scholarly journals Resolution of the M-shape Pattern of the Outdoor Air Temperature Environmental Kuznets Curve (EKC) for Metropolitan Areas in a Country: Using Long-term Monthly Level Data of Taipei City as Empirical Evidence

2021 ◽  
pp. 25-36
Author(s):  
Wu-Jang Huang
Keyword(s):  
Growth Rate ◽  
Air Temperature ◽  
Metropolitan Areas ◽  
Co2 Concentration ◽  
Rate Change ◽  
Solar Irradiation ◽  
Outdoor Air ◽  
Heating Source ◽  
Taipei City

In Taiwan, the heat island effect is the most significant in Taipei City. Thus this research provides a causal explanation for why urban outdoor air temperature has an M-shape EKC pattern for metropolitan areas in a country. Results show that the growth rate change in CO2 concentration can induce changes to the periods of the La Nino effect and EI Nino effect, causing high fluctuations in rain accumulation. The amount of rain then alters A-type evaporation, and so the evaporation amount is the top factor for the diffusion of a city’s heat. This fluctuation plays as a cooling and heating source for the V region of the M shape in the outdoor air temperature EKC pattern. In our previous studies, the growth rate change in CO2 concentration correlates to the energy structure. Therefore, a heat sinking model has been proposed to explain the accumulation of heat in a city, in which a proportion process for the solar irradiation source from buildings and remodeling engineering from a public housing policy and the private sector can play as a heating source of the two peaks of the M shape and present long-term linear growth in the outdoor air temperature EKC pattern.

scholarly journals Thirty Years of Atmospheric CO2 Observations at the Plateau Rosa Station, Italy

Atmosphere ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 418 ◽  
Author(s):  
Francesco Apadula ◽  
Claudio Cassardo ◽  
Silvia Ferrarese ◽  
Daniela Heltai ◽  
Andrea Lanza
Keyword(s):  
Growth Rate ◽  
Co2 Concentration ◽  
Western Alps ◽  
Data Series ◽  
The North ◽  
Global Trend ◽  
Background Data ◽  
Multivariate Enso Index ◽  
Climatic System

The atmospheric background CO2 concentration is a key quantity for the analysis and evaluation of the ongoing climate change. Long-term CO2 observations have been carried out at the high Plateau Rosa mountain station, in the north-western Alps since 1989. The complete time series covers thirty years, and it is suitable for climatological analysis. The continuous CO2 measurements, collected since 1993, were selected, by means of a BaDS (Background Data Selection) filter, to obtain the hourly background data. The monthly background data series was analysed in order to individuate the parameters that characterise the seasonal cycle and the long-term trend. The growth rate was found to be 2.05 ± 0.03 ppm/year, which is in agreement with the global trend. The increased background CO2 concentration at the Plateau Rosa site is the consequence of global anthropic emissions, whereas the natural variability of the climatic system taken from the SOI (South Oscillation Index) and MEI (Multivariate ENSO Index) signals was detected in the inter-annual changes of the Plateau Rosa growth rate.

scholarly journals Increased Atmospheric CO2 Growth Rate during El Niño Driven by Reduced Terrestrial Productivity in the CMIP5 ESMs

2016 ◽  
Vol 29 (24) ◽  
pp. 8783-8805 ◽  
Author(s):  
Jin-Soo Kim ◽  
Jong-Seong Kug ◽  
Jin-Ho Yoon ◽  
Su-Jong Jeong
Keyword(s):  
Growth Rate ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Net Primary Production ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Climate Projections ◽  
Tropical Regions

Abstract Better understanding of factors that control the global carbon cycle could increase confidence in climate projections. Previous studies found good correlation between the growth rate of atmospheric CO2 concentration and El Niño–Southern Oscillation (ENSO). The growth rate of atmospheric CO2 increases during El Niño but decreases during La Niña. In this study, long-term simulations of the Earth system models (ESMs) in phase 5 of the Coupled Model Intercomparison Project archive were used to examine the interannual carbon flux variability associated with ENSO. The ESMs simulate the relationship reasonably well with a delay of several months between ENSO and the changes in atmospheric CO2. The increase in atmospheric CO2 associated with El Niño is mostly caused by decreasing net primary production (NPP) in the ESMs. It is suggested that NPP anomalies over South Asia are at their maxima during boreal spring; therefore, the increase in CO2 concentration lags 4–5 months behind the peak phase of El Niño. The decrease in NPP during El Niño may be caused by decreased precipitation and increased temperature over tropical regions. Furthermore, systematic errors may exist in the ESM-simulated temperature responses to ENSO phases over tropical land areas, and these errors may lead to an overestimation of ENSO-related NPP anomalies. In contrast, carbon fluxes from heterotrophic respiration and natural fires are likely underestimated in the ESMs compared with offline model results and observational estimates, respectively. These uncertainties should be considered in long-term projections that include climate–carbon feedbacks.

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

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.

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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