scholarly journals Trend Analysis of Temperature and Humidity in Kwara State, Nigeria

2020 ◽  
Vol 13 (3-4) ◽  
pp. 44-50
Author(s):  
Adeniyi Adedapo
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Human Health ◽  
Trend Analysis ◽  
Maximum Temperature ◽  
Temperature Minimum ◽  
Significant Difference ◽  
Temperature And Humidity ◽  
Anomaly Index

Abstract This paper examines the trend analysis of temperature and relative humidity in Kwara State. Climatic data on annual mean temperature (minimum and maximum) and relative humidity for 40 years (1978-2017) were collected from Nigerian Meteorological Agency (NIMET) Ilorin. Semi-Average method, Mann- Kendull statistics and regression method were used to analyse the trend in temperature and relative humidity. The Standardized Anomaly Index (SAI) was also used to examine the changes in temperature and humidity over the period of 1978-2017. The result of the analysis indicates that temperature (minimum and maximum) and relative humidity exhibit an upward trend. This implies that temperature and relative humidity increase over the period of 1978-2017. The Mann-Kendull statistics values show that there is no significant difference in the values of temperature (minimum and maximum) and relative humidity. The result of the Standardized Anomaly Index (SAI) also revealed that the values of temperature and humidity fluctuated around the long –term mean. About 50% of the annual average relative humidity falls above the long term average while 40% of the annual mean maximum temperature falls above the long term average. It can therefore, be deduced that there is the possibility of increment in the values of temperature and relative humidity which could cause a serious challenge to human health and climate change. The study therefore, suggests that increase and fluctuations in temperature and relative humidity should be a critical factor in designing strategies to mitigate the effect of climate change on the environment and human health.

scholarly journals Relationship Between Climate Change and Rice Production of Bangladesh: A data analysis using MCMC method

2021 ◽  
Author(s):  
Atiqur Chowdhury
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Rice Production ◽  
Predictor Variables ◽  
Maximum Temperature ◽  
Temperature Minimum ◽  
Model Sets ◽  
Average Rainfall ◽  
Temperature And Humidity ◽  
The Relationship

Abstract In this study, we analyzed publicly available agricultural data on rice production in Bangladesh between 2008 to 2017 to address the relationship between climate changes and rice production in Bangladesh by estimating predictor variables, i.e., average rainfall and maximum temperature, minimum temperature, and humidity. A generalized linear regression model sets up for each rice (Aush, Aman, Boro) with the climate variables (average rainfall, maximum temperature, minimum temperature, and humidity). We used Markov-Chain-Monte-Carlo's (MCMC)'s Gibbs sampling on the collected data to approximate marginal posterior distribution from the prior distribution to see the profound relationship between those predictor variables and the predicted variables (Aush, Aman, Boro). We also saw whether any storm's impact could modify the relationship between climate change variables and rice production in Bangladesh.

IAGOS Research Infrastructure for Global-Scale Atmosphere Monitoring by Instrumented Passenger Aircraft

2021 ◽  
Author(s):  
Andreas Petzold ◽  
Valerie Thouret ◽  
Christoph Gerbig ◽  
Andreas Zahn ◽  
Martin Gallagher ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Relative Humidity ◽  
Water Vapour ◽  
Research Infrastructure ◽  
Good Resolution ◽  
European Research ◽  
Commercial Aircraft ◽  
High Quality

<p>IAGOS (www.iagos.org) is a European Research Infrastructure using commercial aircraft (Airbus A340, A330, and soon A350) for automatic and routine measurements of atmospheric composition including reactive gases (ozone, carbon monoxide, nitrogen oxides, volatile organic compounds), greenhouse gases (water vapour, carbon dioxide, methane), aerosols and cloud particles along with essential thermodynamic parameters. The main objective of IAGOS is to provide the most complete set of high-quality essential climate variables (ECV) covering several decades for the long-term monitoring of climate and air quality. The observations are stored in the IAGOS data centre along with added-value products to facilitate the scientific interpretation of the data. IAGOS began as two European projects, MOZAIC and CARIBIC, in the early 1990s. These projects demonstrated that commercial aircraft are ideal platforms for routine atmospheric measurements. IAGOS then evolved as a European Research Infrastructure offering a mature and sustainable organization for the benefits of the scientific community and for the operational services in charge of air quality and climate change issues such as the Copernicus Atmosphere Monitoring Services (CAMS) and the Copernicus Climate Change Service (C3S). IAGOS is also a contributing network of the World Meteorological Organization (WMO).</p> <p>IAGOS provides measurements of numerous chemical compounds which are recorded simultaneously in the critical region of the upper troposphere – lower stratosphere (UTLS) and geographical regions such as Africa and the mid-Pacific which are poorly sampled by other means. The data are used by hundreds of groups worldwide performing data analysis for climatology and trend studies, model evaluation, satellite validation and the study of detailed chemical and physical processes around the tropopause. IAGOS data also play an important role in the re-assessment of the climate impact of aviation.</p> <p>Most important in the context of weather-related research, IAGOS and its predecessor programmes provide long-term observations of water vapour and relative humidity with respect to ice in the UTLS as well as throughout the tropospheric column during climb-out and descending phases around airports, now for more than 25 years. The high quality and very good resolution of IAGOS observations of relative humidity over ice are used to better understand the role of water vapour and of ice-supersaturated air masses in the tropopause region and to improve their representation in numerical weather and climate forecasting models. Furthermore, CAMS is using the water vapour vertical profiles in near real time for the continuous validation of the CAMS atmospheric models. </p>

scholarly journals Aeroallergens and Climate Change in Tulsa, Oklahoma: Long-Term Trends in the South Central United States

2021 ◽  
Vol 2 ◽  
Author(s):  
Estelle Levetin
Keyword(s):  
Climate Change ◽  
United States ◽  
Pollen Season ◽  
Maximum Temperature ◽  
The South ◽  
South Central ◽  
Pollen Types ◽  
Central United States ◽  
Total Pollen

Climate change is having a significant effect on many allergenic plants resulting in increased pollen production and shifts in plant phenology. Although these effects have been well-studied in some areas of the world, few studies have focused on long-term changes in allergenic pollen in the South Central United States. This study examined airborne pollen, temperature, and precipitation in Tulsa, Oklahoma over 25 to 34 years. Pollen was monitored with a Hirst-type spore trap on the roof of a building at the University of Tulsa and meteorology data were obtained from the National Weather Service. Changes in total pollen intensity were examined along with detailed analyses of the eight most abundant pollen types in the Tulsa atmosphere. In addition to pollen intensity, changes in pollen season start date, end date, peak date and season duration were also analyzed. Results show a trend to increasing temperatures with a significant increase in annual maximum temperature. There was a non-significant trend toward increasing total pollen and a significant increase in tree pollen over time. Several individual taxa showed significant increases in pollen intensity over the study period including spring Cupressaceae and Quercus pollen, while Ambrosia pollen showed a significant decrease. Data from the current study also indicated that the pollen season started earlier for spring pollinating trees and Poaceae. Significant correlations with preseason temperature may explain the earlier pollen season start dates along with a trend toward increasing March temperatures. More research is needed to understand the global impact of climate change on allergenic species, especially from other regions that have not been studied.

scholarly journals Temperature trends in Fiji: a clear signal of climate change

2013 ◽  
Vol 31 (1) ◽  
pp. 27 ◽  
Author(s):  
Ravind Kumar ◽  
Mark Stephens ◽  
Tony Weir
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Pacific Islands ◽  
Extreme Values ◽  
Maximum Temperature ◽  
Temperature Trends ◽  
Clear Signal ◽  
Rate Of Increase ◽  
Significant Difference ◽  
Using Data

This paper analyses trends in temperature in Fiji, using data from more stations (10) and longer periods (52-78 years) than previous studies. All the stations analysed show a statistically significant trend in both maximum and minimum temperature, with increases ranging from 0.08 to 0.23°C per decade. More recent temperatures show a higher rate of increase, particularly in maximum temperature (0.18 to 0.69°C per decade from 1989 to 2008). This clear signal of climate change is consistent with that found in previous studies of temperatures in Fiji and other Pacific Islands. Trends in extreme values show an even stronger signal of climate change than that for mean temperatures. Our preliminary analysis of daily maxima at 6 stations indicates that for 4 of them (Suva, Labasa, Vunisea and Rotuma) there has been a tripling in the number of days per year with temperature >32°C between 1970 and 2008. The correlations between annual mean maximum (minimum) temperature and year are mostly strong: for about half the stations the correlation coefficient exceeds 60% over 50+ years. Trends do not vary systematically with location of station. At all 7 stations for which both trends are available there is no statistically significant difference between the trends in maximum and minimum temperatures.

A Comparison of Daily Temperature-Averaging Methods: Spatial Variability and Recent Change for the CONUS

2018 ◽  
Vol 31 (3) ◽  
pp. 979-996 ◽  
Author(s):  
Jase Bernhardt ◽  
Andrew M. Carleton ◽  
Chris LaMagna
Keyword(s):  
Spatial Variability ◽  
Daily Temperature ◽  
Maximum Temperature ◽  
Underlying Assumption ◽  
First Order ◽  
Daily Average ◽  
Hourly Temperature ◽  
Significant Difference ◽  
Weather Stations

Abstract Traditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin), over a 24-h period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-yr normals) and recent temperature trends and changes. However, many first-order weather stations—such as those at airports—also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day’s temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging [i.e., (Tmax + Tmin)/2; average of 24 hourly temperature values] for 215 first-order weather stations across the conterminous United States (CONUS) over the 30-yr period 1981–2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method [(Tmax + Tmin)/2], particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice-daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially coherent pattern in the difference compared to the most recent part of the temperature record (2001–15). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change.

scholarly journals Effects of Climate Change on Epidemics of Powdery Mildew in Winter Wheat in China

Plant Disease ◽  
2017 ◽  
Vol 101 (10) ◽  
pp. 1753-1760 ◽  
Author(s):  
Xiuli Tang ◽  
Xueren Cao ◽  
Xiangming Xu ◽  
Yuying Jiang ◽  
Yong Luo ◽  
...  
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Powdery Mildew ◽  
Winter Wheat ◽  
Weather Conditions ◽  
Early Spring ◽  
Long Term Effects ◽  
Temperature Changes ◽  
Disease Epidemics

Powdery mildew is a highly destructive winter wheat pathogen in China. Since the causative agent is sensitive to changing weather conditions, we analyzed climatic records from regions with previous wheat powdery mildew epidemics (1970 to 2012) and investigated the long-term effects of climate change on the percent acreage (PA) of the disease. Then, using PA and the pathogen’s temperature requirements, we constructed a multiregression model to predict changes in epidemics during the 2020s, 2050s, and 2080s under representative concentration pathways RCP2.6, RCP4.5, and RCP8.5. Mean monthly air temperature increased from 1970 to 2012, whereas hours of sunshine and relative humidity decreased (P < 0.001). Year-to-year temperature changes were negatively associated with those of PA during oversummering and late spring periods of disease epidemics, whereas positive relationships were noted for other periods, and year-to-year changes in relative humidity were correlated with PA changes in the early spring period of disease epidemics (P < 0.001). Our models also predicted that PA would increase less under RCP2.6 (14.43%) than under RCP4.5 (14.51%) by the 2020s but would be higher by the 2050s and 2080s and would increase least under RCP8.5 (14.37% by the 2020s). Powdery mildew will, thus, pose an even greater threat to China’s winter wheat production in the future.

Climatology of the relative humidity in the Carpathian Valley

2020 ◽  
Author(s):  
Anikó Cséplő ◽  
István Geresdi ◽  
Ákos Horváth
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Meteorological Station ◽  
Seasonal Trends ◽  
Annual Trend ◽  
Changing Climate ◽  
It Impacts

&lt;p&gt;The reports about the climate change mostly focus about the trend of the temperature or precipitation. However, the relative humidity is also an important characteristic of the atmosphere, e.g. it impacts both the cloud and fog formation. The trends of the relative humidity in the changing climate have been found to be rather uncertain. &amp;#160;In this research the climatological trend of the relative humidity in the Carpathian Valley was studied. Analysis of the long-term observed database from eight meteorological stations was used to present the annual and seasonal trends of the relative humidity. The annual trend was found to be between 2-3% in every meteorological station. The results show that the relative humidity has decreased every season but in autumn, when the trend of it has not been consistent. While the most significant decrease has been occurred during spring, the decrease was negligible during autumn.&lt;/p&gt;

Assessment of the future climate potential for tourism over Europe using a combination of downscaling approaches and quantitative impact models

2020 ◽  
Author(s):  
Maria Francisca Cardell ◽  
Arnau Amengual ◽  
Romualdo Romero
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Future Climate ◽  
Cumulative Distribution ◽  
Maximum Temperature ◽  
Model Errors ◽  
Present Climate ◽  
Ensemble Strategy ◽  
The Future

&lt;p&gt;Europe and particularly, the Mediterranean countries, are among the most visited tourist destinations worldwide, while it is also recognized as one of the most sensitive regions to climate change. Climate is a key resource and even a limiting factor for many types of tourism. Owing to climate change, modified patterns of atmospheric variables such as temperature, rainfall, relative humidity, hours of sunshine and wind speed will likely affect the suitability of the European destinations for certain outdoor leisure activities.&lt;/p&gt;&lt;p&gt;Perspectives on the future of second-generation climate indices for tourism (CIT) that depend on thermal, aesthetic and physical facets are derived using model projected daily atmospheric data and present climate &amp;#8220;observations&amp;#8221;. Specifically, daily series of 2-m maximum temperature, accumulated precipitation, 2-m relative humidity, mean cloud cover and 10-m wind speed from ERA-5 reanalysis are used to derive the present climate potential. For projections, the same daily variables have been obtained from a set of regional climate models (RCMs) included in the European CORDEX project, considering the rcp8.5 future emissions scenario. The adoption of a multi-model ensemble strategy allows quantifying the uncertainties arising from the model errors and the GCM-derived boundary conditions. To properly derive CITs at local scale, a quantile&amp;#8211;quantile adjustment has been applied to the simulated regional scenarios. The method detects changes in the continuous CIT cumulative distribution functions (CDFs) between the recent past and successive time slices of the simulated climate and applies these changes, once calibrated, to the observed CDFs.&amp;#160;&lt;/p&gt;&lt;p&gt;Assessments on the future climate potential for several types of tourist activities in Europe (i.e., sun, sea and sand (3S) tourism, cycling, cultural, football, golf, nautical and hiking) will be presented by applying suitable quantitative indicators of CIT evolutions adapted to regional contexts. It is expected that such kind of information will ultimately benefit the design of mitigation and adaptation strategies of the tourist sector.&lt;/p&gt;

scholarly journals Investigation of Climate Change Anomaly by Using Nonparametric Test for Navsari District of South Gujarat

2021 ◽  
Vol 9 (3) ◽  
pp. 266-275
Author(s):  
Neeraj Kumar ◽  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Relative Humidity ◽  
Nonparametric Test ◽  
Maximum Temperature ◽  
Rainfall Trend ◽  
Regression Slope ◽  
Sunshine Hours ◽  
Increasing Trend ◽  
Daily Sunshine

Navsari district of rainfall was shows highest increasing rainfall trend obtained September and negative January, July, October, November and December. The regression slope of the yearly time series is about 12.35 mm/36 years. Maximum temperature shows the highest increasing trend in month October, followed by December and August. The month highest decreasing trend was noticed that January, followed by February and July. The regression slope of the yearly time series is about 0.025°C/36 years. Minimum temperature highest values of the slope (0.109°C/36 year) with high value of regression Slope of determination (0.111°C), the annual Kendall’s tau statistic (0.492°C/36 year), the Kendall Score (310). All the month January to December shows increasing trend. The highest increasing trend found that November, followed by March and July, respectively. This finding shows that all the month shows increasing trend with the range between 0.308°C to 0.390°C. In case of RH-I the highest increasing trend shows September, followed by April and June. Similarly decreasing trend was found that January, followed by February and October, respectively. Relative humidity-II increasing trend was found only at the September month 0.084%, the increasing trend was detected in January to August and October to December, respectively. The strongest trend in the Bright sunshine hour’s decline of all month’s average daily sunshine hours was for the Navsari district. No significant trends were detected in all months and seasons for all weather elements. A similar trend was found in Sen’s slope and regression slope all the months for all the weather elements.

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