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Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 252
Dmitriy Bantcev ◽  
Dmitriy Ganyushkin ◽  
Anton Terekhov ◽  
Alexey Ekaykin ◽  
Igor Tokarev ◽  

The objective of this study is to reveal the isotopic composition of ice and meltwater in glaciated regions of South-Eastern Altai. The paper depicts differences between the isotopic composition of glacier ice from several types of glaciers and from various locations. Detected differences between the isotopic composition of glacier ice in diversified parts of the study region are related to local climate patterns. Isotopic composition of meltwater and isotopic separation for glacier rivers runoff showed that in the Tavan-Bogd massif, seasonal snow participates more in the formation of glacier runoff due to better conditions for snow accumulation on the surface of glaciers. In other research areas pure glacier meltwater prevails in runoff.

2022 ◽  
Vol 15 (1) ◽  
pp. 251-268
Anna Vaughan ◽  
Will Tebbutt ◽  
J. Scott Hosking ◽  
Richard E. Turner

Abstract. A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies.

Ethan David Coffel ◽  
Corey Lesk ◽  
Jonathan M Winter ◽  
Erich C Osterberg ◽  
Justin Staller Mankin

Abstract U.S. maize and soy production have increased rapidly since the mid-20th century. While global warming has raised temperatures in most regions over this time period, trends in extreme heat have been smaller over U.S. croplands, reducing crop-damaging high temperatures and benefiting maize and soy yields. Here we show that agricultural intensification has created a crop-climate feedback in which increased crop production cools local climate, further raising crop yields. We find that maize and soy production trends have driven cooling effects approximately as large as greenhouse gas induced warming trends in extreme heat over the central U.S. and substantially reduce them over the southern U.S., benefiting crops in all regions. This reduced warming has boosted maize and soy yields by 3.3 (2.7 – 3.9; 13.7 – 20.0%) and 0.6 (0.4 – 0.7; 7.5 – 13.7%) bu/ac/decade, respectively, between 1981 and 2019. Our results suggest that if maize and soy production growth were to stagnate, the ability of the crop-climate feedback to mask warming would fade, exposing U.S. crops to more harmful heat extremes.

2022 ◽  
Vol 6 (1) ◽  
pp. 20-26
Yanli Sun

The construction of high-density primary schools in Shenzhen is facing new challenges. Therefore, it is essential to look for ways to meet the demand for degrees, realize education and teaching reform, eliminate the shackles of design code, and adapt to the local climate. From the perspective of design methodology, this article discusses the design strategy of Shenzhen’s high-density primary school campus from four aspects: compact layout, multiple functions, open space, and personalized design, aiming to provide ideas for the construction of primary school campuses.

2022 ◽  
Vol 14 (2) ◽  
pp. 850
Fupeng Zhang ◽  
Lei Shi ◽  
Simian Liu ◽  
Jiaqi Shi ◽  
Qian Ma ◽  

In this study, climate-responsive solutions used in traditional dwellings in the North Dong region of China were identified, and the impact of these solutions on the indoor physical environment and energy consumption was analysed. First, over the course of a year, sample dwellings and short-term on-site indoor physical environment measurements were selected from the local climate. Then, three building materials, namely, brick, wood, and rammed earth, and different structural forms were selected to simulate the indoor thermal environment, ventilation conditions, and energy consumption of traditional dwellings. The study also summarised the advantages and disadvantages of the physical environment of traditional dwellings in response to climate characteristics. The results showed that the fluctuation in indoor temperature and humidity of typical dwellings in the North Dong region is approximately 5 °C, which is 14% lower than that outdoors. Traditional Dong dwellings have good indoor conditioning abilities. Traditional wood structure dwellings can save 26% and 39% of energy per year compared with those of raw earth and brick wood, respectively. Traditional dwellings in the Dong region are well adapted to the local climate in terms of form, materials, and structure and contribute to climate-responsive buildings in the harsh climatic conditions of the region. The solutions used in these dwellings can also be used to design new climate-responsive buildings; however, the indoor thermal comfort is not entirely satisfactory. We proposed an effective adaptation strategy for Dong traditional dwellings.

Yingzuo Qin ◽  
Yan Li ◽  
Ru Xu ◽  
Chengcheng Hou ◽  
Alona Armstrong ◽  

Abstract The development of wind energy is essential for decarbonizing energy supplies. However, the construction of wind farms changes land surface temperature (LST) and vegetation by modifying land surface properties and disturbing land-atmosphere interactions. In this study, we used MODIS satellite data to quantify the impacts of 319 wind farms on local climate and vegetation in the United States. Our results indicated insignificant impacts on LST during the daytime but significant warming of 0.10°C on annual mean nighttime LST averaged for all wind farms, and 0.36°C for those 61% wind farm samples with warming. The nighttime LST impacts exhibited seasonal variations, with stronger warming in winter and autumn up to 0.18°C but weaker effects in summer and spring. We observed a decrease in peak NDVI for 59% of wind farms due to infrastructure construction, with an average decrease of 0.0067 compared to non-wind-farm areas. The impacts of wind farms depended on wind farm size, with winter LST impacts for large and small wind farms ranging from 0.21°C to 0.14°C, and peak NDVI impacts ranging from -0.009 to -0.006. The LST impacts declined with the increasing distance from the wind farm, with detectable impacts up to 10 km. In contrast, the vegetation impacts on NVDI were only evident within the wind farm locations. Wind farms built in grassland and cropland showed larger warming effects but weaker vegetation impact compared to those built on forest land. Furthermore, spatial correlation analyses with environmental factors suggest limited geographical controls on the heterogeneous wind farm impacts and highlight the important role of local factors. Our analyses based on a large sample offer new observational evidence for the wind farm impacts with improved representativeness. This knowledge is important to fully understand the climatic and environmental implications of energy system decarbonization.

Management ◽  
2022 ◽  
Vol 34 (2) ◽  
pp. 18-25
Liudmyla Hanushchak-Yefimenko

BACKGROUND AND OBJECTIVES. Improving the energy performance of buildings is one of the least expensive ways to reduce energy consumption and greenhouse gas emissions. Building energy performance certification increases public knowledge about energy conservation and allows consumers and other decision makers to compare buildings based on their lifetime performance. In addition, energy performance certifications are an incentive for owners to improve the efficiency of existing buildings.METHODS. It is proposed to use in the process of energy certification and energy audit of university buildings collection and evaluation of basic information (including information about local climate, method of use, value of thermal conductivity coefficient and building envelope area, orientation) to determine the level of energy efficiency of the building on a generally accepted scale. In the Certificate of energy efficiency to take into account the calculated results from the assessment of the energy performance of the building.FINDINGS. It is suggested that the results of the energy certification of university buildings be presented in a simple, clear form, to ensure clarity, ease of use and comparability. For the energy certification of university buildings, a comparative labeling from A to G is proposed for use. The scale, on which the current national building standard is at "C," provides ample room for improving the rating of both new and existing buildings. If necessary, the scale should be expanded to add a label such as A1, A2, or A+, A++ when it comes to high-performance buildings.CONCLUSION. Accurate and reliable energy performance certification is a necessary foundation that will help ensure consumer confidence and the success of the certification program. The certification program must be clearly coordinated to ensure a smooth transition of the construction industry to the new rules.

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