Effect of Solar Radiation Modification on Freezing Level Heights across the world

Dynamic controls of the interannual and interdecadal variations of the freezing level height over the Tibetan Plateau

Land surface hydrological modeling is sensitive to near‐surface air temperature, which is especially true for the cryosphere. The lapse rate of near‐surface air temperature is a critical parameter when interpolating air temperature from station data to gridded cells. To obtain spatially distributed, fine‐resolution near‐surface (2 m) air temperature in the mainland China, monthly air temperature from 553 Chinese national meteorological stations (with continuous data from 1962 to 2011) are divided into 24 regional groups to analyze spatiotemporal variations of lapse rate in relation to surface air temperature and relative humidity. The results are as follows: (1) Evaluation of estimated lapse rate shows that the estimates are reasonable and useful for temperature‐related analyses and modeling studies. (2) Lapse rates generally have a banded spatial distribution from southeast to northwest, with relatively large values on the Tibetan Plateau and in northeast China. The greatest spatial variability is in winter with a range of 0.3°C–0.9°C · 100 m−1, accompanied by an inversion phenomenon in the northern Xinjiang Province. In addition, the lapse rates show a clear seasonal cycle. (3) The lapse rates maintain a consistently positive correlation with temperature in all seasons, and these correlations are more prevalent in the north and east. The lapse rates exhibit a negative relationship with relative humidity in all seasons, especially in the east. (4) Substantial regional differences in temporal lapse rate trends over the study period are identified. Increasing lapse rates are more pronounced in northern China, and decreasing trends are found in southwest China, which are more notable in winter. An overall increase of air temperature and regional variation of relative humidity together influenced the change of lapse rate.

PDF HTML阅读 XML下载 导出引用 引用提醒 城市公园景观秋冬季动态热效应 DOI: 10.5846/stxb201407151437 作者: 作者单位: 西南大学地理科学学院,中国科学院生态环境研究中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目(41471150;41230633); 城市与区域生态国家重点实验室自主项目(SKLURE2013-1-02) Diurnal variations of landscape thermal effect in city parks from the later autumn to early winter Author: Affiliation: College of Geography Science, Southwest University,research center for Eco-Environmental Sciences, Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在北京市3个公园中选择裸地、道路、林地、草地4种主要景观类型,通过2013年10月、11月、12月的实地监测数据,定量揭示不同景观类型地表温度、近地表空气温度和湿度的日间变化特征,并分析日间温湿度的均值、幅度大小、空气温湿度的匹配程度。4种景观类型日间地表和空气温度均呈单峰变化趋势,裸地、道路、林地、草地的地表温度最高值分别出现在14:00、12:00、14:00、12:00,空气温度最高值则都在14:00,相对湿度最大值出现在8:00,最小值在14:00。从日间均值来看,地表温度为裸地>道路>草地、林地,裸地的日均空气温度最高,4种景观的相对湿度差异不显著。从日间变化幅度来看,所有景观类型的地表温度均明显大于空气温度,尤其是裸地和道路的日间变化比林地和草地更加明显。通过定量分析4种景观类型的日间热环境动态变化,可以为城市热环境改善、景观设计和生态规划提供科学依据。 Abstract:The urban landscape pattern is increasing changed as cities replace their natural land cover with impervious surfaces, buildings, and other infrastructure. The impacts of landscape changes have become a growing concern particularly for the ecosystem service in recent decades. It is well-known that city parks could provide multiple ecosystem services including temperature regulation services in different seasons. Some thermal characteristics of city parks may directly contribute to the degree of comforts for the mankind, such as the land surface temperature (LST), near-surface air temperature (NAT), and relative humidity (RH). Generally, landscape types and pattern in parks are responsible for the extent and degree of local thermal effects. In this study, three city parks were selected to analysis the diurnal variations of thermal effects in Beijing. We measured LST, NAT, and RH in the grassland, forest, road, and bare land for three months from the later autumn to early winter. The study aims to assess the variations of LST, NAT, and RH as well as their relationships among different landscape types. The results showed that: (1) the lowest LST and NAT are at 8:00 whereas the highest values are different according to the types of landscape. The highest values of LST and NAT are at 14:00,12:00,14:00,12:00, respectively. The relative humidity reaches its lowest value at 14:00 (2) In the daytime, the decreasing orders of mean LST, NAT, and RH are bare land > road > grassland≈forest, bare land > forest≈grassland=road, grassland≈road≈forest≈bare land, respectively. The LST and NAT are increasing with the decreasing RH for all landscape types. (3) The maximum difference in LST, NAT, and RH decrease as: bare land (13.5℃) > road (10.0℃) > grassland (7.5℃) > forest (5.9℃), bare land (7.4℃) ≈ road (7.3℃) > forest (6.3℃) > grassland (5.7℃), bare land (20.8%) = road (20.8%) ≈grassland (19.5%)≈forest (18.8%), respectively. (4) The relationship between LST and NAT are complex due to the various solar radiations in a day. Specifically, the daily variation of LST is greater than that of NAT for all the landscapes. The daily variation of LST and NAT is low in forest compared with the high variations in temperature of the grassland, bare land, and road. (5) The combination of temperature and humidity is represented by the difference between NAT and dew-point temperature. The hydrothermal environment reaches the driest and hottest conditions at 14:00. By quantifying thermal effects of different landscape types in city parks, this study can provide useful implication on the urban management to improve the thermal environment and related ecosystem services. 参考文献 相似文献 引证文献

Soil moisture plays a crucial role for the energy partitioning at Earth’s surface. Changing fractions of latent and sensible heat fluxes caused by soil moisture variations can affect both near-surface air temperature and precipitation. In this study, a simple framework for the dependence of evaporative fraction (the ratio of latent heat flux over net radiation) on soil moisture is used to analyze spatial and temporal variations of land–atmosphere coupling and its effect on near-surface air temperature. Using three different data sources (two reanalysis datasets and one combination of different datasets), three key parameters for the relation between soil moisture and evaporative fraction are estimated: 1) the frequency of occurrence of different soil moisture regimes, 2) the sensitivity of evaporative fraction to soil moisture in the transitional soil moisture regime, and 3) the critical soil moisture value that separates soil moisture- and energy-limited evapotranspiration regimes. The results show that about 30%–60% (depending on the dataset) of the global land area is in the transitional regime during at least half of the year. Based on the identification of transitional regimes, the effect of changes in soil moisture on near-surface air temperature is analyzed. Typical soil moisture variations (standard deviation) can impact air temperature by up to 1.1–1.3 K, while changing soil moisture over its full range in the transitional regime can alter air temperature by up to 6–7 K. The results emphasize the role of soil moisture for atmosphere and climate and constitute a useful benchmark for the evaluation of the respective relationships in Earth system models.

An extinction of incoming solar radiation is taking place by absorption and scattering by dust, water droplets, and gaseous molecules. Such phenomena are responsible for altering meteorological variables. In the present study, temporal analysis of the aerosol optical thickness (AOT) and black carbon (BC) surface mass concentration was undertaken using an ozone monitoring instrument (OMI) and modern-era retrospective analysis for research and applications, version 2 (MERRA-2) satellite from the year 2018 to 2022. The study was mainly focused on the western states of India which are Rajasthan, Gujarat, and Maharashtra. The correlation of AOT and BC surface mass concentration with near-surface temperature (2m above ground level) was analyzed. BC and temperature shows strong negative correlation as BC is known for its absorption of radiation. It accumulates in the atmosphere and contributes to atmospheric warming while simultaneously bringing down the near-surface air temperature due to the reduced sunlight reaching the ground. Also, seasonal analysis was conducted for winter, summer, monsoon, and post-monsoon, which shows the higher values of AOT in monsoon; however, seasonal average BC surface mass concentration was found high in winter in each year for all three states. AERONET data from Jaipur, Rajasthan, and Pune, Maharashtra for the year 2021 was used to further evaluate the AOT generated from OMI. The results demonstrated a significant connection, with R2 values of 0.62 and 0.69, respectively. The temperature retrieved from MERRA-2 was also validated with ground truth data of the Continuous Ambient Air Quality Monitoring Station (CAAQMS) at both stations showing high agreement with R2 > 0.70.

PURPOSE: Moss covers a vast area from the polar to the tropics on a global scale and has important regulatory effects on the biogeochemical processes in the soil. Previous studies had shown that moss on soil could reduce soil temperature in the warm period and plays an important role in the stability of the soil carbon pool in the context of global warming. The cooling effect of soil temperature by moss may be due to its ability to accelerate soil water evaporation. Most vascular plants achieve limited homeothermy by transpiration, but moss has no organs which can regulate transpiration. Can moss also regulate soil water evaporation as the transpiration of vascular plants? The objective of this study was to determine the differences in temperature and evaporation between moss-covered and bare soil. MATERIALS AND METHODS: A year-round mesocosm experiment was carried out to investigate soil temperature and evaporation with and without moss. We used a high time-resolution instrument which could simultaneously monitor soil temperature beneath moss layers, photosynthetically active radiation, and near-surface air temperature (20 cm above the soil) in a 10-min interval. In the meantime, we used a chamber method to monitor soil evaporation and heat flux inside the mesocosms simultaneously. RESULTS AND DISCUSSION: We found that the evaporation of the moss increased drastically when near-surface air temperature exceeded 30 °C, which kept maximum soil temperature around 30 °C. This finding was different from many previous studies which reported that the evaporation of moss was always greater than bare soil or similar to open water surface. The phenomenon we found may be important for moss, which allowed moss to minimize the loss of water at high temperature and maintain a relatively constant temperature. It is known that moss is a C₃ plant, and the high-temperature threshold for the photosynthesis of C₃ vascular plants is also around 30 °C. Thus, the temperature-regulating behavior could improve the net carbon gain for moss and benefit for its survival. To our best knowledge, there is no research reporting this phenomenon. In the context of global warming, the temperature-regulating behavior of moss is very important for its controls on soil carbon dynamics and its other ecological functions, especially at lower latitudes with higher soil temperature. CONCLUSIONS: Our result showed that moss can also achieve limited homeothermy through soil water evaporation similarly as the vascular plant through transpiration, and the temperature threshold was around 30 °C. This homeothermy led to the decoupling of soil temperature and near-surface air temperature. How mosses elevate water evaporation in response to high temperature remains to be a challenge for future research.

Near-surface air temperature lapse rate (NSATLR) is vital for hydrological simulation and mountain climate research in snowmelt-dominated regions. In this study, NSATLRs of two vertical zones (i.e., mountain grassland–coniferous forest belt and alpine meadow belt) of the Manasi River basin on the northern slope of the Tianshan Mountains were calculated using the near-surface air temperature data from 18 observation stations. Furthermore, temporal variations of NSATLRs of these two vertical zones at seasonal, monthly, and daily scales were analyzed, combined with altitudinal differences of local environments. The results show that the temporal variations of NSATLRs are different between these two vertical zones. The steepest monthly NSATLR occurs in July in the mountain grassland–coniferous forest belt and in April in the alpine meadow belt. In spring, summer, and autumn, the hourly NSATLRs in the mountain grassland–coniferous forest belt generally steepen with increasing solar radiation and vice versa, contrary to those in the alpine meadow belt. During winter, the hourly NSATLRs on sunny days are overall positive at night but negative during the day in the mountain grassland–coniferous forest belt. The findings of this study indicate that it is necessary to divide mountains with similar local environments to the study area into different vertical zones to accurately estimate NSATLR, and the use of a fixed NSATLR for different months and vertical zones is not suitable for snowmelt runoff modeling in snow-dominated regions such as the northern slope of the Tianshan Mountains. Significance Statement This study aims to investigate the altitudinal and temporal variations of near-surface air temperature lapse rate (NSATLR) on the northern slope of the Tianshan Mountains and how mountain environments affect NSATLR. This is important because altitudinal differences of mountain environments lead to different NSATLRs, and these altitudinal variations on the northern slope of the Tianshan Mountains are different from those on the Alps at the same latitude. Our results explain how altitudinal differences of mountain environments affect NSATLRs; hence, using a fixed NSATLR for different months and vertical zones is inappropriate, and estimating NSATLRs for different vertical zones is necessary.

The estimation of spatially resolved near-surface air temperature (NSAT) has been extensively performed in previous studies using satellite-derived land surface temperature (LST) from MODIS. However, there remains a need for estimating daily NSAT based on LST data from other satellites, which has important implications for integrating multi-source LST in estimating NSAT and ensuring the continuity of satellite-derived estimates of NSAT over long-term periods. In this study, we conducted a comprehensive comparison of LST derived from Metop with MODIS LST in the modeling and mapping of daily NSAT. The results show that Metop LST achieves consistent predictive performance with MODIS LST in estimating daily NSAT, and models based on Metop LST or MODIS LST have overall predictive performance of about 1.2–1.4 K, 1.5–2.0 K, and 1.8–1.9 K in RMSE for estimating Tavg, Tmax, and Tmin, respectively. Compared to models based on nighttime LST, daytime LST can improve the predictive performance of Tmax by about 0.26–0.28 K, while performance for estimating Tavg or Tmin using different schemes of LST is comparable. Models based on Metop LST also exhibit high consistency with models utilizing MODIS LST in terms of the variability in predictive performance across months, with RMSE of 1.03–1.82 K, 1.3–2.49 K, and 1.26–2.66 K for Tavg, Tmin, and Tmax, respectively. This temporal variability in performance is not due to sampling imbalance across months, which is confirmed by comparing models trained using bootstrapped samples in balance, and our results imply that sampling representativeness, complicated by retrieval gaps in LST, is an important issue when analyzing the variability in predictive performance for estimating NSAT. To fully assess the predictive capability of Metop LST in estimating daily NSAT, more studies need to be performed using different methods across areas with a range of scales and geographical environments.

Assessment of constructing canopy urban heat island temperatures from thermal images: An integrated multi-scale approach

<ul><li>The occurrence of extreme weather events has increased during the two last decades.  European heat waves are responsible for social, economic and environmental damage and are projected to increase in magnitude, frequency and duration under global warming, heightening the  interest about the contribution of different drivers. </li> <li>By using the ERA5 Re-analysis product, we performed a two-sided composite analysis to investigate a potential relation between North Atlantic sea surface temperatures (SSTs) and the near-surface air temperature (T2m) over the European continent. Here, we show that in the presence of cold North Atlantic SSTs during summer, the distribution of European T2m shifts towards positive anomalies a few days later, increasing the likelihood for heat waves. During these events a predominant wave number three pattern in addition to regionally confined Rossby wave activity  contribute to a trough-ridge pattern in the North Atlantic-European sector. Specifically, five of 17 European heat waves within the period of 1979 to 2019 could be related to a cold North Atlantic SST event a few days in advance. In the upstream analysis we identify eleven of 17 European heat waves co-existent with cold North Atlantic SSTs. </li> <li>In order to confirm the crucial role of North Atlantic SSTs for European heat waves, we analysed output from a coupled climate model, HadGEM3, with three different horizontal resolutions. The high-resolution run revealed the closest resemblance to the ERA5 data, suggesting that mechanisms on the mesoscales (<50 km) play a role in the relationship between North Atlantic SSTs and European T2m. Results also highlight the importance of using a climate model with a high horizontal resolution for the purpose of studying the variability of European heat waves.</li> <li>Based upon our results, conducted with ERA5 Re-analysis and HadGEM3 data, North Atlantic SSTs provide potential predictive skill of European heat waves.</li> </ul>

Abstract. Geoengineering by stratospheric sulfate aerosol injection may help preserve mountain glaciers by reducing summer temperatures. We examine this hypothesis for the glaciers in high-mountain Asia using a glacier mass balance model driven by climate simulations from the Geoengineering Model Intercomparison Project (GeoMIP). The G3 and G4 schemes specify use of stratospheric sulfate aerosols to reduce the radiative forcing under the Representative Concentration Pathway (RCP) 4.5 scenario for the 50 years between 2020 and 2069, and for a further 20 years after termination of geoengineering. We estimate and compare glacier volume loss for every glacier in the region using a glacier model based on surface mass balance parameterization under climate projections from three Earth system models under G3, five models under G4, and six models under RCP4.5 and RCP8.5. The ensemble projections suggest that glacier shrinkage over the period 2010–2069 is equivalent to sea-level rise of 9.0 ± 1.6 mm (G3), 9.8 ± 4.3 mm (G4), 15.5 ± 2.3 mm (RCP4.5), and 18.5 ± 1.7 mm (RCP8.5). Although G3 keeps the average temperature from increasing in the geoengineering period, G3 only slows glacier shrinkage by about 50 % relative to losses from RCP8.5. Approximately 72 % of glaciated area remains at 2069 under G3, as compared with about 30 % for RCP8.5. The widely reported reduction in mean precipitation expected for solar geoengineering is unlikely to be as important as the temperature-driven shift from solid to liquid precipitation for forcing Himalayan glacier change. The termination of geoengineering at 2069 under G3 leads to temperature rise of about 1.3 °C over the period 2070–2089 relative to the period 2050-2069 and corresponding increase in annual mean glacier volume loss rate from 0.17 to 1.1 % yr−1, which is higher than the 0.66 % yr−1 under RCP8.5 during 2070–2089.

Accurate estimates of long-term land surface temperature (Ts) and near-surface air temperature (Ta) at finer spatio-temporal resolutions are crucial for surface energy budget studies, for environmental applications, for land surface model data assimilation, and for climate change assessment and its associated impacts. The Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Aqua satellite provide a unique opportunity to estimate both temperatures twice daily at the global scale. In this study, differences between Ta and Ts were assessed locally over regions of North America from 2009 to 2013 using ground-based observations covering a wide range of geographical, topographical, and land cover types. The differences between Ta and Ts during non-precipitating conditions are generally 2–3 times larger than precipitating conditions. However, these differences show noticeable diurnal and seasonal variations. The differences between Ta and Ts were also investigated at the global scale using the AIRS estimates under clear-sky conditions for the period 2003–2015. The tropical regions showed about 5–20 °C warmer Ts than Ta during the day-time, whereas opposite characteristics (about 2–5 °C cooler Ts than Ta) are found over most parts of the globe during the night-time. Additionally, Ts estimates from the AIRS and the MODIS sensors were inter-compared. Although large-scale features of Ts were essentially similar for both sensors, considerable differences in magnitudes were observed (> 6 °C over mountainous regions). Finally, Ta and Ts estimates from the AIRS and MODIS sensors were validated against ground-based observations for the period of 2009–2013. The error characteristics notably varied with ground stations and no clear evidence of their dependency on land cover types or elevation was detected. However, the MODIS-derived Ts estimates generally showed larger biases and higher errors compared to the AIRS-derived estimates. The biases and errors increased steadily when the spatial resolution of the MODIS estimates changed from finer to coarser. These results suggest that representativeness error should be properly accounted for when validating satellite-based temperature estimates with point observations.

The Last Interglacial period (129–116 ka BP) is characterized by a strong orbital forcing which leads to a different seasonal and latitudinal distribution of insolation compared to the pre-industrial period. In particular, these changes amplify the seasonality of the insolation in the high latitudes of the northern hemisphere. Here, we investigate the Arctic climate response to this forcing by comparing the CMIP6 lig127k and pi-Control simulations performed with the IPSL-CM6A-LR model. Using an energy budget framework, we analyse the interactions between the atmosphere, ocean, sea ice and continents. In summer, the insolation anomaly reaches its maximum and causes a near-surface air temperature rise of 3.2 °C over the Arctic region. This warming is primarily due to a strong positive surface downwelling shortwave radiation anomaly over continental surfaces, followed by large heat transfers from the continents back to the atmosphere. The surface layers of the Arctic Ocean also receives more energy, but in smaller quantity than the continents due to a cloud negative feedback. Furthermore, while heat exchanges from the continental surfaces towards the atmosphere are strengthened, the ocean absorbs and stores the heat excess due to a decline in sea ice cover. However, the maximum near-surface air temperature anomaly does not peak in summer like insolation, but occurs in autumn with a temperature increase of 4.0 °C relative to the pre-industrial period. This strong warming is driven by a positive anomaly of longwave radiations over the Arctic ocean enhanced by a positive cloud feedback. It is also favoured by the summer and autumn Arctic sea ice retreat (−1.9 × 106 and −3.4 × 106 km2 respectively), which exposes the warm oceanic surface and allows heat stored by the ocean in summer and water vapour to be released. This study highlights the crucial role of the sea ice cover variations, the Arctic ocean, as well as changes in polar clouds optical properties on the Last Interglacial Arctic warming.

Abstract. The Last Interglacial period (129–116 ka) is characterised by a strong orbital forcing which leads to a different seasonal and latitudinal distribution of insolation compared to the pre-industrial period. In particular, these changes amplify the seasonality of the insolation in the high latitudes of the Northern Hemisphere. Here, we investigate the Arctic climate response to this forcing by comparing the CMIP6 lig127k and piControl simulations performed with the IPSL-CM6A-LR (the global climate model developed at Institut Pierre-Simon Laplace) model. Using an energy budget framework, we analyse the interactions between the atmosphere, ocean, sea ice and continents. In summer, the insolation anomaly reaches its maximum and causes a rise in near-surface air temperature of 3.1 ∘C over the Arctic region. This warming is primarily due to a strong positive anomaly of surface downwelling shortwave radiation over continental surfaces, followed by large heat transfer from the continents to the atmosphere. The surface layers of the Arctic Ocean also receive more energy but in smaller quantity than the continents due to a cloud negative feedback. Furthermore, while heat exchange from the continental surfaces towards the atmosphere is strengthened, the ocean absorbs and stores the heat excess due to a decline in sea ice cover. However, the maximum near-surface air temperature anomaly does not peak in summer like insolation but occurs in autumn with a temperature increase of 4.2 ∘C relative to the pre-industrial period. This strong warming is driven by a positive anomaly of longwave radiation over the Arctic Ocean enhanced by a positive cloud feedback. It is also favoured by the summer and autumn Arctic sea ice retreat (-1.9×106 and -3.4×106 km2, respectively), which exposes the warm oceanic surface and thus allows oceanic heat storage and release of water vapour in summer. This study highlights the crucial role of sea ice cover variations, Arctic Ocean, as well as changes in polar cloud optical properties on the Last Interglacial Arctic warming.

Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters.