Extreme Minimum Temperature Research Articles

Variability and predictability of winter cold nights in Argentina

Extreme cold events can cause crop damage as well as an increase in energy demand and even in the mortality rate. Identifying the atmospheric processes associated with the occurrence of these extremes would allow decision-makers to implement preventive and mitigation actions. The goal of this study is to identify predictors of cold extremes of minimum temperature during winter in Argentina (north of 40 °S), and their modulation by the different El Niño-Southern Oscillation (ENSO) phases for the period 1970–2015. Lagged correlation analysis between winter cold nights and several global and regional climate indices identified ENSO, subtropical jet intensity, position and intensity of the South Pacific Anticyclone (SPA), and a blocking index as the dominating forcings. The El Niño phase is associated with a decrease in the occurrence of cold extremes. In addition, an increase in the number of blocking events together with a weaker subtropical jet and, a less intense and northward shift of the SPA also inhibit the occurrence of cold extremes of the minimum temperature. Our results indicate persistence in the winter cold nights series compared with the previous season in eastern Argentina. Two winters with opposite extreme conditions (high and low frequency of cold nights) were selected as case studies. Since both cases occurred under ENSO neutral conditions, we also searched for cold nights predictors considering only neutral years. We detected the presence of an equivalent barotropic structure located in southeastern Pacific in both events, which confirmed the importance of the SPA intensity and blocking events as predictors in absence of an active El Niño.

A note on analysis of extreme minimum temperatures with the GAMLSS framework

Estimation of return levels, based on extreme value distributions, is of importance in the earth and environmental sciences. To incorporate non-stationarity in the modelling, the statistical framework of generalised additive models for location, scale and shape is an option, providing flexibility and with a wide range of distributions implemented. With a large set of selections possible, model choice is an issue. As a case study, we investigate annual minimum temperatures from measurements at a location in northern Sweden. For practical work, it turns out that care must be taken in examining the obtained distributions, not solely relying on information criteria. A simulation study illustrates the findings.

Responses of radial growth to climate change in Pinus massoniana at different altitudes and slopes.

With dendrochronology method, standard and residual chronologies of Pinus massoniana were established at low altitude (260 m), middle altitude (460 m), high altitude (690 m), sunny slope (270 m), and shady slope (265 m). Relationships between the tree-ring width and the climatic factors were quantified using correlation analysis and redundancy analysis (RDA). The optimal multiple regression models for the radial growth of P. massoniana and the climatic factors were established. We analyzed the change rule of radial growth and its relationship with the climatic factors along with the altitude and slope. The results showed that the radial growth of P. massoniana was significantly affected by precipitation and temperature across the altitude gradient and the slope level, respectively. Among the 120 climatic variables, precipitation in December of last year and the extreme minimum temperature in February of current year had the most significant negative effects on the radial growth at different altitudes and slopes, respectively. This study quantitatively described the impacts of climate change on the radial growth of P. massoniana in the subtropical region, and provided a scientific basis for the planting and management of P. massoniana forest in Jiangle Country under the climate warming background.

Environmental predictors of livestock predation: a lion's tale

AbstractNegative interactions between people and large carnivores are common and will probably increase as the human population and livestock production continue to expand. Livestock predation by wild carnivores can significantly affect the livelihoods of farmers, resulting in retaliatory killings and subsequent conflicts between local communities and conservationists. A better understanding of livestock predation patterns could help guide measures to improve both human relationships and coexistence with carnivores. Environmental variables can influence the intensity of livestock predation, are relatively easy to monitor, and could potentially provide a useful predictive framework for targeting mitigation. We chose lion predation of livestock as a model to test whether variations in environmental conditions trigger changes in predation. Analysing 6 years of incident reports for Pandamatenga village in Botswana, an area of high human–lion conflict, we used generalized linear models to show that significantly more attacks coincided with lower moonlight levels and temperatures, and attack severity increased significantly with extreme minimum temperatures. Furthermore, we found a delayed effect of rainfall: lower rainfall was followed by a significantly increased severity of attacks in the following month. Our results suggest that preventative measures, such as introducing deterrents or changing livestock management, could be implemented adaptively based on environmental conditions. This could be a starting point for investigating similar effects in other large carnivores, to reduce livestock attacks and work towards wider human–wildlife coexistence.

Simulation of extreme temperatures using a new method: TIN‐copula

AbstractIn climatology, there is a clear need for more reliable data, especially in regions where no meteorological stations exist. Different statistical methods as well as regional climate models are usually used for covering areas with limited data. However, important biases between real and simulated climate parameters are observed, especially with respect to extremes. The present study introduces a new statistical method that combines triangular irregular networks and copulas for the simulation of extreme maximum and minimum temperatures. According to the new method, a studied region can be divided into triangles, and a data series can be simulated for every unknown x‐point in each triangle. The simulation of new data series is based on both the distances between the unknown point and the triangle vertices and the observational data available at each vertex. The statistical evaluation of this new method was successful and further demonstrated that the size of the triangle as well as the climatic characteristics of the stations at the triangle vertices can significantly affect the final results. The present investigation proposes the triangular irregular network‐copula method for the simulation of extreme temperatures, as the projections provided by this method are shown to approach the observed extremes values for individual meteorological stations.

Within-winter flexibility in muscle and heart lipid transport and catabolism in passerine birds.

Small birds in cold climates may show within-winter metabolic flexibility to match metabolic capacities to prevailing weather conditions. This flexibility may occur over periods of days to weeks, but the underlying mechanisms for such flexibility are not well understood. Because lipids are the primary fuel for sustained thermogenesis, we examined whether lipid transport and catabolism can mediate within-winter metabolic flexibility in two small temperate-zone wintering passerine birds, dark-eyed juncos (Junco hyemalis) and house sparrows (Passer domesticus). We used simple and multiple regression analyses to test for correlations of several lipid transporters in pectoralis muscle (plasma membrane-bound and cytosolic fatty acid-binding proteins, FABP; fatty acyl translocase, FAT/CD36) and regulatory enzymes (carnitine acyl transferase, CPT; β-hydroxyacyl CoA dehydrogenase, HOAD) in pectoralis and heart with short-term (ST, 0-7days), medium-term (MT, 14-30days) and long-term (LT, 30-year mean daily and extreme minimum temperatures, day of winter season) temperature variables. We hypothesized negative correlations between these regulators and temperature variables. Juncos showed negative correlations for FABPs with ST or MT temperature variables, but other lipid transporters and regulatory enzymes showed positive correlations with ST or MT temperatures for juncos, suggesting no consistent pathway-wide response to within-winter temperatures. LT temperature variables showed several significant associations with lipid transporters and enzymes for juncos, but also not in consistent directions. House sparrows showed the expected negative correlations with LT temperatures for FABPpm, but positive correlations with temperature variables for FABPc, CPT and HOAD. Different species-specific patterns of variation and the absence of consistent pathway-wide responses to temperature suggest that the lipid transport and catabolism pathway is not a uniform mediator of within-winter metabolic flexibility among small birds.

Hemlock woolly adelgid niche models from the invasive eastern North American range with projections to native ranges and future climates

Abstract: The hemlock woolly adelgid (Adelges tsugae Annand - HWA) is invasive in eastern North America where it causes extensive mortality to hemlock communities. The future of these communities under projected climate change is an issue of landscape ecological interest and speculation. We employed the MaxEnt algorithm with the random subset feature selection algorithm (RSFSA) in creating HWA niche models. Final models were ensembles of 12 statistically best models with six predictors each. Out of 119 climatic, topographic, and soil variables, 42 were used in at least one final model. Soil features, followed by climate and topographic features, were most common in selected models. The three most important variables among all models were November potential evapotranspiration, slope, and percent Ochrepts soil. The potential distributions of HWA within eastern North America were projected under historical and four future climate scenarios for 2050 and 2070 under low and high CO2 emissions. The mean of the minimum values for the minimum temperature of the coldest month from the 12 MaxEnt model projections in eastern North America was -15.8°C. This value was close to -15°C, the extreme minimum temperature found for both HWA occurrence points and previously reported HWA cold temperature limits. These results indicate that HWA may be close to equilibrium distribution in eastern North America under current climate. We also reverse-casted the eastern North American MaxEnt model back onto the HWA native ranges in eastern Asia and western North America. The projections match best with native ranges in Asian islands, such as Japan, and the Cascade Mountains in western North America. Statistically significant HWA range shifts of 221-468 km northwards and 110-164 km eastwards were projected by the 12 models for 2050-2070. The 2070 high CO2 emission scenario models projects HWA suitability throughout most of the northern range of eastern hemlock.

Spatial patterns of trends in seasonal extreme temperatures in India during 1980–2010

Station level daily maximum and minimum temperature data spread across India were analyzed for trends in seasonal extreme temperature indices. The trends were calculated for the recent period of 1980–2010, when some of the warmest temperatures were recorded at the global scale. The results of the analysis revealed substantial variations in the spatial patterns of the trends. The majority of the study area experienced declining trends in diurnal temperature range during all seasons. However, substantial variations were observed for the different extreme temperature indices. During the first half of the year, which constitutes the drier summer and winter months, a declining trend in extreme maximum and minimum temperature indices were observed over most of peninsular India and the reverse in northern India. The spatial patterns were in general similar for the first half of the year, winter and summer, which constitute the drier months. The spatial patterns can be attributed to both local and regional level processes, such as aerosol levels and land use land cover changes.

Conservation of genetic diversity hotspots of the high-valued relic yellowhorn (Xanthoceras sorbifolium) considering climate change predictions.

Genetic structure and major climate factors may contribute to the distribution of genetic diversity of a highly valued oil tree species Xanthoceras sorbifolium (yellowhorn). Long‐term over utilization along with climate change is affecting the viability of yellowhorn wild populations. To preserve the species known and unknown valuable gene pools, the identification of genetic diversity “hotspots” is a prerequisite for their consideration as in situ conservation high priority. Chloroplast DNA (cpDNA) diversity was high among 38 natural populations (H d = 0.717, K = 4.616, Tajmas’ D = −0.22) and characterized by high genetic divergence (F ST = 0.765) and relatively low gene flow (N m = 0.03), indicating populations isolation reflecting the species’ habitat fragmentation and inbreeding depression. Six out of the studied 38 populations are defined as genetic diversity “hotspots.” The number and geographic direction of cpDNA mutation steps supported the species southwest to northeast migration history. Climatic factors such as extreme minimum temperature over 30 years indicated that the identified genetic “hotspots” are expected to experience 5°C temperature increase in next following 50 years. The results identified vulnerable genetic diversity “hotspots” and provided fundamental information for the species’ future conservation and breeding activities under the anticipated climate change. More specifically, the role of breeding as a component of a gene resource management strategy aimed at fulfilling both utilization and conservation goals.

Assessment of climate extremes in future projections downscaled by multiple statistical downscaling methods over Pakistan

Climate change is a phenomenon that is unequivocally altering the natural systems in all parts of the world but the alteration in climate extremes may pose more severe and unexpected impacts on Pakistan. The current study provides a comprehensive outlook of observation (1976–2005) and changes in climate extremes between the reference (1976–2005) and future periods (2020s: 2006–2035, 2050s: 2036–2065 and 2080s: 2066–2095). The analysis was conducted across six sub-regions of Pakistan including North Pakistan (NP), Monsoon Region (MR), Khyber Pakhtunkhwa (KP), Southern Punjab (SP), Balochistan and Sindh for which Coupled Model Intercomparison Project Phase 5 (CMIP5) 14 General Circulation Models (GCMs) under Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 were downscaled and bias corrected by three statistical downscaling methods. The spatial disaggregation and quantile delta mapping (SDQDM) method was used for future projections in this study. Changes in climate extremes were detected by Expert Team on Climate Change Detection and Indices (ETCCDI). In case of temperature, the results indicate a projected increase in frequencies and magnitudes for warm extremes, while it is decreasing for cold extremes in the 21st century. The corresponding trends of maximum and minimum temperature extremes are greater than the mean temperature trend; where the frequency and magnitude of minimum temperature extremes is higher than maximum temperature extremes over Pakistan particularly over North in last half of the 21st century for both RCPs. Also, the average of temperature extremes (TXx, TXn, TNx and TNn) are severe in the order of NP (+4.8 °C), KP (+4.6 °C) and MR (+4.5 °C). In the case of precipitation extremes, most of the sub-regions across Pakistan show a higher increase in total annual precipitation and intense precipitation events with the highest increase in MR, KP and NP and the least increase in Sindh. Despite the increase in total precipitation, numbers of consecutive dry days (CDD) are increasing while consecutive wet days (CWD) are decreasing which can give rise to drought conditions particularly in Sindh. The study provides complementary and consistent climate extremes information over Pakistan for local decision makers to incorporate into policy-making, disaster management, and infrastructure planning.

Climatic Experiment on Guizhou Red Heart Kiwifruit Ulcer Disease

Using the climatic data, such as extreme minimum temperature and relative humidity, in 15 bases in the main planting area of Guizhou Red Heart Kiwifruit from December 1st to next April 30th, respectively, from 2010 to 2018, combined with the monitoring of red heart kiwifruit ulcer disease, we analyze the occurrence and prevalence of meteorological factors and dynamic characteristics of the red heart kiwifruit ulcer disease in Guizhou, China. The results showed that the main period of occurrence and prevalence of red heart kiwifruit ulcer was from March to April. In winter, when the daily average temperature of 5 d and above is ≤2°C, the daily extreme minimum temperature is ≤−2°C, and the daily average relative humidity is ≥75%, it is beneficial for the ulcer pathogen to lurk and propagate in the plant. It is suggested that this meteorological condition should be used as an early warning indicator for the prevention of ulcer disease of red heart kiwifruit, in order to actively organize prevention after the emergence of early warning indicators to improve the prevention and treatment effect. In spring, the average daily temperature of 5 d and above is ≤18°C, and the daily average relative humidity is ≥80%, which is easy to induce ulcer disease. It can be seen that the first is the winter climatic conditions to meet the breeding of ulcer disease, and then the spring climatic conditions must also meet the rapid propagation of ulcer disease, ulcer disease can occur, develop and popularize. It is of great practical significance to carry out experiments on the relationship between the occurrence of ulcer disease in Guizhou red heart kiwifruit and meteorological conditions.

基于格网的南昌市生态环境脆弱性评价

PDF HTML阅读 XML下载 导出引用 引用提醒 基于格网的南昌市生态环境脆弱性评价 DOI: 10.5846/stxb201811112441 作者: 作者单位: 江西师范大学地理与环境学院;,江西师范大学地理与环境学院;鄱阳湖湿地与流域教育部重点实验室,江西师范大学地理与环境学院,江西师范大学地理与环境学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41761020）；江西省自然科学基金（20151BAB203041） Gridded assessment of eco-environmental vulnerability in Nanchang city Author: Affiliation: School of Geography and Environment,Jiangxi Normal University;China;,School of Geography and Environment,Jiangxi Normal University;China;Key Lab of Poyang Lake Wetland Watershed Research of Ministry of Education;China,School of Geography and Environment,Jiangxi Normal University;China;,School of Geography and Environment,Jiangxi Normal University;China; Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:科学评价生态环境的脆弱性，对合理利用土地资源和促进区域生态安全具有重要意义。以公里格网为基本评价单元，以快速城市化区域典型代表南昌市为研究区，通过SRP概念模型构建评价指标体系并选取17个评价指标，基于空间主成分分析法、全局Moran's I指数以及LISA聚类图，量化分析了2000-2015年南昌市生态环境脆弱性的时空分布特征及驱动力。结果表明：（1）2000-2015年南昌市生态环境脆弱性呈现由轻度脆弱向中度脆弱演变的趋势；（2）2000-2015年南昌市各县（区）生态环境脆弱性差异明显，东湖区、西湖区、青山湖区、青云谱区、湾里区脆弱性程度最高，其他县（区）脆弱性增幅较大；（3）2000和2015年南昌市生态环境脆弱性具有明显的空间自相关性，且为正相关，高高聚集区主要分布在极度脆弱区，低低聚集区与微度和轻度脆弱区相关；（4）研究时段内，南昌市生态环境脆弱性主要驱动力为人口密度、人均GDP、人均耕地、坡度、地形起伏度、年均气温、极端最高温、极端最低温、年均降水量。 Abstract:Scientific evaluation of the fragility of the ecological environment is of great significance for the rational use of land resources and regional ecological security. Taking the Nanchang City as a case study area, we developed an index-based system for eco-environmental vulnerability assessment based on the ecological sensitivity-ecological recovery-ecological pressure model. Utilizing the spatial principal component analysis method, the global Moran' I index and LISA clustering map, seventeen indicators were selected and used to analyze the spatial and temporal distribution and driving factors of the eco-environmental vulnerability of Nanchang City from 2000 to 2015. Our analyses show there was an increasing trend from light to medium vulnerability in Nanchang City from 2000 to 2015. The spatial heterogeneity of eco-environmental vulnerability in Nanchang City is obvious in both 2000 and 2015. Among the administrative divisions in Nanchang City, the areas with the highest environmental vulnerability were mainly distributed in Donghu, West Lake, Qingshan Lake and Wanli districts. In 2000 and 2015, there was significant global spatial autocorrelation of the eco-environmental vulnerability. High-high (HH) areas were mainly distributed in extremely vulnerable regions, and low-low (LL) regions were associated with negligible and lightly vulnerable areas. From 2000 to 2015, the drivers of eco-environmental vulnerability in Nanchang City were population density, per capita GDP, per capita arable land, slope, topographic relief, average annual temperature, extreme maximum temperature, extreme minimum temperature, and average annual precipitation. 参考文献 相似文献 引证文献

Climate Suitability and Vulnerability of Winter Wheat Planting in Gansu under the Background of Global Warming

Winter wheat is the main food crop in China. Gansu Province is a traditional winter wheat growing area, and its planting range is limited by the thermal conditions of winter. The average temperature in Gansu Province increased by 0.28°C per decade, higher than the China’s and global average, and the warming in winter was more obvious. Therefore, it is necessary to study the climate suitability and vulnerability of winter wheat planting in Gansu. In this paper, the maximum entropy model Maxent and Arcgis software are used to select six major climatic factors including annual total radiation, annual precipitation, the warmest monthly average temperature, the coldest monthly average temperature, annual average temperature, and annual extreme minimum temperature, which construct winter wheat planting distribution-climate relationship model that studies the climate suitability and vulnerability of winter wheat during the period 1961-2015. Studies have shown that the average cold weather and annual extreme minimum temperature are the most important climatic factors affecting winter wheat in Gansu, which can reflect the low temperature conditions that winter wheat can tolerate. However, the main winter wheat planting areas in Gansu Province are distributed in arid and semi-arid rain-fed agriculture areas. Precipitation and total annual radiation are also very important constraints. At the same time, climate change has little effect on winter wheat in Gansu Province, and the area of suitable area fluctuates slightly. It shows moderate adaptation in each evaluation period.

Seasonal and Regional Patterns of Future Temperature Extremes: High‐Resolution Dynamic Downscaling Over a Complex Terrain

AbstractThe combined impacts of projected/simulated climate change and variability on temperature extremes over a complex topographical terrain along the eastern Mediterranean are assessed in this study. High‐resolution dynamical temperature downscaling is conducted for the past (2008) and the near future (until 2050) during eight extreme hot and dry years under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The Weather Research and Forecasting (WRF) model is used for the downscaling process with two nested resolutions of 9 and 3 km, forced by the global High‐Resolution Atmospheric Model (HiRAM) at a resolution of 25 km. Climate indices resultant from daily simulated temperature illustrate considerable changes in daily maximum summer and minimum winter temperature extremes during the future simulated years in comparison with the historic 2008 selected as reference and control period. While average yearly temperatures increase in both scenarios and most regions as expected, the seasonal variability is forecasted to intensify even more significantly resulting in colder winter and warmer summer conditions. Moreover, the interseasonal variability was most pronounced in the years with the highest averaged temperatures. A notable increase in the annual incidence of hot nights and heat wave events relative to 2008 conditions is also expected. The orographic complexity resulted in significant regional differences, with the most affected regions experiencing more than a doubling of extreme indices during the hottest and driest years of near‐future decades. This strong spatial variability highlights the need for high‐resolution downscaling.

Modelling frost generates insights for managing risk of minimum temperature extremes

Frosts and extreme minimum temperature events cause significant damage to grapevines. In the Victorian vine growing regions of Australia, these events may cause complete wipe-out of a season's grape production, and millions of dollars of losses. Frost is an important risk to be managed by viticulturalists, and is likely to change under future climatic conditions with impacts on existing wine-grape growing regions. This case-study applies high spatial resolution analysis of minimum night-time temperatures to explore the impact of current and future frost risks. Multivariate Adaptive Regression Splines (MARS) are used to model high-resolution (30 m grid) minimum temperatures for a topographically diverse region in the Yarra Valley wine region in southeastern Australia. Remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) night-time temperatures, elevation, terrain indices and meteorological station data were used to develop the model of minimum night-time temperatures. The model is formally validated against independent vineyard minimum temperature records (R2 = 0.68) as well as current distribution of viticulture land-use, which was compared with viticulturists' perceptions of a frost-line in the region. Historical temperature records were then adjusted to produce high-resolution maps of frost occurrence under a hot-dry future climate scenario, and a warm-wet scenario, for the years 2030 and 2050. All the future climate scenarios project down-elevation movement of the frost line of between 10 m and 30 m depending on scenarios. This work has implications for viticulturists who might plan to expand growing areas to lower elevations, or might consider changing to different grape varieties. The method developed here could be applied to other regions or used to explore other future climate scenarios.

Projected changes in seasonal temperature extremes across China from 2017 to 2100 based on statistical downscaling

Abstract Based on observations of daily temperature at 2052 ground stations and outputs of General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we study the changes in seasonal temperature extremes and their spatio-temporal patterns across China during the period from 2071 to 2100 relative to the period from 1971 to 2000. The grid-scale extremes estimated from GCM outputs are statistically downscaled to the site scale by transfer functions developed based on the quantiles of grid-scale simulations and on-site observations of the extremes. The assessment of the downscaled seasonal temperature extremes based on Taylor diagram and two-sample Kolmogorv-Smirnov test show that the seasonal maximum/minimum of daily maximum and minimum temperature can be well represented by the downscaled GCM outputs. The seasonal extremes of daily maximum and minimum temperature increase faster in autumn and winter compared to those in spring and summer. The warm nights defined as seasonal fraction of time with daily minimum temperature higher than its 90th percentile centered on a 5-day window are projected to increase substantially in summer. In general, temperature extremes in northern China increase faster than southern China, especially in autumn and winter. The largest increases in seasonal minimum are identified in the most northeastern China in autumn and winter, with magnitudes of >8 °C. In southern China, the increases of seasonal extremes of daily maximum and minimum temperature are generally

Spatiotemporal Patterns of Extreme Temperature across the Huai River Basin, China, during 1961–2014, and Regional Responses to Global Changes

Global warming and its relevant effects have aroused increasing human concerns in recent decades. These anomalies are likely influencing vegetation dynamics and ecosystem stability. This paper aims to dissect extreme temperature variations in both space and time, and related regional responses to global warming in the Huai River Basin. Using the daily maximum and minimum air temperature at 153 stations across the Huai River Basin, China, covering the period of 1961–2014, trends and relations amongst extreme air temperature indices were analyzed, and regional responses of the Huai River Basin to global warming process were also investigated. The results indicated that: (1) widespread increasing trends can be observed in maximum and minimum air temperature, with the largest increasing magnitude of 0.4 °C per decade and 1.3 °C per decade, respectively. The minimum air temperature regimes have a larger increasing magnitude than the maximum air temperatures. (2) There is an increasing trend in the extreme maximum temperature indices, and the increasing rates of TN90p and TR20 are greater than those of the other extreme maximum temperature indices. However, the extreme minimum temperature indices, except for consecutive icy days (CID), show significant decreasing trends. The growing season length (GSL) would increase due to the joint action of (i) an increase in maximum temperature indices, and (ii) a decrease in minimum temperatures indices. Although increases in the GSL would be beneficial for increasing the growing time of crops, a decreased extreme minimum temperature and increased extreme maximum temperature may reduce the winter wheat yield. (3) Extreme low temperature indices show a larger changing magnitude than the extreme high temperature indices. Temporally, a larger changing magnitude can be identified for temperature indices during night-time than during daytime. Moreover, a larger changing magnitude and higher significance of trends of extreme temperature indices can be observed in the eastern Huai River Basin than in the western Huai River Basin. Particularly, we detect an even higher changing rate of extreme temperature indices in the southeastern Huai River Basin. These findings can be well explained by urban heat island effects resulting from urban aggregation across the Yangtze Delta. (4) The correlation between the extreme low temperature indices is slightly higher than that of the extreme high temperature indices. The changing trends of the most extreme temperature indices are closely related to latitude and altitude. Trends in the main extreme temperature indices of the Huai River Basin are essentially similar to those in other regions around the world, implying consistent spatial patterns of extreme temperature indices across the globe.

Possible future changes in South East Australian frost frequency: an inter-comparison of statistical downscaling approaches

Anthropogenic climate change has already been shown to effect the frequency, intensity, spatial extent, duration and seasonality of extreme climate events. Understanding these changes is an important step in determining exposure, vulnerability and focus for adaptation. In an attempt to support adaptation decision-making we have examined statistical modelling techniques to improve the representation of global climate model (GCM) derived projections of minimum temperature extremes (frosts) in Australia. We examine the spatial changes in minimum temperature extreme metrics (e.g. monthly and seasonal frost frequency etc.), for a region exhibiting the strongest station trends in Australia, and compare these changes with minimum temperature extreme metrics derived from 10 GCMs, from the Coupled Model Inter-comparison Project Phase 5 (CMIP 5) datasets, and via statistical downscaling. We compare the observed trends with those derived from the “raw” GCM minimum temperature data as well as examine whether quantile matching (QM) or spatio-temporal (spTimerQM) modelling with Quantile Matching can be used to improve the correlation between observed and simulated extreme minimum temperatures. We demonstrate, that the spTimerQM modelling approach provides correlations with observed daily minimum temperatures for the period August to November of 0.22. This represents an almost fourfold improvement over either the “raw” GCM or QM results. The spTimerQM modelling approach also improves correlations with observed monthly frost frequency statistics to 0.84 as opposed to 0.37 and 0.81 for the “raw” GCM and QM results respectively. We apply the spatio-temporal model to examine future extreme minimum temperature projections for the period 2016 to 2048. The spTimerQM modelling results suggest the persistence of current levels of frost risk out to 2030, with the evidence of continuing decadal variation.

Observed changes in extremes of daily rainfall and temperature in Jemma Sub-Basin, Upper Blue Nile Basin, Ethiopia

Climate variability has been a threat to the socio-economic development of Ethiopia. This paper examined the changes in rainfall, minimum, and maximum temperature extremes of Jemma Sub-Basin of the Upper Blue Nile Basin for the period of 1981 to 2014. The nonparametric Mann-Kendall, seasonal Mann-Kendall, and Sen’s slope estimator were used to estimate annual trends. Ten rainfall and 12 temperature indices were used to study changes in rainfall and temperature extremes. The results showed an increasing trend of annual and summer rainfall in more than 78% of the stations and a decreasing trend of spring rainfall in most of the stations. An increase in rainfall extreme events was detected in the majority of the stations. Several rainfall extreme indices showed wetting trends in the sub-basin, whereas limited indices indicated dryness in most of the stations. Annual maximum and minimum temperature and extreme temperature indices showed warming trend in the sub-basin. Presence of extreme rainfall and a warming trend of extreme temperature indices may suggest signs of climate change in the Jemma Sub-Basin. This study, therefore, recommended the need for exploring climate induced risks and implementing appropriate climate change adaptation and mitigation strategies.

A three-stage hybrid model for regionalization, trends and sensitivity analyses of temperature anomalies in China from 1966 to 2015

Temperature anomalies have received increasing attention due to their potentially severe impacts on ecosystems, economy and human health. To facilitate objective regionalization and examine regional temperature anomalies, a three-stage hybrid model with stages of regionalization, trends and sensitivity analyses was developed. Annual mean and extreme temperatures were analyzed using the daily data collected from 537 stations in China from 1966 to 2015, including the annual mean, minimum and maximum temperatures (Tm, TNm and TXm) as well as the extreme minimum and maximum temperatures (TNe and TXe). The results showed the following: (1) subregions with coherent temperature changes were identified using the rotated empirical orthogonal function analysis and K-means clustering algorithm. The numbers of subregions were 6, 7, 8, 9 and 8 for Tm, TNm, TXm, TNe and TXe, respectively. (2) Significant increases in temperature were observed in most regions of China from 1966 to 2015, although warming slowed down over the last decade. This warming primarily featured a remarkable increase in its minimum temperature. For Tm and TNm, 95% of the stations showed a significant upward trend at the 99% confidence level. TNe increased the fastest, at a rate of 0.56 °C/decade, whereas 21% of the stations in TXe showed a downward trend. (3) The mean temperatures (Tm, TNm and TXm) in the high-latitude regions increased more quickly than those in the low-latitude regions. The maximum temperature increased significantly at high elevations, whereas the minimum temperature increased greatly at middle-low elevations. The most pronounced warming occurred in eastern China in TNe and northwestern China in TXe, with mean elevations of 51 m and 2098 m, respectively. A cooling trend in TXe was observed at the northwestern end of China. The warming rate in TNe varied the most among the subregions (0.63 °C/decade).