Diurnal Temperature Range Research Articles

Impacts of forest management on stand and landscape-level microclimate heterogeneity of European beech forests

ContextForest microclimate influences biodiversity and plays a crucial role in regulating forest ecosystem functions. It is modified by forest management as a result of changes in forest structure due to tree harvesting and thinning.ObjectivesHere, we investigate the impacts of even-aged and uneven-aged forest management on stand- and landscape-level heterogeneity of forest microclimates, in comparison with unmanaged, old-growth European beech forest.MethodsWe combined stand structural and topographical indices derived from airborne laser scanning with climate observations from 23 meteorological stations at permanent forest plots within the Hainich region, Germany. Based on a multiple linear regression model, we spatially interpolated the diurnal temperature range (DTR) as an indicator of forest microclimate across a 4338 ha section of the forest with 50 m spatial resolution. Microclimate heterogeneity was measured as α-, β-, and γ-diversity of thermal niches (i.e. DTR classes).ResultsEven-aged forests showed a higher γ-diversity of microclimates than uneven-aged and unmanaged forests. This was mainly due to a higher β-diversity resulting from the spatial coexistence of different forest developmental stages within the landscape. The greater structural complexity at the stand-level in uneven-aged stands did not increase α-diversity of microclimates. Predicted DTR was significantly lower and spatially more homogenous in unmanaged forest compared to both types of managed forest.ConclusionIf forest management aims at creating a wide range of habitats with different microclimates within a landscape, spatially co-existing types of differently managed and unmanaged forests should be considered, instead of focusing on a specific type of management, or setting aside forest reserves only.

A robust gap-filling approach for European Space Agency Climate Change Initiative (ESA CCI) soil moisture integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning

Abstract. Spatiotemporally continuous soil moisture (SM) data are increasingly in demand for ecological and hydrological research. Satellite remote sensing has potential for mapping SM, but the continuity of satellite-derived SM is hampered by data gaps resulting from inadequate satellite coverage, snow cover, frozen soil, radio-frequency interference, and so on. Therefore, we propose a new gap-filling approach to reconstruct daily SM time series using the European Space Agency Climate Change Initiative (ESA CCI). The developed approach integrates satellite observations, model-driven knowledge, and a machine learning algorithm that leverages both spatial and temporal domains. Taking SM in China as an example, the reconstructed SM showed high accuracy when validated against multiple sets of in situ measurements, with a root mean square error (RMSE) and a mean absolute error (MAE) of 0.09–0.14 and 0.07–0.13 cm3 cm−3, respectively. Further evaluation with a 10-fold cross-validation revealed median values of the coefficient of determination (R2), RMSE, and MAE of 0.56, 0.025, and 0.019 cm3 cm−3, respectively. The reconstructive performance was noticeably reduced both when excluding one explanatory variable and keeping the other variables unchanged and when removing the spatiotemporal domain strategy or the residual calibration procedure. In comparison with gap-filled SM data based on a satellite-derived diurnal temperature range (DTR), the gap-filled SM data from bias-corrected model-derived DTRs exhibited relatively lower accuracy but higher spatial coverage. Application of our gap-filling approach to long-term SM datasets (2005–2015) produced a promising result (R2=0.72). A more accurate trend was achieved relative to that of the original CCI SM when assessed with in situ measurements (i.e., 0.49 versus 0.28, respectively, in terms of R2). Our findings indicate the feasibility of integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning to fill gaps in short- and long-term SM time series, thereby providing a potential avenue for applications to similar studies.

Changes in Diurnal Temperature Range Over Nigeria from 1960 to 2019

Diurnal temperature range (DTR) is an important derived variable used in detecting signature of observed climate changes. Understanding its changes in recent time is important for managing and coping with climate change induced risks. This study analysed the long term trend and abrupt changes in diurnal temperature range over Nigeria from 1960 to 2019. Descriptive statistics, Mann-Kendall trend test, Sen’s slope and Pettit’s tests were used to assess the characteristics, trend, abrupt change and significance in annual DTR time series. Pearson correlation was used to examine the spatial relationship between DTR and minimum temperature, maximum temperature, precipitation and cloud cover. Results showed that mean DTR amount varied across spatio-temporal scales with steady increase from the coastal region, coupled with a weak inter-annual variability (CV < 3%). The trend analysis showed a significant decreasing DTR in most grid points (GP) and regions of Nigeria, with the exception of Enugu, Ado-Ekiti and Warri. Also a significant negative trend was observed at the Guinea, Sudan and Sahel savanna regions. Abrupt changes occurred in the 1970s in the entire regions and most of the GPs with Ado-Ekiti, Calabar, and Enugu occurred in 1991 while Maiduguri and Jos experienced such changes in 2011. Furthermore, significant abrupt changes were observed at the following GPs and regions; Ikeja, Kaduna, Kano, Katsina, Sokoto, Yelwa, Yola, Guinea, Sudan and Sahel Savannas. This decrease in DTR over most of the GP and the entire region indicate that the climate of Nigeria is becoming warmer possibly due to environmental changes with precipitation and cloud cover being the major drivers of DTR variability in Nigeria. Thus, proper climate actions should be taken through adaptation and resilient planning for the sustainable development of socio-economic and natural systems.

Dataset of temperature and precipitation over the major Belt and Road Initiative regions under different temperature rise scenarios

ABSTRACT Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems. In this study, we focus on the major Belt and Road Initiative (BRI) regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C, 2°C, and 3°C above pre-industrial levels under the Representative Concentration Pathway (RCP) 8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848° (~5 km) globally from EnviDat. The temperature variables include the daily maximum (Tmax), minimum (Tmin) and average (Tmp) surface air temperatures, and the diurnal temperature range (DTR). We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986–2005. The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986–2005. Furthermore, we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C, 2°C, and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean. Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions, and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions. The dataset is available at https://doi.org/10.57760/sciencedb.01850.

Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities

In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used to advance the estimation because it allowed for statistical analysis and comparison of climatological elements such time series, means, extremes, and trends. During these 69years, a more erratic DTR trend was seen in the research area. This study investigates the suitability of three deep neural networks for one-step-ahead DTR time series (DTRTS) forecasting, including recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and auto-regressive integrated moving average exogenous (ARIMAX). To evaluate the effectiveness of models in the testing set, six statistical error indicators, including root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), percent bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd), were chosen. The Wilson score approach was used to do a quantitative uncertainty analysis on the prediction error to forecast the outcome DTR. The findings show that the LSTM outperforms the other models in terms of its capacity to forget, remember, and update information. It is more accurate on datasets with longer sequences and displays noticeably more volatility throughout its gradient descent. The results of a sensitivity analysis on the LSTM model, which used RMSE values as an output and took into account different look-back periods, showed that the amount of history used to fit a time series forecast model had a direct impact on the model's performance. As a result, this model can be applied as a fresh, trustworthy deep learning method for DTRTS forecasting.

Diurnal temperature range impacts on outpatients department visits for allergic rhinitis in Lanzhou, China.

This study aims to investigate the association between the diurnal temperature range (DTR) and allergic rhinitis (AR) outpatient visits in Lanzhou, China, utilizing more than 7years of participant surveys. Our study used the distributed lag non-linear model (DLNM) aimed to evaluate the association between DTR and AR outpatient visits. We also performed subgroup analyses in order to find susceptible populations by gender and age groups. In 2013-2019, DTR in Lanzhou demonstrates a non-linear correlation with outpatient visits for AR, which is S-shaped. In addition, when DTR was located in the 0.9-5.3°C and 12-20°C compared with 12°C, the risk of outpatient visits for AR increased. Moreover, males appeared to be more vulnerable to the DTR effect than females, the risk of children visits exceeded both the adult and the elderly groups at the higher DTR. Our study adds to the evidence that DTR is a possible risk factor for outpatient visits for AR; therefore, the public health sector and medical staff should take DTR into account when it comes to preventing AR onset.

Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

Temperature rise is a concern for future agriculture in different regions of the globe. This study aimed to reveal the future changes and variabilities in minimum temperature (Tmin) and maximum temperature (Tmax) in the monthly, seasonal, and annual scale over Bangladesh using 40 General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 5 (CMIP5) for two radiative concentration pathways (RCPs, RCP4.5 and RCP8.5). The statistical downscaling climate model (SimCLIM) was used for downscaling and to ensemble temperature projections (Tmax and Tmin) for the near (2021–2060) and far (2071–2100) periods compared to the base period (1986–2005). Multi-model ensemble (MME) exhibited increasing Tmax and Tmin for all the timescales for all future periods and RCPs. Sen’s slope (SS) analysis showed the highest increase in Tmax and Tmin in February and relatively less increase in July and August. The mean annual Tmax over Bangladesh would increase by 0.61°C and 1.75°C in the near future and 0.91°C and 3.85°C in the far future, while the mean annual Tmin would rise by 0.65°C and 1.85°C in the near future and 0.96°C and 4.07°C in the far future, for RCP4.5 and RCP8.5, respectively. The northern and northwestern parts of the country would experience the highest rise in Tmax and Tmin, which have traditionally been exposed to temperature extremes. In contrast, the southeastern coastal region would experience the least rise in temperature. A higher increase in Tmin than Tmax was detected for all timescales, signifying a future decrease in the diurnal temperature range (DTR). The highest increase in Tmax and Tmin will be in winter compared to other seasons for both the periods and RCPs. The spatial variability of Tmax and Tmin changes can be useful for the long-term planning of the country.

Trend analysis and prediction of temperature change in the continental, Thailand

Diurnal Temperature Range (DTR) is one of global warming indicator which using only daily minimum temperature (TMin) and daily maximum temperature (TMax) for calculation. The study aims 1) to analyse trend of TMax, TMin and DTR over the early period of 21st century (1987 - 2020) and 2) to forecast and analyse trend of TMax, TMin and DTR over the mid and late period of 21st century (2021 - 2040 and 2041-2100) using MIROC5 (rcp85) model, cooperated with EQM Statistical downscaling method. TMax, TMin, and and DTR trends of all periods were investigated by Mann Kendall Trend Test and Sen?s slope, then presented in the form of spatial maps. The most of TMax and TMin trends for all season and all regions tended to be increasing from year by year with the large increasing of changing in Northern and Northeast region especially in Winter, except for the last period which had a large increasing in Summer. However, DTR trends were quietly fluctuated with the large decreasing of changing in Eastern and Southern part, but the trends become to increase in the last period. A large DTR decreasing of changing usually occurred in Winter and Rainy season. The TMax and TMin of all periods were expanding year by year and leading the DTR decreased in the first and second period, however, a changing of TMax and TMin rate leaded DTR increasing for the last period. It is a good signal for heat transfer performance which can help the earth cooling in night time.

Asian climate warming since 1901: observation and simulation

Land surface air temperature in Asia has been increasing significantly since the 1950s. However, current understanding of Asian warming since 1901 in terms of observations and simulations is still poor. Based on a newly developed observation dataset with 2658 stations and Coupled Model Intercomparison Project Phase 5/6 (CMIP5/6) output data, we analyze changes in mean (Tmean), maximum (Tmax), and minimum (Tmin) temperature, and diurnal temperature range (DTR) over Asia during 1901-2100. Annual mean land surface air temperature over Asia increased significantly, and Tmean, Tmax, and Tmin increased by 1.81, 1.47, and 2.15°C during 1901-2020, respectively. The Tmin warming rate is about 1.5× that of Tmax, resulting in a decline of Asian DTR by 0.68°C since 1901. We also found that Asia has experienced more substantial warming than the global case and the Northern Hemisphere, and the decline in DTR is more substantial in Asia. Spatially, Asia exhibits a general warming trend with a gradual increase in spatial warming from low to high latitudes, and the effect of high-latitude warming has gradually strengthened since the 2000s. Seasonally, Asian warming in the cold season is stronger than in the warm season. Furthermore, CMIP5/6 can capture the Asian warming in the historical period 1901-2020. However, it underestimated Tmin and overestimated Tmax, contributing to their poor performance in simulating the historical change of DTR. Under Shared Socioeconomic Pathway (SSP) 2-4.5, Asian Tmean, Tmax, and Tmin are projected to increase by 3.5, 3.4, and 3.7°C century-1 during 2021-2100, respectively. The Asian warming rate under SSP5-8.5 is about 2× that of SSP2-4.5.

Long-term changes in surface air temperature over the Chinese mainland during 1901-2020

The magnitude of long-term surface climate warming over some regions, such as the Chinese mainland, is still uncertain due to the lack of observational data early in the 20th century. In this study, the monthly data series of the average, maximum, and minimum temperatures in the Chinese mainland during 1901-2020 were constructed based on the daily surface air temperature observations from 60 stations across the country, and the characteristics of the average, maximum, and minimum temperature, and diurnal temperature range (DTR) changes were analyzed. Results show that (1) regional average annual mean temperature in the Chinese mainland rose by 0.14°C per decade, maximum temperature rose by 0.07°C per decade, minimum temperature rose by 0.19°C per decade, and DTR decreased by 0.13°C per decade. All these trends are statistically significant (p < 0.01); (2) the largest annual mean maximum temperature increase occurred in spring, followed by winter and autumn/summer, and the largest annual mean minimum temperature increase was in winter and spring, followed by autumn and summer; (3) annual mean DTR decreased significantly at a rate of -0.08, -0.12, -0.12, and -0.13°C per decade (p < 0.01) in spring, summer, autumn, and winter, respectively; (4) the stations with drops in maximum temperature were mainly in Central China, southern North China, the southeastern coastal areas, and the middle and lower reaches of the Yangtze River, and the stations with significant increases in minimum temperature were located in North China, Northeast China, and Northwest China; (5) the areas with the fastest dropping DTR were mainly located in Northeast China and North China. The maximum and minimum temperature series for China based on climate anomalies are comparable to those based on other currently available datasets.

The Influence of Local Rainy and Dry Seasons on the Diurnal Temperature Range in Nigeria

The Influence of Local Rainy and Dry Seasons on the Diurnal Temperature Range in Nigeria

Observed changes in temperature and precipitation over Asia, 1901-2020

Asia is the largest continent in the world and home to 4.7 billion people. Climate change on this continent, therefore, attracts a significant amount of attention from scientists and policy-makers. However, observational studies of long-term climate change over the continent as a whole are lacking. Using updated, homogenized observational data from stations in Asia since 1901 and systematic-bias-adjusted data from stations in China after 1950, we analyzed the long-term trends of surface air temperature (SAT) and precipitation in Asia and China from 1901-2020. The results showed that (1) in the 120 yr between 1901 and 2020, the annual mean SAT rose significantly at rates of 0.13 ± 0.01 and 0.14 ± 0.03°C decade-1 in Asia and China, respectively. The year 2020 in Asia may have been the warmest year since the beginning of the 20th century. (2) Since 1901, in both Asia and China, the annual mean minimum temperature increased more than twice as fast as the maximum temperature, and the diurnal temperature range (DTR) dropped significantly. (3) From 1901-2019, the annual precipitation anomaly percentage in Asia showed a significant increasing trend at an average rate of 0.52 ± 0.10% decade-1, and the increase was more obvious in high latitudes than in low to mid-latitudes. (4) Since 1901, there has been no significant change in annual precipitation in China, but there was a weak and non-significant decrease in the first half of the 20th century and a significant increase after the mid 20th century. The results presented in this paper can help us understand the spatio-temporal patterns and causes of climate change in the Asian continent and Chinese mainland.

Updated analysis of surface warming trends in North China based on in-depth homogenized data (1951-2020)

The reliability of climate change detection and research is significantly impacted by the inhomogeneity of surface climate observation data. However, there is an ongoing debate regarding whether comprehensive homogenization has been performed in large-scale homogenized data sets. In this study, we examined the homogeneity of the original maximum and minimum temperature (Tmax and Tmin) data for 662 meteorological stations in North China by using multiple methods and combining with metadata. The quantile matching method was employed to adjust the daily Tmax and Tmin series. In order to avoid the potential systematic bias resulting from homogenization, no reference series were introduced during the adjustment process. The adjustment results indicate that Tmin in North China is significantly affected by non-climatic factors, particularly station relocations and environmental changes around the stations. The application of homogenization in this study led to a notable increase in the overall temperature trends of the stations, with Tmin exhibiting a larger increase and the diurnal temperature range demonstrating a more significant downward trend. Based on the homogenized data, the annual and seasonal mean temperature trends in North China from 1951 to 2020 were re-evaluated. These temperature trends generally surpass those reported in previous research for the same period from 1961 to 2000. The higher estimate of temperature trends may be attributed to the recovered urbanization effect in the newly homogenized data. Thus, the obtained homogenization data still exhibit a significant urbanization bias that requires further assessment and adjustment.

The impact of meteorological factors on involuntary admission in Attica, Greece.

Few studies in the literature have examined the effect of meteorological factors, especially temperature, on psychiatric hospitalization and even less on their association with involuntary admission. This study aimed to investigate the potential association of meteorological factors with the involuntary psychiatric hospitalization in the region of Attica, Greece. The research was conducted at the Psychiatric Hospital of Attica "Dafni". This was a retrospective time series study of 8 consecutive years of data (2010 to 2017) and included 6887 involuntarily hospitalized patients. Data on daily meteorological parameters were provided from the National Observatory of Athens. Statistical analysis was based on Poisson or negative binomial regression models with adjusted standard errors. Analyses were initially based on univariable models for each meteorological factor separately. All meteorological factors were taken into account through factor analysis and then, through cluster analysis, an objective grouping of days with similar weather type was performed. The resulting types of days were examined for their effect on the daily number of involuntary hospitalizations. Increases in maximum temperature, in average wind speed and in minimum atmospheric pressure values were associated with an increase in the average number of involuntary hospitalizations per day. Increase of the maximum temperature above 23 °C at lag 6 days before admission did not affect significantly the frequency of involuntary hospitalizations. Low temperature and average relative humidity above 60% levels had a protective effect. The predominant day type at lag 1 to 5 days before admission showed the strongest correlation with the daily number of involuntary hospitalizations. The cold season day type, with lower temperatures and a small diurnal temperature range, northerly winds of moderate speed, high atmospheric pressure and almost no precipitation, was associated with the lowest frequency of involuntary hospitalizations, whereas the warm season day type, with low daily temperature and small daily temperature range during the warm season, high values of relative humidity and daily precipitation, moderate wind speed/gust and atmospheric pressure, was associated with the highest. As climate change increases the frequency of extreme weather events, it is necessary to develop a different organizational and administrative culture of mental health services.

LSTM model for predicting the daily number of asthma patients in Seoul, South Korea, using meteorological and air pollution data.

Asthma is a common respiratory disease that is affected by air pollutants and meteorological factors. In this study, we developed models that predict the daily number of patients receiving treatment for asthma using air pollution and meteorological data. A neural network with long short-term memory (LSTM) and fully connected (FC) layers was used. The daily number of asthma patients in the city of Seoul, the capital of South Korea, was collected from the National Health Insurance Service. The data from 2015 to 2018 were used as the training and validation datasets for model development. Unseen data from 2019 were used for testing. The daily number of asthma patients per 100,000 inhabitants was predicted. The LSTM-FC neural network model achieved a Pearson correlation coefficient of 0.984 (P < 0.001) and root mean square error of 3.472 between the predicted and original values on the unseen testing dataset. The factors that impacted the prediction were the number of asthma patients in the previous time step before the predicted date, type of day (regular day and day after a holiday), minimum temperature, SO2, daily changes in the amount of cloud, and daily changes in diurnal temperature range. We successfully developed a neural network that predicts the onset and exacerbation of asthma, and we identified the crucial influencing air pollutants and meteorological factors. This study will help us to establish appropriate measures according to the daily predicted number of asthma patients and reduce the daily onset and exacerbation of asthma in the susceptible population.

Spatial-Temporal Dynamics of Diurnal Temperature Range: Russian Far East as a Case Study

Short-term fluctuations in air temperature, called the daily temperature range (DTR), or its daily amplitude, have a strong impact on ecosystems, as well as on the health and well-being of people. The pronounced effect of DTR on mortality from all causes and especially on cardiovascular mortality is well documented in the scientific literature, but little is known about spatial, inter-annual, and inter-seasonal fluctuations of DTR in the Russian Far East (RFE), an area with high annual dynamics of air temperature. Data from 99 weather stations for the period from 2000 to 2019 was used to evaluate spatial and temporal DTR patterns at the RFE. A higher DTR, up to 16 °C in Toko on the border with Sakha (Yakutia) as a mean for the entire period, is typical for continental areas further to the north. Lower values are observed at continental weather stations in the south and in coastal climates (4.7 °C in Mys Alevina, Magadan Region). In general, a distinct seasonal change in DTR was found for continental locations with a significant difference from month to month throughout the year. On the annual cycle, the maximum DTR at the continental northern stations is observed in April and June–July, and at the continental southern areas in February; the minimum DTR is shown in November and December. The DTR ranges as much as from 6 °C in December to 17 °C in April in the continental Korkodon in the far north. Locations with a marine climate are characterized by a smoothed seasonal change in DTR, with obvious peaks in February–March and October, and a minimum in July–August. The downward trend in DTR for the period from 2000 to 2019, up to −0.7 °C in coastal Bolsheretsk, is based on a faster increase in the minimum daily temperature compared to the maximum, which is typical for most weather stations at the RFE and is known worldwide as a diurnal asymmetry of global warming. At the same time, an increase in the daily amplitude of air temperature (up to +0.6 °C in continental Dolinovka) was found for some localities, associated with a higher positive trend of maximum temperatures, which contradicts global patterns.

Long-Term Impacts of Diurnal Temperature Range on Mortality and Cardiovascular Disease: A Nationwide Prospective Cohort Study.

Previous studies have documented the associations between short-term diurnal temperature range (DTR) exposure and cardiovascular disease (CVD) via time-series analyses. However, the long-term impacts of DTR through a population-based prospective cohort have not been elucidated thoroughly. This study aimed to quantify the longitudinal association of DTR exposure with all-cause mortality and CVD in a nationwide prospective cohort and, by extension, project future DTR changes across China under climate change. We included 22,702 adults (median age 56.1 years, 53.7% women) free of CVD at baseline from a nationwide cross-sectional study in China during 2012-2015, and examined three health outcomes during a follow-up survey in 2018-2019. We estimated the chronic DTR exposure as baseline annual mean daily maximum minus minimum temperature. The Cox proportional hazards regression was adopted to assess the multivariable-adjusted hazard ratio and its corresponding 95% confidence interval (95% CI). We employed 31 downscaled global climate models under two shared socioeconomic pathways for future projection. During the median follow-up period of ~5 years, 1096 subjects died due to all causes while 993 and 597 individuals developed fatal or nonfatal CVD and fatal or nonfatal stroke, respectively. The cumulative incidence rates of all-cause mortality, CVD, and stroke were 10.49, 9.45, and 5.64 per 1000 person-years, respectively. In the fully adjusted models, the risks for all-cause mortality, CVD, and stroke would increase by 13% (95% CI: 8-18%), 12% (95% CI: 7-18%), and 9% (95% CI: 2-16%) per 1 °C increment in DTR, respectively. Moreover, linear positive associations for the concentration-response curves between DTR and mortality and CVD were observed. We also found significantly greater DTR-related mortality risks among rural residents than their urban counterparts. The DTR changes featured a dipole pattern across China under a warming climate. The southern (northern) China would experience increased (decreased) DTR exposure by the end of 21st century. The present study indicates that chronic DTR exposure can exert long-term impacts on mortality and CVD risks, which may inform future public health policies on DTR-related susceptible population and regions.

Projecting the excess mortality related to diurnal temperature range: A nationwide analysis in China

The projection of excess mortality due to diurnal temperature range (DTR) in future has not been evaluated yet in China. Based on daily cause-specific mortality data from 266 cities in China, this study aimed to examine the association between DTR and mortality, which help project the future mortality burden attributable to DTR by considering the modification effects of altitude and population migration. We first found that every 10 °C increase in the DTR would result in a 3.3 % (95 % confidence interval: 2.6 %–4.1 %) excess risk of non-accidental mortality. The unit risk of DTR-associated cause-specific mortality at moderate or high altitudes was significantly lower than at lower altitudes, especially for cardiovascular disease. Subsequently, DTR-associated excess mortality in 2017 in China was 233,154 deaths (with a population-weighted attributable fraction of 2.9 %). Furthermore, we projected DTR-attributable additional mortality in the future, with the associated mortalities to be 221,860 deaths in 2050–2059 (2050s) and 132,305 deaths in 2090–2099 (2090s), under the SSP1–2.6 scenario. Meanwhile, the regional inequalities were exacerbated by 18 % in 2050s and 13 % in 2090s when considering the modification effects of city altitude. Future population migration would increase excess mortality in most areas in central and southern China, and reduce the disease burden in most areas in eastern, western, and northern China. Our findings underpinned that regional strategies should be adopted to mitigate excess mortality attributable to global climate change.

Analysis of non-parametric trend and climatic parameter homogeneity tests in a data-scarce region: a spatio-temporal perspective in the Tawang River basin, Eastern Himalayas

This study is aimed at studying long–term historical and future (1950–2099) trends for the RCP 4.5 and RCP 8.5 on approximately 30-year timescale at annual and seasonal for precipitation and at annual, seasonal, monthly, and diurnal temperature ranges (DTR) for temperature maximum (T_max), temperature minimum (T_min) variations using statistical trend analysis techniques—Mann–Kendall test (MK) and Sen’s slope estimator (S) and the homogeneity test using Pettitt’s test. The study is carried out in three spatial points across the Tawang Chu in the district of Tawang, Arunachal Pradesh. The summer mean precipitation for RCP 4.5 (2006–2065) shows a positive trend with Z = 0.126 (2006–2035), Z = 0.205 (2036–2065) for point 1; Z = 0.080 (2006–2035), Z = 0.200 (2036–2065) at point 2 and Z = 0.048 (2006–2035), Z = 0.205 (2036–2065) at point 3, with a rise in precipitation between 1.56 and 9.94 mm in all the study points. The mean annual precipitation statistics for all the points show an increase for RCP 4.5 in 2006–2052 and 2053–2099 timescale. During the study, all points in both RCPs 4.5 and 8.5 display a uniform rise in mean annual T_min (Z = 0.260 to 0.738) and T_max (Z = 0.329 to 0.674). Still, the inter-decadal temperature statistical analysis shows that the increase in mean annual T_min is greater than the increase in T_max, indicating a decreasing trend in DTR. It is anticipated that this study’s outcomes will contribute to a better understanding of the relationship between change in climate and the regional hydrological behaviour. It can benefit the society to develop a regional strategy for water resource management and can serve as a resource for climate impact research scope- assessments, adaptation, mitigation, and disaster management strategies for India’s north-eastern region.

Corrigendum

Global Ecology and BiogeographyVolume 32, Issue 1 p. 191-192 CORRIGENDUMFree Access Corrigendum This article corrects the following: Plant mycorrhizal status, but not type, shifts with latitude and elevation in Europe C. Guillermo Bueno, Mari Moora, Maret Gerz, John Davison, Maarja Öpik, Meelis Pärtel, Aveliina Helm, Argo Ronk, Ingolf Kühn, Martin Zobel, Brody Sandel, Volume 26Issue 6Global Ecology and Biogeography pages: 690-699 First Published online: March 31, 2017 First published: 16 December 2022 https://doi.org/10.1111/geb.13606AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat In the article by Bueno et al. (2017) entitled, “Plant mycorrhizal status, but not type, shifts with latitude and elevation in Europe”, which was published in Global Ecology and Biogeography 26(6) (pages 690–699), contained errors in Figures 1e, 3a, and 3c. The corrected versions are provided below: Figure 1e. The maximum value of the legend was corrected. Figure 3a, The symbols in the plot were fixed to match the ones in the legend. Figure 3c. The order of the legend elements and the colour code were corrected to match the presentation of the same categories in other figures in the paper. Figure 1e. The share of obligately mycorrhizal (OM) plants in 50 km × 50 km grid cells in Europe. Values in legend represent the respective maximum, median and minimum proportions. ‘M' stands for mycorrhizal plant species. Figure 3a. Model averaged (ΔAIC <7) predictor coefficients with one confidence interval describing the proportion of plant species with plant mycorrhizal types in Europe. AM, arbuscular mycorrhizal; ECM, ectomycorrhizal; ERM, ericoid mycorrhizal; NM, non-mycorrhizal; NPP, net primary productivity; MAT(r), residuals of mean annual temperature excluding the effect of pH and NPP; MDR, mean diurnal temperature range; APP, annual precipitation; PPS, precipitation seasonality. Figure 3c. Model averaged (ΔAIC <7) variable importance plots describing the proportion of plant species with plant mycorrhizal statuses in Europe. The importance for each variable was calculated as the sum of the Akaike weights for all the models where each particular variable appears. Variable importance is used to rank the relevance of the predictors, as it indicates the relative probability of each variable belonging to the best model set. AM, arbuscular mycorrhizal; ECM, ectomycorrhizal; ERM, ericoid mycorrhizal; NM, non-mycorrhizal; OM, obligate mycorrhizal; NPP, net primary productivity; MAT(r), residuals of mean annual temperature excluding the effect of pH and NPP; MDR, mean diurnal temperature range; APP, annual precipitation; PPS, precipitation seasonality. REFERENCE Bueno, C. G., Moora, M., Gerz, M., Davison, J., Öpik, M., Pärtel, M., Helm, A., Ronk, A., Kühn, I., & Zobel, M. (2017). Plant mycorrhizal status, but not type, shifts with latitude and elevation in Europe. Global Ecology and Biogeography, 26, 690– 699. https://doi.org/10.1111/geb.12582 Volume32, Issue1January 2023Pages 191-192 ReferencesRelatedInformation