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Abstract Deep convection in the Indo-Pacific warm pool is vital in driving global atmospheric overturning circulations. Year-to-year variations in the strength and location of warm pool precipitation can lead to significant local and downstream hydroclimatic impacts, including floods and droughts. While the El Niño-Southern Oscillation (ENSO) is recognized as a key factor in modulating interannual precipitation variations in this region, atmospheric internal variability is often as important. Here, through targeted atmospheric model experiments, we identify an intrinsic low-frequency atmospheric mode in the warm pool region during the austral summer, and show that its impact on seasonal rainfall is comparable to ENSO. This mode resembles the horizontal structure of the Madden-Julian Oscillation (MJO), and may play a role in initiating ENSO as stochastic forcing. We show that this mode is not merely an episodic manifestation of MJO events but primarily arises from barotropic energy conversion aided by positive feedback between convection and circulation. 

Abstract In contrast to surface greenhouse warming, surface greenhouse cooling has been less explored, especially on multi-century timescales. Here, we assess the processes controlling the pacing and magnitude of the multi-century surface temperature response to instantaneously doubling and halving atmospheric carbon dioxide concentrations in a modern global coupled climate model. Over the first decades, surface greenhouse warming is larger and faster than surface greenhouse cooling both globally and at high northern latitudes (45–90° N). Yet, this initial multi-decadal response difference does not persist. After year 150, additional surface warming is negligible, but surface cooling and sea ice expansion continues. Notably, the equilibration timescale for high northern latitude surface cooling (∼437 years) is more than double the equivalent timescale for warming. The high northern latitude responses differ most at the sea ice edge. Under greenhouse cooling, the sea ice edge slowly creeps southward into the mid-latitude oceans amplified by positive lapse rate and surface albedo feedbacks. While greenhouse warming and sea ice loss at high northern latitudes occurs on multi-decadal timescales, greenhouse cooling and sea ice expansion occurs on multi-century timescales. Overall, this work shows the importance of multi-century timescales and sea ice processes for understanding high northern latitude climate responses. 

Abstract Arctic amplification (AA), the greater Arctic surface warming compared to the global average, has been widely attributed to increasing concentrations of greenhouse gases (GHG). However, less is known about the impacts of other forcings - notably, anthropogenic aerosols (AER) - and how they may compare to the impacts of GHG. Here we analyze sets of climate model simulations, specifically designed to isolate the AER and GHG effects on global climate. Surprisingly, we find stronger AA produced by AER than by GHG during the 1955–1984 period, when the strongest global AER increase. This stronger AER-induced AA is due to a greater sensitivity of Arctic sea ice, and associated changes in ocean-to-atmosphere heat exchange, to AER forcing. Our findings highlight the asymmetric Arctic climate response to GHG and AER forcings, and show that clean air policies which have reduced aerosol emissions may have exacerbated the Arctic warming over the past few decades. 

A typical Madden–Julian Oscillation (MJO) generates a large region of enhanced rainfall over the equatorial Indian Ocean that moves slowly eastward into the western Pacific. Tropical cyclones often form on the poleward edges of the MJO moist-convective envelope, frequently impacting both southeast Asia and northern Australia, and on occasion Eastern Africa. This paper addresses the question of whether these MJO-induced tropical cyclones will become more numerous in the future as the oceans warm. The Lagrangian Atmosphere Model (LAM), which has been carefully tuned to simulate realistic MJO circulations, is used to study the sensitivity of MJO modulation of tropical cyclogenesis (TCG) to global warming. A control simulation for the current climate is compared with a simulation with enhanced radiative forcing consistent with that for the latter part of the 21st century under Shared Socioeconomic Pathway (SSP) 585. The LAM control run reproduces the observed MJO modulation of TCG, with about 70 percent more storms forming than monthly climatology predicts within the MJO’s convective envelope. The LAM SSP585 run suggests that TCG enhancement within the convective envelope could reach 170 percent of the background value under a high greenhouse gas emissions scenario, owing to a strengthening of Kelvin and Rossby wave components of the MJO’s circulation. 

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system. 

Abstract Arctic amplification (AA), referring to the phenomenon of amplified warming in the Arctic compared to the warming in the rest of the globe, is generally attributed to the increasing concentrations of carbon dioxide (CO₂) in the atmosphere. However, little attention has been paid to the mechanisms and quantitative variations of AA under decreasing levels of CO₂, when cooling where the Arctic region is considerably larger than over the rest of the planet. Analyzing climate model experiments forced with a wide range of CO₂concentrations (from 1/8× to 8×CO₂, with respect to preindustrial levels), we show that AA indeed occurs under decreasing CO₂concentrations, and it is stronger than AA under increasing CO₂concentrations. Feedback analysis reveals that the Planck, lapse-rate, and albedo feedbacks are the main contributors to producing AAs forced by CO₂increase and decrease, but the stronger lapse-rate feedback associated with decreasing CO₂level gives rise to stronger AA. We further find that the increasing CO₂concentrations delay the peak month of AA from November to December or January, depending on the forcing strength. In contrast, decreasing CO₂levels cannot shift the peak of AA earlier than October, as a consequence of the maximum sea-ice increase in September which is independent of forcing strength. Such seasonality changes are also presented in the lapse-rate feedback, but do not appear in other feedbacks nor in the atmospheric and oceanic heat transport processeses. Our results highlight the strongly asymmetric responses of AA, as evidenced by the different changes in its intensity and seasonality, to the increasing and decreasing CO₂concentrations. These findings have significant implications for understanding how carbon removal could impact the Arctic climate, ecosystems, and socio-economic activities. 

Abstract Emission of anthropogenic greenhouse gases has resulted in greater Arctic warming compared to global warming, known as Arctic amplification (AA). From an energy‐balance perspective, the current Arctic climate is in radiative‐advective equilibrium (RAE) regime, in which radiative cooling is balanced by advective heat flux convergence. Exploiting a suite of climate model simulations with varying carbon dioxide () concentrations, we link the northern high‐latitude regime variation and transition to AA. The dominance of RAE regime in northern high‐latitudes under reduction relates to stronger AA, whereas the RAE regime transition to non‐RAE regime under increase corresponds to a weaker AA. Examinations on the spatial and seasonal structures reveal that lapse‐rate and sea‐ice processes are crucial mechanisms. Our findings suggest that if concentration continues to rise, the Arctic could transition into a non‐RAE regime accompanied with a weaker AA.