scholarly journals Relationship Between Sunshine Duration and Air Temperature in Poland (1971–2020)

2021 ◽  
Author(s):  
Dorota Matuszko ◽  
Krzysztof Bartoszek ◽  
Jakub Soroka
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Air Temperature ◽  
Sunshine Duration ◽  
Temperature Trends ◽  
Daily Data ◽  
Source Data ◽  
Basic Source ◽  
Global Dimming ◽  
Contemporary Climate

Abstract The aim of the work is to characterize the trends of sunshine duration (SDU) and air temperature, which may help understand the mechanism of contemporary climate change and explain its causes. The daily totals of SDU and daily data on air temperature from the years 1971–2020, from 25 synoptic stations in Poland are the basic source data. There was a growing trend in both SDU and air temperature. The series of records of the two variables showed that the points of change in the level of stabilization of the value of SDU and air temperature are close to each other, and confirm known in the literature “global dimming” and “global brightening” periods. The linear regression model confirmed that sunshine duration explains well the variability of, and increase in day-time air temperature in Poland in the April-September period. In turn, changes in sunshine duration during winter have no impact on air temperature trends.

scholarly journals The Relationship Between Sunshine Duration And Air Temperature In Poland In The Face Of Contemporary Climate Change

2021 ◽  
Author(s):  
Dorota Matuszko ◽  
Krzysztof Bartoszek ◽  
Jakub Soroka
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Sunshine Duration ◽  
Temperature Trends ◽  
Daily Data ◽  
The Face ◽  
Basic Source ◽  
Global Dimming ◽  
The Relationship ◽  
Contemporary Climate

Abstract The aim of the work is to characterize the trends of sunshine duration (SDU) and air temperature, which may help understand the mechanism of contemporary climate change and explain its causes. The daily totals of SDU and daily data on air temperature from the years 1971–2019, from 25 synoptic stations in Poland are the basic source data. There was a growing trend in both SDU and air temperature. The series of records of the two variables showed that the points of change in the level of stabilization of the value of SDU and air temperature are close to each other, and confirm known in the literature “global dimming” and “global brightening” periods. The linear regression model confirmed that sunshine duration explains well the variability of, and increase in day-time air temperature in Poland in the April-September period. In turn, changes in sunshine duration during winter have no impact on air temperature trends.

Empirical Equations to Estimate Evapotranspiration in Mosul City

2020 ◽  
Vol 27 (4) ◽  
pp. 98-102
Author(s):  
Haqqi Yasin ◽  
Luma Abdullah
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Daily Data ◽  
Daily Sunshine ◽  
Measured Wind Speed

Average daily data of solar radiation, relative humidity, wind speed and air temperature from 1980 to 2008 are used to estimate the daily reference evapotranspiration in the Mosul City, North of Iraq. ETo calculator software with the Penman Monteith method standardized by the Food and Agriculture Organization is used for calculations. Further, a nonlinear regression approach using SPSS Statistics is utilized to drive the daily reference evapotranspiration relationships in which ETo is function to one or more of the average daily air temperature, actual daily sunshine duration, measured wind speed at 2m height and relative humidity

scholarly journals A data portal for regional climatic trend analysis in a Peruvian High Andes region

2011 ◽  
Vol 6 (1) ◽  
pp. 219-226 ◽  
Author(s):  
M. Schwarb ◽  
D. Acuña ◽  
Th. Konzelmann ◽  
M. Rohrer ◽  
N. Salzmann ◽  
...  
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Climate Change Adaptation ◽  
Air Temperature ◽  
Data Series ◽  
Adaptation Measures ◽  
Climatic Trend ◽  
Temperature And Precipitation ◽  
Temperature Trends ◽  
Data Portal

Abstract. In the frame of a Swiss-Peruvian climate change adaptation initiative (PACC), operational and historical data series of more than 100 stations of the Peruvian Meteorological and Hydrological Service (SENAMHI) are now accessible in a dedicated data portal. The data portal allows for example the comparison of data series or the interpolation of spatial fields as well as download of data in various data formats. It is thus a valuable tool supporting the process of data homogenisation and generation of a regional baseline climatology for a sound development of adequate climate change adaptation measures. The procedure to homogenize air-temperature and precipitation data series near Cusco city is outlined and followed by an exemplary trend analysis. Local air temperature trends are found to be in line with global mean trends.

scholarly journals Air temperature trends and extremes in Chisinau (Moldova) as evidence of climate change

2010 ◽  
Vol 42 (3) ◽  
pp. 247-256 ◽  
Author(s):  
R Corobov ◽  
S Sheridan ◽  
A Overcenco ◽  
N Terinte
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Temperature Trends

scholarly journals ANALISIS KECENDERUNGAN (TREND) SUHU UDARA DAN CURAH HUJAN DI PULAU FLORES (LABUAN BAJO, RUTENG, MAUMERE, DAN LARANTUKA)

2020 ◽  
Vol 5 (1) ◽  
pp. 42-56
Author(s):  
Fifi Novita Ambi ◽  
Hadi Imam Sutadji ◽  
Apolinaris S Geru ◽  
Andreas Christian Louk
Keyword(s):  
Climate Change ◽  
Data Processing ◽  
Air Temperature ◽  
Rainfall Trend ◽  
Upward Trend ◽  
Regression Methods ◽  
Temperature Trends ◽  
Air Temperatures ◽  
Rainfall Trends ◽  
Key Words Climate Change

Abstrak Telah dilakukan penelitian  analisis kecenderungan (trend) suhu udara dan curah hujan di Pulau Flores (Labuan Bajo, Ruteng, Maumere dan Larantuka). Tujuan penelitian ini untuk mengetahui profil suhu udara dan curah hujan serta mengetahui trend suhu udara dan curah hujan di Pulau Flores untuk daerah Labuan Bajo, Ruteng, Maumere, dan Larantuka. Data yang digunakan adalah data sekunder yang diperoleh dari BMKG Stasiun Klimatologi Kupang. Pengolahan data dengan  menghitung rata-rata untuk mengetahui profil curah hujan dan suhu udara serta menggunakan metode regresi linear untuk menghitung trend suhu udara dan curah hujan. Berdasarkan pengolahan data, profil curah hujan di Pulau Flores untuk daerah Labuan Bajo, Ruteng, Maumere, dan Larantuka adalah Pola hujan Monsun, untuk profil suhu udara di Pulau Flores suhu udara rata-rata tertinggi terjadi pada bulan November sebesar 29,90C dan suhu udara rata-rata terendah terjadi pada bulan Juli sebesar 18,50C. Untuk trend curah hujan di Labuan Bajo mengalami trend penurunan sebesar -0,919 mm, Ruteng mengalami trend peningkatan sebesar 1,2688 mm, Maumere mengalami trend peningkatan sebesar 0,1442 mm, Larantuka mengalami trend peningkatan sebesar 0,2734 mm. Untuk Trend suhu udara di Labuan Bajo mengalami trend peningkatan sebesar 0,03470C, Ruteng mengalami trend peningkatan sebesar 0,0050C, Maumere mengalami trend peningkatan sebesar 0,01440C, dan Larantuka mengalami trend peningkatan sebesar 0,0360C. Kata kunci: Perubahan iklim, trend, suhu udara, curah hujan ANALYSIS OF TREND OF AIR TEMPERATURE AND RAINFALL IN THE FLORES ISLAND (LABUAN BAJO, RUTENG, MAUMERE AND LARANTUKA)    ABSTRACT Analysis of  the trend rainfall and air temperature has been conducted on Flores Island (Labuan Bajo, Ruteng, Maumere and Larantuka). The purpose of this study was to determine the profile of air temperature and rainfall and determine air temperature and rainfall trend on the island of Flores for the areas of Labuan Bajo, Ruteng, Maumere, and Larantuka. Data obtained from BMKG Kupang Climatology Station. Data processing by calculating the average to determine the profile of rainfall and air temperature and using linear regression methods to calculate air temperature and rainfall trends. Based on data processing, rainfall profiles on Flores Island for the areas of Labuan Bajo, Ruteng, Maumere, and Larantuka are Monsoon Rain Patterns, for the temperature profile on Flores Island the highest average air temperatures occur in November at 29.90C and the temperature the lowest average air occurred  in July of 18,50C. Rainfall trend in Labuan Bajo experienced a downward trend of -0.919 mm, Ruteng experienced an upward trend of 1.2688 mm, Maumere experienced an upward trend of 0.1442 mm, Larantuka had an upward  trend of 0.2734 mm. For air temperature trends in Labuan Bajo experiencing an upward  trend of 0.03470C, Ruteng experiencing an upward trend of 0.0050C, Maumere experiencing an upward trend of 0.01440C,and Larantuka experiencing an upward trend of 0.0360C. Key words: climate change, trends, air temperature, rainfall

Anthropo-generated Climate Change in Europe

1992 ◽  
Vol 19 (4) ◽  
pp. 349-353 ◽  
Author(s):  
Robert C. Balling ◽  
Sherwood B. Idso
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Strong Support ◽  
Surface Air Temperature ◽  
The United States ◽  
The Past ◽  
Temperature And Precipitation ◽  
Temperature Trends ◽  
Wide Range ◽  
Northern Hemispheric

In reviewing the results of our analyses of European temperature and precipitation data, we see patterns that are similar to those discovered in our prior studies of the United States and the British Isles: precipitation begins to increase at about the time that Northern Hemispheric SO2 emissions began their rapid ascension, while prior upward trends of surface-air temperature are dramatically truncated.We also find that surface-air temperature trends of different localities over the past three-and-a-half decades are closely tied to the amount of aerosol sulphates in the atmosphere above them. The wide range and thrust of these several observations, along with their theoretical expectation, provides strong support for the premise that anthropo-generated climate change is indeed occurring in Europe, but that it may well be SO2-induced rather than CO2-induced.

scholarly journals A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models

2020 ◽  
Vol 12 (15) ◽  
pp. 2434 ◽  
Author(s):  
Lucille Alonso ◽  
Florent Renard
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Air Temperature ◽  
Urban Areas ◽  
Partial Least Square ◽  
Landsat 8 ◽  
Statistical Regression ◽  
Wide Range

Climate change is a major contemporary phenomenon with multiple consequences. In urban areas, it exacerbates the urban heat island phenomenon. It impacts the health of the inhabitants and the sensation of thermal discomfort felt in urban areas. Thus, it is necessary to estimate as well as possible the air temperature at any point of a territory, in particular in view of the ongoing rationalization of the network of fixed meteorological stations of Météo-France. Understanding the air temperature is increasingly in demand to input quantitative models related to a wide range of fields, such as hydrology, ecology, or climate change studies. This study thus proposes to model air temperature, measured during four mobile campaigns carried out during the summer months, between 2016 and 2019, in Lyon (France), in clear sky weather, using regression models based on 33 explanatory variables from traditionally used data, data from remote sensing by LiDAR (Light Detection and Ranging), or Landsat 8 satellite acquisition. Three types of statistical regression were experimented: partial least square regression, multiple linear regression, and a machine learning method, the random forest regression. For example, for the day of 30 August 2016, multiple linear regression explained 89% of the variance for the study days, with a root mean square error (RMSE) of only 0.23 °C. Variables such as surface temperature, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) have a strong impact on the estimation model. This study contributes to the emergence of urban cooling systems. The solutions available vary. For example, they may include increasing the proportion of vegetation on the ground, facades, or roofs, increasing the number of basins and water bodies to promote urban cooling, choosing water-retaining materials, humidifying the pavement, increasing the number of public fountains and foggers, or creating shade with stretched canvas.

scholarly journals Special Issue on Climate Migration

2021 ◽  
pp. 1-3
Author(s):  
Anda David ◽  
Frédéric Docquier
Keyword(s):  
Climate Change ◽  
Human Mobility ◽  
Economic Research ◽  
Special Issue ◽  
Low Latitude ◽  
Weather Shocks ◽  
Temperature Trends ◽  
Agricultural Areas ◽  
Slow Onset

How do weather shocks influence human mobility and poverty, and how will long-term climate change affect future migration over the course of the 21st century? These questions have gained unprecedented attention in public debates as global warming is already having severe impacts around the world, and prospects for the coming decades get worse. Low-latitude countries in general, and their agricultural areas in particular, have contributed the least to climate change but are the most adversely affected. The effect on people's voluntary and forced displacements is of major concern for both developed and developing countries. On 18 October 2019, Agence Française de Développement (AFD) and Luxembourg Institute of Socio-Economic Research (LISER) organized a workshop on Climate Migration with the aim of uncovering the mechanisms through which fast-onset variables (such as weather anomalies, storms, hurricanes, torrential rains, floods, landslides, etc.) and slow-onset variables (such as temperature trends, desertification, rising sea level, coastal erosion, etc.) influence both people's incentives to move and mobility constraints. This special issue gathers five papers prepared for this workshop, which shed light on (or predict) the effect of extreme weather shocks and long-term climate change on human mobility, and stress the implications for the development community.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

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