Mean Monthly Temperature Research Articles

Seasonal Amphibian Species Richness and Population Fluctuations at Powdermill Nature Reserve, Pennsylvania, U.S.A.

Amphibians are a highly vulnerable taxonomic group that have suffered population declines worldwide. As amphibians serve critical links between trophic levels and facilitate nutrient recycling, it is critical to understand how their species richness, abundance, and phenology shift over time. Because amphibian populations naturally fluctuate, short-term monitoring can provide insight into how amphibian communities respond to proximate changes in weather conditions, which is helpful for predicting long-term responses to climate change. We used pitfall traps to examine interannual variation of the amphibian community at Powdermill Nature Reserve (PNR) in Pennsylvania during 2020–2021 to provide critical details for future long-term monitoring. Mean monthly temperature and precipitation did not vary significantly between years, but monthly captures per sampling effort declined and monthly biomass per sampling effort increased from 2020 to 2021. Despite a lack of significant relationships between total amphibian captures and temperature and precipitation, peak abundance shifted earlier, and individuals were larger in 2021 compared to 2020. Interestingly, individual species biomass was predicted by an interaction between temperature and precipitation, suggesting individual species will vary in vulnerability to climate change, but the amphibian community as a whole at PNR is resilient to minor fluctuations in temperature and precipitation.

Seasonal Variation of Peritonsillar Abscess Presentation to an Emergency Department in an Atlantic Canadian Climate: A Retrospective Review.

Patients with a peritonsillar abscess (PTA) often present to emergency departments as the first point of medical contact. Upper respiratory tract infections (URTIs) are more frequent in the winter. Therefore, we hypothesize that the incidence of PTAs will be more frequent in colder winter months as well. This is the first study assessing the seasonal variation and epidemiology of PTA presentations to an emergency department in Atlantic Canada, home to a unique maritime climate. A retrospective cohort study was conducted through a chart review of all patients who presented to the Saint John Regional Hospital Emergency Department from January 1, 2015, to December 31, 2020. Patient characteristics, treatment, and microbiology were reported. A chi-square goodness-of-fit test assessed the seasonal variation of PTA. Pearson correlations assessed PTA incidence per mean monthly temperature and humidity. A total of 75 patients were included. 57.3% were male and 42.7% were female, with a mean age (±SD) of 35.9 ± 14.0. Most patients presented afebrile (82.7%, cutoff ≥ 38.0°C). Approximately half of all patients had an elevated WBC count (49.3%, cutoff ≥ 10.9 × 109). The most common bacteria isolated were Streptococcus species followed by anaerobic bacteria (17.9%). No significant variation was found with respect to season (X2(3) = 1.0, P = .801), temperature (r(70) = 0.198, P = .096), or humidity, (r(70) = 0.063, P = .599). This study did not find a seasonal variation of PTA in a maritime climate. These findings question the anecdotal hypothesis that PTA is associated with progression from acute URTIs and therefore would be more common in the winter months.

Evolution of land surface feedbacks on extreme heat: Adapting existing coupling metrics to a changing climate

Episodes of extreme heat are increasing globally, and dry land surface states have been implicated as an amplifying factor in several recent heat waves. Metrics used to quantify land-heat coupling in the current climate, relating sensible heat fluxes to near-surface air temperature, are applied to multimodel simulations of the past, present, and future climate to investigate the evolving role of land–atmosphere feedbacks in cases of extreme heat. Two related metrics are used: one that describes the climatological state of land-heat coupling and one that gives an episodic estimate of land feedbacks, here defined as the metric’s value at the 90th percentile of monthly mean temperatures. To provide robust statistics, seasonal multimodel medians are calculated, with the significance of changes determined by the degree of model consensus on the sign of the change. The climatological land-heat coupling mirrors other metrics of land–atmosphere interaction, peaking in transition regions between arid and humid climates. Changes from preindustrial to recent historical conditions are dominated by decreased land surface controls on extreme heat, mainly over the broad areas that have experienced expanded or intensified agriculture over the last 150 years. Future projections for increased atmospheric CO2 concentrations show a waning of areas of weakened land-heat feedbacks, while areas of increasing feedbacks expand over monsoon regions and much of the midlatitudes. The episodic land-heat metric is based on anomalies, which creates a quandary: how should anomalies be defined in a nonstationary climate? When the episodic coupling is defined relative to the means and variances for each period, a broadly similar evolution to the climatological metric is found, with historically dominant decreases giving way to widespread moderate increases in future climate scenarios. Basing all statistics on preindustrial norms results in huge increases in the coupling metric, showing its sensitivity to the definition of anomalies. When the metric is reformulated to isolate the impact of changing land and temperature variability, the tropics and Western Europe emerge as regions with enhanced land feedbacks on heatwaves, while desert areas and much of the remainder of the midlatitudes show reduced land-heat coupling.

High‐resolution climate characteristics of the Arabian Gulf based on a validated regional reanalysis

AbstractThe regional climate of the Arabian Gulf (AG) and its variability are examined based on a 40‐year (1980–2019), 5‐km regional reanalysis of the Arabian Peninsula (AP reanalysis). The AP reanalysis fields were first validated against the available observations over the AG, suggesting that this high‐resolution reanalysis well reproduces the spatio‐temporal features of the AG atmospheric circulations. The validated AP reanalysis fields were then analysed to examine the climatic characteristics over the AG including the monthly mean, maximum and minimum temperatures, and the seasonal variations in winds, relative humidity and rainfall over the AG. The AG climate is mostly dry between May and October, and experiences moderate rainfall between December and January. The higher (lower) pressure difference between the northwest and southeast AG during summer (winter) generates the northwesterly Shamal winds over the north (central) AG. The mean Shamal winds are relatively stronger (weaker) and prolonged (shorter) during summer (winter); however, the short lived Shamal jet events in winter can be occasionally stronger than summer. In terms of interannual variability, the Shamal winds are stronger and more persistent in summer during El Niño years and in winter during La Niña years. These differences are mainly associated with changes in temperature gradients between the eastern AG and northwestern AP.

Circular Pattern of Temperature Changes and Its Effect on Workers’ Attitude (A Case Study of the Federal Polytechnic Offa)

This study is to determine the effect of temperature on workplace attitude, which led to the investigation of a circular pattern of temperature changes. The idea of time series data being presented in circular form since time itself rotates around the circle of a clock was the moving force in studying the pattern and behaviour of air temperature to identify peak periods. The data on air temperature (0C) in Offa was collected monthly from 2016 to 2020 from a reliable online source tcktcktck.org which tally with an available short period of temperature recorded by the Metrological Garden of Science Laboratory Department, The Federal Polytechnic Offa. While the data on workers’ attitudes was from a questionnaire with an 8.5 reliability index. The study adopted a descriptive approach in the first instance, where responses from the questionnaire were in tabular form and the monthly means temperature in a circular plot before the application of cosinor regression to the original monthly temperature data for further examinations. The result of the circular plot indicated seasonality in the data because the monthly mean temperature differs, making the plot not perfectly circular. The presence of seasonality noticed in the cosinor plot was confirmed in the cosinor regression analysis as being significant at a 5% level of significance. The temperature amplitude from 2016 to 2020 had its peaks in February when 50.9% to 56% of workers are likely to experience fatigue (exhausted). The analysis for 2016 indicates temperature amplitude in Offa at 2.30C which peaks in early February. While in 2020, the temperature amplitude of 3.80C peaks at the beginning of February. The increase between 2016 and 2020 is due to climate change. The government and other stakeholders should provide facilities to reduce workers’ fatigue and do more about climate change.

Spatial-Temporal Changes of Carbon Source/Sink in Terrestrial Vegetation Ecosystem and Response to Meteorological Factors in Yangtze River Delta Region (China)

As an important part and the core link of a terrestrial ecosystem, terrestrial vegetation is the main means for human to regulate climate and mitigate the increase in atmospheric CO2 concentration. The Yangtze River Delta (YRD) region is an urban agglomeration with the strongest comprehensive strength among developing countries (China). In the context of global climate change, a rapid, comprehensive, and detailed understanding of the spatio-temporal characteristics and variation tendency of the net ecosystem productivity (NEP) of vegetation and its response to climate during the rapid development of the YRD region is important for protecting ecological land, strengthening land management, and optimizing urban planning. The monthly mean temperature and rainfall data from 63 meteorological stations, the MODIS net primary productivity product, and a land cover product in the YRD region were used to estimate the NEP from 2000 to 2019 based on the soil respiration model, and the correlation between NEP and meteorological factors (such as temperature and rainfall) was analyzed. The results showed that: (1) From 2000 to 2019, the carbon sink area was much larger than the carbon source area in terrestrial vegetation in the Yangtze River Delta, the mean NEP of the vegetation ecosystem in the past 20 years was 253.2 g C·m−2·a−1, and the spatial distribution presented a trend that was higher in the south and lower in the north, higher in the east and lower in the west, and that gradually increased from northwest to southeast; moreover, the NEP of mountain areas was generally higher than that of river courses and urban surroundings. The interannual fluctuation of NEP was small, but presented a slightly increasing trend, and the interannual variation of NEP was significantly correlated with the maximum NEP in this region. (2) The carbon sink capacity of different vegetation cover types was (from strong to weak): forestlands > grasslands > wetlands ≈ croplands. (3) The area with the NEP change rate (θslope) > 0 accounted for 69.0%; however, there was certain spatial difference, the proportions of the areas with θslope < 0 were (from large to small) 14.50% (Zhejiang Province, China), 9.10% (Anhui Province, China), 6.65% (Jiangsu Province, China), and 0.79% (Shanghai, China). In terms of the individual changes of these provinces and municipalities, Shanghai > Zhejiang Province > Jiangsu Province ≈ Anhui Province. (4) There was a correlation between NEP and the annual mean temperature and annual precipitation in some regions.

Аssessment of the influence of physical factors of the external environment on the heat feeling of a human in the conditions of Kamchatka

OBJECTIVE: Characterize the climate impact on the population of Petropavlovsk-Kamchatsky regarding warmth sensation in different periods of the year.MATERIALS AND METHODS: The length of a warm and cold period of the year was determined. Based on mean monthly temperatures, relative humidity, wind speed, maximum wind and minimum temperature, taken from the website of weather condition records in Petropavlovsk-Kamchatsky for 2010–2020, the equivalent-effective temperature (EET) has been calculated that allows to estimate the warmth sensation of a man, stripped to the waist.RESULTS AND DISCUSSION: According to the average temperature, the warm period is in June-September, the cold one is 8 months. As for EET, thermal comfort is in July-August; it’s cool in June and September; it’s cool/moderately cool in October; it’s moderately cool in April-May and November; it’s very chilly in December-March. Speaking about minimum temperature and maximum wind, it’s warm in August, warm/cold in September; 10 months is a cold period of the year. By comparing EET, it’s cool in July and August; it’s moderately cool in May, June, September and October; it’s very chilly in April and November, moderately cold in December and March and cold in January. In February conditions are moderately cold/cold.CONCLUSION: Amidst high humidity the wind produces a meaningful effect on a bioclimatic discomfort.

Distribution characteristics of Spodoptera frugiperda (Lepidoptera: Noctuidae) winter populations in typical Mountain agricultural area of Southwest China

Fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae) is one of the most damaging crop pests worldwide and has become a significant threat to the food security in many countries. The Lancang River (Mekong) valley and Nujiang River (Salween) valley, which are typical mountain agricultural areas located in the Hindu Kush-Himalayan (HKH) mountain range, are the key channels for the south to north migration of FAW in southwestern China. The purpose of this study was to analyze the population structure and distribution of FAW winter populations in the Lancang River and Nujiang River basins and relate these findings with respect to altitude, latitude, and mean monthly winter temperature in order to explore the spatial and temporal distribution characteristics of the winter population. Our results indicated that overwintering populations of FAW mainly appeared in winter cornfields with a few pupae found in summer cornfields near the middle and lower reaches of the Lancang River basin (south 26.04° N and below 1600 m) and the Nujiang River basin (south of 26.80° N and below 1500 m). Winter corn appeared to be a prerequisite for the occurrence of FAW in winter. The mean monthly temperature of the coldest month being greater than 10 °C is one critical factor determining where FAW populations will occur in winter. Some control options that could be used in these river basins include adjusting crop planting patterns and developing a large-scale non-host isolation band.

Long‐term (2001–2020) trend analysis of temperature and rainfall and drought characteristics by in situ measurements at a tropical semi‐arid station from southern peninsular India

AbstractWith the effects of climate change prevailing worldwide, it is essential to understand the long‐term rainfall and temperature trends at regional scales to plan for adaptation strategies. Investigating the temporal dynamics of meteorological variables in climate‐induced changes, particularly in rain‐fed agriculture countries such as India, is needful. The present study focused on the temporal variation of rainfall and temperature trends to analyse the drought characteristics over the past 20 years (2001–2020) using ground‐based Automatic Weather Station (AWS) data at a semi‐arid station Anantapur in southern peninsular India. The seasonal maximum mean temperature (Tmean) was observed in summer (32.20 ± 1.36°C), followed by the monsoon (29.08 ± 1.02°C), postmonsoon (26.45 ± 2.44°C), and winter (25.45 ± 1.01°C) during the study period. The Mann–Kendall trend test result showed the decreasing trend in seasonal mean temperature during the drying period (winter [−0.069°C·year−1] and summer [−0.081°C·year−1]) and increasing trend in the wetting period (monsoon [0.012°C·year−1] and postmonsoon [0.034°C·year−1]). On the other hand, the seasonal rainfall decreases in the postmonsoon (−2.276 mm·year−1) while increasing in winter (0.5 mm·year−1), summer (2.137 mm·year−1), and monsoon (6.901 mm·year−1) seasons. The annual trends of minimum, maximum, and mean monthly temperatures decrease at the rate of −0.121, −0.123, and −0.022°C·year−1, respectively, while the rainfall increases at the rate of 8.979 mm·year−1. Furthermore, the temporal evolution of meteorological drought characteristics was estimated with the help of the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) on multiple timescales ranging from 1 to 24 months on a monthly basis during the study period. The 24‐month SPI values showed that 1 “severely dry” and the 29 “moderately dry” long‐term drought events were observed. From 24‐month SPEI, a total of 15 “severely dry” and 20 “moderately dry” long‐term droughts were observed during the study period. The severely dry years were observed in 2002 and 2003, while the wettest periods were 2007 and 2010. Both indices are identified as more severe dry periods from 2002 to 2006 and 2013 to 2017 than rest periods. Also, a significant decreasing tendency of droughts was observed in both indices. The SPI values are compared with El Niño–Southern Oscillation (ENSO) and normalized difference vegetation index (NDVI) and found that the SPI versus ENSO index negatively correlates with the coefficient of −0.58 significantly, while the SPI versus NDVI correlation is insignificant. The study region converted to wetter conditions during the last 20 years.

Noah‐MP With the Generic Crop Growth Model Gecros in the WRF Model: Effects of Dynamic Crop Growth on Land‐Atmosphere Interaction

AbstractIn this paper we coupled a crop growth model to the Weather Research and Forecasting model with its land surface model Noah‐MP and demonstrated the influence of the weather driven crop growth on land‐atmosphere (L‐A) feedback. An impact study was performed at the convection permitting scale of 3 km over Germany. While the leaf area index (LAI) in the control simulation was the same for all cropland grid cells, the inclusion of the crop growth model resulted in heterogeneous crop development with higher LAI and stronger seasonality. For the analyses of L‐A coupling, a two‐legged metric was applied based on soil moisture, latent heat flux and convective available potential energy. Weak atmospheric coupling is enhanced by the crop model, the terrestrial coupling determines the regions with the L‐A feedback. The inclusion of the crop model turns regions with no L‐A feedback on this path into regions with strong positive coupling. The number of non‐atmospherically controlled days between April and August is increased by 10–15 days in more than 50% of Germany. Our work shows that this impact results in a reduction of both cold bias and warm biases and thus improves the metrics of distributed added value of the monthly mean temperatures. The study confirms that the simulation of the weather driven annual phenological development of croplands for the regional climate simulations in mid‐latitudes is crucial due to the L‐A feedback processes and the currently observed and expected future change in phenological phases.

Changes in the means of temperatures and rainfall of the period 1981-2010 with respect to mean values of 1951-80 over the Indian stations

In this paper, the changes in monthly and annual means of maximum temperature, minimum temperature and rainfall at well distributed 178 stations of India for the period 1981-2010 as compared to the respective means of 1951-80, are determined and analysed. To test the significant differences at the station, Student’s t test has been applied.
 Increasing trend in monthly and annual mean maximum temperature is found at most places of southern and western regions of India. There is a decrease in maximum temperature in Indo-Gangetic Plains (IGP) region in January to June months. Significant rise in maximum temperature in all months except August is found at more places in southern part (south of 20° N) of India than northern part(north of 20° N). Significant fall in the temperature is found at a few places of the country. The same trend is found in annual maximum temperature. Minimum temperature has increased at most places of the country in all months except April to June where in it decreased over northern, central and eastern regions. During November to March months, minimum temperature increased more at most places of northern part of the country. Annual minimum temperature has increased at most places of the country except over UK, Significant fall in minimum temperature is observed at some places of the country.
 There is a less change in monthly rainfall all over the country. Annual rainfall slightly decreased at most places of the country except Meghalaya, WB and Kerala where it increased. There is a significant fall in annual rainfall at some places of the country.

Information entropy‐based projection of temperature variability under the warming trend in China

AbstractQuantifying the predictability of temperature in the future is a priority to inform policy and adaptation planning. Based on information entropy theory, this study uses global climate models within the Coupled Model Intercomparison Project Phase 6 (CMIP6) to project spatiotemporal changes and variability of temperature in China in the 21st century under different shared socio‐economic pathway scenarios (SSP2‐4.5 and SSP5‐8.5). Results show that the multimodel ensembled values can accurately capture the spatiotemporal pattern of temperature in China. The warming rates of annual mean temperature in China under SSP2‐4.5, SSP5‐8.5 scenarios are 0.42 and 0.61°C·decade−1 in the near‐term (2021–2050), 0.20 and 0.87°C·decade−1 in the long‐term (2071–2100), respectively. Warming is found to be the greatest in winter. Northeast, northwest, and the first region of southwest are anticipated to increase particularly rapidly, with larger increases than the rest of China. The intrayear variability of temperature (the variability of monthly mean temperature within the year or different seasons) in China shows obvious latitudinal differences. The variability of monthly mean temperature exhibits a decreasing trend in parts of northeast and the first region of southwest, and an increasing trend in northwest and parts of north. This is mainly resultant from the variability of monthly mean temperatures within spring and autumn. And this trend is more significant under the SSP5‐8.5 scenario than that under the SSP2‐4.5 scenario. The interannual variability of temperature (the variability of annual or seasonal mean temperature) in China shows significant regional difference, and two obvious regions with extreme variability (northeast and the first region of southwest). The trend of variability increases and then decreases under the SSP2‐4.5 scenario, whereas it keeps increasing under the SSP5‐8.5 scenario. This study helps to understand the spatiotemporal characteristics of temperature variability in China in the 21st century.

Groundwater Level Fluctuations in Coastal Aquifer: Using Artificial Neural Networks to Predict the Impacts of Climatical CMIP6 Scenarios

Groundwater resources play a crucial role in supplying water for domestic, industrial, and agricultural use. In this study ACCESS-CM2, HadGEM3-GC31-LL, and NESM3 were selected for validation from Coupled Model Intercomparison Project Phase 6 (CMIP6). In the following, the feedforward neural network was employed to predict monthly groundwater level (GWL) based on the emission scenarios of the sixth IPCC report (SSP2-4.5 and SSp5-8.5) for the next two decades (2021–2040) in the Sari-Neka coastal aquifer near the Caspian Sea, Iran. In this regard, the monthly maximum and minimum temperature, precipitation, and water table of previous month from four piezometers from 2000 to 2019 were used as input variables to forecast GWL. The evaluation of the three GCM models demonstrated that the ACCESS-CM2 provided the best values of the R2 and RMSE with observation parameters. The results of r, R2, RMSE, and MAE were evaluated for the model and indicated good performance of the model. The results also illustrated that under such mentioned scenarios, the mean monthly temperature would rise approximately from 0.1–1.2 °C. In addition, the mean monthly precipitation is likely to witness changes from -10% to 78% in the next two decades. As a result, this seems to lead to improvement and recharge of groundwater level for the near future. The results can help managers and policymakers to identify adaptation strategies more precisely for basins with similar climates.

Forty Years of Air Temperature Change over Iran Reveals Linear and Nonlinear Warming

Spatiotemporal analysis of long-term changes in air temperature is of prime importance for climate change research and the development of effective mitigation and adaptation strategies. Although there are considerable studies on air temperature change across the globe, most of them have been on linear trends and time series analysis of nonlinear trends have not received enough attention. Here, spatiotemporal patterns of monthly and annual mean (Tmean), maximum (Tmax), and minimum (Tmin) air temperature at 47 synoptic stations across climate zones in Iran for a 40-yr period (1978–2017) are analyzed. A polynomial fitting scheme (Polytrend) is used to both monthly and annual air temperature data to detect trends and classify them into linear and nonlinear (quadratic and cubic) categories. The significant (non-significant) trends in Tmean, Tmax, and Tmin across all climate zones are 41.1% (58.9%), 34.1% (65.9%), and 46% (54%), respectively. The highest magnitude of increasing trends is observed in the annual Tmin (0.47°C decade−1) and the lowest magnitude is for the annual Tmax (0.4°C decade−1). Across the country, increasing trends (bar x = 37.2% ) have higher spatial coverage than the decreasing trends (bar x = 3.2% ). Warming trends in Tmean (65.3%) and Tmin (73.1%) are mainly observed in humid climate zone while warming trends in Tmax are in semi-arid (43.9%) and arid (34.1%) climates. Linear change with a positive trend is predominant in all Tmean (56.7%), Tmax (67.8%), and Tmin (71.2%) and for both monthly and annual data. Further, the linear trends have the highest warming rate in annual Tmin (0.83°C decade−1) and Tmean (0.46°C decade−1) whereas the nonlinear trends have the highest warming rate in annual Tmax (0.52°C decade−1). The linear trend type is predominant across the country especially in humid climate zones whereas the nonlinear trends (quadratic and cubic) are mainly observed in the arid climate zones. This study highlights nonlinear changes and spatiotemporal trends in air temperature in Iran and contributes to a growing body of climate change literature that is necessary for the development of effective mitigation and adaptation strategies in the Middle East.

Influence of climatic factors on single-center peritoneal dialysis-associated peritonitis.

Peritoneal dialysis associated peritonitis (PDAP), one of the most common complications of peritoneal dialysis, is generally believed as an important factor causing technique failure and increasing hospital admission and fatality rate in peritoneal dialysis patients. Moreover, the prevention and treatment for PDAP have always been considered as an important link in clinical peritoneal dialysis. Thus, the study is performed to investigate the impact of climate factors on the incidence and prognosis of single-center PDAP in Hunan Province, China, which can provide a clinical basis for the prevention and treatment strategy for PDAP. A total of 885 patients on peritoneal dialysis, who were admitted to Department of Nephrology, Third Xiangya Hospital, Central South University (hereinafter referred to as our hospital) from January 1, 2009 to December 31, 2017, were enrolled as the subjects, and the demographic and clinical data for patients with PDAP from 2009 to 2017 as well as climate data of Hunan Province were collected.The patients were divided into different season groups, and the general information and clinical biochemical indicators in different season groups were compared. The incidence, the proportion of pathogenic bacteria and the clinical outcome of PDAP in the different season groups were compared. The risk factors of PDAP in different season groups were analyzed by using a multivariate logistic regression. Pearson's correlation analysis was used to analyze the correlations of the overall incidence of PDAP with the monthly mean temperature and the monthly mean humidity. There were 448 cases of PDAP that occurred in the peritoneal dialysis center of our hospital from 2009 to 2017 (9 years), the incidence was 0.17 time/patient-years. The incidence of overall PDAP (0.246 time/patient-years) and the incidence of Gram-negative (G-) bacterial PDAP (0.048 time/patient-years) in the summer group were the highest compared with the other season groups, with significant difference (all P<0.05). Compared with the spring group (OR=0.321, 95% CI 0.144-0.713; P<0.01), the autumn group (OR=0.419, 95% CI 0.187, 0.937; P<0.05) and the winter group (OR=0.326; 95% CI 0.139, 0.763; P<0.05), the probability suffered from PDAP in the summer group were higher. The patients with low body mass index (BMI) in the spring group were more likely to suffer from PDAP in the summer group. The overall incidences of PDAP (r=0.258, P<0.05) and G- bacterial PDAP (r=0.278, P<0.05) were significantly positively correlated with the monthly mean temperature in Hunan Province, which was not correlated with the monthly average humidity. The overall cure rate of PDAP was 83%, and there was no difference in the treatment outcomes among the different season groups (all P>0.05). The climate factors in Hunan, China have a significant impact on the incidence of PDAP in peritoneal dialysis patients in our hospital. Summer is the peak of PDAP, especially G- bacterial PDAP. Intestinal dysfunction and low BMI are risk factors for the onset of PDAP in summer. High temperature rather than high humidity is correlated with increased incidence of overall PDAP, especially for G- bacterial PDAP.

The Influence of Extratropical Weather Regimes on Wintertime Temperature Variations in the Arctic during 1979–2019

In this study, the Arctic sea ice cover in the sector 30° W–60° E in February, and the monthly mean temperature (averaged over the polar cap north of 70° N and 700–1000 hPa, Tcap) in winter during 1979–2019 were analyzed using established change-point detection methods. Step changes were found in 2004, with lower sea ice cover and higher air temperature during 2005–2019 than 1979–2004 (with Tcap anomalies of 1.05 K and −0.63 K, respectively). Two combinations of weather regimes were associated with the anomalously warm months (1.61 K): (1) Scandinavian trough and Ural blocking, and (2) Atlantic ridge and Ural blocking. The first causes a “polar express” for the poleward transport of heat and moisture from mid-latitude East Europe. The second causes a “two-stage heat pump” that transports heat and moisture from the subarctic Atlantic. Their opposite combinations were associated with the anomalously cold months (−0.73 K), which occurred more frequently during 1979–2004. These trends in weather regimes could account for 25% of the step-change in Arctic winter temperature, with the remainder likely caused by changes in sea ice cover, ocean heat transport, and concentrations of aerosol and greenhouse gases.

Evaluation of Long-Term and High-Resolution Gridded Precipitation and Temperature Products in the Qilian Mountains, Qinghai–Tibet Plateau

Long-term and high-resolution gridded products of precipitation and temperature data are highly important to study the changes in climate and environment under global warming. Considering the uncertainties of these products in mountainous areas, it is necessary to evaluate the data reliability. This study evaluates the performances of the CMFD (China Meteorological Forcing Dataset) and ERA5-Land in simulating precipitation and temperature in the Qilian Mountains over the period of 1980–2018. We use the observation data of 28 basic meteorological stations in the Qilian Mountains to compare with the reanalysis products. Error metrics (the correlation coefficient (CC), the root mean square error (RMSE), the mean absolute error (MAE), and the relative bias (BIAS)) are used to quantify the monthly differences in existence between the observed data and reanalysis data. Our findings indicate that both CMFD and ERA5-Land could well reproduce the spatial distribution of mean monthly precipitation and temperature in the region. A good correlation is found between CMFD and OBS under different amounts of monthly precipitation conditions. The monthly average temperatures of CMFD and ERA5-Land reveal a high correlation with the observed results. Moreover, the CC values of CMFD and ERA5-Land precipitation products are the highest in autumn and the lowest in winter, and the CC values of both CMFD and ERA5-Land temperature products are higher in spring and autumn. However, we find that both reanalysis products underestimate the temperature to varying degrees, and the amount of precipitation is overestimated by ERA5-Land. The results of the evaluation show that the errors in precipitation yielded by CMFD as a whole are distinctly fewer than those yielded by ERA5-Land, while the errors in air temperature yielded by both ERA5-Land and CMFD are nearly identical to each other. Overall, ERA5-Land is more suitable than CMFD for studying the trends of temperature changes in the Qilian Mountains. As for simulation of precipitation, CMFD performs better in the central and eastern parts of the Qilian Mountains, whereas ERA5-Land performs better in the western part of the Qilian Mountains.

Multifractality of the standardized precipitation index: influence of pan evaporation and virtual temperature-based potential evapotranspiration

Hydrological extremes experience an increase in some regions and a decrease in other zones. The objectives of the present work were (i) to introduce Class A pan evaporation data and virtual temperature-based potential evapotranspiration (PETv hereafter) into the Standardized Precipitation–Evapotranspiration Index (SPEIm hereafter) computation and (ii) to describe small and large fluctuations of SPI and SPEIm through multifractal detrended fluctuation (MF-DFA) and multifractal detrended cross-correlation (MF-DCCA) analyses. We used 40 years data (1974–2013) of monthly rainfall (P), mean, minimum and maximum air temperature, pan evaporation (E), relative humidity (RH) and relative sun brightness (RSB). Meteorological variables were collected from Puyo meteorological station, Pastaza Province, Ecuador. SPI time series for 1 and 6 months timescales were determined following two approaches. We computed SPI values using precipitation as the only input variable. Additionally, we incorporated pan evaporation and virtual temperature-based potential evapotranspiration into the standard SPEI computation (SPEIm). The SPEIm revealed some differences as compared with the classical SPI methodology. Five out of fifteen Asymmetry Index (AI) values were positive (0.095 ≤ AI ≤ 0.419).This indicates the relevance of high fluctuations at different time scales. Joint multifractal spectra between SPI (1,6)/SPEIm(1,6) versus RH and RSB rendered negative AI values which suggests the importance of low fluctuations at shorter time scales. The DCCA cross-correlation coefficient allows one to identify those time scales where SPI and SPEIm are influenced by other meteorological variables. Long-term correlation and sub-Gaussian behaviour of meteorological variables (apart from air temperature) are the main causes of multifractal structures.

Environmental Factors Affecting Near-Surface Oxygen Content Vary in Typical Regions of the Qinghai-Tibet Plateau

Hypoxia threatens the health of people in highland regions around the world. Recent studies have shown that altitude has a significant effect on near-surface oxygen content at broad geographic scales, and whether this effect is manifested at the landscape scale, particularly on the Qinghai-Tibetan Plateau (QTP) in China, has not been documented. We investigated the relationship between altitude, vegetation (Net Primary Productivity, NPP; Leaf Area Index, LAI; Fractional Vegetation Cover, FVC; Length of the Growing Season, LOS and Vegetation Type), climate variables (Mean Monthly Temperature, MMT; Mean Monthly Precipitation, MMP and Wind Speed, WS), and near-surface oxygen content in five typical regions of the QTP, including the Qilian Mountains (QLM), the Three-River Headwater Area (THA), Ngari Prefecture (NAP), Hengduan Mountains (HDM) and Yajiang Valley (YJV). The results showed that 1) altitude, NPP, FVC, LOS, MMP and MMT were important variables for predicting near-surface oxygen content; 2) the local-scale relationship between altitude and oxygen content exhibited negative or no correlations across broad regions; and 3) oxygen content exhibited a great deal of spatial heterogeneity, with altitude being the main affecting factor, followed by vegetation. Different drivers of near-surface oxygen content should be incorporated into Earth system models to reduce uncertainty in predicting oxygen dynamics and its potential feedback to global warming.

Variability in Growth Patterns and Tree-Ring Formation of East European Scots Pine (Pinus sylvestris L.) Provenances to Changing Climatic Conditions in Lithuania

An increase in the mean monthly temperature in July and a lack of precipitation during the vegetation period will cause an increase in the frequency and severity of droughts during the vegetation period in Lithuania. Thus, the aim of this study was to assess the growth response to climate change of East European pine provenances in Lithuania. The research was performed based on a long-term pine provenance experiment that was established in 1975 in Lithuania. The results showed that central populations demonstrated the same or better survival and growth results compared with western populations that also included local Lithuanian provenances. Even though southern populations were characterized by the same productivity, their low survival rate suggests a negative introduction effect. Analysis of temporal variation in climate sensitivity showed a higher resistance of central and southern Scots pine provenances to the negative climatic changes, compared to the western provenances. Provenances from the central, southern and especially western locations demonstrated an increasing statistically significant negative effect of monthly temperatures in July. Additionally, provenances from western locations were more sensitive to precipitation amounts, water balance and droughts in June and July. Considering that central and southern provenances demonstrated the consistent effect of growth tolerance, the incorporation of the genetic material from the southern part of European Russia and Ukraine into local breeding programs of Scots pine might amplify positive effects of the transfer. Moreover, knowledge gaps remain regarding the growth performance of Scots pine provenances that originated from central or western European regions. Thus, Scots pine origins from Poland and the northeastern part of Germany should be included in Lithuanian provenance experiments.