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Free access
Jingwen Wu
and
Chen Zhang

Abstract

Improving the performance and resilience of the transportation system in cities is an important way to combat climate change. However, the relationship between weather conditions and traffic congestion remains unclear. This study investigates the association between weather conditions and traffic congestion (Congestion Delay Index, CDI) using a dataset encompassing 98 cities in China from 2015 to 2019. The results reveal that temperature exerts a significant negative effect on CDI, particularly during weekends. Conversely, rain, wind speed, and relative humidity exhibit significant positive effects on CDI. Specifically, traffic congestion would decrease by 6% when the temperature exceeds 25 °C, while it increases by 2% to 5.6% with rainfall increases on workdays. Besides, the precipitation-CDI relationship shows an inverse-U shape, especially on weekends. Although subways could mitigate the impact of temperature on cities compared to those without subways, the supplementary effect is mild on rainy days.

Restricted access
Joshua B. Wadler
,
Joseph J. Cione
,
Samantha Michlowitz
,
Benjamin Jaimes de la Cruz
, and
Lynn K. Shay

Abstract

This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the prestorm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best-performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJ cm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJ cm−2 (SST cooling is always less than 2°C), which demonstrates the importance of the prestorm oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical–dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts.

Significance Statement

The ocean provides the heat and moisture necessary for tropical cyclone (TC) intensification. Since the heat and moisture transfer depend on the sea surface temperature (SST), we create statistical equations for the prediction of SST underneath the storm. The variables we use combine the initial SST before the storm arrives, the upper-ocean thermal structure, and the strength and translation speed of the storm. The predictive equations for SST are evaluated for how well they improve TC intensity forecasts. The best-performing equations can be used for prediction in operational statistical models, which can aid intensity forecasts.

Restricted access
Alexander W. Fisher
and
Nicholas J. Nidzieko

Abstract

Measurements collected by a REMUS 600 AUV off the coast of southern California demonstrate large-scale coherent wave-driven vortices, consistent with Langmuir turbulence (LT), played a dominant role in structuring turbulent dissipation within the oceanic surface boundary layer. During a 10-hour period with sustained wind speeds of 10 m s−1, Langmuir circulations were limited to the upper third of the surface mixed layer by persistent stratification within the water column. The ensemble-averaged circulation, calculated using conditional averaging of AD2CP velocity profiles using elevated backscattering intensity associated with subsurface bubble clouds, indicates that LT vortex pairs were characterized by an energetic downwelling zone flanked by broader, weaker upwelling regions with vertical velocity magnitudes similar to previous numerical studies of LT. Horizontally-distributed microstructure estimates of turbulent kinetic energy dissipation rates were lognormally-distributed near the surface in the wave mixing layer with the majority of values falling between wall layer scaling and wave transport layer scaling. Partitioning dissipation rates between downwelling centers and ambient conditions suggests that LT may play a dominant role in elevating dissipation rates in the OSBL by redistributing wave breaking turbulence.

Restricted access
Paul E. Roundy
and
Crizzia Mielle De Castro

Abstract

The Madden–Julian oscillation (MJO) propagates eastward as a disturbance of mostly zonal wind and precipitation along the equator. The initial diagnosis of the MJO spectral peak at 40–50-day periods suggests a reduction in amplitude associated with slower MJO events that occur at lower frequencies. If events on the low-frequency side of the spectral peak continued to grow in amplitude with reduced phase speed, the spectrum would just be red. Wavelet regression analysis of slow and fast eastward-propagating MJO signals during northern winter assesses how associated moisture and wind patterns could explain why slow MJO events achieve lower amplitude in tracers of moist convection. Results suggest that slow MJO events favor a ridge anomaly over Europe, which drives cool dry air equatorward over Africa and Arabia as the active convection develops over the Indian Ocean. We hypothesize that dry air tracing back to this source, together with a longer duration of the events, leads to associated convection diminishing along the equator and instead concentrating in the Rossby gyres off the equator.

Significance Statement

The Madden–Julian oscillation (MJO) dominates the subseasonal variability of the tropical atmosphere. This work suggests that it favors maximum convective activity in the 40–50-day period range because lower-frequency MJO signals tend to import more cool dry air from the extratropics and along the equator, thereby weakening the slower events.

Open access
Martin Jucker
,
Chris Lucas
, and
Deepashree Dutta

Abstract

The amount of water vapor injected into the stratosphere after the eruption of Hunga Tonga-Hunga Ha’apai (HTHH) was unprecedented, and it is therefore unclear what it might mean for surface climate. We use chemistry climate model simulations to assess the long-term surface impacts of stratospheric water vapor (SWV) anomalies similar to those caused by HTHH, but neglect the relatively minor aerosol loading from the eruption. The simulations show that the SWV anomalies lead to strong and persistent warming of Northern Hemisphere landmasses in boreal winter, and austral winter cooling over Australia, years after eruption, demonstrating that large SWV forcing can have surface impacts on a decadal timescale. We also emphasize that the surface response to SWV anomalies is more complex than simple warming due to greenhouse forcing and is influenced by factors such as regional circulation patterns and cloud feedbacks. Further research is needed to fully understand the multi-year effects of SWV anomalies and their relationship with climate phenomena like El Nino Southern Oscillation.

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Zhen Liu
,
Changlin Chen
,
Guihua Wang
,
Shouwei Li
, and
Shouhua Liu

Abstract

Using a range of Detection and Attribution Model Intercomparison Project (DAMIP) simulations from phase 6 of Coupled Model Intercomparison Project (CMIP6), we study the response of dynamic sea level (DSL) to external anthropogenic climate forcing [greenhouse gases (GHGs), aerosols, and stratospheric ozone] with a focus on the differences over the twentieth and twenty-first century. In the second half of the twentieth century, the DSL nonuniformity in the Northern Hemisphere (NH) was relatively small due to a cancellation between the effects of increasing GHGs and aerosols. In contrast, the DSL signal in the Southern Hemisphere (SH) over this period was large because stratospheric ozone depletion reinforced the effects of increasing GHGs. In the twenty-first century, the DSL response has been intensified in the NH because the warming effects of diminishing aerosols have acted to reinforce the effects of increasing GHGs. Meanwhile, the distribution of SH DSL has also become uneven although stratospheric ozone recovery has partially offset the effects of rising GHGs. Using a global ocean circulation model, we decompose the changes in the twenty-first century DSL into distinct responses to surface forcings including sea surface temperature, salinity, and wind stress. Our results show that the dipole-like pattern of DSL in the North Pacific can be attributed largely to sea surface warming, while the dipole-like pattern in the North Atlantic is attributed to subpolar surface salinity freshening. The belted pattern of DSL changes in the Southern Ocean is induced by both surface warming and intensifying/poleward-shifting westerly winds.

Restricted access
Yuqiong Zheng
,
Shangfeng Chen
,
Wen Chen
,
Renguang Wu
,
Zhibiao Wang
,
Bin Yu
,
Peng Hu
, and
Jinling Piao

Abstract

The spring Pacific meridional mode (PMM) is an important precursor of El Niño–Southern Oscillation (ENSO). However, recent studies reported that only about half of the spring PMM events were followed by ENSO events. This study examines the role of internal climate variability in modulating the impact of PMM on ENSO using 100-member ensemble simulations of the Max Planck Institute Earth System Model (MPI-ESM). The relationship between spring PMM and following winter ENSO shows a large spread among the 100 members. The variation of spring Aleutian low (AL) intensity is identified to be an important factor modulating the PMM–ENSO relation. The spring AL affects the PMM–ENSO relationship by modifying PMM-generated low-level zonal wind anomalies over the tropical western Pacific. The strengthening of the spring AL is accompanied by westerly wind anomalies over the midlatitude northwestern Pacific, leading to sea surface temperature (SST) cooling there via an enhancement of upward surface heat flux. This results in increased meridional SST gradient and leads to northerly wind anomalies over the subtropical northwestern Pacific, which turn to surface westerly wind anomalies after reaching the equatorial western Pacific due to the conservation of potential vorticity. Thus, the low-level westerly (easterly) wind anomalies over the tropical western Pacific associated with the positive (negative) spring PMM were strengthened (weakened), which further contributes to an enhanced (a weakened) PMM–ENSO relation. The mechanism for the modulation of the AL on the spring PMM–ENSO relationship is verified by a set of AGCM simulations. This study suggests that the condition of the spring AL should be considered when predicting ENSO on the basis of the PMM.

Significance Statement

Spring Pacific meridional mode (PMM) is a predictor of ENSO, but not all spring PMM events are accompanied by the occurrence of ENSO events. This study aims to explore the influence of internal climate variability on the relationship between spring PMM and following ENSO. It is revealed that the Aleutian low exerts a crucial modulation on the spring PMM–ENSO relationship. The underlying physical mechanisms for the impact of the Aleutian low on the relationship between spring PMM and ENSO are further examined. The results of this study have important implications for improving the prediction of ENSO.

Restricted access
Dana M. Tobin
,
Joshua S. Kastman
,
James A. Nelson
, and
Heather D. Reeves

Abstract

Development of an impact-based decision support forecasting tool for surface-transportation hazards requires consideration for what impacts the product is intended to capture, and how to scale forecast information to impacts to then categorize impact severity. In this first part of the series, we discuss the motivation and intent of such a product, in addition to outlining the approach we take to leverage existing and new research to develop the product. Traffic disruptions (e.g., crashes, increased travel times, roadway restrictions or closures) are the intended impacts, where impact severity levels are intended to scale to reflect the increasing severity of adverse driving conditions that can correlate with a need for enhanced mitigation efforts by motorists and/or transportation agencies (e.g., slowing down, avoiding travel, imposing roadway restrictions or closures). Previous research on how weather and road conditions impact transportation – and novel research herein to create a metric for crash impact based on precipitation type and local hour of the day – are both intended to help scale weather forecasts to impacts. Impact severity classifications can ultimately be determined through consideration of any thresholds used by transportation agencies, in conjunction with the scaling metrics.

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