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Free access
Jangho Lee

Abstract

This study utilizes hourly land surface temperature (LST) data from the Geostationary Operational Environmental Satellite (GOES) to analyze the seasonal and diurnal characteristics of surface urban heat island intensity (SUHII) across 120 largest U.S. cities and their surroundings. Distinct patterns emerge in the classification of seasonal daytime SUHII and nighttime SUHII. Specifically, the enhanced vegetation index (EVI) and albedo (ALB) play pivotal roles in influencing these temperature variations. The diurnal cycle of SUHII further reveals different trends, suggesting that climate conditions, urban and nonurban land covers, and anthropogenic activities during nighttime hours affect SUHII peaks. Exploring intracity LST dynamics, the study reveals a significant correlation between urban intensity (UI) and LST, with LST rising as UI increases. Notably, populations identified as more vulnerable by the social vulnerability index (SVI) are found in high UI regions. This results in discernible LST inequality, where the more vulnerable communities are under higher LST conditions, possibly leading to higher heat exposure. This comprehensive study accentuates the significance of tailoring city-specific climate change mitigation strategies, illuminating LST variations and their intertwined societal implications.

Open access
Hong Wang
,
Liang Gao
,
Lei Zhu
,
Lulu Zhang
, and
Jiahao Wu

Abstract

Accurately assessing cyclone intensity changes due to global warming is crucial for predicting and mitigating sequential hazards. This study develops a high-resolution, fully coupled air–sea model to investigate the impact of global warming on Supertyphoon Mangkhut (2018). A numerical sensitivity analysis is conducted using the pseudo–global warming (PGW) technique based on multiple global climate models (GCMs) from phase 6 of Coupled Model Intercomparison Project (CMIP6). Under ocean warming scenarios, the increasing average sea surface temperature (SST) by 2.26°, 2.44°, 3.45°, and 4.53°C results in reductions in the minimum sea level pressure by 9.2, 10.6, 15.7, and 19.4 hPa, respectively, compared to the original state of Typhoon Mangkhut. Rising SST increases the turbulent heat flux; to be specific, an average SST increase of 2.26°–4.53°C changes the turbulent heat flux into 177%–272% of the original value. Besides, stronger winds enhance SST cooling, including upwelling and entrainment, leading to an increase in the mixed layer depth (MLD). Tropical cyclone heat potential (TCHP) tends to be enhanced under the combined influences as the SST rises. An average increase in the SST of 2.26°, 2.44°, 3.45°, and 4.53°C leads to an increase in the TCHP of 9.94%, 9.85%, 14.67%, and 15.30%, respectively. However, future changes in atmospheric temperature and humidity will moderate typhoon intensification induced by ocean warming. Considering atmospheric conditions, the maximum wind speed decreases by approximately 10% compared to only considering ocean warming. Nevertheless, typhoon intensity is projected to strengthen under future climate change.

Significance Statement

This study examines the role of global warming in typhoon intensity and the response of typhoon events to changes in the oceanic thermal structure. Sensitivity experiments considering future warming climates are conducted using a fully coupled air–sea numerical model. An average increase in sea surface temperature (SST) by 4.53°C can lead to a reduction in the minimum sea level pressure by 19.4 hPa. Ocean warming enhances oceanic mixing, potentially increasing the availability of heat energy for typhoon’s development (i.e., an average increase in SST by 4.53°C leads to a 15.30% increase in heat energy). However, future changes in atmospheric temperature and humidity will moderate the intensification of typhoons induced by ocean warming. These results are expected to provide information for assessing the future changes in typhoon intensity under a warming climate, which is important for predicting and reducing sequential risks.

Restricted access
Free access
Julia Olson
and
Patricia Pinto da Silva

Abstract

The use of oral histories in social scientific approaches to climate change has enabled richly detailed explorations of the situated, meaning-laden dimensions of local experiences and knowledge. But “big data” approaches have been increasingly advocated as a means to scale up understandings from individual projects, through better utilizing large collections of qualitative data sources. This article considers the issues raised by such secondary analysis, using the NOAA Voices Oral History Archives, an online database with a focus on coastal communities and groups thought especially vulnerable to climatic changes. Coupling larger-scale methods such as text mining with more traditional methods such as close reading reveals variations across time and space in the ways people talk about environmental changes, underscoring how memories and experiences shape understandings and the subtlety with which these differences are articulated and culturally inscribed. Looking across multiple collections illuminates those shared understandings, points of contention, and differences between communities that might be obscured if decontextualized, showing the importance of “small data” approaches to big data to fully understand the deeply cultural understandings, perceptions, and histories of environmental changes such as climate change.

Open access
Xiaoqin Yan
,
Wangjie Yao
, and
Youmin Tang

Abstract

Utilizing ensemble hindcast data from the Community Earth System Model (CESM) spanning the years 1900–2014, the multidecadal changes in the seasonal potential predictability of the winter Pacific–North American (PNA) teleconnection pattern and associated circulation anomalies have been investigated by using an information-based metric of relative entropy and the method of the most predictable component analysis. Results show that the seasonal potential predictability of winter PNA has significant multidecadal changes, with values much higher at the two ends of the twentieth century and much lower in between particularly in the 1930s and 1940s. The changes in the seasonal potential predictability of winter PNA are mostly reflected by the temporal evolutions of PNA rather than the location changes of active centers. Further, the changes are mostly contributed by the external forcing of El Niño–Southern Oscillation (ENSO)-related sea surface temperature anomalies in tropical central and eastern Pacific. In particular, the combined effects of lower amplitudes, reduced persistence, and a more eastward shift in warming centers lead to the reduced seasonal potential predictability of PNA and associated circulation changes in the 1930s and 1940s.

Significance Statement

Seasonal prediction of the winter Pacific–North American (PNA) teleconnection pattern and associated circulation anomalies is very important due to its profound climate impacts. Understanding the multidecadal fluctuations and its driving sources of the potential predictability of winter PNA and associated circulation anomalies are meaningful for skillful seasonal prediction of winter PNA and circulation anomalies as well as related climate variations. This study for the first time shows that the multidecadal fluctuations of the potential predictability of winter PNA are quite significant and the changes are mostly reflected by its temporal evolutions rather than spatial shifts of active centers. Furthermore, this study shows that the strength, persistence, and warming center locations of ENSO-related sea surface temperatures in tropical Pacific play a crucial role on the multidecadal changes of the potential predictability of winter PNA and associated circulation anomalies.

Restricted access
John Uehling
and
Carl J. Schreck III

Abstract

Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians.

Significance Statement

The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities.

Restricted access
Dillon Elsbury
,
Amy Butler
,
Yannick Peings
, and
Gudrun Magnusdottir

Abstract

The quasi-biennial oscillation (QBO) is thought to influence boreal winter surface conditions over Asia and around the North Atlantic. Confirming if these responses are robust is complicated by the QBO having multiple pathways to influence surface conditions as well as internal variability. The reanalysis record suggests that sudden stratospheric warmings (SSWs), breakdowns of the polar vortex that can elicit persistent surface impacts, are more frequent during easterly QBO (EQBO). Hence, this modulated frequency of SSWs may account for some of the EQBO surface responses. However, many climate models do not reproduce this QBO–SSW relationship, perhaps because it is noise or because the model QBOs are deficient. We circumvent these issues by using an ensemble of fixed boundary condition branched simulations in which a realistic EQBO is prescribed in control simulations previously devoid of a QBO, allowing us to isolate the transient atmospheric response to EQBO. Imposing EQBO accelerates the tropical upper-tropospheric wind, shifts the subtropical jet poleward, and attenuates the polar vortex. Interestingly, the latter is not entirely dependent on the statistically significant increase in SSW frequency due to EQBO. Corroborating observations, EQBO is associated with warmer surface temperatures over Asia and negative North Atlantic Oscillation (NAO) conditions. We then subsample the branched/control simulations based on which EQBO members have SSWs. The negative NAO response is primarily associated with more frequent SSWs, while the Asia warming develops irrespective of SSWs. These results have implications for wintertime predictability and clarify the pairing of particular QBO teleconnections with certain surface impacts.

Significance Statement

The QBO is one of the few parts of the Earth system that is predictable months in advance and that also elicits global effects on surface temperature, circulation, and precipitation. Unfortunately, climate models and operational forecast systems do not simulate the QBO well and it is not always clear how robust the global impacts of the QBO are. Here, we impose the QBO in idealized model simulations, which modulates wintertime surface temperature and precipitation over Asia, the North Atlantic, Europe, and Africa in a manner consistent with observations. This work substantiates the importance of climate and forecast models properly simulating the QBO.

Restricted access
Ligia Bernardet
,
Lisa Bengtsson
,
Patrick A. Reinecke
,
Fanglin Yang
,
Man Zhang
,
Kyle Hall
,
James Doyle
,
Matus Martini
,
Grant Firl
, and
Lulin Xue

Abstract

The Common Community Physics Package (CCPP) is a state-of-the-art infrastructure designed to facilitate community-wide development of atmospheric physics parameterizations, support their interoperability among different modeling centers, and enable the transition of research to operations in NWP and climate modeling. The CCPP consists of two elements: the Physics (a repository of parameterizations) and the Framework (an infrastructure for interfacing the parameterizations with host models). The CCPP is a community resource: its latest release has 23 primary parameterizations, which can be organized into six supported suites. It is distributed with a single-column model to facilitate physics development and experimentation. The Developmental Testbed Center provides support to users and developers. A key aspect of the CCPP is its interoperability, that is, its ability to be used by multiple host models. This enables synergistic collaboration among groups dispersed over various institutions and working on various models. In this article we provide an overview of the CCPP and how it is being used in two leading modeling systems. The CCPP is part of the Unified Forecast System (UFS), is included in the NOAA operational Hurricane Analysis and Forecast System (HAFS) version one, and is slated for use in all upcoming NOAA global and limited-area UFS applications for operations. Similarly, the CCPP has been integrated into the Navy Environmental Prediction System Using a Nonhydrostatic Engine (NEPTUNE) model and is undergoing testing for upcoming transition to operations. These experiences make physics interoperability a reality and open the doors for much broader collaborative efforts on ESM development.

Open access