JP

Synopsis: A clear signal of human influence on upper tropospheric ozone trends is identifiable with high statistical confidence in a 17-year satellite dataset.

Abstract: Tropospheric ozone (O3) is a strong greenhouse gas, particularly in the upper troposphere (UT). Limited observations point to a continuous increase in UT O3 in recent decades, but the attribution of UT O3 changes is complicated by large internal climate variability.

We show that the anthropogenic signal (“fingerprint”) in the patterns of UT O3 increases is distinguishable from the background noise of internal variability. The time-invariant fingerprint of human-caused UT O3 changes is derived from a 16-member initial-condition ensemble performed with a chemistry-climate model (CESM2-WACCM6). The fingerprint is largest between 30°S and 40°N, especially near 30°N. In contrast, the noise pattern in UT O3 is mainly associated with the El Niño–Southern Oscillation (ENSO). The UT O3 fingerprint pattern can be discerned with high confidence within only 13 years of the 2005 start of the OMI/MLS satellite record. Unlike the UT O3 fingerprint, the lower tropospheric (LT) O3 fingerprint varies significantly over time and space in response to large-scale changes in anthropogenic precursor emissions, with the highest signal-to-noise ratios near 40°N in Asia and Europe.

Our analysis reveals a significant human effect on Earth’s atmospheric chemistry in the UT and indicates promise for identifying fingerprints of specific sources of ozone precursors.

 

Zero hunger. Affordable and clean energy. Reduced inequalities. These are among the  sustainable development goals that the United Nations has established in pursuit of the long-term well-being of the Earth and its inhabitants. But achieving goals like these—whether by the UN’s 2030 deadline or beyond—requires a detailed understanding of the many complex, interconnected, co-evolving natural, social and technological systems upon which all life depends.

Abstract: The transition from fossil-fuel-based transportation systems to those reliant on low- and zero-emission technologies marks a crucial paradigm shift, necessitating a reevaluation of resilience metrics and strategies. As infrastructure investments adapt to a changing climate and the risk of extreme events, our paper identifies the complexities of resilience within the transportation sector, which now integrates a broad array of energy sources like electricity, hydrogen, and synthetic fuels. This deepening integration increases the complexity of maintaining transportation resilience, highlighting the inadequacy of traditional resilience metrics designed for centralized systems under stable climate conditions. We propose a Multi-System Dynamics (MSD) framework to develop new, system-level resilience metrics to effectively manage emerging risks associated with diverse energy sources and extreme weather conditions. This study emphasizes the need for robust scenario analysis and the integration of Cost-Benefit Analysis (CBA) tools that account for resilience, offering a framework to evaluate the economic impacts and benefits of resilience investments. Our proposed approach encompasses evaluating resilience at the system level to identify and mitigate new risks introduced by the adoption of low-carbon technologies and the interconnectedness of modern energy and transportation infrastructures. Through rigorous scenario analysis, we aim to support robust decision-making that can withstand and adapt to the unpredictabilities of a low-carbon future. By advancing these areas, the paper contributes to the strategic planning necessary to foster a resilient, sustainable transportation ecosystem capable of facing both current and future challenges.

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