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Climate involves different combinations of temperature and precipitation, and each year's combination of factors can be assigned a climatic year type (CYT; e.g. Warm-Humid). Describing the changes in the CYT provides more informat...
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Climate involves different combinations of temperature and precipitation, and each year's combination of factors can be assigned a climatic year type (CYT; e.g. Warm-Humid). Describing the changes in the CYT provides more information than describing the temperature or precipitation data alone. In this study, we defined nine CYTs using the probability density function of annual temperature and precipitation. Recent and future spatiotemporal changes in CYT were analysed using 507-station observational data and projected data obtained from the CMIP5 multi-model ensemble under RCP2.6, RCP4.5, and RCP8.5 scenarios. China was divided into six subregions to analyse the spatiotemporal changes. Obvious differences in spatial patterns among the various CYTs reflect the climate regime throughout China. The warmth-associated CYTs (Warm-Humid, Warm-Dry, and Warm-Normal) mainly occur in West China (e.g. Southwest China). The cold-associated CYTs (Cold-Humid, Cold-Dry, and Cold-Normal) dominate at high latitudes and high altitudes (e.g. Northeast China and the Tibetan Plateau). The climate in China changed from cold to warm in the last half-century, accompanying the transformation of Cold-Humid, Cold-Dry, and Cold-Normal before the early 1990s to Warm-Humid, Warm-Dry, and Warm-Normal from the early 1990s onward. In the 21st century, the projected CYTs are mainly Warm-Humid, Warm-Dry, and Warm-Normal in China. Warm-Humid dominates in West China, North China, and Northeast China. Warm-Dry is mainly projected in the Yellow River Valley and South China. High-frequencyWarm-Normal is projected in the Yellow River Valley. Warm-Humid is projected to increase whereas Warm-Dry and Warm-Normal are projected to decrease from 2015 to 2099. All three CYTs are projected to exhibit larger changes in trends under stronger versus weaker RCPs (RCP8.5> RCP4.5> RCP2.6). Compared with temperature or precipitation data alone, CYTs provide more complete information on climate change and more accurately characterize regional differences in climate throughout China.
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An assessment of the plausible climate change in precipitation and surface air temperature (SAT) over the European region by the end of the 21st century is provided. The assessment is based on the results of output of the ocean-at...
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An assessment of the plausible climate change in precipitation and surface air temperature (SAT) over the European region by the end of the 21st century is provided. The assessment is based on the results of output of the ocean-atmosphere models participating in the Coupled Model Intercomparison Project, phase 5 (CMIP5). Six climate models that best reproduce the historical behaviour of SAT over greater Europe were selected from the CMIP5 project using a performance-based selection method of CMIP5 general circulation models for further assessments. The analysis of historical simulations within the scope of the CMIP5 project reveals that six models (namely, CNRM-CM5, HadGEM2ES, GFDL-CM3, CanESM2, MIROC5 and MPI-ESM-LR) sufficiently reproduce historical tendencies and natural variability over the region of interest. The climate change in SAT and precipitation by the end of the 21st century (2070-2099) was examined within the scope of RCP4.5 and RCP8.5 scenarios for these selected models. Typical regional warming due to RCP4.5 (RCP8.5) scenario is assessed as 3-4.5 degrees C (as 4-8 degrees C) in summer and winter, while a significant reduction of precipitation (typically 20-40%) is obtained only in summer.
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The relationship between polar sea ice anomalies and the precipitation and temperature anomalies over China is investigated by performing singular value decomposition (SVD) analyses. The first three coupling modes have been studie...
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The relationship between polar sea ice anomalies and the precipitation and temperature anomalies over China is investigated by performing singular value decomposition (SVD) analyses. The first three coupling modes have been studied. Analyses show that there exist key areas of polar sea ice which are highly related with The precipitation and temperature anomalies over China. Different spatial anomaly Patterns of these areas of polar sea ice are followed by different spatial anomaly patterns Of the precipitation and temperature over China.
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Abstract Despite the availability of global and continental climate datasets and climate change information, locally relevant quantification of historic trends in climate variables is still lacking in developing countries, especia...
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Abstract Despite the availability of global and continental climate datasets and climate change information, locally relevant quantification of historic trends in climate variables is still lacking in developing countries, especially at local scales. This is particularly true in the Department of Arequipa, Peru. An arid region with a booming population, substantial mining activities, and large irrigated agriculture, which is highly susceptible to climate change. This study aims to evaluate climate trends from 1988 to 2017 in the Arequipa Department and provide information that can facilitate stakeholders' adaptation to the rapid‐changing climate. The daily precipitation (Prec), and maximum (Tmax) and minimum (Tmin) daily air temperature data used in this study came from the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) and the National Ocean and Atmospheric Administration's (NOAA) Global Summary of the Day (GSOD). Data passed through a quality checking process for removal of implausible data, data gap filling, and inhomogeneity detection. The Mann–Kendall test, at a significance level of 0.10, was used to determine trends and the Theil–Sen slope (Sen's slope) was used to estimate the magnitude of the change. Sen's slope was also calculated spatially, using the gridded Arequipa Climate Maps (ACM) dataset. Results indicate that precipitation seasonality has been increasing, as the observed increase in annual precipitation is happening mostly in the rainy season (December–March) and the start and end of the rainy season are delayed. Positive temperature trends were dominant in the whole region. Tmin is increasing more than Tmax, especially at higher altitudes. Exceptions to increasing temperatures were found in areas influenced by irrigation projects that underwent great expansion. The effect of increasing temperature on glaciers was evaluated by mapping the change in the area with average annual temperature below 0°C between the decades of 1988–1997 and 2008–2017, which reduced by 73.2%, with small areas disappearing and larger contiguous areas shrinking.
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Characteristic patterns and changes in precipitation and temperature over the Greater Horn of Africa during the 20th and 21st century are analysed based on a sample of Coupled Model Intercomparison Project version 3 (CMIP3) models...
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Characteristic patterns and changes in precipitation and temperature over the Greater Horn of Africa during the 20th and 21st century are analysed based on a sample of Coupled Model Intercomparison Project version 3 (CMIP3) models output. Analysis of the 11 CMIP3 models indicates that the equatorial eastern Africa region (including the entire Greater Horn of Africa (GHA)) have been experiencing a significant increase in temperature beginning in the early 1980s, in both A1B and A2 scenarios. All the Atmosphere Ocean Global Circulation Models (AOGCMs) analysed represent the correct mean annual cycle of precipitation, but there is a fairly large spread among the models in capturing the dominant bimodal peaks. In particular, all the models tend to overestimate the peak of the October-November-December (OND) season, while at the same time the peak of the March-April-May (MAM) season tends to be centered on May in the models instead of April as observed. The projected changes and probability distribution of minimum (T_(min)) and maximum (T_(max)) temperatures over the GHA sub-region based on PDFs constructed from daily values showed very diverse distributions for the present (1981-2000) and future (2046-2065; 2081-2100) periods. Whereas in the reference (1981-2000) the probability distribution functions (PDFs) constructed for both T_(min) and T _(max), and during all the seasons had a near normal (but narrow) distribution, those of the future periods were quite diverse but generally very elongated, with significant shifts toward the positive tail. This generally implies that there is consensus among models and the ensemble mean about high likelihood of increase in extreme warmer T min and T max (more so T_(min)) in the future over the GHA region. Our results also show significant increase in the number of days with T_(min) and T_(max) greater the 2 °C (above 1981-2000 average) by the middle as well as the end of 21st century in both the A1B and A1 scenarios. This is especially so during the June, July, and August (JJA) season where all the 92 days of the season indicate projected minimum temperature to increase by more than 2 °C above the 1981-2000 average by the end of 21st century in both scenarios.
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Abstract Knowledge about the El Niño-Southern Oscillation (ENSO) is the scientific foundation for short-term climate prediction, due to its global influence. In operation and research communities, the ENSO state is often represen...
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Abstract Knowledge about the El Niño-Southern Oscillation (ENSO) is the scientific foundation for short-term climate prediction, due to its global influence. In operation and research communities, the ENSO state is often represented by various ENSO indices. However, it is unclear which index is the strongest for capturing ENSO’s global climate influence. By examining the correlations of eleven ENSO indices with monthly mean global precipitation and surface temperature (TS), we illustrate the similarities and differences in the connections, identify the strongest index, and discuss the physics behind the differences. For the global average, the Niño3.4 and relative Niño3.4 indices are the two strongest indices and the warm pool index is the weakest one for capturing the impact of ENSO on global precipitation, while the Niño4 and Niño3.4 indices are the two strongest indices and the Modoki index is the weakest one for capturing the ENSO’s influence on TS variations. In addition to the dependence on the variables and ENSO indices, the representations of climate variability associated with ENSO depend on the region. For example, in Australia, the southern oscillation index has the most significant correlations with precipitation and its correlations with TS are relatively weaker than those of some of the other indices. These differences associated with the various ENSO indices may be due to their representation of the deep convection in the tropical Pacific. These results can serve as a benchmark to understand the global picture of monthly mean precipitation and TS influenced by ENSO and to verify model’s ability in capturing these connections.
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This study presents a dynamic forecast combination approach adapted to incorporate multiple sources of precipitation. Dynamic combination serves to utilise the varying merit each data source exhibits with time. The dynamic model c...
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This study presents a dynamic forecast combination approach adapted to incorporate multiple sources of precipitation. Dynamic combination serves to utilise the varying merit each data source exhibits with time. The dynamic model combination framework presented merges a nonparametric k-nearest neighbour (k-nn) estimation of radar precipitation with Thin Plate Spline (TPS) interpolated gauge precipitation. Since air temperature is an essential variable to discriminate the phase of the precipitation in cold climates, this study uses radar precipitation and air temperature as the two variables in the dynamic combination algorithm. The merging of k-nn and TPS estimates is shown to reduce the RMSE by 25% compared to the radar precipitation rates. The usefulness of air temperature is found not to be as significant in the combination as it is in the formulation of the nonparametric radar precipitation fields for cold incident temperatures.
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Precipitable water vapour (PWV) is a vital component of the atmosphere and appreciably controls many atmospheric processes. The PWV is not easy to measure with sufficient spatial and time resolution under all weather conditions. I...
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Precipitable water vapour (PWV) is a vital component of the atmosphere and appreciably controls many atmospheric processes. The PWV is not easy to measure with sufficient spatial and time resolution under all weather conditions. In this paper, three precipitable water vapour models; the Smith, Won and Leckner's models were evaluated and compared for Owerri (Latitude 5.48°N, Longitude 7.00°E, and 91 m above sea level) using meteorological parameters of monthly average daily maximum temperature, minimum temperature and relative humidity during the period of sixteen years (2000-2015). The Leckner's model was found most suitable and therefore recommended for estimating PWV for the location with range between 3.253 and 4.662 cm. The highest PWV occurred in June for Won and Leckner's models while for Smith's model it occurred in September; the lowest PWV occurred in January for all the evaluated models. The result showed that high values of dew point temperature (T_(dew)), PWV and relative humidity (RH) were observed during the raining season and low values in the dry season; this is an indication that the dew point temperature is a reflection of the PWV and RH. The dew point temperature is an opposite reflection of the virtual temperature (T_(virtual)), potential temperature (T_(potential)) and mean temperature (T_(mean)). The dew point temperature increases and decreases with mean temperature in the months from January to March and in July respectively for the location under investigation. The values of the dew point temperature indicated that the air is stable signifying no development of severe weather condition like thunderstorms. The maximum and minimum virtual temperature correction of 3.3246℃ and 2.3371℃ occurred in June and January respectively while for the dew point depression, it occurred in the months of January and September with 8.7514℃ and 2.1094℃. The descriptive statistical analysis shows that the dew point temperature, potential temperature, mean temperature and virtual temperature correction data spread out more to the left of their mean value (negatively skewed), while the virtual temperature and dew point depression data spread out more to the right of their mean value (positively skewed). The dew point temperature and the virtual temperature correction data have positive kurtosis which indicates a relatively peaked distribution and possibility of a leptokurtic distribution while the virtual temperature, potential temperature, mean temperature and dew point depression data have negative kurtosis which indicates a relatively flat distribution and possibility of platykurtic distribution.
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Data from 3 commercial rendering companies located in different regions of California were analyzed from September 2003 through August 2005 to examine the relationship of dairy calf and cow mortality to monthly average daily tempe...
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Data from 3 commercial rendering companies located in different regions of California were analyzed from September 2003 through August 2005 to examine the relationship of dairy calf and cow mortality to monthly average daily temperature and total monthly precipitation respectively. Yearly average mortality varied between rendering regions from 2.1 to 8.1% for mature cows. The relationship between cow and calf monthly mortality and monthly average daily temperature was U-shaped. Overall, months with average daily temperatures less than 14 and greater than 24℃ showed substantial increases in both calf and cow mortality with calf mortality being more sensitive to changes in these temperature ranges than cow mortality. Temperature changes were reflected in a 2-fold difference between the minimum and maximum mortality in cows and calves. Precipitation showed a weak effect with calf mortality and no effect with cow mortality. Data from Dairy Herd Improvement Association were used from 112 California herds tested over a 24-mo period to examine the relationship of milk production and quality with monthly average daily temperature and monthly precipitation. Somatic cell count and percent milk fat were either weakly or not associated with monthly average daily temperature and total monthly precipitation. However, total monthly precipitation was negatively associated with test day milk per milking cow regardless of the dairy's geographical location. Housing-specific associations for test day milk per milking cow were greater for total monthly precipitation than monthly average daily temperature, with the strongest negative asso-
ciation seen for dairies that do not provide shelter for cows. This suggests that providing suitable housing for lactating dairy cattle may ameliorate the precipitation-associated decrease in test day milk per milking cow.
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Because global warming accompanied by alteration of precipitation regime became a real threat, worldwide, and potato is one of the most important cultures, increased interest was focused on the influence of the interaction of the ...
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Because global warming accompanied by alteration of precipitation regime became a real threat, worldwide, and potato is one of the most important cultures, increased interest was focused on the influence of the interaction of the climatic factors on potato most aggressive pathogen Phytophtora infestans (Mont) de Bary, which produces the disease called late blight. Based on this reality, our study was aimed to test at low scale the possibility of estimating the effect of Phytophtora infestans L. attack degree (AD) on a potato culture developed in Transylvania, Romania, under conditions of climate change, using the multiregression model. The specific objectives of the study were to annually evaluate the interdependence between temperature, precipitation quantities and Phytophtora infestans AD%, during three experimental years, 2009, 2010 and 2011, between 1st of April and 30th of September each year, to simulate the impact of the possible climatic scenarios on Phytophtora infestans AD% evolutions, and to evaluate the opportunity of using the multiregression model in predicting Phytophtora infestans AD% on potato cultures function of climatic factors, under conditions of conventional treatments. The use of the multiregression model explained 85% of the variation of the Phytophtora infestans attack degree in response to climatic conditions in studied area. The independent variables are in different relationships with Phytophtora infestans AD%, precipitation presents a lower relationship than the temperature. The same interaction was found when climatic change scenarios were developed. When temperature increases or decreases with 2oC and precipitation increases or decreases by 50%, precipitation quantities explain less on Phytophtora infestans AD%, compared with temperature. The study demonstrates the possibility of using the multiregression model in order to predict Phytophtora infestans attack degree on potato under specific climatic conditions.
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