ALBERT

Description: We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies.

Description: Tipping elements are components of the Earth system that may shift abruptly and irreversibly from one state to another at specific thresholds. It is not well understood to what degree tipping of one system can influence other regions or tipping elements. Here, we propose a climate network approach to analyse the global impacts of a prominent tipping element, the Amazon Rainforest Area (ARA). We find that the ARA exhibits strong correlations with regions such as the Tibetan Plateau (TP) and West Antarctic ice sheet. Models show that the identified teleconnection propagation path between the ARA and the TP is robust under climate change. In addition, we detect that TP snow cover extent has been losing stability since 2008. We further uncover that various climate extremes between the ARA and the TP are synchronized under climate change. Our framework highlights that tipping elements can be linked and also the potential predictability of cascading tipping dynamics.

Description: The Pan-Third Pole (PTP) owns complex geography and demographic features where aerosol roles and their impact cannot be neglected as it jeopardizes both the environment and human health. Therefore, we analyzed spatio-temporal aerosol concentration, the influence of meteorological conditions, and underlying aerosol transport mechanisms over the PTP by leveraging observation, satellite dataset, and model outputs. The observation and model simulation result showed that aerosol concentrations exceeded the world health organization (WHO) and China guideline values in most of the locations. This study revealed distinctive seasonality with the highest and lowest aerosol concentrations during the winter and summer seasons, respectively, which could be favored by meteorological conditions and emissions from biomass burning. In response to higher aerosol concentrations, the maximum aerosol optical depth (AOD) values were observed over the major hotspot regions however, interestingly summer high (AOD 〉 0.8) was observed over the Indo Gangetic Plain (IGP) in South Asia. The columnar aerosol profile indicated that the higher aerosol concentrations were limited within 1–2 km elevation over the densely populated regions over South Asia and Eastern China. However, the significant aerosols concentrations found to be extended as high as 10 km could potentially be driven by the deep convection process and summer monsoon activities. Regionally, the integrated aerosol transport (IAT) for black carbon (BC) and organic carbon (OC) was found to be maximum over SA. Noticeable OC IAT anomaly (~5 times 〉 annual mean) found during spring that was linked with the biomass burning events. Yet, the dust transportation was found to be originated from the arid land and deserts that prolonged especially during summer followed by spring seasons. This study highlights the driver mechanism in aerosol seasonality, transport mechanism, and further motivates the additional assessment into potential dynamic relation between aerosol species, aerosol atmospheric river, and its societal impact.

Description: Eco-efficiency enhancement is an inherent requirement of green development and an important indicator of high-quality development in general. It aims to achieve the coordinated development of nature, the economy, and society. Therefore, eco-efficiency measurements should focus on not only total factor input, but also process analysis. Based on the “full world” model in ecological economic theory, this study constructed a theoretical framework for a composite economic-environmental-social system that reflects human welfare and sustainability. To this end, using network data envelopment analysis (DEA), this study established a staged eco-efficiency evaluation model that uses economic, environmental, and social factors to measure the overall and staged eco-efficiency of China’s provinces from 2003 to 2016 and analyze its spatiotemporal characteristics. A geographically weighted regression (GWR) model was also used to analyze the influencing factors of eco-efficiency changes and the spatial differentiation in their effect intensity. The findings were as follows: (1) China’s overall eco-efficiency is still at a low level. It varies significantly from region to region, and only three regions are at the frontier of production. The eastern region has the highest eco-efficiency, followed by the central region, and the gap between the central and western regions has gradually narrowed. In terms of staged efficiency, the level of eco-efficiency in the production stage is less than in the environmental governance stage, which is less than that in the social input stage. (2) In terms of the efficiency of each stage, the efficiency level of the production stage showed a downward trend throughout the entire process, and the decline in the central and western regions was more obvious. The social input stage and the environmental governance stage both showed upward trends. The social input stage showed a higher level, and the increase was relatively flat during the period of study. Efficiency continued to rise during the environmental governance stage from 2003 to 2010 and rose overall, but with some fluctuations from 2011 to 2016. (3) Geographically weighted regression showed that the effects of the influencing factors on eco-efficiency had obvious spatial heterogeneity. The factors affecting overall, production stage, and social input eco-efficiency were, in order of effect intensity from high to low, economic growth level, marketization level, and social input level. In terms of environmental governance, social input level had the greatest impact, followed by economic growth; marketization level did not show a significant impact.

Description: Rural energy consumption not only significantly affects the national economy but also informs us about the living conditions of rural residents. A comprehensive survey of households in the agropastoral area of Qinghai Province was conducted from 2017 to 2018 to identify its energy consumption characteristics. In this paper, a typical household energy flow model was established. The results show that 1) the proportion of noncommercial energy in the agropastoral area of Qinghai Province is 52.89%, and it is affected by the ‘returning farmland to forest’ (RFF) policy and the ‘returning grazing land to grassland project’ (RGLGP). Furthermore, the household energy consumption structure has shifted from traditional biomass to coal and a combination of other energy sources. 2) Households of different cultural backgrounds have different energy consumption patterns. 3) High-income households consume more energy and have more frequent energy flows compared with low-income households. The results of this survey will help policymakers and scholars to formulate strategies for energy conservation and more effectively assess energy policies.

Description: Strew pellet production not only contribute to regional sustainable development and localized energy transition, but also help to mitigate global greenhouse gas emissions. With the development strew pellet products in China, it is critical to uncover the embodied emissions, land use and economic cost effectiveness from producing strew pellet. In order to reach such a target, two main categories of biomass pellet production including a large-scale centralized factory and a small-scale distributed workshop are investigated. Compared with raw coal production, the unit co-benefits in terms of per gigajoule of straw pellets from centralized factory are 1687 kg CO2, 8.65 g SO2, 3.21 g NOx, and 3.897 g PM10, and 0.33 m2 land use, and those for straw pellets from centralized factory are 1352 kg CO2, 8.46 g SO2, 3.12 g NOx, and 4.22 g PM10, and 0.33 m2 land use. Cost-effectiveness for the two straw pellets production system were also uncovered so that the relevant interested agents such as decision makers, business investors or environmental researchers can see the potential economic performance from developing such kind biomass plants. We conclude that environmental performance of the straw pellets whether from centralized factor or decentralized workshop have attractive alternatives to coal production.

Description: Evaluating the embodied environmental impact of solar photovoltaic (PV) technology has been an important topic in addressing the sustainable development of renewable energy. While monetization of environmental externality is a remaining issue, which should be carried out in order to allow for an easy-to-understand comparison between direct economic and external cost. In this study, the environmental impact of solar PV power is monetized through conversion factors between midpoint and endpoint categories of life cycle analysis and the monetization weighting factor. Then, the power generation capacity and generation life of PV and coal-fired power plants are assumed to be consistent in order to compare the total cost of PV and coal-fired power generation. Results show that the cost of PV technology is higher than coal-fired in 2026 to 2030, taking into account environmental external costs and production costs. However, by 2030, the total cost of coal-fired power will be higher than that of solar PV. The life span cost per kWh is $3.55 for solar PV and $116.25 for coal-fired power. Although solar PV power seems more environmentally effective than coal-fired power in the life span, our results reveal the high environmental external cost of producing solar photovoltaic modules, which reminds us to pay more attention to the environmental impact when conducting cost-benefit analysis of renewable technologies. Without incorporating the environmental cost, the real cost of renewable technology will be underestimated.

Description: The availability of water resources in Central Asia depends greatly on snow accumulation in the mountains of Tien-Shan and Pamir. It is important to precisely forecast water availability as it is shared by several countries and has a transboundary context. The impact of climate change in this region requires improving the quality of hydrological forecasts in the Naryn river basin. This is especially true for the growing season due to the unpredictable climate behavior. A real-time monitoring and forecasting system based on hydrological watershed models is widely used for forecast monitoring. The study’s main objective is to simulate hydrological forecasts for three different hydrological stations (Uch-Terek, Naryn, and Big-Naryn) located in the Naryn river basin, the main water formation area of the Syrdarya River. We used the MODSNOW model to generate statistical forecast models. The model simulates the hydrological cycle using standard meteorological data, discharge data, and remote sensing data based on the MODIS snow cover area. As for the forecast at the monthly scale, the model considers the snow cover conditions at separate elevation zones. The operation of a watershed model includes the effects of climate change on river dynamics, especially snowfall and its melting processes in different altitude zones of the Naryn river basin. The linear regression models were produced for monthly and yearly hydrological forecasts. The linear regression shows R2 values of 0.81, 0.75, and 0.77 (Uch-Terek, Naryn, and Big-Naryn, respectively). The correlation between discharge and snow cover at various elevation zones was used to examine the relationship between snow cover and the elevation of the study. The best correlation was in May, June, and July for the elevation ranging from 1000–1500 m in station Uch-Terek, and 1500–3500 m in stations Naryn and Big-Naryn. The best correlation was in June: 0.87; 0.76; 0.84, and May for the elevation ranging from 1000–3500 m in station Uch-Terek, and 2000–3000 m in stations Naryn and Big-Naryn. Hydrological forecast modeling in this study aims to provide helpful information to improve our under-standing that the snow cover is the central aspect of water accumulation.

Description: The popular probability integral transform (PIT) uniform plot presents informative empirical illustrations of five types of ensemble forecasts, i.e., reliable, under-confident, over-confident, negatively biased and positively biased. This paper has built a novel two-stage framework upon the PIT uniform plot to quantitatively examine the forecast attributes of bias and reliability. The first stage utilizes the test statistic on bias to examine whether the mean of PIT values is equal to the theoretical mean of standard uniform distribution. Then, the second stage uses the test statistic on reliability to examine whether the mean squared deviation from the theoretical mean is equal to the theoretical variance of standard uniform distribution. Therefore, by using the two-tailed bootstrap hypothesis testing, the first stage identifies unbiased ensemble forecasts, negatively biased forecasts and positively biased forecasts; the second stage focuses on unbiased ensemble forecasts to furthermore identify reliable forecasts, under-confident forecasts and over-confident forecasts. Numerical experiments are devised for the National Centers for Environmental Prediction (NCEP)’s Climate Forecast System version 2 (CFSv2) ensemble forecasts of global precipitation. The results highlight the existence of various shapes of the PIT uniform plots. Due to extreme values of observed precipitation, the PIT uniform plots in some cases can substantially deviate from the 1:1 line even though the mean and variance of ensemble forecasts are respectively in accordance with the mean and variance of observations. Nevertheless, the two-stage framework along with the two test statistics serves as a robust tool for the verification of ensemble hydroclimatic forecasts.

Description: The normal modes (i.e. Earth's free oscillations) are long-period low-frequency seismic signals, which are excited by a variety of factors, such as earthquakes, volcanic eruption, landslide, avalanche and so on, are an essential vehicle for global seismic tomography to elucidate large-scale heterogeneities within the deep Earth. Accurate extraction of signals on normal mode spectrum is a prerequisite for the imaging inversion, providing the differences between the observed and synthetic normal mode spectrum. However, the normal mode spectrum has great complexity due to many structural factors within the Earth, so unacceptable false and dismissed selections of the signals always occur, which hinder the development of exploration of the deep Earth’s deep interior based on normal mode data. To address these problems, we build a deep-learning based neural network, named ModeNet, which is capable of precisely and efficient selecting the frequency windows to cover the target normal modal signals on a noisy spectrum, which could outperform the conventional spectrum-FLEXWIN method without relying on comparisons with synthetics. We also define our own method to evaluate the performance of ModeNet on the testing set and obtain a precision as high as ~0.98. Moreover, ModeNet achieves good generalization in processing seismograms of different events with different noise levels, components, and time window data, as well as superconductivity-gravimeter observations. Therefore, ModeNet could be implemented as a valuable tool for the future deep Earth inversion.