scholarly journals Relationship between several meteorological factors and phenological features of apple cultivars

2008 ◽  
Vol 14 (1-2.) ◽  
Author(s):  
L. Lakatos ◽  
T. Szabó ◽  
S. Zhongfu ◽  
Y. Wang ◽  
J. Racskó ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Gene Bank ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time ◽  
Apple Cultivars

The trees observed are grown at Ofeherto, Eastern Hungary in the plantation of an assortment (gene bank) with 586 apple cultivars. Each of the cultivars were observed as for their dates of subsequent phenophases, the beginning of bloom, main bloom and the end of bloom over a period between 1984 and 2001. during this period the meteorological data-base keeps the following variables: daily means of temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), daily precipitation sums (mm), daily sums of sunny hours, daily means of the differences between the day-time and night-time temperatures (°C), average differences between temperatures of successive daily means (°C). Between the 90th and 147th day of the year over the 18 years of observation. The early blooming cultivars start blooming at 10-21April. The cultivars of intermediate bloom start at the interval 20 April to 3 May, whereas the late blooming group start at 2-10 May. Among the meteorological variables of the former autumnal and hibernal periods, the hibernal maxima were the most active factor influencing the start of bloom in the subsequent spring.

scholarly journals Relationship between several meteorological factors and phenological features of pear cultivars

2016 ◽  
Vol 18 (2) ◽  
Author(s):  
L. Lakatos ◽  
T. Szabó ◽  
G. Kocsisné Molnár ◽  
J. Racskó ◽  
M. Soltész ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Daily Precipitation ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Gene Bank ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time

The aim of this paper was to investigate the fl owering characteristic of apples and their relationship to meteorological parameters. The trees observed are grown at Újfehértó, Eastern Hungary in the plantation of an assortment (gene bank) with 586 apple varieties. Each of the varieties were observed as for their dates of subsequent phenophases, the beginning of bloom, main bloom and the end of bloom over a period between 1984 and 2001 during this period the meteorological data-base keeps the following variables: daily means of temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), daily precipitation sums (mm), daily sums of sunny hours, daily means of the differences between the day-time and night-time temperatures (°C), average differences between temperatures of successive daily means (°C). Between the 90th and 147th day of the year over the 18 years of observation. The early blooming varieties start blooming at 10–21April. The varieties of intermediate bloom start at the interval 20 April to 3 May, whereas the late blooming group start at 2–10 May. Among the meteorological variables of the former autumnal and hibernal periods, the hibernal maxima were the most active factor infl uencing the start of bloom in the subsequent spring.

scholarly journals Effects of weather characteristics on blooming dates in an apple gene bank plantation between 1984 and 2001

2006 ◽  
Vol 12 (2) ◽  
Author(s):  
L. Lakatos ◽  
T. Szabó ◽  
J. Racskó ◽  
M. Soltész ◽  
Z. Szabó ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Daily Precipitation ◽  
Meteorological Data ◽  
Gene Bank ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time ◽  
Weather Characteristics

The aims of this paper was to investigate the flowering characteristic of apples and their relationship to meteorological parameters. The trees observed are grown at Ujfehert6, Eastern Hungary in the plantation of an assortment (gene bank) with 586 apple varieties. Each of the varieties were observed as for their dates of subsequent phenophases, the beginning of bloom, main bloom and the end of bloom over a period between 1984 and 2001. During this period the meteorological data-base keeps the following variables: daily means of temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), daily precipitation sums (mm), daily sums of sunny hours, daily means of the differences between the day-time and night-time temperatures (°C), average differences between temperatures of successive daily means (°C). Between the 90th and 147th day of the year over the 18 years of observation. The early blooming varieties start blooming at 10-21 April. The varieties of intermediate bloom start at the interval 20 April to 3 May, whereas the late blooming group start at 2-10 May. Among the meteorological variables of the former autumnal and hibernal periods, the hibernal maxima were the most active factor influencing the start of bloom in the subsequent spring.

scholarly journals Relationship beetwen the phenological features of pear cultivars and the main meteorological parameters in a gene bank with 555 pear

2009 ◽  
Vol 15 (1-2) ◽  
Author(s):  
L. Lakatos ◽  
S. Musacchi ◽  
T. Szabó ◽  
G. Kocsisné Molnár ◽  
Z. Szabó ◽  
...  
Keyword(s):  
Gene Bank ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Night Time ◽  
Correlation And Regression Analysis ◽  
Fruit Growing ◽  
Autumn And Winter ◽  
The Relationship

The trees observed are grown at Ujfehert6, Eastern Hungary in a gene bank with 555 pear cultivars. Each of the cultivars was monitored for its dates of: the beginning of bloom, main bloom and the end of bloom and ripe phenophasis separately between I 984 and 2002. We analyzed the statistical features, frequency, distribution of these phenophasis and its' correlation the meteorological variables bet ween the interval. During this period the meteorological database recorded the following variables: daily mean temperature (°C), daily maximum temperature (0C), daily mini m um temperature (0C), daily precipitation (mm), daily hours of bright sunshine, daily means or the differences between the day-time and night-time temperatures (0C). For the analysis of data the cultivars have been grouped according to dates of maturity, blooming period as well as types of the seasons. Groups of maturity dates: summer ripe, autumnal ripening, winter ripe cultivars. Groups of blooming dates: early blooming, intermediate blooming, late blooming cultivars. At all the separated groups we analyzed the relationship between phenophasis and meteorological variables. During the 18 years of observation , the early blooming cultivars started blooming on 10-21 April, those of intermediate bloom date started flowering bet ween 20 April and 3 May, whereas the late blooming group started on 2- 10 May. Among the meteorological variables of the former autumn and winter periods, the winter maxima were the most active factor influencing the start dates of bloom in the subsequent spring. For the research of fruit growing-weather relationships we used simple, well known statistical methods, correlation and regression analysis. We used the SPSS 1 1.0 software for the linear regression fitting and for calculation of dispersions as well. The 1ables made by Excel programme.

scholarly journals Meteorological factors affecting the risk of transmission of HPAI in Miyazaki, Japan

2019 ◽  
Vol 6 (1) ◽  
pp. e000341 ◽  
Author(s):  
Genki Arikawa ◽  
Yoshinori Fujii ◽  
Maiku Abe ◽  
Ngan Thi Mai ◽  
Shuya Mitoma ◽  
...  
Keyword(s):  
Bird Migration ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Migratory Waterfowl ◽  
Generalised Linear Mixed Model ◽  
Fulica Atra ◽  
The Relationship

Highly pathogenic avian influenza (HPAI) outbreaks engender a severe economic impact on the poultry industry and public health. Migratory waterfowl are considered the natural hosts of HPAI virus, and HPAI viruses are known to be transmitted over long distances during seasonal bird migration. Bird migration is greatly affected by the weather. Many studies have shown the relationship between either autumn or spring bird migration and climate. However, few studies have shown the relationship between annual bird migration and annual weather. This study aimed to establish a model for the number of migratory waterfowl involved in HPAI virus transmission based on meteorological data. From 136 species of waterfowl that were observed at Futatsudate in Miyazaki, Japan, from 2008 to 2016, we selected potential high-risk species that could introduce the HPAI virus into Miyazaki and defined them as ‘risky birds’. We also performed cluster analysis to select meteorological factors. We then analysed the meteorological data and the total number of risky birds using a generalised linear mixed model. We selected 10 species as risky birds: Mallard (Anas platyrhynchos), Northern pintail (Anas acuta), Eurasian wigeon (Anas penelope), Eurasian teal (Anas crecca), Common pochard (Aythya ferina), Eurasian coot (Fulica atra), Northern shoveler (Anas clypeata), Common shelduck (Tadorna tadorna), Tufted duck (Aythya fuligula) and Herring gull (Larus argentatus). We succeeded in clustering 35 meteorological factors into four clusters and identified three meteorological factors associated with their migration: (1) the average daily maximum temperature; (2) the mean value of global solar radiation and (3) the maximum daily precipitation. We thus demonstrated the relationship between the number of risky birds and meteorological data. The dynamics of migratory waterfowl was relevant to the risk of an HPAI outbreak, and our data could contribute to cost and time savings in strengthening preventive measures against epidemics.

Spatio-temporal variation of extreme indices derived from observed and reanalysis products for detection of climate change across India

2020 ◽  
Author(s):  
Sachidanand Kumar ◽  
Kironmala Chanda ◽  
Srinivas Pasupuleti
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Spatio Temporal ◽  
Precipitation And Temperature

<p><strong>Abstract</strong></p><p>This article reports the research findings in a recent study (Kumar et al., 2020) that utilizes eight indices of climate change recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) for analyzing spatio-temporal trends in extreme precipitation and temperature at the daily scale across India. Observed gridded precipitation (1971-2017) and temperature (1971-2013) datasets from India Meteorological Department (IMD) are used along with reanalysis products from Climate Prediction Centre (CPC). The trends are estimated using non-parametric Mann-Kendall (MK) test and regression analysis. The trends in ‘wet days’ (daily precipitation greater than 95<sup>th</sup> percentile) and ‘dry days’ (daily precipitation lower than 5<sup>th</sup> percentile) are examined considering the entire year (annual) as well as monsoon months only (seasonal). At the annual scale, about 13% of the grid locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme ‘wet days’) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme ‘dry days’). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The spatio-temporal trends in ‘warm days’ (daily maximum temperature greater than 95<sup>th</sup> percentile), ‘warm nights’ (daily minimum temperature greater than 95<sup>th</sup> percentile), ‘cold days’ (daily maximum temperature lower than 5<sup>th</sup> percentile) and ‘cold nights’ (daily minimum temperature lower than 5<sup>th</sup> percentile) are also investigated for the aforementioned period. The number of ‘warm days’ per year increased significantly at 14% of the locations, while the number of ‘cold days’, ‘warm nights’ and ‘cold nights’ per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices are also investigated for the future using CanESM2 projected data for RCP8.5 after suitable bias correction. Most of the locations (49% to 84%) indicate significant increasing (decreasing) trend in ‘warm days’ (‘cold days’) in the three epochs, 2006-2040, 2041-2070 and 2071-2100. Moreover, most locations (60% to 81%) show an increasing trend in ‘warm nights’ and a decreasing trend in ‘cold nights’ in all the epochs. A similar investigation for the historical and future periods using CPC data as the reference indicates that the trends, on comparison with IMD observations, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.</p><p><strong>Keywords: extreme precipitation and temperature, climate change indices, spatio-temporal variation, India</strong></p><p><strong>References:</strong></p><p>Kumar S., Chanda, K., Srinivas P., (2020), Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India, Theoretical and Applied Climatology, Springer, In press, DOI: 10.1007/s00704-020-03088-5.</p>

scholarly journals Joint Occurrence of Daily Temperature and Precipitation Extreme Events over Canada

2014 ◽  
Vol 53 (9) ◽  
pp. 2148-2162 ◽  
Author(s):  
Bárbara Tencer ◽  
Andrew Weaver ◽  
Francis Zwiers
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Negative Consequences ◽  
Temperature And Precipitation ◽  
Joint Occurrence

AbstractThe occurrence of individual extremes such as temperature and precipitation extremes can have a great impact on the environment. Agriculture, energy demands, and human health, among other activities, can be affected by extremely high or low temperatures and by extremely dry or wet conditions. The simultaneous or proximate occurrence of both types of extremes could lead to even more profound consequences, however. For example, a dry period can have more negative consequences on agriculture if it is concomitant with or followed by a period of extremely high temperatures. This study analyzes the joint occurrence of very wet conditions and high/low temperature events at stations in Canada. More than one-half of the stations showed a significant positive relationship at the daily time scale between warm nights (daily minimum temperature greater than the 90th percentile) or warm days (daily maximum temperature above the 90th percentile) and heavy-precipitation events (daily precipitation exceeding the 75th percentile), with the greater frequencies found for the east and southwest coasts during autumn and winter. Cold days (daily maximum temperature below the 10th percentile) occur together with intense precipitation more frequently during spring and summer. Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

scholarly journals Observed Trends in Indices of Daily Temperature Extremes in South America 1960–2000

2005 ◽  
Vol 18 (23) ◽  
pp. 5011-5023 ◽  
Author(s):  
L. A. Vincent ◽  
T. C. Peterson ◽  
V. R. Barros ◽  
M. B. Marino ◽  
M. Rusticucci ◽  
...  
Keyword(s):  
Climate Change ◽  
South America ◽  
Climate Extremes ◽  
Daily Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Increasing Trends

Abstract A workshop on enhancing climate change indices in South America was held in Maceió, Brazil, in August 2004. Scientists from eight southern countries brought daily climatological data from their region for a meticulous assessment of data quality and homogeneity, and for the preparation of climate change indices that can be used for analyses of changes in climate extremes. This study presents an examination of the trends over 1960–2000 in the indices of daily temperature extremes. The results indicate no consistent changes in the indices based on daily maximum temperature while significant trends were found in the indices based on daily minimum temperature. Significant increasing trends in the percentage of warm nights and decreasing trends in the percentage of cold nights were observed at many stations. It seems that this warming is mostly due to more warm nights and fewer cold nights during the summer (December–February) and fall (March–May). The stations with significant trends appear to be located closer to the west and east coasts of South America.

Assessing the contribution of multiple forcings to changes in temperature extremes 1981–2020 using CMIP6 climate models

2021 ◽  
Author(s):  
Mastawesha Misganaw Engdaw ◽  
Andrew Ballinger ◽  
Gabriele Hegerl ◽  
Andrea Steiner
Keyword(s):  
Climate Change ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Cold Temperatures

<p>In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.</p><p><strong>Keywords:</strong> climate change, temperature, extreme events, attribution, CMIP6</p><p> </p><p><strong>Acknowledgement:</strong> This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)</p>

scholarly journals Monthly Temperature Observations for Uganda

2013 ◽  
Vol 52 (10) ◽  
pp. 2363-2372 ◽  
Author(s):  
John R. Christy
Keyword(s):  
Time Series ◽  
Public Access ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Record Keeping ◽  
Monthly Average ◽  
Weather Observations ◽  
Temperature Observations

AbstractThe International Surface Temperature Initiative is a worldwide effort to locate weather observations, digitize them for public access, and attach provenance to them. As part of that effort, this study sought documents of temperature observations for the nation of Uganda. Although scattered reports were found for the 1890s, consistent record keeping appears to have begun in 1900. Data were keyed in from images of several types of old forms as well as accessed electronically from several sources to extend the time series of 32 stations with at least 4 yr of data back as far as data were available. Important gaps still remain; 1979–93 has virtually no observations from any station. Because many stations were represented by more than one data source, a scheme is described to extract the “best guess” values for each station of monthly averages of the daily maximum, minimum, and mean temperature. A preliminary examination of the national time series indicates that, since the early twentieth century, it appears that Uganda experienced essentially no change in monthly-average daily maximum temperature but did experience a considerable rise in monthly-average daily minimum temperature, concentrated in the last three decades. Because there are many gaps in the data, it is hoped that readers with information on extant data that were not discovered for this study will contact the author or the project so that the data may be archived.

scholarly journals Effects of diurnal warming on soil respiration are not equal to the summed effects of day and night warming in a temperate steppe

2009 ◽  
Vol 6 (8) ◽  
pp. 1361-1370 ◽  
Author(s):  
J. Xia ◽  
Y. Han ◽  
Z. Zhang ◽  
Z. Zhang ◽  
S. Wan
Keyword(s):  
Soil Respiration ◽  
Temperature Increase ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperate Steppe ◽  
Positive Effects ◽  
Diurnal Warming ◽  
Night Warming

Abstract. The magnitude of daily minimum temperature increase is greater than that of daily maximum temperature increase under climate warming. This study was conducted to examine whether changes in soil respiration under diurnal warming are equal to the summed changes under day and night warming in a temperate steppe in northern China. A full factorial design with day and night warming was used in this study, including control, day (06:00 a.m.–06:00 p.m., local time) warming, night (06:00 p.m.–06:00 a.m.) warming, and diurnal warming. Day warming showed no effect on soil respiration, whereas night warming significantly increased soil respiration by 7.1% over the 3 growing seasons in 2006–2008. The insignificant effect of day warming on soil respiration could be attributable to the offset of the direct positive effects of increased temperature by the indirect negative effects via aggravating water limitation and suppressing ecosystem C assimilation. The positive effects of night warming on soil respiration were largely due to the stimulation of ecosystem C uptake and substrate supply via overcompensation of plant photosynthesis. Changes in both soil respiration (+20.7 g C m−2 y−1) and GEP (−2.8 g C m−2 y−1) under diurnal warming are smaller than their summed changes (+40.0 and +24.6 g C m−2 y−1, respectively) under day and night warming. Our findings that the effects of diurnal warming on soil respiration and gross ecosystem productivity are not equal to the summed effects of day and night warming are critical for model simulation and projection of climate-carbon feedback.

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