Observed temperature changes in Emilia-Romagna: mean values and extremes

Summary Possible changes of mean climate and the frequency of extreme temperature events in Emilia-Romagna, over the period 2070–2100 compared to 1960–1990, are assessed. A statistical downscaling technique, applied to HadAM3P experiments (control, A2 and B2 scenarios) performed at the Hadley Centre, is used to achieve this objective. The method applied consists of a multivariate regression based on Canonical Correlation Analysis (CCA), using as possible predictors mean sea level pressure (MSLP), geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850), and as predictands the seasonal mean values of minimum and maximum surface temperature (Tmin and Tmax), 90 th percentile of maximum temperature (Tmax90), 10 th percentile of minimum temperature (Tmin10), number of frost days (Tnfd) and heat wave duration (HWD) at the station level. First, the statistical model is optimised and calibrated using NCEP=NCAR reanalysis to evaluate the large-scale predictors. The observational data at 32 stations uniformly distributed over Emilia-Romagna are used to compute the local predictands. The results of the optimisation procedure reveal that T850 is the best predictor in most cases, and in combination with MSLP, is an optimum predictor for winter Tmax90 and autumn Tmin10. Finally, MSLP is the best predictor for spring Tmin while Z500 is the best predictor for spring Tmax90 and heat wave duration index, except during autumn. The ability of HadAM3P to simulate the present day spatial and temporal variability of the chosen predictors is tested using the control experiments. Finally, the downscaling model is applied to all model output experiments to obtain simulated present day and A2 and B2 scenario results at the local scale. Results show that significant increases can be expected to occur under scenario conditions in both maximum and minimum temperature, associated with a decrease in the number of frost days and with an increase in the heat wave duration index. The magnitude of the change is more significant for the A2 scenario than for the B2 scenario.

In this study, eight extreme temperature event indices were calculated based on daily maximum, minimum, and mean temperature data recorded at 699 National Reference Stations in China during 1961–2020. The yearly change of mean temperature and the magnitude, frequency, and duration of extreme temperature events in six temperature zones were evaluated. All temperature zones had a trend of an increase in mean temperature (rate: 2.1–3.3 °C per 10 years), and the warming was more significant in the warm temperate zone and the Qinghai–Tibet Plateau zone (QPZ). For extreme temperature events, the extreme maximum and minimum temperatures in most temperature zones showed significant trends of increase, and the rates of increase were greater in the northern zones and QPZ. The rate of increase in extreme minimum temperature was substantially (up to three times) higher than the rate of increase in extreme maximum temperature in the same temperature zone; however, the finding was the opposite for the cold temperate zone (CTZ), which is the northernmost region of China. The rate of increase in extreme maximum temperatures was the greatest (0.35 °C per 10 years), whereas the rate of increase in extreme minimum temperatures was the smallest (0.17 °C per 10 years). The number of warm days/nights and the warm spell duration index also showed significant trends of increase that were most obvious in the southern zones and QPZ. In the tropical zone (TZ), which is the southernmost part of mainland China, the number of warm nights was only 15.3 days in 1961–1970, whereas it increased to 61.9 days in 2011–2020 (an increase of 303.9%). The rate of increase in warm nights in TZ (8.8 days per 10 years) was four times that in CTZ (2.2 days per 10 years). The number of cold days/nights and the cold spell duration index showed significant trends of decrease, with the greatest rates of reduction in QPZ and TZ. In evaluating the frequency of extreme temperature events, the amplitude of warming of the night index was found to be greater than that of the day index. In evaluating the duration of extreme temperature events, the variation of the cold index was found to be greater than that of the warm index. The notable asymmetries found in the variations of the minimum/maximum temperatures, day/night indices, and cold/warm spell durations in China are direct manifestations of global warming.

To predict the impact of climate change on our beef animals and systems, we need a better understanding of how beef cattle traits are affected by varying weather and frequency of extreme events. We analysed the effect of minimum and maximum temperatures and average daily precipitation on a range of important carcass traits, including age at slaughter, cold carcass weight, carcass growth rate and conformation and fat score (N = >1.6 million), as well as calf 200-day live weight and growth rate (N = >270 000), using data from abattoirs across Britain (carcass traits) and calves in Scottish suckler beef herds (live weights and growth). Animals which experienced higher daily maximum and minimum temperatures had slower carcass and calf growth rates. Increased precipitation also led to poorer cold carcass weights, conformation scores, calf 200-day weights and calf growth. We also analysed the effect of frequency of extreme weather events, including heatwaves, cold waves, and dry and wet days. The frequency of heatwaves, dry and wet days were shown to have significant negative effects on almost all traits considered, for example, predicting that an increase in the frequency of heatwaves by 1 day per 100 days of life would reduce cold carcass weights by about 200 g and increase age at slaughter by about 3 days. Results show that varying weather and frequency of extreme weather, across the lifetime of a beef animal, influences traits which affect the potential profit for a beef farmer. These effects may be due to several factors, including direct effects on the animal, as well as feed availability and management decisions made by the farmer. However, there is potential to mitigate negative effects through a range of animal management strategies.

Radley Horton,1,a Daniel Bader,1,a Yochanan Kushnir,2 Christopher Little,3 Reginald Blake,4 and Cynthia Rosenzweig5 1Columbia University Center for Climate Systems Research, New York, NY. 2Ocean and Climate Physics Department, Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY. 3Atmospheric and Environmental Research, Lexington, MA. 4Physics Department, New York City College of Technology, CUNY, Brooklyn, NY. 5Climate Impacts Group, NASA Goddard Institute for Space Studies; Center for Climate Systems Research, Columbia University Earth Institute, New York, NY

Abstract. Using 5520 observatories covering the whole territory of Spain (about 1 station per 90 km2 considering the whole period), a daily gridded maximum and minimum temperature was built covering a period from 1901 to 2014 in peninsular Spain and 1971 to 2014 in the Balearic and Canary Islands. A comprehensive quality control was applied to the original data, and the gaps were filled on each day and location independently. Using the filled data series, a grid of 5 km × 5 km spatial resolution was created by estimating daily temperatures and their corresponding uncertainties at each grid point. Four daily temperature indices were calculated to describe the spatial distribution of absolute maximum and minimum temperature, number of frost days and number of summer days in Spain. The southern plateau showed the maximum values of maximum absolute temperature and summer days, while the minimum absolute temperature and frost days reached their maximums at the northern plateau. The use of all the available information, the complete quality control and the high spatial resolution of the grid allowed for an accurate estimate of temperature that represents a precise spatial and temporal distribution of daily temperatures in Spain. The STEAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/8622 and can be cited as Serrano-Notivoli et al. (2019).

We downscaled atmospheric reanalysis data using linear regression and Bayesian neural network (BNN) ensembles to obtain daily maximum and minimum temperatures at ten weather stations in southern Quebec and Ontario, Canada. Performance of the linear and non-linear downscaling models was evaluated using four different sets of predictors, not only in terms of their ability to reproduce the magnitude of day-to-day variability (i.e., “weather,” mean absolute error between the daily values of the predictand(s) and the downscaled data) but also in terms of their ability to reproduce longer time scale variability (i.e., “climate,” indices of agreement between the predictand's observed annual climate indices and the corresponding downscaled values). The climate indices used were the 90th percentile of the daily maximum temperature, 10th percentile of the daily minimum temperature, number of frost days, heat wave duration, growing season length, and intra-annual temperature range.Our results show that the non-linear models usually outperform their linear counterparts in the magnitude of daily variability and, to a greater extent, in annual climate variability. In particular, the best model simulating weather and climate was a BNN ensemble using stepwise selection from 20 reanalysis predictors, followed by a BNN ensemble using the three leading principal components from the aforementioned predictors. Finally, we showed that, on average, the first three indices presented higher skills than the growing season length, number of frost days, and the heat wave duration.

Proportion of oleic acid in olive oil as influenced by the dimensions of the daily temperature oscillation

Seasonal and annual trends in Australian minimum and maximum temperatures were studied. Records of daily minimum and maximum temperatures averaged over each month, extending as far back as 1856 were examined. Over 1/2 million monthly temperature values were retrieved from the Australian Bureau of Meteorology for 299 stations. Each station had an average of 89 years of observations. Significant step discontinuities affected the maximum temperature data in the 19th century when Stevenson screens were installed. The temperature trends were found after such spurious data were removed and averaged over all stations. The resulting trend in the minimum (maximum) daily temperature was 0.67 ± 0.19 (0.58 ± 0.26) oC per century for the period 1907-2014. Decadal fluctuations were evident in the maximum daily temperature with most of the increase occurring in the late 20th century. The minimum and maximum daily temperature trends were also found for the various seasons. The minimum daily temperature trend exceeded the maximum daily temperature trend for all seasons except during June to August. The largest increases in minimum temperature as well as the smallest maximum temperature increases were found for the region north of 30 oS latitude and east of 140 oE longitude. There was also evidence that urban stations had greater increases in maximum daily temperature than those located in a rural environment.

A number of indices have been developed to investigate recent changes in the annual frequencies of extreme temperature events in Australia. A high-quality daily temperature dataset including 88 station records is used to determine trends in these indices, generally over the period 1957 to 1996. Indices investigated include measures of the frequencies of daily maximum and minimum temperatures above and below fixed temperature thresholds, as well as frequencies above and below specified percentile levels. Trends in consecutive days and nights of extreme temperature are also considered. These trends indicate that occurrences of warm temperature extreme events have generally increased over the investigation period, whilst numbers of extremely cool temperature events have decreased. The trends are particularly strong for indices based on minimum temperature, with many statistically significant at the 95 per cent confidence level. Some of the trends display considerable regional variation. Examples of this are a downward trend in the frequency of warm extremes in parts of the far southeast, counter to the national trend, and an especially strong decrease in the frequency of relatively cool days along the east coast. A number of measures of daily temperature variability are also examined with many records showing significant declines for these indices. These trends may provide the first evidence of decreases in intraseasonal temperature variability consistent with those observed over large parts of the northern hemisphere landmass.

Cold temperature and associated extremes often impact adversely human health and environment and bring disruptions in economic activities during winter over Canada. This study investigates projected changes in winter (December to March) period cold extreme days (i.e., cold nights, cold days, frost days, and ice days) and cold spells over Canada based on 11 regional climate model (RCM) simulations for the future 2040–2069 period with respect to the current 1970–1999 period. These simulations, available from the North American Regional Climate Change Assessment Program, were obtained with six different RCMs, when driven by four different Atmosphere–Ocean General Circulation Models, under the Special Report on Emissions Scenarios A2 scenario. Based on the reanalysis boundary conditions, the RCM simulations reproduce spatial patterns of observed mean values of the daily minimum and maximum temperatures and inter-annual variability of the number of cold nights over different Canadian climatic regions considered in the study. A comparison of current and future period simulations suggests decreases in the frequency of cold extreme events (i.e., cold nights, cold days and cold spells) and in selected return levels of maximum duration of cold spells over the entire study domain. Important regional differences are noticed as the simulations generally indicate smaller decreases in the characteristics of extreme cold events over western Canada compared to the other regions. The analysis also suggests an increase in the frequency of midwinter freeze–thaw events, due mainly to a decrease in the number of frost days and ice days for all Canadian regions. Especially, densely populated southern and coastal Canadian regions will require in depth studies to facilitate appropriate adaptation strategies as these regions are clearly expected to experience large increases in the frequency of freeze–thaw events.

Nowadays, the intensification of global warming leads to the increased frequency of extreme temperature events. Many studies reported that different regions are facing the threat of extreme hot and cold temperature in some degree. The Qinghai-Tibet Plateau Transportation Project is a major project in China, and it is beneficial for public to study the extreme temperature events along the railway and avoid the risk induced by the extreme temperature. This study estimated the daily maximum, minimum and average near-surface air temperature along the railway. Sixteen extreme temperature indices defined by ETCCDI (the Expert Team on Climate Change Detection and Indicators) were used to represent the extreme temperature events, and Mann-Kendall trend test and Sen's slope estimation method were employed to explore the spatial-temporal variation trends of the extreme temperature along the Qinghai-Tibet Plateau Transportation Corridor from 1981 to 2019. In addition, the response of extreme temperature events to altitude was discussed.The results show that the climate becomes warming along the Qinghai-Tibet Plateau Transportation Corridor from 1981 to 2019, and the extreme hot events are detected in most areas, while the extreme cold events mainly occurs in the east and southwest part. The significant increasing trend is found according to the indices representing the hot events (SU25, TR20, TX90p, TN90p, TXx, TXn, TNx, TNn and WSDI), while the indices representing the cold events (FD0, ID15, TX10p, TN10p and CSDI) show a significant decreasing trend in most areas over the past nearly 40 years. Besides, the extreme temperature events is highly related to altitude variations. Compared with the middle altitude zones, extreme high temperature events tend to occur in the lower altitude zones and the higher altitude zones. It is of great significant to schedule the train in advance and reduce the disasters by investigating the long-term variation trends of extreme temperature events along the Qinghai-Tibet Plateau Transportation Corridor.

Identifying climatic drivers that dominate in determining crop yield variations at a regional scale is important for predicting regional crop production. In this study, a statistical method was used to quantify the relationship between reported shire wheat yields and climate factors during the wheat-growing season across the New South Wales (NSW) wheat belt in eastern Australia from 1922 to 2000. The results show that recent climatic trends have increased wheat yield by 8.5 to 21.2% in 4 different climatic regions of NSW over the last few decades: In the eastern slopes, growing season maximum and minimum temperatures and number of heat stress days (> 34C) were identified as the dominant climatic factors affecting wheat yield, accounting for 36% of its variation. The wheat yield variation in the remaining 3 regions were as follows: 41% in the northern region from maximum temperature, pre-growing season rainfall (December to April), and number of frost days (< 2C); 47% in the south from rainfall, temperature, and number of frost and heat stress days; while in southwest NSW, rainfall was the main factor responsible for 31% of the variation. Frost was less important in the eastern slopes because farmers manage frost occurrence by sowing late and using late-flowering cultivars. However, the opposite occurs in the northern parts of the wheat belt where farmers sow earlier and select shortseason varieties to avoid heat stress, but thereby expose their crops to possible frost conditions. Understanding the impact of climate variations on crop yield is important for developing sustainable agricultural production under future climate change.

Future changes of seasonal minimum and maximum temperature over Northern Italy are assessed for the periods 2021–2050 and 2070–2099 against 1961–1990. A statistical downscaling technique, applied to the ENSEMBLES-Stream1 and CIRCE global simulations (A1B scenario), is used to reach this objective. The statistical scheme consists of a multivariate regression based on Canonical Correlation Analysis. The set-up of the statistical scheme is done using large-scale fields (predictors) derived from ERA40 reanalysis and seasonal mean minimum and maximum temperature (predictands) derived from observational data at around 75 stations, distributed over Northern Italy, over the period 1960–2002. A similar technique is also applied to the number of frost days and ice days at a reduced number of stations in order to construct projections on change of the selected extreme temperature indices for the two future periods. The evaluation of future projections for these extreme indices is relevant due to its impacts on transports, health, and agriculture. The downscaling scheme constructed using observed data is then applied to large-scale fields simulated by global models (A1B scenario), in order to construct scenarios on future change of seasonal temperature, mean and extreme indices, at local scale. The significance of changes is tested from the statistical point of view. The results show that significant increases could be expected to occur under scenario conditions in both minimum and maximum temperature, associated with a decrease in the number of frost and ice days in both periods and more intense to the end of the century.

In this paper the statistics of daily maximum and minimum surface air temperature at weather stations in the southeast United States are examined as a function of the El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) phase. A limited number of studies address how the ENSO and/or AO affect U.S. daily—as opposed to monthly or seasonal—temperature averages. The details of the effect of the ENSO or AO on the higher-order statistics for wintertime daily minimum and maximum temperatures have not been clearly documented. Quality-controlled daily observations collected from 1960 to 2009 from 272 National Weather Service Cooperative Observing Network stations throughout Florida, Georgia, Alabama, and South and North Carolina are used to calculate the first four statistical moments of minimum and maximum daily temperature distributions. It is found that, over the U.S. Southeast, winter minimum temperatures have higher variability than maximum temperatures and La Niña winters have greater variability of both minimum and maximum temperatures. With the exception of the Florida peninsula, minimum temperatures are positively skewed, while maximum temperatures are negatively skewed. Stations in peninsular Florida exhibit negative skewness for both maximum and minimum temperatures. During the relatively warmer winters associated with either a La Niña or AO+, negative skewnesses are exacerbated and positive skewnesses are reduced. To a lesser extent, the converse is true of the El Niño and AO−. The ENSO and AO are also shown to have a statistically significant effect on the change in kurtosis of daily maximum and minimum temperatures throughout the domain.

From 1955 to 2000, China has experienced a decrease in the number of frost days, while the length of the frost‐free season between the last spring freeze and the first fall frost has increased. Three distinct regimes can be detected in the time series: up to about 1973, the annual number of frost days was about 2 d higher than the 1961–1990 average; from 1973 to 1985, the annual number of frost days held close to that average with remarkably little interannual variability; and after 1985, the annual number of frost days decreased rapidly with distinct reversal around 1992. The dates of first and last frost show two patterns: before 1980, these dates fluctuated around the 1961–1990 average, but after 1980 (and especially from 1993) the frost‐free season was rapidly lengthened. The numbers of frost days are highly correlated with minimum temperature (Tmin) in north China in spring and fall; while in south China frost dates correlate with minimum temperatures in winter. Generally, the seasonal relationships between Tmin and frost days are significant in both the temporal and spatial domains when seasonal average Tmin falls within a range of ±10°C. Analyzing annual and seasonal influences on the number of frost days, we find that water vapor plays a significant role. Regionally, the greater influence on the length of the frost‐free season in south China has been the delayed onset of the autumn frost, while in north China the spring and autumn dates each have a comparable influence on the length of the frost‐free season. The initial lengthening of the frost‐free season lagged about 10 years behind the rapid increase in daily minimum temperatures, while the decrease in the annual number of frost days lagged by about 15 years.