Lag Correlation Research Articles

Temporal Associations Between Polarimetric Updraft Proxies and Signatures of Inflow and Hail in Supercells

Recurring polarimetric radar signatures in supercells include deep and persistent differential reflectivity (ZDR) columns, hail inferred in low-level scans, and the ZDR arc signature. Prior investigations of supercell polarimetric signatures reveal positive correlations between the ZDR column depth and cross-sectional area and quantitative characteristics of the radar reflectivity field. This study expands upon prior work by examining temporal associations between supercell polarimetric radar signatures, incorporating a dataset of relatively discrete, right-moving supercells from the continental United States observed by the Weather Surveillance Radar 1988-Doppler (WSR-88D) network. Cross-correlation coefficients were calculated between the ZDR column area and depth and the base-scan hail area, ZDR arc area, and mean ZDR arc value. These correlation values were computed with a positive and negative lag time of up to 45 min. Results of the lag correlation analysis are consistent with prior observations indicative of storm cycling, including temporal associations between ZDR columns and inferred hail signatures/ZDR arcs in both tornadic and nontornadic supercells, but were most pronounced in tornadic storms.

Evaluation of wastewater percent positive for assessing epidemic trends - A case study of COVID-19 in Shangrao, China

ObjectiveThis study aims to assess the feasibility of evaluating the COVID-19 epidemic trend through monitoring the positive percentage of SARS-CoV-19 RNA in wastewater. MethodThe study collected data from January to August 2023, including the number of reported cases, the positive ratio of nucleic acid samples in sentinel hospitals, the incidence rate of influenza-like symptoms in students, and the positive ratio of wastewater samples in different counties and districts in Shangrao City. Wastewater samples were obtained through grabbing and laboratory testing was completed within 24 h. The data were then normalized using Z-score normalization and analyzed for lag time and correlation using the xcorr function and Spearman correlation coefficient. ResultsA total of 2797 wastewater samples were collected. The wastewater monitoring study, based on sampling point distribution, was divided into two phases. Wuyuan County consistently showed high levels of positive ratio in wastewater samples in both phases, reaching peak values of 91.67% and 100% respectively. The lag time analysis results indicated that the peak positive ratio in all wastewater samples in Shangrao City appeared around 2 weeks later compared to the other three indicators. The correlation analysis revealed a strong linear correlation across all four types of data, with Spearman correlation coefficients ranging from 0.783 to 0.977, all of which were statistically significant. ConclusionThe positive ratio of all wastewater samples in Shangrao City accurately reflected the COVID-19 epidemic trend from January to August 2023. This study confirmed the lag effect of wastewater percent positive and its strong correlation with the reported incidence rate and the positive ratio of nucleic acid samples in sentinel hospitals, supporting the use of wastewater percent positive monitoring as a supplementary tool for infectious disease surveillance in the regions with limited resources.

Response of bottom dissolved oxygen reduction to net ecosystem production observed by a wave-driven profiler in the Changjiang River Plume

Coastal hypoxia, exacerbated by the combined influence of eutrophication and global warming, presents a significant environmental challenge. However, the lag correlation between organic matter (OM) export from the upper layers and bottom dissolved oxygen (DOBOT) reduction still lack clear elucidation. This study investigated the coupling between net ecosystem production (NEP, representing the maximum OM export) and DOBOT in the Changjiang River plume (CRP), using a wave-driven profiler system. The high-resolution profiles revealed rhythmic fluctuations in water column NEP, with sediment-water exchange (−74.6%) and NEP (−4.0%) dominating DOBOT reduction. Notably, surface NEP impacts DOBOT with a lag time of 25.65 h, indicating an OM sinking speed of 1.32 mm s−1. NEP at a depth of 3.4 m exerted the most significant influence on DOBOT, explaining a 12% reduction. These findings elucidate the response mechanism of DOBOT reduction to upper OM export and provide insights for hypoxia prediction in coastal and estuarine areas.

Distinctive water bodies surrounding lakes: An effective indicator for drought monitoring and assessment

The response mechanisms of surface water to drought and the potential and performance of water body changes in drought monitoring and assessment remain insufficiently elucidated. We take the Poyang Lake Basin, which suffers from drastic water body changes and frequent droughts, as a representative. Through integrating multisource data to extract long-term precise water bodies, we construct standardized water body area/number anomaly indices (SAAI/SNAI). Subsequently, we quantify the correlation and time lag response between SAAI, SNAI and drought. Our findings reveal that overall, water body area and number in the Poyang Lake Basin all exhibit a significant correlation with typical drought indices. Among them, the large water body area has a higher correlation coefficient with drought, making it a more effective indicator for drought monitoring. Water body changes of Poyang Lake surrounding area offer a more precise reflection of drought conditions than other sub-basins and exhibit greater sensitivity to drought. Meanwhile, water body area changes in Poyang Lake surrounding area lag behind meteorological drought by approximately half a month. And the basin’s water body area can typically respond with changes to some prolonged and severe hydrological droughts about 1 to 2 months in advance. We propose an integrated mechanism for drought monitoring and assessment informed by these conclusions and use actual drought events for qualitative validation. The results indicate that it is possible to assess water body drought conditions for the next approximately half month based on current meteorological drought conditions. Also, combining water body area changes with meteorological drought severity can aid in evaluating hydrological drought conditions for the following about 1–2 months. We extend our investigation to explore the universality of these phenomenon across eight shallow lakes globally characterized by significant water fluctuations. The results reveal that the water body changes in most of these lakes exhibit good potential for drought monitoring and assessment, and present a strong consistency with deep soil moisture, allowing for the accurate reflection of deep soil drought conditions. The lag response patterns between water bodies and drought in Dongting Lake, Hongze Lake, and Tonlé Sap Lake—also situated in the monsoon climate zone—are more similar to those observed in Poyang Lake. These distinctive lake water bodies can serve as an innovative indicator for drought monitoring and assessment, providing potential support for endeavors in drought prevention and mitigation.

The Recent Increase in Invasive Bacterial Infections: A Report From the National COVID Cohort Collaborative

Background: When coronavirus disease 2019 (COVID-19) mitigation efforts waned, viral respiratory infections (VRIs) surged, potentially increasing the risk of postviral invasive bacterial infections (IBIs). We sought to evaluate the change in epidemiology and relationships between specific VRIs and IBIs [complicated pneumonia, complicated sinusitis and invasive group A streptococcus (iGAS)] over time using the National COVID Cohort Collaborative (N3C) dataset. Methods: We performed a secondary analysis of all prospectively collected pediatric (<19 years old) and adult encounters at 58 N3C institutions, stratified by era: pre-pandemic (January 1, 2018, to February 28, 2020) versus pandemic (March 1, 2020, to June 1, 2023). We compared the characteristics and outcomes of patients with prespecified VRIs and IBIs, including correlation between VRI cases and subsequent IBI cases. Results: We identified 965,777 pediatric and 9,336,737 adult hospitalizations. Compared with pre-pandemic, pandemic-era children demonstrated higher mean monthly cases of adenovirus (121 vs. 79.1), iGAS (5.8 vs. 3.3), complicated pneumonia (282 vs. 178) and complicated sinusitis (29.8 vs. 16.3), P < 0.005 for all. Among pandemic-era children, peak correlation between RSV cases and subsequent complicated sinusitis cases occurred with a 60-day lag (correlation coefficient 0.56, 95% confidence interval: 0.52–0.59, P < 0.001) while peak correlation between influenza and complicated sinusitis occurred with a 33-day lag (0.55, 0.51–0.58, P < 0.001). Correlation among other VRI–IBI pairs was modest during the pandemic and often lower than during the pre-pandemic era. Conclusions: Since COVID-19 emerged, mean monthly cases of iGAS, complicated pneumonia, and complicated sinusitis have been higher. Pandemic-era RSV and influenza cases were correlated with subsequent cases of complicated sinusitis in children. However, many other VRI–IBI correlations decreased during the pandemic.

Application of GRACE-based satellite estimates in the assessment of flood potential: A case study of Gangetic-Brahmaputra basin, India.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

The mesolimbic system and the loss of higher order network features in schizophrenia when learning without reward.

Schizophrenia is characterized by a loss of network features between cognition and reward sub-circuits (notably involving the mesolimbic system), and this loss may explain deficits in learning and cognition. Learning in schizophrenia has typically been studied with tasks that include reward related contingencies, but recent theoretical models have argued that a loss of network features should be seen even when learning without reward. We tested this model using a learning paradigm that required participants to learn without reward or feedback. We used a novel method for capturing higher order network features, to demonstrate that the mesolimbic system is heavily implicated in the loss of network features in schizophrenia, even when learning without reward. fMRI data (Siemens Verio 3T) were acquired in a group of schizophrenia patients and controls (n=78; 46 SCZ, 18 ≤ Age ≤ 50) while participants engaged in associative learning without reward-related contingencies. The task was divided into task-active conditions for encoding (of associations) and cued-retrieval (where the cue was to be used to retrieve the associated memoranda). No feedback was provided during retrieval. From the fMRI time series data, network features were defined as follows: First, for each condition of the task, we estimated 2nd order undirected functional connectivity for each participant (uFC, based on zero lag correlations between all pairs of regions). These conventional 2nd order features represent the task/condition evoked synchronization of activity between pairs of brain regions. Next, in each of the patient and control groups, the statistical relationship between all possible pairs of 2nd order features were computed. These higher order features represent the consistency between all possible pairs of 2nd order features in that group and embed within them the contributions of individual regions to such group structure. From the identified inter-group differences (SCZ ≠ HC) in higher order features, we quantified the respective contributions of individual brain regions. Two principal effects emerged: 1) SCZ were characterized by a massive loss of higher order features during multiple task conditions (encoding and retrieval of associations). 2) Nodes in the mesolimbic system were over-represented in the loss of higher order features in SCZ, and notably so during retrieval. Our analytical goals were linked to a recent circuit-based integrative model which argued that synergy between learning and reward circuits is lost in schizophrenia. The model's notable prediction was that such a loss would be observed even when patients learned without reward. Our results provide substantial support for these predictions where we observed a loss of network features between the brain's sub-circuits for a) learning (including the hippocampus and prefrontal cortex) and b) reward processing (specifically constituents of the mesolimbic system that included the ventral tegmental area and the nucleus accumbens. Our findings motivate a renewed appraisal of the relationship between reward and cognition in schizophrenia and we discuss their relevance for putative behavioral interventions.

Surface water dynamics of Lake Chad Basin (Sahelian Africa) based on daily temporal resolution Earth observation time series

ABSTRACT Water availability is vital for the sustenance of livelihoods in the Lake Chad Basin. However, the daily and seasonal dynamics of open water bodies are not well understood. This study aims to (1) analyze the daily and seasonal dynamics of water bodies, (2) estimate changes in surface water area extent including trends and change points, and (3) assess the connection between surface water extent and seasonal rainfall variation. To achieve this, we used the Global WaterPack and ERA5-Land daily aggregated datasets. We employed time series decomposition, trends analysis, and temporal lag correlation in our analysis. The results showed strong seasonal patterns of natural lakes compared to reservoirs/dams. Between 2003 and 2022, Lake Chad averaged 2,475.64 km2. The Northern pool of Lake Chad exhibited significant fluctuations, remaining below 600 km² between 2005 and 2012, from 2016 to 2019), with less than 350 km2 lasting only for a few days annually. The Southern pool averaged between 2,200 and 2,400 km2, except during drought years (2006–2007), specifically between the days of the year to approximately 66, and days 301–365/6. In Lake Fitri, the yearly maximum and minimum water extents were observed between days 1–59 and 305–365/6, and between days 60 and 304, respectively.

Relationships between water quality of a long-distance inter-basin water diversion project and air pollution emissions along the canal: Distributions, lag effects, and nonlinear responses

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators—permanganate index (CODMn), NH3-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)—and six air pollution indicators—O3, CO, NO2, SO2, 2.5 μm particulate matter (PM2.5), and inhalable particles (PM10)—using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann–Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for CODMn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO2, SO2, PM2.5, and PM10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for CODMn, NH3-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.

Effects of Meteorological Conditions in Summer and Early Autumn on PM2.5 and O3 Pollution in Beijing-Tianjin-Hebei and Surrounding Areas

PM2.5 pollution remains prominent in autumn, whereas O3 pollution gradually manifests in summer. To understand the dual high characteristics and meteorological effects of PM2.5 and O3 in the summer and early autumn of 2021 in the Beijing-Tianjin-Hebei and surrounding areas, the spatiotemporal distribution characteristics of PM2.5 and O3 concentrations, as well as meteorological conditions, subtropical high index, and weather situation in the Beijing-Tianjin-Hebei and surrounding areas were analyzed. The results showed that PM2.5 concentration and DPO3 （O3 daily maximum 8h mean minus O3 concentration at 06：00） from June to September 2021 decreased compared with those in the same period in 2020 and 2022, and high concentrations were mainly occurring in June. The overall PM2.5 concentration and DPO3 showed a gradual decrease from the middle to the north and south, with synchronous spatiotemporal changes. The hourly value of PM2.5 concentration presented a multimodal distribution, reaching the peak at 07：00-10：00 and 22：00-24：00. O3 concentration showed an opposite trend of change with PM2.5 concentration, reaching their peak from 14：00-16：00. When DPO3 and the concentration of PM2.5 were high, the characteristics of near-surface meteorological elements were characterized by temperatures ranging from 24.0-28.0℃, relative humidity concentrated at 58.4%-76.3%, and wind speeds ranging from 1.5-3 m·s-1. There was a high lag correlation between the subtropical high index and DPO3. When the subtropical high was farther and stronger from the Beijing-Tianjin-Hebei and surrounding areas, DPO3 was higher. The major weather patterns with both high PM2.5 and O3 concentrations in the study area were near surface low-pressure front, high-pressure type, and frontal type. The high altitude was controlled by high-pressure ridges, and the subtropical high ridge line was stable between 21°-28°N.

The wildfire impacts of the 2017-2018 precipitation whiplash event across the Southern Great Plains

The Southern Great Plains of the United States is a region with a sharp zonal precipitation gradient that is prone to rapid transitions in precipitation extremes. Transitions from pluvial to drought conditions can lead to the green-up of vegetation during extreme rainfall, posing a considerable fire risk as the region rapidly transitions into drought. Such transitions have been studied in depth across regions such as California; however, limited studies have examined their impacts across the Southern Great Plains. The aim of this study was to examine the role of preceding precipitation whiplash events in providing fuel for wildfires, with 2017–2018 investigated as a case study. This study specifically demonstrates the relationship between precipitation, vegetation, and wildfires for the first time across the Southern Great Plains. Lag correlation analysis of historical data at our study site showed anomalously high precipitation 8 months prior to Spring wildfires, resulting in a significantly higher number of wildfires and acres burned. In particular, this study examined a highly impactful precipitation whiplash event that occurred during the Fall of 2017 across the Oklahoma and Texas panhandles, which preceded a mega-fire event in the Spring of 2018. Precipitation anomalies that were 137% of normal during the 2017 growing season rapidly cascaded into drought conditions with precipitation anomalies 21% of normal throughout the cool winter season. Excessive precipitation supported vigorous vegetation recovery and growth, with vegetation indices peaking at approximately 1 standard deviation above average during August 2017. However, the subsequent drought period rapidly desiccated the terrestrial surface. As a result, dozens of wildfires burned a total of 556 347 acres during March and April 2018, resulting in at least two fatalities, dozens of homes destroyed, and over 500 personnel dispatched to fight and mitigate the fires. Overall, this study highlights the significant role of preceding Fall precipitation whiplash events in fueling Spring wildfires across the Southern Great Plains, particularly exemplified by the impactful 2017–2018 case, highlighting the complex dynamics between extreme precipitation, vegetation growth, and subsequent fire risks in the region.

Comparative analysis of the relationship between leukemia trends with forest fires and solar activity in different age groups

BACKGROUND: Studying the etiology of leukemia is necessary to develop measures to prevent this pathology. AIM: Conduct a comparative analysis of the relationship between trends in the incidence of leukemia with forest fires and solar activity in different age groups. MATERIAL AND METHODS: Information on the incidence of leukemia in Russia in 1990–2019 was provided by the Moscow Research Oncological Institute named after P.A. Hertsen. Data on solar activity (Wolf numbers) and the number of forest fires were taken from open sources. A Pearson correlation analysis of dynamic series of environmental factors and the incidence of leukemia was carried out in 11 iterations with a time delay (lag) of 0–10 years. The obtained data was compared with similar information for the regions of Russia, the USA and Canada. RESULTS: A trend towards an increase in the incidence of leukemia in children and adults has been established in the populations of Russia, the USA and Canada. A relationship between the number of forest fires and the incidence of leukemia was found in 35 regions of Russia; the average correlation coefficient and lag were comparable to those previously identified in the Khabarovsk Territory. The correlation of Wolf numbers with the frequency of leukemia has been established in pediatric and full-age populations of Russia, Canada and the USA. In the full-age population of Russia, a tendency towards an increased connection between the frequency of leukemia and solar activity was revealed: in 1990–1999, the correlation was 0.697; in 2000–2009 it increased to 0.815; in 2010–2019 reached a very strong level (0.920), while the lag decreased from 6 to 4 years. CONCLUSION: Fluctuations in the incidence of leukemia in all age groups in Russia correlate with the number of forest fires and solar activity.

The Impact of Climate Change and Natural Disasters on Pakistan's GDP Growth: An ARDL Analysis

The main objective of this study is to determine the impact of climate change and natural disasters on GDP growth rate. In order to examine long run association between climate change, natural disasters and GDP, an Auto Regressive Distributed Lag (ARDL) model and correlation matrix were employed, using 20 years data spanning from 2001 to 2021. The focus of the study was to investigate into how CO2 emission causes climate change and climate change, in turn, causes natural disasters and, in turn, natural disasters cause human and crop losses as well as high inflation. The empirical evidence reveals that climate change, CO2 emissions and natural disasters have negative association with GDP because natural disasters in the shape of earthquakes, heavy rains, and floods cause huge human and crop losses, have negative impact on GDP. The variables, agriculture production and employment, have positive and significant association with GDP growth because increase in agriculture production and employment will definitely have significant and positive impact on GDP growth in the long run. The study suggests to build resilient technologies to cope with climate change, control CO2 emissions and inflation as these three variables have negative impact on GDP growth in the long run. This study significantly contributes by highlighting the link between climate change and natural disasters and their consequential negative impact on GDP growth.

The role of upper-ocean heat content in the regional variability of Arctic sea ice at sub-seasonal timescales

Abstract. In recent decades, the Arctic Ocean has undergone changes associated with enhanced poleward inflow of Atlantic and Pacific waters and increased heat flux exchange with the atmosphere in seasonally ice-free regions. The associated changes in upper-ocean heat content can alter the exchange of energy at the ocean–ice interface. Yet, the role of ocean heat content in modulating Arctic sea ice variability at sub-seasonal timescales is still poorly documented. We analyze ocean heat transports and surface heat fluxes between 1980–2021 using two eddy-permitting global ocean reanalyses, C-GLORSv5 and ORAS5, to assess the surface energy budget of the Arctic Ocean and its regional seas. We then assess the role of upper-ocean heat content, computed in the surface mixed layer (Qml) and in the 0–300 m layer (Q300), as a sub-seasonal precursor of sea ice variability by means of lag correlations. Our results reveal that in the Pacific Arctic regions, sea ice variability in autumn is linked with Qml anomalies leading by 1 to 3 months, and this relationship has strengthened in the Laptev and East Siberian seas during 2001–2021 relative to 1980–2000, primarily due to reduced surface heat loss since the mid-2000s. Q300 anomalies act as a precursor for wintertime sea ice variability in the Barents and Kara seas, with considerable strengthening and expansion of this link from 1980–2000 and 2001–2021 in both reanalyses. Our results highlight the role played by upper-ocean heat content in modulating the interannual variability of Arctic sea ice at sub-seasonal timescales. Heat stored in the ocean has important implications for the predictability of sea ice, calling for improvements in forecast initialization and a focus upon regional predictions in the Arctic region.

Impact of climatic anomalies and reservoir induced seismicity on earthquake generation using Federated Learning

In this article, impact of climatic anomalies and artificial hydraulic loading on earthquake generation has been studied using federated learning (FL) technique and a model for the prediction of earthquake has been proposed. Federated Learning being one of the most recent techniques of machine learning (ML) guarantees that the proposed model possesses the intrinsic ability to handle all concerns related to data involving data privacy, data availability, data security, and network latency glitches involved in earthquake prediction by restricting data transmission to the network during different stages of model training. The main objective of this study is to determine the impact of artificial stresses and climatic anomalies on increase and decrease in regional seismicity. Experimental verification of proposed model has been carried out within 100 km radial area from 34.708o N, 72.5478o E in Western Himalayan region. Regional data of atmospheric temperature, air pressure, rainfall, water level of reservoir and seismicity has been collected on hourly bases from 1985 till 2022. In this research, four client stations at different points within the selected area have been established to train local models by calculating time lag correlation between multiple data parameters. These local models are transmitted to central server where global model is trained for generating earthquake alert with ten days lead time alarming a specific client that reported high correlation among all selected parameters about expected earthquake.

Impact of permafrost degradation on the extreme increase of dissolved iron concentration in the Amur river during 1995–1997

Primary production in the Sea of Okhotsk is largely supported by dissolved iron (dFe) transported by the Amur river, indicating the importance of dFe discharge from terrestrial environments. However, little is known about the mechanisms of dFe discharge into the Amur river, especially in terms of long-term change in dFe concentration. In the Amur river, extreme increase in dFe concentration was observed between 1995 and 1997, the cause of which remains unclear. As a cause of this iron anomaly, we considered the impact of permafrost degradation. To link the permafrost degradation to long-term variation in dFe concentration, we examined the changes in annual air temperature (Ta), accumulated temperature (AT), and net precipitation for three regions (northeast, south, and northwest) of the basin between 1960 and 2006. Ta and AT were relatively high in one out of every few years, and were especially high during 1988–1990 continuously. Net precipitation in late summer (July to September) has increased since 1977 and has stayed positive until 2006 throughout the basin. Most importantly, we found significant correlations between Ta and late summer dFe concentration with a 7-year lag (r = 0.54–0.69, p < 0.01), which indicate a close relationship between high Ta in year Y and increased late summer dFe concentration in year Y + 7. This correlation was the strongest in northeastern Amur basin where permafrost coverage is the highest. Similar 7-year lag correlation was also found between AT in the northeastern basin and late summer dFe concentration (r = 0.51, p < 0.01). Based on our findings, we propose the following hypothesis as a cause of iron anomaly. (1) Increased net precipitation since 1977 has increased soil moisture, which created suitable conditions for microbial dFe generation; (2) permafrost degradation during the warm years of 1988–1990 promoted iron bioavailability and led to the intensive dFe generation in the deeper part of the active layer; and (3) dFe took approximately 7 years to reach the rivers and extremely increased dFe concentration during 1995–1997. This is the first study to suggest the time-lagged impact of permafrost degradation on iron biogeochemistry in the Amur river basin.

Effect of climate change on the seasonal variation in photosynthetic and non-photosynthetic vegetation coverage in desert areas, Northwest China

Efficient prediction of the response of vegetation to future climate change requires an in depth understanding of the effect of climate change on the land cover by photosynthetic (fPV) and non-photosynthetic vegetation (fNPV) and its driving mechanism. Here, we aimed to estimate fPV and fNPV, and analyze the impacts of climate factors on their seasonal variability in the arid desert of northwestern China. First, optimal vegetation indices (VIs) were selected to estimate fPV and fNPV based on measured spectral reflectance, and the accuracy of estimated fPV and fNPV using Sentinel-2 images was evaluated with data from an unmannedaerialvehicle (UAV). Then, partial correlation, multi-correlation and time‐lag effect analyses were used to obtain the correlation coefficients and lag times between fPV, fNPV and climate change. The results showed that Normalized Difference Vegetation Index (NDVI) and Dead Fuel Index (DFI) were the optimal VIs to estimate fPV and fNPV in the desert, with the accuracy of 67 and 52 % for fPV and fNPV, respectively. The average fPV and fNPV were 8.15 and 9.26 % from March to November, their seasonal variability was consistent with plant phenology, and there was a decrease in fPV and fNPV from east to west with a decrease in precipitation. The variability in fPV was driven by precipitation while that of fNPV was dominated bytemperature. The time-lag of temperature and precipitation effect on fPV was 1.28 ± 1.28 and 1.61 ± 1.16 months, respectively, and on fNPV was 1.28 ± 1.2 and 1.19 ± 1.24 months, respectively. The time-lag of temperature-precipitation interaction on fPV and fNPV were 1.4 ± 1.22 and 1.2 ± 1.28 months, respectively. Accumulated precipitation had a positive effect on fPV and a negative effect on fNPV, while the impact of accumulated temperature was the opposite. We conclude that there were time-lag and cumulative effects of climate change on fPV and fNPV variability in desert areas. This study helps us better understand the climate-vegetation interactions and provides a scientific basis for desert vegetation management under climate change.

Analysis of Dual-Polarimetric Radar Observations of Precipitation Phase during Snowstorm Events in Jiangsu Province, China

Based on ground observed data, S-band dual-polarization radar data, and ERA-5 reanalysis data, the statistical characteristics of polarimetric parameters and the application of melting layer (ML) and hydrometeor classification (HCL) products during eight snowstorm events in Jiangsu Province from 2020 to 2022 were investigated. A heavy snowstorm that went through different phases of rain, sleet, and pure snow and that occurred on 29 December 2020 was also analyzed as a typical example. The results showed the following: During the phase transition between rain and snow in the Jiangsu region, the basic reflectivity factor ZH ≥ 27 dBZ, the zero-order lag correlation coefficient CC ≤ 0.93, and the differential reflectivity ZDR ≥ 1.0 dB were important indicators for judging the melting layer while the specific differential phase KDP changed slightly. The snowstorm event was well observed and recorded by the Yancheng dual-polarimetric radar, whose low value area of CC coincided mostly with the melting layer. The ML products and HCL products based on fuzzy-logic hydrometeor classification algorithms can help identify the melting layer and the properties of precipitation particles. ML products are more reliable when the melting layer is high and can better show the trends of melting layer decline. They can certainly serve as a reference for detecting and judging precipitation phase changes in winter in Jiangsu Province.

The Non-Agricultural Labour Productivity Effects of Working Time: South Africa’s Case

The purpose of the current study is to assess the impact of working time on non-agricultural labour productivity in the South African economy. To achieve this objective, the study utilised the Autoregressive Distributed Lag Model (ARDL) and correlation analysis to analyse the effects of different working time arrangements on labour productivity. The results of the study indicated that weekly working hours have a significant influence on worker productivity. Working between 30 and 39 hours per week was found to greatly enhance productivity both in the short and long term. Additionally, working between 15 and 29 hours per week showed a slight positive effect on productivity. Conversely, working more than 39 hours per week (between 40 and 45 hours) only increased productivity in the short term, while working less than 29 hours and exceeding 45 hours per week had a negative impact on labour productivity. These findings suggest that an optimal working schedule to maximise labour productivity is between 30 and 40 hours per week, with overtime not exceeding 5 hours per week. Establishing a proper schedule of weekly working hours is crucial for maximising labour productivity and reducing work-related stress, which can impede productivity.

Time Variability Correction of CMIP6 Climate Change Projections

AbstractAccurate projections of climate change and associated extreme events under differing emission scenarios are linked to realistic representations of the temporal variability of the atmosphere at a variety of time scales, for example, annual, seasonal, synoptic, and daily. Here a new method is employed to explicitly quantify a model's ability to accurately represent covariance at and between differing time scales. From our global‐scale analysis, on average, raw Coupled Model Intercomparison Project Phase 6 (CMIP6) models misrepresent temporal variances at differing time scales for maximum temperature (tasmax) by a considerable margin, particularly at 183‐, 92‐ and 46‐day time scales. To ameliorate such variability errors, we propose a novel Time Variability Correction (TVC) method that corrects temporal covariances while preserving the essential time‐event sequence of the model simulations. We adopt a model‐as‐truth framework to evaluate the effectiveness of the TVC method under future forcing conditions when applied to daily tasmax simulations from 23 CMIP6 models for 1% of the global grid cells. TVC‐corrected temperatures generally show improved matching of temporal variance and lag correlations in the out‐of‐sample projection period compared to simple mean‐corrected projections. By imparting more realistic temporal‐correlations to model series, TVC is expected to improve the projections of extreme events associated with persistent heat, such as heatwaves. Applying TVC to future temperature projections using actual observations significantly increases the temperature variance in most middle to high latitude land regions in the Northern Hemisphere, while decreasing it in most low to middle latitude land regions, compared to simple mean‐corrected projections.