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When attempting to estimate the impacts of future climate change it is important to reflect on information gathered during the past. Understanding historical trends may also aid in the assessment of likely future agricultural and horticultural changes. The timing of agricultural activities, such as grape harvest dates, is known to be influenced by climate and weather. However, fewer studies have been carried out on grapevine yield and quality. In this paper an analysis is undertaken of long-term data from the period 1805-2010 on grapevine yield (hl/ha) and must sugar content (°Oe) and their relation to temperature. Monthly mean temperatures were obtained for the same time period. Multiple regression was used to relate the viticulture variables to temperature, and long-term trends were calculated. Overall, the observed trends over time are compatible with results from other long term studies. The findings confirm a relationship between yield, must sugar content and temperature data; increased temperatures were associated with higher yields and higher must sugar content. However, the potential increase in yield is currently limited by legislation, while must sugar content is likely to further increase with rising temperatures.

Heat waves are becoming more intense and extreme as a consequence of global warming. Epidemiological evidence reveals the health impacts of heat waves in mortality and morbidity outcomes, however, few studies have been conducted in tropical regions, which are characterized by high population density, low income and low health resources, and susceptible to the impacts of extreme heat on health. The aim of this paper is to estimate the effects of heat waves on cardiovascular and respiratory mortality in the city of Rio de Janeiro, Brazil, according to sex, age, and heat wave intensity. We carried out a time-stratified case-crossover study stratified by sex, age (0-64 and 65 or above), and by sex for the older group. Our analyses were restricted to the hot season. We included 42,926 participants, 29,442 of whom died from cardiovascular and 13,484 from respiratory disease, between 2012 and 2017. The death data were obtained from Rio de Janeiro's Municipal Health Department. We estimated individual-level exposure using the inverse distance weighted (IDW) method, with temperature and humidity data from 13 and 12 stations, respectively. We used five definitions of heat waves, based on temperature thresholds (90th, 92.5th, 95th, 97.5th, and 99th of individual daily mean temperature in the hot season over the study period) and a duration of two or more days. Conditional logistic regression combined with distributed lag non-linear models (DLNM) were used to estimate the short-term and delayed effects of heat waves on mortality over a lag period (5 days for cardiovascular and 10 for respiratory mortality). The models were controlled for daily mean absolute humidity and public holidays. The odds ratios (OR) increase as heat waves intensify, although some effect estimates are not statistically significant at 95% level when we applied the most stringent heat wave criteria. Although not statistically different, our central estimates suggest that the effects were greater for respiratory than cardiovascular mortality. Results stratified by sex and age were also not statistically different, but suggest that older people and women were more vulnerable to the effects of heat waves, although for some heat wave definitions, the OR for respiratory mortality were higher among the younger group. The results also indicate that older women are the most vulnerable to heat wave-related cardiovascular mortality. Our results show an increase in the risk of cardiovascular and respiratory mortality on heat wave days compared to non-heat wave ones. These effects increase with heat wave intensity, and evidence suggests that they were greater for respiratory mortality than cardiovascular mortality. Furthermore, the results also suggest that women and the elderly constitute the groups most vulnerable to heat waves.

This paper describes a publicly available, long-term (1915–2011), hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of 1 16 0 latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915–2011 versus 1950–2000), 2) increased the spatial resolution from ⅛° to 1 16 0 , and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community.

Over the past few decades, a growing body of epidemiological studies found the effects of temperature on cardiovascular disease, including the risk for acute myocardial infarction (AMI). Our study aimed to investigate whether there is an association between extremely temperature and acute myocardial infarction hospital admission in Beijng, China. We obtained 81029 AMI cases and daily temperature data from January 1, 2013 to December 31, 2016. We employed a time series design and modeled distributed lag nonlinear model (DLNM) to analyze effects of temperature on daily AMI cases. Compared with the 10th percentile temperature measured by daily mean temperature (Tmean), daily minimum temperature (Tmin) and daily minimum apparent temperature (ATmin), the cumulative relative risks (CRR) at 1st percentile of Tmean, Tmin and ATmin for AMI hospitalization were 1.15(95% CI: 1.02, 1.30), 1.24(95% CI: 1.11, 1.38) and 1.41(95% CI: 1.18, 1.68), respectively. Moderate low temperature (10th vs 25th) also had adverse impact on AMI events. The susceptive groups were males and people 65 years and older. No associations were found between high temperature and AMI risk. The main limitation of the study is temperature exposure was not individualized. These findings on cold-associated AMI hospitalization helps characterize the public health burden of cold and target interventions to reduce temperature induced AMI occurrence.

As climate change intensifies, ambient temperatures have become a global concern, leading to an increasing number of studies examining the impact of temperature on human health. Extreme weather events, including heatwaves and cold spells, are becoming more frequent and severe. Numerous studies have highlighted the positive correlation between non-optimal ambient temperatures and mortality. Understanding these impacts is crucial for developing targeted public health interventions and accurately predicting the future health burden associated with climate variability. This study aims to estimate the relative risks and mortality burden associated with temperature extremes over the past decade, focusing on the contributions of both heat and cold, as well as mild and extreme temperatures, and identifying vulnerable populations. By doing so, filling a regional research gap in Guiyang. We collected the daily weather and mortality data from 2013 to 2023. Descriptive analysis was conducted to characterize overall weather patterns and mortality trends during the study period. A quasi-Poisson regression with a distributed lag non-linear model (DLNM), incorporating a 21-day lag and controlling for trends, air pollutants, and the day of the week, was applied to estimate the cumulative relative risks of non-accidental mortality due to non-optimal and extreme temperatures. We calculated attributable fractions and attributable numbers for heat and cold (defined as temperatures above and below the daily mean temperature), mild temperatures (defined using cutoffs at the minimum mortality temperature, with mild heat ranging from the minimum mortality temperature to the 97.5th temperature percentile and mild cold ranging from the 2.5th temperature percentile to the minimum mortality temperature) and extreme temperatures (defined as temperatures below the 2.5th temperature percentile for extreme cold and above the 97.5th temperature percentile for extreme heat). A total of 140,099 non-accidental deaths were included in the study.Temperature and mortality showed U-shaped associations, except for 0-64 years age group. For extreme low temperatures, the effects appeared in lag 2 to 4 days and lasted for approximately 18 days, peaking on lag day 5, yielding a cumulative relative risks (RRs) of 1.24% (95% CI 1.14% to 1.36%) for non-accidental mortality. For extreme high temperatures, the strongest effect was observed on the same day, with an RR of 1.18%(95% CI 1.03% to 1.35%). The attributable fraction of non-accidental mortality associated with non-optimal temperatures was 9.21% (95% eCI: 5.32% to 12.15%). The mortality burden from heat and cold was 5.55% (95% eCI: 2.04% to 8.59%) and 3.67% (95% eCI: 1.45% to 5.80%), respectively. Mild heat was responsible for the majority of the mortality burden. Extreme low temperatures had higher cumulative relative risk and a prolonged effect compared to extreme high temperatures. The attributable fraction associated with non-optimal temperatures was highest for respiratory-related deaths. Mild heat was responsible for the majority of the mortality burden. Additionally, males and the individuals aged 65 years and above were particularly vulnerable populations.

The frequency and intensity of extreme high temperature (EHT) in the Northern Hemisphere exhibit remarkable low-frequency (LF) variations (longer than 10 years) in summer during 1951–2017. Five hotspots featuring large LF variations in EHT were identified, including western North America–Mexico, eastern Siberia, Europe, central Asia, and the Mongolian Plateau. The probability density functions show that the higher EHT occurrences over these hotspots in recent decades is consistent with the shifted average and increased variances in daily mean temperature. The common features of the LF variation in EHT frequency over all domains are the remarkable increasing trends and evident decadal to multidecadal variations. The component of decadal to multidecadal variations is the main contribution to the LF variations of temperature in the last century. Further analysis shows that the coherent variability of decadal to multidecadal temperature variations over western North America–Mexico, eastern Siberia, Europe, and the Mongolian Plateau are the footprints of a dominant natural internal signal: the Atlantic multidecadal oscillation. It contributes to the variations in temperature over these hotspots via barotropic circumglobal teleconnection, which imposes striking anomalous pressure over these regions. This study implies that natural internal variability plays an important role in making hotspots more vulnerable to EHT.

Anthropogenic climate change is affecting the snowpack freshwater storage, with socioeconomic and ecological impacts. We present an assessment of maximum snow water equivalent (SWEmax) change in large river basins of the northwestern North America region using the Canadian Regional Climate Model 50-member ensemble under 1.0 °C to 4.0 °C global warming thresholds above the pre-industrial period. The projections indicate steep SWEmax decline in the warmer coastal/southern basins (i.e., Skeena, Fraser and Columbia), moderate decline in the milder interior basins (i.e., Peace, Athabasca and Saskatchewan), and either a small increase or decrease in the colder northern basins (i.e., Yukon, Peel, and Liard). A key factor for these spatial differences is the proximity of winter mean temperature to the freeze/melt threshold, with larger SWEmax declines for the basins closer to the threshold. Using the random forests machine-learning model, we find that the SWEmax change is primarily temperature controlled, especially for warmer basins. Further, under a categorical framework of below-normal SWEmax defined as snow drought (SD), we find that above-normal temperature and precipitation are the dominant conditions for SD occurrences under higher global warming thresholds. This implies a limited capacity of precipitation increase to compensate the temperature-driven snowpack decline. Additionally, the frequency and severity of SD occurrences are projected to be most extreme in the southern basins where current water demands are highest. Overall, the results of this study, including insights on snowpack changes, their climatic controls, and the framework for SD classification, are applicable for basins spanning a range of hydro-climatological regimes.

Global mean temperature change simulated by climate models deviates from the observed temperature increase during decadal-scale periods in the past. In particular, warming during the ‘global warming hiatus’ in the early twenty-first century appears overestimated in CMIP5 and CMIP6 multi-model means. We examine the role of equatorial Pacific variability in these divergences since 1950 by comparing 18 studies that quantify the Pacific contribution to the ‘hiatus’ and earlier periods and by investigating the reasons for differing results. During the ‘global warming hiatus’ from 1992 to 2012, the estimated contributions differ by a factor of five, with multiple linear regression approaches generally indicating a smaller contribution of Pacific variability to global temperature than climate model experiments where the simulated tropical Pacific sea surface temperature (SST) or wind stress anomalies are nudged towards observations. These so-called pacemaker experiments suggest that the ‘hiatus’ is fully explained and possibly over-explained by Pacific variability. Most of the spread across the studies can be attributed to two factors: neglecting the forced signal in tropical Pacific SST, which is often the case in multiple regression studies but not in pacemaker experiments, underestimates the Pacific contribution to global temperature change by a factor of two during the ‘hiatus’; the sensitivity with which the global temperature responds to Pacific variability varies by a factor of two between models on a decadal time scale, questioning the robustness of single model pacemaker experiments. Once we have accounted for these factors, the CMIP5 mean warming adjusted for Pacific variability reproduces the observed annual global mean temperature closely, with a correlation coefficient of 0.985 from 1950 to 2018. The CMIP6 ensemble performs less favourably but improves if the models with the highest transient climate response are omitted from the ensemble mean.

This study examines seasonal and annual mean temperature changes from 1970 to 2014. Climatologically, the June–July–August season exhibited the highest (27 °C) mean temperatures over the country, followed by March–April–May (MAM; 20.2 °C), September–October–November (SON; 19.1 °C), and December–January–February (DJF; 8.8 °C), while the annual mean was 18.8 °C. The southern region exhibited higher mean temperatures than the northern region for the DJF, MAM, JJA, SON, and annual timescales during the study period. The seasonal trend across the country was highest in MAM (0.027 °C/year), followed by SON (0.025 °C/year), DJF (0.023 °C/year), and JJA (0.016 °C/year). The interannual trend increased significantly at 0.023 °C/year across the country. Across the north, MAM showed the highest increase (nonsignificant) in trends at 0.025 °C/year, followed by a significant increase during SON (0.018 °C/year), DJF (0.017 °C/year), and JJA (0.010 °C/year), while annual trends were the lowest (0.017 °C/year). Examination of abrupt change over Pakistan showed nonsignificant change during JJA, while MAM, JJA, SON, and annual timescales demonstrated significant positive and negative changes. Decadal anomalies showed long-term positive tendencies in DJF (since 1990–2014) temperature, followed by JJA, SON, annual, and MAM timescales (2000–2014). In conclusion, the observed changes in temperature were significantly robust and promise increasing signal patterns in all time scales.

Summers have become hotter in recent decades, with earlier onsets in many regions. Here, we explore changes in the summer length under global warming based on the observations and CMIP6 models, identify the influences of external forcing and internal variability, and use CMIP6 models to project variations of the future summer length. Summer is defined as when the daily mean temperature is above the 1961–1990 75th percentile for 5 consecutive days. The summer length significantly increases, and the observed trends show marked regional differences. External forcing is the main contributor to the lengthening of summer, while internal variability may explain the regional differences. Our results reveal that a 1 ℃ global surface mean temperature increase is associated with 15 days of the summer length increase during 1961–2014 in the observations, while a 1 ℃ temperature increase corresponds to 10 days of the summer length increase in CMIP6 models. CMIP6 models are also used to project the change of the summer length in the future, and it is found that the summer length will continue to increase in the future. Summer will last 142 days (175 days) under SSP2-4.5 (SSP5-8.5) scenario of global warming by the end of the twenty-first century, equivalent to an approximate 1.2 (1.5)-fold increase relative to that of 2014.