No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Trend analysis of cold extremes in South Africa: 1960‐2016
International Journal of Climatology
Abstract
Extreme cold events ('cold waves’) have disastrous impacts on ecosystem and human health. Evidence shows that these events will still occur under current increasing mean temperatures. Little research has been done on extreme cold events, especially in developing countries such as South Africa. These events pose a significant threat due to the low adaptive capacity, urgent development needs and relatively inadequate infrastructure in South Africa. This study presents annual and seasonal, spatial and temporal trend analyses of extreme cold temperature events for the period 1960‐2016. We apply the World Meteorological Organisation Commission for Climatology and Indices Expert Team on Sector‐Specific Climate Indices (ET‐SCI) to South Africa for the first time, with comparison to the World Meteorological Organisation Expert Team on Climate Change Detection (ETCCDI) indices previously used in South Africa . The extreme cold indices are calculated using the RClimDex and ClimPACT, respectively. Trends were calculated using the non‐parametric Mann‐Kendall test, Spearman Rank Correlation Coefficient and Sen's slope estimates. A decreasing trend is found for annual cold spell duration and cold wave frequency, at rates of 0.10 days.y‐1 and 0.02 events.y‐1, respectively. Seasonally, coldest day temperatures increased in autumn, with increases of 0.02°C.y‐1 for the period 1960‐2016. Regionally, increasing trends in annual cold spell duration days were evident in stations located in the Western Cape, Eastern Cape, North‐West Province, at a rate of 0.03 days.y‐1. Increasing trends in cold waves were observed for stations in Northern Cape, Gauteng, KwaZulu‐Natal and the Eastern Cape Province, at a rate of 0.01 events.y‐1. These results contribute to the awareness and recognition of the incidence and duration of cold extreme events in South Africa, seeing that studies suggest that anomalously cold events may persist in a warming world.
... Another study by Colyn et al. (2018) indicated that the increase in temperature could lead to a decline in plant and animal species, particularly in the fynbos biome, this will have severe consequences for biodiversity conservation and ecotourism. Although research on low temperature extremes is very scarce in South Africa, there is an indication that the impacts of cold waves will decrease with the changing climate (e.g., Grab and Simpson 2000;Van Der Walt and Fitchett 2021). ...
... The low risk of extreme low temperatures is also indicated by decreasing annual trends for the number of cold nights, temperature of coldest nights and cold spells. Similar results have been reported by Kruger et al. (2019), Engelbrecht (2019), Kapwata et al. (2022), Mbokodo et al. (2020) and Van der Walt and Fitchett (2021). The other major result from this study is the comparison of inland versus coastal stations. ...
... TX90P, TXx, WSDI) for coastal cities were lower (increasing trend) than the inland cities. Similar trends are reported by Van der Walt and Fitchett (2021). Alternatively, the general trends of lower extreme indices (such as TX10P, TN10P, TXn and CSDI3) for coastal cities was higher (decreasing trend) than the inland cities. ...
In this era of climate change, extreme weather events are expected to become more intense and frequent. This study analysed the long-term future climate data from the mean of five climate model intercomparison project phase 6 (CMIP6) global climate models (GCMs) to examine the impacts of climate change on extreme temperature in four major South African cities. The bias correction was successfully conducted using the CMhyd software program. The analysis of extreme temperatures was conducted using indices developed by the World Meteorological Organization's Expert Team on Sector-specific Climate Indices (ET-SCI) and calculated using the R-based Climpact2 software. All statistical metrics (mean, R 2 and RMSE) show that bias correction was fairly good, and further analysis and conclusions could also be drawn using the adjusted dataset. The overall result shows that annual trends of all temperature indices analysed in this study are significantly increasing for both scenarios (SSP2-4.5 and SSP5-8.5) except for some lower extreme temperature indices (i.e., number of cool days, cold nights and cold spells). In the historical time scale, however, some indices showed no trend for some stations. The study also found that coastal cities had a slower increase in higher extreme weather indices as compared to inland cities. However, for lower extreme indices (such as number of cool days, cold nights, cool day's temperature and cold spells), the opposite was true. This information is important for policymakers, development agents and disaster prevention workers to make informed decisions about adapting to and mitigating extreme weather events.
... TEEs have been measured and analysed extensively 26,[30][31][32][33][34][35][36][37] . While heat extremes are the most studied events, much less studies focus on cold extremes 23,38,39 , and even fewer on both. These studies also mainly deal with summertime heat waves and wintertime cold waves, and adequate analysis still lacks regarding cooler season warm spells and warmer season cold spells 36 . ...
... A few studies with a global focus 18,26 also detected the increasing trends of heat extremes in frequency and magnitude, on which a general consensus has been reached 1 . Cold extremes were reported to get milder in some regional studies 7,10,23,38,39,50 , and no global conclusions have been drawn yet to our best knowledge. Trends of F and M in all five climate zones were consistent with global ones, except that the increasing trends of heat extremes in the Polar zone were not significant (Supplementary Table S1). ...
Temperature extremes with altered characteristics are one of the most threatening impacts of global warming. How their characteristics have changed is uncertain, and varies by region. Here we analyse ERA5 reanalysis data for the period 1980 to 2018 to illuminate the spatio-temporal characteristics of single and multiple successive temperature extreme events at the global scale. We show that in the global average, the magnitude of heat extremes significantly increased while that of cold extremes decreased at a faster rate. As a result, the prevailing climate shifted from a prevalence of cold extremes to one of heat extremes, and the overall magnitude of extremes decreased. Globally, the magnitude of multiple successive temperature extreme events constituted about a quarter of all identified events. These trends were not globally uniform. The most noteworthy trends were observed in the Tropical and Polar zones; the Middle East and North Africa, including the Mediterranean region, were identified as hotspots of climate shifts. On global average, the magnitude of hot temperature extremes increased less fast than the magnitude of cold extremes decreased, with much regional variability and a hotspot in the Middle East and North Africa, suggests a global analysis of extremes between 1980 and 2018.
... Given the historical, current, and projected climate change trajectories associated with extreme events (see Kodra et al., 2011;Jury, 2013;LTAS, 2013;Peings et al., 2013;Harnik et al., 2016;Kim & Lee, 2019), although biased toward increases in mean temperatures, low-temperature events remain a critical concern because they are likely to increase in frequency or duration concurrent with increasing mean temperature (see e.g. Adriaan & Fitchett, 2021). As such, investigating effects of low-temperature stress on essential ecosystem services remains essential as climate change ensues in arid tropical regions (Adriaan & Fitchett, 2021). ...
... Adriaan & Fitchett, 2021). As such, investigating effects of low-temperature stress on essential ecosystem services remains essential as climate change ensues in arid tropical regions (Adriaan & Fitchett, 2021). ...
1. Low temperatures affect insect functioning and population dynamics. Although temperate species cope with low temperatures better than their tropical counterparts, increasing temperature variability due to climate change exposes tropical species to frequent cold stress. For keystone insect species providing important ecosystem services, low-temperature tolerances, and behavioural responses remain unknown, hampering predictions under climate change.
2. The present study examined low-temperature physiology [critical thermal minima (CTmin) and chill coma recovery time (CCRT)] of six dung beetle species across three activity times: diurnal Allogymnopleurus indigaceous (Reiche) and Euoniticellus intermedius (Reiche); crepuscular Onthophagus alexis (Klug) and Onthophagus gazella (Fabricius), and; nocturnal Copris elephenor (Klug) and Scarabaeus zambezianus (Peringuey). Further, ecological service delivery (dung removal) was examined between diurnal and nocturnal species across the temperature regimes.
3. Nocturnal species had significantly greater cold tolerance than both crepuscular and diurnal species, while CCRT was significantly shortest in diurnal than both crepuscular and nocturnal species. Dung ball production between diurnal and nocturnal species interacted with temperature, with diurnal species producing significantly fewer balls at low temperatures, while nocturnal beetles were not significantly affected. In turn, nocturnal species produced significantly larger balls than the diurnal species across temperatures. Effects of temperature regime shifts were intertwined with the foraging ecology of individual species.
4. Future research should quantify species' functional responses toward different amounts of dung masses as stressful temperatures increase.
5. Results are significant for determination of species thermal ranges and predicting costs of low-temperature stress through reduced ecological services under shifting thermal environments.
Changes in temperature and its extremes have widely been among key indicators in the global climate change discourse. In this study, we analysed the spatial and temporal trends of temperature extremes over Malawi in Southern Africa during 1961 to 2015. The analysis was based on observed daily maximum and minimum temperatures (Tmax and Tmean) from 25 stations across the country. We derived 17 extreme temperature indices based on the WMO/CLIVAR Expert Team on Climate Change Detection and Indices (ETCCDI). These indices were analysed for significance of trend using the non-parametric Mann-Kendal test at α = 0.05 while their slopes were quantified using linear regression. The results suggest a countrywide warming pattern, with statistically significant increases in warming indices: SU25 at 7.56 days/decade; TR20 at 17.81 days/decade; Tmin at 0.46 oC/decade; TXx at 2.72 oC/decade; TNx at 2.20 oC/decade; TXn at 0.84 oC/decade; TN90p at 5.17 days /decade,; TX90p at 5.11 days/decade; and WSDI at 14.26 days/decade. In addition, the countrywide pattern suggest significant decreases in cold spell indices: CSDI at -2.17 days/decade, TN10p at -3.53 days/decade at TX10p at -30.40 days/decade. No significant changes were found in Tmax, TNn and DTR. The lack of trends in the DTR and TNn, coupled with the significant trends in Tmin and TNx suggest that the Tmin increased more than the Tmax across the country. The spatial pattern of the warming over Malawi is typical of regions experiencing considerable deforestation and urbanization.
This study examined the occurrences of severe cold harmattan events in Northwestern Nigeria from 1961 to 2020. Monthly minimum temperatures of the harmattan seasons (November to February) for five (5) selected stations: Kaduna, Kano, Gusau, Katsina, and Sokoto, were collected and analyzed over 60 years. A modified Standard Anomaly Index (SAI) was applied to determine the severe cold harmattan (SCH) events for the harmattan seasons. Findings revealed that SCH events occurred 55 times in the study area throughout the study period. In terms of geographical distribution, Sokoto experienced the most frequent SCH events (16 times in 60 years), followed by Kano and Katsina (15 times each). Kaduna had the lowest frequency (3 times), next to Gusau (6 times). Among decades, the second decade (1971-1980) had the highest occurrences of SCH in the study area (15 times in 10 years or 27.27%). Sokoto experienced the highest occurrence in this decade, while both Kaduna and Gusau witnessed the lowest. In regards to total decadal frequency, the most recent decade (2011-2020) and the first decade (1961-1970) tied for second. In general, all stations experienced SCH in the most recent decade (2011-2020). The study suggests that measures should be put in place to mitigate against SCH events that focus on climate change abatement strategies such as conventional mitigation, reduction of negative emissions, and unpleasant land use practices because climate change can increase the frequency of SCH and may put human health at risk of cold-related illnesses.
Extreme temperature events have traditionally been detected assuming a unimodal distribution of temperature data. We found that surface temperature data can be described more accurately with a multimodal rather than a unimodal distribution. Here, we applied Gaussian Mixture Models (GMM) to daily near‐surface maximum air temperature data from the historical and future Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for 46 land regions defined by the Intergovernmental Panel on Climate Change. Using the multimodal distribution, we found that temperature extremes, defined based on daily data in the warmest mode of the GMM distributions, are getting more frequent in all regions. Globally, a 10‐year extreme temperature event relative to 1985–2014 conditions will occur 13.6 times more frequently in the future under 3.0°C of global warming levels (GWL). The frequency increase can be even higher in tropical regions, such that 10‐year extreme temperature events will occur almost twice a week. Additionally, we analyzed the change in future temperature distributions under different GWL and found that the hot temperatures are increasing faster than cold temperatures in low latitudes, while the cold temperatures are increasing faster than the hot temperatures in high latitudes. The smallest changes in temperature distribution can be found in tropical regions, where the annual temperature range is small. Our method captures the differences in geographical regions and shows that the frequency of extreme events will be even higher than reported in previous studies.
The 6th Assessment of the Intergovernmental Panel on Climate Change projects increasing thermal‐associated morbidity and mortality under anthropogenically induced warming. Over 100 indices exist to quantify thermal stress, and among these, the Universal Thermal Climate Index (UTCI) was developed for regional investigations of thermal stress influences on human health. Although by definition a universal index, current applications are mainly limited to Europe. For regions such as Africa, use of the UTCI has been hampered by a lack of available requisite input variables from ground‐based meteorological stations. To overcome this, a gridded dataset, derived from ERA5 reanalysis, of UTCI equivalent temperatures was developed by the European Centre for Medium‐Range Weather Forecasts. Using this dataset for daily average, minimum and maximum UTCI values, we explore spatiotemporal patterns and changes thereof over annual, seasonal, and monthly scales across southern Africa from 1979 to 2021. Across these scales, 9 of 10 UTCI thermal stress categories were observed, ranging from very strong cold stress to extreme heat stress. Spatially, no thermal stress was most widespread for daily mean values, whereas for daily maximum (minimum) values there was a wider heat (cold) stress incidence, with frequent occurrences of moderate and strong heat stress (slight and moderate cold stress). Interannually, a clear El Niño–Southern Oscillation influence on thermal stress was evident during summer, with El Niño (La Niña) phases extending (reducing) heat stress incidences by up to 13.8% (2.9%). Over the study period, heat stress increased at statistically significant rates in many instances, with the strongest, most widespread increases, for the daily average and maximum (minimum), during spring (summer), averaging 0.28 and 0.29°C·decade⁻¹ (0.23°C·decade⁻¹); few regions experienced statistically significant decreasing trends. Overall, the trend results highlight regions vulnerable to significant thermal climate changes, and thus should be considered in decision‐making regarding outdoor activities.
This book explores the nature of climate change in southern Africa, its impacts on tourism and the resilience, adaptation and governance needs in various tourism operations and environments. Previous studies on climate change and tourism have mainly focused on the Global North and specific forms of tourism such as snow-based winter activities. Drawing on case studies from a wide range of countries including South Africa, Lesotho, Namibia, Botswana and Zimbabwe, this book fills this lacuna by describing and analysing the climate change and tourism nexus in the southern African context. The book begins by providing an overview of the current and estimated impacts of climate change to the tourism industry in the region, highlighting the deepening socio-economic inequities, and environmental and social injustices. It focuses on the importance of sustainable tourism in tackling these issues and highlights that resilience and robust governance and policy systems are essential for a tourism destination to successfully adapt to change. By synthesising the key lessons learned through this analysis, Climate Change and Tourism in Southern Africa also draws attention to specific adaptation and policy strategies which have value for other regions in the Global South. This book will be of great interest to students and scholars of climate change, tourism and environmental policy and justice.
Across South Africa, a wide range of activities is influenced by differences in seasonality. In a South African context, there is little consensus on the timing of seasonal boundaries. Inconsistency exists through the use of ad-hoc approaches to define seasonal boundaries across South Africa. In this paper, we present one of the very first uniform statistical classifications of South African seasonal divisions on the basis of daily temperature data. Daily maximum and minimum temperature data were obtained from 35 selected South African Weather Service meteorological stations that had sufficiently complete data sets and homogeneous time series, spanning the period 1980–2015. An Euclidean cluster analysis was performed using Ward’s D method. We found that the majority of the stations can be classified into four distinct seasons, with the remaining 12 stations’ data best classified into three seasons, using Tavg as the classifier. The statistically classified seasonal brackets include summer (October/November/December/ January/February/March), early autumn (April) and late autumn (May), winter (June/July/August), and spring (September). Exploring the boundaries of seasons, the start of summer and end of winter months follow a southwest to northeastwards spatial pattern across the country. Summers start later and winters end later in the southwestern parts of the country, whereas in the northeast, summers start earlier and winters end earlier.
Significance:
• The findings contribute to the common knowledge of seasonality in South Africa.
• New seasonal divisions in South Africa are proposed.
Globally, subtropical circulation in the lower troposphere is characterized by anticyclones over the oceans. Subtropical anticyclones locate over subtropical belts, modulating weather and climate patterns in those regions. The Mascarene High is an anticyclone located over the Southern Indian Ocean and has a vital role in weather and climate variability over Southern Africa. The warm Western Indian Ocean is a major source of moisture for the subcontinent also permitting tropical cyclone genesis. In this study, we review the dynamics of the Mascarene High, its interactions with the ocean, and its impact on weather and climate over Southern Africa. We also review studies on the evolution of subtropical anticyclones in a future warmer climate. The links between SST modes over the Indian Ocean and the strengthening and weakening of the Mascarene High have been demonstrated. One important aspect is atmospheric blocking due to the Mascarene High, which leads to anomalous rainfall and temperature events over the subcontinent. Blocking leads to landfall of tropical cyclones and slow propagation of cut-off lows resulting in severe weather and flooding over the subcontinent. Understanding how expansion of the Mascarene High due to warming will alter trade winds and storm tracks and change the mean climate of Southern Africa is crucial.
Plain Language Summary
On any given day, some region of the globe is likely to be affected by extreme cold. Depending on the severity, extreme cold periods may be deemed cold air outbreaks (CAOs). These CAOs can be detrimental to the agricultural industry, energy industry, and human health, especially in regions with a warmer climate that may be less well prepared to mitigate the impacts. This study uses a set of criteria to examine CAOs across the globe from 1979–2018 and to determine how CAOs have changed over the last 40 years. We found that CAOs occur most frequently in the Northern Hemisphere, with as many as eight CAO days per year in North America and Eurasia. CAOs were found to have decreased in size, intensity, frequency, and duration across much of the globe, with the largest decreases in Alaska, Canada, and the North Atlantic, while an increase in CAOs was observed in Eastern Europe, Central Eurasia, and the Southern Ocean. Early and late winter CAOs have also become much less frequent in most regions.
Owing to unique topographic and ecological diversity, central Himalayan state of Nepal is exposed to adverse impacts of climate change and associated disasters. However, countrywide historical assessment of mean and extreme temperature changes, a prerequisite for devising adequate adaptation strategies, is still lacking. Here, we present a comprehensive picture of mean and extreme temperature trends across Nepal over the 1980–2016 period, based on high‐quality daily temperature observations from 46 stations. Our results suggest that besides winter cooling in southern lowlands, the country features a widespread warming, which is higher for maximum temperature (~0.04°C⋅year⁻¹) than for minimum temperature (~0.02°C⋅year⁻¹), over the mountainous region than in valleys and lowlands and during the pre‐monsoon season than for the rest of the year. Consistently, we found a higher increasing trend for warm days (13 days⋅decade⁻¹) than for warm nights (4 days⋅decade⁻¹), whereas the rates of decrease for cold days and cold nights are the same (6 days⋅decade⁻¹). Further investigations reveal that pronounced warming in maximum temperature over mountain regions can be attributed to less cloud cover and snowfall in recent decades during non‐monsoon seasons as a result of positive geopotential height anomalies and strengthening of anticyclonic circulations in the mid‐to‐upper troposphere. Similarly, increased stability of lower atmosphere during winter and post‐monsoon seasons caused prolonged and frequent periods of fog over lowlands, resulting in significant winter cooling there.
Among the most devastating extreme weather events, cold surge (CS) events frequently impact northern China. It has been reported that extreme weather events will increase in the global warming context. However, the direct evidence of this hypothesis is limited. Here, we investigated the changes in frequency, number, duration, and temperature of CS events in northern China using the daily minimum temperature dataset of 331 stations from 1960 to 2016. The results indicate that the annual CS events in terms of frequency and number decreased, and the duration shortened as the starting date was later and the ending date earlier. Meanwhile, the annual CS temperature increased. In addition, spatial trends in the CS events in terms of frequency, number, and duration decreased while the CS temperature increased in most regions of northern China. We interpreted these variations as a response to global warming. However, the extreme CS events in terms of frequency, number and the earliest starting date and the latest ending date showed little change though the extreme CS temperature increased, implying climate warming had not limited extreme CS events. The adverse effect of CS events on agriculture and human health remain concerning.
Background:
In the context of global warming, most researches have been conducted on health influences of heat waves, with limited understanding of health impacts of cold spells, especially for developing countries.
Methods:
We collected daily mortality and meteorological data for 31 capital cities across China during the maximum period of 2007-2013. A quasi-Poisson regression model combined with a distributed lag non-linear model was used to estimate the short-term effects of cold spells on mortality in cold seasons (November to March). 19 definitions of cold spell were clearly compared, including three definitions from the China Meteorological Administration (CMA) and 16 definitions by combining two temperature indicators (daily minimum and mean temperature), two temperature thresholds (3rd and 5th percentile) and four durations of at least 2-5 days. Then, a random effect meta-analysis was applied to pool the effect estimates at national level. Furthermore, a stratified analysis was constructed to identify the vulnerable subpopulations to cold spells.
Results:
The definition, in which daily mean temperature falls below 5th percentile for at least two consecutive days, produced the optimum model fit performance. Generally, the mortality risk increased to the maximum after 3-6 days' exposure to cold spell and then leveled off in the next 3 weeks. The pooled relative risks (RR) of non-accidental mortality for cold spells were 1.03 (95% CI: 1.01-1.05), 1.27 (1.19-1.35) and 1.55 (1.40-1.70) at lag 0, lag 0-14 and lag 0-27 days, respectively. The greatest effect estimates of cold spells were found among total respiratory diseases and COPD, with RR of 1.88 (1.65-2.11) and 1.88 (1.58-2.19), respectively. The elderly, less-educated individuals and residents in southern China were more vulnerable to cold spells.
Conclusion:
There are remarkable mortality effects of cold spells, with effect estimates varying with the definition of cold spell and subpopulations. Using the official definition of cold spells may fail to capture the mortality risk associated with cold spells. These findings may facilitate the development of cold alert warning system and preventive actions to the vulnerable populations.
Motivated by the risks posed by global warming, historical trends and future projections of near-surface temperature in South Africa have been investigated in a number of previous studies. These studies included the assessment of trends in average temperatures as well as extremes. In this study, historical trends in near-surface minimum and maximum temperatures as well as extreme temperature indices in South Africa were critically investigated by comparing quality-controlled station observations with downscaled model projections. Because climate models are the only means of generating future global warming projections, this critical point comparison between observed and downscaled model simulated time series can provide valuable information regarding the interpretation of model-generated projections. Over the historical 1951–2005 period, both observed data and downscaled model projections were compared at 22 point locations in South Africa. An analysis of model projection trends was conducted over the period 2006–2095. The results from the historical analysis show that model outputs tend to simulate the historical trends well for annual means of daily maximum and minimum temperatures. However, noteworthy discrepancies exist in the assessment of temperature extremes. While both the historical model simulations and observations show a general warming trend in the extreme indices, the observational data show appreciably more spatial and temporal variability. On the other hand, model projections for the period 2006–2095 show that for the medium-to-low concentration Representative Concentration Pathway (RCP) 4.5, the projected decrease in cold nights is not as strong as is the case for the historically observed trends. However, the upward trends in warm nights for both the RCP4.5 and the high concentration RCP8.5 pathways are noticeably stronger than the historically observed trends. For cool days, future projections are comparable to the historically observed trends, but for hot days noticeably higher. Decreases in cold spells and increases in warm spells are expected to continue in future, with relatively strong positive trends on a regional basis. It is shown that projected trends are not expected to be constant into the future, in particular trends generated from the RCP8.5 pathway that show a strong increase in warming towards the end of the projection period.
Significance:
• Comparison between the observed and simulated trends emphasises the necessity to assess the reliability of the output of climate models which have a bearing on the credibility of projections.
• The limitation of the models to adequately simulate the climate extremes, renders the projections conservative.
The temporal and spatial trends of 16 climate extreme indices based on daily maximum and minimum temperatures during the period 1987–2016 at 28 stations distributed across Israel and Palestine territories in the Levant region were annually and seasonally analysed. The nonparametric Man‐Kendall test and the Sen's slope estimator were employed for the trend analysis. Results showed that the region has significantly experienced a dominant warming trend for the last three decades, with more intense changes for minimum temperatures than for maximum. At annual scale, maximum values of minimum temperatures exhibited significant increasing trends up to 0.68°C/decade. For percentile‐based extreme temperature indices, changes detected were more pronounced than those for the absolute extreme temperature indices, with 93 and 89% of stations significantly showed increasing trends in TX90p and TN90p, respectively. The duration and fixed threshold extreme indices confirmed the trend toward a warming, with the 86% of the stations exhibited significant increasing trends in the annual occurrence of summer days (SU25) and tropical nights (TR20). Moreover, 57% of stations showed significant increasing trends in their very summer days (SU30) index. At seasonal scale, the analysis of trends for extreme temperature indices showed intense and broad significant increasing trends in all absolute extreme temperature indices. In summer, more than 75% of total stations exhibited significant increasing trends for warm days and warm nights (TX90p and TN90p). In winter and spring, 71% of the total stations also showed significant increasing trends in SU25 index, whereas the percentage of stations reached 82% in summer and 64% in autumn for significant increasing trends in TR20 index. Finally, the influence of large‐scale circulation patterns on temperature extremes was examined. The results highlighted the presence of significant correlations between most of the selected extreme temperature indices and the North Sea‐Caspian pattern at annual and seasonal scales.
This study examines the 1950–2017 temporal changes in climate extremes in Israel, which is located in the East Mediterranean (EM), a region which suffers from a scarcity of long and reliable datasets. It is well known that most long‐term records are affected by artificial shifts most commonly caused by station relocation, instrumental modification and local environmental changes. Therefore, for the first time, a thorough homogenization (detection and correction) routine was developed and implemented in the long‐term records. Consequently, a new daily adjusted dataset has been generated, including 34 temperature stations and 60 precipitation stations. Based on this comprehensive dataset, 38 extreme indices recommended by the Expert Team on Climate Change Detection and the Expert Team on Sector‐specific Climate Indices have been calculated. These indices will help various sectors to plan properly mitigation actions and adaptation for climate change, in addition to facilitating future studies for the EM. The results showed highly significant changes in temperature extremes associated with warming, especially for those indices derived from the daily minimum temperature (TN, 1950–2017), whereas the maximum temperature (TX) exhibited a similar increasing magnitude of the TN (~0.55°C/decade) in the last 30 years. The warming trends, which are non‐monotonic, seem to have been particularly strong since the early 1990s. The coastal area is characterized by higher heat stress during the nighttime, while mountains exhibit a strong tendency towards increasing temperatures during the noon hours. A reduction in the total precipitation amount and in the number of wet days with a tendency towards more intense wet days was found. Although all the regional trends of the precipitation indices were not statistically significant (p ≤ .05), they showed a fine spatial coherence.
The temporal structure of heat waves having substantial human impact varies widely, with many featuring a compound structure of hot days interspersed with cooler breaks. In contrast, many heat wave definitions employed by meteorologists include a continuous threshold-exceedance duration criterion. This study examines the hazard of these diverse sequences of extreme heat in the present, and their change with global warming. We define compound heat waves to include those periods with additional hot days following short breaks in heat wave duration. We apply these definitions to analyze daily temperature data from observations, NOAA Geophysical Fluid Dynamics Laboratory global climate model simulations of the past and projected climate, and synthetically generated time series. We demonstrate that compound heat waves will constitute a greater proportion of heat wave hazard as the climate warms and suggest an explanation for this phenomenon. This result implies that in order to limit heat-related mortality and morbidity with global warming, there is a need to consider added vulnerability caused by the compounding of heat waves.
Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world's population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations.
The Urban Heat Island (UHI), the tendency for urban areas to be hotter than rural regions, represents a significant health concern in summer as urban populations are exposed to elevated temperatures. A number of studies suggest that the UHI increases during warmer conditions, however there has been no investigation of this for a large ensemble of cities. Here we compare urban and rural temperatures in 54 US cities for 2000–2015 and show that the intensity of the Urban Heat Island, measured here as the differences in daily-minimum or daily-maximum temperatures between urban and rural stations or ΔT, in fact tends to decrease with increasing temperature in most cities (38/54). This holds when investigating daily variability, heat extremes, and variability across climate zones and is primarily driven by changes in rural areas. We relate this change to large-scale or synoptic weather conditions, and find that the lowest ΔT nights occur during moist weather conditions. We also find that warming cities have not experienced an increasing Urban Heat Island effect.
Periods of extreme cold impact the mid-latitudes every winter. Depending on the magnitude and duration of the occurrence, extremely cold periods may be deemed cold air outbreaks (CAOs). A systematic CAO index and ranking system was developed from 20 surface weather stations from 1948 through 2016, based on a set of criteria concerning magnitude, duration, and spatial extent. Standard deviations in temperature were used to identify extreme temperatures relative to the station. A total of 49 CAOs occurred during the 67-year period, with the majority occurring during mid-winter. The number of CAOs proved to be largely dependent on the stations latitude and maritime influence. The duration, magnitude, and spatial extent were dependent on the time of the winter season in which the CAO occurred. Furthermore, two prominent clusters of an increased number of CAOs occurred during the 67-year period, suggesting multi-decadal circulations may be a factor in CAOs.
Cold and heat waves represent a significant problem for the electricity generation sector. The disruptions cold and heat waves can cause in power production are beyond their consumption impacts through, for instance, higher peak demand. Unexpected stops at thermal or nuclear power plants by excessively high-temperature water constitute clear examples of this. In this invited paper, we use past case studies to analyze the impact of these kinds of events on power production. Subsequently we discuss how events of this nature may evolve over the future in view of their association to climate change. Although the review is not exhaustive, we do expose some ideas that may be relevant for decision making in this area.
The Paris Agreement calls for global warming to be limited to 1.5-2 °C. For the first time, this study investigates how different regional heatwave characteristics (intensity, frequency and duration) are projected to change relative to increasing global warming thresholds. Increases in heatwave days between 4-34 extra days per season are projected per °C of global warming. Some tropical regions could experience up to 120 extra heatwave days/season if 5 °C is reached. Increases in heatwave intensity are generally 0.5-1.5 °C above a given global warming threshold, however are higher over the Mediterranean and Central Asian regions. Between warming thresholds of 1.5 °C and 2.5 °C, the return intervals of intense heatwaves reduce by 2-3 fold. Heatwave duration is projected to increase by 2-10 days/°C, with larger changes over lower latitudes. Analysis of two climate model ensembles indicate that variation in the rate of heatwave changes is dependent on physical differences between different climate models, however internal climate variability bears considerable influence on the expected range of regional heatwave changes per warming threshold. The results of this study reiterate the potential for disastrous consequences associated with regional heatwaves if global mean warming is not limited to 2 degrees.
The meteorology of air pollution episodes on South Africa’s Highveld was studied using OMI and AIRS satellite estimates, MERRA2 reanalysis model products and in-situ weather data. Surface-layer SO2 and NO2 display high concentrations during winter (May-July) and provide a focus for statistical analysis of monthly and daily time series. Highveld area averaged monthly model SO2 was temporally correlated with boundary layer height (-0.76) and temperature lapse rate (+0.65) in the period 1980-2015, but relationships with winds were weak. Daily Highveld area satellite NO2 was related to dewpoint temperature (-0.59) and exhibited pulsing in the range 7-24 days in the period 2005-2015. High concentrations of these short-lived locally-generated air pollutants were found over and southeast of Johannesburg due to urban and industrial emissions.
The spatial regression of daily NO2 onto regional sea level air pressure fields in May-July 2005-2015 revealed the slow eastward movement of an anticyclone. At the climate timescale, Pacific La Nina conditions favored an increase of May-July SO2 concentrations when SST in the equatorial Atlantic were warmer than normal. The meteorological pattern underlying the highest ranked air pollution event of 18-25 July 2008 was characterized by sharp anticyclonic curvature of low level winds that induce subsidence, and consequently a stable lapse rate and low dewpoint temperature (-5C). The wind vorticity exerted a stronger influence on dispersion than the surface divergence. This new understanding will underpin better air quality forecasts over the South African Highveld.
Weather and climate are often important factors determining the success of a tourism destination and resultant satisfaction among tourists. This is particularly true for South Africa due the predominance of outdoor tourist attractions. Increasing numbers of international tourists have visited South Africa since the fall of apartheid, particularly those from the United States (U.S.), which is an important market for South African tourism. Therefore, this paper seeks to examine a sample of American tourists’ experience with day-to-day weather and climatic conditions in South Africa. The results show that although respondents did not feel that climatic conditions were an important factor in motivations to visit the country, the day-to-day weather did often impact the enjoyment of their visit. Most notably, weather controlled their ability to participate in outdoor activities. In correlating accounts of unpleasant weather conditions with the meteorological records, a close association emerged, particularly for excessively high temperatures. This indicates that the experiences of American tourists are an accurate indication of climatic unsuitability for tourism, which poses threats to the South African outdoor tourism sector.
Understanding what drives changes in heatwaves is imperative for all systems impacted by extreme heat. We examine short- (13 yr) and long-term (56 yr) heatwave frequency trends in a 21-member ensemble of a global climate model (Community Earth System Model; CESM), where each member is driven by identical anthropogenic forcings. To estimate changes dominantly due to internal climate variability, trends were calculated in the corresponding pre-industrial control run. We find that short-term trends in heatwave frequency are not robust indicators of long-term change. Additionally, we find that a lack of a long-term trend is possible, although improbable, under historical anthropogenic forcing over many regions. All long-term trends become unprecedented against internal variability when commencing in 2015 or later, and corresponding short-term trends by 2030, while the length of trend required to represent regional long-term changes is dependent on a given realization. Lastly, within ten years of a short-term decline, 95% of regional heatwave frequency trends have reverted to increases. This suggests that observed short-term changes of decreasing heatwave frequency could recover to increasing trends within the next decade. The results of this study are specific to CESM and the 'business as usual' scenario, and may differ under other representations of internal variability, or be less striking when a scenario with lower anthropogenic forcing is employed.
Climate change is expected to have profound effects on weather patterns and temperatures worldwide in the coming decades, with serious implications for public health.1 Among the many ways in which global warming bears on human health,2,3,4,5,6 few are more readily apparent than the trend of increasing heat waves, which are often regarded as the deadliest of all natural disasters.7,8 And despite current and future adaptation efforts,9,10 the overall health burden of heat waves could grow as average temperatures continue their upward tick and extreme heat events become more frequent, severe, and long-lasting.11
A statistical analysis reveals projections of consistently larger increases in the highest percentiles of summer and winter temperature maxima and minima versus the respective lowest percentiles, resulting in a wider range of temperature extremes in the future. These asymmetric changes in tail distributions of temperature appear robust when explored through 14 CMIP5 climate models and three reanalysis datasets. Asymmetry of projected increases in temperature extremes generalizes widely. Magnitude of the projected asymmetry depends significantly on region, season, land-ocean contrast, and climate model variability as well as whether the extremes of consideration are seasonal minima or maxima events. An assessment of potential physical mechanisms provides support for asymmetric tail increases and hence wider temperature extremes ranges, especially for northern winter extremes. These results offer statistically grounded perspectives on projected changes in the IPCC-recommended extremes indices relevant for impacts and adaptation studies.
Background
Many studies have investigated heat wave related mortality, but less attention has been given to the health effects of cold spells in the context of global warming. The 2008 cold spell in China provided a unique opportunity to estimate the effects of the 2008 cold spell on mortality in subtropical regions, spatial heterogeneity of the effects, stratification effect and added effects caused by sustained cold days.
Methods
Thirty-six study communities were selected from 15 provinces in subtropical China. Daily mortality and meteorological data were collected for each community from 2006 to 2010. A distributed lag linear non-linear model (DLNM) with a lag structure of up to 27 days was used to analyze the association between the 2008 cold spell and mortality. Multivariate meta-analyses were used to combine the cold effects across each community.
Results
The 2008 cold spell increased mortality by 43.8% (95% CI: 34.8% ~ 53.4%) compared to non-cold spell days with the highest effects in southern and central China. The effects were more pronounced for respiratory mortality (RESP) than for cardiovascular (CVD) or cerebrovascular mortality (CBD), for females more than for males, and for the elderly aged ≥75 years old more than for younger people. Overall, 148,279 excess deaths were attributable to the 2008 cold spell. The cold effect was mainly from extreme low temperatures rather than sustained cold days during this 2008 cold spell.
Conclusions
The 2008 cold spell increased mortality in subtropical China, which was mainly attributable to the low temperature rather than the sustained duration of the cold spell. The cold effects were spatially heterogeneous and modified by individual-specific characteristics such as gender and age.
Observed trends in seasonal and annual total rainfall, number of rain days and daily maximum and minimum temperature were calculated for a number of stations in South Africa for the period 1960-2010. Statistically significant decreases in rainfall and the number of rain days are shown over the central and northeastern parts of the country in the autumn months and significant increases in the number of rain days around the southern Drakensberg are evident in spring and summer. Maximum temperatures have increased significantly throughout the country for all seasons and increases in minimum temperatures are shown for most of the country. A notable exception is the central interior, where minimum temperatures have decreased significantly. Regionally aggregated trends for six water management zones covering the entire country are not evident for total rainfall, but there are some significant trends for the number of rain days. Temperature in these zones has increased significantly for most seasons, with the exception of the central interior. Comparison of the observed trends with statistically downscaled global climate model simulations reveals that the models do not represent the observed rainfall changes nor the cooling trend of minimum temperature in the central interior. Although this result does not rule out the possibility of attributing observed local changes in rainfall to anthropogenically forced global change, it does have major implications for attribution studies. It also raises the question of whether an alternative statistical downscaling method or dynamical downscaling through the use of a regional climate model might better represent regional and local climatic processes and their links to global change.
The focus of the great majority of climate change impact studies is on changes in mean climate. In terms of climate model output, these changes are more robust than changes in climate variability. By concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated. Here we briefly review the possible impacts of changes in climate variability and the frequency of extreme events on biological and food systems, with a focus on the developing world. We present new analysis that tentatively links increases in climate variability with increasing food insecurity in the future. We consider the ways in which people deal with climate variability and extremes and how they may adapt in the future. Key knowledge and data gaps are highlighted. These include the timing and interactions of different climatic stresses on plant growth and development, particularly at higher temperatures, and the impacts on crops, livestock and farming systems of changes in climate variability and extreme events on pest-weed-disease complexes. We highlight the need to reframe research questions in such a way that they can provide decision makers throughout the food system with actionable answers, and the need for investment in climate and environmental monitoring. Improved understanding of the full range of impacts of climate change on biological and food systems is a critical step in being able to address effectively the effects of climate variability and extreme events on human vulnerability and food security, particularly in agriculturally-based developing countries facing the challenge of having to feed rapidly growing populations in the coming decades. This article is protected by copyright. All rights reserved.
Heatwaves have profound socio-economic impacts. Increases in temperature
variability would exacerbate these impacts but debate rages in the
literature about whether the climate has or will become more variable.
There is currently no firm evidence that temperature variability has or
will increase because questions have been raised about the methods used
to reach this conclusion. However, irrespective of changing temperature
variability, the impact from increases in the frequency and intensity of
heatwaves will be a major problem for the future.
This study presents a second generation of homogenized monthly mean surface air temperature data set for Canadian climate trend analysis. Monthly means of daily maximum and of daily minimum temperatures were examined at 338 Canadian locations. Data from co-located observing sites were sometimes combined to create longer time series for use in trend analysis. Time series of observations were then adjusted to account for nation-wide change in observing time in July 1961, affecting daily minimum temperatures recorded at 120 synoptic stations; these were adjusted using hourly temperatures at the same sites. Next, homogeneity testing was performed to detect and adjust for other discontinuities. Two techniques were used to detect non-climatic shifts in de-seasonalized monthly mean temperatures: a multiple linear regression based test and a penalized
maximal t test. These discontinuities were adjusted using a recently developed quantile-matching algorithm: the adjustments were estimated with the use of a reference series. Based on this new homogenized temperature data set, annual and seasonal temperature trends were estimated for Canada for 1950–2010 and Southern Canada for 1900–2010. Overall, temperature has increased at most locations. For 1950–2010, the annual mean temperature averaged over the country shows a positive trend of 1.5�C for the past 61 years. This warming is slightly more pronounced in the minimum temperature than in the maximum temperature; seasonally, the greatest warming occurs in winter and spring. The results are similar for Southern Canada although the warming is considerably greater in the minimum temperature compared to the maximum temperature over the period 1900–2010.
In February 2012 Italy was hit by an exceptional cold spell with extremely low temperatures and heavy snowfall. The aim of this work is to estimate the impact of the cold spell on health in the Italian cities using data from the rapid surveillance systems. In Italy, a national mortality surveillance system has been operational since 2004 in 34 cities for the rapid monitoring of daily mortality. Data from this system were used to evaluate the impact of the February 2012 cold spell on mortality shortly after the occurrence of the event. Furthermore, a cause-specific analysis was conducted in Roma using the Regional Mortality Registry and the emergency visits (ER) surveillance system. Cold spell episodes were defined as days when mean temperatures were below the 10(th) percentile of February distribution for more than three days. To estimate the impact of the cold spell, excess mortality was calculated as the difference between observed and daily expected values. An overall 1578 (+25%) excess deaths among the 75+ age group was recorded in the 14 cities that registered a cold spell in February 2012. A statistically significant excess in mortality was observed in several cities ranging from +22% in Bologna to +58% in Torino. Cause-specific analysis conducted in Roma showed a statistically significant excess in mortality among the 75+ age group for respiratory disease (+64%), COPD (+57%), cardiovascular disease +20% ischemic heart disease (14%) and other heart disease (+33%). Similar results were observed for ER visits. Surveillance systems need to become are a key component of prevention plans as they can help improve public health response and are a valid data source to rapidly quantify the impact on health. Cold-related mortality is still an important issue and should not be underestimated by public health Authorities.
Persistent ocean warming has caused the rapid poleward shift of various tropical marine organisms. However, extreme cold events have been reported to have greater impacts on corals, yet no reports have elaborated how such cold events affect range-shifting tropical reef fishes in temperate waters. This study assessed benthic cover and assemblage structure of tropical reef fishes (Pomacentridae, Chaetodontidae, Scaridae, and Acanthuridae) on two reefs, dominated by tabulate Acropora corals, in Tosa Bay (33°N, 133°E), southwestern Japan. The study was conducted during winter and summer within 2 years (2017–2018), fortuitously covering periods before, during, and after the occurrence of an extreme cold event in early 2018. The event resulted in an approximately 2-month extremely low sea surface temperature (SST) of < 15 °C, causing massive bleaching and mortality of > 90% corals. About 80% of the fish species richness and > 80% of their abundance declined during the winter of 2018, with a slow increase in their populations during the summer of 2018 as a consequence of the coral loss. The extremely low SST negatively affected non-established and breeding fish populations, while the massive coral loss severely affected corallivorous fishes. This study demonstrates the potential impact of extreme cold events on the persistent establishment of tropical reef fishes in temperate waters. Thus, our study highlights the potential instability of temperate waters as a refuge for range-shifting tropical reef fishes since extreme climatic events are predicted to increase amidst climate change. This may potentially impair benefits from the tropicalized temperate reef ecosystem services (e.g., local tourism and fishery industries).
Background: While numerous studies have shown that the impact of cold waves is decreasing as result of various processes of adaptation, far fewer have analysed the time trend shown by such impact, and still fewer have done so for the different provinces of a single country, moreover using a specific cold waves definition for each. This study thus aimed to analyse the time trend of the impact of cold days on daily mortality in Spain across the period 1983-2003.
Methods: For study purposes, we used daily mortality data for all natural causes except accidents in ten Spanish provinces. The time series was divided into three subperiods. For each period and province, the value of Tthreshold was obtained via the percentile corresponding to the cold day’s definition for that province obtained in previous studies. Relative Risks (RRs) and Population Attributable Fraction (PARs) were calculated using Generalised Linear Models (GLMs) with the Poisson regression link. Seasonalities, trends and autoregressive components were controlled. Global RRs and PARs were calculated with a meta-analysis with random effects for each of the periods.
Results: The results show that the RRs for Spain as a whole were 1.12 (95% CI: 1.08 1.16) for the first period, 1.15 (95% CI: 1.09 1.22) for the second and 1.18 (95% CI: 1.10 1.26) for the third. The impact of cold days has risen slightly over time, though the differences were not statistically significant. These findings show a clearly different behaviour pattern to that previously found for heat.
Conclusion: The results obtained in this study do not show a downward trend for colds days. The complexity of the biological mechanisms involved and the lack of robust results mean that more research must be done in this particular field of public health.
The weather and climate of a particular region is a function of multi-scale controls in both space and time. South Africa exhibits a range of present day climate regimes that result from these controls, giving rise to generally hot, wet summers and cold, dry winters, except in the southwestern parts that are dry in summer and wet in winter. This chapter examines the drivers of the regional climates through the lens of scales in space and time. It presents selected synoptic circulation patterns responsible for weather over different parts of the country, and smaller scale features associated with relevant synoptic circulation patterns, as appropriate. Also included is a discussion of how synoptic processes may be modulated by larger scale atmospheric and oceanic processes. The chapter concludes by linking socioeconomic activities such as agriculture, water resources and health to the regional climate.
Objectives:
Understanding the dynamics of the temperature-mortality relationship is an asset to support public health interventions. We investigated the lag structure of the mortality response to cold and warm temperatures in 18 French cities between 2000 and 2010.
Methods:
A distributed lag non-linear generalized model using a quasi-Poisson distribution and controlling for classical confounding factors was built in each city. A fitted meta-analytical model combined the city-specific models to derive the best linear unbiased prediction of the association, and a meta-regression explored the influence of background characteristics of the cities. The fraction of mortality attributable to cold and heat was estimated with reference to the minimum mortality temperature.
Results:
Between 2000 and 2010, 3.9% [CI 95% 3.2:4.6] of the total mortality was attributed to cold, and 1.2% [1.1:1.2] to heat. The immediate increase in mortality following high temperatures was partly compensated by a harvesting effect when temperatures were below the 99.2 percentiles of the mean temperature distributions.
Discussion:
Cold represents a significant public health burden, mostly driven by moderate temperatures (between percentiles 2.5 and 25). The population is better adapted to warm temperatures, up to a certain intensity when heat becomes an acute environmental health emergency (above percentile 99). The rapid increase in mortality risk at very high temperatures percentiles calls for an active adaptation in a context of climate change.
Introduction: Direct health effects of extreme temperatures are a significant environmental health problem in Lithuania, and could worsen further under climate change. This paper attempts to describe the change in environmental temperature conditions that the urban population of Vilnius could experience under climate change, and the effects such change could have on excess heat-related and cold-related mortality in two future periods within the 21st century.
Methods: We modelled the urban climate of Vilnius for the summer and winter seasons during a sample period (2009–2015) and projected summertime and wintertime daily temperatures for two prospective periods, one in the near (2030–2045) and one in the far future (2085–2100), under the Representative Concentration Pathway (RCP) 8.5. We then analysed the historical relationship between temperature and mortality for the period 2009–2015, and estimated the projected mortality in the near future and far future periods under changing climate and population, assuming alternatively no acclimatization and acclimatization to heat and cold based on a constant threshold percentile temperature.
Results: During the sample period 2009–2015 in summertime we observed an increase in daily mortality from a maximum daily temperature of 30ºC (the 96th percentile of the series), with an average of around 7 deaths per year. Under a no acclimatization scenario, annual average heat-related mortality would rise to 24 deaths/year (95 % CI: 8.4-38.4) in the near future and to 46 deaths/year (95 % CI: 16.4-74.4) in the far future. Under a heat acclimatization scenario, mortality would not increase significantly in the near or in the far future. Regarding wintertime cold-related mortality in the sample period 2009-2015, we observed increased mortality on days on which the minimum daily temperature fell below -12ºC (the 7th percentile of the series), with an average of around 10 deaths a year. Keeping the threshold temperature constant, annual average cold-related mortality would decrease markedly in the near future, to 5 deaths/year (95 % CI: 0.8-7.9)) and even more in the far future, down to 0.44 deaths/year (95 % C: 0.1-0.8)). Assuming a “middle ground” between the acclimatization and non-acclimatization scenarios, the decrease in cold wave mortality will not compensate the increase in heat wave mortality.
Conclusion: Thermal extremes, both heat and cold, constitute a serious public health threat in Vilnius, and in a changing climate the decrease in mortality attributable to cold will not compensate for the increase in mortality attributable to heat. Study results reinforce the notion that public health prevention against thermal extremes should be designed as a dynamic, adaptive process from the inception.
The present study examines trends in extreme temperature absolute indices [warmest day (TXx), coldest day (TXn), warmest night (TNx) and coldest night (TNn)] using the newly homogenized daily minimum and maximum temperature series from 21 stations in Nigeria for the period 1971–2012. The indices provide an understanding of the characteristics of changing temperature, in light of the present anthropogenic global warming. Trends in the indices and the statistical significance are obtained using the modified Mann–Kendall test in the R-package. The results show that many of the stations have significant increasing trends in the absolute indices, with the warming most pronounced in Southern Nigeria. Averaged over the country, the TXx, TXn, TNx and TNn have absolute warming trends of 0.59, 0.17, 0.67and 0.97 °C, respectively, for the 42 years since 1971. The warming rate is highest during the winter (DJF) season for TNn. The present study also examines the possible influence of large-scale teleconnection patterns on extreme temperature variations by determining the correlation between the absolute indices and the North Atlantic Oscillation (NAO) index on monthly, seasonal and annual timescales. Our results show that warming over Nigeria during the dry season months from November to February is significantly correlated with NAO. The NAO has significant correlation with TXx and TXn in the north, and with TNx and TNn in the south. The analysis is repeated with ERA-20C-gridded data set, and similar correlations are found. The warming nights, in particular, in Nigeria can lead to sleeplessness and widespread mosquito-related diseases as the mosquito population and its parasites thrive in warmer conditions. The results of the analyses are relevant for decision-making process, especially for the development of early warning systems for extreme heat events.
The analyses of temperature extremes can be very useful because they are highly representative of the climatic tendency of an area. In this study, 19 temperature series registered in southern Italy have been analysed for detecting trends through the Mann–Kendall non-parametric test. The tendencies have been detected since 1951 for the maximum and minimum monthly temperatures and for several indices of daily extremes. As a result, the minimum and maximum monthly temperatures mainly denote a positive trend for spring and summer months and a marked negative one in September. Moreover, the trend analysis on the extreme temperature indices shows opposite behaviours: a clear positive tendency in the frequency and intensity of the highest values and some negative trends for the lowest ones.
Previous assessments of historical trends of measured surface temperature in South Africa have all shown a general upward trend, in both mean and extreme values, over recent decades. In addition, some regional differences in trends have been identified. Most of these studies focused on the period from about 1961 up to the last year that could be included before publication, and only climate stations situated in the same position for the entire analysis period were analysed. A data homogenization procedure enabled the combination of time series of stations from which trend analysis could be applied, extending the common analysis period for this study back to around 1931. The trend results, based on the World Meteorological Organization Expert Team on Climate Change Detection and Indices, continue to show the general warming trend shown in previous analyses, with a general increase in extreme warm events and a general decrease in extreme cold events across South Africa. The analysis of seasonal trends show that, while there are noteworthy differences on a regional basis, austral summer shows on average the strongest warming, followed by autumn, winter and spring. The central interior, which exhibited significant cooling in previous analyses, now shows non-significant or similar trends when compared to the other parts of South Africa. There is no countrywide acceleration in the warming trends, but some regional consistencies in the temporal changes in trends could be determined, i.e. increases in trends in the central interior and decreases in trends along most of the coastal region.
The forecasted changes in global climate include not only shifts in average conditions, but also changes in the frequency and intensity of climatic extremes. One such climate extreme expected to change in the future are extreme cold spells. Although these disturbances may become less frequent, extreme cold spells are believed to persist in the foreseeable future and will likely have profound effects on species distributions, community organization, and ecosystem function. However, generalities about how ecosystems respond to these disturbances remain understudied. In 2008, an extreme once-in- 50- year cold spell affected sub-tropical China, causing catastrophic damage to natural systems within the swath of the disturbance. In this study, we investigated the effects of this disturbance on a community of butterflies in the Nanling Mountain Preserve in southern China. Butterfly count surveys were conducted from 2006 to 2011. We predicted that the 2008 cold spell would have disproportionate effects on tropical butterfly species, increasing community dominance of more broadly tolerant temperate species. Over the course of the study, we counted 3403 butterflies from 249 species. Our results showed that the cold spell reduced abundances of temperate and tropical species by 50% and 88%, respectively. The disproportionate loss of tropical butterfly species changed butterfly community structure, resulting in a post cold spell community nearly completely dominated by temperate species. Butterfly communities remained dominated by temperate species for 2 years before the abundances of tropical species return to predisturbance conditions in 2011. As cold spells change in frequency and intensity in the future, we should expect their role in structuring sub-tropical communities, in particular, the presence and dominance of tropical species to also change.
In this study, maximum daily and monthly winter temperatures over the energy consuming province of Gauteng, South Africa, are analysed to understand mechanisms underlying cold spells and cold winter seasons. Composite sequences of daily weather data for cold spells implicate large Rossby waves advecting polar air from the South Atlantic. The potential for cold late winters increases when the meridional temperature gradient between the poles and tropics is diminished. Reduced zonal thermal winds enhance the penetration of polar air. Statistical models are developed to take advantage of climatic signals preceding cold winters. The predictability of winter temperatures over Gauteng is found to be reasonable, using a number of candidate predictors over a short training period 1975-1993. Long-range prediction of winter temperatures, based on both pattern recognition and preliminary models, could assist strategic planning efforts for electricity demand and other economic activities affected by cold weather.
This study provides the comprehensive analysis of changes in mean and extreme temperature indices of India to assist the climate change mitigation and adaptation strategies and to add information for the global comparisons, using a high-resolution daily gridded temperature data set (1° × 1°) during 1971–2005. In addition to the indices recommended by the World Meteorological Organization/CLIVAR Expert Team on Climate Change Detection and Indices, few more indices having social and agricultural implication are investigated at the seasonal and annual scales, utilizing widely adopted statistical methodologies in climate research. The results show, in general, a robust signal of warming, broadly consistent with what has been observed and predicted in other parts of the world in the context of global warming. The frequency and intensity of warm extremes, especially representing the daily minimum temperature, have increased with simultaneous decreases in cold extremes in large parts of the country, but the spatial distribution of the trend magnitude reflects the complex natural climatic settings of India and its possible interaction with the anthropogenic forcing. Seasonal analysis reveals a faster warming in day and night temperatures in winter affecting the major wheat crop. In summer, however, both human and ecosystems appear to be more vulnerable to the increasing tendency of the heatwave occurrences, particularly during night-time, since the 1990s. The relationship with the large-scale natural climatic modes indicates that the warming indices tend to increase in the year following the El Niño events as evident from the correlation with the NINO3.4 index, with a relatively higher association in the monsoon season. Moreover, the concurrent correspondence of the summer heatwaves with the north Indian Ocean sea surface temperature suggests a degree of predictability of the heat stress episode.
Trends in daily maximum and minimum extreme temperature indices were investigated for 28 weather stations in South Africa, not only for the common period of 1962–2009, but also for longer periods which the individual record lengths of the stations would allow. The utilized weather stations had limited gaps in their time series, did not undergo major moves, or had their exposure compromised during the study period, as to influence the homogeneity of their time series. The indices calculated were forthcoming from those developed by the WMO/CLIVAR Expert Team on Climate Change Detection and Indices (ETCCDI), but only those applicable to the South African climate were selected. The general result is that warm extremes increased and cold extremes decreased for all of the weather stations. The trends however vary on a regional basis, both in magnitude and statistical significance, broadly indicating that the western half, as well as parts of the northeast and east of South Africa, show relatively stronger increases in warm extremes and decreases in cold extremes than elsewhere in the country. These regions coincide to a large degree with the thermal regimes in South Africa which are susceptible to extreme temperatures. The annual absolute maximum and minimum temperatures do not reflect the general trends displayed by the other indices, showing that individual extreme events cannot always be associated with observed long‐term climatic trends. The analyses of longer time series than the common period indicate that it is highly likely that warming accelerated since the mid‐1960s in South Africa. Copyright © 2012 Royal Meteorological Society
Mid-tropospheric closed-lows (cold-core cut-off lows and warm-core tropical lows) are important rain producing weather systems for the southern Africa region. Over South Africa, most wide-spread flood events are caused by these systems. It is therefore important to explore the potential impact of anthropogenic forcing on the occurrence of closed-lows and extreme rainfall events over the region. Coupled global circulation models (CGCMs) can not be directly applied for this purpose because of their relatively low spatial resolution—some form of downscaling is required to adequately resolve these systems and the rainfall they cause. In this study, a variable-resolution atmospheric global circulation model is applied as a regional climate model to simulate closed-low characteristics over southern Africa under current and future forcings. The model is forced with greenhouse gas concentrations according to the A2 SRES scenario and with sea surface temperatures (SSTs) and sea-ice as specified by the CSIRO Mk3 CGCM. The model projects a general decrease in closed-low frequencies over the region, which occurs in association with a general strengthening of the subsiding branch of the Hadley cell. However, the climate-change signal shows variation in time and space and certain sub-regions are projected to experience an increase in closed-low frequencies during certain seasons. A general increase in extreme rainfall events is projected over southern Africa despite the projected decrease in closed-low frequencies. It is deduced that this increase in extreme rainfall events is driven by intense convective rainfall events occurring within more frequently forming tropical-temperate cloud bands. Over Mozambique, extreme rainfall events are projected to increase in association with more frequently occurring closed-lows. Copyright © 2012 Royal Meteorological Society
Major circulation patterns affecting southern Africa have been well documented, particularly as they affect rainfall, but few seasonal or longer-period synoptic-scale studies of temperature have been undertaken, and those that do exist are spatially limited. There is therefore a need to investigate the relationship between surface temperature, surface wind and atmospheric circulation on diurnal and seasonal time scales and on meso and synoptic spatial scales over an extended period for southern Africa. Hourly summer and winter surface temperature and wind data are used to examine the relationships between synoptic circulation patterns, winds and surface temperature fields. Data for four coastal and three inland weather stations show that seasonal and diurnal variations of surface temperature are related to dynamic and advective effects, synoptic-scale winds and local air circulations, and thermo-topographic boundary layer effects.
Analyses of climate model simulations and observations reveal that extreme cold events are likely to persist across each land-continent even under 21st-century warming scenarios. The grid-based intensity, duration and frequency of cold extreme events are calculated annually through three indices: the coldest annual consecutive three-day average of daily maximum temperature, the annual maximum of consecutive frost days, and the total number of frost days. Nine global climate models forced with a moderate greenhouse-gas emissions scenario compares the indices over 2091-2100 versus 1991-2000. The credibility of model-simulated cold extremes is evaluated through both bias scores relative to reanalysis data in the past and multi-model agreement in the future. The number of times the value of each annual index in 2091-2100 exceeds the decadal average of the corresponding index in 1991-2000 is counted. The results indicate that intensity and duration of grid-based cold extremes, when viewed as a global total, will often be as severe as current typical conditions in many regions, but the corresponding frequency does not show this persistence. While the models agree on the projected persistence of cold extremes in terms of global counts, regionally, inter-model variability and disparity in model performance tends to dominate. Our findings suggest that, despite a general warming trend, regional preparedness for extreme cold events cannot be compromised even towards the end of the century.
Investigations of extreme rainfall events in the southern African region are limited by the paucity of the observational network. Furthermore, the lack of full radar coverage for South Africa makes quantitative precipitation estimation difficult. Therefore, numerical modeling represents the most effective method for improving the understanding of the mechanisms that contribute to extreme rainfall events in this region with the caveat that accurate validation of model simulations is hampered by the limited observations in the region. This paper describes an intense cutoff low event over South Africa that led to record rainfall and flash flooding along the south coast of the country and adjoining hinterland. Analyses from the Global Forecast System model showed that the cutoff aloft was accompanied by a strong low-level jet (LLJ) impinging onto the south coast where rainfall was heaviest, and that lapse rates were steep in the lower troposphere. Simulations of the event were carried out using a numerical model [i.e., the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5)], which showed that severe convection occurred over the ocean on the right-hand side of the LLJ, and at its leading edge where it impinged on the coastal topography. This topography was also very important in providing additional forcing for the ascent of moist air. A factor separation technique was used to show that surface heat fluxes from the warm sea surface temperature (SST) of the Agulhas Current were important in enhancing low-level cyclogenesis, and that topography was important in maintaining the position of the low-level coastal depression, which led to favorable conditions for rainfall remaining in the same area for an extended period of time. It is suggested that improved representation of the tight topographic and SST gradients of the southern African region in NWP models or postprocessing systems would help to provide more accurate forecasts of the amount and location of heavy precipitation during cutoff low events where surface forcing is important.
This study proposes an empirical approach to account for lag-1 autocorrelation in detecting mean shifts in time series of white or red (first-order autoregressive) Gaussian noise using the penalized maximal t test or the penalized maximal F test. This empirical approach is embedded in a stepwise testing algorithm, so that the new algorithms can be used to detect single or multiple changepoints in a time series. The detection power of the new algorithms is analyzed through Monte Carlo simulations. It has been shown that the new algorithms work very well and fast in detecting single or multiple changepoints. Examples of their application to real climate data series (surface pressure and wind speed) are presented. An open-source software package (in R and FORTRAN) for implementing the algorithms, along with a user manual, has been developed and made available online free of charge.