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Identifying regions that mediate regional propagation of atmospheric perturbations is important to assessing the susceptibility and resilience of complex hydroclimate systems. Detecting the regional gateways through causal inference, can help unravel the interplay of physical processes and inform projections of future changes. In this study, we characterize the causal interactions among nine climate regions in the contiguous United States using long‐term (1901–2018) precipitation data. The constructed causal networks reveal the cross‐regional propagation of precipitation perturbations. Results show that the Ohio Valley region acts as an atmospheric gateway for precipitation and moisture transport in the U.S., which is largely regulated by the regional convective uplift. The findings have implications for improving predicative capacity of hydroclimate modeling of regional precipitation.

 

Critical transitions of the state variable (temperature) in dynamic climate systems often lead to catastrophic consequence, whereas the effort to reverse the transitions usually lags behind. However, these transitions are characterized by the slowing down of recovery from perturbations, carrying early‐warning signals that can be used to predict system bifurcation. In this study, we employ the conceptual framework of pitchfork bifurcation and analyze the early‐warning signals in temperature time series for critical slowing down prior to both the early 20th century global warming and heat waves. We also investigate the urban signature in these heat waves. The emergence of early‐warning signals before heat waves provides new insights into the underlying mechanisms (e.g., possible feedback via land‐atmosphere interactions). In particular, given the increasing frequency and intensity of heat extremes, the results will facilitate the design of countermeasures to reserve the tipping and restore the resilience of climate systems.

 

As important determinants of urban thermal environment, surface roughness and morphology have been extensively studied for sustainable urban development. In this study, we quantify the effect of urban roughness and morphology on the surface urban heat island (SUHI) intensity and its spatiotemporal patterns, over seventeen major cities in six urban agglomerations of China. We employ multisource dataset and derive multiple measures, representative of the roughness and horizontal/vertical indicators of urban morphology. The results show that the correlation between the SUHI intensity and urban morphological indices is significantly strengthened with the heat island intensity, manifested by the contrasting Pearson’s r in summer (r = 0.59 ± 0.13) and winter (0.11 ± 0.35). In general, the impact assessed using different measures of surface morphology is consistent on the SUHI intensity, while the one-dimensional (1D) roughness emerges as an adequate index not inferior to more complex morphological parameters. Our study also shows that the impact of urban morphology varies in different geographic and climatic regions, as well as with different urban management, which highlights the importance of locality and site-specific design in implementing effective urban heat mitigation strategies. 

Abstract. One critical challenge of studying Earth's hydroclimate system, in the face of global environmental changes, is to predict whether the system approaches a critical threshold. Here, we identified the critical transitions of hydrological processes, including precipitation and potential evapotranspiration, by analyzing their early-warning signals and system-based network structures. The statistical early-warning signals are manifest in increasing trends of autocorrelation and variance in the hydrologic system ranging from regional to global scales, prior to climate shifts in the 1970s and 1990s, in agreement with observations. We further extended the conventional statistics-based measures of early-warning signals to system-based network analysis in urban areas across the contiguous United States. The topology of an urban precipitation network features hub-periphery (clustering) and modular organization, with strong intra-regional connectivity and inter-regional gateways (teleconnection). We found that several network parameters (mean correlation coefficient, density, and clustering coefficient) gradually increased prior to the critical transition in the 1990s, signifying the enhanced synchronization among urban precipitation patterns. These topological parameters can not only serve as novel system-based early-warning signals for critical transitions in hydrological processes but also shed new light on structure–dynamic interactions in the complex hydrological system.