Mean Trend Research Articles

Evolution of weather parameters and trend analysis over Junagadh, Gujarat

Climate change is one of the most important worldwide issues talked among the scientists and researchers. The temperature and rainfall trends are analyzed for meteorological data of Junagadh station in Gujarat. India over approximately last three decades stretching between years 1971 to 2013. The long–term change in temperature and rainfall has been assessed by linear trend analysis. The increasing trend in mean monthly maximum temperature (MMAX) and total mean rainfall (TMRF) is confirmed by Mann-Kendall trend test. It is evident from that monthly mean temperature have increased significantly for all the months except the months of April and October for which a very weak decrease in mean maximum temperature is observed. This implies that at Junagadh, the highest increase in temperature occurred in November (0.0210C) during 1980-2011 period. The annual mean of monthly maximum temperatures (MMAX) observed an increasing trend having an annual decreasing of 0.0040C year during 1980-2011. It is evident TMRF have increased for the months April, May August and September whereas it shows decreasing trend in January, Feb, March, June, July, October, November and December. This implies that at Junagadh, the highest increase in TMRF occurs in August and increased by (0.029 mm) during the last 1980-2011. The highest decrease in TMRF occurs in June and decreased by (2.153mm) during the same period. The annual mean of total monthly rainfall observed a decreasing trend having an decrease of 0.249 mm/year during the last 31 years.

Simulated Twentieth‐Century Ocean Warming in the Amundsen Sea, West Antarctica

AbstractRapid ice loss is occurring in the Amundsen Sea sector of the West Antarctic Ice Sheet. This ice loss is assumed to be a long‐term response to oceanographic forcing, but ocean conditions in the Amundsen Sea are unknown prior to 1994. Here we present a modeling study of Amundsen Sea conditions from 1920 to 2013, using an ensemble of ice‐ocean simulations forced by climate model experiments. We find that during the early twentieth century, the Amundsen Sea likely experienced more sustained cool periods than at present. Warm periods become more dominant over the simulations (mean trend 0.33°C/century) causing an increase in ice shelf melting. The warming is likely driven by an eastward wind trend over the continental shelf break that is partly anthropogenically forced. Our simulations suggest that the Amundsen Sea responded to historical greenhouse gas forcing, and that future changes in emissions are also likely to affect the region.

Agricultural management effects on mean and extreme temperature trends

Abstract. Understanding and quantifying land management impacts on local climate is important for distinguishing between the effects of land management and large-scale climate forcings. This study for the first time explicitly considers the radiative forcing resulting from realistic land management and offers new insights into the local land surface response to land management. Regression-based trend analysis is applied to observations and present-day ensemble simulations with the Community Earth System Model (CESM) version 1.2.2 to assess the impact of irrigation and conservation agriculture (CA) on warming trends using an approach that is less sensitive to temperature extremes. At the regional scale, an irrigation- and CA-induced acceleration of the annual mean near-surface air temperature (T2m) warming trends and the annual maximum daytime temperature (TXx) warming trends were evident. Estimation of the impact of irrigation and CA on the spatial average of the warming trends indicated that irrigation and CA have a pulse cooling effect on T2m and TXx, after which the warming trends increase at a greater rate than the control simulations. This differed at the local (subgrid) scale under irrigation where surface temperature cooling and the dampening of warming trends were both evident. As the local surface warming trends, in contrast to regional trends, do not account for atmospheric (water vapour) feedbacks, their dampening confirms the importance of atmospheric feedbacks (water vapour forcing) in explaining the enhanced regional trends. At the land surface, the positive radiative forcing signal arising from enhanced atmospheric water vapour is too weak to offset the local cooling from the irrigation-induced increase in the evaporative fraction. Our results underline that agricultural management has complex and non-negligible impacts on the local climate and highlight the need to evaluate the representation of land management in global climate models using climate models of higher resolution.

A Pilot Study Investigating the Methodology for Studying the Effectiveness of Intervention for Toddlers with Vestibular Over Responsivity

ABSTRACT Sensory over-responsivity in the vestibular domain (aka gravitational insecurity) has been frequently identified in the literature. However, little has been written about the effectiveness of interventions, especially in toddlers with these idiopathic challenges (e.g. without a diagnosis of autism). The objective of this pilot study was to test the feasibility of the methodology and sensitivity of outcome measures for treatment based on the principles of Ayres Sensory Integration and the STAR Frame of Reference for toddlers with vestibular over – responsivity. A single subject design was attempted. Three toddlers with vestibular over responsivity received intervention 1–2 time per week for 15 weeks. Data were collected on a clinic outcome measure (e.g. time on a swing) and a home outcome measure (e.g. protests during diapering). However, baseline data could not be lengthened or staggered to meet the criteria for a multiple baseline study. Preliminary support is provided for the procedures, coding, and feasibility of including a clinic and a home outcome. Data analysis procedures consistent with single subject designs were used to evaluate sensitivity of the measures through an examination of changes in mean level, trend, and variability. Findings suggest this methodology and these outcome measures may be useful in future research; changes were noted in duration of play on a piece of suspended equipment and a reduction in protests during diapering activities at home following an early treatment of toddlers with vestibular over – responsivity. Lessons learned from this study can inform future study of intervention effectiveness for this population.

Global Trends of Mean and Inequality in Multidimensional Wellbeing: Analysis of 1.2 Million Individuals From 162 Countries, 2009-2019.

IntroductionHuman flourishing is a multidimensional concept characterized by a state of complete wellbeing. However, much of the prior research on wellbeing has principally focused on population averages assessed using a single item of wellbeing. This study examined trends in population averages and inequalities for a multidimensional index of wellbeing and compared emergent patterns with those found for Cantril's ladder, a measure of life satisfaction commonly used as a unidimensional index of wellbeing.MethodsData were from the Gallup World Poll from the years 2009 to 2019, a repeated cross-sectional survey of nationally representative samples comprising ~1.2 million individuals from 162 countries. We assessed five domains of flourishing: (1) happiness, (2) health, (3) purpose, (4) character, and (5) social relationships. We used the Gini Index to estimate inequalities in wellbeing within populations. We examined and compared country ranking, global and region-specific trajectories of mean and inequality, and relationships with age for flourishing and Cantril's ladder.ResultsAlthough all trends were highly correlated across the two metrics of wellbeing, we identified distinct patterns in flourishing concerning geography, time, and age relationships that were not observed for Cantril's ladder. Temporal trends and age relationships were different across domains of flourishing. Evidence of changing inequalities in wellbeing was also found, even when population averages were high or stable over time.ConclusionComprehensive measures of wellbeing are needed to capture the complex and changing patterns of wellbeing both within and across populations.

Impact of acoustic Doppler current profiler (ADCP) motion on structure function estimates of turbulent kinetic energy dissipation rate

Abstract. Turbulent mixing is a key process in the transport of heat, salt, and nutrients in the marine environment, with fluxes commonly derived directly from estimates of the turbulent kinetic energy dissipation rate, ε. Time series of ε estimates are therefore useful in helping to identify and quantify key biogeochemical processes. The velocity structure function method can be used to determine time series of ε estimates using along-beam velocity measurements from suitably configured acoustic Doppler current profilers (ADCPs). Shear in the background current can bias such estimates; therefore, standard practice is to deduct the mean or linear trend from the along-beam velocity over the period of an observation burst. This procedure is effective if the orientation of the ADCP to the current remains constant over the burst period. However, if the orientation of the ADCP varies, a proportion of the velocity difference between bins is retained in the structure function and the resulting ε estimates will be biased. Long-term observations from a mooring with three inline ADCPs show the heading oscillating with an angular range that depends on the flow speed: from large, slow oscillations at low flow speeds to smaller, higher-frequency oscillations at higher flow speeds. The mean tilt was also determined by the flow speed, whilst the tilt oscillation range was primarily determined by surface wave height. Synthesised along-beam velocity data for an ADCP subject to sinusoidal oscillation in a sheared flow indicate that the retained proportion of the potential bias is primarily determined by the angular range of the oscillation, with the impact varying between beams depending on the mean heading relative to the flow. Since the heading is typically unconstrained in a tethered mooring, heading oscillation is likely to be the most significant influence on the retained bias for a given level of shear. Use of an instrument housing designed to reduce oscillation would mitigate the impact, whilst if the shear is linear over the observation depth range, the bias can be corrected using a modified structure function method designed to correct for bias due to surface waves.

Interhemispheric asymmetry of climate change projections of boreal winter surface winds in CanESM5 large ensemble simulations

A recent study of global wind power using an ensemble of ten CMIP5 climate simulations indicated an interhemispheric asymmetry of wind power changes over the twenty-first century, featured by power decreases across the Northern Hemisphere mid-latitudes and increases across the tropics and subtropics of the Southern Hemisphere. Here we analyze future projections of surface mean and extreme winds by means of a single-model initial-condition 50-member ensemble of climate simulations generated with CanESM5, the Canadian model participated in CMIP6. We analyze the ensemble mean and spread of boreal winter mean and extreme wind trends over the next half-century (2021–2070) and explore the contribution of internal climate variability to these trends. Surface wind speed is projected to mostly decrease in northern mid-low latitudes and southern mid-latitudes and increase in northern high latitudes and southern tropical and subtropical regions, with considerable regional variations. Large ensemble spreads are apparent, especially with remarkable differences over northern parts of South America and northern Russia. The interhemispheric asymmetry of wind projections is found in most ensemble members, and can be related to large-scale changes in surface temperature and atmospheric circulation. The extreme wind has similar structure of future projections, whereas its reductions tend to be more consistent over northern mid-latitudes. The projected mean and extreme wind changes are attributed to changes in both externally anthropogenic forced and internal climate variability generated components. The spread in wind projections is partially due to large-scale atmospheric circulation variability.

A study of energy consumption for office buildings in Vietnam for sustainable energy and climate change mitigation

This study used survey data to evaluate the current status of electrical energy use of commercial office (CO) and governmental office (GO) buildings in Hanoi and Ho Chi Minh City (HCMC) in Vietnam. Main data used in the analyses including floor area and monthly electricity consumption which were gathered from the questionnaire survey for 57 CO and GO buildings in two cities – conducted by the Vietnam Clean Energy Program in 2015. The basic statistical analysis techniques were used to evaluate the electricity consumption intensity of office buildings and preliminarily analyze key factors affecting the energy consumption of these buildings. The results generally showed that CO buildings consumed significantly more electricity than GO buildings in all months of the year and the variation and trend of mean monthly electricity consumption intensity of the buildings strongly depend on that of ambient air temperature. In addition, the energy use intensities (EUIs) of CO buildings, GO buildings, and both buildings (CO+GO) in HCMC (in a hot climate all year round) were higher than those in Hanoi (in a climate with four distinct seasons). The mean EUIs of these buildings in Hanoi, HCMC, and both cities were 105.9, 116.4, and 109.6 kWh/m2 floor. year. The findings of this study are useful information on the status of energy use and energy efficiency of CO and GO buildings in Hanoi and HCMC, thereby contribute scientific bases to the development of policies and solutions to promote the energy efficiency of CO and GO buildings in the coming years and urgent actions for sustainable energy and climate change mitigation in Vietnam.

The Agro-Climatic Change Characteristics across China during the Latest Decades

Climate change has been highlighted over the last decades worldwide, with pronounced higher warming trends for China. Induced by climate change, to some extent, agricultural production has changed, as well as the climatic resources during the agricultural growing season. An obvious longer potential agricultural growing season (PAGS) was detected in the latest decades from 1961–2020. The spatial–temporal characteristics of change and variation of climatic resources during the PAGS were explored, utilizing comparisons of the differences in mean magnitude, standard deviation, and trends. In the period of 1991–2020 relative to 1961–1990, alterations in PAGS were characterized by increases of 0–1.5 °C in mean air temperature overall, 0.8–23.8% increases in precipitation in the southeast, northeast, and west, as well as a decrease of 2.1–10.2% in insolation in central-south regions but an increase of 0.3–6.7% in the north and west. The features were pronounced during the PAGS in the primary agricultural zones as follows: (1) Northeast China, increasing and stable temperature but unstable precipitation and insolation; (2) North, eastern-west, and Southwest China, increasing but unstable temperature, decreasing but stable precipitation as well as decreasing and unstable insolation; (3) Southeast China, increasing but unstable temperature and precipitation as well as decreasing and unstable insolation; (4) West China, increasing but unstable temperature and precipitation as well as increasing but unstable insolation. Further comparisons between agro-climatic change and climate change indicated that temperature alterations during the PAGS were less numerous while precipitation and insolation were more unstable than the changes during the whole year. These findings can assist the understanding of regional agricultural climate changes and guide agricultural production practices in response.

Testing mean air temperature trends in southern Greece: A Bayesian approach

AbstractThis study investigates the variability and trends of the mean annual and seasonal surface air temperature in southern Greece, namely in Crete and in the islands of the southern Aegean Sea. The climatic data used are the mean monthly homogenized air temperature values of seven meteorological stations of the Hellenic Meteorological Service, for the period 1960–2004. A Bayesian trend analysis is performed by fitting alternative stochastic‐trend models based on the Log‐normal, the Normal, the Generalized Extreme Value and the Gumbel distributions. Posterior simulations results based on the best fitting model reveal the locations and time‐periods with the strongest and the weakest interannual trends for both mean and variance. Furthermore, a model comparison experiment based on Bayes Factors compares the above stochastic‐trend models with the corresponding ones with no‐time trends. The results are very significant since they reveal the locations and the seasons where interannual trends for both mean and variance have real grounds.

Trend assessment in climate variable by Mann Kendall test of Bastar district of Chhattisgarh

Climate change is a natural phenomenon but in present decades its variability of change mainly due to anthropogenic activities is alarming. Agriculture of Chhattisgarh state is mainly dependant on monsoon rain and its distribution. Considering this fact, the present study has been tried to analyze the most important climatic variables, viz., precipitation and temeperature for analyzing their trend in the area. The trends of maximum atmospheric temperature, rainfall and rainy days are analysed statistically for meteorological data of Jagdalpur station of Bastar district, over last three decades stretching between years 1980 to 2014. The long term change in temperature, rainfall and rainy days has been analysed by correlation and linear trend analysis. The annual MMAX temperature has decreased at a rate of -0.465 °C per year during this period at Jagdalpur station and decreasing trend for rainy days during monsoonal season (June to September) is also found and is confirmed by Mann-Kendall trend test. Very weak increasing trend is observed in total month rainfall (TMRF) during season June to September. There are decreasing trends of mean monthly rainfall and south west (June - September) rainfall observed in Bastar district of Chhattisgarh. The agricultural planning and utilization of water is dependent on monsoon rainfall and more than 75% of rainfall occurring during the monsoon season is uneven both in time and space. Therefore its analysis is important for crop planning.

Prediction of interface of geological formations using generalized additive model

ABSTRACT Geological information such as geological interfaces is important for the design of underground excavation and supporting measures. This in turn requires a method to predict accurately the locations of geological interfaces for the gap areas between boreholes. This study presents a generalized additive model (GAM) to predict the location of the geological interfaces. The performance of the GAM method is evaluated using both simulated data and borehole data for the determination of rockhead in two different geological formations in Singapore. The results show that the GAM method can provide a reasonable confidence interval (CI) of the mean trend and the prediction interval (PI) in the sense that the 95% CI covers about 95% of the actual mean curve while the 95% PI covers around 95% of testing data. Furthermore, the geological complexity can be well reflected as the prediction uncertainty in the geologically complex area is larger than that in the geologically regular area. More importantly, the users can impose prior information or personal judgment regarding the shape of the geological profile on the model. This is an important feature to enable further improvement in the accuracy of the prediction. Highlights A generalized additive model is used to predict the location of the geological interfaces The performance of the GAM method is evaluated using both simulated data and actual borehole data from Singapore The results show that the GAM method can provide a reasonable confidence interval of the mean trend and the prediction interval of a prediction Geological complexity can be well reflected in the sense that the prediction uncertainty in the geologically complex area is relatively large Expert judgment or knowledge on the geological profile can be applied to the model, which improves the prediction accuracy

Climatological Trends of Mean and Extreme Daily Precipitation in Arizona (USA)

Climatological Trends of Mean and Extreme Daily Precipitation in Arizona (USA)

Random regression models for multi-environment, multi-time data from crop breeding selection trials

Context In order to identify best crop genotypes for recommendation to breeders, and ultimately for use in breeding, evaluation is usually conducted in field trials across a range of environments, known as multi-environment trials. Increasingly, many breeding traits are measured over time, for example with high-throughput phenotyping at different growth stages in annual crops or repeated harvests in perennial crops. Aims This study aims to provide an efficient, accurate approach for modelling genotype response over time and across environments, accounting for non-genetic sources of variation such as spatial and temporal correlation. Methods Because the aim is genotype selection, genetic effects are fitted as random effects, and so the approach is based on random regression, in which linear or non-linear models are used to model genotype responses. A method for fitting random regression is outlined in a multi-environment situation, using underlying cubic smoothing splines to model the mean trend over time. This approach is illustrated on six wheat experiments, using data on grain-filling over thermal time. Key results The method correlates genetic effects over time and environments, providing predicted genotype responses while incorporating spatial and temporal correlation between observations. Conclusions The approach provides robust genotype predictions by accounting for temporal and spatial effects simultaneously under various situations including those in which trials have different measurement times or where genotypes within trials are not measured at the same times. The approach facilitates investigation into genotype by environment interaction (G × E) both within and across environments. Implications The models presented have potential to increase accuracy of predictions over measurement times and trials, provide predictions at times other than those observed, and give a greater understanding of G × E interaction, hence improving genotype selection across environments for repeated-measures traits.

ТРЕНДЫ ДАВЛЕНИЯ НА УРОВНЕ МОРЯ В ЮЖНОМ ОКЕАНЕ И АНТАРКТИКЕ ПО РЕЗУЛЬТАТАМ РЕАНАЛИЗА И НАЗЕМНЫМ ДАННЫМ

Trends in the mean sea-level pressure (SLP) in Antarctica in the last four decades (1980– 2020) have been examined using in situ observations and reanalysis data. The analysis involved time series of monthly mean, season-mean and yearly-mean values of the SLP derived from four reanalysis datasets, NCEP/NCAR, ERA5, JRA55, MERRA2, and from surface observations acquired from the Reference Antarctic Data for Environmental Research (READER) dataset. With this data we have evaluated the trends, characterized their seasonal peculiarities and variation across the high-latitude region of the Southern Hemisphere. The results of the analysis confirmed the dominance of decreasing trends in the annual mean SLP in Antarctica. Larger negative trends were found in the Western Antarctica with the most pronounced pressure drop in the South Pacific. The long-term decrease in the annual mean SLP in Antarctica was due to strong negative pressure trends in the austral summer and fall season whereas in winter and in spring the trends turn to mixed and mostly positive. The comparison of multiyear time series of SLP reanalysis data with in situ observations at Antarctic stations revealed a considerable overestimate of negative SLP trends in the NCEP/NCAR dataset. Among the four examined reanalysis datasets, ERA5 provided the best agreement with the station data on the annual mean and monthly mean SLP trend values.

Model assessments and future projections of spring climate extremes in China based on CMIP6 models

AbstractThis study reports model assessments and future projections of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating spring climate extremes over China and its eight subregions. The CMIP6 models are generally able to capture mean climate extremes and trends but have common deficiencies such as topography‐related cold‐temperature biases and precipitation overestimations. The CMIP6 multimodel median ensemble (MME) typically outperforms the individual models in simulating spring climate extremes. However, in terms of the model performance metrics, almost no models show better performance in reference to all indices compared to the observations. Furthermore, we investigate the future changes in these indices on national and regional scales over the near‐term (2021–2040), mid‐term (2041–2060), and long‐term (2081–2100) future periods relative to the 1995–2014 reference period under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. By the end of the 21st century, wet and warm extreme climate events are projected to increase, particularly in northern and western China. While dry and cold extreme climate events are projected to decrease, particularly in southern China. Additionally, the future projections of both temperature and precipitation extreme indices are remarkable under the high‐greenhouse‐gas (GHG)‐emission scenarios, with the projected changes exceeding those expected under the low‐GHG scenarios.

Cognitive Benefits of Learning Additional Languages in Old Adulthood? Insights from an Intensive Longitudinal Intervention Study

Abstract Second language (L2) learning has been promoted as a promising intervention to stave off age-related cognitive decline. While previous studies based on mean trends showed inconclusive results, this study is the first to investigate nonlinear cognitive trajectories across a 30-week training period. German-speaking older participants (aged 64–75 years) enrolled for a Spanish course, strategy game training (active control) or movie screenings (passive control). We assessed cognitive performance in working memory, alertness, divided attention, and verbal fluency on a weekly basis. Trajectories were modeled using Generalized Additive Mixed Models to account for temporally limited transfer effects and intra-individual variation in cognitive performance. Our results provide no evidence of cognitive improvement differing between the Spanish and either of the control groups during any phase of the training period. We did, however, observe an effect of baseline cognition, such that individuals with low cognitive baselines increased their performance more in the L2 group than comparable individuals in the control groups. We discuss these findings against the backdrop of the cognitive training literature and Complex Dynamic Systems Theory.

Spatiotemporal Variations in Meteorological Disasters and Vulnerability in China During 2001–2020

Based on the Yearbook of Meteorological Disasters in China, we analyzed the spatiotemporal variations in major meteorological disaster (MD) losses at the provincial scale during 2001–2020 to determine the spatiotemporal variations in MDs and vulnerability in China. Our results suggest that the impacts of MDs, including floods, droughts, hail and strong winds (HSs), low temperature and frosts (LTFs), and typhoons, have been substantial in China. MDs in China affect an average of 316.3 million people and 34.3 million hectares of crops each year, causing 1,739 deaths and costing 372.3 billion yuan in direct economic losses (DELs). Floods and droughts affected more of the population in China than the other MDs. Fatalities and DELs were mainly caused by floods, and the affected crop area was mainly impacted by drought. The national average MD losses decreased significantly, except for DELs. The trends in the affected population and crop area were mainly caused by droughts, and the trends in fatalities and DELs were dominated by floods. Floods and typhoons showed increasing influence in the last two decades relative to other disasters. The annual mean and long-term trends in MD losses exhibited regional heterogeneity and were subject to different dominant hazards in different regions. The disaster losses and their trends in southeastern China were mainly attributed to typhoons. The affected population, crop area, and DELs were all significantly and positively correlated with exposure. The vulnerability of the population, crops, and economy tended to decrease. Economic development reduced the vulnerability of the population and economy but showed no significant influence on the vulnerability of crops. Our findings suggest that more focus should be placed on the impacts of floods and typhoons and that socioeconomic development has an important influence on the vulnerability of the population and economy. These results provide a foundation for designing effective disaster prevention and mitigation measures.

Instantaneous, Dual-Frequency, Multi-GNSS Precise RTK Positioning Using Google Pixel 4 and Samsung Galaxy S20 Smartphones for Zero and Short Baselines.

The recent development of the smartphone Global Navigation Satellite System (GNSS) chipsets, such as Broadcom BCM47755 and Qualcomm Snapdragon 855 embedded, makes instantaneous and cm level real-time kinematic (RTK) positioning possible with Android-based smartphones. In this contribution we investigate the instantaneous single-baseline RTK performance of Samsung Galaxy S20 and Google Pixel 4 (GP4) smartphones with such chipsets, while making use of dual-frequency L1 + L5 Global Positioning System (GPS), E1 + E5a Galileo, L1 + L5 Quasi-Zenith Satellite System (QZSS) and B1 BeiDou Navigation Satellite System (BDS) code and phase observations in Dunedin, New Zealand. The effects of locating the smartphones in an upright and lying down position were evaluated, and we show that the choice of smartphone configuration can affect the positioning performance even in a zero-baseline setup. In particular, we found non-zero mean and linear trends in the double-differenced carrier-phase residuals for one of the smartphone models when lying down, which become absent when in an upright position. This implies that the two assessed smartphones have different antenna gain pattern and antenna sensitivity to interferences. Finally, we demonstrate, for the first time, a near hundred percent (98.7% to 99.9%) instantaneous RTK integer least-squares success rate for one of the smartphone models and cm level positioning precision while using short-baseline experiments with internal and external antennas, respectively.

Efficient Iterative Timing Recovery of Low-Density Parity-Check Decoding Metrics Using the Steepest Descent Algorithm for Satellite Communications at Low SNRs

An efficient iterative timing recovery via steepest descent of low-density parity-check (LDPC) decoding metrics is presented. In the proposed algorithm, a more accurate symbol timing synchronization is achieved at a low signal-to-noise (SNR) without any pilot symbol by maximizing the sum of the square of all soft metrics in LDPC decoding. The principle of the above-proposed algorithm is analyzed theoretically with the evolution trend of the probability mean of the soft LDPC decoding metrics by the Gaussian approximation. In addition, an efficiently approximate gradient descent algorithm is adopted to obtain excellent timing recovery with rather low complexity and global convergence. Finally, a complete timing recovery is accomplished where the proposed scheme performs fine timing capture, followed by a traditional Mueller–Müller (M&M) timing recovery, which acquires timing track. Using the proposed iterative timing recovery method, the simulation results indicate that the performance of the LDPC coded binary phase shift keying (BPSK) scheme with rather large timing errors is just within 0.1 dB of the ideal code performance at the cost of some rational computation and storage. Therefore, the proposed iterative timing recovery can be efficiently applied on occasions of the weak signal timing synchronization in satellite communications and so on.