Impact of Climatic Changes on Earth's Survival

Every species' survival on earth is dependent on each other for their demand and dependent on the environment and various other sources. These resources include fresh food, clean drinking water, timber for construction, natural gas and coal for industries, fibers for clothing. All the human activity affects the environment severely in different ways. The biggest threats to the environment are climatic changes. Climate is an important factor that affects all survival on earth. The different pollutants, transport, dispersion, chemical transformation, as well as the deposition can be affected by meteorological variable such as humidity, wind, temperature. Climatic changes are expected to worsen the quality of air and water by changing the atmospheric processes and chemistry. Not only human beings but every aspect of the ecosystem is affected due to the changing climate. This chapter will explore the impacts of climatic changes on biodiversity by various activities of humans. Additionally, it will sketch how the impacts can be reduced by plants.

A Relationship between Micro-Meteorological and Personal Variables of Outdoor Thermal Comfort: A Case Study in Kitakyushu, Japan

Outdoor thermal comfort is an important indicator to create a quality and livable environment. This study examines a relationship between micro-meteorological and personal variables of outdoor thermal comfort conditions in an urban park. The data collection of outdoor thermal comfort is carried out using two methods in combination: micro-meteorological measurement and questionnaire survey. This finding shows that most of the respondents were comfortable with the thermal, wind, and humidity condition. The acceptability and satisfaction level of thermal comfort were positive. The most significant micro-meteorological variable for the physiologically equivalent temperature (PET) value is mean radiant temperature (Tmrt). As the Tmrt value is influenced by how much shading is produced from the presence of vegetation or buildings around the measurement location, this finding shows that the shadow was very important to the thermal comfort conditions in the Green Park Kitakyushu. The most influential micro-meteorological variable for the three different personal variables (TSV, WFSV, and HSV) is air temperature. The strongest relationship among the four variables is between TSV and PET. The findings will be the basis for the city authorities in preparing regional development plans, especially those related to the planning of city parks or tourist attractions.

METEOROLOGICAL VARIABLES AND SENSORIAL QUALITY OF COFFEE IN THE MANTIQUEIRA REGION OF MINAS GERAIS

<p>The objective of this study was to identify meteorological variables related to the sensorial quality of the coffees from Mantiqueira region in Minas Gerais. Meteorological conditions are strongly related to the coffee’s sensorial characteristics, however, there aren’t many studies quantifying this relation. Air temperature and rainfall data were collected and spatialized for regional analysis. These were associated to the 2007 through 2011 coffees’ beverage scores. The region is stratified according to relief characteristics. The bigger frequency of high scores occurred on the region’s central-south, where coffee cultivation is performed above 900 m altitude. For the <em>in loco </em>study, meteorological data and coffee samples were collected in selected pilot areas. Coffee crops were selected in three altitude ranges: below 1000 m, between 1000 and 1200 m, and over 1200 m. Above 1000 m the meteorological variable that presented the biggest variation was the air temperature. Above 1000 m the smallest thermal amplitude occurred, which provided superior quality coffees. The study demonstrates the importance of the meteorological variable characterization aiming to identify locations with greater vocation to the specialty coffees production.</p>

Impact of Climatic Changes on Earth's Survival

Every species' survival on earth is dependent on each other for their demand and dependent on the environment and various other sources. These resources include fresh food, clean drinking water, timber for construction, natural gas and coal for industries, fibers for clothing. All the human activity affects the environment severely in different ways. The biggest threats to the environment are climatic changes. Climate is an important factor that affects all survival on earth. The different pollutants, transport, dispersion, chemical transformation, as well as the deposition can be affected by meteorological variable such as humidity, wind, temperature. Climatic changes are expected to worsen the quality of air and water by changing the atmospheric processes and chemistry. Not only human beings but every aspect of the ecosystem is affected due to the changing climate. This chapter will explore the impacts of climatic changes on biodiversity by various activities of humans. Additionally, it will sketch how the impacts can be reduced by plants.

CHARACTERIZATION OF THE PREDOMINANT WIND SPEED AND DIRECTION IN SANTA CATARINA, BRAZIL

Although the state of Santa Catarina has little variation in latitude, it presents significant spatial variations in its climate. Wind is considered an important meteorological variable, but it is not intensively studied and there is a shortage of information on this subject in the region of Santa Catarina. Thus, the objective of this article was to analyze the behavior of the winds in five regions of the state, with different aspects. For that, daily data from National Institute of Meteorology of direction and speed of the winds were used from 1974 to 2016. The conventional meteorological stations used were: Chapecó, Campos Novos, Lages, Indaial and Florianópolis. Regarding wind speed, Florianópolis and Campos Novos registered the highest speeds during the whole year. In the analysis of wind direction, the prevalence of South Atlantic subtropical anticyclone was observed in most of the year in Forianópolis (circulation going north), Lages and Campos Novos (turning northeast) and Chapecó (predominant wind direction in the este). For Indaial, the prevalence was the effect of the valley-mountain breeze.

PREVISÃO CLIMÁTICA DE PRECIPITAÇÃO PARA A REGIÃO SUL POR REDE NEURAL AUTOCONFIGURADA

Climate prediction for precipitation field is a key issue, because such meteorological variable is the challenge for climate and weather forecasting due to the high spatial and temporal variability with strong impact on the society. A method based on the artificial neural network is applied to monthly and seasonal precipitation forecast in southern Brazil. The use of neural networks as a predictive model is widespread in different applications. The best configuration for the neural network is automatically calculated. The autoconfiguration scheme is described as an optimization problem.

Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis

Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations. The ERA-Interim reanalysis data de-biased at FLUXNET sites can be downloaded from the PANGAEA data centre (http://doi.pangaea.de/10.1594/PANGAEA.838234).

Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-interim reanalysis

Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 sites registered and up to 250 of them sharing data (Free Fair Use dataset). Many modelling groups use the FLUXNET dataset for evaluating ecosystem model's performances but it requires uninterrupted time series for the meteorological variables used as input. Because original in-situ data often contain gaps, from very short (few hours) up to relatively long (some months), we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-interim) and high temporal resolution spanning from 1989 to today. These data are however not measured at site level and for this reason a method to downscale and correct the ERA-interim data is needed. We apply this method on the level 4 data (L4) from the LaThuile collection, freely available after registration under a Fair-Use policy. The performances of the developed method vary across sites and are also function of the meteorological variable. On average overall sites, the bias correction leads to cancel from 10 to 36% of the initial mismatch between in-situ and ERA-interim data, depending of the meteorological variable considered. In comparison to the internal variability of the in-situ data, the root mean square error (RMSE) between the in-situ data and the un-biased ERA-I data remains relatively large (on average overall sites, from 27 to 76% of the standard deviation of in-situ data, depending of the meteorological variable considered). The performance of the method remains low for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations. The ERA-interim reanalysis data debiased at FLUXNET sites can be downloaded from the PANGAEA data center (http://doi.pangaea.de/10.1594/PANGAEA.838234).