Abstract
Bubble plumes play a significant role in the air–sea interface by influencing processes such as air–sea gas exchange, aerosol production, modulation of oceanic carbon and nutrient cycles, and the vertical structure of the upper ocean. Using acoustic Doppler current profiler (ADCP) data collected off the west coast of Ireland, we investigate the dynamics of bubble plumes and their relationship with sea state variables. In particular, we describe the patterns of bubble plume vertical extension, duration, and periodicity. We establish a power-law relationship between the average bubble penetration depth and wind speed, consistent with previous findings. Additionally, the study reveals a significant association between whitecapping coverage and observed acoustic volume backscatter intensity, underscoring the role of wave breaking in bubble plume generation. The shape of the probability distribution of bubble plume depths reveals a transition toward stronger and more organized bubble entrainment events during higher wind speeds. Furthermore, we show that deeper bubble plumes are associated with turbulent Langmuir number La t ∼ 0.3, highlighting the potential role of Langmuir circulation on the transport and deepening of bubble plumes. These results contribute to a better understanding of the complex interactions between ocean waves, wind, and bubble plumes, providing valuable insights for improving predictive models and enhancing our understanding of air–sea interactions.
Significance Statement
This research contributes to understanding bubble plume dynamics in the upper ocean and their relationship with sea state variables. The establishment of a power-law relationship between the bubble penetration depth and wind speed, along with the association between whitecapping coverage and acoustic backscatter intensity, contributes to improved predictive capabilities for air–sea interactions and carbon dioxide exchange. The identification of the potential influence of Langmuir circulation on bubble plume dynamics expands our understanding of the role of coherent circulations in transporting bubble plumes. Additionally, this study presents a clear methodology using commercial sensors such as an ADCP, which can be easily replicated by researchers worldwide, leading to potential advancements in our comprehension of bubble plume dynamics.
Abstract
Bubble plumes play a significant role in the air–sea interface by influencing processes such as air–sea gas exchange, aerosol production, modulation of oceanic carbon and nutrient cycles, and the vertical structure of the upper ocean. Using acoustic Doppler current profiler (ADCP) data collected off the west coast of Ireland, we investigate the dynamics of bubble plumes and their relationship with sea state variables. In particular, we describe the patterns of bubble plume vertical extension, duration, and periodicity. We establish a power-law relationship between the average bubble penetration depth and wind speed, consistent with previous findings. Additionally, the study reveals a significant association between whitecapping coverage and observed acoustic volume backscatter intensity, underscoring the role of wave breaking in bubble plume generation. The shape of the probability distribution of bubble plume depths reveals a transition toward stronger and more organized bubble entrainment events during higher wind speeds. Furthermore, we show that deeper bubble plumes are associated with turbulent Langmuir number La t ∼ 0.3, highlighting the potential role of Langmuir circulation on the transport and deepening of bubble plumes. These results contribute to a better understanding of the complex interactions between ocean waves, wind, and bubble plumes, providing valuable insights for improving predictive models and enhancing our understanding of air–sea interactions.
Significance Statement
This research contributes to understanding bubble plume dynamics in the upper ocean and their relationship with sea state variables. The establishment of a power-law relationship between the bubble penetration depth and wind speed, along with the association between whitecapping coverage and acoustic backscatter intensity, contributes to improved predictive capabilities for air–sea interactions and carbon dioxide exchange. The identification of the potential influence of Langmuir circulation on bubble plume dynamics expands our understanding of the role of coherent circulations in transporting bubble plumes. Additionally, this study presents a clear methodology using commercial sensors such as an ADCP, which can be easily replicated by researchers worldwide, leading to potential advancements in our comprehension of bubble plume dynamics.
Estimation of Urban Greenhouse Gas Fluxes from Mole Fraction Measurements Using Monin–Obukhov Similarity TheoryAbstract
The purpose of this study is to determine whether urban greenhouse gas (GHG) fluxes can be quantified from tower-based mole fraction measurements using Monin–Obukhov similarity theory (MOST). Tower-based GHG mole fraction networks are used in many cities to quantify whole-city GHG emissions. Local-scale, micrometeorological flux estimates would complement whole-city estimates from atmospheric inversions. CO2 mole fraction and eddy-covariance flux data at an urban site in Indianapolis, Indiana, from October 2020 through January 2022 are analyzed. Using MOST flux–variance and flux–gradient relationships, CO2 fluxes were calculated using these mole fraction data and compared to the eddy-covariance fluxes. MOST-based fluxes were calculated using varying measurement heights and methods of estimating stability. The MOST flux–variance relationship method showed good temporal correlation with eddy-covariance fluxes at this site but overestimated flux magnitudes. Fluxes calculated using flux–gradient relationships showed lower temporal correlation with eddy-covariance fluxes but closer magnitudes to eddy-covariance fluxes. Measurement heights closer to ground level produce more precise flux estimates for both MOST-based methods. For flux–gradient methods, flux estimates are more accurate and precise when low-altitude measurements are combined with a large vertical separation between measurement heights. When stability estimates based on eddy-covariance flux measurements are replaced with stability estimates based on the weather station or net radiation data, the MOST-based fluxes still capture the temporal patterns measured via eddy covariance. Based on these results, MOST can be used to estimate the temporal patterns in local GHG fluxes at mole fraction tower sites, complementing the small number of eddy-covariance flux measurements available in urban settings.
Abstract
The purpose of this study is to determine whether urban greenhouse gas (GHG) fluxes can be quantified from tower-based mole fraction measurements using Monin–Obukhov similarity theory (MOST). Tower-based GHG mole fraction networks are used in many cities to quantify whole-city GHG emissions. Local-scale, micrometeorological flux estimates would complement whole-city estimates from atmospheric inversions. CO2 mole fraction and eddy-covariance flux data at an urban site in Indianapolis, Indiana, from October 2020 through January 2022 are analyzed. Using MOST flux–variance and flux–gradient relationships, CO2 fluxes were calculated using these mole fraction data and compared to the eddy-covariance fluxes. MOST-based fluxes were calculated using varying measurement heights and methods of estimating stability. The MOST flux–variance relationship method showed good temporal correlation with eddy-covariance fluxes at this site but overestimated flux magnitudes. Fluxes calculated using flux–gradient relationships showed lower temporal correlation with eddy-covariance fluxes but closer magnitudes to eddy-covariance fluxes. Measurement heights closer to ground level produce more precise flux estimates for both MOST-based methods. For flux–gradient methods, flux estimates are more accurate and precise when low-altitude measurements are combined with a large vertical separation between measurement heights. When stability estimates based on eddy-covariance flux measurements are replaced with stability estimates based on the weather station or net radiation data, the MOST-based fluxes still capture the temporal patterns measured via eddy covariance. Based on these results, MOST can be used to estimate the temporal patterns in local GHG fluxes at mole fraction tower sites, complementing the small number of eddy-covariance flux measurements available in urban settings.
Abstract
It has been over 75 years since the concept of directly suppressing lightning by modifying thunderstorm cloud processes was first proposed as a technique for preventing wildfire ignitions. Subsequent decades produced a series of successful field campaigns that demonstrated several techniques for interrupting storm electrification, motivated in part by the prospect of protecting Apollo-era rocket launches from lightning strike. Despite the technical success of these research programs, funding and interest diminished until the final field experiment in 1975 marked the last large-scale activity toward developing lightning prevention technology. Having lost widespread awareness over the ensuing 50 years, these pioneering efforts in experimental cloud physics have largely been forgotten, and this approach for mitigating lightning hazards has fallen into obscurity. At the present day, risks from lightning-ignited wildfires to lives, property, and infrastructure are once again a major topic of concern. Similarly, the rapid development of an emerging commercial space sector is placing new demands on airspace management and launch scheduling. These modern challenges may potentially be addressed by a seemingly antiquated concept—lightning suppression—but considerations must be made to understand the consequences of deploying this technology. Nonetheless, the possible economic, environmental, and societal benefits of this approach merit a critical reevaluation of this hazard mitigation technology in the current era.
Abstract
It has been over 75 years since the concept of directly suppressing lightning by modifying thunderstorm cloud processes was first proposed as a technique for preventing wildfire ignitions. Subsequent decades produced a series of successful field campaigns that demonstrated several techniques for interrupting storm electrification, motivated in part by the prospect of protecting Apollo-era rocket launches from lightning strike. Despite the technical success of these research programs, funding and interest diminished until the final field experiment in 1975 marked the last large-scale activity toward developing lightning prevention technology. Having lost widespread awareness over the ensuing 50 years, these pioneering efforts in experimental cloud physics have largely been forgotten, and this approach for mitigating lightning hazards has fallen into obscurity. At the present day, risks from lightning-ignited wildfires to lives, property, and infrastructure are once again a major topic of concern. Similarly, the rapid development of an emerging commercial space sector is placing new demands on airspace management and launch scheduling. These modern challenges may potentially be addressed by a seemingly antiquated concept—lightning suppression—but considerations must be made to understand the consequences of deploying this technology. Nonetheless, the possible economic, environmental, and societal benefits of this approach merit a critical reevaluation of this hazard mitigation technology in the current era.
Abstract
Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of “anchor” observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes.
Significance Statement
This study aims to evaluate the impact of aircraft reconnaissance dropsondes on the assimilation of satellite radiance data, utilizing observations from the 2020 Atmospheric River Reconnaissance program. The key findings reveal a substantial enhancement in the model first guess and improved estimates of radiance biases. Notably, there is a significant 5%–10% increase in microwave radiance observations over the northeastern Pacific and North America, with positive yet modest effects observed in tropospheric infrared radiance. These findings underscore the crucial role of atmospheric river reconnaissance dropsondes as anchor data, enhancing the assimilation of radiance observations. In essence, the inclusion of these dropsondes in routine networks is particularly valuable for optimizing data assimilation in regions with sparse observational data.
Abstract
Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of “anchor” observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes.
Significance Statement
This study aims to evaluate the impact of aircraft reconnaissance dropsondes on the assimilation of satellite radiance data, utilizing observations from the 2020 Atmospheric River Reconnaissance program. The key findings reveal a substantial enhancement in the model first guess and improved estimates of radiance biases. Notably, there is a significant 5%–10% increase in microwave radiance observations over the northeastern Pacific and North America, with positive yet modest effects observed in tropospheric infrared radiance. These findings underscore the crucial role of atmospheric river reconnaissance dropsondes as anchor data, enhancing the assimilation of radiance observations. In essence, the inclusion of these dropsondes in routine networks is particularly valuable for optimizing data assimilation in regions with sparse observational data.
Abstract
Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.
Abstract
Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.
Abstract
Marine heatwaves (MHWs) are prolonged extremely high sea surface temperature (SST) events. In 2021 summer, an intense MHW occurred over the central North Pacific; the SST in September 2021 was the highest in September since 1900, and the warming signal was distributed not only near the sea surface but also below the ocean mixed layer (∼300 m depth). Atmosphere reanalysis data showed westward expansion of the North Pacific Subtropical High (NPSH) in 2021 summer, but both an increase in downward shortwave radiation and a decrease in upward latent heat flux were not so large, and ocean mixed layer heat budget analysis, which also used ocean reanalysis data, revealed that the atmosphere-induced heating is insufficient to form the record-breaking MHW. Argo profiling floats indicated that, in 2021 summer, the Central Mode Water (CMW) – a huge water mass characterized by vertically uniform properties in depths of 100–500 m – decreased extremely, the thickness of which was less than 20% of the normal. Statistical analysis showed that, from the sea surface to the upper boundary of CMW, the heavier isopycnal surfaces are deeper associated with the decrease in CMW, leading to a weakening of the seasonal pycnocline. Then this causes the weakening of cooling heat flux associated with the entrainment of subsurface waters into the mixed layer, resulting in surface ocean warming, which in turn contributed to form the MHW in 2021 summer.
Abstract
Marine heatwaves (MHWs) are prolonged extremely high sea surface temperature (SST) events. In 2021 summer, an intense MHW occurred over the central North Pacific; the SST in September 2021 was the highest in September since 1900, and the warming signal was distributed not only near the sea surface but also below the ocean mixed layer (∼300 m depth). Atmosphere reanalysis data showed westward expansion of the North Pacific Subtropical High (NPSH) in 2021 summer, but both an increase in downward shortwave radiation and a decrease in upward latent heat flux were not so large, and ocean mixed layer heat budget analysis, which also used ocean reanalysis data, revealed that the atmosphere-induced heating is insufficient to form the record-breaking MHW. Argo profiling floats indicated that, in 2021 summer, the Central Mode Water (CMW) – a huge water mass characterized by vertically uniform properties in depths of 100–500 m – decreased extremely, the thickness of which was less than 20% of the normal. Statistical analysis showed that, from the sea surface to the upper boundary of CMW, the heavier isopycnal surfaces are deeper associated with the decrease in CMW, leading to a weakening of the seasonal pycnocline. Then this causes the weakening of cooling heat flux associated with the entrainment of subsurface waters into the mixed layer, resulting in surface ocean warming, which in turn contributed to form the MHW in 2021 summer.
