Changes in Canada's Climate: Trends in Indices Based on Daily Temperature and Precipitation Data

ABSTRACT

Trends 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.     

北京1960—2008年气候变暖及极端气温指数变化特征

应用均一化逐日气象观测资料,分析了北京地区1960—2008年气候变暖及主要极端气温指数的统计特征。结果表明：近49年来北京年平均气温增温速率约为0.39℃/10a,最高、最低气温变化具有明显的非对称性。霜冻日数和气温年较差呈现下降趋势,暖夜指数及热浪指数呈现上升趋势,除气温年较差外,其他极端气温指数的气候变率均在加大。北京年平均气温及极端气温指数主要存在21年、15～17年及准10年周期特征。年平均气温与极端气温指数之间存在较强相关性,气候变暖突变发生前后某些极端气温指数发生频率表现出明显差异。自1980年起,北京市区极端最高气温及其增温率明显高于近郊和远郊,高温日数市区多于近郊,近郊多于远郊;近、远郊极端最低气温温差高于城、近郊温差。     

Changes in Daily Climate Extremes of Observed Temperature and Precipitation in China

Daily precipitation for 1960–2011 and maximum/minimum temperature extremes for 1960–2008 recorded at 549 stations in China are utilized to investigate climate extreme variations.A set of indices is derived and analyzed with a main focus on the trends and variabilities of daily extreme occurrences.Results show significant increases in daily extreme warm temperatures and decreases in daily extreme cold temperatures,defined as the number of days in which daily maximum temperature(Tmax)and daily minimum temperature(Tmin)are greater than the 90th percentile and less than the10th percentile,respectively.Generally,the trend magnitudes are larger in indices derived from Tmin than those from Tmax.Trends of percentile-based precipitation indices show distinct spatial patterns with increases in heavy precipitation events,defined as the top 95th percentile of daily precipitation,in western and northeastern China and in the low reaches of the Yangtze River basin region,and slight decreases in other areas.Light precipitation,defined as the tail of the5th percentile of daily precipitation,however,decreases in most areas.The annual maximum consecutive dry days(CDD)show an increasing trend in southern China and the middle-low reach of the Yellow River basin,while the annual maximum consecutive wet days(CWD)displays a downtrend over most regions except western China.These indices vary significantly with regions and seasons.Overall,occurrences of extreme events in China are more frequent,particularly the night time extreme temperature,and landmasses in China become warmer and wetter.     

非对称性增温对农业生态系统影响研究进展

该文概述了北半球和我国气候变暖中增温的非对称性特征:北半球气候变暖存在明显的季节差异和昼夜不同步性, 大部分地区冬、春季升温高于夏、秋季, 日最低气温升幅是日最高气温升幅的2~3倍; 近50年我国近地表气温升高主要是最低气温明显上升的结果, 日最低气温升幅是日最气高温升幅的2~3倍, 与北半球基本一致; 升温最显著的季节为冬季和春季。在此基础上概述了非对称性增温对农业生态系统的影响, 论述了非对称性增温对农作物物候和农作物产量的影响, 得出最低气温升高促使整个生长季延长, 促使早春作物物候期提前, 但最低气温和最高气温对不同作物的物候以及同一作物的不同发育阶段影响不同。现有研究多采用模型或统计的方法研究气候变暖对作物生长的影响, 认为温度升高对作物有“强迫成熟”效应; 而现有的最低气温升高和最高气温升高对作物生长影响的研究结果并不一致。非对称性增温对农作物影响的实验研究极少, 且缺乏对模型模拟结果的实验验证。     

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec,Canada

Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m^?2?d^?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m^?2?d^?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.