Search

In the current context of climate change, the increase in minimum temperatures recorded in recent decades has received special scientific attention due to its importance for the proper night's sleep and health of the population, among other considerations. One of the main indicators usually considered refers to the frequency of tropical nights (≥20°C), which have begun to become widespread in regions hitherto excluded from this type of event. This paper analyses tropical nights in Spain between 1970 and 2023, addressing their mean annual occurrence, their intensity, their monthly distribution and the average number of consecutive tropical nights recorded, as well as their relationship with relative humidity. The results, based on the analysis of 75 homogenized series located in 44 different provinces, allow us to differentiate seven large areas based on the minimum temperature in which there has been an increase in the frequency and intensity of tropical nights, which progressively extend the season in which they can appear. Similarly, a generalized increase in the maximum number of consecutive nights thermally above 20°C was identified, in addition to the presence of high relative humidity in coastal areas during these episodes. [ABSTRACT FROM AUTHOR]

It is often desirable to simulate a single temperature series or a collection (network) of temperature series. Accurate simulations can enhance our understanding of temperature trends and variabilities. Simulation can also be used to generate data with known specifications, which are useful in assessing climate data processing routines such as quality control and homogenization algorithms. Possessing multiple realistic temperature series is often beneficial as only one natural record of our climate is available. The current popularity of general circulation models (GCMs) demonstrates how important simulation techniques are. However, even with sophisticated downscaling, it is difficult to replicate station temperature data that have realistic seasonal cycles as well as realistic temporal and spatial correlations. This paper reviews and studies statistical time series methods that can replicate a single series or a network of series from data. The methods are purely statistical—no atmospheric dynamics are involved—and attempt to produce replicates of the records under study. The work here develops (a) methods that simulate temperatures from a fixed season (say a particular day or month of the year) that match the distributional characteristics of the observed data; (b) methods for simulating an entire series that matches the series' temporal autocorrelations and seasonal cycle; and (c) methods for simulating a network of series that reproduce the data's observed seasonal cycles and spatial and temporal autocorrelations. Applications are given throughout, including one where a GCM series and local station data are used in tandem to describe long‐term trends and inject realistic station‐level short‐term fluctuations. This paper can be used as a tutorial for the simulation of a single climate observation, an entire climate series, or a network of multiple climate series simultaneously. Extensions of the ideas that involve GCMs are also examined. [ABSTRACT FROM AUTHOR]

In this paper we analyse the relationship between the mean and extreme precipitation for five stations in Hungary and the circulation types at the 500 hPa level, for a 52‐year period, 1958–2010. We used daily rainfall data for each station and a calendar of the daily circulation types at the 500 hPa geopotential height for Hungary based on an automatic classification scheme. A trend analysis showed that, in general, the mean and extreme precipitation rarely exhibit significant trends, similarly to previous studies. The only exception was found for the number of annual and summer rainy days in Budapest with significant negative trends (p = .05), as well as the number of days with rainfall amounts greater than the 95% probability level for Debrecen with a significant positive trend. It was found that the circulation types that are mainly responsible for the largest precipitation amounts overall show (in some cases significant) negative trends, as well as the precipitation amounts that are associated with them (Cwsw, Cse, Cwnw). In most cases, the circulation types that are prevailing during the extreme precipitation events are associated with western or almost‐western atmospheric circulations (Cwsw, Ase) and Mediterranean‐ or Atlantic‐origin depressions. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper documents the changes in climate extremes over Indonesia during the past three decades (1983-2012) based on a subset of extreme indices recommended by the Expert Team on Climate Change Detection and Indices ( ETCCDI). The extreme indices were calculated based on the quality controlled daily observational data (minimum and maximum temperature and precipitation) from 88 weather stations. Overall, we found significant and spatially coherent trends of warming in the temperature indices over Indonesia, consistent with other studies conducted at different countries within the Southeast Asia. The frequency of cool days ( TX10p) and cools nights ( TN10p) had decreased whereas warm days ( TX90p) and warm nights ( TN90p) were observed more frequently. Averaged over the country, the annual mean of daily maximum ( TXmean) and minimum temperature ( TNmean) had increased significantly by 0.18 and 0.30 °C decade−1, respectively. Other temperature indices also showed significant warming trends. In contrast, trends in the precipitation extremes indices were generally not significant and less spatially coherent. However, a tendency towards wetter conditions was observed, in agreement with the results at the global scale. The daily precipitation intensity ( SDII) had increased significantly over the country by 0.21 mm day−1 decade−1 during the period studied. At the regional scale, we observed a significant wetting trend in the annual highest daily amount ( RX1day) and the rainfall amount contributed by the extremely very wet days (R99p) in the northern part of the country. The wetting trends of a number of extreme precipitation indices were depicted prominently in December-January-February ( DJF) and/or March-April-May ( MAM) seasons, both at country and regional levels. However, for the southern region of Indonesia, a drying tendency was observed for June-July-August ( JJA), September-October-November ( SON) and MAM. [ABSTRACT FROM AUTHOR]

Seventeen years of 3‐hr, 0.25° resolution, precipitation data from the Tropical Rainfall Measurement Mission (TRMM) multi‐satellite precipitation analysis (TMPA) product are used to develop a global climatology of precipitation in tropical cyclones (TCs). Due to very large SDs for rainfall in each stratification by intensity class or cyclone basin, our methodology concentrates on frequency distributions and percentage representation in different rainfall rate categories. The stratifications reveal that the TC rainfall climatologies are dependent on three inter‐related factors: TC intensity, TC size and TC basin. The interdependence of the three is examined. The distributions of TC intensity classes in the different basins are not a significant contributor to the fact that certain basins (Northwest Pacific, North Atlantic) have higher TC rainfall rates than other basins (Northeast Pacific, South Indian). In contrast to this, the distributions of TC size classes between basins are a significant contributor to why some basins are wetter than others. A climatology is also presented of lifetime accumulated rainfall (LAR) in TCs. The record LAR belongs to hurricane Ivan in 2004, with 300 km3 of rain over the 0–350 km radius, and 432 km3 over 0–500 km. The largest LAR values occur almost exclusively in two cyclone basins: The Northwest Pacific and the North Atlantic. Not unexpectedly, LAR is determined primarily by TC duration, which accounts for around 70% of the variance. Examination of the full 17‐year dataset reveals a decreasing trend in both median and extreme TC rainfall rates in all basins except the Northeast Pacific. However, mechanisms responsible for this decrease are yet to be identified and may be primarily due to the sample size or data inhomogeneities. The difference between the trends and those expected from physical principles is a concern which we hope will be taken up by other investigators. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents an analysis of winter climate variability based on daily mean temperature and precipitation data since 1970 in the Chic-Chocs Mountain range (located in the Gaspé Peninsula, Eastern Quebec, Canada). Mountain environments are particularly sensitive to rapid climate change and are therefore good indicators of recent global warming. The main goal of this study is to demonstrate how joint probability temperature/precipitation distributions can be used to estimate winter condition changes (trends) for six meteorological stations in the study area (the altitudinal range for the stations is from 5 to 574 m). The presence and persistence of snow cover was also estimated. Previous studies have shown a lack of evidence of significant trends in snow-cover characteristics (density, depth and snow water equivalent (SWE)) from the early 1980s to the present, despite an increase in temperature over the same period. A reanalysis of these data sets in addition to the use of a combination of temperature and precipitation data categorized into four modes (warm/wet, warm/dry, cold/wet and cold/dry) was also performed. Despite this new analysis, no clear evidence of climate change could be found in the study area over the last four decades. The results revealed that patterns and trends are quite different from one station to another, but when the environment is taken into account (valley or plateau, coastal versus inland) some apparent patterns emerge. [ABSTRACT FROM AUTHOR]

In the context of climate change characterized by rising temperature and more extreme precipitation regimes, drought is one of the most relevant natural disasters. This paper presents maps of global drought frequency, duration, and severity for the periods 1951-1970, 1971-1990, and 1991-2010, to give an overview of the respective drought hot spots. Drought frequency is defined as the number of drought events occurred, drought duration as the number of months in drought conditions, and drought severity as the sum of the integral area below zero of each event. Because drought is mainly driven by rainfall deficits, we chose the Standardized Precipitation Index (SPI) as the base indicator to derive drought-related quantities. SPI-12 has been calculated on a monthly basis using a Gamma distribution fitted to a 60-year baseline period (1951-2010). Global grids (0.5° ×0.5°) of the Full Data Reanalysis Version 6.0 dataset provided by the Global Precipitation Climatology Centre (GPCC) have been used as precipitation data input. The regions most exposed to prolonged and severe droughts during 1951-1970 were the Central United States, the Argentinian Pampas, Russia, and Central Australia; during 1971-1990 they were Southern Chile, the Sahel, and Siberia; during 1991-2010 they were the Amazon Forest, the Congo River Basin, Mongolia, North Eastern China, and Borneo. A linear trend analysis between 1951 and 2010 shows a small global increase in each drought component, but drought frequency decreased in the Northern Hemisphere. The increase in drought frequency, duration, and severity is found to be significant in Africa, Eastern Asia, Mediterranean region, and Southern Australia, while the Americas and Russia show a decrease in each drought component. [ABSTRACT FROM AUTHOR]

This paper gives the complete details of the protocols applied for developing a spatially and temporarily high-resolution dataset of temperature for northeastern Spain. Our methodologies used data from a large number of observatories (1583) spanning some portions of the period between 1900 and 2006. The raw dataset was first tested for internal and external consistency to check data quality. To improve data completeness, a linear regression model was then utilized to infill gaps in the daily temperature series using the best correlated data from nearby sites. Discontinuities in the reconstructed series were determined by combining the results of three homogeneity-relative tests: the Standard Normal Homogeneity Test (SNHT), the Eastrerling and Peterson two-phased regression method and the Vincent test. To assess the possible impact of data homogenisation on trends and statistical properties of the final series, a set of tests (e.g. semivariance models and L-moment statistics) was applied to the series before and after correction. Semivariance models suggest a significant improvement in the spatial dependence of the corrected dataset on both seasonal and annual timescales. Also, L-moments gave no evidence of significant changes in the probability distribution of daily temperature series after correction. Taken together, the newly compiled dataset seems to be more robust and reveals more coherent spatial and temporal patterns of temperature compared with the original dataset. From the temporal and spatial perspectives, the new dataset comprises the most complete register of temperature in northeast Spain (1900-2006), with a reasonably spatial coverage. Accordingly, this database can provide a more reliable base for studying temperature changes and variability in the region. This dataset can also be of particular relevance to a number of meteorological, ecological, hydrological and agricultural applications on local, regional and continental scales. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper examines changes in rainfall in New South Wales (NSW), Australia using daily rainfall data from 200 rainfall stations covering the period of 1945–2014. The Mann–Kendall test is applied to identify trends in the selected rainfall indices, while the Pettitt change point test is employed to determine the direction and timing of a change point. Van Bell and Hughes homogeneity test is applied to examine homogeneity of the observed trends. It is found that the southern half of NSW is dominated by a negative trend in annual total rainfall, and autumn and spring rainfall. Based on the Pettitt change point test, it is found that annual maximum 24-h rainfall data in NSW is dominated by a negative shift. Based on the Van Belle and Hughes method, it is found that NSW is dominated by non-homogeneous trends in monthly maximum daily rainfall data. Overall, the results do not show any consistent pattern of rainfall trends in NSW. The findings of this study contribute towards the growing body of knowledge on the nature of changes in rainfall based on the latest data in NSW. [ABSTRACT FROM AUTHOR]

ABSTRACT In a recent article, Diem (2012) examined the impact of the North Atlantic Subtropical High ( NASH) and atmospheric moistening on summer rainfall variability in the Atlanta, Georgia region. In his paper, he indicated that the results concerning the variability of the NASH western ridge discussed in Li et al. (2011) are incorrect. We present new evidence here to show that the comments by Diem (2012) are unjustified; the main conclusions in Li et al. (2011) were drawn according to the data over a 60-year (1948-2007) and 45-year (1958-2002) periods of NCEP/ NCAR and ERA-40 reanalysis, whereas conclusions of Diem (2011) were based upon the trend analysis of a similar but different index of the NASH using sub-periods of 1948-2009 (since late 1970s). The comment emphases the importance of climate dynamics to study precipitation variability over the Southeastern US and further strengthens the conclusions originally put forth in Li et al. (2011): over the last 60 years (1948-2007), the NASH has shown a significant trend of westward movement, and the meridional movement of the NASH western-ridge (i.e. its latitudinal change) has enhanced in the recent three decades. [ABSTRACT FROM AUTHOR]

Snowfall and snow accumulation play a crucial role in shaping ecosystems and human activities in the Alpine region. This resource is under threat as a consequence of the visible effects of global warming, and, therefore, it appears urgent to understand how snowfall trends have changed in time and space. In this context, we recovered data from over a hundred snowfall (HN) time series covering the period 1980–2020 over the mountain region of Trentino‐South Tyrol in the northeastern Italian Alps and analysed them to understand snowfall climatology in the region, recent trends and their dependence on elevation and timing of the season. Negative, although not always statistically significant, trends were found in the lowest elevation range (0–1,000 m a.s.l.) over the whole winter season, while some positive and even significant trends were found from January to March above 2,000 m a.s.l. The intermediate elevation range (1,000–2,000 m a.s.l.) exhibits a strong variability with no clear trend. Negative and statistically significant trends were found in April for all elevations. An attribution analysis was performed using precipitation (P), mean air temperature (TMEAN), and large‐scale synoptic descriptors, such as the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) indices. The analysis shows that, overall, P is the driver that best explains the snowfall trends, but, for low elevations, especially during mid‐winter, TMEAN is more relevant. Low elevations are facing a clear decrease in HN due to a significant increase in mean temperatures, while high elevations during mid‐winter display a slight increase in HN, associated with a general increase in precipitation. NAO and AO indices exhibit no significant correlations with HN, except at the lowest elevations and at the beginning of the season. [ABSTRACT FROM AUTHOR]

State‐of‐the‐art homogenisation approaches for any test site rely upon the availability of a sufficient number of neighbouring sites with similar climatic conditions and a sufficient quantity of overlapping measurements. These conditions are not always met, particularly in poorly sampled regions and epochs. Modern sparse‐input reanalysis products which are constrained by observed sea surface temperatures, sea‐ice and surface pressure observations, continue to improve, offering independently produced surface temperature estimates back to the early 19th century. This study undertakes an exploratory analysis on the applicability of sparse‐input reanalysis to identify breakpoints in available basic station data. Adjustments are then applied using a variety of reanalysis and neighbour‐based approaches to produce four distinct estimates. The methodological independence of the approach may offer valuable insights into historical data quality issues. The resulting estimates are compared to Global Historical Climatology Network version 4 (GHCNMv4) at various aggregations. Comparisons are also made with five existing global land surface monthly time series. We find a lower rate of long‐term warming which principally arises in differences in estimated behaviour prior to the early 20th century. Differences depend upon the exact pair of estimates, varying between 15 and 40% for changes from 1850–1900 to 2005–2014. Differences are much smaller for metrics starting after 1900 and negligible after 1950. Initial efforts at quantifying parametric uncertainty suggest this would be substantial and may lead to overlap between these new estimates and existing estimates. Further work would be required to use these data products in an operational context. This would include better understanding the reasons for apparent early period divergence including the impact of spatial infilling choices, quantification of parametric uncertainty, and a means to update the product post‐2015 when the NOAA‐CIRES‐DOE 20CRv3 sparse input reanalysis product, upon which they are based, presently ceases. [ABSTRACT FROM AUTHOR]

Long‐term instrumental climate records are a major source of data for historical trends and extreme event analysis. Time series analysis of several rain gauges shows that the mid‐latitudes of Brazil experienced an increase in both annual and extreme rainfall over the last 60 years (1961–2020). The annual time series shows a significant upward trend, while changes in the frequency and intensity of heavy rainfall indicate a shortening of the recurrence interval for extreme precipitation events. In support of these results, the standardized precipitation index (SPI) also shows a positive trend for rainfall anomalies. Thus, the flood risk is higher at present than a few decades ago, while drought intensity is lower for both short and long monthly intervals used for the SPI calculation. The 60‐year instrumental rainfall data for southern Brazil shows that the amount of rainfall and changes in intensity could be higher than climate projections from models based on a broader spatial scale. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the performance of 46 global climate models (GCMs) from the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and temperature, and select the best performing GCMs for future projection across China and three major river basins. This study uses four shared socioeconomic pathways (SSPs), namely SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 relative to the base period (1961–2014). Initially, 46 models were evaluated across China employing an improved Taylor diagram method. Based on relatively better performance, 10 best‐performing models (TBMs) were selected out of 46 models for further evaluation. The results show that historical temperature was well reproduced by CMIP6 over the study regions with a high correlation coefficient (CC). All the TBMs produced good CC ranging from 0.8 to 0.99 presenting the precipitation and temperature distribution well. Meanwhile, EC‐Earth3 and EC‐Earth3‐Veg well simulated the precipitation and temperature amounts as well as trends over selected three river basins. The multimodel ensemble mean (MME) underestimates temperature over China and selected three basins with bias values of −0.53, −0.21, −0.91, and −0.68°C, respectively. In contrast, MEM overestimated the simulated precipitation with the amount of 27.7, 32.4, 21.0, and 104.6% across China and selected three basins. During future projections, increased precipitation and temperature trends are projected over three selected river basins as well as all across China. The increasing trend of future precipitation over China under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios are 0.65, 0.86, 1.29, and 0.76 mm·a−1, whereas, the increasing trend of temperature is 0.008, 0.028, 0.050, and 0.065°C·a−1, respectively. Comparatively, the greater the radiation force, the higher projected increases in precipitation and temperature across China and three major river basins were observed. The extent of CMIP6 models over the target region and its river basins calls for further deep assessment of the attribution and possible implementation of robust methods that can accurately simulate the observed patterns for future practice. [ABSTRACT FROM AUTHOR]

The Okavango River Basin (ORB) is a highly sensitive and biodiverse region in southern Africa whose climate, vegetation and river discharge characteristics are not well understood. This study investigated relationships between rainfall, temperature, Normalized Difference Vegetation Index (NDVI) and river discharge over the region as well as their trends and interannual variability. It is found that spatial patterns of NDVI are closely related to those of rainfall, but less so with temperature at monthly and seasonal time scales. The relationships between NDVI and rainfall/temperature differ north of 18.9°S where rainfall is higher than to its south. Typically, there are lags of 1–2‐months between NDVI and either rainfall or temperature. Also, there are large areas across the region that show significant warming trends in all seasons as well as wetting (mainly in the north). This increasing trend in surface temperature may act to worsen the impacts of extreme events such as severe drought and fire in the region. There is also pronounced interannual variability with significant correlations found with El Niño‐Southern Oscillation (ENSO), the subtropical South Indian Ocean Dipole and the Botswana High for rainfall and temperature, and for NDVI with ENSO. For the Southern Annular Mode, significant correlations were found with rainfall and temperature only in December and April. On longer time scales, focus was also placed on the wet 2006–2013 period relative to much drier 1999–2005 epoch for October–December. The wetter conditions during 2006–2013 appear related to La Niña Modoki conditions and warmer sea surface temperature near Angola as well as regional circulation differences. [ABSTRACT FROM AUTHOR]

Given the key roles of the Indo–China Peninsula (ICP) in weather and climate systems, hydrometeorology, and ecology, the annual and monthly changes in the Food and Agriculture Organization‐56 Penman–Monteith reference evapotranspiration (ET0), which was calculated based on the Climatic Research Unit datasets, were investigated in ICP during 1961–2017. The annual ICP ET0 significantly (p <.05) increased, with different increasing tendencies in most months. In particular, larger and more significant (p <.05) ET0 values were found during October–December. The annual and monthly ET0 changes showed evident spatial differences, characterized by increases in more than 50% of the ICP area except for decreases in around 70% of that area during March–May. A sensitivity experiment‐based separation method was utilized to evaluate the contribution of each influential factor, and the corresponding determinants were identified by comparing the contributions. Results showed that the annual ICP ET0 increase was attributed to the increased vapour pressure deficit (Vpd). However, the annual determinants varied spatially, with net solar radiation (Rn) in the southern region of ICP, wind speed (Wnd) in the northeast, and Vpd in the remaining regions. The monthly ICP determinant was Wnd in January, March–May and December, and Vpd for the remaining months. Despite different spatial patterns of monthly dominants, Vpd and Wnd were the determinants with the most extensive distributions over ICP (>75% of ICP in total). The results of this study can significantly fulfil the research gap regarding the ICP ET0 changes and the underlying mechanisms. Meanwhile, this study provides fundamental and necessary information for protecting biodiversity and understanding hydrometeorological extreme events, thus promoting specific measures to sustain the ICP development. [ABSTRACT FROM AUTHOR]

Several temperature and precipitation indices, with special focus on extremes, were analysed in different sub‐regions of southern South America during 1979–2017 using multiple reanalyses, the CPC gridded data set and the most extended network of meteorological stations employed in regional climate studies up to date. Reanalyses generally well represented the spatial patterns of the indices, although they showed some differences in extreme indices over large portions of southern South America and tended to overestimate precipitation maximums, especially in southern Chile. Furthermore, ERA‐Interim presented clear difficulties in reproducing precipitation near the Andes Mountains, exhibiting the largest overestimations. This seemed to be improved in the new generation of ERA reanalyses (ERA5). When evaluating the long‐term changes, most of the data sets agreed in general warming conditions, stronger and more homogeneous for the maximum temperature. NCEP1 and NCEP2 reanalyses showed contrary temporal changes in almost all the temperature indices. Precipitation indices exhibited less consistent changes among reanalyses, although significant upward trends were detected for precipitation extremes in southeastern South America and downward trends were detected in southern Chile in the observational data sets. In addition, most of the data sets agreed in drier conditions in the arid diagonal region of Argentina as reflected by significant positive trends for dry spells and negative trends for the total annual precipitation. In terms of the inter‐annual correspondence, reanalyses usually presented good correlations to the stations reference in the regional averaged series, mainly for temperature indices and more variable for precipitation indices. Overall, no reanalysis was found to perform best. The use of reanalyses data to perform regional climate studies should consider the existent differences among them and with observational data. Moreover, using multiple sources of information is strongly recommended to account for observational uncertainty, especially in regions like southern South America, where data availability and its resolution are often limited. [ABSTRACT FROM AUTHOR]

Trends of 15 extreme and non‐extreme daily rainfall indices for Mexico and the southern United States are analyzed for the extended boreal winter (November–March) and summer (June–October) during 1981–2010. The analysis includes gridded observations (CHIRPS and Livneh), reanalyses (ERA5, ERA‐Interim, MERRA2, and CFSR), the regional climate model RegCM4.7 (forced by ERA‐Interim), and four general circulation models (GCMs) from the CMIP5 (HadGEM2‐ES, CNRM‐CM5, MPI‐ESM‐MR, and GFDL‐ESM2M). The domain is divided into 13 regions with similar seasonal rainfall patterns according to the k‐means algorithm applied to CHIRPS monthly precipitation, as a reference. Gridded observations and reanalyses (O&R) show similar regional annual cycles and metrics of precipitation. The two observational data sets show the largest differences in the magnitude of some indices in the wettest region (southeastern Mexico) and in the monsoon. During summer, O&R show positive significant trends in extreme rainfall indices in regions commonly affected by tropical cyclones (southern Baja California Peninsula, northeastern Mexico, and the southeastern United States), while negative significant trends of seasonal accumulation of rainfall are seen in the Mediterranean Californias and the Sonoran Desert region. There is an agreement among most data sets that the frequency and intensity of extreme winter precipitation indices have declined in northwest Mexico and the southwestern United States, but trends are not statistically significant, while southeastern Mexico shows significant increases in 5‐days accumulation (rx5d). ERA5 followed by MERRA2 show the best performance. RegCM4.7 reproduces the annual cycle of precipitation in most of the regions and captures the main trend signals especially in winter, but it has difficulties in reproducing the summer interannual variability of few indices. The GCMs tend to overestimate (underestimate) the annual cycle of precipitation and the median values of extreme indices in the driest (wettest) regions. They are not able to capture the regional trends of the indices. [ABSTRACT FROM AUTHOR]

For the first time, the spatial and temporal variability of rainfall in the Republic of Djibouti is investigated using data from 14 weather stations over the period 1946–2017. Due to limited data availability, high‐resolution long‐term satellite rainfall products (CHIRPS, PERSIANN‐CDR, TAMSATv3, ARC2) and ERA5 reanalysis also contribute to document time–space rainfall variability at monthly, seasonal and annual scales. Principal component analysis identifies two spatially coherent regions of rainfall variability in the east (coastal zone) and the west (inland zone) of the country. Annual rainfall amounts are everywhere very low (60–300 mm), but with contrasted regimes. At seasonal scale, the highest rainfall amounts in the eastern part of the country are found between October–December (OND) and March–May (MAM), while July–September is the wettest season in the western part. The monthly rainfall regimes are relatively well reproduced by most products. ERA5 displays the highest monthly correlations with observations, followed by PERSIANN‐CDR and CHIRPS. Trend analysis since 1983 shows a significant decrease of rainfall during MAM which is in agreement with other parts of East Africa. On the other hand, nonsignificant decreasing trends are observed in January–February (JF) and OND. Only June–September (JJAS) revealed a nonsignificant increasing trend, but it follows a prior drying trend since the 1950s. The impact of large‐scale background climate on rainfall variability is assessed with focus given on El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). Rainfall variability during OND shows a significant correlation with IOD, while in JJAS it is significantly negatively correlated with ENSO. In general, ERA5, CHIRPS and PERSIANN datasets are best able to reproduce rainfall patterns in Djibouti and suitable for further analysis. The fact that the interannual and decadal‐scale rainfall variations in Djibouti show large‐scale teleconnections with global sea‐surface temperature fields, as demonstrated in this study, provides good prospects for the prediction of rainfall variations at a range of different temporal scales. [ABSTRACT FROM AUTHOR]

This study presents the monthly 10‐m wind speed climatology, decadal variability and possible trends in the North Atlantic and Europe from ERA5 reanalysis from 1979 to 2018 and investigates the physical reasons for the decadal variability. Additionally, temporal time series are examined in three locations: the central North Atlantic, Finland and Iberian Peninsula. The 40‐year mean and the 98th percentile wind speeds emphasize a distinct land‐sea contrast and a seasonal variation with the strongest winds over the ocean and during winter. The strongest winds and the highest variability are associated with the storm tracks and local wind phenomena such as the mistral. The extremeness of the winds is examined with an extreme wind factor (the 98th percentile divided by mean wind speeds) which in all months is higher in southern Europe than in northern Europe. Mostly no linear trends in 10‐m wind speeds are identified in the three locations but large annual and decadal variability is evident. The decadal 10‐m wind speeds were stronger than average in the 1990s in northern Europe and in the 1980s and 2010s in southern Europe. These decadal changes were largely explained by the positioning of the jet stream and storm tracks and the strength of the north–south pressure gradient in the North Atlantic. The 10‐m winds have a positive correlation with the North Atlantic Oscillation in the central North Atlantic and Finland on annual scales and during cold season months and a negative correlation in Iberian Peninsula mostly from July to March. The Atlantic Multi‐decadal Oscillation has a moderate negative correlation with the winds in the central North Atlantic but no correlation in Finland and Iberian Peninsula. Overall, our results emphasize that while linear trends in wind speeds may show a general long‐term trend, more information on the changes is obtained by analysing long‐term variability. [ABSTRACT FROM AUTHOR]

As the Arctic continues to warm, a weakening of upper‐tropospheric westerly winds is hypothesized to induce a meandering jet stream and slower propagation of Rossby waves. As such, current hypotheses suggest an increase in Greenland blocking due to increased stationarity of the high amplitude waves. These hypotheses have been supported observationally with the Greenland blocking index (GBI). However, given an expected increase in overall geopotential heights corresponding to increased temperatures in the region, we assess the robustness of trends in Greenland blocking using additional blocking metrics in addition to the GBI, which has largely been the focused blocking metric for this region to date. Our results show sensitivity of the GBI‐based increases in blocking to global and zonally averaged 500‐hPa geopotential heights, which results in inconsistent increasing trends over the 1979–2018 period when compared with other blocking metrics. Seasonal blocking frequencies of the GBI show a significant increase in blocking for JJA, though no significant trend in JJA blocking occurs for most metrics. Other indices suggest a decrease in blocking frequency in September–November (SON) and December–February (DJF), though these trends are not statistically significant. Yet, when smoothed using a 5‐year running mean, these other metrics suggest an increase in both DJF and JJA blocking with a decrease only in SON blocking, which are consistent with findings of significant changes in GBI. We report no best metric for identifying Greenland blocking. Instead, we present some shortcomings of the different metrics used in this study. These results provide insight into selection of Greenland blocking events for future research, as over‐ or under‐estimation of blocking activity can impact estimates of surface mass balance of the ice sheet. [ABSTRACT FROM AUTHOR]

Precipitation extremes have been investigated throughout the last decades in different regions of the Andes. However, little attention has been paid to the Altiplano region (Central Andes), especially to the Peruvian Altiplano (PA) that represents a complex area and is highly vulnerable to extreme events, such as floods and droughts, driven by the strong variability of precipitation. This study focuses on the analysis of 11 extreme precipitation indices (EPIs) in the period 1971–2013. In this context, commonly used statistical trend and break analyses were applied and a false discovery procedure was used in order to reduce the number of artificial significant tests. Additionally, the relative dominance of precipitation frequency and intensity in interannual precipitation datasets was determined. Finally, the correlation between EPIs and six oceanic‐atmospheric indices were analysed. The results indicate that there is no significant global trend towards wet or dry conditions in the PA, although a signal of a more slightly decrease of precipitation is presented in the Southern PA. Additionally, interannual variability of total precipitation is mainly dominated by precipitation frequency. The Central Tropical Pacific sea surface temperature plays a major role for the maximum and average length of wet periods as well as for total precipitation. This finding is particularly relevant for the southwestern PA. Our results have important implications for risk management and adaptation planning related to extreme hydrological events in the PA. [ABSTRACT FROM AUTHOR]

This study aims to characterize temporal and spatial trends of sunshine duration in Poland based on data from the years 1971–2018 and to make an attempt to explain their causes. Daily sunshine duration came from 31 Polish meteorological stations. Data from 10 stations came only from a traditional Campbell–Stokes heliograph, while in the other 21 synoptic stations Campbell–Stokes heliograph was replaced with an automatic sensor in 2014 and since then only such data are available. The smallest sunshine duration occurs in December (average areal total—39.7 hr), and the highest in July (average areal total—230.1 hr). The spatial distribution of sunshine duration in Poland shows great diversity associated with different day lengths depending on latitude. In the cool part of the year (November, December, January, and February) the isolines of totals of sunshine duration run from the highest values in the south to the lowest in the north of the country, while in the months of the warm part of the year (March to September) the system is reversed. The multi‐annual trends in sunshine duration in Poland are similar to the changes taking place in other parts of the world. The years 1971–1980 mark the end of "global dimming," which was manifested in very low values of actual and relative sunshine duration. "Global brightening" confirms a statistically significant growing trend in sunshine duration at all the analysed meteorological stations in Poland from April to September. There is a strong statistically significant relationship between the areal totals of sunshine duration and the type of pressure system, average monthly cloud cover, and atmospheric optical depth. [ABSTRACT FROM AUTHOR]

This study investigated the temporal variability and changes in snow cover duration and the average snow depth from December to April in the Pyrenees at 1,500 and 2,100 m a.s.l. for the period 1958–2017. This is the first such analysis for the entire mountain range using SAFRAN‐Crocus simulations run for this specific purpose. The SAFRAN‐Crocus simulations were evaluated for the period 1980–2016 using 28 in situ snow depth data time series, and for the period 2000–2017 using MODIS observations of the snow cover duration. Following confirmation that the simulated snow series satisfactorily reproduced the observed evolution of the snowpack, the Mann–Kendall test showed that snow cover duration and average depth decreased during the full study period, but this was only statistically significant at 2,100 m a.s.l. The temporal evolution in the snow series indicated marked differences among massifs, elevations, and snow variables. In general, the most western massifs of the French Pyrenees underwent a greater decrease in the snowpack, while in some eastern massifs the snowpack did not decrease, and in some cases increased at 1,500 m a.s.l. The results suggest that the trends were consistent over time, as they were little affected by the start and end year of the study period, except if trends are computed only starting after 1980, when no significant trends were apparent. Most of the observed negative trends were not correlated with changes in the atmospheric circulation patterns during the snow season. This suggests that the continuous warming in the Pyrenees since the beginning of the industrial period, and particularly the sharp increase since 1955, is a major driver explaining the snow cover decline in the Pyrenees. [ABSTRACT FROM AUTHOR]

Seasonality in Argentina is examined using precipitation observations from 56 weather stations north of 42°S and CRU gridded precipitation. The Walsh and Lawler Seasonality Index (SI) and Precipitation Concentration Index (PCI) are calculated to determine the different seasonality regimes. The main characteristic is an eastwest gradient, where precipitation tends to be more evenly distributed throughout the different months of the year in the east while rainfall in the west is concentrated in 3 months or less. High replicability values indicate that the wettest month generally occurs during a same period of months in central and northern Argentina. Several stations in eastern humid Argentina have significant positive changes in the mean of seasonality indices between 1961 and 1985 and 1986 and 2010, and increasing trend. In most cases this leads to a change in the regime from rather seasonal with a short dry season to a seasonal regime. Through a PAM Clustering technique a new regionalization of precipitation seasonality is obtained. The mean series in each cluster show a tendency towards more seasonal and highly seasonal regimes in all regions. The SI and PCI indices were calculated using the precipitation outputs of 18 global climate models (GCMs) from the Coupled Model Inter‐comparison Project Phase 5 (CMIP5) to examine the ability of these models to represent seasonality spatial distribution and changes in Argentina. To some extent the spatial distribution of seasonality is captured by models; however, there are considerable discrepancies in the location and extension of the eastwest gradient. The sign and location of trends and changes in the mean SI and PCI are adequately reproduced in only a few models. The low ability of GCMs to represent seasonality and its changes is a consequence of the difficulties GCMs have simulating precipitation characteristics in Argentina reported by different authors. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs), defined as narrow, transient corridors of strong moisture transport in the lower troposphere, are important phenomena for freshwater recharge and water resources, especially along the west coast of North America. This study presents the variability and trends of landfalling ARs (LARs) along the higher (53.5°–60.0°N) and lower (47.0°–53.5°N) latitudes of British Columbia and southeastern Alaska (BCSAK) during the 1948–2016 period. Moreover, we present the synoptic evolution and distribution of LARs in BCSAK during different phases of ocean–atmosphere climate variability using a six‐hourly AR catalogue from the Scripps Institution of Oceanography and reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research. During 1948–2016, BCSAK averages 35 ± 5 LARs annually, with the highest frequency during fall (13 ± 2) and lowest during spring (5 ± 2). The frequency of LARs across BCSAK rises during the study period, and the increase between 1979 and 2016 is statistically significant (p <.05). A strong ridge over the Pacific Northwest and BC and a trough over the Gulf of Alaska and the Northeastern Pacific Ocean favours AR landfalls at the higher and lower latitudes, respectively. BCSAK experiences greater numbers of LARs during neutral phases of El Niño/Southern Oscillation, the 2013/2014 Pacific oceanic blob, and during the positive phases of the Pacific Decadal Oscillation and Pacific North American Pattern. [ABSTRACT FROM AUTHOR]

The objective of this research was to estimate probable maximum precipitation (PMP) for the duration of 1 to 3 days in Thailand. Rainfall data were collected from 217 meteorological stations. Two methods for estimating PMP were applied: the generalized method which analysed actual storms data using meteorological process, and the Hershfield's statistical method which applied the frequency analysis of the annual maximum rainfall. Both methods were commonly used and were recommended by the World Meteorological Organization (WMO) for estimating PMP. PMP estimation results were presented as isohyetal maps. For the generalized method, most PMP values were found higher in the east and decreased towards the west. However, there was no clear trend with the statistical method in the PMP's spatial distribution. An analysis of the 3‐day PMP averages showed that PMP estimation using the generalized method is 10% higher than that of the statistical method. The study results were then applied to estimate PMP of six large dams in Thailand and compared with the ones used in the design of the spillways. It was found that PMP estimation based on the generalized method was closer to the value used in the spillway design of those dams than PMP estimation based on the statistical method. [ABSTRACT FROM AUTHOR]

Near‐surface seasonal and annual mean wind speed in Switzerland is investigated using homogenized observations, Twentieth Century Reanalysis (20CRv2c) data and raw model output of a 75 member EURO‐COoRdinated Downscaling EXperiment regional climate model (RCM) ensemble for present day and future scenarios. The wind speed observations show a significant decrease in the Alps and on the southern Alpine slopes in the period 1981–2010. However, the 20CRv2c data reveal that the recent trends lie well within the decadal variability over longer time periods and no clear signs of a systematic wind stilling can be found for Switzerland. The ensemble of RCMs shows large biases in the annual mean wind speed over the Jura mountains, and some members also show large biases in the Alps compared to station observations. The spatial distribution of the model biases varies strongly between the RCMs, while the resolution and the driving global model have less impact on the pattern of the model bias. The RCMs are mostly able to represent the seasonality of wind speed on the Plateau but miss important details in complex terrain related to local wind systems. Most models show no significant changes in near‐surface mean wind speed until the end of the 21st century. The model ensemble changes range from a 7% decrease to a 6% increase with an ensemble mean decrease of 1 to 2%. Due to model biases, the scale mismatch between model grid and station observations and the missing representation of local winds in the simulations, the changes need to be interpreted with utmost care. Future assessments might lead to major revisions even for the sign of the projected changes, in particular over complex terrain. [ABSTRACT FROM AUTHOR]

This study investigates the long‐term variability of surface air temperature (SAT) over the Arabian Peninsula (AP), using data from the Climate Research Unit (TS 3.22) for the 1960–2010 period. The long‐term climatology suggests that the warmest AP mean temperatures occur during summer, with the highest temperatures over the northern AP (NAP), due to the monsoon–desert mechanism. During winter, the NAP exhibits low SATs under the influence of western disturbances originating from the Mediterranean. The southwestern AP exhibits the lowest temperatures because of its proximity to the Arabian Sea cold waters, and also because of the orographic effects. The inter‐annual variability of the SAT is stronger during winters. A linear trend analysis reveals a significant increase in the SAT anomaly (0.10 °C/decade) across the AP, consistently with the global temperature anomalies. Besides the local convective heating, summer SAT variability is associated with the weakening of the Asian jet stream and a Rossby wave train from the Indian Ocean. This variability is also influenced by the anomalous low pressure over the North Atlantic and the Sahara, a high‐pressure system over Siberia and the northwest Pacific. Both in spring and autumn, sea surface temperature (SST) variations over the Indo‐western Pacific are highly influenced the AP SATs, whereas winter SATs are modulated by the subtropical jet stream and the Middle East jet stream. In all seasons, the AP SAT is strongly influenced by the SST variations over the tropical oceans. The temperature variability is closely associated with the El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Arctic Oscillation (AO). The warm phase of ENSO (i.e., El Niño) is one possible reason behind the inter‐annual increase in SAT over the southern AP. The negative phases of NAO and AO also play a role in increasing AP SAT. Seasonal and annual mean surface air temperature (°C, shaded) and standard deviation (contours) over the Arabian Peninsula for the period 1960–2010. [ABSTRACT FROM AUTHOR]

Non‐meteorological factors may bias wind speed measurements in a number of ways, among them the types of instruments used, their calibration and standards of maintenance, station relocations, and changes in the physical surroundings of a given station. Moreover, homogenisation of series of such measurements is more complicated than that of other climatic variables. This contribution uses figures from the Czech Republic as an example to demonstrate that measured (raw) data may produce different results in mean daily wind speeds from those acquired in homogenous series. A basic set of measurements taken in 1961–2015 at 178 meteorological stations was quality‐checked and then homogenized using the Standard Normal Homogeneity test (SNHT) and the Maronna‐Yohai test. Subsequent analyses were based on homogenized series from 119 stations. Station relocations and automation of wind speed measurements were identified as the most important sources of break‐points in series of mean daily wind speeds. Generally lower mean wind speeds (as well as wind speed variability) were obtained from homogenous series in comparison with measured data, with reflections also found in their spatial distribution around the territory of the Czech Republic. Statistically significant decreasing linear trends calculated from measured and homogenized daily wind speed series from 119 stations confirm the existence of wind stilling as a typical feature of land mid‐latitudes in recent decades. However, trends from measured data are highly overestimated compared with those from homogenized series. Fluctuations in mean annual wind speed series based on measured (1) and homogenized (2) data in the 1961–2015 period for four altitudinal groups (I: ≤300 m asl, II: 301–600 m asl, III: 601–900 m asl, IV: >900 m asl) and the entire Czech Republic (CR). Linear trends and their values (m s−1 dec−1; left below) for both types of series are included [ABSTRACT FROM AUTHOR]

A comprehensive analysis of the spatial and temporal variability of daily precipitation in Spain in the period 1950–2012 was carried out based on the high‐resolution gridded data set SPREAD at a 5 × 5 km spatial resolution. Temporal trends of 12 daily precipitation indices were computed by application of the Sen's slope and the Mann–Kendall tests. The spatial distribution of the trends showed (a) a global slight increase in the duration of the precipitation events, especially along the Mediterranean fringe; (b) an increase of the frequency of low precipitation events and a decrease of the frequency of high and very high events (inverse in the Mediterranean coast); and (c) a slight decrease of the intensity when considering single events (1‐ or 5‐day duration), but a significant negative trend in mean and median precipitation when considering all the precipitation days (p > 0), especially in the Mediterranean coast. [ABSTRACT FROM AUTHOR]

High‐resolution numerical simulations were carried out for the most recent 30‐year period (1987–2016), focusing on the Euro‐Mediterranean region. A sophisticated thermo‐physiologically significant thermal index, namely the physiologically equivalent temperature (PET), was computed for assessing the thermal bioclimate of the study area and for investigating the presence of long‐term trends, focusing on bioclimatic extremes. Results indicate that the Euro‐Mediterranean thermal bioclimate follows a zonal pattern, upon which topography acts to delineate regional maxima and minima. The conducted time series analysis reveals statistically significant trends that are generally more pronounced for cold extremes. In particular, it is found that the number of days with cold (hot) stress has decreased (increased), with the distribution of PET exhibiting warming of both its cold and warm tail. The south–east Mediterranean and the Balkans are found to be the most responsive to bioclimatic changes, showing coherent and statistically significant warming trends. Overall, the results of this study provide a new point of view of the Euro‐Mediterranean climate, which could be of usefulness in a wide range of future applications. The detailed spatio‐temporal bioclimatic data could be exploited, for instance, to support applications related to tourism and recreation, highlighting regions with favourable or less favourable thermal comfort conditions. Furthermore, the trend data could be employed for quantifying the vulnerability of certain regions to cold and heat stress, in the context of public health applications. [ABSTRACT FROM AUTHOR]

We present the Caribbean's Virgin Islands’ climate variability over the last 60 years from indices of extreme temperature and precipitation, as well as their quantitative support for a recent climatic shift (1983.2 ± 5.5 years). The region's climate indices (defined via time series averages) and trends (defined via linear least squares regression fitting) of extreme temperature and precipitation were cross‐examined from 1952, 1961, 1978, and 1983–2014 to diagnose: the area's climatic extremes; a climatic transition's role in assessing decadal climate anomaly rates; and to provide an analytically independent test of the transition's existence. Analyses relied on the use of varying physical constraints to include weighting from statistical and non‐statistical uncertainties in our climatic outcomes. We report that diurnal warming and enhanced precipitation accompanied each interval, however, non‐negligible deviations existed in comparisons between the same anomalies in any 2 intervals. Our Virgin Islands’ long (1952, 1961–2014) versus short (1978, 1983–2014) climate variation comparisons provide evidence for the manifestation of decadal (or longer) shifts, and thus, biases, in recent Caribbean reports. Our climatic transition's overlap with the renowned 1976/1977 Pacific event insinuates a local Caribbean teleconnection, and contributes to the evidence growing globally for 1980s’ shifts in an abundance of feedback measures of Earth's energy budget. [ABSTRACT FROM AUTHOR]

ABSTRACT: Observational analyses of changing climate extremes over the West Africa region have been limited by the availability of long and high‐quality datasets. To help address this gap, a climate extremes indices workshop was held in the Gambia in December 2011 with participants from 14 West African countries. The resulting analysis utilized 15 annual indices derived from observed daily temperatures and 10 annual indices derived from observed daily precipitation. The analysis was conducted for 166 meteorological stations in 13 countries for 2 periods: 1960–2010 and 1981–2010. The analyses of trends in the annual mean temperature indices have identified statistically significant increases of 0.16 °C/decade and 0.28 °C/decade for mean annual maximum and mean annual minimum temperatures, respectively, averaged over all available land stations in the region during the last 50 years. The seasonal‐temperature‐related indices show significant patterns of warming in all seasons. The annual mean of daily minimum temperature has increased more than the annual mean of daily maximum temperature leading to a decreasing trend in the diurnal temperature range. Warm days and warm nights have become more frequent, and cold days and cold nights have become less frequent. The analyses of precipitation‐based indices indicate spatially non‐coherent changes throughout the study area with few statistically significant trends over the longer period. Exceptions to this are the simple daily intensity index and maximum 5‐day precipitation, which show significant increasing regional trends over both the shorter and longer periods. Additionally, over the recent period (1981–2010) most of the precipitation related indices show significant trends towards wetter conditions. However, this period of increased rainfall follows a decade of significantly drier conditions in the region – it is not clear whether the recent upward trends reflect the ‘recovery’ from this long drought period or represents a long‐term response to warming. [ABSTRACT FROM AUTHOR]

ABSTRACT: This study provides an assessment of recent changes in near‐surface air temperature over the central Adriatic region of Italy, focusing on extreme events at annual and seasonal scales. It investigates trends in selected indices calculated from quality controlled and homogenized daily temperature data recorded from 1980 to 2012 at 34 meteorological stations distributed over the territory. The results reveal overall warming tendencies, particularly pronounced in spring and summer, with significant increases in the duration of warm spells (WSDI) and in the frequency of warm days (TX90p) and warm nights (TN90p), summer days (SU) and tropical nights (TR). Moreover, cold‐related extremes (cold spell duration (CSDI), cool days (TX10p) and cool nights (TN10p), frost days (FD) and icing days (ID)) show significant reductions, although of lower magnitudes, thus confirming the recent warming over the study domain. Prevalent increasing tendencies are also observed for absolute extreme temperature indices (highest and lowest daily values of maximum and minimum temperatures), but with more mixed and less uniform spatial patterns. Finally, the influence of large‐scale circulation modes on temperature extremes is examined. The results highlight the presence of significant correlations between most of the selected extreme temperature indices and the East‐Atlantic pattern, in particular for the warm season. [ABSTRACT FROM AUTHOR]

ABSTRACT: Assessing warming over the Western Himalayan Region (WHR) of India is challenging due to its limited station data availability and poor data quality. The missing values in the station data were replaced using the Multiple Imputation Chained Equation technique. Finally, 16 stations having continuous records during 1969–2009 were considered as the ‘reference stations’ for assessing the warming/cooling trends in addition to evaluate the Coupled Model Intercomparison, phase 5 (CMIP5), Global Circulation Model (GCM). Station data indicates winter (DJF) warming is higher and rapid (1.41 °C) than the other seasons and less warming was observed in the post‐monsoon (0.31 °C) season. Overall mean annual warming over WHR is ∼0.84 °C during 1969–2009. The performance of 34 CMIP5 models was evaluated based on three different criteria namely (1) mean seasonal cycle, (2) temporal trends and (3) spatial correlation between simulated and observed signals for common available period of 1969–2003 over the study area. Models are provided a final rank on the basis of the cumulative rank obtained in each of three approaches. CMCC‐CM, GISS‐E2‐H and MIROC 5 are three top‐ranked models while MIROC‐ESM, MIROC‐ESM‐CHEM and bcc‐csm1‐1 are three bottom‐ranked models over the WHR. The study also extended to judge whether the selected top‐ranked models perform well through two alternative data sources namely European Reanalysis (ERA)‐interim and Climate Research Unit (CRU), which have not used in the process of model evaluation. The spatial patterns of top‐ranked GCM are similar to the spatial pattern obtained through ERA‐interim and CRU while zoomed in to WHR but bottom‐ranked models fail to reproduce such spatial patterns indicating the top‐ranked GCMs would offer more reliability for projecting future climate over WHR. [ABSTRACT FROM AUTHOR]

ABSTRACT This study provides a better understanding of the relationships between the trends of mean and extreme precipitation in two observed precipitation data sets: the Climate Prediction Center Unified daily precipitation data set and the Global Precipitation Climatology Program ( GPCP) pentad data set. The study employs three kinds of definitions of extreme precipitation: (1) percentile, (2) standard deviation and (3) generalize extreme value ( GEV) distribution analysis for extreme events based on local statistics. Relationship between trends in the mean and extreme precipitation is identified with a novel metric, i.e. area aggregated matching ratio ( AAMR) computed on regional and global scales. Generally, more (less) extreme events are likely to occur in regions with a positive (negative) mean trend. The match between the mean and extreme trends deteriorates for increasingly heavy precipitation events. The AAMR is higher in regions with negative mean trends than in regions with positive mean trends, suggesting a higher likelihood of severe dry events, compared with heavy rain events in a warming climate. AAMR is found to be higher in tropics and oceans than in the extratropics and land regions, reflecting a higher degree of randomness and more important dynamical rather than thermodynamical contributions of extreme events in the latter regions. [ABSTRACT FROM AUTHOR]

ABSTRACT Quantifying precipitation variability and trends is difficult and merits regular reassessment. In the present study, the seasonal homogenized precipitation series in Switzerland is investigated for the period 1901-2013 in terms of trends, inter-annual variability and the influence of large-scale flow. An objective clustering is applied, resulting in 32 distinct precipitation regions. Trends are analysed for all 32 regions with moving trend windows, and statistical significance ( p < 0.05) is assessed with Theil-Sen trend estimates and nonparametric Mann-Kendall trend tests. Inter-annual precipitation varies considerably regionally and seasonally. Of 2720 analysed trend windows of 33-113 years in length, 194 (7.1%) show a significantly positive trend and 10 (0.4%) a significantly negative trend. Most of the significantly positive trends are found for 50+ year series for winter, autumn and the year. 81% of the annual and 72% of the winter series show positive trends (0-3% per decade) for the 1901-2013 period. The trends are significant for 34% (22%) of the annual (winter) series. Long-term trends are generally in line with earlier estimates in literature considering somewhat shorter time periods. Significantly negative trends are only found for winter in the most recent decades. No significant trends are found for inter-annual variability and changes in the 10th and 90th percentile seasonal extreme precipitation thresholds. The influence of large-scale flow on precipitation variability is strongest in winter. The leading pattern for northern Switzerland ( nS) is the Euro-Atlantic blocking pattern. For southern Switzerland ( sS), the Eastern Atlantic (Scandinavian) pattern is dominant in winter (autumn). In nS, wet (dry) seasons are linked to south-westerly (north-easterly) flow. In sS, wet (dry) seasons are linked to southerly (northerly) flow. Strong evidence for imprints of atmospheric blocking on seasonal precipitation variability is found in winter and autumn. In spring, a high pressure anomaly stretching from the mid-Atlantic to central Europe is indicative of dryness in nS. [ABSTRACT FROM AUTHOR]

ABSTRACT This article presents a climatology of total cloud cover ( TCC) in the area of the three inland Eurasian seas (Black, Caspian, and Aral Sea). Analyses are performed on the basis of 20 years of data (1991-2010), collected from almost 200 ground stations. Average TCC is 49%, with broad spatial and seasonal variability: minimum TCC values are found in summer and to the southeast, whereas maximum values correspond to winter and to the northwest. For the whole area, linear trend analyses show that TCC did not vary during the study period. We only detected a statistically significant positive trend (+1.2% decade−1) in autumn. We obtained different results for the regions delimited by means of a principal component analysis: a clear decrease, both for the annual, spring, and summer series, was detected for the south of Black Sea, while increasing TCC was found for the annual, autumn, and winter series in the north Caucasus and the west and north of Black Sea. We also analysed the TCC data from global gridded products, including satellite projects [International Satellite Cloud Climatology Project ( ISCCP), Pathfinder Atmospheres Extended ( PATMOS-x), cLoud, Albedo & Radiation ( CLARA)], reanalyses [ ERA-interim, National Centers for Environmental Prediction/Department of Energy ( NCEP/ DOE), Modern-Era Retrospective Analysis for Research and Applications ( MERRA)], and surface observations [Climatic Research Unit ( CRU)]. Although all these products capture the seasonal evolution over the study area, they differ substantially both among them and in relation to the ground observations: reanalyses produce much lower values of TCC, while ISCCP and CLARA provide a summer minimum that is too high. Trend analyses applied to these data generally showed a decrease in TCC; only CRU and NCEP/ DOE tally with the ground data as regards the absence of overall trends. These results are discussed in relation to previous studies presenting trends of other variables such as sunshine duration, diurnal temperature range, or precipitation; we also discuss the connections with changes in synoptic patterns and environmental changes, in particular in the Aral Sea region. [ABSTRACT FROM AUTHOR]

The past two decades of the 20th century and the first of the 21st century have been characterized by global temperature rise and increased frequency of weather-induced extreme events such as floods, droughts, heavy rainfall, and heat waves. We investigated the heat and the cold waves in the Carpathian Region, an area whose rich biosphere is endangered by extreme events. We used the daily minimum (TN) and maximum (TX) temperature data collected in the framework of the CARPATCLIM project. Such high-resolution (0.1° × 0.1°) gridded data range from January 1961 to December 2010. In this study, a heat wave occurs when temperature is above the 90th percentile for at least five consecutive days and a cold wave occurs when temperature is below the 10th percentile for at least five consecutive days. The percentiles have been computed over the baseline period 1971-2000. We distinguish between night-time and daytime events and we discuss heat (and cold) waves considering at least five consecutive night and days with temperature above (below) the selected percentile. For each heat or cold wave event, we assigned duration, severity, and intensity. For these parameters and for frequency, we performed linear trend analysis for the period 1961-2010. The trends have been computed on an annual and seasonal basis and tested for statistical significance. Different spatial patterns of heat and cold waves characterize the Carpathian Region: heat wave events show general increase in all the parameters considered, while cold wave events show a decrease in all the variables West to the Carpathians and an increase North-East to the Carpathians. We also compiled a list of the most relevant heat waves that hit the Carpathian Region from 1961 to 2010: out of seven events, four occurred from 2000 to 2010. Instead, the 1960s and the 1980s have been the decades most hit by severe cold waves. [ABSTRACT FROM AUTHOR]

ABSTRACT Changes in annual and seasonal temperatures were studied from 18 stations, distributed all over the Basilicata region (southern Italy), for the 1951-2010 period. The analysis is based on high-quality and homogenous daily minimum and maximum temperatures. Both minimum ( Tmin) and maximum ( Tmax) temperatures increase, especially after 1971. Seasonal results show upward trends in Tmin in winter, spring and summer, whereas they show downward trends in Tmin in autumn, especially in the last normal 1981-2010. Tmax also shows upward trends in spring and summer, whereas it tends to decrease during winter and autumn. The intra-annual extreme temperature range (ETR) index also shows a general positive trend, especially during spring. Eleven indices were used to assess the changes in both the cold and warm tails of the daily temperature distribution. The presence of trends was assessed by means of the Mann-Kendall test. The results reveal a general upward tendency on warm days (TX90), warm nights (TN90) and tropical nights (T20) especially because of an increase in temperature after 1971. This datum is fully confirmed in summer which is the season mainly responsible for this trend. The annual occurrence of summer days (SU) and very warm days (TX99) is weakly increasing. The majority of cold extremes, i.e. very cold nights (TN1), cold days (TX10), cold nights (TN10), frost days (FD) and ice days (ID) showed negative trends, thus confirming the overall warming trend in the Basilicata region. This negative trend could stem from the strong increase of Tmin during winter, spring and summer. [ABSTRACT FROM AUTHOR]

ABSTRACT Trends of annual maximum 1-day precipitation in three major Hawaiian Islands are investigated using a nonparametric Mann-Kendall method and Sen's test ( MKS). The records are from 24 stations on Oahu, Maui, and Hawaii, and the period of analysis ranges from 1960 to 2009. To complement the MKS method, a non-stationary three-parameter generalized extreme value ( GEV) distribution is also used to detect trends in precipitation extremes. Both methods demonstrate that negative trends prevail for Oahu and Maui but positive trends dominate the Island of Hawaii. The influence of the location and the scale parameter in the GEV model on different return levels (2-year, 20-year, and 100-year) are explicitly described. The return-level threshold values are found to change with time considerably. As a result, a rare storm with daily precipitation of 300 mm (20-year return period) in 1960 has become a rather common storm event (3-5-year return period) in 2009 on the Island of Hawaii. The opposite trend behavior in extreme events is observed on Oahu and Maui, where rainfall extremes have become less frequent in the last five decades. A positive relationship is found between the precipitation extremes and Southern Oscillation Index ( SOI), implying greater extreme events during La Niña years and the opposite for El Niño years. [ABSTRACT FROM AUTHOR]

ABSTRACT Hailstorms have become a serious meteorological risk in mid-latitude countries because of their small-scale development and short time span, and their forecasting remains problematic. Moreover, the lack of reliable hailstorm databases is an important handicap for Numerical Weather Prediction (NWP) model validation, time trend studies and establishment of hailstorm relationships with global warming and climate change. However, this can be overcome by ascertaining physical relationships between favourable synoptic patterns and mesoscale trigger factors for hailstorm development in a local study area. North-eastern Spain is one of the European regions with largest number of hailstorm days ( HD) observed in the summer. Since 2001, from May through September, reliable databases of hailstorm occurrence in the middle Ebro Valley have been constructed, which include intensity, temporal frequency and spatial coordinate data. June and July 2006 had the largest number of HD in 2001-2010. With the objective of analysing meteorological factors responsible for this anomaly, atmospheric patterns at low and mid-tropospheric levels were studied. We determined a set of special synoptic configurations, with an evident deviation from climatic values generating anomalies of 850 hPa temperature relative to the characteristic values of 2001-2010 and 1950-2010. An analysis of these anomalies detected a positive trend of 850 hPa temperature and geopotential height in the western Mediterranean area. As a consequence, the characteristic synoptic circulation has changed since 1950, towards low-level patterns favouring adequate thermodynamic environments for hailstorm development in north-eastern Spain. Finally, after a cluster analysis, we studied periodicities in the monthly 850 hPa temperature field to improve knowledge for seasonal forecasting of hailstorms. [ABSTRACT FROM AUTHOR]

ABSTRACT A workshop was held at the University of the West Indies, Jamaica, in May 2012 to build capacity in climate data rescue and to enhance knowledge about climate change in the Caribbean region. Scientists brought their daily observational surface temperature and precipitation data from weather stations for an assessment of quality and homogeneity and for the calculation of climate indices helpful for studying climate change in their region. This study presents the trends in daily and extreme temperature and precipitation indices in the Caribbean region for records spanning the 1961-2010 and 1986-2010 intervals. Overall, the results show a warming of the surface air temperature at land stations. In general, the indices based on minimum temperature show stronger warming trends than indices calculated from maximum temperature. The frequency of warm days, warm nights and extreme high temperatures has increased while fewer cool days, cool nights and extreme low temperatures were found for both periods. Changes in precipitation indices are less consistent and the trends are generally weak. Small positive trends were found in annual total precipitation, daily intensity, maximum number of consecutive dry days and heavy rainfall events particularly during the period 1986-2010. Correlations between indices and the Atlantic multidecadal oscillation ( AMO) index suggest that temperature variability and, to a lesser extent, precipitation extremes are related to the AMO signal of the North Atlantic surface sea temperatures: stronger associations are found in August and September for the temperature indices and in June and October for some of the precipitation indices. [ABSTRACT FROM AUTHOR]

This study analyses the spatial and temporal variability and trends of rainfall, mean maximum and minimum temperatures at seasonal and annual timescales over the Upper Blue Nile River Basin, Ethiopia. Statistical and geostatistical techniques were applied to 1634 points on 10 × 10 km gridded data reconstructed from weather stations and meteorological satellite records. The data were obtained from the National Meteorological Agency of Ethiopia and cover the period between 1981 and 2010. Trends were evaluated from slopes of regression lines using the least squares method. The F-distribution test was used to determine the statistical significance of the trends. Minimum temperatures significantly increased in northern, central, southern and southeastern parts of the Basin in all seasons. At the annual scale, maximum and minimum temperatures significantly increased in over 33% of the Basin at a rate of 0.1 and 0.15 °C per decade, respectively; however, the western part (12%) of the Basin experienced declining trends on annual and seasonal timescales. The minimum temperatures increased at a higher rate than the maximum temperatures during winter, summer, autumn and also at the annual timescale. Mean annual minimum and maximum temperatures increased from 12.69 to 13.32 °C and 26.43 to 26.91 °C from 1981 to 2010, respectively. Rainfall showed statistically non-significant increasing trends of 35 mm per decade at the annual timescale. All seasons except spring season exhibited similar statistically non-significant trends. The spring season, however, showed a statistically non-significant declining trend in the north eastern (11%) part of the Basin. [ABSTRACT FROM AUTHOR]

ABSTRACT This article presents an analysis of observed climate extremes over the Arabian Peninsula ( AP); a region for which little such analysis has been available. A set of climate extremes indices developed by the Expert Team on Climate Change Detection and Indices ( ETCCDI) were computed and analysed for trends including the periods 1970-2008 and 1986-2008 at 23 stations covering six countries (Bahrain, Qatar, Kuwait, Oman, Saudi Arabia and UAE) and for Bahrain (1950-2008), Kuwait (1962-2008), Masirah and Salalah (1943-2008). The results indicate a general decreasing trend of cold temperature extremes and increasing trends of the warm temperature extremes during the periods of analysis. Over the whole region, a remarkable and highly significant increase in very warm nights during 1986-2008 has occurred. In general higher temperature trends (magnitudes/significance) are reported over the northern AP in the day-time extremes while for the night-time extremes the trends are higher and significant for the southern region especially during the recent decades. The precipitation indices trends are weak and insignificant except for the annual count of days when precipitation exceeds 10 mm which shows a significant decrease during 1986-2008. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article we examine trends in rainfall in the Fiji, using records from 14 stations in Fiji. These records cover more stations and significantly longer periods (>90 years for several stations) than those used in any previous studies. We find that over a period of nearly 100 years there is high interannual variability but no significant long-term trend in annual rainfall in Fiji in either 'wet-side' or 'dry-side' stations. This result is consistent with the more restricted results of almost all previous studies. We also find no significant trends in 'wet season' and 'dry season' rainfall, considered separately. Thus unlike temperature data, rainfall data from Fiji provides little evidence of long-term climate change. There is an indication that the few dry seasons with rainfall more than one standard deviation below the mean have occurred more frequently in the most recent 50 years than in the previous 50. Our results confirm that there is a significant influence of the El Niño Southern Oscillation on rainfall in Fiji, especially on the 'dry' side of the larger islands. However, we find that the Interdecadal Pacific Oscillation (IPO) does not modulate the correlation between rainfall in Fiji and the Southern Oscillation Index; contrary to the case in eastern Australia, this correlation is strongly positive for all phases of the IPO. [ABSTRACT FROM AUTHOR]

An analysis of recent-past changes in rainfall records from the three major transnational basins in Iberia was performed, using data from Spain and Portugal which are generally considered separately. Change point (to detect abrupt changes in rainfall) and trend analysis were performed in the basins of rivers Douro, Tagus and Guadiana for the period 1961 to 2009. Nonparametric tests (Pettitt, cusum and Mann-Kendall) were used in order not to assume a specific distribution for the data. Field significance was taken into account when applying the tests for trends and change points. The importance of spatial correlation when calculating field significance was demonstrated. As well as finding changes in rainfall which have great significance for water resources, some important issues are raised as to the nature of changes in rainfall to be expected. Significant decreases in rainfall were found for the month of February and, to a lesser extent, March. Significant increases in rainfall were found for October in the Spanish side of Douro and Tagus catchments. The variability of the NAO index was considered a likely explanation for the changes detected. It was also demonstrated that changes in rainfall cannot be interpreted as trends or change points always because the pattern of change can be more complex than these two simplistic ways of describing change. Furthermore, the magnitude of the change can be completely different depending on the type of change assumed. Therefore the quantification of the change must be made with care, as the widely used linear trend can result in an overestimation of the change and, in principle, is not coherent with the multiple alternative hypotheses (different admissible monotonic patterns) implied by the Mann-Kendall test. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT We present a climate analysis of nine unique Swiss Alpine new snow series that have been newly digitized. The stations cover different altitudes (450-1860 m asl) and all time series cover more than 100 years (one from 1864 to 2009). In addition, data from 71 stations for the last 50-80 years for new snow and snow depth are analysed to get a more complete picture of the Swiss Alpine snow variability. Important snow climate indicators such as new snow sums ( NSS), maximum new snow ( MAXNS) and days with snowfall ( DWSF) are calculated and variability and trends analysed. Series of days with snow pack ( DWSP) ≥ 1 cm are reconstructed with useful quality for six stations using the daily new snow, local temperature and precipitation data. Our results reveal large decadal variability with phases of low and high values for NSS, DWSF and DWSP. For most stations NSS, DWSF and DWSP show the lowest values recorded and unprecedented negative trends in the late 1980s and 1990s. For MAXNS, however, no clear trends and smaller decadal variability are found but very large MAXNS values (>60 cm) are missing since the year 2000. The fraction of NSS and DWSP in different seasons (autumn, winter and spring) has changed only slightly over the ∼150 year record. Some decreases most likely attributable to temperature changes in the last 50 years are found for spring, especially for NSS at low stations. Both the NSS and DWSP snow indicators show a trend reversal in most recent years (since 2000), especially at low and medium altitudes. This is consistent with the recent 'plateauing' (i.e. slight relative decrease) of mean winter temperature in Switzerland and illustrates how important decadal variability is in understanding the trends in key snow indicators. [ABSTRACT FROM AUTHOR]

ABSTRACT In this work, the trends of six climate indices based on the daily maximum and minimum temperatures during the period 1949-2009 at 15 stations distributed across Serbia were analysed. The results showed seasonal changes in the minimum and maximum temperature extremes. An analysis of the extreme temperature indices suggested that the Serbian climate generally tended to become warmer in the last 61 years. The most significant temperature trends were revealed for the summer season. The influence of large-scale variables on the temperature was examined by means of the empirical orthogonal function and correlation. It was found that the East Atlantic pattern dominated during the winter, spring and summer, while the East Atlantic/West Russia pattern governed during the autumn. In addition, the North Atlantic Oscillation dominated the Serbian extreme temperature variability during the winter. [ABSTRACT FROM AUTHOR]