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Abstract Identifying and understanding various causal relations are fundamental to climate dynamics for improving the predictive capacity of Earth system modeling. In particular, causality in Earth systems has manifest temporal periodicities, like physical climate variabilities. To unravel the characteristic frequency of causality in climate dynamics, we develop a data‐analytic framework based on a combination of causality detection and Hilbert spectral analysis, using a long‐term temperature and precipitation dataset in the contiguous United States. Using the Huang–Hilbert transform, we identify the intrinsic frequencies of cross‐regional causality for precipitation and temperature, ranging from interannual to interdecadal time scales. In addition, we analyze the spectra of the physical climate variabilities, including El Niño‐Southern Oscillation and Pacific Decadal Oscillation. It is found that the intrinsic causal frequencies are positively associated with the physics of the oscillations in the global climate system. The proposed methodology provides fresh insights into the causal connectivity in Earth's hydroclimatic system and its underlying mechanism as regulated by the characteristic low‐frequency variability associated with various climatic dynamics. 

The IPCC’s Special Report on Climate Change and Cities shows how cities must adapt to climate risks. Urban planners need to create solutions that fit each city’s needs, enhancing urban adaptability and resilience in the context of increasing climate-related risks. Sustainable urban planning, increased citizen awareness, and resilient infrastructure design are crucial in mitigating the growing impacts of climate change on human settlements. Addressing these challenges requires the integration of perspectives from diverse disciplines, including the natural sciences, social sciences, and engineering fields. This article draws on insights from a collaborative effort among experts in these areas, promoting a more coordinated and interdisciplinary approach. By bridging this expertise, we aim to advance resilience practices and awareness, fostering effective urban climate solutions in Texas and beyond. 

The Atlantic Meridional Overturning Circulation (AMOC) is a significant component of the global ocean system, which has so far ensured a relatively warm climate for the North Atlantic and mild conditions in regions, such as Western Europe. The AMOC is also critical for the global climate. The complexity of the dynamical system underlying the AMOC is so vast that a long-term assessment of the potential risk of AMOC collapse is extremely challenging. However, short-term prediction can lead to accurate estimates of the dynamical state of the AMOC and possibly to early warning signals for guiding policy making and control strategies toward preventing AMOC collapse in the long term. We develop a model-free, machine-learning framework to predict the AMOC dynamical state in the short term by employing five datasets: MOVE and RAPID (observational), AMOC fingerprint (proxy records), and AMOC simulated fingerprint and CESM AMOC (synthetic). We demonstrate the power of our framework in predicting the variability of the AMOC within the maximum prediction horizon of 12 or 24 months. A number of issues affecting the prediction performance are investigated. 

Abstract Global climate changes, especially the rise of global mean temperature due to the increased carbon dioxide (CO₂) concentration, can, in turn, result in higher anthropogenic and biogenic greenhouse gas emissions. This potentially leads to a positive loop of climate–carbon feedback in the Earth’s climate system, which calls for sustainable environmental strategies that can mitigate both heat and carbon emissions, such as urban greening. In this study, we investigate the impact of urban irrigation over green spaces on ambient temperatures and CO₂exchange across major cities in the contiguous United States. Our modeling results indicate that the carbon release from urban ecosystem respiration is reduced by evaporative cooling in humid climate, but promoted in arid/semi-arid regions due to increased soil moisture. The irrigation-induced environmental co-benefit in heat and carbon mitigation is, in general, positively correlated with urban greening fraction and has the potential to help counteract climate–carbon feedback in the built environment. 

Urban areas experience numerous environmental challenges, among which the anthropogenic emissions of heat and carbon are two major contributors, the former is responsible for the notorious urban heat effect, the latter longterm climate changes. Moreover, the exchange of heat and carbon dioxide are closely interlinked in the built environment, and can form positive feedback loops that accelerate the degradation of urban environmental quality. Among a handful countermeasures for heat and carbon mitigation, urban irrigation is believed to be effective in cooling, yet the understanding of its impact on the co-evolution of heat and carbon emission remains obscure. In this study, we conducted multiphysics urban climate modeling for all urban areas in the contiguous United States, and evaluated the irrigation-induced cooling and carbon mitigation. Furthermore, we assessed the impact of urban irrigation on the potential heat-carbon feedback loop, with their strength of coupling quantified by an advanced causal inference method using the convergent cross mapping algorithms. It is found that the impact of urban irrigation varies vastly in geographically different cities, with its local and non-local effect unraveling distinct pathways of heat-carbon feedback mechanism. 

Abstract Global climate change has been shown to cause longer, more intense, and frequent heatwaves, of which anthropogenic stressors concentrated in urban areas are a critical contributor. In this study, we investigate the causal interactions during heatwaves across 520 urban sites in the U.S. combining complex network and causal analysis. The presence of regional mediators is manifest in the constructed causal networks, together with long-range teleconnections. More importantly, megacities, such as New York City and Chicago, are causally connected with most of other cities and mediate the structure of urban networks during heatwaves. We also identified a significantly positive correlation between the causality strength and the total populations in megacities. These findings corroborate the contribution of human activities e.g., anthropogenic emissions of greenhouse gases or waste heat, to urban heatwaves. The emergence of teleconnections and supernodes are informative for the prediction and adaptation to heatwaves under global climate change. 

The exacerbated thermal environment in cities, the urban heat island (UHI) effect as a prominent example, has been the source of many adverse urban environmental issues, including the increase of health risks, degradation of air quality and ecosystem services, and reduced resiliency of engineering infrastructure. Last decades have witnessed tremendous efforts and resources being invested to find sustainable solutions for urban heat mitigation, whereas the relative contributions of different UHI attributes and their patterns of spatio-temporal variability remain obscure. In this study, we employed the random forest (RF) method to quantify the relative importance of four categories of urban surface characteristics that regulate the surface UHI, namely the urban greenery fraction, land surface albedo, urban morphology, and level of human activities. We selected seventeen major cities from six megaregions in China as our study areas, with the RF training and test sets obtained from multi-sourced remote sensing and observational data products. It is found that the urban greenery coverage manifests as the most important environmental determinants of UHI, followed by surface albedo. The results are informative for urban planners, policymakers, and engineering practitioners to design and implement sustainable strategies for urban heat mitigation.