Observed Changes in Canada’s Snowfall as Inferred from Precipitation and Daily Mean Temperatures

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

ABSTRACTTrends in indices based on daily temperature and precipitation are examined for two periods: 1948–2016 for all stations in Canada and 1900–2016 for stations in the south of Canada. These indices, a number of which reflect extreme events, are considered to be impact relevant. The results show changes consistent with warming, with larger trends associated with cold temperatures. The number of summer days (when daily maximum temperature >25°C) has increased at most locations south of 65°N, and the number of hot days (daily maximum temperature >30°C) and hot nights (daily minimum temperature >22°C) have increased at a few stations in the most southerly regions. Very warm temperatures in both summer and winter (represented by the 95th percentile of their daily maximum and minimum temperatures, respectively) have increased across the country, with stronger trends in winter. Warming is more pronounced for cold temperatures. The frost-free season has become longer with fewer frost days, consecutive frost days, and ice days. Very cold temperatures in both winter and summer (represented by the 5th percentile of their daily maximum and minimum temperatures, respectively) have increased substantially across the country, again with stronger trends in the winter. Changes in other temperature indices are consistent with warming. The growing season is now longer, and the number of growing degree-days has increased. The number of heating degree-days has decreased across the country, while the number of cooling degree-days has increased at many stations south of 55°N. The frequency of annual and spring freeze–thaw days shows an increase in the interior provinces and a decrease in the remainder of the country. Changes in precipitation indices are less spatially coherent. An increase in the number of days with rainfall and heavy rainfall is found at several locations in the south. A decrease in the number of days with snowfall and heavy snowfall is observed in the western provinces, while an increase is found in the north. There is no evidence of significant changes in the annual highest 1-day rainfall and 1-day snowfall. The maximum number of consecutive dry days has decreased, mainly in the south.

In ants, social thermal regulation is the collective maintenance of a nest temperature that is optimal for individual colony members. In the thermophilic ant Aphaenogaster iberica, two key behaviours regulate nest temperature: seasonal nest relocation and variable nest depth. Outside the nest, foragers must adapt their activity to avoid temperatures that exceed their thermal limits. It has been suggested that social thermal regulation constrains physiological and morphological thermal adaptations at the individual level. We tested this hypothesis by examining the foraging rhythms of six populations of A. iberica, which were found at different elevations (from 100 to 2,000m) in the Sierra Nevada mountain range of southern Spain. We tested the thermal resistance of individuals from these populations under controlled conditions. Janzen's climatic variability hypothesis (CVH) states that greater climatic variability should select for organisms with broader temperature tolerances. We found that the A. iberica population at 1,300m experienced the most extreme temperatures and that ants from this population had the highest heat tolerance (LT50=57.55°C). These results support CVH's validity at microclimatic scales, such as the one represented by the elevational gradient in this study. Aphaenogaster iberica maintains colony food intake levels across different elevations and mean daily temperatures by shifting its rhythm of activity. This efficient colony-level thermal regulation and the significant differences in individual heat tolerance that we observed among the populations suggest that behaviourally controlled thermal regulation does not constrain individual physiological adaptations for coping with extreme temperatures.

This study examines the trends and variations in several indices of daily and extreme temperature and precipitation in Canada for the periods 1950–2003 and 1900–2003 respectively. The indices are based on homogenized daily temperature and adjusted daily precipitation measurements which are special datasets that include adjustments for site relocation, changes in observing programs and corrections for known instrument changes or measurement program deficiencies. For 1950–2003, the analysis of the temperature indices indicates the occurrence of fewer cold nights, cold days and frost days, and conversely more warm nights, warm days and summer days across the country. The results are generally similar for 1900–2003 but they also include a decrease in the diurnal temperature range in southern Canada and a decrease in the standard deviation of the daily mean temperatures for many stations in western Canada. The analysis of the precipitation indices for 1950–2003 reveals more days with precipitation, a decrease in daily intensity and a decrease in the maximum number of consecutive dry days. The annual total snowfall significantly decreased in the south and increased in the north and north‐east during the second half of the twentieth century. The results are generally similar for 1900–2003. The national series for the century shows an increase in annual snowfall from 1900 to the 1970s followed by a considerable decrease until the 1980s which also corresponds to a pronounced downward trend in the frequency of frost days. No consistent changes were found in most of the indices of extreme precipitation for both periods.

This study assesses the simulations of the daily mean, maximum, and minimum temperatures and daily precipitation over China during the period 1990–1999, based on phase 3 and phase 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). Fourteen CMIP3 models and 14 CMIP5 models were investigated over eight regions across China. Skill scores quantifying the match between the simulated and observed probability density functions (PDFs) were applied to evaluate the performance of the models. For daily mean, maximum, and minimum temperatures, the results revealed that CMIP3 and CMIP5 models captured the basic pattern of the observed PDFs in all regions. However, the probabilities at lower values were overestimated in most models. In all regions except the west of Northwest China (region 7), all CMIP5 models captured more than 80% of the observed PDFs. Compared with performance at the annual time scale, the models tended to perform relatively worse over the period June to August. The performances of the CMIP5 and CMIP3 models were not as good for daily precipitation as for daily temperature, and the skill scores for precipitation were generally lower than 0.7 in all regions. The amount of drizzle (daily precipitation < 5 mm) was overestimated notably in all regions. The amount of very heavy precipitation (daily precipitation ≥ 20 mm) tended to be underestimated in humid regions but overestimated in arid regions. Compared with CMIP3, CMIP5 models showed some improvements in the simulation of daily mean, maximum, and minimum temperatures, but there was a lack of apparent improvement for simulation of daily precipitation.

The application of trivariate thin-plate smoothing splines to the interpolation of daily weather data is investigated. The method was used to develop spatial models of daily minimum and maximum temperature and daily precipitation for all of Canada, at a spatial resolution of 300 arc s of latitude and longitude, for the period 1961–2003. Each daily model was optimized automatically by minimizing the generalized cross validation. The fitted trivariate splines incorporated a spatially varying dependence on ground elevation and were able to adapt automatically to the large variation in station density over Canada. Extensive quality control measures were performed on the source data. Error estimates for the fitted surfaces based on withheld data across southern Canada were comparable to, or smaller than, errors obtained by daily interpolation studies elsewhere with denser data networks. Mean absolute errors in daily maximum and minimum temperature averaged over all years were 1.1° and 1.6°C, respectively. Daily temperature extremes were also well matched. Daily precipitation is challenging because of short correlation length scales, the preponderance of zeros, and significant error associated with measurement of snow. A two-stage approach was adopted in which precipitation occurrence was estimated and then used in conjunction with a surface of positive precipitation values. Daily precipitation occurrence was correctly predicted 83% of the time. Withheld errors in daily precipitation were small, with mean absolute errors of 2.9 mm, although these were relatively large in percentage terms. However, mean percent absolute errors in seasonal and annual precipitation totals were 14% and 9%, respectively, and seasonal precipitation upper 95th percentiles were attenuated on average by 8%. Precipitation and daily maximum temperatures were most accurately interpolated in the autumn, consistent with the large well-organized synoptic systems that prevail in this season. Daily minimum temperatures were most accurately interpolated in summer. The withheld data tests indicate that the models can be used with confidence across southern Canada in applications that depend on daily temperature and accumulated seasonal and annual precipitation. They should be used with care in applications that depend critically on daily precipitation extremes.

Investigating the response of crop production to climate change can help to optimize local agricultural practices, and then ensure food and ecological security. Crop models can provide a useful way to examine the effects of a range of climatic condition, management or crop cultivar on crop growth and yield in field and pasture. This work investigated the effects of precipitation and air temperature changes on the production of winter wheat, maize and lucerne in rain-fed agriculture area located in the central and western Loess Plateau by field experiment and crop simulation model. The field experiment was conducted at Qingyang Loess Plateau Experimental Station of Lanzhou University through 2001 to 2010, and the Agricultural Production Systems Simulator (APSIM) was applied in this study to simulate the growing process of winter wheat, maize and lucerne. The APSIM was validated with the experimental data firstly, and then the APSIM was applied to simulate the yield variability of the crops under the combinations 5 precipitation levels and 5 air temperature levels based on historical climatic data from 1961 to 2010. Temperature levels were: 1) -1.5 decrease in daily mean temperature (T1); 2) -1 decrease in daily mean temperature (T2); 3) historical daily temperature (T2); 4) 1 increase in daily mean temperature (T4); and 5) 1.5 increase in daily mean temperature (T5). Precipitation levels were: 1) 20% decrease in daily precipitation (P1); 2) 10% decrease in daily precipitation (P2); 3) historical daily precipitation (P3); 4) 10% increase in daily precipitation (P4); and 5) 20% increase in daily precipitation (P5). Results showed that the APSIM can predict the grain yield and biomass of the 3 crops accurately with the determination coefficients varied between 0.80-0.93, the normalized root mean square errors varied between 11.35%-22.48%, and the model efficiency varied between 0.53-0.91; Overall, APSIM was powerful to simulate the crop grain yield and biomass of winter wheat, maize and lucerne in study site. Winter wheat and lucerne maintained the greatest yield increase when the air temperature decreased and the precipitation increased during 1961-2010, which was 29.8% and 51.7%. Maize reached its greatest yield, which improved 22% when the precipitation increased and the air temperature remained unchanged. The maximal reduction of yield of 3 crops were 38.7%, 40.3% and 41.8%, respectively, which presented in the scenarios with low precipitation level and high temperature level. In addition, the variation range of winter wheat yield was reduced by increasing air temperature and precipitation while lucerne yield exhibited a smaller variation range when precipitation decreased and temperature increased. According to the trend of winter wheat and lucerne, the variation range of maize yield tended to boost by increasing precipitation, otherwise, maize yield also showed a wider range under the temperature level varied from T1to T3; but when temperature level hoisted up the T5, variation range of maize yield tended to be narrower. Overer, lucerne could adapt to the climate change better than winter wheat and maize with relatively inferior changes of yield variation under different climatic scenarios. In conclusion, the 3 crops were more sensitive to precipitation and they had positive linear relationships with precipitation level by slopes of 14.3-16.0, 11.8-15.5 and 15.0-18.9, respectively. The results should offer better comprehension and consultation for future studies and actual production about long-term of chief crop production when climate changes. Future agricultural production should attach importance to change crop management such as sowing date and cultivar to avoid heat or moisture stress. Otherwise, more efforts should be paid to explore the effect of interaction by CO2, solar radiation, precipitation and air temperature on crop production on the western of Loess Plateau. © 2016, Chinese Society of Agricultural Engineering. All right reserved.

The internode of each stalk in three varieties, NCo 310, F 161 and Yomitanzan rapidly elongated and thickened within 10 days after the time of unfolding of the leaf attached at each node and attained the maximum size within 20 days after the time. Dry matter weight and Brix of the internode in NCo 310 and F 161 continuously increased within 100 days after the time of unfolding of the leaf, while the dry weight in Yomitanzan reached the maximum within 70 days afrer the time. The moisture content of the internode gradually decreased with time. The length and diameter of internodes in the main stalk of F 161 planted every month from June, 1980 to May, 1981 showed the pronounced variation. The length of all upper internodes from 11th node order was significantly correlated with mean daily air temperature, mean daily solar radiation and precipitation during the growing period of each internode. Partial correlations between the length of internodes from 11th to 40th node order and precipitation and between the length of upper internodes from 11th and mean daily solar radiation were positive, but those between the length of internodes from 11th to 40th and the mean daily temperature were negative. The each diameter of internodes from 11th to 30th node order was negatively correlated with the mean daily air temperature and precipitation, but the each diameter of upper node order from 31th was positively correlated with the three climatic factors. Partial correlations of the diameter with mean daily air temperature were highly negative, but those of the diameter with mean daily solar radiation and precipitation were positive.

Substrate and soil temperatures were analyzed throughout 14 representative fruit–vegetable crop cycles and treatments grown in low-cost Mediterranean greenhouses, mostly around the cold winter period. The mean daily temperatures of most common substrates (perlite and coconut-coir bags) were lower (between 0.9 and 3.0 °C) and more variable than root zone temperatures of the most common soil (enarenado soil) throughout all crop cycles and treatments studied, particularly during the cold period. The mean daily temperature of the perlite and coconut-coir bags was low (around 15 °C) during most of the cold periods, and the minimum daily temperature was very low (around 12 °C) during some crop periods. These low temperatures are generally considered suboptimal for greenhouse production. The seasonal dynamics of the minimum and mean daily temperature of the substrate bags were similar to those observed for the mean daily greenhouse air temperature. The minimum daily temperature of substrate bags was close to the mean daily greenhouse air temperature for all the studied crop cycles. A simple and practical relationship was found for predicting mean daily temperatures of substrate bags from mean daily greenhouse air temperatures (MAE = 0.87 °C; RMSE = 1.15 °C). A substantial spatial and temporal variation in the hourly temperature was found in the cross-section of the coconut-coir bag, but not for the mean daily temperature. No differences were found for the mean daily temperature along the longitudinal section of the bag. In general, representative measurements of the coconut-coir bag can be taken by installing the sensors horizontally and especially vertically around the middle part of the bag.

This study analyzed future projections on daily mean values and extremes for temperature and daily precipitation over Seoul metropolitan city using bias-corrected high-resolution multi-regional climate models. The factors of uncertainty for the future projection of climate variables were defined. In the time series analysis of future projections for regional climate models, the average daily temperature and the number of days of the hot day-hot night were predicted to have a stable trend in the RCP2.6 scenario, and showed a tendency to increase continuously in the RCP8.5 scenario. The daily mean precipitation and RX1day (annual daily maximum precipitation) had large annual variabilities in the models. In the estimation of the fraction of total variance, the daily mean temperature was dominated by the internal variability in the early 21st century and the most contributing to the scenario uncertainty in the late 21st century. The daily mean precipitation showed a remarkable contribution from the internal variability over the entire period. The number of days of the hot day-hot night showed a similar contribution pattern to that of the daily mean temperature. For the RX1day, the internal variability dominated over the entire period, and the scenario uncertainty had little contribution. This study will help establish more scientific climate change adaptation policies by providing the uncertainty information for future climate change projection.

Effect of vegetation change from forest to herbaceous vegetation cover on soil moisture and temperature regimes and soil water chemistry

Because of the unique climate characteristics, the runoff law in mid-temperate zone is very different from other regions in spring. Accurate runoff simulation and forecasting is of great importance to spring flood control and efficient use of water resources. Baishan reservoir is located in the upper Second Songhua River Basin in Northeast China, where snowmelt is an important source of runoff that contributes to the water supply. This study utilized long-term hydrometeorological data, in the contributing area of Bashan reservoir, to investigate factors and time-lag effects on spring snowmelt and to establish a snowmelt-runoff model. Daily precipitation, temperature, and wind data were collected from three meteorological stations in this region from 1987–2016. Daily runoff into the Baishan reservoir was selected for the same period. The snowmelt period was identified from March 23 to May 4 through baseflow segmentation with the Eckhardt recursive digital filtering method combined with statistical analyses. A global sensitivity analysis, based on the back propagation neural network method, was used to identify daily radiation, wind speed, mean temperature, and precipitation as the main factors affecting snowmelt runoff. Daily radiation, precipitation, and mean temperature factors had a two-day lag effect. Based on these factors, an empirical snowmelt runoff model was established by genetic algorithm (GAS) to estimate the snowmelt runoff in this area. The model showed an acceptable performance with coefficient of determination (R2) of 73.6%, relative error (Re) of 25.10%, and Nash-Sutcliffe efficiency coefficient (NSE) of 66.2% in the calibration period of 1987–2010, while reasonable performance with R2 of 62.3%, Re of 27.2%, and NSE of 46.0% was also achieved during the 2011–2016 validation period.

Associations between cold spells of different time types and coronary heart disease severity

A new nonlinear objective prediction scheme has been developed for predicting 24h daily maximum and minimum temperature forecasts at 14 stations in Guangxi, China during Jan, 2015-Jun, 2018 using Recurrent Neural Network (RNN) and based on the daily average, maximum, minimum temperature and precipitation data. Taking the climatology and persistence predictors as primary factors, the conditional attribute reduction method of rough set theory is adopted. By eliminating the unrelated attributes, the predictors direct correlated with the predictand (maximum and minimum temperature) are taken as the RNN model input by means of attribute reduction. This new scheme is validated with 24h short-range forecasts spanning Jan to Jun, 2018. Using identical predictors and sample cases, predictions of the RNN model are compared with the stepwise regression method, and results show that the former is more accurate. The mean absolute errors of RNN at 14 stations in Guangxi are lower than those of the stepwise regression method. The mean forecast accuracy with absolute errors being less than 2°C (1°C) of RNN is higher than that of the stepwise regression method. Moreover, the number of forecast errors larger than 2°C and the system deviation of daily maximum (minimum) temperature prediction are significantly reduced by RNN model, indicating a potentially better operational weather prediction tool.

Air temperature data from many locations worldwide are only available as series of daily minima and maxima temperatures. Historically, several different approaches have been used to estimate the actual daily mean temperature, as only in the last two or three decades automatic thermometers are able to compute its actual value. The most common approach is to estimate it by averaging the daily minima and maxima. When only daily minima and maxima are available, an alternative approach, proposed by Dall’Amico and Hornsteiner in 2006, uses the two daily extremes together with next day minima temperature and a coefficient related to the local daily astronomical sunset time. Additionally, the method uses two optimizable coefficients related to the region’s temperature profile. In order to use this approach it is necessary to optimize the region’s unknown parameters. For this optimization, it is necessary a dataset containing the maxima, minima, and the actual daily mean temperatures for at least one year. In this research, for the period 2007-2014, we used three datasets of minima, maxima and actual mean temperatures obtained at three automatic meteorological stations located in the Douro Valley to optimize the two unknown parameters in the Dall’Amico and Hornsteiner approach. Moreover, we compared the actual mean daily temperatures available from the three datasets with the correspondent values estimated by using i) the usual approach of averaging the daily maxima and minima temperatures and ii) the Dall’Amico and Hornsteiner approach. Results show that the former approach overestimates, on average, the daily mean temperatures by 0.5ºC. The Dall’Amico and Hornsteiner approach showed to be a better approximation of mean temperatures for the three meteorological stations used in this research, being unbiased relative to the actual mean values of daily temperatures. In conclusion, this research confirms that the Dall’Amico and Hornsteiner is a better approach to estimate the mean daily temperatures and provides the optimized parameters for three sites located at each of the three sub-regions of the Douro Valley (Baixo Corgo, Cima Corgo and Douro Superior).