Using seven well-replicated Qilian juniper (Sabina przewalskii Kom.) ring-width chronologies developed at Zongwulong and Shalike Mts. in the northeastern part of the Qaidam Basin annual precipitation from previous July to current June in the recent 1000 years was reconstructed for Delingha. The reconstruction can capture 63.1% of precipitation variance and the equation was stable over time. For the reconstructed precipitation, wet periods occurred in AD1520-1633 and 1933-2001, whereas dry intervals in 1429-1519 and 1634-1741. In addition, the magnitude in precipitation variation was lower before 1430 with about 15 mm, but it increased to 30 mm during the period of 1430 to 1850. After 1850, the precipitation variance decreased again. In contrast to the increase in temperature, a decrease in annual precipitation was evident since the 1990s. The agreement in low-frequency variation between the reconstruction and the glacier accumulation and particulate content in Dunde ice cores during the recent several hundred years suggested that the precipitation reconstructed in this study was rather reliable, and represented a regional signal. This 1000-year reconstruction could benefit our understanding of climatic variation in decadal to century-scale in this region, and provide basic data to climate models and to prediction of future climate in the 21st century. Keywords: northeastern part of the Qaidam Basin, tree-ring width series of Qilian juniper, precipitation reconstruction in 1000 years.

Global development has been heavily reliant on the overexploitation of natural resources since the Industrial Revolution. With the extensive use of fossil fuels, deforestation, and other forms of land-use change, anthropogenic activities have contributed to the ever-increasing concentrations of greenhouse gases (GHGs) in the atmosphere, causing global climate change. In response to the worsening global climate change, achieving carbon neutrality by 2050 is the most pressing task on the planet. To this end, it is of utmost importance and a significant challenge to reform the current production systems to reduce GHG emissions and promote the capture of CO 2 from the atmosphere. Herein, we review innovative technologies that offer solutions achieving carbon (C) neutrality and sustainable development, including those for renewable energy production, food system transformation, waste valorization, C sink conservation, and C-negative manufacturing. The wealth of knowledge disseminated in this review could inspire the global community and drive the further development of innovative technologies to mitigate climate change and sustainably support human activities.

Abstract. While climate change mitigation targets necessarily concern maximum mean state changes, understanding impacts and developing adaptation strategies will be largely contingent on how climate variability responds to increasing anthropogenic perturbations. Thus far Earth system modeling efforts have primarily focused on projected mean state changes and the sensitivity of specific modes of climate variability, such as the El Niño–Southern Oscillation. However, our knowledge of forced changes in the overall spectrum of climate variability and higher-order statistics is relatively limited. Here we present a new 100-member large ensemble of climate change projections conducted with the Community Earth System Model version 2 over 1850–2100 to examine the sensitivity of internal climate fluctuations to greenhouse warming. Our unprecedented simulations reveal that changes in variability, considered broadly in terms of probability distribution, amplitude, frequency, phasing, and patterns, are ubiquitous and span a wide range of physical and ecosystem variables across many spatial and temporal scales. Greenhouse warming in the model alters variance spectra of Earth system variables that are characterized by non-Gaussian probability distributions, such as rainfall, primary production, or fire occurrence. Our modeling results have important implications for climate adaptation efforts, resource management, seasonal predictions, and assessing potential stressors for terrestrial and marine ecosystems.