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Abstract
El Niño–Southern Oscillation (ENSO) is the leading mode of interannual ocean–atmosphere coupling in the tropical Pacific, greatly influencing the global climate system. Seasonal phase locking, which means that ENSO events usually peak in boreal winter, is a distinctive feature of ENSO. In model future projections, the ENSO sea surface temperature (SST) amplitude in winter shows no significant change with a large intermodel spread. However, whether and how ENSO phase locking will respond to global warming are not fully understood. In this study, using Community Earth System Model Large Ensemble (CESM-LE) projections, we found that the seasonality of ENSO events, especially its peak phase, has advanced under global warming. This phenomenon corresponds to the seasonal difference in the changes in the ENSO SST amplitude with an enhanced (weakened) amplitude from boreal summer to autumn (winter). Mixed layer ocean heat budget analysis revealed that the advanced ENSO seasonality is due to intensified positive meridional advective and thermocline feedback during the ENSO developing phase and intensified negative thermal damping during the ENSO peak phase. Furthermore, the seasonal variation in the mean El Niño–like SST warming in the tropical Pacific favors a weakened zonal advective feedback in boreal autumn–winter and earlier decay of ENSO. The advance of the ENSO peak phase is also found in most CMIP5/6 models that simulate the seasonal phase locking of ENSO well in the present climate. Thus, even though the amplitude response in the winter shows no model consensus, ENSO also significantly changes during different stages under global warming.
Abstract
El Niño–Southern Oscillation (ENSO) is the leading mode of interannual ocean–atmosphere coupling in the tropical Pacific, greatly influencing the global climate system. Seasonal phase locking, which means that ENSO events usually peak in boreal winter, is a distinctive feature of ENSO. In model future projections, the ENSO sea surface temperature (SST) amplitude in winter shows no significant change with a large intermodel spread. However, whether and how ENSO phase locking will respond to global warming are not fully understood. In this study, using Community Earth System Model Large Ensemble (CESM-LE) projections, we found that the seasonality of ENSO events, especially its peak phase, has advanced under global warming. This phenomenon corresponds to the seasonal difference in the changes in the ENSO SST amplitude with an enhanced (weakened) amplitude from boreal summer to autumn (winter). Mixed layer ocean heat budget analysis revealed that the advanced ENSO seasonality is due to intensified positive meridional advective and thermocline feedback during the ENSO developing phase and intensified negative thermal damping during the ENSO peak phase. Furthermore, the seasonal variation in the mean El Niño–like SST warming in the tropical Pacific favors a weakened zonal advective feedback in boreal autumn–winter and earlier decay of ENSO. The advance of the ENSO peak phase is also found in most CMIP5/6 models that simulate the seasonal phase locking of ENSO well in the present climate. Thus, even though the amplitude response in the winter shows no model consensus, ENSO also significantly changes during different stages under global warming.
Abstract
Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as postprocessing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initialization dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict monthly average precipitation and 2-m temperature 2 weeks in advance for the continental United States. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models (a multimodel approach based on the prediction of the individual ML models). Unlike previous ML approaches that often use ensemble mean alone, we leverage information embedded in the ensemble forecasts to enhance prediction accuracy. Additionally, we investigate extreme event predictions that are crucial for planning and mitigation efforts. Considering ensemble members as a collection of spatial forecasts, we explore different approaches to using spatial information. Trade-offs between different approaches may be mitigated with model stacking. Our proposed models outperform standard baselines such as climatological forecasts and ensemble means. In addition, we investigate feature importance, trade-offs between using the full ensemble or only the ensemble mean, and different modes of accounting for spatial variability.
Significance Statement
Accurately forecasting temperature and precipitation on subseasonal time scales—2 weeks–2 months in advance—is extremely challenging. These forecasts would have immense value in agriculture, insurance, and economics. Our paper describes an application of machine learning techniques to improve forecasts of monthly average precipitation and 2-m temperature using lagged physics-based predictions and observational data 2 weeks in advance for the entire continental United States. For lagged ensembles, the proposed models outperform standard benchmarks such as historical averages and averages of physics-based predictions. Our findings suggest that utilizing the full set of physics-based predictions instead of the average enhances the accuracy of the final forecast.
Abstract
Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as postprocessing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initialization dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict monthly average precipitation and 2-m temperature 2 weeks in advance for the continental United States. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models (a multimodel approach based on the prediction of the individual ML models). Unlike previous ML approaches that often use ensemble mean alone, we leverage information embedded in the ensemble forecasts to enhance prediction accuracy. Additionally, we investigate extreme event predictions that are crucial for planning and mitigation efforts. Considering ensemble members as a collection of spatial forecasts, we explore different approaches to using spatial information. Trade-offs between different approaches may be mitigated with model stacking. Our proposed models outperform standard baselines such as climatological forecasts and ensemble means. In addition, we investigate feature importance, trade-offs between using the full ensemble or only the ensemble mean, and different modes of accounting for spatial variability.
Significance Statement
Accurately forecasting temperature and precipitation on subseasonal time scales—2 weeks–2 months in advance—is extremely challenging. These forecasts would have immense value in agriculture, insurance, and economics. Our paper describes an application of machine learning techniques to improve forecasts of monthly average precipitation and 2-m temperature using lagged physics-based predictions and observational data 2 weeks in advance for the entire continental United States. For lagged ensembles, the proposed models outperform standard benchmarks such as historical averages and averages of physics-based predictions. Our findings suggest that utilizing the full set of physics-based predictions instead of the average enhances the accuracy of the final forecast.
Abstract
Using the past to improve future predictions requires an understanding and quantification of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fingerprints into a joint study. We find that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fingerprint). These results are robust across fingerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([−0.86, −0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010–19 relative to 1850–1900 using the combined signals of GMT and GMLP.
Significance Statement
Aerosols are small liquid or solid airborne particles. They are predominantly the secondary result of emissions of aerosol precursor gases emitted via industrial or natural processes. While greenhouse gases warm the climate, aerosols can have a cooling effect on the climate system, thus offsetting some of the greenhouse gas–related warming. We expect greenhouse gas concentrations in the atmosphere to continue to increase, while aerosol concentrations are likely going to decline due to their impacts on human health. Our study uses observed temperature and precipitation changes to quantify how much aerosols have offset warming from past greenhouse gas emissions. This can help constrain future predictions of global warming.
Abstract
Using the past to improve future predictions requires an understanding and quantification of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fingerprints into a joint study. We find that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fingerprint). These results are robust across fingerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([−0.86, −0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010–19 relative to 1850–1900 using the combined signals of GMT and GMLP.
Significance Statement
Aerosols are small liquid or solid airborne particles. They are predominantly the secondary result of emissions of aerosol precursor gases emitted via industrial or natural processes. While greenhouse gases warm the climate, aerosols can have a cooling effect on the climate system, thus offsetting some of the greenhouse gas–related warming. We expect greenhouse gas concentrations in the atmosphere to continue to increase, while aerosol concentrations are likely going to decline due to their impacts on human health. Our study uses observed temperature and precipitation changes to quantify how much aerosols have offset warming from past greenhouse gas emissions. This can help constrain future predictions of global warming.
Abstract
Marine Isotope Stage 3 (MIS 3) is characterized by significant millennial-scale climatic oscillations between cold stadials and mild interstadials, which presents a valuable case for understanding hydrological response to abrupt climate change. Through a set of coupled model simulations, our results broadly show an antiphased interhemispheric change in land monsoonal precipitation during the present-day relative to MIS 3 interstadial and the stadial–interstadial transition, with a general decrease in the Northern Hemisphere but an increase in the Southern Hemisphere. The antiphased pattern is largely caused by the change in orbital insolation during the present-day relative to MIS 3 interstadial, whereas by the weakened Atlantic meridional overturning circulation during the interstadial–stadial transition. However, there are obvious discrepancies in precipitation response and underlying mechanisms among individual monsoon domains and across different periods. Based on the moisture budget analysis, we indicate that the dynamic factor mainly explains the decreased monsoonal rainfall in the Northern Hemisphere during the present-day relative to the MIS 3 interstadial, whereas the thermodynamic term is largely responsible for the increased precipitation in the Southern Hemisphere. In contrast, the dynamic factor plays an important role in the variation of precipitation over all the monsoon zones from the MIS 3 interstadial to stadial states, with the thermodynamic term mainly contributing to the decreased tropical monsoonal precipitation in the colder Northern Hemisphere. Our results help improve the understanding of global monsoon variations under intermediate glacial climate conditions and shed light on their behaviors under potentially rapid climate change in the future.
Abstract
Marine Isotope Stage 3 (MIS 3) is characterized by significant millennial-scale climatic oscillations between cold stadials and mild interstadials, which presents a valuable case for understanding hydrological response to abrupt climate change. Through a set of coupled model simulations, our results broadly show an antiphased interhemispheric change in land monsoonal precipitation during the present-day relative to MIS 3 interstadial and the stadial–interstadial transition, with a general decrease in the Northern Hemisphere but an increase in the Southern Hemisphere. The antiphased pattern is largely caused by the change in orbital insolation during the present-day relative to MIS 3 interstadial, whereas by the weakened Atlantic meridional overturning circulation during the interstadial–stadial transition. However, there are obvious discrepancies in precipitation response and underlying mechanisms among individual monsoon domains and across different periods. Based on the moisture budget analysis, we indicate that the dynamic factor mainly explains the decreased monsoonal rainfall in the Northern Hemisphere during the present-day relative to the MIS 3 interstadial, whereas the thermodynamic term is largely responsible for the increased precipitation in the Southern Hemisphere. In contrast, the dynamic factor plays an important role in the variation of precipitation over all the monsoon zones from the MIS 3 interstadial to stadial states, with the thermodynamic term mainly contributing to the decreased tropical monsoonal precipitation in the colder Northern Hemisphere. Our results help improve the understanding of global monsoon variations under intermediate glacial climate conditions and shed light on their behaviors under potentially rapid climate change in the future.
Abstract
Accurate subseasonal forecasts for snow cover have significant socioeconomic value. This paper evaluates subseasonal forecasts for winter snow cover in the Northern Hemisphere as predicted by three numerical models: the Model for Prediction Across Scales–Atmosphere (MPAS-A), the China Meteorological Administration (CMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. While these models can generally simulate the spatial distribution of winter snow cover climatology and subseasonal variability, they tend to underestimate both the climatology and the intensity of subseasonal variability. Compared to persistence forecasts, these models demonstrate skill in subseasonal snow cover forecasting. Notably, the ECMWF model outperforms the MPAS and CMA models. The sensitivity of the surface air temperature subseasonal forecast skill to the predicted snow cover was also investigated using the MPAS. The results show that for forecasts with lead times of 1–2 weeks, the predicted snow cover contributes to the temperature forecasting skill. However, for forecasts with lead times of 3–4 weeks, the predicted snow cover does not enhance the temperature forecasting skill. Furthermore, part of the errors in temperature forecasts can be attributed to inaccuracies in snow cover forecasts with lead times of 2 weeks or more. These findings suggest that refining snow cover parameterization schemes and effectively exploiting predictability from snow cover can enhance the skill of subseasonal atmospheric forecasts.
Significance Statement
Snow cover is a crucial variable in hydrometeorology. Subseasonal forecasting, which involves predicting snow cover anomalies 1–4 weeks in advance, has socioeconomic value. We conducted an evaluation of the subseasonal forecasts for Northern Hemisphere winter snow cover produced by three numerical models. This evaluation provides insights into the accuracy and reliability of these models, which could contribute to their enhancement. Furthermore, we examined the impact of the predicted snow cover on the skill of surface air temperature subseasonal forecasts. The results suggest that improvements in snow cover modeling and forecasting can lead to more accurate subseasonal atmospheric forecasts. Therefore, future efforts to refine snow cover parameterization schemes suitable for subseasonal forecasting are promising and worthwhile.
Abstract
Accurate subseasonal forecasts for snow cover have significant socioeconomic value. This paper evaluates subseasonal forecasts for winter snow cover in the Northern Hemisphere as predicted by three numerical models: the Model for Prediction Across Scales–Atmosphere (MPAS-A), the China Meteorological Administration (CMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. While these models can generally simulate the spatial distribution of winter snow cover climatology and subseasonal variability, they tend to underestimate both the climatology and the intensity of subseasonal variability. Compared to persistence forecasts, these models demonstrate skill in subseasonal snow cover forecasting. Notably, the ECMWF model outperforms the MPAS and CMA models. The sensitivity of the surface air temperature subseasonal forecast skill to the predicted snow cover was also investigated using the MPAS. The results show that for forecasts with lead times of 1–2 weeks, the predicted snow cover contributes to the temperature forecasting skill. However, for forecasts with lead times of 3–4 weeks, the predicted snow cover does not enhance the temperature forecasting skill. Furthermore, part of the errors in temperature forecasts can be attributed to inaccuracies in snow cover forecasts with lead times of 2 weeks or more. These findings suggest that refining snow cover parameterization schemes and effectively exploiting predictability from snow cover can enhance the skill of subseasonal atmospheric forecasts.
Significance Statement
Snow cover is a crucial variable in hydrometeorology. Subseasonal forecasting, which involves predicting snow cover anomalies 1–4 weeks in advance, has socioeconomic value. We conducted an evaluation of the subseasonal forecasts for Northern Hemisphere winter snow cover produced by three numerical models. This evaluation provides insights into the accuracy and reliability of these models, which could contribute to their enhancement. Furthermore, we examined the impact of the predicted snow cover on the skill of surface air temperature subseasonal forecasts. The results suggest that improvements in snow cover modeling and forecasting can lead to more accurate subseasonal atmospheric forecasts. Therefore, future efforts to refine snow cover parameterization schemes suitable for subseasonal forecasting are promising and worthwhile.
Abstract
This study explores how future SST warming in remote ocean basins may affect the western North Pacific (WNP) wet season climate by applying a high-resolution atmospheric general circulation model to conduct a series of numerical experiments. A marked precipitation and tropical cyclone (TC) activity reduction, as well as enhanced anticyclonic circulation, in the WNP is projected in AMIP experiments forced by SST change in a future warming scenario. The sensitivity experiments reveal that various SST warming phenomena (e.g., in the global SST warming pattern, the tropical ocean belt, the Indian Ocean, the tropical Atlantic, and the subtropical northeast Pacific) and the increase in greenhouse gas concentration could weaken the precipitation, TC activity, and circulation. By contrast, the SST warming in the WNP and eastern equatorial Pacific has opposite and mixed effects, respectively, and tends to weakly offset the dominant influences of remote ocean warming. These results indicate that the WNP, being the epicenter of the global teleconnection of divergent and rotational flow, is susceptible to the influence of the SST warming in remote ocean basins. The remote forcing as projected in future scenarios would overwhelm the enhancing effect of local SST warming and weaken the circulation, convection, and TC activity in the WNP. These findings further the understanding of how the decreased precipitation and enhanced subtropical high in the WNP may be easily triggered by remote SST warming as revealed in the AMIP-type simulations. How this effect would be affected by air–sea coupling needs further investigation.
Abstract
This study explores how future SST warming in remote ocean basins may affect the western North Pacific (WNP) wet season climate by applying a high-resolution atmospheric general circulation model to conduct a series of numerical experiments. A marked precipitation and tropical cyclone (TC) activity reduction, as well as enhanced anticyclonic circulation, in the WNP is projected in AMIP experiments forced by SST change in a future warming scenario. The sensitivity experiments reveal that various SST warming phenomena (e.g., in the global SST warming pattern, the tropical ocean belt, the Indian Ocean, the tropical Atlantic, and the subtropical northeast Pacific) and the increase in greenhouse gas concentration could weaken the precipitation, TC activity, and circulation. By contrast, the SST warming in the WNP and eastern equatorial Pacific has opposite and mixed effects, respectively, and tends to weakly offset the dominant influences of remote ocean warming. These results indicate that the WNP, being the epicenter of the global teleconnection of divergent and rotational flow, is susceptible to the influence of the SST warming in remote ocean basins. The remote forcing as projected in future scenarios would overwhelm the enhancing effect of local SST warming and weaken the circulation, convection, and TC activity in the WNP. These findings further the understanding of how the decreased precipitation and enhanced subtropical high in the WNP may be easily triggered by remote SST warming as revealed in the AMIP-type simulations. How this effect would be affected by air–sea coupling needs further investigation.
Abstract
Flash droughts (FDs) have attracted widespread attention in recent years due to their sudden onset and rapid intensification with significant impacts on ecosystems, water resources, and agriculture. These features of FDs pose unique challenges for their forecast, monitoring, and mitigation. The impact of FDs on society can vary depending on several factors, such as the frequency of their occurrence, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study developed a novel approach to quantitatively define FD based on the aridity index. This new approach was used to examine spatiotemporal characteristics (including trends) and triggers of FDs over 25 river basins across India from 1981 to 2021. The hydrometeorological conditions, including soil moisture percentiles, anomalies of precipitation, temperature, and vapor pressure deficit were investigated at different stages of FD. Results suggest that FDs with high intensification rates are more common in humid areas compared to subhumid and semiarid areas. Both precipitation and temperature are primary triggers of FDs over a major part of the study area. The individual effects of soil moisture and precipitation also act as a trigger across some regions (like northeast India and the Western Ghats). Additionally, atmospheric aridity can create conditions conducive to FDs, and when combined with depleted soil moisture, it can accelerate their rapid onset. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Significance Statement
Flash droughts have attracted widespread attention due to their sudden onset and rapid intensification with significant impacts on multiple vectors. The impact of flash drought on society depends on their frequency, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study develops a novel approach to quantitatively define flash drought based on the aridity index. This new approach is used to examine spatiotemporal characteristics and triggers of flash drought over 25 river basins across India from 1981 to 2021. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Abstract
Flash droughts (FDs) have attracted widespread attention in recent years due to their sudden onset and rapid intensification with significant impacts on ecosystems, water resources, and agriculture. These features of FDs pose unique challenges for their forecast, monitoring, and mitigation. The impact of FDs on society can vary depending on several factors, such as the frequency of their occurrence, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study developed a novel approach to quantitatively define FD based on the aridity index. This new approach was used to examine spatiotemporal characteristics (including trends) and triggers of FDs over 25 river basins across India from 1981 to 2021. The hydrometeorological conditions, including soil moisture percentiles, anomalies of precipitation, temperature, and vapor pressure deficit were investigated at different stages of FD. Results suggest that FDs with high intensification rates are more common in humid areas compared to subhumid and semiarid areas. Both precipitation and temperature are primary triggers of FDs over a major part of the study area. The individual effects of soil moisture and precipitation also act as a trigger across some regions (like northeast India and the Western Ghats). Additionally, atmospheric aridity can create conditions conducive to FDs, and when combined with depleted soil moisture, it can accelerate their rapid onset. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Significance Statement
Flash droughts have attracted widespread attention due to their sudden onset and rapid intensification with significant impacts on multiple vectors. The impact of flash drought on society depends on their frequency, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study develops a novel approach to quantitatively define flash drought based on the aridity index. This new approach is used to examine spatiotemporal characteristics and triggers of flash drought over 25 river basins across India from 1981 to 2021. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
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
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.
