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In this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the variability and its temporal‐scale components of daily air temperature and precipitation simulated by two regional climate models (RCMs) and two driving global climate models (GCMs) from the EURO‐CORDEX and CMIP5 projects. The analysis was performed for eight geographical areas of central Europe for the period 1971–2000. After a brief evaluation of total variance, we focus on the ability of the simulations to represent short‐term, seasonal and long‐term variability components. This analysis was carried out for original time series as well as for series with the annual cycle removed. A fast Fourier transformation and a Kolmogorov–Zurbenko filter were used to separate the three temporal components. The two methods of time series decomposition generally agree, which confirms the robustness of the results. Although the observed total variability of daily air temperature and precipitation is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. Regarding the air temperature, the results for the time series with the annual cycle removed are generally somewhat worse than for original series. The simulations tend to underestimate the short‐term component of temperature variability, while the results for the other two components are inconclusive. Moreover, it was found that temperature variability caused by the annual cycle is overestimated by the simulation of HadGEM2 GCM and CCLM4‐8‐17 RCM driven by HadGEM2, which explains the relatively poor results of these simulations regarding air temperature total variance and short‐term as well as seasonal components in the original temperature series. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components. Although the observed total variability of daily air temperature and precipitation over selected European areas is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components. In the figure, location of the eight studied areas situated in the vicinity of central Europe and the orography of the E‐OBS data set. [ABSTRACT FROM AUTHOR]

In the paper we focus on the notes of botanist and meteorologist Gottfried Reyger on the weather conditions in Gdańsk, Poland. In our estimation, this chronicle, covering the period from December 1721 to June 1786 (the longest weather chronicle recorded by a single person), is a reliable source of information with the potential to contribute to the understanding of climate variability in 18th‐century Gdańsk, the significant old Hanseatic city in northern Poland. The temperature and precipitation series for the period 1721–1786 were indexed against contemporary conditions using the calculated percentages of cold, normal and warm decades. The climate in the 18th century in Gdańsk was colder than nowadays; the springs were much cooler, as were the winters, while the summers were warmer. A few relatively cold periods and several warm periods were identified. The first half of the analysed period was very humid, whereas the second was quite dry. The reconstructed indexed data series were compared to annual and seasonal mean values of air temperature and precipitation derived from regular instrumental observations made in Gdańsk since 1739. The linear regression method was applied to calibrate the reconstructed index series against anomalies from the 1961–1990 average. The correlation coefficient, standard error of estimate and the root‐mean‐square error statistics were used to diagnose the quality of calibration and verification. The highest correlation between the temperature indices and the observational series was found for winter. The reconstructed series for precipitation has a smaller variability than in reality. The reconstructed values for the years prior to 1739 indicate cold years for 1725–1732 and a wet period in the 1730s. The chronicle of botanist and meteorologist Gottfried Reyger, covering the period 1721–1786, characterizes the thermal and precipitational conditions in Gdańsk, Poland. The series were indexed against contemporary conditions, compared with instrumental data and reconstructed for early non‐instrumental period. The 18th‐century Gdańsk was colder than nowadays, particularly on case of springs, while the summers were warmer. The first decades of the period were very humid, whereas the latter ones quite dry. In the figure, annual air temperature (left panel) and precipitation (right panel) reconstruction for Gdańsk for the period 1722–1785 presented in the context of observational series, 1739–1785 for air temperature and 1739–1772 for precipitation. Solid line shows observational data; dotted line shows reconstructed series. [ABSTRACT FROM AUTHOR]

ABSTRACT: Extreme precipitation events are widely thought to intensify in a warmer atmosphere through the Clausius‐Clapeyron equation. The temperature‐extreme precipitation scaling was proposed to analyse the temperature dependency of short‐duration extreme precipitation and since then, the concept has been widely used in climatology. Bao et al. (2017) suggest that the apparent scaling reflects not only how surface air properties affect extreme precipitation, but also reflects how synoptic conditions and localized cooling due to the storm itself affect the scaling – implying two‐way causality. We address here critical issues of this paper and provide evidence that dew point temperature drives extreme precipitation, with the direction of causality reversed only for the storm's peak intensity. This physical inference may serve as a basis to better quantify scaling rates and to help establish the relationship between extreme precipitation and environmental conditions in the current climate, and thereby provide insights into future changes to precipitation extremes due to climate change. [ABSTRACT FROM AUTHOR]

In a changing climate, it is likely that precipitation patterns will experience shifts in amount and seasonality, with a tendency towards increased drought in many parts of Europe. In order to investigate what could be some of the feedbacks of dry conditions on temperature, this paper has investigated close to 30 European locations to assess the influence of observed dry summer days on daily maximum temperature ( Tmax) as well as a clustering of rainless days, and finally the temperature impact related to drought conditions as defined by the Standardized Precipitation Index. The results show that for the three main European climatic regions investigated ('Mediterranean', 'Maritime', 'Continental'), the influence of rainless days is discernible, with an average difference between mean summer Tmax and Tmax during rainless days only of up to 1.5 °C. A further aim of the study is to determine whether recent shifts in the occurrence of rainless days during the summer have a bearing on temperature trends. Results show that shifts in rainless days explain only a fraction of the observed temperature change at the locations studied, thereby hinting that the enhanced greenhouse effect remains the dominant mechanism for temperature change. Finally, using a regional climate model, very preliminary insight into a 'greenhouse climate' highlights the fact that changes in dry conditions seem to confirm the findings for current climate, i.e. they account for only a fraction of the increase in temperatures. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT The development of Adapted Caussinus-Mestre Algorithm for homogenising Networks of Temperature series ( ACMANT), one of the most successful homogenisation methods tested by the European project COST ES0601 ( HOME) has been continued. The third generation of the software package ' ACMANT3' contains six programmes for homogenising temperature values or precipitation totals. These incorporate two models of the annual cycle of temperature biases and homogenisation either on a monthly or daily time scale. All ACMANT3 programmes are fully automatic and the method is therefore suitable for homogenising large datasets. This paper describes the theoretical background of ACMANT and the recent developments, which extend the capabilities, and hence, the application of the method. The most important novelties in ACMANT3 are: the ensemble pre-homogenisation with the exclusion of one potential reference composite in each ensemble member; the use of ordinary kriging for weighting reference composites; the assessment of seasonal cycle of temperature biases in case of irregular-shaped seasonal cycles. ACMANT3 also allows for homogenisation on the daily scale including for break timing assessment, gap filling and ANOVA application on the daily time scale. Examples of efficiency tests of monthly temperature homogenisation using artificially developed but realistic test datasets are presented. ACMANT3 can be characterized by improved efficiency in comparison with earlier ACMANT versions, high missing data tolerance and improved user friendliness. Discussion concerning when the use of an automatic homogenisation method is recommended is included, and some caveats in relation to how and when ACMANT3 should be applied are provided. [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]

ABSTRACT This paper investigates the interannual variability of precipitation over the South Asian monsoonal region in May, during which the South Asian summer monsoon is generally established. The South Asian monsoonal region in this study is referred to as a broad area that includes the Arabian Sea, the Indian peninsula, the Bay of Bengal, and the Indochina peninsula. The precipitation exhibits a significant enhancement in May, compared to that in the previous month. The present results indicate that the interannual variability of May precipitation in this region is significantly regulated by two independent factors: The Europe-West Asia ( EWA) teleconnection and El Niño Southern Oscillation. The EWA teleconnection pattern is characterized by the see-saw pattern of upper-tropospheric geopotential height anomalies between the Europe and the Iranian plateau. Both the factors modulate the meridional gradient of tropospheric temperature and lower-tropospheric zonal winds, and thus affect the rainfall in the monsoonal region. Therefore, both the extratropical circulation teleconnection and tropical forcing are crucial for inducing rainfall variability in May over the monsoonal region. [ABSTRACT FROM AUTHOR]

The homogenization of climate observational series is a needed process before undertaking confidently any study of their internal variability, since changes in the observation methods or in the surroundings of the observatories, for instance, can introduce biases in the data of the same order of magnitude than the underlying climate variations and trends. Many methods have been proposed in the past to remove the unwanted perturbations from the climatic series, and some of them have been implemented in software packages freely available on the Internet. The Spanish project MULTITEST was intended to test their performance in an automatic way with synthetic monthly series of air temperature and atmospheric precipitation, to update inter‐comparison results from former projects, especially those of the COST Action ES0601. Several networks representing different climates and station densities were used to test a variety of homogenization packages on hundreds of random samples. Results were evaluated mainly in the form of root mean squared errors and errors in the trend of the series, showing that ACMANT, followed by Climatol, minimized these errors. However, other packages performed also relatively well, even outperforming them when there were simultaneous biases of the same sign in most or all the test series. [ABSTRACT FROM AUTHOR]

This paper considers problems in calculating the heat island magnitude of coastal cities. We examined the influence on heat island magnitude (1) when a rural observatory located in an inland area is compared with an urban observatory and (2) when an observatory in an atypical countryside setting is chosen as the rural site. The results showed that the urban–rural temperature difference was bigger in winter than in summer when the rural observatory was located in a relatively inland area in comparison with the corresponding urban observatory. This tendency was more marked at night than in the daytime. When an observatory in an atypical rural setting was used, the urban–rural temperature difference was greater from 11:00 p.m. to midnight in summer. Therefore, only observatories located in typical rural areas and located roughly the same distance from the sea as the corresponding urban observatories should be selected for investigation of urban heat island magnitude. Copyright © 2005 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper studies the temporal and spatial patterns of precipitation anomalies over southern central South America (SCSA; 22–40°S and 54–70°W), and their relationship with the sea-surface temperature (SST) variability over the surrounding tropical and subtropical Atlantic and Pacific Oceans. The data include monthly precipitation from 68 weather stations in central–northern Argentina and neighbouring Brazil, Paraguay and Uruguay, and monthly SSTs from the NOAA dataset with a 2° resolution for the period 1961–93. We use the method of canonical correlation analysis (CCA) to study the simultaneous relationship between bi-monthly precipitation and SST variability. Before applying the CCA procedure, standardized anomalies are calculated and a prefiltering is applied by means of an empirical orthogonal function (EOF) analysis. Thus, the CCA input consists of 10 EOF modes of SST and between 9 and 11 modes for precipitation and their corresponding principal components, which are the minimum number of modes necessary to explain at least 80% of the variance of the corresponding field. The results show that November–December presents the most robust association between the SST and SCSA precipitation variability, especially in northeastern Argentina and southern Brazil, followed by March–April and May–June. The period January–February, in contrast, displays a weak relationship with the oceans and represents a temporal minimum of oceanic influence during the summer semester. Based on the CCA maps, we identify the different oceanic and SCSA regions, the regional averages of SST and precipitation are calculated, and linear correlation analysis are conducted. The periods with greater association between the oceans and SCSA precipitation are November–December and May–June. During November–December, every selected region over SCSA reflects the influence of several oceanic regions, whereas during May–June only a few regions show a direct association with the oceans. The Pacific Ocean regions have a greater influence and are more widespread over SCSA; the Atlantic Ocean regions have an influence only over the northwestern and the southeastern parts of SCSA. In general, the relationship with the equatorial and tropical Atlantic and Pacific Oceans is of the type warm–wet/cold–dry, whereas the subtropical regions of both oceans show the opposite relationship, i.e. warm–dry/cold–wet. Copyright © 2004 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

When a correlation-based circulation pattern classification was applied to daily 700 hPa heights for 25 summer seasons, and the circulation types were related to surface temperature and precipitation for the Lake Superior basin, within-type variability was found to be high. Regarding temperature, the difference between warm and cold days of a type is a positive height anomaly centred to the east of the lake, which is very similar to the height anomaly for winter temperatures identified in an earlier study. The difference between wet and dry days of a type is a pattern quite unlike that for temperature. It is a dipole pattern consisting of a negative anomaly to the northwest of the lake and a positive anomaly to the east and southeast. These small-scale circulation components were missed by the correlation-based classification because they are much smaller than the differences in height between circulation types. Regression models, relating monthly mean temperature anomalies to circulation type frequencies, produced results comparable to those obtained for winter temperature. For precipitation, the variance explained by the regression models is only half that obtained for temperature. Two approaches were used to reduce within-type variability and to improve the regression results: the division of the first few circulation types into warm/cold and wet/dry subtypes on the basis of differences in 700 hPa vorticity (an approach introduced in the earlier paper), and a modified correlation-based circulation pattern classification. The modification consisted of raising the correlation threshold for a few selected grid-points to capture the small-scale circulation component and also, of lowering the threshold for the comparison between maps and map sectors in order to minimize the number of unclassified days. Of the two approaches used to reduce within-type variability, the modified classification scheme produced superior results. In particular, the variance in precipitation explained by the regression models is about 30% higher than that achieved using vorticity to create subtypes. Copyright © 2000 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model overspecification. We focus on: (a) model structure: simple (generalized linear models, GLMs) versus complex (artificial neural networks, ANNs) models. (b) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM and ANN) to downscale wet‐day precipitation amounts. While no downscaling formulation is optimal for all predictands and at 10 locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for Tmin than Tmax. Use of PCA‐derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet‐day threshold increases and models using PCA‐derived predictors tend to outperform those based on grid cell predictors. Each model for wet‐day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN‐based models and those with larger predictor suites tend to have the smallest bias. [ABSTRACT FROM AUTHOR]

Circulation‐type classifications (CTCs) are useful tools for systematically describing atmospheric circulation and studying its variation and impact on surface climate. However, few circulation classification methods (CCMs) have been applied in the Tibetan Plateau (TP). In this study, six frequently used CCMs using 500 hPa geopotential height data were assessed with respect to their abilities in explaining observed daily mean temperature and precipitation variations in the central and eastern TP for the period 1980–2014. Two statistical measures, the explained variance and the Kolmogorov–Smirnov test, were used to quantify the performance of the CTCs in describing climate variable variations. SAN (simulated annealing and diversified randomization clustering) and KRZ (Kruizinga's eigenvector‐based scheme) were identified as optimum CCMs for synoptic typing over the study region. The daily mean temperatures are well conditioned on SAN‐CTC with 43.9–62.4% explained variance at annual scale and 11.1–25.2% at seasonal scale. For daily precipitation variations, the explained variance at the annual scale is less than 40%. The spatial characters of climate variation affected by large‐scale circulation are identified. Daily mean temperature variations in the Qaidam Basin and the Qilian Mountains (90°–103°E and 35°–40°N) all year round and in the region between the Tanggula Mountains and the Himalayan Mountains (88°–97°E and 30°–34°N) in wet season (May–September) are well resolved by the CTCs. Daily precipitation variations in the eastern TP in the dry season (October–April) and a region from the Brahmaputra River basin to the Tanggula Mountains (85°–92°E and 28°–32°N) and the southern Hengduan Mountains (98°–102°E and 27°–31°N) in the wet season show a close relationship with the identified CTCs by SAN. Schematic diagram of the circulation systems related to TP climate variations. The boundaries of the four spatial domains for circulation classification are drawn with different kinds of dashed lines (domain 1: 40°–135°E, 5°S–70°N, domain 2: 50°–125°E, 5°–60°N, domain 3: 60°–115°E, 15°–50°N, domain 4: 70°–105°E, 25°–40°N). [ABSTRACT FROM AUTHOR]

Simulations and future projections of year‐to‐year variation in global monsoon precipitation (GMP), defined as the summer precipitation amount per unit area within the global monsoon domain, are investigated using the 33 models that participated in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). The inter‐annual standard deviation of GMP, which presents the amplitude of year‐to‐year variability in monsoon rainfall, is simulated well by the multi‐model ensemble (MME) mean of the 33 models and of the best five (B5) models. The B5 models show superior skills in reproducing the climatological monsoon precipitation and its inter‐annual variability. The inter‐annual standard deviations of 25‐year GMP in the present day (1979–2005) derived from the MME average of the 33 models and of the B5 models are 0.17 and 0.15 mm/day per unit area, respectively, both being close to the observation (0.16 mm/day per unit area). Consistent with the observed El Niño–Southern Oscillation (ENSO)–GMP relationship, the simulated variability of GMP is negatively correlated with the sea surface temperature in the Niño areas at the inter‐annual timescale. Projections by the 33 CMIP5 models under the RCP4.5 scenario indicate that wet and dry monsoon years would occur more intensively in a warmer climate. Around 61% (20 out of the 33) of the models show enhanced inter‐annual variability of GMP by the end of the 21st century (2072–2098) compared to the period 1979–2005. The MME of the B5 models suggests that the amplitude of projected GMP variability would increase by ~20% from present day to the late 21st century. However, the increasing rate of GMP variability detected by the 33 models' MME is relatively small (~6%), which is even smaller than the inter‐model standard deviation, suggesting the amplitude changes in GMP variability feature non‐negligible uncertainty among the 33 models. Under global warming, the inter‐annual variation of GMP would still be tightly connected with ENSO, although the projected amplitude changes in ENSO variability are noticeably diverse across the 33 CMIP5 models. The enhanced amplitude of inter‐annual GMP variability may be attributable to the increase in mean‐state moisture associated with global warming. The dynamic effect related to changes in the variability of monsoon circulation is small and less robust among the 33 CMIP5 models. Taking advantage of the large number of members in phase 5 of the Coupled Model Intercomparison Project (CMIP5), we identified robust changes in monsoon rainfall variability at the inter‐annual timescale under global warming. Around 61% (20 out of 33) of the models show enhanced inter‐annual variability of GMP by the end of the 21st century (2072–2098) compared to the period 1979–2005. ENSO is still the major driving force behind the inter‐annual variability of global monsoon precipitation under global warming, although the amplitude changes in ENSO variability are remarkably diverse in future projections. From the moisture budget perspective, the enhanced amplitude of inter‐annual GMP variability may be attributable to the increase in mean‐state moisture associated with global warming. Figure shows (a) differences in the inter‐annual variability (defined as the inter‐annual standard deviation) of GMP (red), NHMP (blue) and SHMP (green) between the historical run (1979–2005) and RCP4.5 (2072–2098) in the CMIP5 models. The rightmost bars are the results derived from the average of CMAP and GPCP precipitation (unit: mm/day per unit area). (b) TCCs between the GMP and 2‐m air temperature anomalies (shading), and between the GMP and 850‐hPa wind anomalies (vectors), during the period 2072–2098. White dots mark the regions with correlation coefficients at the 95% confidence level. (c) Relative contributions of mean‐state specific humidity and the variability of vertical motion to the enhanced variability of GMP under GW (unit: 10−5 kg m−1 s−3). [ABSTRACT FROM AUTHOR]

In December 2015, the Paris Agreement was reached in an effort to limit global warming to below 1.5 °C. However, there is few scientific literature assessing changes in the climate with 1.5 °C of warming over China. We investigated changes in climate extremes in China that generally present high impacts on society. The results indicated that an additional warming of 0.5 °C would lead to significant increases in temperature and precipitation extremes across China. Both the temperatures on the hottest days and the frequencies of heat events across China are estimated to be lower when limiting warming to 1.5 °C compared to 2.0 °C. Events such as the record heat case in the summer of 2013 over eastern China would be approximately 28% less likely to occur if warming was limited to below 1.5 °C. Moreover, China would experience reduced precipitation extremes, although this projection is accompanied by a relatively lower confidence level than for changes in temperature extremes. High‐impact heavy rain events similar to that in the summer of 2012 over northern China (Beijing) that led to severe urban waterlogging and loss of life would be less likely if there is no more than 0.5 °C of warming. Similarly, the odds of wide‐ranging severe droughts, as witnessed in the summer of 2014 over north China, are projected to decrease clearly. The positive effects of limiting warming on changes in climate extremes are thus clear, and limiting warming should be encouraged regardless of the political and socio‐economic goals of a country. [ABSTRACT FROM AUTHOR]

We present a homogenized Greenland blocking index (GBI) daily record from 1851 to 2015, therefore significantly extending our previously published monthly/seasonal GBI analysis. This new time series is analysed for evidence of changes in extreme events, and we investigate the underlying thermodynamic and dynamic precursors. We compare occurrences and changes in extreme events between our GBI record and a recently published, temporally similar daily North Atlantic Oscillation (NAO) series, and use this comparison to test dynamic meteorology hypotheses relating negative NAO to Greenland blocking. We also compare daily GBI changes and extreme events with long‐running indices of England and Wales temperature and precipitation, to assess potential downstream effects of Greenland blocking on UK extreme weather events and climate change. In this extended analysis we show that there have been sustained periods of positive GBI during 1870–1900 and from the late 1990s to present. A clustering of extreme high GBI events since 2000 is not consistently reflected by a similar grouping of extreme low NAO events. Case studies of North Atlantic atmospheric circulation changes linked with extreme high and low daily GBI episodes are used to shed light on potential linkages between Greenland blocking and jet‐stream changes. Particularly noteworthy is a clustering of extreme high GBI events during mid‐October in 4 out of 5 years during 2002–2006, which we investigate from both cryospheric and dynamic meteorology perspectives. Supporting evidence suggests that these autumn extreme GBI episodes may have been influenced by regional sea‐ice anomalies off west Greenland but were probably largely forced by increases in Rossby‐wave train activity originating from the tropical Pacific. However, more generally our results indicate that high GBI winter anomalies are co‐located with sea‐ice anomalies, while there seems to be minimal influence of sea‐ice anomalies on the recent significant increase in summer GBI. [ABSTRACT FROM AUTHOR]

ABSTRACT: It is generally agreed that models that better simulate historical and current features of climate should also be the ones that more reliably simulate future climate. This article describes the ability of a selection of global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to represent the historical and current mean climate and its variability over northeastern Argentina, a region that exhibits frequent extreme events. Two types of simulations are considered: Long‐term simulations for 1901–2005 in which the models respond to climate forcing (e.g. changes in atmospheric composition and land use) and decadal simulations for 1961–2010 that are initialized from observed climate states. Monthly simulations of precipitation and temperature are statistically evaluated for individual models and their ensembles. Subsets of models that best represent the region's climate are further examined. First, models that have a Nash–Sutcliffe efficiency of at least 0.8 are taken as a subset that best represents the observed temperature fields and the mean annual cycle. Their temperature time series are in phase with observations (r > 0.92), despite systematic errors that if desired can be corrected by statistical methods. Likewise, models that have a precipitation Pearson correlation coefficient of at least 0.6 are considered that best represent regional precipitation features. GCMs are able to reproduce the annual precipitation cycle, although they underestimate precipitation amounts during the austral warm season (September through April) and slightly overestimate the cold season rainfall amounts. The ensembles for the subsets of models achieve the best evaluation metrics, exceeding the performance of the overall ensembles as well as those of the individual models. [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 aims to relate the intra‐seasonal rainfall variability over the Amazon basin to atmospheric circulation patterns (CPs), with particular attention to extreme rainfall events in the Amazon–Andes region. The CPs summarize the intra‐seasonal variability of atmospheric circulation and are defined using daily low‐level winds from the ERA‐Interim (1.5° × 1.5°) reanalysis for the 1979–2014 period. Furthermore, observational data of precipitation and high‐resolution TRMM 3B42 (∼25 km), 2A25 PR (∼5 km) and CHIRPS (∼5 km) data products are related to the CPs throughout the Amazon basin. Nine CPs are determined using a hybrid method that combines a neural network technique (self‐organizing maps, SOM) and hierarchical ascendant classification. The CPs are characterized by a specific cycle with alternative transitions and a duration of 14 days on average. This configuration initially results in northerly winds to southerly winds towards the northern or eastern Amazon basin. The related rainfall suggests that it is driven mainly by CP dynamics. In addition, we demonstrate a good agreement amongst the four rainfall data sets: observed precipitation, TRMM 3B42, TRMM 2A25 PR and CHIRPS. Furthermore, special attention is given to the Amazon–Andes transition region. Over this region, two particular CPs (CP4 and CP5) are identified as the key contributors of maximum and minimum daily rainfall, respectively. Thus, during the dry season, 40.8% (11.4%) of the CP5 (CP4) days demonstrate rainfall of less than 1 mm day−1, while during the wet season, 6.2% (14.6%) of the CP5 (CP4) days show rainfall amounts higher than the seasonal 90th percentile (10.4 mm day−1). This study provides additional information concerning the intra‐seasonal circulation variability in Amazonia and demonstrates the value of using remote sensing precipitation data in this region as a tool for forecast in areas lacking observable information. [ABSTRACT FROM AUTHOR]

ABSTRACT: During the last few decades, weather circulation type classifications (CTCs) have been widely used to gain insight into processes at the synoptic scale, but also for studying the relationship between atmospheric circulation and surface climate variability. This study aims to evaluate the best performing CTCs based on COST733 software for the stratification of daily ground‐level precipitation and surface air temperature across Italy by means of four statistical metrics. Six classification methods belonging to the four COST733 groups (threshold‐based, PCA‐based, leader algorithms and optimization algorithms) were investigated on 32 and 26 data time series derived from Italian weather stations for daily mean temperature and daily mean precipitation, respectively. CTCs were computed using gridded mean sea level pressure and geopotential height at 500 hPa derived from the NCEP Reanalysis 2 dataset between 1979 and 2015 and tested on three different numbers of classes (8/9, 11/12 and 18 circulation types). Evaluation metrics showed an evident seasonal variability and high‐spatial heterogeneity reflecting the geographical complexity of the Italian territory. The study points out that the best classification, both for temperature and precipitation, is strongly dependent on the classification variable (mean sea level pressure and geopotential height at 500 hPa) showing relevant differences between surface temperature and precipitation. A low number of circulation types (8/9) resulted as the most appropriate grouping for the Italian domain and the Principal Component Transversal and Simulated Annealing were the best performing classification procedures for ground‐level precipitation and temperature stratification, respectively. [ABSTRACT FROM AUTHOR]

ABSTRACT: This study documents monthly temperature and precipitation anomalies over Korea during the different phases of El Niño‐Southern Oscillation (ENSO) for the two types of ENSO evolution. The evolution process of ENSO can be classified into two groups based on whether El Niño turns into La Niña in the subsequent year. The first group involves the transition process from El Niño to La Niña, while the second group shows the prolonged El Niño or neutral conditions after the mature phase of El Niño. Because the mid‐latitude atmospheric responses as well as the equatorial heating anomalies for the two groups of ENSO are different each other, the ENSO‐related climate variation over Korea are investigated separately for the two ENSO evolution groups. In particular, this study focuses on the entire monthly evolution of the temperature and precipitation over Korea during the different phases of ENSO. The statistically robust signals can be found in several particular months, which provides statistical basis for predicting monthly climate over Korea. In addition to the observational data analyses, we further investigate the forecast skill of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble (MME) in simulating two groups of ENSO evolution and their impacts on Korean climate. The result shows that the MME reasonably predicts the two different evolution of ENSO as in observation but their prediction skills for the ENSO‐related Korean climate are diverse, which largely depends on the phase of ENSO. [ABSTRACT FROM AUTHOR]

ABSTRACT: While global climate models (GCMs) are useful for simulating climatic responses to perturbations in the Earth's climate system, there are many instances where higher spatial resolution information is necessary. In all instances, interpretation of interpolated or downscaled GCMs must be done cautiously because each method has its own set of assumptions and potential disadvantages. Here, we present an update to the Global Climate Data (GCD) package, which enables the package to efficiently bias correct and interpolate precipitation and air temperature output from GCM simulations to very high spatial resolutions using the delta change method. While the delta change method is relatively simple, it has previously been shown to enhance the physical representation of interpolated climate time‐series compared to directly interpolating the gridded climate time‐series to a higher spatial resolution. The bias correction methods programmed into the GCD package are univariate empirical quantile mapping (QM) and bivariate empirical joint bias correction (JBC). The skill of QM and JBC for improving GCM simulations processed with the delta change method is evaluated through comparing the cumulative distribution functions (CDFs) of the interpolated GCM simulations to the CDFs of Global Historical Climatology Network (GHCN) station observations for three test regions: Oregon (in the USA), the Alps (spanning several countries in Europe), and the Ganges Delta (in India and Bangladesh). We also assess the representation of precipitation and mean temperature joint probability distributions relative to those present in GHCN station observations. Overall, GCM simulations that are bias corrected with QM prior to being input to the delta change method perform best under our analysis. [ABSTRACT FROM AUTHOR]

Progresses in understanding climate variation and its ecosystem responses and feedback in Central Asia have been limited by availability of high resolution and quality climate data. To ease this limitation, the Weather Research and Forecasting (WRF) model is implemented in Central Asia in this study after repeated yearly sensitivity evaluations. The optimized WRF is used to downscale the ERA-Interim reanalysis data, producing a new regional dataset of 20 × 20 km spatial resolution covering 1980-2015. The downscaled precipitation and daily maximum and minimum temperatures of seven years from 1980-1986, when the observational data are the most abundant, are compared in statistical measures to station observations and also contrasted with the comparison results between the ERA-Interim data and the observations. Results of those comparisons show that the downscaled data have overall comparable accuracy in precipitation and daily extreme temperatures to the ERA-Interim data. An outstanding improvement of the downscaled data is in their describing the variance in precipitation and temperatures at daily to annual timescales, while the downscaled extreme precipitation and temperature events also show more accurate comparisons to the observed at some sub-regions in Central Asia. The validated WRF model, which can be used to further downscale past as well as future climate data for specific research needs, and the downscaled high resolution and quality data present both a tool and a useful dataset for ecological and detailed climate research in Central Asia. [ABSTRACT FROM AUTHOR]

ABSTRACT Climate change affects mean and extreme temperature and precipitation, increasing the risk of related disasters. Assessing the performance of current models in simulating these aspects is therefore crucial to future projection and policymaking. Based on a large ensemble ( LE; 30 members) of the Community Earth System Model ( CESM- LE), this study seeks to answer the following two questions: (1) Are the simulated trends of mean and extreme temperature and precipitation over China consistent with observations? (2) What is the range of model uncertainty due to internal variability? The results show that CESM-LE can capture the trends of observed annul mean temperature and extremely low temperature days ( ELD) over 16-26% of China, but has difficulty in reproducing the observed trends in extremely high temperature days ( EHD). For precipitation, CESM-LE demonstrates similar capability in reproducing the observed trends in annual mean precipitation and extreme precipitation days ( EPD). For consecutive dry days ( CDD), the decreasing trends over part of northwestern China can be captured, but the increasing trends over northern China cannot be reproduced by CESM-LE. Regarding the uncertainty of the model resulting from natural internal variability, because all 30 ensemble members of CESM use the same model and external forcing, CESM-LE's spread (uncertainty) is mainly due to internal variability. Larger uncertainty in simulated trends is found over northern China and the Tibetan Plateau for annual mean temperature, over northeastern China and the Huang-Huai region for ELD, and over the Tibetan Plateau for EHD. The trends in annual mean precipitation show uniform uncertainty over nearly all of China, except for the northwest. Larger uncertainty trends appear over southern and northwestern China for EPD, and over the Tibetan Plateau and northern China for CDD. [ABSTRACT FROM AUTHOR]

ABSTRACT This study compares the multivariate predictions of daily temperature, temperature range, precipitation, surface wind and solar radiation of a single-model analogue approach with an original multi-model analogy over 12 regions in Europe and Maghreb. Both approaches are based on two-level analogue models where atmospheric predictors are either dynamic or thermodynamic. In the multi-model approach, independent analogue models with predictand-specific predictors are used. In the single-model one, a unique analogue model and its associated set of predictors is applied to all predictands. Testing numerous large-scale predictors, we first identify the best predictor sets for each modelling strategy. Those obtained for the single-model approach are significantly different from those of the predictand-specific models. This is especially the case for local temperature and wind speed. Both methods perform similarly for precipitation, temperature range and radiation. We next assess the ability of both approaches to simulate physically coherent multivariate weather scenarios. With the single-model method, weather scenarios are obtained for each prediction day from observations sampled simultaneously on one analogue day. The physical consistency between variables is thus automatically fulfilled each day. This allows the single-model method to reproduce well the observed inter-predictand correlations, especially the significant correlations between radiation and precipitation and between radiation and temperature range. These results suggest a hybrid analogue model using a single-model for radiation, temperature range and precipitation, combined with a univariate approach for wind. Two options are proposed for temperature for which either the predictand-specific method or a single-model approach with an additional correction are conceivable. This hybrid approach leads to a possible compromise between reasonable univariate prediction skills and realistic inter-predictands correlations, both classically required for many impact studies. [ABSTRACT FROM AUTHOR]

ABSTRACT Using station data from the United States Historical Climatology Network, we preformed a running trend analysis of temperature, precipitation and drought in the Alabama-Coosa-Tallapoosa and Apalachicola-Chattahoochee-Flint river basins for the time period 1895-2012 on annual and monthly timescales. Our study found that long-term precipitation and temperature trends were statistically detectible but relatively slight in the order of an increase at 3 mm per decade for precipitation and a decline of 0.02 °C per decade for temperature. Running trend analyses for the time period 1895-2012 found field-significant and relatively large annual precipitation increases and temperature decreases between the 1950s and early 1980s associated with the 'warming hole'. The magnitude of precipitation trends for the aforementioned period was in the range of 7 mm per year for precipitation and 0.6 °C per decade for temperature. For temperature, we observed generally decreasing maximum, minimum and mean temperatures in the 1960s and 1970s and increasing temperatures from the 1970s to present. Minimum temperatures particularly showed a strong increase in recent summer months in the range of 0.3 °C per decade. Trends in the diurnal temperature range showed a recent narrowing during summer and fall months. Differences in the standardized precipitation index ( SPI) and standardized precipitation evapotranspiration index ( SPEI) did not show a strong effect of temperature on drought for this region. [ABSTRACT FROM AUTHOR]

ABSTRACT The temporal and spatial trends of four climate extreme indices for Guangdong Province of China were calculated based on observed daily precipitation, daily maximum, and daily minimum temperatures at 24 weather stations as well as the frequency of typhoon events during the period of 1957-2013. An integrated extreme climatic index ( IECI) was derived from four individual climate extreme indices (high temperature days ( Htd), low temperature days ( Ltd), intensive precipitation days ( Ipd) and number of typhoon events ( TT)), considering their weights using an information theory model. A simple linear regression model was utilized to analyze the spatiotemporal trends of the climate extremes. The correlation analysis between the above extreme climatic indices and the socioeconomic losses [affected area of crops ( AAC), direct losses of the economy ( DLE), and affected population ( AP)] was calculated. The results indicate that Htd increased while Ltd decreased significantly at the 5% significance level. By contrast, there were no significant long-term changes in Ipd and TT. The rate of change of IECI was 0.006 per decade and no long-term trend was detected in IECI. Spatially, a positive trend was observed in the Yuexi River Basin ( YXRB) for Ipd, while a negative trend was found in the East River Basin ( ERB). Ltd followed an opposite spatial pattern to that of Htd. Extreme temperature days occurred more frequently in the Pearl River Delta ( PRD) than in other parts of Guangdong Province. Regions with positive IECI rates of change were mostly in the YXRB, the coastal area of the Yuedong River Basin ( YDRB) and the northern and southern part of the ERB. The correlation coefficients between IECI and AAC, DLE, and AP are 0.375, 0.424 and 0.603, respectively. In general, IECI correlated more strongly with the loss indices caused by extreme climatic events than did the four individual extreme climatic indices. [ABSTRACT FROM AUTHOR]

ABSTRACT In 2015, large parts of Europe were characterized by extraordinary high summer temperatures, accompanied by very dry conditions, particularly in central-eastern Europe. Several major heat episodes occurred from the end of June until mid-September. We provide an ad-hoc evaluation of the observed climatological extremes in a secular context, by using a set of long station time series in daily resolution. Our data set comprises 42 temperature and 43 precipitation records, predominantly starting already in the 19th century. To investigate local record values, the individual full record length is analysed for each station, while regionally averaged analyses are presented for the core study period of 1901-2015. The study area covers Europe's central latitudes (44° to 52°N), extending from England in the west up to the central Ukraine in the east. During summer 2015, various indices representing extremely high maximum and minimum temperatures (strongly) exceeded previous record high values, mainly in an area extending from eastern Germany to western Ukraine. Additionally, severely to extremely dry conditions with unusually frequent dry days were prevailing particularly in the (central-) eastern part of the study area. Drought indices combining temperature and precipitation revealed drought conditions comparable or even worse than those of former extreme summers like 2003. [ABSTRACT FROM AUTHOR]

ABSTRACT Eurasian snow cover fraction ( SCF) prediction is analyzed using the recently developed National Centers for Environmental Prediction ( NCEP) Climate Forecast System version 2 ( CFSv2) monthly retrospective forecasts for 1983-2009. The CFSv2 is generally capable of reproducing the observed Eurasian SCF seasonal cycle and climatology. This study focuses on the prediction skill and predictability of Eurasian SCF in snowmelt and snowfall seasons because the intensive variability occurs in the two seasons. The CFSv2 reasonably predicts the interannual variations, long-term trend and leading pattern in snowmelt season several months ahead. In comparison with the snowmelt season, the CFSv2 shows a better prediction skill in climatological values but a worse skill in the interannual variability in snowfall season. In addition, the forecasted downward trend of SCF in the snowfall season is opposite to that in the observation. The biases of Eurasian SCF in the snowmelt and snowfall seasons are significantly related with those of temperature and precipitation in the CFSv2. The forecasted cooler and wetter atmosphere is suggestive of the overestimation of the mean SCF. Meanwhile, the underestimation in the variability of both temperature and precipitation in the CFSv2 may be the important factor for the underestimated variability of SCF, especially for the damped variability of SCF in the snowfall season. Generally, the CFSv2 shows a higher and more stable prediction skill after late-1990s than before in the two seasons. The change in the initial condition in the CFSv2 and the observed SCF in late-1990s might be the plausible reason for it. [ABSTRACT FROM AUTHOR]

ABSTRACT We examine the ability of an ensemble of 10 Regional Climate Models ( RCMs), driven by ERA-Interim reanalysis, in skillfully reproducing key features of present-day precipitation and temperature (1990-2008) over West Africa. We explore a wide range of time scales spanning seasonal climatologies, annual cycles and interannual variability, and a number of spatial scales covering the Sahel, the Gulf of Guinea and the entire West Africa. We find that the RCMs show acceptable performance in simulating the spatial distribution of the main precipitation and temperature features. The occurrence of the West African Monsoon jump, the intensification and northward shift of the Saharan Heat Low ( SHL), during the course of the year, are shown to be realistic in most RCMs. They also capture the mean annual cycle of precipitation and temperature, including, single and double-peaked rainy seasons, in terms of timing and amplitude over the homogeneous sub-regions. However, we should emphasize that the RCMs exhibit some biases, which vary considerably in both magnitude and spatial extent from model to model. The interannual variability of seasonal anomalies is best reproduced in temperature rather than precipitation. The ensemble mean considerably improves the skill of most of the individual RCMs. This highlights the importance of performing multi-model assessment in properly estimating the response of the West African climate to global warming at seasonal, annual and interannual time scales. [ABSTRACT FROM AUTHOR]

ABSTRACT Ethiopia has wide eco-environmental diversity ranging from extreme heat at one of the lowest places in the world to one of the coolest summits in Africa. Associated with this environmental diversity and climate change, climatic extremes are expected to change over time and also vary across eco-environments in the country. This study was conducted to examine the trends of past precipitation and temperature extremes over three eco-environments in Ethiopia. The study involved analysis of 20 extreme indices computed from daily temperature and precipitation data spanning over 42 years (1967-2008). The climate data were obtained from 11 stations selected from three major eco-environments (pastoral, agropastoral and highland). The results indicated positive trends for maximum value of the maximum temperature ( TXx), warm days ( TX90p), warm nights ( TN90p) and warm spell duration indicators ( WSDI) and negative trends for cool days ( TX10p), cool nights ( TN10p) and cold spell duration indicators ( CSDI) in more than 8 of the 11 stations studied. However, most of the trends were not significant at many of the stations and the significant trends were not uniquely differentiated by eco-environments. Unlike temperature extremes, precipitation extreme trends showed high variability among nearby stations within eco-environments and were not significant at many of the stations studied. It is concluded that trends of temperature and precipitation extremes vary considerably among stations located within a given eco-environment indicating that the response of local climate to global warming could be different in physiographically diverse regions. [ABSTRACT FROM AUTHOR]

New monthly, long-running, continuous surface air temperature records for four island chains throughout the Macaronesian biogeographical zone in the North Atlantic Ocean are presented. The records run from 1865 for the Azores and Madeira, 1885 for the Canary Islands and 1895 for Cape Verde. Recent (1981-2010) warming across these islands is significant in summer ( JJA) for the Canary Islands, Cape Verde and Madeira, ranging from 0.40 to 0.46 °C per decade. Annually, the temperature trends across this period range from 0.30 to 0.38 °C per decade across all four island chains (significant for all but the Canary Islands), which exceed the station-based, average global temperature rise by up to 0.10 °C per decade. Precipitation records from multiple islands across Macaronesia are also presented in addition to sea-level pressure records from the Azores and Cape Verde. Cape Verde wet season ( ASO) precipitation is found to have significantly increased at two of our three sites from 1981 to 2010. The Azores, Canary Islands and Madeira precipitation trends display no significant changes, although the three Azores stations display a recent positive tendency. The extended Azores pressure record allows us to construct an entirely station-based Azores-Iceland North Atlantic Oscillation index ( NAOI) from 1865 to 2012 and extend the daily station-based index back to 1944, further than the longest previous daily NAOI by 6 years. In addition, we use the sea-level pressure difference between the Azores and Cape Verde to create a novel method of characterizing trade wind strength across Macaronesia, the Trade Wind index ( TWI), which points towards a recent, statistically significant increase (since 1973) throughout the region. Links between the winter and summer NAOI, TWI and Macaronesian temperature and precipitation are explored, as are the differences in warming trends between Macaronesia and analogous subtropical island chains, most of which are found to be warming at slower rates than the Macaronesia stations. © 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 Changes observed in nine meteorological variables obtained from the Three Rivers Source Region ( TRSR) between 1960 and 2009 were investigated using a fitted linear model, Mann-Kendall test, moving t-test, and Morlet wavelet. Analysis of the regionally scaled annual series from 1960 to 2009 showed that minimum ( Tmin), maximum ( Tmax), mean ( Tmean) air temperature, precipitation ( P), potential evaporation ( Ep), and sunshine hours ( SH) increased while relative humidity ( RH) and wind speed ( W) decreased. Trends were significant at a 99% confidence level for air temperature and at a 95% confidence level for Ep and W. With the exception of SH, regional scale changes observed in all variables in the past decade (2000-2009) when compared to climate norms (means of all climatic variables) from 1961 to 1990 were consistent with their corresponding linear trends from 1960 to 2009. Tmax, Tmean, and drought index ( DI) exhibited one climate jump, Tmin and RH two, and SH, Ep, and W three at a significance level of α = 0.05. On a regional scale, the period from 1986 to 1997 experienced a warmer, drier climate due to higher than average air temperatures, lower P, and higher DI compared to means from 1960 to 2009. The majority of meteorological variables of the TRSR experienced significant ( α = 0.05) short periodical cycling between 2 and 5 years. In terms of spatial distribution, seven out of 12 meteorological stations underwent warmer and wetter periods from 1960 to 2009, whereas the other five situated in the southeastern section of the TRSR underwent warmer, drier periods. [ABSTRACT FROM AUTHOR]

The article presents a study on the temperature and precipitation extreme in South America based on the Providing Regional Climate for Impacts Studies (PRECIS) regional climate modeling (RCM) system. The study compared the simulation trends in the 1961-1990 of the HadRM3P, the RCM within PRECIS, with the actual observations. It reveals that HadRM3P simulated trends show the similarity in the patterns of negative trends in the cold nights in South America and Southeastern South America.

The article investigates the impact of large scale climate circulations on snow variability as well as wintertime precipitation and temperature variables in eastern United States. Two snowfall variables: total seasonal snowfall (TSF) and number of snow days (NSD) as well as data from 124 snowfall stations were examined using principal component analysis. It was found that the North Atlantic Oscillation is the leading mode of variability for both TSF and NSD, while the second mode of variability for TSF and NSD, are the Pacific/North American pattern (PNA) and a dipole pattern, respectively. Findings reveal that these patterns provide prospects for seasonal predictions of snow variables.

This article presents a study regarding the trends in extreme daily temperature and precipitation near Lake Hövsgöl, Mongolia. From 1963 to 2002, a long-term set of daily maximum and minimum temperature and precipitation data are analyzed for the Lake Hövsgöl area in Mongolia. It examines eight indices of extreme precipitation and six indices of extreme temperature. Results revealed that climate conditions over northern Mongolia are changing as showed by a warming trend identified during the research period.