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Abstract Recent global warming has not been ubiquitous: there are seasons, regions, and time periods with negligible or even negative air temperature trends (frequently referred to as warming holes). This paper presents a novel method enabling a proper localization of specific trend events, such as periods of warming holes, of a particularly strong warming, and of rapid transitions of trend amplitudes during the calendar year. The method consists in analyzing trends for periods of a given length (10 to 90 days) that are sliding over the year with a one day step. This allows a detailed description of the annual cycle of trends. The analysis is conducted for daily maximum and minimum temperature at 135 stations in Europe in 1961–2000. Despite an overall warming in Europe, several warming holes are uncovered during various parts of the year, not only in autumn when a warming hole has already been reported. The autumn warming hole concentrates in Eastern Europe, but it changes its strength and spatial extent: it spreads into Western Europe in September and retreats to Eastern Europe in November when it intensifies especially north of the Black Sea. Three shorter warming holes are detected: In February and March, cooling occurs in the Eastern Mediterranean and Iceland, while in early April, cooling is detected over Central, Southern, and Southeastern Europe. Another large-scale cooling occurs in Central, Northern, and Northwestern Europe in mid-June. The periods of strongest warming occur around the middle of January in Eastern Europe, in early March over almost entire Europe, and in mid-May and early August mainly over Central and Western Europe. Cluster analysis of stations with respect to the annual cycles of trends demonstrates a spatial coherence of the trends; the lack of spatial coherence points to local peculiarities or data problems of individual stations. The method of sliding seasons proves to be much more effective in the identification and localization of notable trend events than the ordinary approach of trend detection for fixed calendar seasons and/or months. Highlights • Annual course of temperature trends in Europe in 1961–2000 using "moving trends" • "Subseasons" of 10–90 days shifted by 1 day during the calendar year • Shorter subseasons = higher temporal and spatial variability of temperature trends • Greatest warming in January in NE Europe due to high interannual variability • "Warming holes" (i.e. cooling) in autumn in Central&E Europe, in June in N Europe [ABSTRACT FROM AUTHOR]

Engineers and planners need information about factors that affect demand for bicycling and walking to plan and manage transportation infrastructure. This paper presents a set of econometric models that summarize the effects of variation in temperature, precipitation, wind speed, dew point, and hours of daylight on daily bicycle and pedestrian trail traffic volumes. We make three contributions to the literature on non-motorized traffic monitoring. First, we summarize trail traffic monitoring results for 32 monitoring stations on multiuse trails in 13 cities in the United States, including locations across seven climate regions and zones classified by the U.S. Department of Energy. The monitoring results include estimates of average daily bicyclists (ADB) and average daily pedestrians (ADP) for the period, January 1, 2014 through February 16, 2016. Second, we introduce the concept of demand returns by testing the parabola form of the weather factors in the models, and measuring the vertex points of demand functions where use shifts from increasing to decreasing or vice versa in response to linear changes in the weather variable. Third, we compare regional elasticities for each weather variable for both bicyclists and pedestrians. Our results show (1) mean daily trail traffic varies substantially in response to variations in weather, with greater elasticities for temperature than precipitation and other weather variables; (2) the parabola form works well for variables such as temperature, where trail use is associated with warmer temperatures, but only up to a point at which higher temperatures then decrease use; and (3) bicyclists and pedestrians respond differently to variations in weather, and their responses vary both within and across regions. Transportation planners and trail managers can use these results to estimate the effects of weather and climate on trail traffic and to plan and manage facilities more effectively. [ABSTRACT FROM AUTHOR]

To address the problems and limitations identified through a comprehensive evaluation in Part I paper, several modifications are made in model inputs, treatments, and configurations and sensitivity simulations with improved model inputs and treatments are performed in this Part II paper. The use of reinitialization of meteorological variables reduces the biases and increases the spatial correlations in simulated temperature at 2-m (T2), specific humidity at 2-m (Q2), wind speed at 10-m (WS10), and precipitation (Precip). The use of a revised surface drag parameterization further reduces the biases in simulated WS10. The adjustment of only the magnitudes of anthropogenic emissions in the surface layer does not help improve overall model performance, whereas the adjustment of both the magnitudes and vertical distributions of anthropogenic emissions shows moderate to large improvement in simulated surface concentrations and column mass abundances of species in terms of domain mean performance statistics, hourly and monthly mean concentrations, and vertical profiles of concentrations at individual sites. The revised and more advanced dust emission schemes can help improve PM predictions. Using revised upper boundary conditions for O 3 significantly improves the column O 3 abundances. Using a simple SOA formation module further improves the predictions of organic carbon and PM 2.5 . The sensitivity simulation that combines all above model improvements greatly improves the overall model performance. For example, the sensitivity simulation gives the normalized mean biases (NMBs) of −6.1% to 23.8% for T2, 2.7–13.8% for Q2, 22.5–47.6% for WS10, and −9.1% to 15.6% for Precip, comparing to −9.8% to 75.6% for T2, 0.4–23.4% for Q2, 66.5–101.0% for WS10, and 11.4%–92.7% for Precip from the original simulation without those improvements. It also gives the NMBs for surface predictions of −68.2% to −3.7% for SO 2 , −73.8% to −20.6% for NO 2 , −8.8%–128.7% for O 3 , −61.4% to −26.5% for PM 2.5 , and −64.0% to 7.2% for PM 10 , comparing to −84.2% to −44.5% for SO 2 , −88.1% to −44.0% for NO 2 , −11.0%–160.3% for O 3 , −63.9% to −25.2% for PM 2.5 , and −68.9%–33.3% for PM 10 from the original simulation. The improved WRF/Chem is applied to estimate the impact of anthropogenic aerosols on regional climate and air quality in East Asia. Anthropogenic aerosols can increase cloud condensation nuclei, aerosol optical depth, cloud droplet number concentrations, and cloud optical depth. They can decrease surface net radiation, temperature at 2-m, wind speed at 10-m, planetary boundary layer height, and precipitation through various direct and indirect effects. These changes in turn lead to changes in chemical predictions in a variety of ways. [ABSTRACT FROM AUTHOR]

Anthropogenic warming may change precipitation patterns, impacting infrastructure performance and reliability. Future precipitation statistics generated using General Circulation Models (GCM) are, however, often biased and not easily applied to problems such as runoff estimation. Stochastic weather generation is hence used as an alternative to GCMs in hydrology and hydraulic modelling. This paper explores the dependence of fine temporal precipitation characteristics on air pressure and air temperature using historic observations. The goal is to develop, based on the key causes of precipitation, a climatological basis for a stochastic precipitation generator for non-stationary precipitation under climate change conditions. The analysis focuses on precipitation in the urban Northeast United States and utilizes pooled observations from meteorological stations in New York City, Philadelphia, and Boston over 60 years. A negative correlation between hourly Probability of Precipitation (POP) and air pressure is observed. When the historical records are discretized using air Pressure Change Events (PCE), Decreasing Pressure Change Events (DePCEs) had a higher POP and a higher Precipitation Depth (PD) than Increasing Pressure Change Events (InPCEs). Temperature was more strongly associated with PD during DePCEs than InPCEs; this association was more pronounced during high magnitude PCEs and extreme events. The potential for simulating future hourly precipitation by associating historic hourly precipitation patterns with PCE’s and monthly temperature is assessed. [ABSTRACT FROM AUTHOR]

Over the last decades, different machine learning techniques have been used to detect climate change patterns, mostly using data from measuring stations located in different parts of the world. Some previous studies focus on temperature as primary variable of study, though there have been other works focused on precipitation or even wind speed as objective variable. In this paper, we use the self-organized Second Order Data Coupled Clustering (SODCC) algorithm to carry out a spatio-temporal analysis of temperature patterns in Europe. By applying the SODCC we identify three different regimes of spatio-temporal correlations based on their geographical extent: small, medium, and large-scale regimes. Based on these regimes, it is possible to detect a change in the spatio-temporal trend of air temperature, reflecting a shift in the extent of the correlations in stations in the Iberian Peninsula and Southern France. We also identify an oscillating spatio-temporal trend in the Western Asia region and a stable medium-scale regime affecting the British Isles. These results are found to be consistent with previous studies in climate change. The patterns obtained with the SODCC algorithm may represent a signal of climate change to be taken into account, and so the SODCC could be used as detection method. [ABSTRACT FROM AUTHOR]

Based on the long-term trends analysis of five meteorological parameters (temperature, precipitation, wind speed, sunshine duration and relative humidity) in the four chosen stations of different settings, this paper attempts to reveal the characteristics of climate change in Urumqi City during 1960–2013. Our analytical results showed that both the temperature and precipitation in the Urumqi City have increased significantly since the middle 1980s. Autumn was the season when the temperature increased most drastically, and winter was the season when the precipitation increased most drastically. The average minimum temperature increased faster than the average maximum temperature, thus resulting in greatly reduced diurnal and annual temperature ranges. The wind speed was weakening significantly since the middle 1980s. The sunshine duration did not display any geographic coherence or consistency among different stations. The relative humidity has maintained more or less a constant, meaning that the absolute amount of water vapor has been increased under warming conditions. Comparatively, the Urumqi station (i.e., the urbanized area) had the most pronounced changes in temperature, precipitation and sunshine duration and the most pronounced changes were possibly due to the combined effects of natural and anthropogenic factors. Climate change in the mountainous area was synchronous with that in the plain area and the underlying surfaces and topographies, together with degrees of human activities, distinguished the regional climatic responses to global warming. [ABSTRACT FROM AUTHOR]

Based on the data of five observed meteorological elements (temperature, precipitation, wind speed, sunshine duration, and relative humidity) from 22 stations, the paper investigated the climate variations in northern Xinjiang over the past 50 years. The analytical results showed that both the temperature and the precipitation have increased significantly (0.35 °C/10 y, 11.2 mm/10 y) during the period of 1960–2011. An abrupt change was detected to have occurred in 1986 for temperature and in 1987 for precipitation. Autumn was the season when the temperature increased most dramatically, and winter and autumn were the seasons when the precipitation increased most dramatically. The twenty years after 1990 were the warmest and wettest two decades of the past ∼50 years, but the warming and wetting paces have slowed down during the same period. The results also showed that the mean annual minimum temperature increased more dramatically than the mean annual maximum temperature, resulting in a greatly reduced diurnal range. The reasons or mechanisms for rising trend of temperature during the past 50 years and also for the faster rising rate of the temperature in northern Xinjiang and the adjacent areas have been reasonably explored in published literature. However, the reasons or mechanisms for the rising trend of the past 50-year precipitation in northern Xinjiang and the adjacent areas have not been reasonably explored. This wetting trend has not extended further to the surrounding areas including Republic of Mongolia, major part of North China, and five republics of the former Soviet Union, and the need is thus pressing to explore the reasons or mechanisms behind this relatively “localized” wetting trend. [ABSTRACT FROM AUTHOR]

The total glacial area of the Cordillera Blanca, Peru, has shrunk by more than 30% in the period of 1930 to the present with a marked glacier retreat also in the recent decades. The aim of this paper is to assess local air temperature and precipitation changes in the Cordillera Blanca and to discuss how these variables could have affected the observed glacier retreat between the 1980s and present. A unique data set from a large number of stations in the region of the Cordillera Blanca shows that after a strong air temperature rise of about 0.31°C per decade between 1969 and 1998, a slowdown in the warming to about 0.13°C per decade occurred for the 30years from 1983 to 2012. Additionally, based on data from a long-term meteorological station, it was found that the freezing line altitude during precipitation days has probably not increased significantly in the last 30years. We documented a cooling trend for maximum daily air temperatures and an increase in precipitation of about 60mm/decade since the early 1980s. The strong increase in precipitation in the last 30years probably did not balance the increase of temperature before the 1980s. It is suggested that recent changes in temperature and precipitation alone may not explain the glacial recession within the thirty years from the early 1980s to 2012. Glaciers in the Cordillera Blanca may be still reacting to the positive air temperature rise before 1980. Especially small and low-lying glaciers are characterised by a serious imbalance and may disappear in the near future. [ABSTRACT FROM AUTHOR]

Abstract: Climate extremes in the Tibetan Plateau may cause serious regional and global consequences, but observation-based research over the western Tibetan Plateau is scarce. In this paper, recent changes in extremes of temperature and precipitation over the western Tibetan Plateau from 1973 to 2011 are investigated. A total of 24 indices that represent extreme climate events are selected and calculated by using daily maximum and minimum temperature and precipitation data. Results demonstrate that most cold-related indices of temperature extremes (frost and ice days, cool nights and cool days, and cold spell duration indicators) show a significant decrease, and that both the coldest night and coldest day have increased during the study period. Warm-related indices of temperature extremes such as summer days, the warmest night and the warmest day, warm nights and warm days, and warm spell duration indicators all have increased. The diurnal temperature ranges show a decreasing trend with statistical significance, whereas growing season lengths have increased significantly. The change trends of precipitation extremes are nonsignificant. Nonsignificant increasing trends are detected for most indices including annual total wet-day precipitation, maximum 1-day and 5-day precipitation, very wet and extremely wet day precipitation, and consecutive wet days. However, nonsignificant decreasing trends are found for the number of heavy precipitation days, consecutive dry days and simple daily intensity index. [Copyright &y& Elsevier]

Abstract: Alpine ecosystems are highly sensitive to global climate changes. The Tibetan Plateau is one of the areas that are most sensitive to global climate change. Increases in temperature and changes in precipitation can impact the plateau''s ecosystem productivity. Net primary productivity (NPP) is one of the most important factors in the carbon cycle of terrestrial ecosystems. In this paper, a light-use-efficiency model was used to estimate the net primary productivity in the Tibetan Plateau. The model is based on a 1-km×1-km-resolution map of vegetation type, multi-temporal 500-m-resolution MODIS data and daily meteorological data. The spatial distribution pattern and dynamic change of the annual NPP from 2001 to 2008 are analyzed. Then, we analyzed the response of the NPP to temperature and precipitation changes. The results show that the mean annual NPP of alpine ecosystems in the Tibetan Plateau is equal to 0.472 Pg C and that the NPP exhibits significant seasonal and interannual variation due to the combined effects of temperature and precipitation changes. Finally, to analyze the effect of temperature and precipitation on the inter-annual change of the NPP, the correlation coefficient between temperature, precipitation and the NPP was computed. It was found that the relations among air temperature, precipitation and the NPP in the Tibetan Plateau region are different. The annual average temperature increase had a significantly positive effect on the vegetation NPP (R2 =0.83). In contrast, the annual precipitation changes had a weakly negative effect on the vegetation NPP (R2 =0.373). [Copyright &y& Elsevier]

Abstract: We have used EML Surface Air Sampling Program (SASP) data to analyze the long-term trend in 7Be surface concentration and address possible correlation between this long-term trend and climatic changes, namely changes in precipitation patterns and temperature. In this paper we present 7Be concentration data from 23 sites, spanning over 25 years, all over the world, and extract long-term trend parameter using two independent techniques. The 7Be concentrations in most stations show a pronounced decreasing trend, potentially corresponding to statistically significant changes in transporting 7Be from upper atmosphere source to these sites. Weak negative correlation between 7Be concentration and amount of precipitation was also observed. However, more data from more representative sites around the world are needed the statistical robustness of this trend. [Copyright &y& Elsevier]

Abstract: The aim of the paper is to employ Empirical Orthogonal Function (EOF) analysis to drought indices including Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Palmer Moisture Anomaly (Z) Index (Z-Index), Weighted Palmer Drought Severity Index (WPDSI), and Water Deficit (P–PET) Index and to compare their resultant spatial patterns across Turkey. In this respect, the PDSI, PHDI, Z, WPDSI, Aridity Index (AI) and P–PET Index values are calculated based on observed monthly temperature and precipitation values of the 96 Turkish meteorological stations for the 1929–2009 period, and gridded available water holding capacities (AWHC) in 1-m soil depth are extracted from the soil data sets of the Oak Ridge National Laboratory Distributed Active Archive Centre (ORNL DAAC). By considering the strong match among the PDSI, PHDI, and WPDSI based on the results of the patterns of the first EOFs (EOF1) with the highest eigenvalue, we suggest that using and applying one of these drought indices could be adequate for a drought analysis in Turkey. On the other hand, it is evident that the significant results from the EOF2, EOF3 and EOF4 loadings of the PHDI and WPDSI indices have a similar pattern across Turkey. Therefore, with respect to the geographical autocorrelation and magnitudes of the loading values, it is explained that the PDSI and WPDSI in particular could have originated from the same physical process, and one may have a preference for the effective usage of a combination of PDSI, PHDI and Z-Index. Consequently, in a multi-purpose medium- and long-term drought management plan for Turkey or in any country having similar climatic and physical geographical conditions, using one of the three drought indices along with the Z-Index could be acceptable for successful applications. [Copyright &y& Elsevier]

Abstract: Climate models provide estimates of climatic change over periods of time in the ancient past. Macrophysical climate models (MCM) differ from the more widely used general circulation models (GCM), in that MCMs provide temporally high-resolution (~100years) and site-specific estimations of monthly values of climate variables such as temperature and precipitation. In this paper, seasonal changes in climate variables are modeled for six 14C-dated fossil localities in North America. Five of these localities represent the time of maximum extent of ice during the most recent glacial episode, the Full Glacial (25+–15ka), including one at the peak of the Last Glacial Maximum (17–15ka). The other locality represents the time as the ice began to recede, the Late Glacial (15–11ka). Seasonal variations in temperature and precipitation modeled by MCM are herein compared with interpretations of seasonal variation based upon oxygen isotopes from serially sampled hypsodont teeth (mostly Equus and Bison) collected from each of these localities. Additionally, the MCM-modeled seasonal variations are used to predict the expected abundances of different plant functional groups (PFG) during those times, especially C3 and C4 functional groups, using modern relationships. These predictions are compared with carbon isotopic values from the same teeth. The importance of atmospheric pCO2 for the relative abundance of plants utilizing the C4 metabolic pathway is discussed, given that glacial episodes are known to have been times of lower atmospheric pCO2. Interpretations of seasonal variability and the relative abundance of C3 versus C4 vegetation based upon isotopes from tooth enamel are in broad agreement with predictions using the MCM and the modern distribution of PFGs with climate variables. The influence of pCO2 on the distribution of C4 vegetation during glacial times appears to be negligible. [Copyright &y& Elsevier]