply and compare different approaches in some simplified case studies. In particular, the well known environmental contour method will be compared to a more novel approach based on sequential sampling and Gaussian processes regression for an ultimate limit state case study on the maximum flapwise blade root bending moment. For one of the case studies, results will also be compared to results from a brute force simulation approach. Interestingly, the comparison is very different from the two case studies. In one of the cases the environmental contours method agrees well with the sequential sampling method but in the other, results vary considerably. Probably, this can be explained by the violation of some of the assumptions associated with the environmental contour approach, i.e. that the short-term variability of the response is large compared to the long-term variability of the environmental conditions. Results from this simple comparison study suggests that the sequential sampling method can be a robust and computationally effective approach for structural reliability assessment.

Short-Term Variability of Both Brachial and Aortic Blood Pressure is Increased in Patients with Immune-mediated Chronic Inflammation.

Blood pressure (BP) variability (BPV) has emerged as an indicator of subclinical organ damage and an independent predictor of cardiovascular disease (CVD) morbidity and mortality in high-risk populations. We aimed to assess short-term variability of both brachial and aortic BP in psoriasis, a common immune-mediated inflammatory disorder characterized by increased CVD risk. Psoriasis patients and non-psoriasis individuals had their BP assessed throughout a 24h period (Mobil-O-Graph device). Brachial and aortic BPV during the 24h and the respective daytime and nighttime periods was calculated from relevant ambulatory BP profiles. In-house software was applied to automatically calculate average real variability (ARV) of brachial and aortic systolic (bSBP, aSBP) and diastolic BP (bDPB, aDBP), and the weighted standard deviation (wSD) of 24h bSBP/aSBP. 24h pulse wave velocity (PWV) and augmentation index (AIx) were used as widely applied markers of arterial stiffness. Psoriasis patients (n = 74) presented increased ARV of 24h and daytime bSBP/aSBP, and increased ARV of 24h and daytime bDBP/aDBP, compared to controls (n = 40). PWV and AIx correlated with ARV of 24h bSBP/aSBP, daytime bSBP/aSBP, while PWV further correlated with ARV of nighttime aSBP. The observed associations with PWV, yet not AIx, with indices of BPV remained significant after adjusting for CVD risk factors. This is the first study reporting increased 24h variability of both brachial and aortic BP in psoriasis. The association of short-term BPV with arterial stiffness implies a potential role of BPV in terms of CVD risk stratification in patients with chronic immune-mediated inflammation.

Agreement between Ultra-Short-Term and Standard Heart Rate Variability Analysis in Resting and Post-Exercise Conditions.

Short-term (5 min) heart rate variability (HRV) analysis is widely used in assessing autonomic nervous system activity during exercise. While shortening the HRV measurement duration can help improve its application efficiency, its accuracy needs to be verified. This study investigated the agreement between ultra-short-term (UST) HRV (3 or 4 min) and standard 5 min HRV and explored the optimal recording duration under resting and post-exercise conditions. Fourteen participants exercised on a cycle ergometer at 60% of their maximum peak power. Data were collected during the rest condition (Pre-E) and three post-exercise conditions (Post-E1, Post-E2, and Post-E3), with indicators of the standard deviation (SDNN) of the ultra-short and short-term RR intervals and the root mean square (RMSSD) of the continuous difference between RR intervals. Repeated measures ANOVA, Cohen's d statistic, Bland-Altman analysis, and interclass correlation coefficients (ICC) assessed the agreement between UST-HRV and ST-HRV. The consistency results of SDNN and RMSSD in resting and post-exercise were different. At the Pre-E, Post-E2, and Post-E3 phases, no statistical differences for SDNN and RMSSD were observed, with ICCs surpassing 0.9, indicating a high level of agreement. However, at Post-E2, there was a significant difference between 3 min RMSSD and 5 min RMSSD (p < 0.05), as well as between 3 min SDNN, 4 min SDNN, and 5 min SDNN (p < 0.05). Furthermore, the limits of agreement were observed to decrease as the time duration increased in Bland-Altman plots. UST-HRV analysis is a reliable substitute for standard 5 min HRV assessment, particularly during resting conditions. For post-exercise measurements, assessing the appropriateness of a 3- or 4 min duration based on the exercise's length is recommended to ensure accuracy and reliability.

CLIMATE CHANGES AND AGRICULTURE SECTOR IN INDIA: ISSUE AND CHALLENGES

Global climate change refers to the long-term shifts in weather patterns that affect different regions of the world. In contrast, "weather" describes the short-term variations in wind, temperature, and precipitation in a specific area. Agriculture is the sector most vulnerable to climate change within the Indian economy. Long-term climate shifts can impact agricultural productivity, growth rates, photosynthesis, transpiration, moisture availability, and both the quantity and quality of crops. According to the Intergovernmental Panel on Climate Change (IPCC), India's temperatures are expected to rise by 3-5°C by the end of the twenty-first century, which could lead to a reduction of 3-26% in net agricultural revenues. This poses a significant threat to global food security as it alters seasonal temperature patterns. A decline in agricultural output could result in increased prices and make food unaffordable for many. Without effective mitigation and adaptation strategies, agricultural revenues may drop by 12-40% in the coming years. This situation is particularly critical for an agrarian country like India, affecting livelihoods, economic development, and food and employment security. It is essential to identify and address the factors contributing to the rise in greenhouse gases and the degradation of soil and water resources. Urgent mitigation and adaptation measures are necessary to tackle the challenges posed by global climate change. Stakeholders must focus on how quickly Indian farmers can adjust their practices to cope with these changes and what policies or technological innovations could facilitate this adaptation. However, in a developing country like India, where many farmers lack financial resources and expertise, rapid adaptation may be challenging.

Interactions between developmental phenology, carbon movement, and storage constrain demography in the understory clonal herb Podophyllum peltatum L.

Little is known about how carbon integration and storage dynamics affect and are affected by demography in field populations. We sought to elucidate this link by examining dynamic patterns of carbon integration relative to the timing of demographically significant developmental decisions regarding shoot type determination in mayapple, Podophyllum peltatum, a clonal plant with large and persistent rhizomes. Individual rhizome systems growing in natural populations were fed 14CO2 either in late-April, early-May, or mid-June, then harvested at intervals throughout the current season and into the next. When distribution of label was examined we found that carbon fixed at different times in the growing season is used differently: April-fixed assimilate remained in the labeled shoot or was moved into the old rhizome, May-fixed assimilate was found predominantly in the old rhizome, while early-June fixed assimilate moved into the old rhizome and the extending new ramet. Movement of assimilate into the old rhizome appeared to have precedence over formation of additional new ramets. Despite significant within season changes in location of dominant sinks within rhizome systems, there was little redistribution of labeled assimilate: early fixed assimilate was not used to fuel later within season growth, however, assimilate was redistributed between seasons. Vegetative and sexual systems differed in the distribution only of April-fixed assimilate. This was observed even though early labeling occurred prior to anthesis. Sexual systems retained a greater proportion of assimilate in the stem than did vegetative ones, which exported more to the old rhizome. 14C-distribution patterns did not vary between systems differing in future demographic status suggesting that the developmental decision regarding shoot type is based on resources acquired in prior years. We explore the hypothesis that preformation and storage are functionally linked traits that permit plants to coordinate the developmental determination of structures differing in cost and demographic function with known resource status. We conclude that demography influences and is influenced by integrative physiology and that physiological restrictions on within season redistribution of assimilates constrain plants' capacities to respond to short-term environmental variation. Such constraints may affect plants' abilities to respond to rapid environmental change in the Anthropocene.

DETERMINANTS OF INDONESIA’S TRADE BALANCE : A VECM MODEL APPROACH

Trade Balance is the main indicator in external competitiveness. The condition of the trade balance in Indonesia tends to experience unbalanced conditions every year, so this study aims to see the relationship between the variables of Exchange Rate, FDI and GDP to the Trade Balance in Indonesia. The data used is a time series for 43 years (1980-2022) using the Vector Error Correction Model (VECM) method to analyze the relationship and response between variables in the term short and long term. Short-term results show that the Exchange Rate, Foreign Direct Investment (FDI) and Gross Domestic Product (GDP) do not affect the surplus or deficit of Indonesia's Trade Balance. While in the long run, Exchange Rate appreciation has a negative effect on the Trade Balance, the amount of FDI has a positive impact on the Trade Balance, and the amount of Gross Domestic Product (GDP) affects the deficit in the Trade Balance. The use of monetary policy can help stab ilize exchange rates, increase exports through increased foreign investment, maintain the value of exports, and reduce trade deficits. Rephrase

Self-adaptive system state optimization based on nonlinear affine transformation for renewable energy volatility

The short-term power variations of renewable energy sources result in system state fluctuations and deviations from the scheduled operating point. Tracking short-term power variations and maintaining the system's optimality is challenging for traditional optimization methods due to their highly time-consuming nature. This paper proposes a short-timescale self-adaptive optimization strategy based on the nonlinear affine transformation to deal with this problem. Firstly, the multi-period optimization model is established and solved by considering static power-frequency characteristics to obtain an optimal operating point on a longer temporal scale, with the state security margins reserved using chance constraint programming. Next, analogously with the Taylor series, the nonlinear relationship between the system state variable and short-term power fluctuations is revealed through an analytic expression of the nonlinear affine transformation. Then, a self-adaptive optimization algorithm based on the nonlinear affine transformation is proposed to achieve frequency and voltage optimizations on a shorter temporal scale. With less communication, self-adaptive optimization is implemented at the local bus level to achieve more optimal states for short-term renewable power variations rapidly. Finally, simulations demonstrate that the proposed optimization strategy can effectively enhance frequency and voltage qualities, and decrease objective function, thereby improving the operation safety and economy.

BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI

PurposeMRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose “BrainLossNet”, a convolutional neural network (CNN)-based method for BVL-estimation.MethodsBrainLossNet uses CNN-based non-linear registration of baseline(BL)/follow-up(FU) 3D-T1w-MRI pairs. BVL is computed by non-linear registration of brain parenchyma masks segmented in the BL/FU scans. The BVL estimate is corrected for image distortions using the apparent volume change of the total intracranial volume. BrainLossNet was trained on 1525 BL/FU pairs from 83 scanners. Agreement between BrainLossNet and SIENA was assessed in 225 BL/FU pairs from 94 MS patients acquired with a single scanner and 268 BL/FU pairs from 52 scanners acquired for various indications. Robustness to short-term variability of 3D-T1w-MRI was compared in 354 BL/FU pairs from a single healthy men acquired in the same session without repositioning with 116 scanners (Frequently-Traveling-Human-Phantom dataset, FTHP).ResultsProcessing time of BrainLossNet was 2–3 min. The median [interquartile range] of the SIENA-BrainLossNet BVL difference was 0.10% [− 0.18%, 0.35%] in the MS dataset, 0.08% [− 0.14%, 0.28%] in the various indications dataset. The distribution of apparent BVL in the FTHP dataset was narrower with BrainLossNet (p = 0.036; 95th percentile: 0.20% vs 0.32%).ConclusionBrainLossNet on average provides the same BVL estimates as SIENA, but it is significantly more robust, probably due to its built-in distortion correction. Processing time of 2–3 min makes BrainLossNet suitable for clinical routine. This can pave the way for widespread clinical use of BVL estimation from intra-scanner BL/FU pairs.

Glycan clock of ageing-analytical precision and time-dependent inter- and i-individual variability.

Ageing is a complex biological process with variations among individuals, leading to the development of ageing clocks to estimate biological age. Glycans, particularly in immunoglobulin G (IgG), have emerged as potential biomarkers of ageing, with changes in glycosylation patterns correlating with chronological age.For precision analysis, three different plasma pools were analysed over 26days in tetraplicates, 312 samples in total. In short-term variability analysis, two cohorts were analysed: AstraZeneca MFO cohort of 26 healthy individuals (median age 20) and a cohort of 70 premenopausal Chinese women (median age 22.5) cohort monitored over 3months. Long-term variability analysis involved two adult men aged 47 and 57, monitored for 5 and 10years, respectively. Samples were collected every 3months and 3weeks, respectively. IgG N-glycan analysis followed a standardized approach by isolating IgG, its subsequent denaturation and deglycosylation followed by glycan cleanup and labelling. Capillary gel electrophoresis with laser-induced fluorescence (CGE-LIF) and ultra-performance liquid chromatography analyses were employed for glycan profiling. Statistical analysis involved normalization, batch correction, and linear mixed models to assess time effects on derived glycan traits.The intermediate precision results consistently exhibited very low coefficient of variation values across all three test samples. This consistent pattern underscores the high level of precision inherent in the CGE method for analysing the glycan clock of ageing. The AstraZeneca MFO cohort did not show any statistically significant trends, whereas the menstrual cycle cohort exhibited statistically significant trends in digalactosylated (G2), agalactosylated (G0) and fucosylation (F). These trends were attributed to the effects of the menstrual cycle. Long-term stability analysis identified enduring age-related trends in both subjects, showing a positive time effect in G0 and bisected N-acetylglucosamine, as well as a negative time effect in G2 and sialylation, aligning with earlier findings. Time effects measured for monogalactosylation, and F remained substantially lower than ones observed for other traits.The study found that IgG N-glycome analysis using CGE-LIF exhibited remarkably high intermediate precision. Moreover, the study highlights the short- and long-term stability of IgG glycome composition, coupled with a notable capacity to adapt and respond to physiological changes and environmental influences such as hormonal changes, disease, and interventions. The discoveries from this study propel personalized medicine forward by deepening our understanding of how IgG glycome relates to age-related health concerns. This study underscores the reliability of glycans as a biomarker for tracking age-related changes and individual health paths.

Commentary on "Short-Term Variability of Both Brachial and Aortic Blood Pressure is Increased in Patients with Immune-Mediated Chronic Inflammation".

Commentary on "Short-Term Variability of Both Brachial and Aortic Blood Pressure is Increased in Patients with Immune-Mediated Chronic Inflammation".

Cardiohemodynamic responses and heart rate variability following an active orthostatic test among male residents of the North with different NOS3 (rs2070744) genotypes

AIM: This study explored associations between the -786TC (rs2070744) genetic polymorphism of the NOS3 gene and the main indices of systemic hemodynamics and heart rate variability following an active orthostatic test in men living in the North. MATERIAL AND METHODS: Eighty-three healthy men aged 33.5±1.5 yеаrs on average, who were permanent residents of the Magadan Region, comprised the sample. The participants underwent both resting and active orthostatic testing modes. Cardiovascular system variables were recorded using an automatic tonometer. Hemodynamic indices were then calculated. Short-term heart rate variability in frequency and spectral areas was simultaneously measured using the Varikard hardsoft complex unit. Genotyping was conducted using polymerase chain reaction. The sample was then divided into two groups: Group 1 consisted of homozygotes with the TT genotype (n=34), while Group 2 comprised carriers of the NOS3*C allele variant (genotypes TC+CC) associated with reduced nitric oxide production (n=49). RESULTS: By examining the complex vasomotor, chrono- and inotropic reactions of hemodynamic and heart rate variables during the active orthostatic test, we were able to discern unique patterns in the response of the two groups under study. Men in Group 1 demonstrated an elevated cardiac output response during orthostatic changes, while maintaining consistent vascular resistance values. This was accompanied by a shift towards sympathetic autonomic activity and an increase in the very low-frequency component of heart rate variability. In Group 2, individuals with sympathetic insufficiency and increased vascular resistance during the orthostatic test showed a 35% increase in the low-frequency component of the cardiac rhythm reflecting the importance of this indicator as a stimulator of blood pressure rhythm fluctuations mediated by baroreflex mechanisms. CONCLUSION: Our findings suggest that men lacking the NOS3*С allele variant in their genotype demonstrate more favorable responses to the active orthostatic test. This may reflect higher level of cardiovascular functional reserves. The 786TC (rs2070744) polymorphism of the NOS3 gene can be considered as a marker of cardiohemodynamic status and autonomic regulations during functional exercise, such as an active orthostatic test.

Temporal upscaling of solar radiation components using an analytical model for variability modeling

To properly design systems that harness the solar resource, reliable measurements or estimates of its availability are needed. Often, this data is only available at temporal resolutions that are not sufficiently fine to capture the short-term fluctuations of the solar resource, creating uncertainty in the design and performance assessment of solar-powered systems. Synthetic irradiance generation techniques aim to create high temporal resolution estimates starting from lower resolution data, however, a literature review on the topic shows opportunities for improvement regarding the generality of the generation and evaluation approaches. This work proposes novel methods to estimate a quantifiable indicator of short-term variability from hourly data, a new temporal upscaling methodology for time series of arbitrary origin and destination temporal resolution, based on explicit mathematical functions and sufficient generality for application in different contexts, and an improved assessment methodology. The method was applied tor ground and satellite upscaling up to 1 min, resulting in time series with characteristics consistent with those of a higher temporal resolution and exhibit minimum deviations against the original time series characteristics. This method has applications not only for upscaling of low temporal resolution data, but also for gap-filling techniques and the upscaling of hourly solar radiation forecasts.

Assessment of Brain Natriuretic Peptide (BNP) and Lactate Dehydrogenase (LDH) levels in type 2 Diabetic Patients with Cardiovascular Disease

The metabolic condition type 2 diabetes mellitus (T2DM) causes organ malfunction and damage due to insulin secretion or resistance abnormalities. The clinical assessment of monitoring short-term blood glucose variability could benefit from the addition of a less interference-prone biomarker for nutritional status and blood cell health. In type 2 diabetic individuals with cardiovascular disease (CVD), this study looks at the significance of serum B-type natriuretic peptide (BNP) and lactate dehydrogenase (LDH) and how it relates to other variables. This study included 100 T2DM patients with cardiovascular disease (57 males and 43 females) with 70 healthy controls (39 males and 31 females ) aged 45 to &gt;74 years with a diabetes duration of 12 to 26 years. the present study shows that in T2DM patients with CVD, there was an increase in Brain Naturiertic Peptide and a high increase in LDH levels. The statistical significance of these differences is very significant (P&gt;0.001). Comparing the patient group to the control group, there is a highly significant increase in BNP and LDH. BNP and LDH were greater in people with type 2 diabetes who also had cardiovascular disease (CVD) and elevated LDH level was associated with elevated BNP. BNP and LDH are useful biomarkers that support diagnosis, and risk assessment, as well as the diagnosis and management of cardiovascular disease in diabetic individuals by facilitating the assessment of heart function and risk factors for cardiovascular disease.

MCNet: Multivariate long-term time series forecasting with local and global context modeling

Time series data typically exhibit various intra-sequence and inter-sequence correlations, resulting in intricate, intertwined dependencies, which pose challenges for accurately predicting future long-term trends. Previous studies have not fully considered the two correlations, and they also still face challenges of excessive time and memory complexity when dealing with long-term predictions. To address these challenges and establish high-precision prediction models, we propose MCNet that consists of a local branch and a global branch. The local branch aims at capturing short-term variations of intra-sequences, as well as capturing inter-sequence correlations. The global branch models long-term dependencies within sequences. Specifically, the local branch consists only of MLP module, which effectively captures short-term variations of intra-sequences by independently modeling the temporal information within and between patches of the most recent time series. Subsequently, inter-sequence dependencies are captured through the channel interaction module, which further explores more key information to improve the performance of MCNet. Meanwhile, global branch models long-term dependencies within the time series through structured global convolution. Experimental results on multiple popular long-term time series forecasting benchmarks demonstrate that MCNet outperforms state-of-the-art methods, yielding a relative improvement of 12% for multivariate time series while also being more efficient.

Occurrence of the calanoid copepod Acartia (Odontacartia) ohtsukai in Laizhou Bay, the Bohai Sea, China, and its relationship with environmental factors

In this study, we examined the short-term variation in the abundance of copepod Acartia specimens, identified previously as Acartia pacifica, in Laizhou Bay, an estuarine bay in the southern Bohai Sea in northern China. Monthly samples were collected from May 2011 to April 2012, excluding December 2011 and January to February 2012 due to ice. Based on its morphological characteristics, Acartia ohtsukai was distinguishable from A. pacifica for the first time in Laizhou Bay. A. ohtsukai was sporadically present from summer to autumn, with the highest abundance and occurrence in September. It appeared in June when the water temperature was above 20°C, and disappeared in November when the water temperature decreased to less than 10°C. During the surveyed months, A. ohtsukai was more commonly found inshore with salinity less than 28 than offshore in the bay. Correlation analysis revealed that temperature and chlorophyll a concentration significantly influenced the monthly variations in A. ohtsukai abundance. We also compared the occurrence of A. ohtsukai with that of three species from the genus Tortanus (Tortanus derjugini, Tortanus forcipatus, and Tortanus spinicaudatus) in Laizhou Bay. The coexistence of A. ohtsukai and T. derjugini in the bay suggests that their ecological habitats are similar to those of brackish-water species.

Trends of Key Greenhouse Gases as Measured in 2009–2022 at the FTIR Station of St. Petersburg State University

Key long-lived greenhouse gases (CO2, CH4, and N2O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always successful. Infrared spectroscopy is a recognized technique for the ground-based long-term monitoring of the gaseous composition of the atmosphere. The current paper is focused on the analysis of new data on CO2, CH4, and N2O total columns (TCs) retrieved from high resolution IR solar spectra acquired during 2009–2022 at the NDACC atmospheric monitoring station of St. Petersburg State University (STP station, 59.88°N, 29.83°E, 20 m asl.). The paper provides information on the FTIR system (Fourier-transform infrared) installed at the STP station, and an overview of techniques used for the CO2, CH4, and N2O retrievals. Trends of key greenhouse gases and their confidence levels were evaluated using an original approach which combines the Lomb–Scargle method with the cross-validation and bootstrapping techniques. As a result, the following fourteen-year (2009–2022) trends of TCs have been revealed: (0.56 ± 0.01) % yr−1 for CO2; (0.46 ± 0.02) % yr−1 for CH4; (0.28 ± 0.01) % yr−1 for N2O. A comparison with trends based on the EMAC numerical modeling data was carried out. The trends of greenhouse gases observed at the STP site are consistent with the results of the in situ monitoring performed at the same geographical location, and with the independent estimates of the global volume mixing ratio growth rates obtained by the GAW network and the NOAA Global Monitoring Laboratory. There is reasonable agreement between the CH4 and N2O TC trends for 2009–2019, which have been derived from FTIR measurements at three locations: the STP site, Izaña Observatory and the University of Toronto Atmospheric Observatory.

Solar Activity and Cosmic Ray Intensity Variation with Geomagnetic Activity during 1996–2022

The average features of diurnal variation have been observed to change with different phases of the solar cycle, with the variance being substantially bigger at higher energies. The events were classified on the basis of different phases of solar cycles, i.e., the minimum solar activity time period, the maximum solar activity time period, and the declining phase of solar cycle. This research looks at the observed results and the influence of solar variability on cosmic rays and the geomagnetic field from 1996 to 2022. The occasional group includes a Forbush effect decline, transitory decrease, and a ground level enhancement (GLE). The 11-year fluctuation in Galactic Cosmic Rays is also known as the long-term variation, whereas the Forbush effect reduction is known as short-term variation. We investigated the long-term change in the cosmic ray intensity and its relationship to the number of Sun spots (Rz), solar wind speed (