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Under global warming scenarios, extreme climate events in South Asia will occur more frequently which will seriously threaten the safety of local residents. South Asia faces dual pressures of the obligation of carbon emissions reduction globally and the demand for a better life for huge populations. Stratospheric Aerosol Injection (SAI) climate engineering provides a potential solution to this dilemma. We compared the evolution of 12 climate extreme indices under historical scenarios, two future scenarios (SSP245, SSP585) and an implementation scenario of SAI climate engineering (G6sulfur). We showed that the intensity and frequency of extreme climates under a G6sulfur scenario would be significantly higher than those under historical scenarios, and that the difference in extreme climates under three scenarios (SSP245, SSP585, and G6sulfur) would be widely varying, with some indices being considerably mitigated while others would reflect a worse set of circumstances than would be the case without SAI climate engineering. Therefore, SAI climate engineering is not an effective tool to mitigate future climate extremes in South Asia under global warming scenarios. [ABSTRACT FROM AUTHOR]

Multifractal detrended fluctuation analysis (DFA) can extract multi-scaling behavior and measure long-range correlations in climatic time series. In this study, with the help of multifractal DFA, we investigated the scaling behavior of daily minimum/maximum temperatures during the years 1989–2019 from 34 meteorological stations in Bangladesh. We revealed spatial patterns, topographic impacts and global warming impacts of long-range correlations embedded in small and large fluctuations in temperature time series. Meanwhile, we developed a multifractal DFA-based algorithm to dynamically determine thresholds to discriminate extreme and non-extreme events in climate systems and applied it to analyze the frequency and trends of temperature extremes in Bangladesh. Compared with widely-used percentile thresholds, the extreme climate events captured in our algorithm are more reliable since they are determined dynamically by the climate system itself. [ABSTRACT FROM AUTHOR]

We studied the robustness of photosynthetic metabolism in the chloroplasts of C[sub3] plants under drought stress and at high CO[sub2] concentration conditions by using a method called Minimization of Metabolic Adjustment Dynamic Flux Balance Analysis (MDFBA). Photosynthetic metabolism in the chloroplasts of C3 plants applies highly cooperative regulation to minimize the fluctuation of metabolite concentration profiles in the face of transient perturbations. Our work suggests that highly cooperative regulation assures the robustness of the biological system and that there is closer cooperation under perturbation conditions than under normal conditions. This results in minimizing fluctuations in the profiles of metabolite concentrations, which is the key to maintaining a system's function. Our methods help in understanding such phenomena and the mechanisms of robustness for complex metabolic networks in dynamic processes. [ABSTRACT FROM AUTHOR]