Search

The alpine grassland ecosystem accounts for 53 % of the Qinghai–Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015–2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m 2) was successfully upscaled to the MODIS pixel scale (62 500 m 2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m -2. Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Terre.tpdc.272587; H. Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

The Nanjing University of Information Science and Technology Earth System Model version 3 (NESM v3) has been developed, aiming to provide a numerical modeling platform for cross-disciplinary Earth system studies, project future Earth climate and environment changes, and conduct subseasonal-to-seasonal prediction. While the previous model version NESM v1 simulates the internal modes of climate variability well, it has no vegetation dynamics and suffers considerable radiative energy imbalance at the top of the atmosphere and surface, resulting in large biases in the global mean surface air temperature, which limits its utility to simulate past and project future climate changes. The NESM v3 has upgraded atmospheric and land surface model components and improved physical parameterization and conservation of coupling variables. Here we describe the new version's basic features and how the major improvements were made. We demonstrate the v3 model's fidelity and suitability to address global climate variability and change issues. The 500-year preindustrial (PI) experiment shows negligible trends in the net heat flux at the top of atmosphere and the Earth surface. Consistently, the simulated global mean surface air temperature, land surface temperature, and sea surface temperature (SST) are all in a quasi-equilibrium state. The conservation of global water is demonstrated by the stable evolution of the global mean precipitation, sea surface salinity (SSS), and sea water salinity. The sea ice extents (SIEs), as a major indication of highlatitude climate, also maintain a balanced state. The simulated spatial patterns of the energy states, SST, precipitation, and SSS fields are realistic, but the model suffers from a cold bias in the North Atlantic, a warm bias in the Southern Ocean, and associated deficient Antarctic sea ice area, as well as a delicate sign of the double ITCZ syndrome. The estimated radiative forcing of quadrupling carbon dioxide is about 7.24Wm-2, yielding a climate sensitivity feedback parameter of -0:98Wm-2 K-1, and the equilibrium climate sensitivity is 3.69 K. The transient climate response from the 1%yr-1 CO2 (1pctCO2) increase experiment is 2.16 K. The model's performance on internal modes and responses to external forcing during the historical period will be documented in an accompanying paper. [ABSTRACT FROM AUTHOR]

Abstract: Air pollution caused by biomass burning has been a major concern over east China in recent decades. The distinct radiative effects of biomass burning aerosol due to its strong absorption also bring great uncertainty to the regional climate change. Therefore, it is urgent to investigate the spatial, temporal and vertical distribution of biomass burning aerosols over eastern China. In this paper, machine learning and multi-threshold method were used to identify biomass burning aerosol. We used a random forests model to identify biomass burning aerosols based on multi-source data includes Himawari-8 with high temporal resolution. The validated method would provide a basis for the study of aerosol radiation effect in China. This study is also useful to monitor smoke pollution in eastern China. [ABSTRACT FROM AUTHOR]

Human activities and climate change are two key factors influencing the variation of the total amount of available surface and groundwater, hereafter termed water resources. Quantitatively separating their impacts remains a challenge. To this end, we used time-varying Budyko-type equations and a hierarchical Bayesian model in this paper to separate their impacts in 31 provincial-level divisions of China. The time-varying Budyko-type equations treat the Budyko equation parameter w as a variable, which depends on human activities (represented by per capita gross regional production) and climate change (represented by temperature and precipitation). The hierarchical model quantifies the uncertainty of parameters and the interrelation between covariates across regions in China. The results show that the time-varying Budyko-type equation can improve the fitting capability for water resources in China. The hierarchical Bayesian model, which considered spatial dependence, reduced the uncertainty of the parameters compared to spatially independent counterparts. For most regions of China, human activities reduce water resources while climate change increases them. Southeastern China is the most influenced area, and its water resources decreased approximately 50 mm because of human activities. This study can provide a basis for water resource management under climate change and human activity constraints in China. [ABSTRACT FROM AUTHOR]

AbstractClimate elements are important indicators of climate change in China's main grainproducing areas during the April–September growth season and affect the growth and yield of crops. This paper combines grain concentration and geographical detector to divide the North and South regions of China's main grain production. The linear trend and Morlet wavelet transform methods are used to analyse the characteristics of climate change based on observational climate data from April to September 1981–2015. The results show that the climate in the North region is warm and dry during the growth season, whereas the climate is warm and humid in the South region. The main periods of the change in temperature in the North and South regions are 3 years, and that in precipitation is 5 years, and that in sunshine hours is 3-4 years. Changes in the climate elements in various provinces show complex, varying and regional characteristics of cold-warm and dry-wet cycles. The changes in climate elements are significant and different climatic conditions and regions have variouspossible impacts on grain production in China during the growth season. For China's agricultural-economy sustainable development and grain security, the study suggests that governments should place more emphasis on climatic elements changes during the growth season, and invest more money in disaster prevention and mitigation, especially in the main grain producing areas.Key words: grain-producing regions; changes in climate elements; growth season; climate trend50 coefficient; climate tendency rate; wavelet analysis [ABSTRACT FROM AUTHOR]

The cloud feedback is very important for climate change. In this paper, the long-term variability of clouds in East Asia and their influence on the radiation budget are studied quantitatively by analyzing satellite observations, so as to determine the cloud feedback on regional warming. We firstly analysis the 18-year's (Mar.2000-Feb.2018) trend of cloud optical thickness (COT) over East Asia based on the retrievals of the Moderate Resolution Imaging Spectrometer (MODIS), and calculate the cloud radiative forcing of water cloud and ice cloud using BCC_RAD radiation transfer model based on the COTs, and then discuss the impact of COT's variation on the cloud radiative forcing. The water cloud COTw is found to increase in northeast China (~0.05 per year) and east China sea (~0.05 per year), and to decrease at south China (~-0.07 per year) and south Japan (~-0.05 per year). The ice cloud COTi is found to increase at northeast China (~0.06 per year) and north Japan (~0.08 per year), whereas decrease significantly at Tibetan plateau (~-0.07 per year). The annual mean shortwave-, longwave- and net-radiative forcing caused by the water cloud in East Asia at the top of the atmosphere are -72.4 W m-2, 15.7 W m-2 and -56.7 W m-2, respectively; the corresponding forcing caused by ice cloud are -88.5 W m-2, 55.7 W m-2 and -32.7 W m-2 respectively. The effects of COTw and COTi variations on radiation in different regions are different remarkably. Both positive feedback (e.g. south China, 0~0.4 W m-2 per year) and negative feedback (e.g. northeast China, -0.4~0 W m-2 per year) occurs, which indicates that the cloud feedback to regional warming is significant and may be opposite at different areas. [ABSTRACT FROM AUTHOR]