Abstract
The paper aims at investigating the effectiveness of estimating vertical profiles of air temperature and PM10 concentrations in Alpine valleys through ground stations positioned at different altitudes on one valley sidewall (i.e. pseudo-vertical profiles). Two case studies in the Italian Alps are investigated: Chiese Valley in Trentino province and Camonica Valley in Lombardy region. Vertical profiles of temperature and PM10 concentrations were derived from airborne measurements at the center of the two valleys by means of low-cost sensors installed on a drone during summer 2019 and a tethered balloon during winter 2020. At the same time, five stations, equipped with the same kind of low-cost sensors, simultaneously monitored the same variables on one mountain slope. Comparisons between pseudo-profiles and airborne soundings revealed that ground stations well approximated temperature and PM10 soundings during the night and early morning, while temperatures along the slopes were higher than in the center of the valley during daytime, due to solar radiative heating, with larger differences in summer than in winter. On the contrary, some episodes with PM10 concentrations slightly higher in the valley center than on the slope were recorded, due to transport events and upslope winds. Nonetheless, the pseudo-profiles based on slope ground measurements faithfully reproduced the vertical gradients of both air temperature and PM10 if compared to those assessed from the soundings performed at the center of the two valleys. Results show that pseudo-vertical profiles can be a reliable and inexpensive method for continuous monitoring of vertical air temperature and PM10 distribution in mountain valleys.
Abstract
The paper aims at investigating the effectiveness of estimating vertical profiles of air temperature and PM10 concentrations in Alpine valleys through ground stations positioned at different altitudes on one valley sidewall (i.e. pseudo-vertical profiles). Two case studies in the Italian Alps are investigated: Chiese Valley in Trentino province and Camonica Valley in Lombardy region. Vertical profiles of temperature and PM10 concentrations were derived from airborne measurements at the center of the two valleys by means of low-cost sensors installed on a drone during summer 2019 and a tethered balloon during winter 2020. At the same time, five stations, equipped with the same kind of low-cost sensors, simultaneously monitored the same variables on one mountain slope. Comparisons between pseudo-profiles and airborne soundings revealed that ground stations well approximated temperature and PM10 soundings during the night and early morning, while temperatures along the slopes were higher than in the center of the valley during daytime, due to solar radiative heating, with larger differences in summer than in winter. On the contrary, some episodes with PM10 concentrations slightly higher in the valley center than on the slope were recorded, due to transport events and upslope winds. Nonetheless, the pseudo-profiles based on slope ground measurements faithfully reproduced the vertical gradients of both air temperature and PM10 if compared to those assessed from the soundings performed at the center of the two valleys. Results show that pseudo-vertical profiles can be a reliable and inexpensive method for continuous monitoring of vertical air temperature and PM10 distribution in mountain valleys.
Abstract
The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the global climate, known to be a tipping element, as it could collapse under global warming. The main objective of this study is to compute the probability that the AMOC collapses within a specified time window, using a rare-event algorithm called Trajectory-Adaptive Multilevel Splitting (TAMS). However, the efficiency and accuracy of TAMS depend on the choice of the score function. Although the definition of the optimal score function, called “committor function” is known, it is impossible in general to compute it a priori. Here, we combine TAMS with a Next-Generation Reservoir Computing technique that estimates the committor function from the data generated by the rare-event algorithm. We test this technique in a stochastic box model of the AMOC for which two types of transition exist, the so-called F(ast)-transitions and S(low)-transitions. Results for the F-transtions compare favorably with those in the literature where a physically-informed score function was used. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities, transition times, and even transition paths for a wide range of model parameters. We then extend these results to the more difficult problem of S-transitions in the same model. In both cases of F-transitions and S-transitions, we also show how the Next-Generation Reservoir Computing technique can be interpreted to retrieve an analytical estimate of the committor function.
Abstract
The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the global climate, known to be a tipping element, as it could collapse under global warming. The main objective of this study is to compute the probability that the AMOC collapses within a specified time window, using a rare-event algorithm called Trajectory-Adaptive Multilevel Splitting (TAMS). However, the efficiency and accuracy of TAMS depend on the choice of the score function. Although the definition of the optimal score function, called “committor function” is known, it is impossible in general to compute it a priori. Here, we combine TAMS with a Next-Generation Reservoir Computing technique that estimates the committor function from the data generated by the rare-event algorithm. We test this technique in a stochastic box model of the AMOC for which two types of transition exist, the so-called F(ast)-transitions and S(low)-transitions. Results for the F-transtions compare favorably with those in the literature where a physically-informed score function was used. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities, transition times, and even transition paths for a wide range of model parameters. We then extend these results to the more difficult problem of S-transitions in the same model. In both cases of F-transitions and S-transitions, we also show how the Next-Generation Reservoir Computing technique can be interpreted to retrieve an analytical estimate of the committor function.
Abstract
A series of idealized numerical simulations is used to examine the generation of mode-one superinertial coastally trapped waves (CTWs). In the first set of simulations, CTWs are resonantly generated when freely propagating mode-one internal tides are incident on the coast such that the angle of incidence of the internal wave causes the projected wavenumber of the tide on the coast to satisfy a triad relationship with the wavenumbers of the bathymetry and the CTW. In the second set of simulations, CTWs are generated by the interaction of the barotropic tide with topography that has the same scales as the CTW. Under resonant conditions, superinertial coastally trapped waves are a leading order coastal process, with alongshore current magnitudes that can be larger than the barotropic or internal tides from which they are generated.
Abstract
A series of idealized numerical simulations is used to examine the generation of mode-one superinertial coastally trapped waves (CTWs). In the first set of simulations, CTWs are resonantly generated when freely propagating mode-one internal tides are incident on the coast such that the angle of incidence of the internal wave causes the projected wavenumber of the tide on the coast to satisfy a triad relationship with the wavenumbers of the bathymetry and the CTW. In the second set of simulations, CTWs are generated by the interaction of the barotropic tide with topography that has the same scales as the CTW. Under resonant conditions, superinertial coastally trapped waves are a leading order coastal process, with alongshore current magnitudes that can be larger than the barotropic or internal tides from which they are generated.
Abstract
The influence of biomass burning (BB) aerosols arising from wildfires and agricultural fires on the transient coupled evolution of ENSO is explored in CESM2. For both El Niño and La Niña, two 20-member ensembles are generated from initial states that are predisposed to evolve into ENSO events. For each ENSO phase, one ensemble is forced with the observed BB emissions during satellite-era ENSO events while the other is forced with a climatological annual cycle, with the responses to anomalous BB emissions estimated from inter-ensemble differences.
It is found that the regional responses to anomalous BB emissions occur mainly during boreal fall, which is also the time of the climatological seasonal maximum in emissions. Transient responses are identified in precipitation, clouds, and radiation in both the tropics and extratropics. At the onset of El Niño, these include an increase precipitation in the northern branch of the ITCZ and an enhancement of cloud albedo and amount across the Maritime Continent and eastern subtropical Pacific Ocean. Additional responses are identified through the course of El Niño and successive La Niña events, the net effect of which is to strengthen SST anomalies in the eastern Pacific Ocean during El Niño and warm the tropical Pacific Ocean during La Niña. These responses improve simulation of ENSO power, diversity, and asymmetry in CESM2.
Abstract
The influence of biomass burning (BB) aerosols arising from wildfires and agricultural fires on the transient coupled evolution of ENSO is explored in CESM2. For both El Niño and La Niña, two 20-member ensembles are generated from initial states that are predisposed to evolve into ENSO events. For each ENSO phase, one ensemble is forced with the observed BB emissions during satellite-era ENSO events while the other is forced with a climatological annual cycle, with the responses to anomalous BB emissions estimated from inter-ensemble differences.
It is found that the regional responses to anomalous BB emissions occur mainly during boreal fall, which is also the time of the climatological seasonal maximum in emissions. Transient responses are identified in precipitation, clouds, and radiation in both the tropics and extratropics. At the onset of El Niño, these include an increase precipitation in the northern branch of the ITCZ and an enhancement of cloud albedo and amount across the Maritime Continent and eastern subtropical Pacific Ocean. Additional responses are identified through the course of El Niño and successive La Niña events, the net effect of which is to strengthen SST anomalies in the eastern Pacific Ocean during El Niño and warm the tropical Pacific Ocean during La Niña. These responses improve simulation of ENSO power, diversity, and asymmetry in CESM2.
Abstract
The joint Taiwan-Area Heavy Rain Observation and Prediction Experiment (TAHOPE)/Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) field campaign between Taiwan and the United States took place from late May to mid-August in 2022. The field campaign aimed to understand the dynamics, thermodynamics, and predictability of heavy rainfall events in the Taiwan area. This study investigated the mechanisms of a heavy rainfall event that occurred on 6–7 June during the intensive observation period-3 (IOP3) of the field campaign. Heavy rainfall occurs on Taiwan’s western coast when a Meiyu front hovers in northern Taiwan. A multiscale radar ensemble data assimilation system based on the successive covariance localization (SCL) method is used to derive a high-resolution analysis for forecasts. Two numerical experiments are conducted with the use of convective-scale (RDA) or multiscale (MRDA) corrections in the assimilation of the radial velocity from operational radars at Chigu and Wufen, and the additional S-Pol radar deployed at Hsinchu during the field campaign. Compared with RDA, MRDA results in large-area wind corrections, which help reshape and relocate a low-level mesoscale vortex, a key element of this heavy rainfall event, offshore of western central Taiwan and enhances the front intensity offshore of northwestern Taiwan. Consequently, MRDA improves the 6-h heavy rainfall prediction over the coast of western Taiwan and better represents the elongated rainband in northern Taiwan during the 3- to 6-h forecast. Sensitivity experiments demonstrate the importance of assimilating winds from Chigu and S-Pol radar in establishing low-level mesoscale vortex and convergence zones.
Abstract
The joint Taiwan-Area Heavy Rain Observation and Prediction Experiment (TAHOPE)/Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) field campaign between Taiwan and the United States took place from late May to mid-August in 2022. The field campaign aimed to understand the dynamics, thermodynamics, and predictability of heavy rainfall events in the Taiwan area. This study investigated the mechanisms of a heavy rainfall event that occurred on 6–7 June during the intensive observation period-3 (IOP3) of the field campaign. Heavy rainfall occurs on Taiwan’s western coast when a Meiyu front hovers in northern Taiwan. A multiscale radar ensemble data assimilation system based on the successive covariance localization (SCL) method is used to derive a high-resolution analysis for forecasts. Two numerical experiments are conducted with the use of convective-scale (RDA) or multiscale (MRDA) corrections in the assimilation of the radial velocity from operational radars at Chigu and Wufen, and the additional S-Pol radar deployed at Hsinchu during the field campaign. Compared with RDA, MRDA results in large-area wind corrections, which help reshape and relocate a low-level mesoscale vortex, a key element of this heavy rainfall event, offshore of western central Taiwan and enhances the front intensity offshore of northwestern Taiwan. Consequently, MRDA improves the 6-h heavy rainfall prediction over the coast of western Taiwan and better represents the elongated rainband in northern Taiwan during the 3- to 6-h forecast. Sensitivity experiments demonstrate the importance of assimilating winds from Chigu and S-Pol radar in establishing low-level mesoscale vortex and convergence zones.
Abstract
The Maryland Mesonet Project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of record. The spatial configuration of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-hour case-study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a ’standard-uncertainty’ configuration tuned to be representative of existing convective-allowing prediction systems, and a ’constrained-uncertainty’ configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We find that the assimilation of mesonet data produces definitive improvements to analysis fields below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.
Abstract
The Maryland Mesonet Project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of record. The spatial configuration of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-hour case-study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a ’standard-uncertainty’ configuration tuned to be representative of existing convective-allowing prediction systems, and a ’constrained-uncertainty’ configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We find that the assimilation of mesonet data produces definitive improvements to analysis fields below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.
Abstract
Information about past floods and historical precipitation records is fundamental to the management of water resources, but observational records usually cover only the last 100–150 years. Using several different data sources, such as newly digitized meteorological data from several stations in the south-eastern part of Romania, from historical newspapers of that time, and daily reanalysis of large-scale data, here we provide a detailed analysis of the atmospheric circulation conditions associated with a devastating flood event which took place in June 1897. The floods in June 1897 were one of the most devastating natural disasters in Romania's history and they were caused by heavy rainfall that started at the beginning of May and continued for several weeks, resulting in widespread flooding, especially in the eastern part of the country. The most affected areas were the cities of Braila and Galati, located on the main course of the Danube River, where the floods caused extensive damage to infrastructure, including homes, bridges, and roads, and disrupted transportation and communication networks. The heavy rainfall events occurring in June 1897 and the associated flood peak were triggered by intrusions of high Potential Vorticity (PV) anomalies toward the southeastern part of Europe, persistent and pivotal cut-off lows over the analyzed region, and increased water vapor transport over the south-eastern part of Romania. We argue that digitizing and analyzing old meteorological records enables researchers to better understand the Earth's climate system and make more accurate predictions about future climate change.
Abstract
Information about past floods and historical precipitation records is fundamental to the management of water resources, but observational records usually cover only the last 100–150 years. Using several different data sources, such as newly digitized meteorological data from several stations in the south-eastern part of Romania, from historical newspapers of that time, and daily reanalysis of large-scale data, here we provide a detailed analysis of the atmospheric circulation conditions associated with a devastating flood event which took place in June 1897. The floods in June 1897 were one of the most devastating natural disasters in Romania's history and they were caused by heavy rainfall that started at the beginning of May and continued for several weeks, resulting in widespread flooding, especially in the eastern part of the country. The most affected areas were the cities of Braila and Galati, located on the main course of the Danube River, where the floods caused extensive damage to infrastructure, including homes, bridges, and roads, and disrupted transportation and communication networks. The heavy rainfall events occurring in June 1897 and the associated flood peak were triggered by intrusions of high Potential Vorticity (PV) anomalies toward the southeastern part of Europe, persistent and pivotal cut-off lows over the analyzed region, and increased water vapor transport over the south-eastern part of Romania. We argue that digitizing and analyzing old meteorological records enables researchers to better understand the Earth's climate system and make more accurate predictions about future climate change.
Abstract
The existence of multiple equilibria (ice-covered and ice-free states) is explored using a set of coupled, nondimensional equations that describe the heat and salt balances in basins, such as the Arctic Ocean, that are subject to atmospheric forcing and two distinct water mass sources. Six nondimensional numbers describe the influences of atmospheric cooling, evaporation minus precipitation, solar radiation, atmospheric temperature, diapycnal mixing, and the temperature contrast between the two water masses. It is shown that multiple equilibria resulting from the dependence of albedo on ice cover exist over a wide range of parameter space, especially so in the weak mixing limit. Multiple equilibria can also occur if diapycnal mixing increases to O(10−4) m2 s−1 or larger under ice-free conditions due to enhanced upward mixing of warm, salty water from below. Sensitivities to various forcing parameters are discussed.
Significance Statement
The purpose of this study is to better understand under what circumstances high-latitude seas, such as the Arctic Ocean, can exist in either an ice-covered or an ice-free state. The temperature and salinity of the ocean, as well as the heat exchange with the atmosphere, are drastically different depending on which state the ocean is in. The theory presented here identifies how forcing from the atmosphere and ocean dynamics determines whether the ocean is ice covered, ice free, or possibly either one.
Abstract
The existence of multiple equilibria (ice-covered and ice-free states) is explored using a set of coupled, nondimensional equations that describe the heat and salt balances in basins, such as the Arctic Ocean, that are subject to atmospheric forcing and two distinct water mass sources. Six nondimensional numbers describe the influences of atmospheric cooling, evaporation minus precipitation, solar radiation, atmospheric temperature, diapycnal mixing, and the temperature contrast between the two water masses. It is shown that multiple equilibria resulting from the dependence of albedo on ice cover exist over a wide range of parameter space, especially so in the weak mixing limit. Multiple equilibria can also occur if diapycnal mixing increases to O(10−4) m2 s−1 or larger under ice-free conditions due to enhanced upward mixing of warm, salty water from below. Sensitivities to various forcing parameters are discussed.
Significance Statement
The purpose of this study is to better understand under what circumstances high-latitude seas, such as the Arctic Ocean, can exist in either an ice-covered or an ice-free state. The temperature and salinity of the ocean, as well as the heat exchange with the atmosphere, are drastically different depending on which state the ocean is in. The theory presented here identifies how forcing from the atmosphere and ocean dynamics determines whether the ocean is ice covered, ice free, or possibly either one.
Abstract
This study investigated trends in satellite-based chlorophyll-a (Chl-a; 1998–2022), sea surface temperature (SST; 1982–2022), and sea level anomaly (SLA; 1993–2021) from the European Space Agency’s Climate Change Initiative records, integrating time series decomposition and spectral analysis. Trends in parameters signify prolonged increases, decreases, or no changes over time. These are time series in the same space as original parameters, excluding seasonalities and noise, and can exhibit nonlinearity. Trend rates approximate the pace of change per time unit. We quantified trends using conventional linear-fit and three incrementally advancing methods for time series decomposition: simple moving average (SMA), seasonal-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL (MSTL), across the global ocean, the Bay of Bengal, and the Chesapeake Bay. Challenges in decomposition include specifying accurate seasonal periods that are derived here by combining Fourier and Wavelet Transforms. Globally, SST and SLA trend upwards, and Chl-a has no significant change, yet regional variations are notable. We highlight the advantage of extracting multiple periods with MSTL and, more broadly, decomposition’s role in disentangling time-series components (seasonality, trend, noise) without resorting to monotonic functions, thereby preventing overlooking episodic events. Illustrations include extreme events temporarily counteracting background trends, e.g., the 2010–2011 SLA drop due to La Niña-induced rainfall over land. The continuous analysis clarifies the warming hiatus debate, affirming sustained warming. Decadal trend rates per grid cell are also mapped. These are ubiquitously significant for SST and SLA, whereas Chl-a trend rates are globally low but extreme across coasts and boundary currents.
Abstract
This study investigated trends in satellite-based chlorophyll-a (Chl-a; 1998–2022), sea surface temperature (SST; 1982–2022), and sea level anomaly (SLA; 1993–2021) from the European Space Agency’s Climate Change Initiative records, integrating time series decomposition and spectral analysis. Trends in parameters signify prolonged increases, decreases, or no changes over time. These are time series in the same space as original parameters, excluding seasonalities and noise, and can exhibit nonlinearity. Trend rates approximate the pace of change per time unit. We quantified trends using conventional linear-fit and three incrementally advancing methods for time series decomposition: simple moving average (SMA), seasonal-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL (MSTL), across the global ocean, the Bay of Bengal, and the Chesapeake Bay. Challenges in decomposition include specifying accurate seasonal periods that are derived here by combining Fourier and Wavelet Transforms. Globally, SST and SLA trend upwards, and Chl-a has no significant change, yet regional variations are notable. We highlight the advantage of extracting multiple periods with MSTL and, more broadly, decomposition’s role in disentangling time-series components (seasonality, trend, noise) without resorting to monotonic functions, thereby preventing overlooking episodic events. Illustrations include extreme events temporarily counteracting background trends, e.g., the 2010–2011 SLA drop due to La Niña-induced rainfall over land. The continuous analysis clarifies the warming hiatus debate, affirming sustained warming. Decadal trend rates per grid cell are also mapped. These are ubiquitously significant for SST and SLA, whereas Chl-a trend rates are globally low but extreme across coasts and boundary currents.
Abstract
A sample of 889 left-moving (LM) supercells of varying rotational strength across the United States was examined to determine if improvements could be made in predicting their motion using an existing hodograph-based technique. This technique was previously applied to a sample of only 30 LM supercells, and it was assumed that the same off-hodograph deviation from the mean wind for right-moving (RM) supercells was appropriate for LM supercells. However, our larger sample herein reveals the average deviation for LM supercells is less than the assumed 7.5 m s−1 based on a subset of 207 observed proximity soundings. At the same time, the 0–6-km mean-wind layer is still optimal for the advective component of storm motion (consistent with that for RM supercells). Applying the same methods to a subset of 678 model-derived RUC/RAP proximity soundings generally confirms these results, but with slightly smaller deviations. These findings support decreasing the deviation parameter to 5.0 m s−1 for predicting LM supercell motion (at least for the United States).
The sample of LM supercells additionally was subdivided based on strength and duration, and then reevaluated using the observed proximity soundings. The predicted motion of moderate-strength mesoanticyclones had the least error, whereas the strong category had the largest errors by about 1 m s−1. Similarly, mesoanticyclones lasting 60–120 min had the least error in predicted motion. These two findings also are consistent with the results when using the RUC/RAP proximity soundings.
Abstract
A sample of 889 left-moving (LM) supercells of varying rotational strength across the United States was examined to determine if improvements could be made in predicting their motion using an existing hodograph-based technique. This technique was previously applied to a sample of only 30 LM supercells, and it was assumed that the same off-hodograph deviation from the mean wind for right-moving (RM) supercells was appropriate for LM supercells. However, our larger sample herein reveals the average deviation for LM supercells is less than the assumed 7.5 m s−1 based on a subset of 207 observed proximity soundings. At the same time, the 0–6-km mean-wind layer is still optimal for the advective component of storm motion (consistent with that for RM supercells). Applying the same methods to a subset of 678 model-derived RUC/RAP proximity soundings generally confirms these results, but with slightly smaller deviations. These findings support decreasing the deviation parameter to 5.0 m s−1 for predicting LM supercell motion (at least for the United States).
The sample of LM supercells additionally was subdivided based on strength and duration, and then reevaluated using the observed proximity soundings. The predicted motion of moderate-strength mesoanticyclones had the least error, whereas the strong category had the largest errors by about 1 m s−1. Similarly, mesoanticyclones lasting 60–120 min had the least error in predicted motion. These two findings also are consistent with the results when using the RUC/RAP proximity soundings.
