Long-term air temperature variation in the Karkonosze mountains according to atmospheric circulation

<p>Long-term trends of local winds such as sea breezes have been less addressed in climate research, despite their impacts on broad environmental and socioeconomic spheres, such as weather and climate, agriculture and hydrology, wind-power industry, air quality or even human health, among many others. In a warming climate, sea breezes could be affected by changes on air temperature, as these onshore winds are thermally-driven by gradients between the sea-land air, but also by ocean-atmosphere oscillations or changes in large-scale atmospheric circulation. In the last few decades, advances in wind trends studies evidenced a recovery in global wind stilling during the last 10 years, and differences in the sign-magnitude of wind speed trends were found at seasonal-scale, suggesting the hypothetic effect of the reinforcement of local wind circulations in the warm seasons.</p><p>In this study, we analyze for the first time the long-term trends, multidecadal variability and possible drivers of the sea-breeze speeds and gusts in Eastern Iberian Peninsula during the last 58 years (1961-2019), using homogenized wind speed and gusts data from 16 meteorological stations. To identify potential sea breeze episodes, we developed a robust automated method based on alternative criteria. Our results suggest a decoupling between the declining sea-breeze speeds and the strengthening of the maximum gusts for much of the 1961-2019 period at annual, seasonal and monthly scales, but differences based on locations were also found. Because sea breeze changes can be driven by multiple complex factors (i.e. land use changes, land-sea air temperature gradient, complex orography, etc.), the attribution of causes is challenging. To better understand the causes behind the opposite trends between sea-breeze speeds and gusts, we investigate the effect of e.g. the changes in large-scale atmospheric circulation or physical-local factors.</p>

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Long-term trend in ground-based air temperature and its responses to atmospheric circulation and anthropogenic activity in the Yangtze River Delta, China

Atmospheric Research ◽

10.1016/j.atmosres.2017.05.013 ◽

2017 ◽

Vol 195 ◽

pp. 20-30 ◽

Cited By ~ 12

Author(s):

Xia Peng ◽

Qiannan She ◽

Lingbo Long ◽

Min Liu ◽

Qian Xu ◽

...

Keyword(s):

Atmospheric Circulation ◽

Air Temperature ◽

Yangtze River ◽

Yangtze River Delta ◽

Anthropogenic Activity ◽

The Yangtze River ◽

Term Trend ◽

Long Term Trend ◽

The Yangtze River Delta

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On the dynamics of distribution of the American White Butterfly, Hyphantria cunea Drury, 1973 (Lepidoptera, Arctiidae), in Ukraine regarding the annual minimal air temperatures

Ukrainian Entomological Journal ◽

10.15421/281806 ◽

2018 ◽

Vol 14 (1) ◽

pp. 44-57

Author(s):

S. N. Shumov

Keyword(s):

Air Temperature ◽

Annual Reports ◽

Complete Elimination ◽

Hyphantria Cunea ◽

White American ◽

Gis Technologies ◽

Data Analyses ◽

Air Temperatures ◽

Negative Temperatures

The spatial analysis of distribution and quantity of Hyphantria cunea Drury, 1973 across Ukraine since 1952 till 2016 regarding the values of annual absolute temperatures of ground air is performed using the Gis-technologies. The long-term pest dissemination data (Annual reports…, 1951–1985; Surveys of the distribution of quarantine pests ..., 1986–2017) and meteorological information (Meteorological Yearbooks of air temperature the surface layer of the atmosphere in Ukraine for the period 1951-2016; Branch State of the Hydrometeorological Service at the Central Geophysical Observatory of the Ministry for Emergencies) were used in the present research. The values of boundary negative temperatures of winter diapause of Hyphantria cunea, that unable the development of species’ subsequent generation, are received. Data analyses suggests almost complete elimination of winter diapausing individuals of White American Butterfly (especially pupae) under the air temperature of −32°С. Because of arising questions on the time of action of absolute minimal air temperatures, it is necessary to ascertain the boundary negative temperatures of winter diapause for White American Butterfly. It is also necessary to perform the more detailed research of a corresponding biological material with application to the freezing technics, giving temperature up to −50°С, with the subsequent analysis of the received results by the punched-analysis.

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Simulation of the internal temperature in the Passol Laboratory, University of Debrecen

International Review of Applied Sciences and Engineering ◽

10.1556/irase.3.2012.1.08 ◽

2012 ◽

Vol 3 (1) ◽

pp. 63-73 ◽

Cited By ~ 4

Author(s):

I. Csáky ◽

F. Kalmár

Keyword(s):

Temperature Variation ◽

Air Temperature ◽

Indoor Air ◽

Storage Capacity ◽

Building Structures ◽

Internal Temperature ◽

The One ◽

Heavy Structure ◽

East West ◽

Made In

Abstract Nowadays the facades of newly built buildings have significant glazed surfaces. The solar gains in these buildings can produce discomfort caused by direct solar radiation on the one hand and by the higher indoor air temperature on the other hand. The amplitude of the indoor air temperature variation depends on the glazed area, orientation of the facade and heat storage capacity of the building. This paper presents the results of a simulation, which were made in the Passol Laboratory of University of Debrecen in order to define the internal temperature variation. The simulation proved that the highest amplitudes of the internal temperature are obtained for East orientation of the facade. The upper acceptable limit of the internal air temperature is exceeded for each analyzed orientation: North, South, East, West. Comparing different building structures, according to the obtained results, in case of the heavy structure more cooling hours are obtained, but the energy consumption for cooling is lower.

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Impact of aerosol on air temperature in Baghdad

Journal of Applied and Advanced Research ◽

10.21839/jaar.2017.v2i6.112 ◽

2017 ◽

Vol 2 (6) ◽

Author(s):

Yaseen Kadhim Abbas Al-Timimi ◽

Ali Challob Khraibet

Keyword(s):

Temperature Variation ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Air Temperature ◽

Strong Correlation ◽

Imaging Spectrometer ◽

Moderate Resolution ◽

Resolution Imaging

Aerosol Optical Depth (AOD) is the measure of aerosol distributed with a Column of air from earth’s surface to the top of atmosphere, in this study, temperature variation of aerosol optical depth (AOD) in Baghdad was analyzed Moderate Resolution Imaging Spectrometer (MODIS) from Terra and its relationship with temperature for the period 2003 – 2015 were examined. The highest values for mean seasonal AOD were observed in spring and summer and the maximum AOD values ranged from 0.50 to 0.58 by contrast minimum AOD values ranging from 0.30 to 0.41 were found in winter and autumn. Results of study also showed that the temperature (max., min., mean air temperature and DTR) have a strong correlation with AOD (0.82, 0.83, 0.82 and 0.65) respectively.

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Prediction of long-term strain in concrete structure using convolutional neural networks, air temperature and time stamp of measurements

Automation in Construction ◽

10.1016/j.autcon.2021.103665 ◽

2021 ◽

Vol 126 ◽

pp. 103665

Author(s):

Byung Kwan Oh ◽

Hyo Seon Park ◽

Branko Glisic

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Air Temperature ◽

Concrete Structure ◽

Time Stamp

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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 ◽

10.3390/w13081109 ◽

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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