ABSTRACT. – TemperatureandprecipitationchangesinTârguMures (Romania) fromperiod1951-2010. The analysis was made based upon meteorological data collected at TârguMures meteorological station (Romania, Mures county, lat. 46°32’N, lon. 24°32’E, elevation 308 m), between 1951and2010. Several climatic parameters were studied (for instance, annual and monthly mean temperature, maximum precipitationin 24 hours, number of summer days, etc). Detected inhomogeneities are not related to instrumental causes or geographical relocation. Positive and statistical significant trends (Mann-Kendall test) are indicated for: mean annual temperatures, mean temperatures of warm months, average of the maximum and minimum temperatures (annual and warm months data), number of days with mean temperature between 20.1-25.0 °C , number of days with precipitation ≥0 mm, and for all parameters of precipitation of September. The sequential version of Mann-Kendall test show a beginning of a trend in 1956 in the case of mean temperature (at same, the two and three parts regression denote this year like a moment of change), years 1965 and 1992 in the case of annual amount of precipitation. CUSUM charts indicate occurs of changes points at 1988, 2005, 2009 (mean temperature) respectively at 1989, 2004 (precipitation), and at 1968, 1992 (daily temperature range). Tendencies of overlapped time series reveal a more important increase at the end of period (mainly for mean temperature). The analysis with RClimDex show for 5 extreme climate indices a significant trend: positive for summer days, warm nights, warm spell duration indicator and negative for cold nights and cold days.
When attempting to estimate the impacts of future climate change it is important to reflect on information gathered during the past. Understanding historical trends may also aid in the assessment of likely future agricultural and horticultural changes. The timing of agricultural activities, such as grape harvest dates, is known to be influenced by climate and weather. However, fewer studies have been carried out on grapevine yield and quality. In this paper an analysis is undertaken of long-term data from the period 1805–2010 on grapevine yield (hl/ha) and must sugar content (uOe) and their relation to temperature. Monthly mean temperatures were obtained for the same time period. Multiple regression was used to relate the viticulture variables to temperature, and long-term trends were calculated. Overall, the observed trends over time are compatible with results from other long term studies. The findings confirm a relationship between yield, must sugar content andtemperature data; increased temperatures were associated with higher yields and higher must sugar content. However, the potential increase in yield is currently limited by legislation, while must sugar content is likely to further increase with rising temperatures.
Several studies have used these indices, associated with GCMs, RCMs and future scenarios, to assess future fire danger. Flannigan et al.  used the CSR, SRES scenarios A1B, A2 and B1, and outputs from three GCMs (CGCM3.1, from the Canadian Centre for Climate Modelling and Analysis, HadCM3, from the Hadley Centre for Climate Prediction in the United Kingdom, and the IPSL-CM4 from France) to examine the potential influence of climate change on future global fire season severity. Their results suggest that significant increases in wildfire events over most of the globe may be attributed to the role temperature plays in fire activity, and that fire seasons will be more severe (in some areas of the globe, in- creasing by 20 days per year). Also using a CWFIS product, Moriondo et al.  evaluated the present and future fire risk in the Mediterranean region using FWI and the output of the HadRM3P GCM along with SRES A2 and B2 scenarios. Results show that the higher risks in forest fire are due to increases in maximum temperatureand decreases inprecipitationand relative humidity, namely during the summer period. Liu et al. , explored fire risk under current and future climate conditions using KBDI and climate variables simulated by four GCMs (HadCM3; CGCM2; CSIRO, from the Commonwealth Sci- entific and Industrial Research Organisation; and NIES, from the National Institute for Environmental Studies in Japan). The GCM simulation for the future was performed for four emission scenarios: SRES A1, A2, B1 and B2. They found that future fire potential is expected to increase significantly in the United States, South America, central Asia, southern Europe, southern Africa and Australia, i.e. mainly areas that currently have significant fire occurrence and many fire-dependent forest types. They have also found that there is an increase in fire season length, as well as a higher likelihood of extreme weather events.
106-yr reference time series covering the scenario period (2010–2115) were composed as a random sequence of years from historical observations of the period 1961–2009. To preserve interannual autocorrelation, spatial coherence, and correlation among climate variables, all months and grid cells for all climate variables were taken from the same year. Prior to resampling, the trend intemperature was removed in a way that the detrended time series of temperature are representative for the climatologic mean of year 2009 ob- tained from the trend analysis. In the process of data prepara- tion, observations of precipitationand cloudiness were found to exhibit strong interannual/interdecadal variability, which negatively affects the robustness of estimated trends. In or- der to avoid spurious effects from removing these trends, the original data were used directly for generating the reference time series for cloudiness andprecipitation. The time series of resampled observations obtained are assumed to represent variability and climatology for the reference year 2009, to be consistent with the reference year for the derived anoma- lies. This consistency between the constructed reference time series, derived anomaly time series, and observations allows for seamless combination of historic observations with future climate projections and thus for transient impact model runs. The combination of the anomalies with the reference time series is a crucial step and related to the general problem of whether to apply climate anomalies as an absolute change: V scen (x, m, y) = V ref (x, m, y) + 1V scen (x, m, y) (5)
observational data set. In part, they are explained by an en- hanced zonal circulation in the GCM simulation that can- not be substantially ameliorated by the RCM rather than be- ing explained by deficiencies within the reconstructions. Al- though reconstructions and the simulation seem to correctly reproduce most of the spatio-temporal variability, there is little agreement in their temporal evolution. The mismatch in the temperature, especially in the last decades, can orig- inate from the missing anthropogenic aerosol forcing in the simulation. Additionally, early instrumental time series can show warm biases caused by the lack of modern thermome- ter screens (Frank et al., 2007a, b). Although we do not nec- essarily expect the reconstructed and simulated temperature evolution to agree in the earlier periods due to the poten- tially dominant internal variability, we also acknowledge that the lack of stratospheric dynamics in both the regional and the global simulation may account for some disagreement. Specifically, too low a top atmospheric layer in the model and no ozone chemistry reduce the ability of the model to correctly represent the potential top–down influences of solar activity changes on the atmospheric circulation in the North Atlantic sector, e.g. the North Atlantic Oscillation, andin turn European climate variability (Shindell et al., 2001; Anet et al., 2013). Finally, the simplification of using reduced TSI for volcanic forcing might be an additional source of errors reducing the agreement between the simulation and recon- structions.
Ivanov S. et al. (2010) pointed out that in region of Eastern Europe the regional changesin the precipitation are in the relationship with changesin the atmospheric circulation at the global level. According to Jovanović G. et al. (2008) there is a dominant influence of the NAO and AO (Arctic Oscillation) phenomenon on precipitation regime in Serbia, especially during the winter seasons, and the Arctic Oscillation have more pronounced effect. Ducić V. and Luković J. (2005) conclude that there is a connection between El Niño Southern Oscillation (ENSO) andchangesin rainfall in Serbia for the period1951-2000., stressing that the connection is made via the general circulation of the atmosphere. Monthly precipitationandtemperature anomalies in Hungary during El Niño and La Niña events were found Bartholy J. and Pongrácz R. (2006). Also, these authors suggest that the strongest connection between these phenomena is realized with a time lag of 2-3 months. The same time lag (3 months) was obtained for monthly temperatures in Serbia and ENSO for period 1950-1998. (Jovanović G., 2010).
The number of outliers has clear annual cycle. For most of the elements (e.g. air temperature), a higher number of out- liers was detected in summer months than in winter months (connected with larger neighbour differences variations due to influence of active surface). More outliers were detected in the morning and evening measurements compared to noon (associated with steeper gradients in the former case). For precipitation there are two maxima per year, in the summer months and then in January and December (in winter it is per- tinent to problems with solid precipitation measurements), while during spring and autumn a lower number of outliers was detected. The number of detected outliers also changes with time. For air temperature, the higher number of out- liers since the late 1990s coincides well with transition to automatic measurements. Our explanation is that all values coming from automated measurements (including errors) are stored straight into database while in the case of manual mea- surements observer revises read values before sending them to meteorological office. On the contrary, in the case of pre- cipitation no increase of errors after automation was encoun- tered.
Abstract. We present an analysis of different sources of impact model uncertainty and combine this with proba- bilistic projections of climate change. Climatic envelope models describing the spatial distribution of palsa mires (mire complexes with permafrost peat hummocks) in north- ern Fennoscandia were calibrated for three baseline periods, eight state-of-the-art modelling techniques and 25 versions sampling the parameter uncertainty of each technique – a to- tal of 600 models. The sensitivity of these models to changesintemperatureandprecipitation was analysed to construct impact response surfaces. These were used to assess the be- haviour of models when extrapolated into changed climate conditions, so that new criteria, in addition to conventional model evaluation statistics, could be defined for determining model reliability. Impact response surfaces were also com- bined with climate change projections to estimate the risk of areas suitable for palsas disappearing during the 21st cen- tury. Structural differences in impact models appeared to be a major source of uncertainty, with 69 % of the models giving implausible projections. Generalized additive mod- elling (GAM) was judged to be the most reliable technique for model extrapolation. Using GAM, it was estimated as very likely (>90 % probability) that the area suitable for pal- sas is reduced to less than half the baseline area by the period 2030–2049 and as likely (>66 % probability) that the en- tire area becomes unsuitable by 2080–2099 (A1B emission scenario). The risk of total loss of palsa area was reduced for a mitigation scenario under which global warming was constrained to below 2 ◦ C relative to pre-industrial climate,
To aid assessments of the impact of climate change on water related activities in the case study regions (CSRs) of the EC-funded project SWURVE, estimates of uncertainty in climate model data need to be developed. This paper compares two methods for estimating uncertainty in annual surface temperatureandprecipitation for the period 20702099. Both combine probability distribution functions for global temperature increase and for scaling variables (i.e. the change in regional temperature/precipitation per degree of global annual average temperature change) to produce a probability distribution for regional temperatureandprecipitation. The methods differ in terms of the distribution used for the respective probability distribution function. For scaling variables, the first method assumes a uniform distribution, whilst the second method assumes a normal distribution. For the probability distribution function of global annual average temperature change, the first method uses a uniform distribution and the second uses a log-normal approximation to a distribution derived from Wigley and Raper, 2001. Although the methods give somewhat different ranges of change, they agree on how temperatureandprecipitationin each of the CSRs are likely to change relative to each other. For annual surface temperature, both methods predict increases in all CSRs, although somewhat less so for NW England (5 th and 95 th percentiles vary between 1.11.9 °C to 3.85.7 °C) and about 1.73.1 °C to 5.38.6 °C for the others. For precipitation,
The increase or reduction in fruit respiration can vary with the exposure to temperature. Kader (1985) and Kluge et al. (2002) have reported that Q 10 values of some fruits change as function of the consid- ered temperature range. The recommended storage tem- perature for guava fruit (Psidium guajava L.) varies from 8 to 10ºC (Carraro & Cunha, 1994; Castro & Sigrist, 1988). In typical, tropical Brazilian climate, guava fruit can easily be exposed to temperatures higher than 10ºC during storage and commercialization period, and undergo physiological stress and loss of shelf life and quality. The objective of this study was to evalu- ate the respiratory activity, ethylene production and Q 10 of Paluma guava cultivar at different storage tempera- tures.
The present study aimed to analyze trends in air temperatureand rainfall for 13 locations in the state of Pará using nonparametric tests. Daily data of maximum and minimum air temperatures andprecipitation covering the period 1970-2006, collected by the Instituto Nacional de Meteorologia (INMET) have been used. From the results obtained it was observed that the number of warm days and nights per year has increased, thereby providing a significant reduction in the number of cool days and nights in the state. Due to the high space-time variability of precipitation, few localities showed statistically significant trends for indices of extremes dependent on this variable. The days and nights in Belém have been hotter in the last two decades. Therefore, these results are important for future planning of public health and energy for the state of Para, which must adapt to future warming scenarios sectors.
Kriging method. The time series of monthly precipitationandtemperature data from 98 meteorological stations were used for the Ordinary Kriging. To evaluate and interpret the results, multivariate statistics (band collection) and cell statistics were applied for the monthly precipitationandtemperature layer series. The results revealed that a significant change inprecipitation regime in the Aegean Region was occurred. It is necessary to pay attention to this change because of multiple environmental effects of the climate changes. In the following studies, prediction of the future trends and determination of the effects of these changes on nature and human health are required.
Because the GCM outputs’ resolution is too coarse to be used directly at the catchment scale, the data were statistically downscaled by a simple bias correction method based on the delta factor approach, using the GCMs simulation data for the control periodfrom 1972 to 1999. Despite its limitations to account for projected variability, this method, also known as change factor or perturbation method, has been successfully applied in previous studies (e.g., Berg et al. 2012, Kwon et al. 2012, Teutschbein and Seibert 2012, Kidmose et al. 2013). According to Holman et al. (2009) and Willems and vrac (2011), the delta factor method assumes that the future climate will be a disturbed version of the present climate. This is accomplished by applying multiplicative (for the precipitation) or additive (for the temperature) correction factors to climatic variables.
Abstract. Climate variation and change influence several ecosystem components including forest fires. To examine long-term temporal variations of forest fire danger, a fire dan- ger day (FDD) model was developed. Using mean temper- ature and total precipitation of the Finnish wildfire season (June–August), the model describes the climatological pre- conditions of fire occurrence and gives the number of fire danger days during the same time period. The performance of the model varied between different regions in Finland being best in south and west. In the study period 1908–2011, the year-to-year variation of FDD was large and no significant increasing or decreasing tendencies could be found. Negative slopes of linear regression lines for FDD could be explained by the simultaneous, mostly not significant increases in pre- cipitation. Years with the largest wildfires did not stand out from the FDD time series. This indicates that intra-seasonal variations of FDD enable occurrence of large-scale fires, de- spite the whole season’s fire danger is on an average level. Based on available monthly climate data, it is possible to estimate the general fire conditions of a summer. However, more detailed input data about weather conditions, land use, prevailing forestry conventions and socio-economical factors would be needed to gain more specific information about a season’s fire risk.
Variation in the base temperature has no significant effect on the precision of spring phenology models [14,30,35]. Generally, the heat unit total depends on the threshold used , as with Leymus chinensis in this study (Tables 3, 4, 5). Therefore, changesin the base temperature induce different thresholds for the accumu- lated temperature, resulting in no significant variation in model accuracy. The threshold measure is a mathematical construct which may or may not be related to the physiological threshold . Physiological parameters can be estimated from simulation experiments, but can not be obtained from the process of parameter optimization. This is because the optimization process is mostly dependent on the precision of observed field data, sample number, and local climate conditions. The biological interpreta- tion of model parameters should not be considered as absolute [34,36]. The base leafing temperature of the same plant can be different simply due to different models, as seen with all plant species in this study (Tables 3, 4, 5). The base temperaturein the growth season index (GSI) is fixed, and the minimum temperature is derived from experimental data [36,37]. In the present study, the leafing dates of woody plants actually changed can be, to a certain extent, explained by GSI using the fixed parameter. However, considering the threshold of 0.5 from the original model , the predicted dates for plant leafing in Northeast China was earlier than the observed values. Thus, the original parameter threshold for GSI was too small for the present study, and the optimal threshold varied with different species.
mass balance is not the appropriate measure to interpret climatic fluctuations. Due to the dynamic response of glaciers to changesin their climatic forcing, the importance of short-term climatic oscillations is overestimated. Taking the changesin glacier geome- try into account, the AMO related climate variations are far less important to the recent mass loss than the trend caused by the gradual warming over the past century.
Prismatic test samples made from a disc of 18Ni C350 maraging steel manufactured by vacuum induction melting and vacuum arc remelting (Table 1), subsequently being sectioned in a cut-off machine and machined to the dimensions showed on Fig. 1. Then, they were deformed by uniaxial compression without lubrication in a 150 Ton friction drive screw press following the conditions shown in Table 2. Right after the deformation, samples were quenched in water, after that all samples were cut in small pieces, which were characterized in either the as deformed state, or after aging for 1, 5, 7,5, 10, 25 and 40 hours at 500 ºC and for 10, 30 min, 1, 5 and 10 hours at 550 ºC. For characterization, the samples were metallographically prepared following the procedures of the ASTM E3 standard, and chemically etched following the guidelines of the ASTM E407 standard, with modified fry etchant (50 ml of HCl, 25 ml of HNO 3 , 1 g of
A population decrease was observed at certain sampling points during the study. This decrease resulted from human activity at points 2 and 3. A drainage procedure was performed on the wet land at point 2. This procedure interfered with the collection of molluscs. These molluscs commonly occur in water tanks, small water reservoirs, irrigation ditches (Coelho and Lima, 2003; Carvalho et al., 2005 ) and back waters (Serra-Freire, 1995). If these areas are drained, the increased exposure to solar radiation at high temperatures may cause mortality in the mollusc populations. Beginning in January 2011, the reservoir at point 3 was used for other farm activities, resulting in a decrease in the amount of available substrate. As a result, the mollusc populations at points 2 and 3 decreased and eventually disappeared. Similarly, Coelho and Lima (2003) observed 5.2% to 3.9% decreases in mollusc populations in an area after removal of aquatic plants and the application of drainage procedures.
The Spanish hotel sector is comprised mainly of city hotels and resorts (sun and sand) aimed at a wide range of tourist segments. This study analyses the impact of the crisis period on the eiciency of a particular model of city hotels by determining whether they responded through organizational and/or technological changesin order to improve eiciency levels. The total-factor productivity (TFP) index was used, as well as its decomposition in technical ei- ciency and technological change. The AC Hotels chain was chosen because it has a strategy that varies with the location of each hotel in the chain, and because of the chain’s continuous innovation, ren- ovation, and infrastructure maintenance practices, using its own design team to ensure customers’ needs are met. In2010, AC Hotels formed a joint venture with leading US company Marriott Interna- tional. Through this joint venture, the AC Hotels by Marriott brand was created to manage the chain. AC Hotels by Marriott is committed to a clear diferentiation in its products and services by reinforc- ing the chain’s characteristics, investing in new technologies such communication, management, and direct sales to customers. The inclusion of AC Hotels in Marriot’s new distribution channels and marketing tools will help the chain to increase its visibility to inter- national travelers, as well as learn the tastes and preferences of its customers in order to ofer them personalized services.
is used to formulate wind stress. The data are detrended through analysis of changesin zonal mean over the ocean (by month) across the full 60 yr period; this has little im- pact except over the Southern Ocean, where the trend is quite significant (Thompson and Solomon, 2002). For any given model calendar year, a random calendar year of wind stress data is applied to the ocean. This approach ensures that both short-term