Snowfall trends and variability in Qinghai, China

Snow is an important component of a climate system, and very sensitive to climate change, and it can be an indicator of checking and monitoring global change. This paper is concerned with identifying changes in the time series of snowfall in Qinghai, located in western China. We used the Mann-Kendall test to evaluate the annual and monthly trends in snowfall in Qinghai during the periods 1957/58–2006/07 based on data obtained from 25 stations. Significant positive and negative trends at the 90, 95, and 99 percent confidence levels were detected for numerous stations. In order to understand the regional pattern of snowfall in Qinghai, the detected trends are spatially interpolated using the inverse distance weighted interpolation method in a GIS environment. The results indicate that in January snowfall in the most area of Qinghai shows a significant upward trend, especially in the Delingha and Nomhon area in northern Qaidam Basin, as well as southeastern Qinghai, however, there is no significant downward trend area. The trend analysis indicates that annual snowfall has a significant upward trend in Delingha and Nomhon area in northern Qaidam Basin, and the downward trends occurred in the Qilian mountain area and Gonghe Basin. This is especially the case for highlighting the spatial structure of snowfall trends. The research results help in further understanding the regional pattern of snowfall in Qinghai and its response to global climatic warming.     

石羊河流域降雪初、终日及雪期特征分析

利用1960—2017年石羊河流域5个气象站点降雪和气温观测资料,采用气候诊断分析方法,分析了石羊河流域降雪初、终日及雪期的时空变化特征及与气温和海拔高度的关系。结果表明:降雪初日为山区早于平原区,平原区早于荒漠区;降雪终日为山区晚于平原区,平原区晚于荒漠区;雪期为山区长于平原区,平原区长于荒漠区。各地降雪初、终日及雪期存在一定的异常性,正常降雪初、终日及雪期年概率均超过67%。年、年代降雪初日呈推迟趋势,降雪终日(除凉州和古浪外)呈提早趋势,雪期呈缩短趋势。降雪初、终日及雪期与气温和海拔高度呈极显著相关性,气温每升高1℃,初日推迟约6.3 d,终日提早约7.6 d,雪期缩短约13.8 d;海拔每升高100 m,初日提早约3.3 d;终日推迟约4.0 d,雪期延长约7.3 d。本研究为降雪预报提供了参考依据,同时对防御雪灾、科学利用降雪资源、保护生态环境和促进区域经济发展有重要意义。     

Discrete wavelet transform-based investigation into the variability of standardized precipitation index in Northwest China during 1960–2014

This study analyses spatio-temporal trends in precipitation, temperature, and river discharge in the northeast of Iran during recent decades (1953–2013). The Pettitt, SNHT, Buishand, Box-Pierce, Ljung-Box, and McLeod-Li methods were applied to examine homogeneity in time series studied. The nonparametric Mann-Kendall and Sen’s slope estimator tests were used to detect possible significant (p < 0.05) temporal trends in hydrometeorological time series and their magnitude, respectively. For time series with autocorrelation, the trend-free pre-whitening (TFPW) method was used to determine significant trends. To explore spatial distributions of trends, their magnitudes were interpolated by the inverse distance whitening (IDW) method. Trend analysis shows that for daily, monthly, and annual precipitation time series, 12.5, 19, and 12.5 % of the stations revealed significant increasing trends, respectively. For mean temperature, warming trends were found at 38, 23, and 31 % of the stations on daily, monthly, and annual timescales, in turn. Daily and monthly river discharge decreased at 80 and 40 % of the stations. Overall, these results indicate significant increases in precipitation and temperature but decreases in river discharge during recent decades. Hence, it can be concluded that decreasing trends in river discharge time series over the northeast of Iran during 1953–2013 are in response to warming temperatures, which increase the rate of evapotranspiration. Differences between the results of our comprehensive large-scale study and those of previous researches confirm the necessity for more model-based local studies on climatic and environmental changes across the northeast of Iran.     

Application of random forest time series,support vector regression and multivariate adaptive regression splines models in prediction of snowfall (a case study of Alvand in the middle Zagros,Iran)

We have conducted a case study to investigate the performance of support vector machine, multivariate adaptive regression splines, and random forest time series methods in snowfall modeling. These models were applied to a data set of monthly snowfall collected during six cold months at Hamadan Airport sample station located in the Zagros Mountain Range in Iran. We considered monthly data of snowfall from 1981 to 2008 during the period from October/November to April/May as the training set and the data from 2009 to 2015 as the testing set. The root mean square errors (RMSE), mean absolute errors (MAE), determination coefficient (R ²), coefficient of efficiency (E%), and intra-class correlation coefficient (ICC) statistics were used as evaluation criteria. Our results indicated that the random forest time series model outperformed the support vector machine and multivariate adaptive regression splines models in predicting monthly snowfall in terms of several criteria. The RMSE, MAE, R ², E, and ICC for the testing set were 7.84, 5.52, 0.92, 0.89, and 0.93, respectively. The overall results indicated that the random forest time series model could be successfully used to estimate monthly snowfall values. Moreover, the support vector machine model showed substantial performance as well, suggesting it may also be applied to forecast snowfall in this area.     

An Overview of the Second Generation Adjusted Daily Precipitation Dataset for Trend Analysis in Canada

A second generation adjusted precipitation daily dataset has been prepared for trend analysis in Canada. Daily rainfall and snowfall amounts have been adjusted for 464 stations for known measurement issues such as wind undercatch, evaporation and wetting losses for each type of rain-gauge, snow water equivalent from ruler measurements, trace observations and accumulated amounts from several days. Observations from nearby stations were sometimes combined to create time series that are longer; hence, making them more useful for trend studies. In this new version, daily adjustments are an improvement over the previous version because they are derived from an extended dataset and enhanced metadata knowledge. Datasets were updated to cover recent years, including 2009. The impact of the adjustments on rainfall and snowfall total amounts and trends was examined in detail. As a result of adjustments, total rainfall amounts have increased by 5 to 10% in southern Canada and by more than 20% in the Canadian Arctic, compared to the original observations, while the effect of the adjustments on snowfall were larger and more variable throughout the country. The slope of the rain trend lines decreased as a result of the larger correction applied to the older rain-gauges while the slope of the snow trend lines increased, mainly along the west coast and in the Arctic. Finally, annual and seasonal rainfall and snowfall trends based on the adjusted series were computed for 1950–2009 and 1900–2009. Overall, rainfall has increased across the country while a mix of non-significant increasing and decreasing trends was found during the summer in the Canadian Prairies. Snowfall has increased mainly in the north while a significant decrease was observed in the southwestern part of the country for 1950–2009.

     

1960—2005年东北地区降雪变化特征研究

利用国家气象信息中心提供的逐日降水和逐日天气现象台站资料,在运用旋转经验正交函数（Rotated Empirical Orthogonal Function, REOF）和相关分析进行降雪分区的基础上,重点研究了46 a来东北地区降雪的时空分布、演变特征和长期气候趋势。结果表明：东北地区的山地是主要的降雪地区,而平原及平原南部是降雪较少的区域,降雪区域差异明显。在空间上,大兴安岭北部（长白山地区）是降雪增加（减少）最大的地区,小兴安岭地区（平原地区）是降雪增加（减少）较明显的地区。在时间上,东北北区降雪量呈增加趋势,且在20世纪90年代发生了突变,目前增加趋势显著,而东北南区降雪量是减少的。     

Estimation of persistence and trends in geostrophic wind speed for the assessment of wind energy yields in Northwest Europe

Wind climate in Northwest Europe is subject to long-term persistence (LTP), also called the Hurst phenomenon. Ignorance of LTP causes underestimation of climatic variability. The quantification of multi-year variability is important for the assessment of the uncertainty of future multi-year wind yields. Estimating LTP requires long homogeneous time series. Such series of wind observations are rare, but annual mean geostrophic wind speed (U) can be used instead. This study demonstrates a method to estimate the 10-year aggregated mean U for the near and the far future and its uncertainty in Northwest Europe. Time series of U were derived from daily sea level pressure from the European Climate Assessment Dataset. Minor inhomogeneities cannot be ruled out, but were shown to hardly affect the estimated Hurst exponent $(\hat{H})$ . A maximum likelihood method was adjusted to remove the biases in $\hat{H}$ . The geostrophic wind speed over the North Sea, the British Isles and along the Scandinavian coast are characterised by statistically significant H between 0.58 and 0.74, (H?=?0.5 implies no LTP). The additional affect of the parameter uncertainty is estimated in a Bayesian way and is highly dependent on the record length. The assessment of structural changes in future wind fields requires general circulation models. An ensemble of seventeen simulations (ESSENCE) with one single climate model (ECHAM5/MPI-OM) was used to evaluate structural trends and LTP. The estimated $\hat{H}$ in the ESSENCE simulations are generally close to 0.5 and not significant. Significant trends in U are found over large parts of the investigated domain, but the trends are small compared to the multi-year variability. Large decreasing trends are found in the vicinity of Iceland and increasing trends near the Greenland coast. This is likely related to the sea ice retreat within the ESSENCE simulations and the associated change in surface temperature gradients.     

乌鲁木齐冬季清雪预报服务研究

以乌鲁木齐冬季清雪工作需求为牵引,利用乌鲁木齐2006—2016年冬季(当年11月—翌年3月)逐日降雪量、积雪深度、气温和173 d降雪日的逐时降雪量资料,分析了近11年冬季各量级降雪的分布特征、降雪持续时间和降雪的日变化特征;对积雪深度和对应降雪量做线性拟合,得出二者比值大体为0.968 cm/mm。得出清雪预报服务的重点是:小雪的有无、降雪起止时间和累积降雪量、积雪深度的预报,难点是中雪、大雪特别是暴雪和对应的积雪深度的精细化预报。     

我国强降雪气候特征及其变化

基于全国气象台站逐日地面降雪观测数据,对我国25°N以北不同气候区强降雪事件的地理分布和年内旬、月变化等气候特征进行分析,并探讨1961—2008年其时间序列演变特征,及1961—2008年和1981—2008年 (气候变暖后) 气候变化趋势。结果表明:强降雪量和强降雪日数在青藏高原东部、新疆和东北北部最多;强降雪强度高值中心出现在云南。东北北部、华北、西北、青藏高原东部强降雪事件多发生于初冬和初春,年内分布呈双峰型;新疆和黄淮地区年内分布呈单峰型,前者多发生在隆冬时节,后者多发生于晚冬;1961—2008年东北北部、新疆、青藏高原东部平均强降雪量和强降雪日数呈明显增加趋势;气候变暖后我国大部年强降雪量增多,强降雪日数增加,强降雪强度增强。     

Observed changes in relative humidity and dew point temperature in coastal regions of Iran

The analysis of trends in hydroclimatic parameters and assessment of their statistical significance have recently received a great concern to clarify whether or not there is an obvious climate change. In the current study, parametric linear regression and nonparametric Mann?CKendall tests were applied for detecting annual and seasonal trends in the relative humidity (RH) and dew point temperature (T _dew) time series at ten coastal weather stations in Iran during 1966?C2005. The serial structure of the data was considered, and the significant serial correlations were eliminated using the trend-free pre-whitening method. The results showed that annual RH increased by 1.03 and 0.28?%/decade at the northern and southern coastal regions of the country, respectively, while annual T _dew increased by 0.29 and 0.15°C per decade at the northern and southern regions, respectively. The significant trends were frequent in the T _dew series, but they were observed only at 2 out of the 50 RH series. The results showed that the difference between the results of the parametric and nonparametric tests was small, although the parametric test detected larger significant trends in the RH and T _dew time series. Furthermore, the differences between the results of the trend tests were not related to the normality of the statistical distribution.     

Watershed-wide trend analysis of temperature characteristics in Karun-Dez watershed, southwestern Iran

Trend estimation of climatic characteristics for a watershed is required to determine developing compatible strategies related to design, development, and management of water resources. In this study, the trends of the annual maximum (T _max), minimum (T _min), and mean (T _mean) air temperature; temperature anomaly (T _anomaly); and diurnal temperature range (DTR) time series at 13 meteorological stations located in the Karun-Dez watershed were analyzed using the Mann–Kendall and linear regression trend tests. The pre-whitening method was used to eliminate the influence of serial correlation on the Mann–Kendall test. The result showed increasing trends in the T _min, T _mean, and T _anomaly series at the majority of stations and decreasing trend in the T _max and DTR series. A geographical analysis of the trends revealed a broad warming trend in most of the watershed, and the cooling trends were observed only in the southern parts. Furthermore, the geographical pattern of the trends in the T _mean and T _anomaly series was similar, and the T _max data did not show any dominant trend for the whole watershed. This study provides temperature change scenarios that may be used for the design of future water resource projects in the watershed.     

北疆冰雪运动气候适宜性及其变化特征

基于北疆43站的降雪量、日平均气温、日最高气温以及日平均风速等资料,应用冰雪运动气候适宜性指数(ISCI),对1961—2017年冬春季北疆的冰雪运动适宜性及其变化特征进行评估,结果如下：(1)北疆大部分站点都适宜开展冰雪运动,阿勒泰等地最为适宜,但伊犁河谷大部分站点、阿拉山口与达坂城等地适宜程度相对较低。(2)冰雪运动气候适宜程度与降雪量并非线性关系,北疆冬春季区域平均降雪量越大,很适宜与不适宜开展冰雪运动的站点都越多。海拔在1200 m以上的站点均适宜开展冰雪运动。(3) ISCI指数的年际差异明显,区域平均ISCI指数随时间推进而减小,但不同站点的ISCI指数线性变化趋势有升有降。1998—2017年期间,北疆适宜冰雪运动的站点较之前大范围减少。(4)阿勒泰地区各站在所有时间阶段中均适宜开展冰雪运动,尤其是阿勒泰北部,冰雪运动气候适宜程度呈阶段性增大,而伊犁河谷大部分站点的冰雪运动适宜程度整体呈阶段性减小。     

Nonlinear dynamics of meteorological variables: multifractality and chaotic invariants in daily records from Pastaza, Ecuador

Weather represents the daily state of the atmosphere. It is usually considered as a chaotic nonlinear dynamical system. The objectives of the present study were (1) to investigate multifractal meteorological trends and rhythms at the Amazonian area of Ecuador and (2) to estimate some nonlinear invariants for describing the meteorological dynamics. Six meteorological variables were considered in the study. Datasets were collected on a daily basis from January 1st 2001 to January 1st 2005 (1,460 observations). Based on a new multifractal method, we found interesting fractal rhythms and trends of antipersistence patterns (Fractal Dimension >1.5). Nonlinear time series analyses rendered Lyapunov exponent spectra containing more than one positive Lyapunov exponent in some cases. This sort of hyperchaotic structures could explain, to some extent, larger fractal dimension values as the Kaplan–Yorke dimension was also in most cases larger than two. The maximum prediction time ranged from ξ?=?1.69 days (approximately 41 h) for E/P ratio to ξ?=?14.71 days for evaporation. Nonlinear dynamics analyses could be combined with multifractal studies for describing the time evolution of meteorological variables.     

Spatial dependence of diurnal temperature range trends on precipitation from 1950 to 2004

This paper analyzes the spatial dependence of annual diurnal temperature range (DTR) trends from 1950–2004 on the annual climatology of three variables: precipitation, cloud cover, and leaf area index (LAI), by classifying the global land into various climatic regions based on the climatological annual precipitation. The regional average trends for annual minimum temperature (T _min) and DTR exhibit significant spatial correlations with the climatological values of these three variables, while such correlation for annual maximum temperature (T _max) is very weak. In general, the magnitude of the downward trend of DTR and the warming trend of T _min decreases with increasing precipitation amount, cloud cover, and LAI, i.e., with stronger DTR decreasing trends over drier regions. Such spatial dependence of T _min and DTR trends on the climatological precipitation possibly reflects large-scale effects of increased global greenhouse gases and aerosols (and associated changes in cloudiness, soil moisture, and water vapor) during the later half of the twentieth century.     

Time trend estimation with breaks in temperature time series

This paper deals with the modelling of the global and northern and southern hemispheric anomaly temperature time series using a novel technique based on segmented trends and fractional integration. We use a procedure that permits us to estimate linear time trends and orders of integration at various subsamples, where the periods for the changing trends are endogenously determined by the model. Moreover, we use a non-parametric approach (Bloomfield P, Biometrika, 60:217–226, 1973) for modelling the I(0) deviation term. The results show that the three series (global, northern and southern temperatures) can be well described in terms of fractional integration with the orders of integration around 0.5 in the three cases. The coefficients associated to the time trends are statistically significant in all subsamples for the three series, especially during the final part of the sample, giving then some support to the global warming theories.     

Trend analysis of extreme thermal indices in south Brazil (1971 to 2014)

Southern Brazil has recently been the subject of many major natural hazards from weather and climatic origin, including violent floods in 2016 caused by very heavy rains. It has also experienced episodes of droughts and heat waves in recent years. The state of Paraná, located at the climatic transition zone, has densely populated urban areas, vast cultivation areas, and hydro-electric reservoirs (more than 20), so those extreme events are letting local populations vulnerable to the vagaries of the climate and their impacts on community, infrastructure, and ecosystem. This study analyzes climatic variability and trends of about ten climatic indices (with RClimDex), calculated by using daily thermal data of ten stations spread mainly in two geographic regions of Paraná. The Mann-Kendall and Pettitt tests were applied for trends analysis and determine the presence of breakpoints in the time series (1971–2014). Our results showed that for the various thermal indices, there is a reduction of the cold days on all the stations, indicating a more pronounced increase of the indices based on the maximum temperatures (TX), especially in the north. On the other hand, the indexes based on minimum temperatures (TN) show a more modest and partial increase, varying accordingly to the location of the stations. Regionally, the trends calculated were similar, that is, North region presents a more expressive increase of the indices of heat whereas in the east predominates a reduction.     

Analysis of trends and sudden changes in long-term environmental data from King George Island (Antarctica): relationships between global climatic oscillations and local system response

The Western Antarctic Peninsula is one of the most rapidly warming regions on earth. It is therefore important to analyze long-term trends and inter-annual patterns of change in major environmental parameters to understand the process underlying climate change in Western Antarctica. Since many polar long-term data series are fragmented and cannot be analysed with common time series analysis tools, we present statistical approaches that can deal with missing values. We applied U-statistics after Pettit and Buishand to detect abrupt changes, dynamic factor analysis to detect functional relationships, and additive modelling to detect patterns in time related to climatic cycles such as the Southern Annular Mode and El Niño Southern Oscillation in a long-term environmental data set from King George Island (WAP), covering 20 years. Our results not only reveal sudden changes for sea surface temperature and salinity, but also clear patterns in all investigated variables (sea surface temperature, salinity, suspended particulate matter and Chlorophyll a) that can directly be related to climatic cycles. Our results complement previous findings on climate related changes in the King George Island Region and provide insight into the environmental conditions and climatic drivers of system change in the study area. Hence, our statistical analyses may prove valuable for other polar environmental data sets and contribute to a better understanding of the regional variability of climate change and its impact on coastal systems.     

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.     

基于HSIC核函数聚类的湖北省降雪气候区划

无资料地区雪灾防御参数常采用周边有资料的气象站参数替代,基于气候背景相似的降雪气候区划可以为代表站的选取提供科学依据。本文利用湖北省76个国家气象站1961—2020年的气象观测资料,选取了降雪初终日、雪日数、积雪日数、降雪量、最大积雪深度等12个多维时间序列指标,采用Hilbert-Schmidt Independence Criterion（HSIC）核函数的有偏估计公式计算12个指标的整体相似性,对湖北省降雪气候进行了聚类分析。结果表明：湖北省降雪气候可以划分为东南部、中部、西北部和西南部4个气候分区,分区的地带性分布特征与湖北省强降雪天气由北方冷空气南下产生的气候背景一致;初雪日从西北部向中部、西南部、东南部降雪区推迟,终雪日则正好相反,西北部的降雪日数和积雪日数最多;东南部代表站为黄石站,中部代表站有麻城、武汉、钟祥,西南部代表站有咸丰、巴东,西北部代表站郧西、老河口。HSIC核函数能很好处理较大年际波动的指标序列集之间的相似性,其聚类方法对湖北省降雪的气候区划较为合理,区划结果为湖北省精细化雪灾防御提供了技术依据。     

Application of stochastic simulation to climatic-change studies

Stochastic modelling provides a tool for exploring the full implications of the statistical behavior of historical records and can be used to predict the changing probabilities that events of various magnitudes will occur for different climatic change scenarios. Two simulation models are presented, one for daily air temperature, and the other for daily precipitation. The simulation procedures are: (1) extract salient parameter values from historical records; (2) simulate long sequences of data using the stochastic models, with or without a climatic change scenario as provided by a general circulation model; and (3) using the simulated data as inputs, derive the probability distributions of other variables based on known deterministic or probabilistic relationships between the input and the predicted variables.Given a doubling of carbon dioxide concentration in the atmosphere, the climatic models produce varying degrees of temperature and precipitation changes. Examples of application, including the derivation of snowfall and riverice data using simulated temperature and precipitation, illustrate that stochastic modelling offers a suitable approach to quantify the possible hydrologic impacts of climatic change.