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Indices for monitoring changes in extremes based on daily temperature and precipitation data
Abstract
Indices for climate variability and extremes have been used for a long time, often by assessing days with temperature or precipitation observations above or below specific physically‐based thresholds. While these indices provided insight into local conditions, few physically based thresholds have relevance in all parts of the world. Therefore, indices of extremes evolved over time and now often focus on relative thresholds that describe features in the tails of the distributions of meteorological variables. In order to help understand how extremes are changing globally, a subset of the wide range of possible indices is now being coordinated internationally which allows the results of studies from different parts of the world to fit together seamlessly. This paper reviews these as well as other indices of extremes and documents the obstacles to robustly calculating and analyzing indices and the methods developed to overcome these obstacles. Gridding indices are necessary in order to compare observations with climate model output. However, gridding indices from daily data are not always straightforward because averaging daily information from many stations tends to dampen gridded extremes. The paper describes recent progress in attribution of the changes in gridded indices of extremes that demonstrates human influence on the probability of extremes. The paper also describes model projections of the future and wraps up with a discussion of ongoing efforts to refine indices of extremes as they are being readied to contribute to the IPCC's Fifth Assessment Report. WIREs Clim Change 2011, 2:851–870. doi: 10.1002/wcc.147
This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change
... The 98th percentile values of P and w max were calculated for each grid cell within the study area over the entire analysis period to identify CPWEs. The percentile method is widely used to identify extremes of these variables due to its simplicity and adaptability to various datasets and regions [40]. Different percentiles of P and w max have been used in CPWE studies across various regions and on a global scale [7,8,11,14,41]. ...
... At least one of these conditions was not met when using the other percentile values. Similar reasoning for using the 98th percentile was also provided in other studies [7,40]. ...
Compound wind and precipitation extremes (CPWEs) pose significant threats to infrastructure, economies, the environment, and human lives. In this study, the recurrence, spatial distribution, intensity, and synoptic conditions leading to the formation of CPWEs were assessed in the eastern part of the Baltic Sea region. Using ERA5 reanalysis data, CPWEs were identified when both daily precipitation and maximum wind speed exceeded the 98th percentile thresholds on the same day at the same grid cell. Due to the proximity of the Baltic Sea and the influence of terrain, CPWEs were most frequent on the windward slopes of highlands in the western part of the investigation area. The most severe CPWEs occurred in the second half of summer and early September. Based on data from the Hess–Brezowsky synoptic classification catalogue and various synoptic datasets, the formation of CPWEs during the cold season (October–March) is associated with intense zonal (westerly) flow, while during the warm season (April–September), it is linked to the activity of southern-type cyclones. The number of CPWEs increased across all seasons, with the largest changes observed during the summer. However, the majority of changes are insignificant according to the Mann–Kendall test.
... We assessed the capability of the CMIP6 models to accurately represent extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) (Zhang et al., 2011). Ten ETCCDI precipitation indices were selected for this study (Table 2). ...
... List of the detailed precipitation extreme indices used in the study (Zhang et al., 2011). (Zhang and Yang, 2004) for the period 1981-2014. ...
... Moderate climate extremes, defined as those that typically occur several times or at least once every year (World Meteorological Organization 2009;Zhang et al. 2011), have intensified significantly due to increasing atmospheric waterholding capacity under human-induced global warming reflected by the Clausius-Clapeyron relationship (Min et al. 2011;Sun et al. 2021). Therefore, there is an urgent need for monitoring, assessing, and projecting potential changes in the characteristics of moderate climate extremes (including frequency, intensity, and duration) and their implications worldwide. ...
... An in-depth discussion on the use of ETCCDI indices was comprehensively documented by the Alexander et al. (2019); World Meteorological Organization (2009); Zhang et al. (2011). Fundamentally, the ETCCDI indices, derived from daily maximum and minimum temperatures and daily rainfall amounts, can be broadly classified into three types, including absolute indices, day-count indices with fixed thresholds, and those based on percentile thresholds. ...
To gain a deeper understanding of the historical changes in climate means and extremes over recent decades in Vietnam’s Mekong Delta (VMD), this study employed a multi-temporal trend analysis methodology. Non-parametric statistical trend tests, including Sen’s slope estimator, as well as classical and modified Mann-Kendall tests were applied to all possible periods with varying beginning and ending times. The outcomes consistently indicate progressive warming trends, with detectable trends at 30-year scales or more estimated to be around 0.04–0.49 °C per decade. Regarding rainfall trends, the outcomes reveal much less coherent patterns across stations and periods analyzed. Trends estimated for long-term periods (record length of 30 years or more) were mainly weak and statistically insignificant, ranging from approximately 0.2–6.9% per decade for stations characterized by increasing trends in annual rainfall and from approximately − 0.1% to -4.5% per decade for those exhibiting declining trends. Meanwhile, time-varying trends at shorter-term time scales appeared to be more mixed with higher magnitudes, and also exhibited alternating increasing and decreasing trend behaviors or even a reversal of the dipole-like pattern. These findings highlight the sensitivity of time-varying trend patterns to the considered periods in terms of trend direction, magnitude, and statistical significance. This study also addressed potential relationships between El Niño-Southern Oscillation (ENSO) and interannual variability of rainfall anomalies based on correlation analysis. The outcomes reveal significant linkages between ENSO and rainfall variations in the VMD. Overall, this study provides more illuminating insights into the trend possibilities of climate means and extremes in the VMD.
... Это происходит потому, что чем экстремальнее событие, тем вероятнее оно является причиной социального или экологического ущерба. Тем не менее, анализ изменений в частоте и интенсивности экстремальных явлений, которые находятся подальше в хвостах распределения, по своей сути является более неопределенным, потому что такие события происходят не так часто, и в итоге имеется меньше данных для определения и характеристики возможных изменений [25]. ...
... Таким образом, эти показатели могут быть универсальны при оценке поведения экстремумов температур воздуха в любом месте земного шара [24,25]. Кроме того, эти показатели были проверены и утверждены Всемирной Метеорологической Организацией [14], Европейской оценкой климата (ECA) 2 , и Европейским проектом Статистического и динамического регионального уменьшения масштаба экстремумов (STARDEX ЕС) 3 для исследования экстремальных температур. ...
Based on multiannual data (1945-2010) of the thermal extreme indices recommended by the World Meteorological Organization and characterizing the thermal regime in the warm period of the year, the trends of changes in these indices in the Republic of Moldova were assessed. The analysis of trends for two time periods of the current climate (“stationary” and “vulnerable”) revealed a change in the sign of practically all indicators of “hot” temperature extremes – during the “stationary” climate (until 1980) the trend is downward, and in the “vulnerable” climate the climate trends are positive. The comparison of the total number of extreme temperature events for the entire observation period showed that over the past 30 years the number of extremes in the warm period has increased significantly. This increase is observed through indicators characterizing night-time temperature conditions. The analysis of time trends in the indices of thermal extremes confirms not only the fact of the general warming of the regional climate, but also demonstrates the "extreme" character of the warming, expressed by the increase in the number of "hot" temperature extremes during the warm period.
... La mayoría de los índices que se encuentran en la literatura son para series diarias, para las que usualmente se utilizan umbrales basados en los percentiles superiores iniciando en el percentil 95. A nivel diario se han propuesto índices como Días secos consecutivos (CDD), días húmedos consecutivos (CWD), precipitación húmeda total (PRCPTOT), número de días de alta precipitación (R10mm), número de días de muy alta precipitación (R20mm), días muy húmedos (R95p), días extremadamente húmedos (R99p), máxima cantidad de precipitación en 1 d (Rx1day), máxima cantidad de precipitación en 5 d (Rx5day), índice de intensidad simpe diaria (SDII) (Zhang et al., 2011). Estos índices son muy populares, pero es sabido que, al trabajar en estos, se pierde información de los eventos más intensos intradiarios que precisamente son los causantes de las inundaciones súbitas en las zonas de montaña. ...
La relación conjunta entre la lluvia media y la respuesta hidrológica en el punto de aforo de una cuenca es lo que consideramos un evento de lluvia. El objetivo de este estudio es analizar los eventos extremos registrados en la cuenca del rio Chinchiná, estación Bosque Popular, ubicada en la ciudad de Manizales. El estudio se realiza mediante la creación de una base de datos que identifica y relaciona las principales variables de estos eventos de lluvia. Por facilidad, se utiliza una metodología que considera únicamente los eventos extremos simples de un solo pico cuyo origen son una sola lluvia. Los datos que recopila la base de datos para cada evento extremo son: el nivel inicial, el nivel pico, el incremento del nivel, y los tiempos asociados a diferentes etapas de la lluvia, como la duración total de la lluvia, la duración de la precipitación máxima y total del evento. Las lluvias medias en la cuenca fueron calculadas mediante el inverso de la distancia al cuadrado. Los resultados indican que la caracterización de las variables de los eventos extremos facilita la identificación de correlaciones significativas entre ellas, y destaca la importancia de seleccionar variables que se puedan medir en tiempo real, lo que permite establecer relaciones precisas para el desarrollo de un protocolo adecuado para un Sistema de Alerta Temprana (SAT) por inundaciones. Adicionalmente, se realiza el estudio para comparar diferentes ventanas temporales móviles. Los resultados sugieren que una mejor comprensión de los eventos extremos puede mejorar la efectividad de los SAT, y contribuyen al avance en la gestión de riesgos por inundaciones en ciudades de alta montaña, lo que puede tener un impacto significativo en la prevención y respuesta ante desastres naturales.
... La mayoría de los índices que se encuentran en la literatura son para series diarias, para las que usualmente se utilizan umbrales basados en los percentiles superiores iniciando en el percentil 95. A nivel diario se han propuesto índices como Días secos consecutivos (CDD), días húmedos consecutivos (CWD), precipitación húmeda total (PRCPTOT), número de días de alta precipitación (R10mm), número de días de muy alta precipitación (R20mm), días muy húmedos (R95p), días extremadamente húmedos (R99p), máxima cantidad de precipitación en 1 d (Rx1day), máxima cantidad de precipitación en 5 d (Rx5day), índice de intensidad simpe diaria (SDII) (Zhang et al., 2011). Estos índices son muy populares, pero es sabido que, al trabajar en estos, se pierde información de los eventos más intensos intradiarios que precisamente son los causantes de las inundaciones súbitas en las zonas de montaña. ...
... An extended analysis focused on lead-lag composites of specific humidity, vertical velocity, and temperature anomalies during the EPEs. Finally, we extended our examination to include six extreme daily climate indices, as defined by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI), to characterize the magnitude and duration of EPEs across the WH (Zhang et al. 2011;Bhattacharyya et al. 2022;). The RX1day index was utilized to gauge the maximum single-day precipitation amount, offering insights into instances of intensity precipitation events. ...
Understanding the precipitation patterns in the western Himalayas (WH) during the Indian summer monsoon (ISM) is crucial across different spatiotemporal scales for societal well-being and effective risk management. This study conducts a comprehensive assessment of summer monsoon (June–September; JJAS) precipitation over WH using high-resolution simulations (at a 10 km grid-spacing) of the Weather Research and Forecasting model (referred to as WRF-HARv2), driven by ERA5 reanalysis for the period of from 1980 to 2020. The finding indicates that the dynamically downscaled WRF model provides a reliable spatial distribution of precipitation patterns during ISM against the ground-based gridded (IMD), satellite-based (GPM-IMERG), and reanalysis (ERA5) datasets. Empirical Orthogonal Function analysis/Principal Component Analysis was employed to investigate precipitation variability, and the results suggest that the WRF-HARv2 model exhibits potential skill in capturing the interannual precipitation variability over the WH. Our results also depict that the WRF-HARv2 effectively capture extreme precipitation events (EPEs) along with interannual variability, as observed in ERA5, indicating that decreasing the horizontal grid spacing of WRF to 10-km can reproduce extreme precipitation over WH. In examining extreme precipitation, finding shows that large amounts of moisture are being transported towards the WH, feeding the EPEs over WH, which is realistically represented by WRF-HARv2. Furthermore, WRF simulations reveal that EPEs over the WH are primarily driven by vertical advection in the moisture budget, with dynamic terms accounting for more than 98% of the moisture budget component. Overall, the analysis shows that WRF improves in representing the spatiotemporal variations of precipitation, interannual variability, and extreme precipitation, providing high-resolution climate information with more accurate comparisons to the observed dataset.
... These climate changes have already influenced the global weather and climate, with marked increases in the frequency and intensity of heat extremes, marine heat waves, heavy rainfall, agricultural, and ecological droughts in some regions, and tropical cyclones, as well as reductions in the Arctic Sea ice, snowpack, and permafrost [1]. In recent decades, climate change has attracted considerable research attention, resulting in the analysis of temperature and precipitation time series through the definition of specific indices that can help identify increasing or decreasing trends in extreme events [2][3][4]. Globally, the last seven years have been the warmest years ever recorded by a considerable margin. Recently, the Copernicus Climate Change Service [5] confirmed that 2023 was the warmest calendar year as per the global temperature data records, which go back to 1850. ...
This paper presents a study on the evolution of extreme temperatures in the Marche region, Central Italy. To this end, a complete dataset compiled using data collected from available thermometric stations over the years 1957–2019 based on minimum and maximum daily temperatures was selected. The yearly mean values of extreme temperature and relative climate indices defined by the Expert Team on Climate Change Detection and Indices were calculated, and a trend analysis was performed. The spatial distribution of the trends was assessed, and the variations in extreme temperatures in the medium–long term were considered by calculating mean values with respect to different climatological standard normals and decades. The analyzed parameters show that extreme heat events characterized by increasing intensity and frequency have occurred over the years, while cold weather events have decreased. A high percentage of stations recorded an increase in all indices related to daily maximum temperatures, and a simultaneous decline of those related to daily minimum values, under both nighttime and daytime conditions. This phenomenon characterizes the entire Marche region. A detailed analysis of the heat wave indices confirms an increasing trend, with a notable increase beginning in the early 1980s.
... These included annual air temperature and precipitation, urbanization intensity, urban green space, and population density. Annual air temperatures and precipitation are well-documented indicators of background climatic conditions [37]. Annual air temperatures and precipitation data were retrieved from the atmospheric reanalysis product. ...
Biodiversity has important implications for the sustainable development of cities. Given the paucity of ground-based experiments, the responses of biodiversity to urbanization and its associated controls on a global scale remain largely unexplored. We present a novel conceptual framework for quantifying the direct and indirect impacts of urbanization on biodiversity in 1523 cities worldwide using the global 100 m grid biodiversity intactness index data (2017–2020) as a proxy for biodiversity. The results show a pervasive positive impact of urbanization on biodiversity in global cities, with a global mean direct and indirect impact of 24.85 ± 9.97% and 16.18 ± 10.92%, respectively. The indirect impact is relatively large in highly urbanized cities in the eastern United States, Western Europe, and the Middle East. The indirect impact is predominantly influenced by urbanization intensity, population density, and background climate. The correlation between urbanization intensity and indirect impact is most pronounced across all climate zones, while the other driving variables influencing the indirect effect exhibited considerable variations. Furthermore, our findings indicate that the biodiversity responses to urbanization are influenced by the biodiversity and development conditions of cities. Our findings have important implications for understanding the impact of urbanization on biodiversity and for future sustainable urban biodiversity.
... As seen in the left panel of Figure 3, the median values remain well below the chosen tn-thresholds (blue dots in the chart). This suggests that more frequent and longer cold spells will be detected in at least 50% of the stations, determining the proposed cold spell duration indicator as an indicator of moderate extremes [42]. The chosen thresholds are well suited for more than 70% of stations in the domain but not for stations in the Csa climate zone. ...
Recent studies have revealed a rise in extreme heat events worldwide, while extreme cold has reduced. It is highly likely that human-induced climate forcing will double the risk of exceptionally severe heat waves by the end of the century. Although extreme heat is expected to have more significant socioeconomic impacts than cold extremes, the latter contributes to a wide range of adverse effects on the environment, various economic sectors and human health. The present research aims to evaluate the contemporary spatio-temporal variations of extreme cold events in Southeastern Europe through the intensity–duration cold spell model developed for quantitative assessment of cold weather in Bulgaria. We defined and analyzed the suitability of three indicators, based on minimum temperature thresholds, for evaluating the severity of extreme cold in the period 1961–2020 across the Köppen–Geiger climate zones, using daily temperature data from 70 selected meteorological stations. All indicators show a statistically significant decreasing trend for the Cfb and Dfb climate zones. The proposed intensity–duration model demonstrated good spatio-temporal conformity with the Excess Cold Factor (ECF) severity index in classifying and estimating the severity of extreme cold events on a yearly basis.
... More frequent and intense extreme events beyond natural climate variability have already caused widespread adverse impacts to nature and people [108]. The Expert Team on Climate Change Detection and Indices (ETCCDI) was established in the late 1990s, and the set of indices developed by the group has become the principal tool for monitoring changes in temperature extremes over land [109]. The terrestrial indices, calculated from daily values of maximum, minimum, or mean temperature values recorded at global terrestrial weather stations have been combined in the HadEX3 dataset to form spatially complete fields of temperature extremes [110]. ...
he Sixth Assessment Report of the Intergovernmental Panel on Climate Change stated that “it is unequivocal that human influence has warmed the atmosphere, ocean and land.” One key piece of evidence for this is the global average of the instrumental record of surface temperature. A change in surface temperature of 1.5°C relative to a reference “preindustrial” period is the measure internationally agreed to mark the transition to a climate where dangerous impacts become common. This chapter will discuss the observational evidence that ensures a reliable record of the changing temperature, some of the challenges that have been overcome, and some that remain in improving that record and more fully understanding its uncertainty.
... Appendix A summarizes potential hydrological metrics used in water management decisions (Jagannathan et al., 2021), statistical assessments of extremes (Zhang et al., 2011), andmodel evaluations (Phillips et al., 2020). The metrics in bold are presented in this paper. ...
The large spatial scale of global Earth system models (ESMs) is often cited as an obstacle to using the output by water resource managers in localized decisions. Recent advances in computing have improved the fidelity of hydrological responses in ESMs through increased connectivity between model components. However, the models are seldom evaluated for their ability to reproduce metrics that are important for and resonate with practitioners or that allow practitioners to situate higher-resolution model outputs within a cascade of uncertainty stemming from different models and scenarios. We draw on the combined experience of the author team and water manager workshop participants to identify salient water management metrics and evaluate whether they are credibly reproduced over the conterminous USA by the Community Earth System Model v2 (CESM2) Large Ensemble. We find that, while the exact values may not match the observations, aspects such as interannual variability can be reproduced by CESM2 for the mean wet day precipitation and length of dry spells. CESM2 also captures the proportion of total annual precipitation that derives from the heaviest rain days in watersheds that are not snow-dominated. Aggregating the 7 d mean daily runoff to two-digit Hydrological Unit Code (HUC2) watersheds also shows that rain-dominated regions capture the timing and interannual variability of annual maximum and minimum flows. We conclude that there is potential for far greater use of large-ensemble ESMs, such as CESM2, in long-range water management decisions to supplement high-resolution regional projections.
... This study selected four extreme climate indices that are significantly affected by urbanization [14,27], recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) [28], to explore historical climate extreme changes in typical urban agglomerations in China. The selected indices include two extreme temperature indices and two extreme precipitation indices. ...
The rapid expansion of urban land is considered one of the primary factors contributing to the enhancement in climate extremes in both frequency and severity. But the effects of urban land expansion on climate extremes are presently unclear, especially in geographically and climatologically complex China. This study investigates evolution laws of temperature and precipitation extremes from 1960 to 2022 over five national-level urban agglomerations in China and explores evolution trends in those under urban land expansion using the WRF model. The results show that the variation characteristics of temperature extremes over urban agglomerations in China show higher consistency compared to precipitation extremes under global warming and urbanization. Both the intensity and frequency of temperature extremes have significantly increased, but those of precipitation extremes have sometimes decreased rather than increased. Furthermore, both temperature and precipitation extremes will strengthen with urban land expansion. Around 30% of the enhancement in temperature and precipitation extremes can be attributed to urban land expansion. The temperature extremes of urban agglomerations at lower latitudes are more significantly affected by urban land expansion, but no significant spatial distribution law is observed in precipitation extremes. The results of this study could provide a scientific reference for better coping with extreme climate changes in urban areas and achieving sustainable development.
... Ambos estão localizados no Semiárido Pernambucano, mas apresentam características climáticas distintas, associadas aos padrões típicos do sertão e do agreste, respectivamente. Santana, A. C. A. de; Rodrigues Junior, J. C.; Barros, T. H. S. da; Rocha, N.; Bender F. D.; Cirilo, J. frequência e intensidade, para constatar mudanças nos padrões de tempo e clima local, regional e mesmo global (Frich et al., 2002;Manton, 2008;Zhang et al., 2011). ...
O objetivo deste trabalho consiste em analisar o comportamento do clima a partir da aplicação de indicadores de extremos climáticos de temperatura e de chuva, do Índice de Aridez (IA) e do Índice de Precipitação Padronizado (SPI), nos municípios de Dormentes e Canhotinho, no Sertão e Agreste pernambucano, respectivamente. Para isso, utilizou-se uma série histórica de 1980-2016 para calcular o SPI e IA e, com o software ClimPACT2 foram estimados os índices extremos. Para a temperatura, os índices extremos relevam tendência de aumento no número de dias e noites quentes (TX90p e TN90p) nos dois municípios. Em relação aos índices de chuva, observa-se uma tendência de aumento de seca no Sertão (DCU, PRCPTOT, R95p, Rx5day, R10) e, de umidade no Agreste (PRCPTOT, R95p, Rx5day, R10, R20, R30). O SPI apontou a ocorrência de eventos secos intensos nas duas regiões e, o IA indicou uma aridez crescente para Dormentes.
... 018). Many indices are defined to characterize extreme precipitation, including R95pTOT. R95pTOT refers to annual total precipitation accumulations when daily precipitation exceeds the 95th percentile of the probability distribution of wet-day (precipitation ≥ 1.0 mm) precipitation over a reference period (e.g., 1961-1990) (e.g., Dong et al., 2021;X. Zhang et al., 2011). Lin et al. (2016) used an Earth system model ensemble simulation to examine the increased rate in extreme precipitation due to anthropogenic GHGs and aerosols globally in the 21st century, showing that the increased rate in extreme precipitation due to surface warming caused by aerosol reduction is 2-4 times higher than that caused by ...
Extreme precipitation is becoming more intense and frequent. The increasing trends in extreme precipitation in China in warm season related to changes in aerosols and greenhouse gases (GHGs) are investigated using observations, reanalysis data and model simulations. A significant accelerating increase in extreme precipitation occurred around 2010, with the trend in accumulated extreme rainfall amount (R95pTOT) increasing from 2.88 mm per decade during 2000–2010 to 22.88 mm per decade during 2010–2023. The sudden acceleration of the increasing extreme precipitation is largely attributed to the reverse in aerosol trends associated with China’s clean air actions, which affects extreme precipitation through perturbing cloud microphysics and atmospheric dynamics, accounting for half of the change in R95pTOT trends. Future aerosol reduction to achieve carbon neutrality is shown to continue to intensify the extreme precipitation, which overweighs the effect induced by GHGs, highlighting the importance of aerosol changes in modulating future climate and weather extremes.
... Present day ( The 99.5th percentile is inherited, for consistency, from Griffin, Kay, Bell, et al. (2022). It is largely arbitrary, intended to yield sufficient data points for statistical analysis (Bloomfield et al. 2023;Griffin, Kay, Sayers, et al. (2022); Martius, Pfahl, and Chevalier 2016;Zhang et al. 2011). It is less than the 2-year return period 'rule of thumb' for bank-full discharge (i.e., 99.9th percentile), although the work this derives from Williams (1978) is highly equivocal (i.e., 1-32 year range) due to factors such as basin characteristics, local climate and flood defences (Berghuijs et al. 2019; e.g., Tian et al. 2019). ...
Insurers and risk managers for critical infrastructure such as transport or power networks typically do not account for flooding and extreme winds happening at the same time in their quantitative risk assessments. We explore this potentially critical underestimation of risk from these co‐occurring hazards through studying events using the regional 12 km resolution UK Climate Projections for a 1981–1999 baseline and projections of 2061–2079 (RCP8.5). We create a new wintertime (October–March) set of 3427 wind events to match an existing set of fluvial flow extremes and design innovative multi‐event episodes (Δt of 1–180 days long) that reflect how periods of adverse weather affect society (e.g., through damage). We show that the probability of co‐occurring wind‐flow episodes in Great Britain (GB) is underestimated 2–4 times if events are assumed independent. Significantly, this underestimation is greater both as severity increases and episode length reduces, highlighting the importance of considering risk from closely consecutive storms (Δt ~ 3 days) and the most severe storms. In the future (2061–2079), joint wind‐flow extremes are twice as likely as during 1981–1999. Statistical modelling demonstrates that changes may significantly exceed thermodynamic expectations of higher river flows in a wetter future climate. The largest co‐occurrence increases happen in mid‐winter (DJF) with changes in the North Atlantic jet stream an important driver; we find the jet is strengthened and squeezed into a southward‐shifted latitude window (45°–50° N) giving typical future conditions that match instances of high flows and joint extremes impacting GB today. This strongly implies that the large‐scale driving conditions (e.g., jet stream state) for a multi‐impact ‘perfect storm’ will vary by country; understanding regional drivers of weather hazards over climate timescales is vital to inform risk mitigation and planning (e.g., diversification and mutual aid across Europe).
... This led to the development of internationally accepted extreme climate indices that can be used for consistently comparing various spatial and temporal domains (Alexander and Herold, 2015). Out of 27 climate indices defined by ETCCDI, 16 are related to temperature extremes, while 11 are related to precipitation (Alexander et al. 2006Zhang et al. 2011). The detailed description is available at etccdi.pacificclimate.org/list_27_indices.shtml. ...
Changes in global or regional climate in recent decades have significantly impacted various aspects. Africa, with immense concern, is facing the consequences of extreme precipitation on socio-economic activities such as poverty, hydropower generation, and many more. This paper aims to evaluate the precipitation extremes in the Volta River Basin (VRB) in West Africa using the extreme climate indices proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI). The precipitation data for the analysis were obtained from Climate Forecast System Reanalysis (CFSR) for 36 years from 1979 to 2013 at a spatial resolution of 0.5° at the tropics and 0.25° at the equator. The temporal distribution of average precipitation in the basin decreases at the rate of 15 mm per year, while the maximum and minimum temperatures increase at the rate of + 0.066 °C and + 0.019 °C, respectively. In addition, the temporal distribution of precipitation indices showed that the dry days (+ 1.1 days/year) are increasing, and the wet days (− 0.25 days/year) are decreasing. The dry years are more pronounced in the northern region of the basin, while the wet days are more pronounced in the southern. The increasing trend of the dry years and decreasing trend of the wet years will lead to face drought events in the future, affecting rain-fed irrigation productivity and hydropower production thereby affecting the nexus of water, agriculture, and socio-economic. With further increases in the dry events in the basin, adaptive climate measures need to be addressed to minimize climate-related hazards. This could be achieved by conducting nexus-based research considering climate science, social science, economics and the environment.
... Figure 2. Bucharest -Băneasa weather-station (a) and its location within Bucharest town-area (b) (Sources: personal image (a) and data processed in QGis with OpenStreetMap (b)) In order to highlight some of Bucharest`s extreme climatic features, a number of 8 out of the 27 existing WMO's Expert Team on Climate Change Detection and Indices (ETCCDI) indices were used in this study. Designed to reflect some of the most relevant aspects of extreme weather and climatic events (Zhang et al., 2011), they have been selected and calculated according to RClimDex or FClimDex methods (CLIMDEX -Datasets for Indices of Climate Extremes, 2022), over the 1980-2015 period, as follows: ...
The energy consumption has become a real concern in choosing the most cost- effective way and resources for indoor-heating. This experimental study tried to estimate both the energy amounts needed to heat up the residential indoor spaces and the resulting average costs that people living in the Bucharest Metropolitan Area might have to pay for heating during the winter months. The daily minimum air-temperatures, incoming solar radiation and wind- speed values provided by the Bucharest-Băneasa weather station were used to calculate the corresponding mean monthly values of an expressive compound index for December, January and February, over the 1980-2015 period. In this respect, the Cooling Energy Consumption (CEC) index has been calculated. Then, its values were related to two different types of individual heating systems (CT): a conventional CT produced by Ariston (net efficiency of 93%) and a gaseous condensation CT produced by Viessmann (net efficiency of 108%). Finally, the results were multiplied by the actual unit cost of energy in Romania (1.3 lei/kWh), provided that the total monthly consumption of electricity per household keeps less than 300 kWh/month, so that some interesting and realistic estimates of heating expenditures could be obtained for either each or all winter months in Bucharest – Romania’s capital city. This method might be useful both to local authorities and inhabitants to estimate and plan in advance their public or domestic budget to more economically sustain their energy resources and expenditures.
... Before proceeding with the actual analysis, the data were subjected to a quality control using Rclimdex, a climate algorithm used with the R environment [44]. This tool is recommended by the ETCCDMI expert group for its ability to identify errors that may be present in daily data during collection and to detect indicators of climate change [45]. No outliers or missing data were observed in the dataset of this study. ...
As global warming continues, extremes in key climate parameters will become more frequent. These extremes are one of the main challenges for the sustainability of cities. The aim of this study is to provide a better understanding of the evolution of extremes in precipitation (pcp) and maximum (Tmax) and minimum (Tmin) temperatures in Grand-Nokoué to improve the resilience of the region. To this end, historical daily precipitation and maximum (Tmax) and minimum (Tmin) temperature data from the Cotonou synoptic station were used from 1991 to 2020. First, the extreme events identified using the 99th percentile threshold were used to analyze their annual and monthly frequency. Secondly, a Generalized Extreme Value (GEV) distribution was fitted to the annual maxima with a 95% confidence interval to determine the magnitude of the specific return periods. The parameters of this distribution were estimated using the method of L moments, considering non-stationarity. The results of the study showed significant upward trends in annual precipitation and minimum temperatures, with p-values of 0.04 and 0.001, respectively. Over the past decade, the number of extreme precipitation and Tmin events has exceeded the expected number. The model provides greater confidence for periods ≤ 50 years. Extreme values of three-day accumulations up to 68.21 mm for pcp, 79.38 °C for Tmin and 97.29 °C for Tmax are expected every two years. The results of this study can be used to monitor hydroclimatic hazards in the region.
... Therefore, it provides temporally and spatially continuous and readily available data [46]. It is stated that daily and monthly reanalysis of mean temperatures demonstrated good agreement with station data [47]. National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data are employed to investigate the temperature changes due to climate change in the eastern part of the Black Sea for the period of 1950-2015 [48]. ...
Climate change will have a tremendous effect on tourism activities. Tourism revenue plays a crucial role in the Turkish economy; therefore, it is vital to assess the impacts of climate change on tourism. This research aims to investigate the effects of climate change on seaside tourism on the Black Sea region in Türkiye. The summer simmer index (SSI) was utilized to determine the climatic comfort conditions in the summer months. Meteorological data, over 30 years, was used to observe the impact of climate change. Mann–Kendall trend analysis and ¸Sen’s innovative trend analysis were applied to reveal the trends. As a result, SSI zones were computed as zones 1, 2, 3, and 4. Zone 4 was rarely observed. Thermal comfort conditions in the summer were found to not pose a health threat to tourists. Both trend methods determined an upward trend of SSI scores in Akçakoca, Samsun, Rize, and Hopa. These destinations are becoming more favorable in terms of seaside tourism due to climate change. The results of this study can be used for destination marketing. Tourism decision makers may benefit from these results for developing coastal tourism in this region.
... Extreme precipitation indices offer valuable tools for quantifying the intensity, frequency, and duration of precipitation extremes, enabling consistent analysis and comparison of trends across different datasets. Zhang et al. (2011) documented the indices of climate extremes defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) from the World Meteorological Organization (WMO) Commission for Climatology (CCI)/Climate Variability and Predictability (CLIVAR)/Joint WMO-Intergovernmental Oceanographic Commission (IOC) Commission on Oceanography and Marine Meteorology (JCOMM). Extreme precipitation indices have been widely employed to quantitatively describe events (L. ...
Impacts of extreme precipitation call for additional attention to their trends and representation. Linear temporal trends in extreme precipitation indices (1981–2022) across the contiguous USA are identified using the 0.25°x0.25° European Centre for Medium-Range Weather Forecast (ECMWF) fifth-generation reanalysis (ERA5) and the Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS). Although statistically significant increases in annual precipitation are limited in space, trends in extreme precipitation indices are more widespread. Annual frequency of days with ≥10 mm (R10) and ≥20 mm (R20) of precipitation showed a statistically significant increase in parts of the Ohio Valley and Northeast and decreased in the parts of the South, Southwest, and West, with broader R10-trending areas. Trends in the maximum five-day precipitation (Rx5day) and total precipitation exceeding the 95th percentile (R95P) show similar spatial patterns as R10 and R20. Trends in the annual maximum number of consecutive wet (CWD) and dry (CDD) days were significant only in isolated areas. CDD increased significantly in much of the Southwest and West. The study’s findings suggest that western regions of the country are experiencing more significant and widespread increase in aridity, while certain areas of the eastern regions are experiencing a trend of increased precipitation extremes.
... The next step is to calculate the extreme rainfall indices and analyze the trend in each extreme wet rainfall indices using the Mann Kendall test. Extreme rainfall parameters use indices that have been developed by the Expert Team for Climate Change Detection and Indices (ETCCDI) and have been widely used [10], [11], [12], [13] and to calculate the indices using RClimDex software. In this study, the analyzed rainfall is wet extremes, therefore 5 indices were selected from 11 ETCCDI extreme rainfall indices, namely: a. Rx1day (Max 1-day precipitation amount) i.e. the maximum amount of one-day precipitation in 1 year (mm). ...
Bandung is one of the areas prone to flood disasters in Indonesia. The areas frequently affected by flooding are located around the Cikapundung Watershed. Floods are triggered by several factors, namely: rainfall, changes in land use, as well as geological and morphological conditions. The aim of this research is to analyze rainfall in the Cikapundung watershed to see whether there is an increase in extreme rainfall from 1977- 2022. The method used in this research is first to test the homogeneity of rainfall over 45 years. Next, the extreme rainfall indices and the trend of each wet extreme rainfall indices were calculated using the Mann-Kendall test. The research results show that statistically, of the 20 indices analyzed, only one indices experienced a significant downward trend, namely the Rx5day indices at Kayu Ambon station. Other indices show a p-value of more than 0.05, which means statistically there is no trend, either an upward trend or a downward trend.
... On the other hand, the Mann-Kendall test is a nonparametric test for randomness against trend. As for the Sen Estimator, the use of the Mann-Kendall test is well documented Atmosphere 2025, 16, 133 5 of 23 in climate extreme analysis [43]. The null hypothesis, H0, is that the data are Independent and Identically Distributed (IID). ...
Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts.
... From these records percentiles, representing temperature values that occur less frequently and that appropriately predict outcomes in subjects of inter-est, such as human health (Pascal et al. 2013;Herold et al. 2017;Christidis et al. 2019). Further, for studies covering wider geographical areas, percentile-based thresholds are deemed to be more evenly distributed spatially and allow for comparisons across regions with complex and differing terrains ( Zhang et al. 2011). ...
This paper provides a review of the main empirical challenges involved in quantitatively estimating the impact of extreme climate events on household welfare at the micro-level. To this end, it first outlines a conceptual framework of extreme climate event damage modeling that can aid in terms of considering the ideal input and damage function requirements to create appropriate proxies. It then considers the use of imperfect versions of these proxies in a general econometric framework designed for typical data contexts, and the implications with regard to the interpretation of the results for the impact on household welfare. Using four extreme climate event type case studies, namely tropical cyclones, flooding, extreme heat, and droughts, the study outlines and discusses their respective challenges within the proposed framework.
... Five extreme temperature indices (Table 1) were computed based on the methodology provided by Expert Team on Climate Change Detection and Indices (ETCCDI) [63][64][65]. Percentile-based threshold indices were computed for each calendar day using data for consecutive 5-day moving windows centered on that calendar day [65] from the 1991-2020 base period. The computation of extreme temperature indices and their corresponding thresholds was carried out using RClimDex v1.0 base package [66]. ...
Analysis of the trends and variability of climate variables and extreme climate events is important for climate change detection in space and time. In this study, the trends and variabilities of minimum, maximum, and mean temperatures, as well as five extreme temperature indices, are analyzed over Rwanda for the period of 1983 to 2022. The Modified Mann–Kendall test and the Theil–Sen estimator are used for the analysis of, respectively, the trend and the slope. The standard deviation is used for the analysis of the temporal variability. It is found, on average, over the country, a statistically significant (α = 0.05) positive trend of 0.17 °C/decade and 0.20 °C/decade in minimum temperature, respectively, for the long dry season and short rain season. Statistically significant (α = 0.05) positive trends are observed for spatially averaged cold days (0.84 days/decade), warm nights (0.62 days/decade), and warm days (1.28 days/decade). In general, maximum temperature represents higher variability compared to the minimum temperature. In all seasons except the long dry season, statistically significant (α = 0.05) high standard deviations (1.4–1.6 °C) are observed over the eastern and north-western highlands for the maximum temperature. Cold nights show more variability, with a standard deviation ranging between 5 and 7 days, than the cold days, warm nights, and warm days, having, respectively, standard deviations ranging between 2 and 3, 4 and 5 days, and 3 and 4, and, especially in the area covering the central, south-western, south-central, and northwestern parts of Rwanda. Temperature increase and its variability have an impact on agriculture, health, water resources, infrastructure, and energy. The results obtained from this study are important since they can serve as the baseline for future projections. These can help policy decision making take objective measures for mitigation and adaptation to climate change impacts.
... Eight key indices of climate extremes recommended by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI) (Zhang et al. 2011) were analysed for inter-annual trends and multi-decadal changes in the baseline period (1991-2020) and projected changes in the climate extremes between the future time horizons and the baseline. The extreme climate indices consisted of four rainfall-based indices (annual rainfall totals, maximum 5-day rainfall totals, heavy rainfall days and very heavy rainfall days) and four temperature-based indices (cool night, cool days, warm nights and warm days), all defined in Table 3. ...
Understanding the evolving patterns of climate extremes is crucial for planning climate change adaptation and safeguarding vulnerable communities in the Lower Volta Basin, Ghana. This study utilised precipitation, temperature and relative humidity data from CHIRPS and ERA5, as well as 13 CORDEX-Africa model projections for RCP4.5 and RCP8.5, to evaluate past and future trends in selected rainfall and temperature indices for the historic (1991–2020), near future (2026–2045), mid future (2046–2065) and far future (2066–2100) time horizons. The significance of the observed trends was determined using the Modified Mann-Kendall non-parametric statistic at 95% confidence level and the magnitude of the trends was estimated with Sen’s statistic. The results reveal insignificant trends in historical rainfall extremes, though multi-decadal changes indicate a 20% increase in maximum 5-day rainfall totals over the last two decades, compared to earlier period. Isolated extreme rainfall events have become more frequent post-2000, increasing flood and erosion risks for vulnerable coastal areas. The temperature analyses show significant decline in the frequency of cool days and nights, while warm days and nights have surged, intensifying human thermal stress. The projections suggest an overall increase in maximum 5-day rainfall totals and very heavy rainfall days. The results also show significant increasing (decreasing) trend for warm (cool) nights and warm (cool) days in the near, middle, and far futures. These findings underscore the urgent need for holistic climate adaptation strategies, to protect livelihoods and promote sustainable development under changing climate in the Lower Volta Basin.
... The indices serve as vital for evaluating the incidence of extreme temperature events, including both cold and warm episodes, which are essential for comprehending the impact of climate change on water temperature. The indicators are selected and utilized based on the approaches established by the Expert Team on Climate Change Detection and Indicators (ETCCDI), providing a standardized framework for analysing climate variability and change (Zhang et al., 2011). ...
Emphasis on future environmental changes grows due to climate change, with simulations predicting rising river temperatures globally. For Poland, which has a long history of thermal studies of rivers, such an approach has not been implemented to date. This study used 9 Global Climate Models and tested three machine-learning techniques to predict river temperature changes. Random Forest performed best, with R2=0.88 and lowest error (RMSE: 2.25, MAE:1.72). The range of future water temperature changes by the end of the 21st century was based on the Shared Socioeconomic Pathway scenarios SSP2-4.5 and SSP5-8.5. It was determined that by the end of the 21st century, the average temperature will increase by 2.1°C (SSP2-4.5) and 3.7°C (SSP5-8.5). A more detailed analysis, divided by two major basins Vistula and Odra, covered about 90% of Poland’s territory. The average temperature increase, according to scenarios SSP2-4.5 and SSP5-8.5 for the Odra basin rivers, is 1.6°C and 3.2°C and for the Vistula basin rivers 2.3°C and 3.8°C, respectively. The Vistula basin’s higher warming is due to less groundwater input and continental climate influence. These findings provide a crucial basis for water management to mitigate warming effects in Poland.
... The study of Adzawla, Azumah, Anani, and Donkoh (2020) discloses that Ghana is very vulnerable to climate change. Besides, weather indices are based on rainfall, hail, wind and temperature, which are climatic data collected at meteorological stations (Zhang et al., 2011). According to the Innovative Insurance Products for the Adaptation to Climate Change (IIPACC) 2014, the average daily temperature is anticipated to rise three degrees Celsius, with rainfall falling between 9% and 27% depending on the region. ...
In Sub-Saharan Africa, agricultural insurance products have been piloted to address the connected climatic risks that farmers confront. However, these products, in general, face low rates of adoption in Ghana. Specific determinants and opportunities of weather index-based insurance are evident in the existing literature. However, empirical studies on the effect of the variables in question seem to be lacking. The purpose of this study was to determine whether farmers in Ghana are willing to demand weather index-based insurance and what factors influence crop insurance participation and purchase. The factors under consideration included farmers' trust in financial institutions, farmers' perception of risk, and farmers' perceptions of policy interventions. This study utilized the theory of planned behavior (TPB) to explain the factors that influenced crop farmers' demand for weather index insurance. Based on the objectives of the research, the study adopted the quantitative research approach. In Ghana's Northern and Southern Savanna regions, a purposive sample technique was used to survey two hundred and three (203) farmers. The variables that affected the likelihood of farmers requesting weather index insurance were identified using Ordered Probit econometric models. The empirical results from the crop farmers show that farmers' trust in financial institutions, farmers' perceived risk, and farmers' perception of public policy were significant predictors of weather index insurance demand. The study found that farmers' trust in financial institutions and perceptions of public policy positively affected the probability of demanding weather index-based insurance. On the contrary, farmers' perceived risk negatively affected farmers' likelihood of demanding weather index insurance. The ordered probit regression analysis showed a significant difference between agroecological zone (p-value = 0.020, z = -2.31), maize crop (p-value = 0.022, z = -2.13), farming experience (p-value = 0.038, z = 1.92) and educational background (p-value = 0.042, z = 1.83) in farmer's willingness to demand weather index insurance. The study finds that farmers with a high level of perceived risk are less likely to demand for weather index-based insurance. Also, farmers with a high level of trust are more likely to demand for weather index-based insurance. Finally, farmers who view policy interventions to be effective are more likely to demand extra weather index-based insurance. The findings provide useful insight for government agencies and policymakers to review existing policies and introduce new ones that would encourage the participation of weather index insurance. Also, insurers can take a clue from the findings to draw up effective programs and workshops that would train farmers on better risk management strategies through the adoption of weather index insurance products. The study recommends the government improve the infrastructure and the quality of weather data. The study also suggests future studies to focus on farmers' saving and access to credit facilities on adopting weather index-based insurance. KEYWORDS: Weather Index-Based Insurance, Climate Risk Mitigation, Sustainable Agriculture, Farmer Perceptions, Trust in Financial Institutions
The net primary productivity (NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the context of the increasing frequency, intensity, and duration of global extreme climate events, the impacts of extreme climate and vegetation phenology on NPP are still unclear, especially on the Qinghai-Xizang Plateau (QXP), China. In this study, we used a new data fusion method based on the MOD13A2 normalized difference vegetation index (NDVI) and the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g datasets to obtain a NDVI dataset (1982–2020) on the QXP. Then, we developed a NPP dataset across the QXP using the Carnegie-Ames-Stanford Approach (CASA) model and validated its applicability based on gauged NPP data. Subsequently, we calculated 18 extreme climate indices based on the CN05.1 dataset, and extracted the length of vegetation growing season using the threshold method and double logistic model based on the annual NDVI time series. Finally, we explored the spatiotemporal patterns of NPP on the QXP and the impact mechanisms of extreme climate and the length of vegetation growing season on NPP. The results indicated that the estimated NPP exhibited good applicability. Specifically, the correlation coefficient, relative bias, mean error, and root mean square error between the estimated NPP and gauged NPP were 0.76, 0.17, 52.89 g C/(m2·a), and 217.52 g C/(m2·a), respectively. The NPP of alpine meadow, alpine steppe, forest, and main ecosystem on the QXP mainly exhibited an increasing trend during 1982–2020, with rates of 0.35, 0.38, 1.40, and 0.48 g C/(m2·a), respectively. Spatially, the NPP gradually decreased from southeast to northwest across the QXP. Extreme climate had greater impact on NPP than the length of vegetation growing season on the QXP. Specifically, the increase in extremely-wet-day precipitation (R99p), simple daily intensity index (SDII), and hottest day (TXx) increased the NPP in different ecosystems across the QXP, while the increases in the cold spell duration index (CSDI) and warm spell duration index (WSDI) decreased the NPP in these ecosystems. The results of this study provide a scientific basis for relevant departments to formulate future policies addressing the impact of extreme climate on vegetation in different ecosystems on the QXP.
Agriculture, a historically crucial sector for Thailand's economy, has been severely impacted in recent years due to global climate change causing widespread alterations in rainfall patterns across the country. Therefore, for developing resilient climate adaptation measures, it is important to understand the inter-annual variability of rainfall and its associated processes. Large-scale oceanic phenomena, such as El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), have played significant roles in controlling the inter-annual variability of rainfall across the Asian continent. In this study, we present a review of past studies with an emphasis on rainfall variability and its association with large-scale oceanic phenomena, such as ENSO and IOD, in Thailand. This study found that trends in annual and seasonal rainfall characteristics were heterogeneous, with both increasing and decreasing trends observed within the country and at the regional scale. Generally, ENSO significantly affects the rainfall variability across Thailand. Above-normal and below-normal rainfall are associated with the La Niña and El Niño years, respectively, in Thailand. However, the magnitude of ENSO's effects on rainfall variability in Thailand varies at both spatial and temporal scales. The review also shows a significant association between major IOD events and the Pacific Decadal Oscillation. The effect of IOD on rainfall variability was found to be weak to moderate across Thailand, although the effect was significant during the co-occurrence of IOD events with the ENSO events. Additionally, tropical depressions, tropical cyclones, and Inter-Tropical Convergence Zone's contribution are mainly associated with torrential rainfall and are an integral part of the interannual variability of rainfall across Thailand. In general, this review found that the contribution of different moisture sources, seasonal to intra-seasonal variations in rainfall, topographical variations, geographical location between Indian and Pacific oceans, and influence of large-scale variations including ENSO and IOD, make Thailand's rainfall highly complex at both spatial and temporal scales. Overall, the findings of this study would help scientists and policymakers understand the process and dynamics of rainfall variability and its association with large-scale oceanic phenomena in the study area.
The latest two generations of climate models (CMIP5 and CMIP6, Coupled Model Intercomparison Projects Phase 5 and 6) show a clear discrepancy in the projected future changes in mean temperature. Different methods have been proposed to reduce this difference, however, very limited studies are focused on extreme low temperatures (ELT). Here we propose a new method to constrain the projection of ELT changes by establishing their quasi‐linear relationships with mean temperature (Tmean) in Eastern China. The results show that the Tmean weighting coefficients considering model performance and independence can effectively reduce the uncertainty range of future ELT. Before constraint, there are substantial differences in the projected ranges of Tmean and ELT between CMIP5 and CMIP6 models. After constraint, the projected ranges of CMIP6 models are considerably reduced, particularly at the warmer end, thus showing better consistency with CMIP5. Under the SSP5‐8.5 scenario, at the end of the 21st century (2081–2100), the original projected changes in Tmean are constrained from 5.1 (3.5–7.9)°C to 5.0 (3.5–6.5)°C, with the warmer end of the projected range decreased by 1.4°C. For the ELT, the decreases of the warmer ends are 1.9°C and 1.2°C for the annual minima of daily minimum (TNn) and maximum temperature (TXn), respectively. The reliability evaluation shows that the differences between pseudo‐observations represented by two large ensemble models and constrained projections are smaller than those for unconstrained projections, thereby confirming the reliability of the weighted method employed in this study.
This study, using a dataset consisting of in-situ daily temperature records and ERA5 reanalysis data, analyses the extreme temperature events (i.e., heat waves and warm spells) occurred in the Apennine Mountains (Italy) over the period 1961–2022. The available dataset has been employed to meet the following two main goals: i) to assess the linear trends of the heat waves and warm spells in terms of number of events, duration, and severity by applying the Seasonal Kendall test; ii) to shed light, on a seasonal basis, on the synoptic climatology of such events. From the linear trend analysis, it emerged that the Apennines, as many other regions of the world, experienced an increasing trend in extreme temperature episodes. In particular, in the last 30-year reference period (1991–2020), the number of regional extreme heat events increased by 134 % in summer and 102 % in spring compared to the 1961–1990 period, while in winter and autumn the increase in warm spells is smaller (53 % and 27 %, respectively) and generally not statistically significant in terms of duration and severity. Using Principal Component Analysis and k-means clustering, several synoptic-scale patterns that can trigger extreme hot conditions in the study area are identified. In the last 30-year period, notable changes in the synoptic climatology of extreme heat events have been detected in summer, as well as in spring and autumn. Specifically, in summer the large-scale patterns characterised by a cyclonic area over the eastern North Atlantic (over the British Islands or off the coasts of Ireland) and by a ridge from North Africa to the eastern Europe provide a larger relative contribution to the total number of events. Such patterns promote the advection, over the study area, of hot subtropical air masses, mainly at mid-tropospheric levels. Summer heat waves in the Apennines are generally preceded and accompanied by negative sea surface temperature anomaly with a negative tendency in the eastern North Atlantic area.
Such results supply new insights about the links between extreme heat events in the central Mediterranean area and large-scale atmospheric types as well as useful tools to improve the predictability of heat waves and warm spells at both meteorological and climatological time scales.
This study, utilizing bias-corrected CMIP AR6 climate models, projects significant increases in heavy rainfall days and overall precipitation in Tirupati district by the end of the century. Key findings include:
An expected rise in heavy rainfall days to 30-40 annually during the southwest monsoon, with a corresponding 8-32% increase in total annual rainfall.
A potential rise in future annual maximum temperatures by 3.3°C by 2100 compared to 1981-2010, with summer temperatures potentially increasing by up to 3.6°C.
The northeast monsoon (Oct-Dec), which contributes 65% of the district's annual rainfall, and cyclones affecting the south coast and northern Tamil Nadu, are expected to further influence the region's climate.
Precipitation is one of the crucial climatic variables that has significant impact on the natural and human systems, with several important sectors of the Earth's system responding to its spatiotemporal variability. Consequently, various studies are conducted on global and regional scales to evaluate changes and trends in precipitation, with more emphasis on extremes. This review assesses existing studies on precipitation trends conducted using in situ data or gauge-based datasets, examining their comparability and consistency to identify regional trends. It also seeks to demonstrate the pressing challenges related to the availability and accessibility of precipitation data, with a particular focus on Africa. The existing gauge-based global and regional studies are limited and generally diverse, making it difficult to infer robust regional trends from their findings. Complex differences are observed in data periods, analysis region, methods, precipitation metrics, and the type of datasets used. This review notes that there is uneven station distribution in each continent, and that this is also mirrored in the existing global datasets, while Africa constitutes one of the least covered global regions. Yet, a few studies agree that long-term precipitation totals exhibit non-significant decreasing trends over northern Africa and significantly decreasing trends in parts of western Africa. Conversely, long-term annual precipitation totals have increased significantly over Asia, northern and central Europe, southern Canada and the eastern United States. Generally, despite accounting for different analysis periods, total and extreme trends match up for most global regions. Areas with significant increasing extreme trends, such as RX1day, RX5day and R95pTOT indices, include South Africa, eastern Asia, Canada, northern and central Europe, northeastern United States, and western Australia. Overall, more efforts are needed to significantly expand station coverage across Africa and ease restrictions to allow greater access to data. Initiatives to establish and monitor climate stations across Africa need to be supported. Regional studies that use in situ or gauge-based datasets need to increase and employ comparable analysis regions and data periods, as well as assess and adjust for systematic biases in precipitation data at urban stations.
Heat waves have emerged as one of the most severe and destructive meteorological phenomena, significantly threatening human health, agricultural productivity, and ecosystems due to their increasing frequency, duration, and intensity. In India, these extreme events predominantly occur during the pre-monsoon months (March to mid-June), with recent years (2016, 2019, 2022, and 2023) showing a clear intensification in their occurrence. This study aims to explore the dynamics of heat waves, synoptic conditions, surface land-atmosphere interactions, and regional variations in recent years across India, utilizing maximum temperature data from the India Meteorological Department (IMD) and heat wave indices to evaluate their intensity and impact. Analysis of maximum temperature data and heatwave indices highlights a notable rise in heatwave frequency and duration, particularly in northern and central India. The 2-meter (2 m) temperature anomaly in north, central, and southern India exceeded 2.5°C, while the 925hPa temperature showed significant warming trends in north and northwest India. The analysis of the spatial distribution of the planetary boundary layer (PBL) and total cloud cover (TCC) indicates reduced cloud cover and an increased PBL, intensifying heat wave conditions across north and central regions. The warm air advection and sinking air in the descending limb of the Walker circulation ensured a stable and drier atmosphere, favoring heatwave conditions. Moreover, a persistent anticyclonic circulation and its associated high-pressure system enabled heat-trapping within the atmosphere, leading to prolonged and intensified heat wave conditions. The study indicates a shift in the position and strength of the subtropical jet stream (STJ) during these years, highlighting its significant role in developing and intensifying heat waves.
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Nos últimos anos, o aumento das atividades antrópicas tem gerado problemáticas ambientais, sendo o aquecimento global uma das mais proeminentes. Esse fenômeno é, em grande parte, impulsionado pelas emissões de Gases de Efeito Estufa (GEE). Portanto, o objetivo do presente estudo é analisar as emissões médias de dióxido de carbono (CO ) resultantes da queima de 2 combustíveis fósseis nos municípios brasileiros entre 1999 e 2022, utilizando sensoriamento remoto
e estatística descritiva, de forma a compreender a correlação entre essas emissões e os problemas socioambientais ocorridos durante o período
analisado. Para tanto, os dados de CO foram 2 extraídos da plataforma EDGARv8.0 e interpolados por meio do interpolador Inverse Distance Weighted (IDW) através do software ArcGIS 10.8 e, para a análise estatística do banco de dados, utilizou-se o
software BIOSTAT 5.0. Os resultados da análise estatística descritiva revelaram valores mínimos de 2 emissão de CO2 de 0,0183 ton/Km no município de
Jacundá – Pará (PA). Os valores máximos de 2 emissão atingiram 152,4231 ton/Km no município de São Lourenço da Mata – Pernambuco (PE). A análise das emissões médias nos 5.570 municípios brasileiros ao longo da série histórica mostrou
variações significativas, que podem ser correlacionadas com questões socioambientais, como crises econômicas, pandemias e o advento de
tecnologias mais eficientes. Assim, as técnicas empregadas mostraram-se eficazes na análise das emissões médias municipais de CO originadas da 2
queima de combustíveis fósseis, pois permitiram relacionar as emissões com questões socioambientais e têm o potencial de contribuir para a compreensão das mudanças climáticas, bem como para a formulação de políticas públicas.
Understanding the spatial and temporal characteristics of precipitation and its extremes for the past and future periods can help identify the driving potential of extreme events, especially in the context of climate change. This study presents the spatial and temporal variation of precipitation on a seasonal and annual scale and their change in future periods (2015–2100) in response to historical periods (1980–2014), using bias-corrected and downscaled nine Coupled Model Intercomparison Project -sixth phase (CMIP6) ensemble members under mid- (SSP245) and high- (SSP585) emission scenarios over Nepal. The precipitation trend and related precipitation extremes (ten indices), distribution, and future changes were also assessed. The bias-corrected CMIP6 models slightly underestimated the observed precipitation amount but generally captured the observed precipitation pattern over the country. The spatial distribution of mean precipitation is similar between the historical and the projected periods, with higher precipitation likely to occur in the future, particularly under SSP585 scenarios. In the historical period, precipitation was found to be increasing in the monsoon season only, which is likely to continue in the future period at a rate of 2.3 mm/year and 5.8 mm/year under SSP245 and SSP585, respectively. The decreasing trend of winter precipitation in the historical period will continue in future periods, whereas precipitation in pre- and post-monsoon will likely increase in future periods. Further, all extreme precipitation indices except for consecutive dry days were found to be increasing, with higher frequency and intensity projected in the future. The increase in high-intensity and wetness-related precipitation extremes are related to the increasing precipitation in the monsoon season, while the increase in dry days-related extremes may be related to reduced winter precipitation. The findings of the present study can help understand an early snapshot of future precipitation and extreme event scenarios, which can serve as a reference for developing strategies to mitigate climate-related risks in the region.
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world’s freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread predictions of hydrological variables such as river flow and water quality has become increasingly urgent due to climate and land use change over the past decades, and their associated impacts on water resources. Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. We review relevant state-of-the art applications of machine learning for streamflow, water quality, and other water resources prediction and discuss opportunities to improve the use of machine learning with emerging methods for incorporating watershed characteristics and process knowledge into classical, deep learning, and transfer learning methodologies. The analysis here suggests most prior efforts have been focused on deep learning frameworks built on many sites for predictions at daily time scales in the United States, but that comparisons between different classes of machine learning methods are few and inadequate. We identify several open questions for time series predictions in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding and spatial context, and explainable AI techniques in modern machine learning frameworks.
Climate change is a matter of global interest. Great part of the research focuses on extreme climate events. This study investigates the patterns of change in extreme climate events in Spain, from 1951 to 2021. A total of 16,156 multivariate time series, based on monthly extreme data of maximum and minimum temperatures as well as precipitation, corresponding to 5 × 5-km² small areas, were analysed. The analysis procedure used reduces the dimensionality of the time series, clustering them and identifying changing patterns for each cluster. To carry out the research, a new multiscale analysis methodology is proposed, based on the financial field (Shi et al. in Phys A Stat Mech Appl, 567:71932008, 2021) and adapted to the climatic field: (1) considering the asymmetry of the precipitation series, (2) extending to the multidimensional case to obtain a better representation of the climatic reality, and (3) adapting the definition of distance between univariate series to multivariate series. The clustering results from applying the novel methodology, divide Spain into twelve zones. Among the results, it is worth highlighting that: extreme maximum temperatures have grown more in the cluster that covers Eastern Andalusia and the interior of Southern Murcia; there are three clusters corresponding mainly to Southern Spain and Middle Mountain that have significantly increased the duration of their droughts. Climate series show cyclical patterns and trends over multiple time scales, making the new methodology very useful. Furthermore, this approach allows detecting possible differences between the evolution of microclimates of small neighbouring territorial areas.
The most disastrous heatwaves are very extreme events with return periods of hundreds of years, but traditionally, climate research has focussed on moderate extreme events occurring every couple of years or even several times within a year. Here, we use three Earth System Model large ensembles to assess whether very extreme heat events respond differently to global warming than moderate extreme events. We find that the warming signal of very extreme heat can be amplified or dampened substantially compared to moderate extremes. This modulation is detectable already in mid-century projections. In the mid-latitudes, it can be explained by changes in event soil moisture-temperature coupling during the hottest day of the year. The changes depend on the interplay of present soil moisture and coupling during heat events as well as projected precipitation changes. This mechanism is robust across models, albeit with large spatial uncertainties. Our findings are highly relevant for climate risk assessments and adaptation planning.
The ability of a large ensemble of 15 state-of-the-art regional climate models (RCMs) to simulate precipitation extremes was investigated. The 99th, 99.9th and 99.99th percentiles of daily precipitation in the models were compared with those in the recently released E-OBS observational database for winter, spring, summer and autumn. The majority of the models overestimated the values of the precipitation extremes compared with E-OBS, on average by approximately 38%, but some models exceeded 50%. To measure model performance, a simple metric is proposed that averages a nonlinear function of the seasonal biases over the European area. The sensitivity of the metric to different assumptions in the construction and the quality of the observational data was explored. Generally, low sensitivities of the metric to spatial and seasonal averaging were found. However, large sensitivities to potential biases in the observational database were found. An alternative metric that measures the spatial pattern of the extremes (which is not sensitive to a potential constant offset in the observational data) was further explored. With this metric, the ranking between the models changed substantially. However, the 2 models with the worst score in the standard metric also displayed the worst scores with this alternative metric. Finally, the regional climate models displayed the largest biases compared with E-OBS in areas where the underlying station density used in E-OBS is low, thus suggesting that data quality is indeed an important issue. In summary, the results show that: (1) there is no metric that guarantees an objective and precise ranking or weighting of the models, (2) by exploring different metrics it nevertheless appears possible to indentify models that perform consistently worse than other models, and (3) the observational data quality should be considered when designing and interpreting metrics.
Projections of changes in climate extremes are critical to assessing the potential impacts of climate change on human and natural systems. Modeling advances now provide the opportunity of utilizing global general circulation models (GCMs) for projections of extreme temperature and precipitation indicators. We analyze historical and future simulations of ten such indicators as derived from an ensemble of 9 GCMs contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR4), under a range of emissions scenarios. Our focus is on the consensus from the GCM ensemble, in terms of direction and significance of the changes, at the global average and geographical scale. The climate extremes described by the ten indices range from heat-wave frequency to frost-day occurrence, from dry-spell length to heavy rainfall amounts. Historical trends generally agree with previous observational studies, providing a basic sense of reliability for the GCM simulations. Individual model projections for the 21st century across the three scenarios examined are in agreement in showing greater temperature extremes consistent with a warmer climate. For any specific temperature index, minor differences appear in the spatial distribution of the changes across models and across scenarios, while substantial differences appear in the relative magnitude of the trends under different emissions rates. Depictions of a wetter world and greater precipitation intensity emerge unequivocally in the global averages of most of the precipitation indices. However, consensus and significance are less strong when regional patterns are considered. This analysis provides a first overview of projected changes in climate extremes from the IPCC-AR4 model ensemble, and has significant implications with regard to climate projections for impact assessments.
The validation of climate model simulations creates substantial demands
for comprehensive observed climate datasets. These datasets need not
only to be historically and geographically extensive, but need also to
be describing areally averaged climate, akin to that generated by
climate models. This paper addresses one particular difficulty found
when attempting to evaluate the daily precipitation characteristics of a
global climate model, namely the problem of aggregating daily
precipitation characteristics from station to area.Methodologies are
developed for estimating the standard deviation and rainday frequency of
grid-box mean daily precipitation time series from relatively few
individual station time series. Temporal statistics of such areal-mean
time series depend on the number of stations used to construct the areal
means and are shown to be biased (standard deviations too high, too few
raindays) if insufficient stations are available. It is shown that these
biases can be largely removed by using parameters that describe the
spatial characteristics of daily precipitation anomalies. These spatial
parameters (the mean interstation correlation between station time
series and the mean interstation probability of coincident dry days) are
calculated from a relatively small number of available station time
series for Europe, China, and Zimbabwe. The relationships that use these
parameters are able to successfully reproduce the statistics of grid-box
means from the statistics of individual stations. They are then used to
estimate the statistics of grid-box means as if constructed from an
infinite number of stations (for standard deviations) or 15 stations
(for rainday frequencies), even if substantially fewer stations are
actually available. These estimated statistics can be used for the
evaluation of daily precipitation characteristics in climate model
simulations, and an example is given using a simulation by the
Commonwealth Scientific and Industrial Research Organisation atmosphere
general circulation model. Applying the authors' aggregation methodology
to observed station data is a more faithful form of model validation
than using unadjusted station time series.
Gridded trends of annual values of various climate extreme indices were
estimated for 1950 to 1995, presenting a clearer picture of the patterns
of trends in climate extremes than has been seen with raw station data.
The gridding also allows one, for the first time, to compare these
observed trends with those simulated by a suite of climate model runs
forced by observed changes in sea surface temperatures, sea ice extent,
and various combinations of human-induced forcings.Bootstrapping
techniques are used to assess the uncertainty in the gridded trend
estimates and the field significance of the patterns of observed trends.
The findings mainly confirm earlier, less objectively derived, results
based on station data. There have been significant decreases in the
number of frost days and increases in the number of very warm nights
over much of the Northern Hemisphere. Regions of significant increases
in rainfall extremes and decreases in the number of consecutive dry days
are smaller in extent. However, patterns of trends in annual maximum
5-day rainfall totals were not significant.Comparisons of the observed
trend estimates with those simulated by the climate model indicate that
the inclusion of anthropogenic effects in the model integrations, in
particular increasing greenhouse gases, significantly improves the
simulation of changing extremes in temperatures. This analysis provides
good evidence that human-induced forcing has recently played an
important role in extreme climate. The model shows little skill in
simulating changing precipitation extremes.
A set of agroclimatic indices representing Canadian climatic conditions for field crop production are analyzed for long-term trends during 1895-2007. The indices are categorized for three crop types: cool season, warm season, and overwintering. Results indicate a significant lengthening of the growing season due to a significantly earlier start and a significantly later end of the growing season. Significant positive trends are also observed for effective growing degree-days and crop heat units at most locations across the country. The occurrence of extremely low temperatures has become less frequent during the nongrowing season, implyinga more favorable climate for overwinter survival. In addition, the total numbers of cool days, frost days, and killing-frost days within a growing season have a decreasing trend. This means that crops may also be less vulnerable to cold stress and injury during the growing season. Extreme daily precipitation amounts and 10-day precipitation totals during the growing season have been increasing. Significant trends associated with increased availability ofwater during the growing season are identified by the standardized precipitation index and seasonal water deficits. The benefit of the increased precipitation may have been offsetby an upward trend in evaporative demand; however, this would depend onthe amount of growth and productivity resulting from increased actual evapotranspiration.
Changes in the frequencies of extremes are investigated by a variety of methods using daily temperature data from the British Isles, and monthly 5 latitude 5 longitude grid-box temperatures over the land and marine regions of the world. The 225 year long daily Central England Temperature record shows no significant increase in very warm days in recent years but there is a marked decrease in the frequency of very cold days. Thus the rise in temperature in the last two decades is principally associated with a reduction in very cold days. Temperatures on days with particular wind circulation or pressure pattern types over the British Isles show multidecadal variations. Analyses using monthly gridded temperature data around the world since 1951 indicate that the recent rise in global surface temperatures is accompanied both by reductions in the areas affected by extremely cool temperatures and by increases in the areas with extremely warm temperatures.
Rainfall in South America as simulated by a 24-ensemble member of the ECHAM 4.5 atmospheric general circulation model is compared and contrasted with observations (in areas in which data are available) for the period 1976–2001. Emphasis is placed on determining the onset and end of the rainy season, from which its length and rain rate are determined.
It is shown that over large parts of the domain the onset and ending dates are well simulated by the model, with biases of less than 10 days. There is a tendency for model onset to occur early and ending to occur late, resulting in a simulated rainy season that is on average too long in many areas. The model wet season rain rate also tends to be larger than observed.
To estimate the relative importance of errors in wet season length and rain rate in determining biases in the annual total, adjusted totals are computed by substituting both the observed climatological wet season length and rate for those of the model. Problems in the rain rate generally are more important than problems in the length.
The wet season length and rain rate also contribute substantially to interannual variations in the annual total. These quantities are almost independent, and it is argued that they are each associated with different mechanisms.
The observed onset dates almost always lie within the range of onset of the ensemble members, even in the areas with a large model onset bias. In some areas, though, the model does not perform well. In southern Brazil the model ensemble average onset always occurs in summer, whereas the observations show that winter is often the wettest period. Individual members, however, do occasionally show a winter rainfall peak. In southern Northeast Brazil the model has a more distinct rainy season than is observed. In the northwest Amazon the model annual cycle is shifted relative to that observed, resulting in a model bias.
No interannual relationship between model and observed onset dates is expected unless onset in the model and observations has a mutual relationship with SST anomalies. In part of the near-equatorial Amazon, there does exist an interannual relationship between onset dates. Previous studies have shown that in this area there is a relationship between SST anomalies and variations in seasonal total rainfall.
Soil quality models developed for ecodistrict polygons (EDP) and the polygons of the soil landscapes of Canada (SLC) to monitor the concentration of soil organic matter require daily climate data as an important input. The objectives of this paper are (i) to provide a method that interpolates the daily station data onto the 894 SLC polygons and 150 EDP in the province of Alberta, Canada, so that the interpolated data fit not only climate mean but also climate variability, especially for the precipitation field, and hence can be used as realistic climate input to soil quality models and (ii) to understand the variability of the Alberta daily climate, such as precipitation frequency. The procedure interpolates the station data onto a dense network of grid points and then averages the gridpoint values inside polygons. The procedure and results for maximum temperature, minimum temperature, and precipitation are reported in detail. The interpolation uses the observed daily data for the period 1 January 1961-31 December 1997 (13 514 days) within the latitude-longitude box (45°-64°N, 116°-124°W). Because the precipitation field can have a short spatial correlation length scale and large variability, a hybrid of the methods of inverse-distance weight and nearest-station assignment is developed for interpolating the precipitation data. This method can reliably calculate not only the number of precipitation days per month, but also the precipitation amount for a day. The temperature field has a long spatial correlation scale, and its data are interpolated by the inverse-distance-weight method. Cross-validation shows that the interpolated results on polygons are accurate and appropriate for soil quality models. The computing algorithm uses all the daily observed climate data; despite that, some stations have a very short time record or only summer records.
Using a Monte Carlo simulation, it is demonstrated that percentile-based temperature indices computed for climate change detection and monitoring may contain artificial discontinuities at the beginning and end of the period that is used for calculating the percentiles (base period). This would make these exceedance frequency time series unsuitable for monitoring and detecting climate change. The problem occurs because the threshold calculated in the base period is affected by sampling error. On average, this error leads to overestimated exceedance rates outside the base period. A bootstrap resampling procedure is proposed to estimate exceedance frequencies during the base period. The procedure effectively removes the inhomogeneity.
A weeklong workshop in Brazil in August 2004 provided the opportunity for 28 scientists from southern South America to examine daily rainfall observations to determine changes in both total and extreme rainfall. Twelve annual indices of daily rainfall were calculated over the period 1960 to 2000, examining changes to both the entire distribution as well as the extremes. Maps of trends in the 12 rainfall indices showed large regions of coherent change, with many stations showing statistically significant changes in some of the indices. The pattern of trends for the extremes was generally the same as that for total annual rainfall, with a change to wetter conditions in Ecuador and northern Peru and the region of southern Brazil, Paraguay, Uruguay, and northern and central Argentina. A decrease was observed in southern Peru and southern Chile, with the latter showing significant decreases in many indices. A canonical correlation analysis between each of the indices and sea surface temperatures (SSTs) revealed two large-scale patterns that have contributed to the observed trends in the rainfall indices. A coupled pattern with ENSO-like SST loadings and rainfall loadings showing similarities with the pattern of the observed trend reveals that the change to a generally more negative Southern Oscillation index (SOI) has had an important effect on regional rainfall trends. A significant decrease in many of the rainfall indices at several stations in southern Chile and Argentina can be explained by a canonical pattern reflecting a weakening of the continental trough leading to a southward shift in storm tracks. This latter signal is a change that has been seen at similar latitudes in other parts of the Southern Hemisphere. A similar analysis was carried out for eastern Brazil using gridded indices calculated from 354 stations from the Global Historical Climatology Network (GHCN) database. The observed trend toward wetter conditions in the southwest and drier conditions in the northeast could again be explained by changes in ENSO.
Understanding how extremes are changing globally, regionally, and locally is an important first step for planning appropriate adaptation measures, as changes in extremes have major impacts. The Intergovernmental Panel on Climate Change's synthesis of global extremes was not able to say anything about western central Africa, as no analysis of the region was available nor was there an adequate internationally exchanged long-term daily data set available to use for analysis of extremes. This paper presents the first analysis of extremes in this climatically important region along with analysis of Guinea Conakry and Zimbabwe. As per many other parts of the world, the analysis shows a decrease in cold extremes and an increase in warm extremes. However, while the majority of the analyzed world has shown an increase in heavy precipitation over the last half century, central Africa showed a decrease. Furthermore, the companion analysis of Guinea Conakry and Zimbabwe showed no significant increases.
A climate change workshop for the Middle East brought together scientists and data for the region to produce the first area-wide analysis of climate extremes for the region. This paper reports trends in extreme precipitation and temperature indices that were computed during the workshop and additional indices data that became available after the workshop. Trends in these indices were examined for 1950–2003 at 52 stations covering 15 countries, including Armenia, Azerbaijan, Bahrain, Cyprus, Georgia, Iran, Iraq, Israel, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Syria, and Turkey. Results indicate that there have been statistically significant, spatially coherent trends in temperature indices that are related to temperature increases in the region. Significant, increasing trends have been found in the annual maximum of daily maximum and minimum temperature, the annual minimum of daily maximum and minimum temperature, the number of summer nights, and the number of days where daily temperature has exceeded its 90th percentile. Significant negative trends have been found in the number of days when daily temperature is below its 10th percentile and daily temperature range. Trends in precipitation indices, including the number of days with precipitation, the average precipitation intensity, and maximum daily precipitation events, are weak in general and do not show spatial coherence. The workshop attendees have generously made the indices data available for the international research community.
An assessment is made of the climate simulations from the NCEP (National Centers for Environmental Prediction) Reanalyses over Europe. This assessment was initiated as part of the European Commission funded study on Atmospheric Circulation Classification and Regional Downscaling (ACCORD) and was designed to test the suitability of the Reanalyses for this type of application. Here NCEP Reanalyses (pressure, temperature and precipitation) from 1958 through 1997 are compared to station data of precipitation and temperature and composites of mean sea level pressure (MSLP) data, The comparison is made over a European window using monthly data with a focus on 3 land areas: Central and Eastern England and Italy, where daily timescale data are employed. MSLP data are generally well simulated; however, an input problem in the NCEP data prior to 1967 results in unrealistically low surface pressure. NCEP surface pressure over Greenland is also shown to be unrealistically high during the winter months. Spatially NCEP MSLP is shown to correlate quite well with UK Meteorological Office (UKMO) MSLP over the ocean and much of northeast Europe, while they correlate less well over high orographical regions. It is shown that, while daily temperature is well simulated, daily precipitation is less so, particularly during the summer months when convective precipitation is dominant. Total precipitation over the 2 UK areas is lower than observed, by as much as 22 % over Central England. The number of rain day events is underestimated over the 3 regions, although the anomaly of rain per rain day is shown to vary between the regions, being overestimated in NCEP in Eastern England and Italy. Mean daily temperature is shown to be much better simulated compared with precipitation, with a slight warm bias in all 3 grid boxes.
A revised framework is presented that quantifies observed changes in the climate of the contiguous United States through analysis of a revised version of the U.S. Climate Extremes Index (CEI). The CEI is based on a set of climate extremes indicators that measure the fraction of the area of the United States experiencing extremes in monthly mean surface temperature, daily precipitation, and drought (or moisture surplus). In the revised CEI, auxiliary station data, including recently digitized pre-1948 data, are incorporated to extend it further back in time and to improve spatial coverage. The revised CEI is updated for the period from 1910 to the present in near–real time and is calculated for eight separate seasons, or periods.
Results for the annual revised CEI are similar to those from the original CEI. Observations over the past decade continue to support the finding that the area experiencing much above-normal maximum and minimum temperatures in recent years has been on the rise, with infrequent occurrence of much below- normal mean maximum and minimum temperatures. Conversely, extremes in much below-normal mean maximum and minimum temperatures indicate a decline from about 1910 to 1930. An increasing trend in the area experiencing much above-normal proportion of heavy daily precipitation is observed from about 1950 to the present. A period with a much greater-than-normal number of days without precipitation is also noted from about 1910 to the mid-1930s. Warm extremes in mean maximum and minimum temperature observed during the summer and warm seasons show a more pronounced increasing trend since the mid-1970s. Results from the winter season show large variability in extremes and little evidence of a trend. The cold season CEI indicates an increase in extremes since the early 1970s yet has large multidecadal variability.
The extremes of near-surface temperature and 24-h and 5-day mean precipitation rates are examined in simulations performed with atmospheric general circulation models (AGCMs) participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2). The extremes are evaluated in terms of 20-yr return values of annual extremes. The model results are validated against the European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction reanalyses and station data. Precipitation extremes are also validated against the pentad dataset of the Global Precipitation Climatology Project, which is a blend of rain gauge observations, satellite data, and model output.
On the whole, the AGCMs appear to simulate temperature extremes reasonably well. Model disagreements are larger for cold extremes than for warm extremes, particularly in wet and cloudy regions, and over sea ice and snow-covered areas. Many models exhibit an exaggerated clustering behavior for temperatures near the freezing point of water. Precipitation extremes are less reliably reproduced by the models and reanalyses. The largest disagreements are found in the Tropics where the parameterizations of deep convection affect the simulated daily precipitation extremes.
In 1990 and 1992 the Intergovernmental Panel on Climate Change (IPCC), in its first assessment of climate change and its supplement, did not consider whether extreme weather events had increased in frequency and/or intensity globally, because data were too sparse to make this a worthwhile exercise. In 1995 the IPCC, in its second assessment, did examine this question, but concluded that data and analyses of changes in extreme events were 'not comprehensive' and thus the question could not be answered with any confidence. Since then, concerted multinational efforts have been undertaken to collate, quality control, and analyse data on weather and climate extremes. A comprehensive examination of the question of whether extreme events have changed in frequency or intensity is now more feasible than it was 15 years ago. The processes that have led to this position are described, along with current understanding of possible changes in some extreme weather and climate events.
The influence of large-scale modes of climate variability on worldwide summer and winter temperature extremes has been analyzed, namely, that of the El Niño-Southern Oscillation, the North Atlantic Oscillation, and Pacific interdecadal climate variability. Monthly indexes for temperature extremes from worldwideland areas are used describe moderate extremes, such as the number of exceedences of the 90th and 10th climatological percentiles, and more extreme events such as the annual, most extreme temperature. This study examines which extremes show a statistically significant (5%) difference between the positive and negative phases of a circulation regime. Results show that temperature extremes are substantially affected by large-scale circulation patterns, and they show distinct regional patterns of response to modes of climate variability. The effects of the El Niño-Southern Oscillation are seen throughout the world but most clearly around the Pacific Rim and throughout all of North America. Likewise, the influence of Pacific interdecadal variability is strongest in the Northern Hemisphere, especially around the Pacific region and North America, but it extends to the Southern Hemisphere. The North Atlantic Oscillation has a strong continent-wide effect for Eurasia, with a clear but weaker effect over North America. Modes of variability influence the shape of the daily temperature distribution beyond a simple shift, often affecting cold and warm extremes and sometimes daytime and nighttime temperatures differently. Therefore, for reliable attribution of changes in extremes as well as prediction of future changes, changes in modes of variability need to be accounted for.
This study integrates a Box–Cox power transformation procedure into a common trend two-phase regression-model-based test (the extended version of the penalized maximal F test, or ‘‘PMFred,’’ algorithm) for detecting changepoints to make the test applicable to non-Gaussian data series, such as nonzero daily precipitation amounts or wind speeds. The detection-power aspects of the transformed method (transPMFred) are assessed by a simulation study that shows that this new algorithm is much better than the corresponding untransformed method for non-Gaussian data; the transformation procedure can increase the hit rate by up to ~70%. Examples of application of this new transPMFred algorithm to detect shifts in real daily precipitation series are provided using nonzero daily precipitation series recorded at a few stations across Canada that represent very different precipitation regimes. The detected changepoints are in good agreement with documented times of changes for all of the example series. This study clarifies that it is essential for homogenization of daily precipitation data series to test the nonzero precipitation amount series and the frequency series of precipitation occurrence (or nonoccurrence), separately. The new transPMFred can be used to test the series of nonzero daily precipitation (which are non Gaussian and positive), and the existing PMFred algorithm can be used to test the frequency series. A software package for using the transPMFred algorithm to detect shifts in nonzero daily precipitation amounts has been developed and made freely available online, along with a quantile-matching (QM) algorithm for adjusting shifts in nonzero daily precipitation series, which is applicable to all positive data. In addition, a similar QM algorithm has also been developed for adjusting Gaussian data such as temperatures. It is noticed that frequency discontinuities are often inevitable because of changes in the measuring precision of precipitation, and that they could complicate the detection of shifts in nonzero daily precipitation data series and void any attempt to homogenize the series. In this case, one must account for all frequency discontinuities before attempting to adjust the measured amounts. This study also proposes approaches to account for detected frequency discontinuities, for example, to fill in the missed measurements of small precipitation or the missed reports of trace precipitation. It stresses the importance of testing the homogeneity of the frequency series of reported zero precipitation and of various small precipitation events, along with testing the series of daily precipitation amounts that a small threshold value, varying the threshold over a set of small values that reflect changes in measuring precision over time.
In this study we analyse gridded observed and multi-model simulated trends in the annual number of warm nights during the second half of the 20th century. We show that there is evidence that external forcing has significantly increased the number of warm nights, both globally and over many regions. We define thirteen regions with a high density of observational data over two datasets, for which we compare observed and simulated trends from 20th century simulations. The main analysis period is 1951-1999, with a sub-period of 1970-1999. In order to investigate if observed trends changed past 1999, we also analysed periods of 1955-2003 and 1974-2003. Both observed and ensemble mean model data from all models analysed show a positive trend for the regional mean number of warm nights in all regions within this 49 year period (1951-1999). The trends tend to become more pronounced over the sub-period 1970-1999 and even more so up to 2003. We apply a fingerprint analysis to assess if trends are detectable relative to internal climate variability. We find that changes in the global scale analysis, and in 9 out of 13 regions, are detectable at the 5% significance level. A large part of the observed global-scale trend in TN90 results from the trend in mean temperature, which has been attributed largely to anthropogenic greenhouse gas increase. This suggests that the detected global-scale trends in the number of warm nights are at least partly anthropogenic.
The interpretation of model precipitation output (e.g., as a grid-point
estimate vs. as an areal mean) has a large impact on the evaluation and
comparison of simulated daily extreme rainfall indices from climate
models. We first argue that interpretation as a grid-point estimate
(i.e., corresponding to station data) is incorrect. We then illustrate
impacts of this interpretation vs. the areal-mean interpretation in the
context of rainfall extremes. A high resolution (0.25°x0.25°
grid) daily observed precipitation dataset for the U.S. (from CPC) is
used as idealized perfect model gridded data. Both 30-year return levels
of daily precipitation (P30) and a simple daily intensity index are
substantially reduced in this data when estimated at coarser resolution
compared to the estimation at finer resolution. The reduction of P30
averaged over the conterminous US is about 9, 15, 28, 33, and 43% when
the data were first interpolated to 0.5°x0.5°, 1°x1°,
2°x2°, 3°x3° and 4°x4° grid boxes, respectively,
before the calculation of extremes. The differences resulting from the
point estimate vs. areal mean interpretation are sensitive to both the
data grid size and to the particular extreme rainfall index analyzed.
The differences are not as sensitive to the magnitude and regional
distribution of the indices. Almost all IPCC AR4 models underestimate US
mean P30 if it is compared directly with P30 estimated from the high
resolution CPC daily rainfall observation. On the other hand, if CPC
daily data is first interpolated to various model resolutions before
calculating the P30 (a more correct procedure in our view), about half
of the models show good agreement with observations while most of the
remaining models tend to overestimate the mean intensity of heavy
rainfall events. A further implication of interpreting model
precipitation output as an areal mean is that use of either simple
multimodel ensemble averages of extreme rainfall or of inter-model
variability measures of extreme rainfall to assess the common
characteristics and range of uncertainties in current climate models is
not appropriate if simulated extreme rainfall is analyzed at a model?|s
native resolution. Owing to the large sensitivity to the assumption
used, we recommend that for analysis of precipitation extremes,
investigators interpret model precipitation output as an area average as
opposed to a point estimate, and then ensure that various analysis steps
remain consistent with that interpretation.
Temperature and precipitation extremes and their potential future changes are evaluated in an ensemble of global coupled climate models participating in the Intergovernmental Panel on Climate Change (IPCC) diagnostic exercise for the Fourth Assessment Report (AR4). Climate extremes are expressed in terms of 20-yr return values of annual extremes of near-surface temperature and 24-h precipitation amounts. The simulated changes in extremes are documented for years 2046–65 and 2081–2100 relative to 1981–2000 in experiments with the Special Report on Emissions Scenarios (SRES) B1, A1B, and A2 emission scenarios.
Overall, the climate models simulate present-day warm extremes reasonably well on the global scale, as compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes, especially in sea ice–covered areas. Simulated present-day precipitation extremes are plausible in the extratropics, but uncertainties in extreme precipitation in the Tropics are very large, both in the models and the available observationally based datasets.
Changes in warm extremes generally follow changes in the mean summertime temperature. Cold extremes warm faster than warm extremes by about 30%–40%, globally averaged. The excessive warming of cold extremes is generally confined to regions where snow and sea ice retreat with global warming. With the exception of northern polar latitudes, relative changes in the intensity of precipitation extremes generally exceed relative changes in annual mean precipitation, particularly in tropical and subtropical regions. Consistent with the increased intensity of precipitation extremes, waiting times for late-twentieth-century extreme precipitation events are reduced almost everywhere, with the exception of a few subtropical regions. The multimodel multiscenario consensus on the projected change in the globally averaged 20-yr return values of annual extremes of 24-h precipitation amounts is that there will be an increase of about 6% with each kelvin of global warming, with the bulk of models simulating values in the range of 4%–10% K−1. The very large intermodel disagreements in the Tropics suggest that some physical processes associated with extreme precipitation are not well represented in models. This reduces confidence in the projected changes in extreme precipitation.
Annual and seasonal trends in heavy daily rainfall, total rainfall and the number of rain days were calculated for the whole of Australia and each State/Territory from 1910 to 1995, using high-quality daily data from 379 stations. Trend significance was determined using the Kendall-tau test and trend magnitudes were computed from linear regression. While many statistically significant trends were found, non-significant trends judged to be of special interest are noted. From 1910-1995, annual total rainfall has undergone secular changes with a significant 14 per cent increase in Victoria and non-significant increases of 15-18 per cent in New South Wales (NSW), the Northern Territory (NT) and South Australia (SA). When analysed seasonally, non-significant changes of 10-40 per cent were found in some States. Heavy rainfall indices were defined as the 99th and 95th percentiles (the highest and 5th highest daily amounts, respectively, in each three-month season). Australian areal-mean heavy rainfall has not changed significantly in any season. However, on a regional basis significant increases in heavy rainfall emerged in SA in summer and NSW in autumn, while significant decreases occurred in southwest Western Australia (SWWA) in winter. Important non-significant increases of 10-45 per cent were also found in some States. There has been a significant 10 per cent rise in the annual Australian-average number of rain days. Significant increases of almost 20 per cent were found in the NT and NSW despite a significant 10 per cent decline in SWWA. Regionally, significant increases of 20-50 per cent have occurred in some States, with large changes in the frequency of light rainfall. Strong correlations exist between interannual variations in temperature, total rainfall, heavy rainfall and the number of rain days. Increases in Australian rainfall since 1910 are generally linked to an increase in heavy rainfall and the number of rain days. ENSO variability is partly responsible, as is enhanced monsoon activity in the 1970s and changes in other large-scale circulation features. Decreased rainfall in southwest WA is also linked to circulation changes.
This article briefly discusses climate mitigation policy in Italy. It first reviews the recent policy action and then assesses the challenges ahead. It shows that emissions in 2012 will be below the Kyoto target only if the carbon intensity of the economies continues to decline at the pre-crisis rate. Even in this favorable case, the use of Kyoto mechanisms will likely be necessary to compensate for the high emissions of 2008. This article then examines the implications of reducing emissions in 2050 by 80% with respect to the present level, as proposed by Major Economies Leaders during the 2009 G8 meeting. In particular, the article focuses on the transformations needed in the power sector. Scenarios developed using the integrated assessment model World Induced Hybrid Technical Change (WITCH) and an analysis of historical data show that (1) electricity in 2050 must come with zero (or possibly negative) emissions, and therefore, absent nuclear power and (2) carbon capture and sequestration (CCS) must play a major role in future power supply, even with a massive development of wind and photovoltaic. The article then proposes five pillars on which a sensible, long-term, ambitious Italian climate mitigation policy should rest. WIREs Clim Change 2012, 3:171–183. doi: 10.1002/wcc.159
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Trends in surface temperature over the last 100, 50, and 30 yr at individual grid boxes in a 5° latitude longitude grid are compared with model estimates of the natural internal variability of these trends and with the model response to increasing greenhouse gases and sulfate aerosols. Three different climate models are used to provide estimates of the internal variability of trends, one of which appears to overestimate the observed variability of surface temperature at interannual and 5-yr time scales. Significant warming trends are found at a large fraction of the individual grid boxes over the globe, a much larger fraction than can be explained by internal climate variations. The observed warming trends over the last 50 and 30 yr are consistent with the modeled response to increasing greenhouse gases and sulfate aerosols in most of the models. However, in some regions, the observed century-scale trends are significantly larger than the modeled response to increasing greenhouse gases and sulfate aerosols in the atmosphere. Warming trends consistent with the response to anthropogenic forcing are detected at scales on the order of 500 km in many regions of the globe.
Formal detection and attribution analyses of changes in daily extremes give evidence of a significant human influence on the increasing severity of extremely warm nights and decreasing severity of extremely cold days and nights. This paper presents an optimal fingerprinting analysis that also detects the contributions of external forcings to recent changes in extremely warm days using nonstationary extreme value theory. The authors' analysis is the first that attempts to partition the observed change in warm daytime extremes between its anthropogenic and natural components and hence attribute part of the change to possible causes. Changes in the extreme temperatures are represented by the temporal changes in a parameter of an extreme value distribution. Regional distributions of the trend in the parameter are computed with and without human influence using constraints from the global optimal fingerprinting analysis. Anthropogenic forcings alter the regional distributions, indicating that extremely warm days have become hotter.
To be confident in the analyses of long-term changes in daily climate extremes, it is necessary for the data to be homogenized because of nonclimatic influences. Here a new method of homogenizing daily tempera- ture data is presented that is capable of adjusting not only the mean of a daily temperature series but also the higher-order moments. This method uses a nonlinear model to estimate the relationship between a candidate station and a highly correlated reference station. The model is built in a homogeneous subperiod before an inhomogeneity and is then used to estimate the observations at the candidate station after the inhomogeneity using observations from the reference series. The differences between the predicted and observed values are binned according to which decile the predicted values fit in the candidate station's observed cumulative distribution function defined using homogeneous daily temperatures before the in- homogeneity. In this way, adjustments for each decile were produced. This method is demonstrated using February daily maximum temperatures measured in Graz, Austria, and an artificial dataset with known inhomogeneities introduced. Results show that given a suitably reliable reference station, this method produces reliable adjustments to the mean, variance, and skewness.
The monitoring and analysis of daily climate records to identify trends in extreme climate events have been primarily limited to data from Canada, the US, the Soviet Union, China, and Australia. Their data was hard to analyze since they used varying measures of extremes. As such, the need to update and converge the findings has been recognized at the November 1999 meeting of what is now known as the joint Expert Team on Climate Change Detection and Indices. The task was to converge the exact formulation of a suite of agreed indices of climate extremes from daily data and the organization of a regional climate-change workshops. As of 2004, a total of five workshops have been conducted in Melbourne. The workshops included participants from neighboring countries and from other areas of the world to provide guidance on the analysis of the climate data.
Spatial and temporal characteristics of heavy precipitation events over Canada (excluding the high Arctic) are examined for the period 1900-98. In southern Canada, about 71% of total precipitation comes from rainfall events. In northern Canada, more than 50% of total precipitation comes from snowfall events. Heavy rainfall and snowfall events are thus defined for each season and station separately by identifying a threshold value that is exceeded by an average of three events per year. Annual and seasonal time series of heavy event frequency are then obtained by counting the number of exceedances per year. Characteristics of the intensity of heavy precipitation events are investigated examining the 90th percentiles of daily precipitation, the annual maximum daily value, and the 20-yr return values. It was found that decadal variability is the dominant feature in both the frequency and the intensity of extreme precipitation events over the country. For the country as a whole, there appear to be no identifiable trends in extreme precipitation (either frequency or intensity) during the last century. The observed increase in precipitation totals in the twentieth century was mainly due to increase in the number of small to moderate events.Stations with coherent temporal variability in the frequency of heavy precipitation events are grouped by cluster analysis and examined on a regional basis. Results show stations belonging to the same group are generally located in a continuous region, indicating that the temporal distribution of the number of events is spatially coherent. It is also found that heavy snowfall events are more spatially coherent than heavy rainfall events. Indices representing temporal variations of regional heavy precipitation display strong interdecadal variability with limited evidence of long-term trends. They vary markedly depending on the precipitation type, season, and region. Spring heavy rainfall events over eastern Canada have shown an increasing trend superimposed on the strong decadal variability. However, heavy rainfall events in other seasons/regions are generally not associated with any trends or decadal variability. The number of heavy snowfall events in southern Canada shows an upward trend from the beginning of the twentieth century until the late 1950s to 1970s, followed by a downward trend to the present. In the last 50 yr, heavy snowfall events in northern Canada have been increasing with marked decadal variation.The majority of stations have a significant positive correlation between the total amount of snowfall contributed by heavy events versus that contributed from nonheavy events. The relationship is strongest over western Canada. On the other hand, relatively few (
Changes due to CO2 doubling in the extremes of the surface climate as simulated by the second-generation circulation model of the Canadian Centre for Climate Modelling and Analysis are studied in two 20-yr equilibrium simulations. Extreme values of screen temperature, precipitation, and near-surface wind in the control climate are compared to those estimated from 17 yr of the NCEP-NCAR reanalysis data and from some Canadian station data.The extremes of screen temperature are reasonably well reproduced in the control climate. Their changes under CO2 doubling can be connected with other physical changes such as surface albedo changes due to the reduction of snow and sea ice cover as well as a decrease of soil moisture in the warmer world.The signal in the extremes of daily precipitation and near-surface wind speed due to CO2 doubling is less obvious. The precipitation extremes increase almost everywhere over the globe. The strongest change, over northwest India, is related to the intensification of the summer monsoon in this region in the warmer world. The modest reduction of wind extremes in the Tropics and middle latitudes is consistent with the reduction of the meridional temperature gradient in the 2×CO2 climate. The larger wind extremes occur in the areas where sea ice has retreated.
Partial duration series methods have been used to estimate the T-year event ZT of a hydrological variable. In this paper the maximum likelihood estimate of ZT is considered for the model with a Poisson number of peaks and independently, exponentially distributed peak magnitudes. The mean and the variance of are evaluated by finite summations. Approximations to the bias of are discussed. Although the use of a first-order asymptotic expression for var() has been criticized by earlier workers, the results from this study indicate that the error is small for the most common values of T. It is further shown that small departures from the assumed model can have a large impact on var().
Observed 1961-2000 annual extreme temperatures, namely annual maximum daily maximum (TXx) and minimum (TNx) temperatures and annual minimum daily maximum (TXn) and minimum (TNn) temperatures, are compared with those from climate simulations of multiple model ensembles with historical anthropogenic (ANT) forcing and with combined anthropogenic and natural external forcings (ALL) at both global and regional scales using a technique that allows changes in long return period extreme temperatures to be inferred. Generalized extreme value (GEV) distributions are fitted to the observed extreme temperatures using a time-evolving pattern of location parameters obtained from model-simulated extreme temperatures under ANT or ALL forcing. Evaluation of the parameters of the fitted GEV distributions shows that both ANT and ALL influence can be detected in TNx, TNn, TXn, and TXx at the global scale over the land areas for which there are observations, and also regionally over many large land areas, with detection in more regions in TNx. Therefore, it is concluded that the influence of anthropogenic forcing has had a detectable influence on extreme temperatures that have impacts on human society and natural systems at global and regional scales. External influence is estimated to have resulted in large changes in the likelihood of extreme annual maximum and minimum daily temperatures. Globally, waiting times for extreme annual minimum daily minimum and daily maximum temperature events that were expected to recur once every 20 yr in the 1960s are now estimated to exceed 35 and 30 yr, respectively. In contrast, waiting times for circa 1960s 20-yr extremes of annual maximum daily minimum and daily maximum temperatures are estimated to have decreased to fewer than 10 and 15 yr, respectively.
The probability of climate extremes is strongly affected by atmospheric circulation This study quantifies the worldwide influence of three major modes of circulation on station based indices of intense precipitation the El Nino-Southern Oscillation the Pacific interdecadal variability as characterized by the North Pacific index (NPI) and the North Atlantic Oscillation Northern Annular Mode The study examines which stations show a statistically significant (5%) difference between the positive and negative phases of a circulation regime Results show distinct regional patterns of response to all these modes of climate variability however precipitation extremes are most substantially affected by the El Nino-Southern Oscillation The effects of the El Nino-Southern Oscillation are seen throughout the world, including in India Africa South America, the Pacific Rim North America and weakly Europe The North Atlantic Oscillation has a strong continent wide effect on Eurasia and affects a small but not negligible percentage of stations across the Northern Hemispheric midlatitudes This percentage Increases slightly if the Northern Annular Mode Index is used rather than the NAO index In that case a region of increase in intense precipitation can also be found in Southeast Asia The NPI influence on precipitation extremes is similar to the response to El Nino and strongest in landmasses adjacent to the Pacific Consistently indices of more rare precipitation events show a weaker response to circulation than indices of moderate extremes, the results are quite similar but of opposite sign for negative anomalies of the circulation indices
A workshop on climate change indices was held at the Mauritius Meteorological Services in October 2009 to produce the first analysis of climate trends for the countries of the western Indian Ocean. Scientists brought their long-term daily temperature and precipitation for a careful assessment of data quality and homogeneity, and for the preparation of climate change indices. This paper reports on the trends in daily and extreme temperature and precipitation indices for 1961-2008. The results indicate a definitive warming of surface air temperature at land stations. Annual means of the daytime and nighttime temperatures have increased at a similar rate, leading to no discernible change in the diurnal temperature range. Significant increasing trends were found in the frequency of warm days and warm nights, while decreasing trends were observed in the frequency of cold days and cold nights. Moreover, it seems that the warm extremes have changed more than the cold extremes in the western Indian Ocean region. Trends in precipitation indices are generally weak and show less spatial coherence. Regionally, a significant decrease was found in the annual total rainfall for the past 48 years. The results also show some increase in consecutive dry days, no change in daily intensity and consecutive wet days, and a decrease in extreme precipitation events. Temperature indices are highly correlated with sea surface temperatures of the region, whereas weak correlations are found with the precipitation indices.