Browse
Abstract
A radar display is a tool that depicts meteorological data over space and time; therefore, an individual must think spatially and temporally in addition to drawing on their own meteorological knowledge and past weather experiences. We aimed to understand how the construal of situational risks and outcomes influences the perceived usefulness of a radar display and to explore how radar users interpret distance, time, and meteorological attributes using hypothetical scenarios in the Tampa Bay area (Florida). Ultimately, we wanted to understand how and why individuals use weather radar and to discover what makes it a useful tool. To do this, construal level theory and geospatial thinking guided the mixed methods used in this study to investigate four research objectives. Our findings show that radar is used most often by our participants to anticipate what will happen in the near future in their area. Participants described in their own words what they were viewing while using a radar display and reported what hazards they expected at the study location. Many participants associated the occurrence of lightning or strong winds with “red” and “orange” reflectivity values on a radar display. Participants provided valuable insight into what was and was not found useful about certain radar displays. We also found that most participants overestimated the amount of time they would have before precipitation would begin at their location. Overall, weather radar was found to be a very useful tool; however, judging spatial and temporal proximity became difficult when storm motion/direction was not easily identifiable.
Significance Statement
The purpose of this study is to understand how and why individuals use weather radar and to discover what makes radar a useful tool. We were particularly interested to explore how distance and time are thought about when using radar. We found that radar is generally a useful tool for decision-making except when a storm event was stationary. Participants use their personal experiences and knowledge of past weather events when they use a radar display. We also discovered that deciding how much time is available before rain occurs is often overestimated. These findings are helpful to understand how individuals use weather radar to make decisions that may help us to better understand protective action behavior.
Abstract
A radar display is a tool that depicts meteorological data over space and time; therefore, an individual must think spatially and temporally in addition to drawing on their own meteorological knowledge and past weather experiences. We aimed to understand how the construal of situational risks and outcomes influences the perceived usefulness of a radar display and to explore how radar users interpret distance, time, and meteorological attributes using hypothetical scenarios in the Tampa Bay area (Florida). Ultimately, we wanted to understand how and why individuals use weather radar and to discover what makes it a useful tool. To do this, construal level theory and geospatial thinking guided the mixed methods used in this study to investigate four research objectives. Our findings show that radar is used most often by our participants to anticipate what will happen in the near future in their area. Participants described in their own words what they were viewing while using a radar display and reported what hazards they expected at the study location. Many participants associated the occurrence of lightning or strong winds with “red” and “orange” reflectivity values on a radar display. Participants provided valuable insight into what was and was not found useful about certain radar displays. We also found that most participants overestimated the amount of time they would have before precipitation would begin at their location. Overall, weather radar was found to be a very useful tool; however, judging spatial and temporal proximity became difficult when storm motion/direction was not easily identifiable.
Significance Statement
The purpose of this study is to understand how and why individuals use weather radar and to discover what makes radar a useful tool. We were particularly interested to explore how distance and time are thought about when using radar. We found that radar is generally a useful tool for decision-making except when a storm event was stationary. Participants use their personal experiences and knowledge of past weather events when they use a radar display. We also discovered that deciding how much time is available before rain occurs is often overestimated. These findings are helpful to understand how individuals use weather radar to make decisions that may help us to better understand protective action behavior.
Abstract
A considerable part of the skill in decadal forecasts often comes from the forcings, which are present in both initialized and uninitialized model experiments. This makes the added value from initialization difficult to assess. We investigate statistical tests to quantify if initialized forecasts provide skill over the uninitialized experiments. We consider three correlation-based statistics previously used in the literature. The distributions of these statistics under the null hypothesis that initialization has no added values are calculated by a surrogate data method. We present some simple examples and study the statistical power of the tests. We find that there can be large differences in both the values and power for the different statistics. In general, the simple statistic defined as the difference between the skill of the initialized and uninitialized experiments behaves best. However, for all statistics the risk of rejecting the true null hypothesis is too high compared to the nominal value. We compare the three tests on initialized decadal predictions (hindcasts) of near-surface temperature performed with a climate model and find evidence for a significant effect of initializations for small lead times. In contrast, we find only little evidence for a significant effect of initializations for lead times longer than 3 years when the experience from the simple experiments is included in the estimation.
Abstract
A considerable part of the skill in decadal forecasts often comes from the forcings, which are present in both initialized and uninitialized model experiments. This makes the added value from initialization difficult to assess. We investigate statistical tests to quantify if initialized forecasts provide skill over the uninitialized experiments. We consider three correlation-based statistics previously used in the literature. The distributions of these statistics under the null hypothesis that initialization has no added values are calculated by a surrogate data method. We present some simple examples and study the statistical power of the tests. We find that there can be large differences in both the values and power for the different statistics. In general, the simple statistic defined as the difference between the skill of the initialized and uninitialized experiments behaves best. However, for all statistics the risk of rejecting the true null hypothesis is too high compared to the nominal value. We compare the three tests on initialized decadal predictions (hindcasts) of near-surface temperature performed with a climate model and find evidence for a significant effect of initializations for small lead times. In contrast, we find only little evidence for a significant effect of initializations for lead times longer than 3 years when the experience from the simple experiments is included in the estimation.
Abstract
The observation error covariance partially controls the weight assigned to an observation during data assimilation (DA). True observation error statistics are rarely known and likely vary depending on the meteorological state. However, operational DA systems often apply static methods that assign constant observation errors across a dataset. Previous studies show that these methods can degrade forecast quality when assimilating ground-based remote sensing datasets. To improve the impact of assimilating such observations, we propose two novel methods for estimating the observation error variance for high-frequency thermodynamic profilers. These methods include an adaptive observation error inflation technique and the Desroziers method that directly estimates the observation error variances using paired innovation and analysis residuals. Each method is compared for a nocturnal mesoscale convective system (MCS) observed during the Plains Elevated Convection at Night (PECAN) experiment. In general, we find that these novel methods better represent the large variability of observation error statistics for high-frequency profiles collected by Atmospheric Emitted Radiance Interferometers (AERIs). When assimilating AERIs by statically inflating retrieval error variances, the trailing stratiform region of the MCS is degraded compared to a baseline simulation with no AERI data assimilated. Assimilating the AERIs using the adaptive inflation or Desroziers method results in better maintenance of the trailing stratiform region and additional suppression of spurious convection. The forecast improvements from these novel methods are primarily linked to increased error variances for some moisture retrievals. These results indicate the importance of accurately estimating observation error statistics for convective-scale DA and suggest that accounting for flow dependence can improve the impacts from assimilating remote sensing datasets.
Abstract
The observation error covariance partially controls the weight assigned to an observation during data assimilation (DA). True observation error statistics are rarely known and likely vary depending on the meteorological state. However, operational DA systems often apply static methods that assign constant observation errors across a dataset. Previous studies show that these methods can degrade forecast quality when assimilating ground-based remote sensing datasets. To improve the impact of assimilating such observations, we propose two novel methods for estimating the observation error variance for high-frequency thermodynamic profilers. These methods include an adaptive observation error inflation technique and the Desroziers method that directly estimates the observation error variances using paired innovation and analysis residuals. Each method is compared for a nocturnal mesoscale convective system (MCS) observed during the Plains Elevated Convection at Night (PECAN) experiment. In general, we find that these novel methods better represent the large variability of observation error statistics for high-frequency profiles collected by Atmospheric Emitted Radiance Interferometers (AERIs). When assimilating AERIs by statically inflating retrieval error variances, the trailing stratiform region of the MCS is degraded compared to a baseline simulation with no AERI data assimilated. Assimilating the AERIs using the adaptive inflation or Desroziers method results in better maintenance of the trailing stratiform region and additional suppression of spurious convection. The forecast improvements from these novel methods are primarily linked to increased error variances for some moisture retrievals. These results indicate the importance of accurately estimating observation error statistics for convective-scale DA and suggest that accounting for flow dependence can improve the impacts from assimilating remote sensing datasets.
Abstract
Recent studies propose that the Asian–Bering–North American (ABNA) teleconnection is a distinct atmospheric pattern that is related to Eurasian and North American winter climate besides the Pacific–North America (PNA) pattern, while its origin remains elusive. This study investigates the interannual variability of the ABNA during the past 42 winters (1979–2020) and the associated prior surface boundary forcings. The ABNA explains coherent surface air temperature changes in northern Asia, eastern Siberia–Alaska, and eastern North America, even after removing the impacts of the PNA, the Arctic Oscillation, the North Atlantic Oscillation, and the North Pacific Oscillation. Surface boundary conditions linked to the ABNA could be traced back to a Eurasian snow cover dipole pattern (ESCDP) and a Maritime Continent sea surface temperature anomaly (MCSST) in November. The ESCDP leads to a displacement of the Arctic stratospheric polar vortex via troposphere–stratosphere coupling. The anomalous polar vortex propagates downward in the following winter and generates the tropospheric ABNA pattern. The MCSST induces a diabatic heating anomaly, which is associated with a tropical western Pacific precipitation anomaly (TWPP) in winter. The TWPP excites a poleward Rossby wave train that propagates across the North Pacific and directly strengthens the ABNA. The above physical processes can be well reproduced by a linear baroclinic model (LBM). Based on the ESCDP and MCSST predictors, an empirical model is established and shows a promising prediction skill of the ABNA during the hindcast period. This can provide a useful strategy for seasonal prediction of winter climate in the Northern Hemisphere extratropics.
Significance Statement
Extreme cold events have influenced both Asian and North American continents during the past decades, causing huge socioeconomic impacts. The Asian–Bering–North American teleconnection is found to be responsible for coherent changes of winter climate in these two continents besides the Pacific–North America pattern. Our results indicate that Eurasian snow cover and the Maritime Continent sea surface temperature are important sources of predictability for the Asian–Bering–North American teleconnection, and can be used to predict coherent and cross-continent variations of winter surface air temperature. We propose that the Asian–Bering–North American teleconnection and these two predictors should be included in operational monitoring and prediction systems, helping to improve the prediction skill of winter climate in the Northern Hemisphere continents.
Abstract
Recent studies propose that the Asian–Bering–North American (ABNA) teleconnection is a distinct atmospheric pattern that is related to Eurasian and North American winter climate besides the Pacific–North America (PNA) pattern, while its origin remains elusive. This study investigates the interannual variability of the ABNA during the past 42 winters (1979–2020) and the associated prior surface boundary forcings. The ABNA explains coherent surface air temperature changes in northern Asia, eastern Siberia–Alaska, and eastern North America, even after removing the impacts of the PNA, the Arctic Oscillation, the North Atlantic Oscillation, and the North Pacific Oscillation. Surface boundary conditions linked to the ABNA could be traced back to a Eurasian snow cover dipole pattern (ESCDP) and a Maritime Continent sea surface temperature anomaly (MCSST) in November. The ESCDP leads to a displacement of the Arctic stratospheric polar vortex via troposphere–stratosphere coupling. The anomalous polar vortex propagates downward in the following winter and generates the tropospheric ABNA pattern. The MCSST induces a diabatic heating anomaly, which is associated with a tropical western Pacific precipitation anomaly (TWPP) in winter. The TWPP excites a poleward Rossby wave train that propagates across the North Pacific and directly strengthens the ABNA. The above physical processes can be well reproduced by a linear baroclinic model (LBM). Based on the ESCDP and MCSST predictors, an empirical model is established and shows a promising prediction skill of the ABNA during the hindcast period. This can provide a useful strategy for seasonal prediction of winter climate in the Northern Hemisphere extratropics.
Significance Statement
Extreme cold events have influenced both Asian and North American continents during the past decades, causing huge socioeconomic impacts. The Asian–Bering–North American teleconnection is found to be responsible for coherent changes of winter climate in these two continents besides the Pacific–North America pattern. Our results indicate that Eurasian snow cover and the Maritime Continent sea surface temperature are important sources of predictability for the Asian–Bering–North American teleconnection, and can be used to predict coherent and cross-continent variations of winter surface air temperature. We propose that the Asian–Bering–North American teleconnection and these two predictors should be included in operational monitoring and prediction systems, helping to improve the prediction skill of winter climate in the Northern Hemisphere continents.
Abstract
Normalized mutual information (NMI) is a nonparametric measure of the dependence between two variables without assumptions about the shape of their bivariate data distributions, but the implementation and interpretation of NMI in the coupled climate system is more complicated than for linear correlations. This study presents a joint approach combining correlation and NMI to examine land and ocean surface forcing of U.S. drought at varying lead times. Based on the distribution of correlation versus NMI between a source variable (local or remote forcing) and target variable [e.g., summer precipitation in the southern Great Plains (SGP)], newly proposed one-tail significance levels for NMI combined with two-tailed significance levels of correlation enable us to discern linearity and nonlinearity dominant regimes in a more intuitive way. Our analysis finds that NMI can detect strong linear relationships like correlations, but it is not exclusively tuned to linear relationships as correlations are. Also, NMI can further identify nonlinear relationships, particularly when there are clusters and blank areas (high density and low density) in joint probability distributions between source and target variables (e.g., detected between soil moisture conditions in eastern Montana from mid-February to mid-August and summer precipitation in the SGP). The linear and nonlinear information are found to be sometimes mixed and rather convoluted with time, for instance, in the subtropical Pacific of the Southern Hemisphere, revealing relationships that cannot be fully detected by either NMI or correlation alone. Therefore, this joint approach is a potentially powerful tool to reveal complex and heretofore undetected relationships.
Abstract
Normalized mutual information (NMI) is a nonparametric measure of the dependence between two variables without assumptions about the shape of their bivariate data distributions, but the implementation and interpretation of NMI in the coupled climate system is more complicated than for linear correlations. This study presents a joint approach combining correlation and NMI to examine land and ocean surface forcing of U.S. drought at varying lead times. Based on the distribution of correlation versus NMI between a source variable (local or remote forcing) and target variable [e.g., summer precipitation in the southern Great Plains (SGP)], newly proposed one-tail significance levels for NMI combined with two-tailed significance levels of correlation enable us to discern linearity and nonlinearity dominant regimes in a more intuitive way. Our analysis finds that NMI can detect strong linear relationships like correlations, but it is not exclusively tuned to linear relationships as correlations are. Also, NMI can further identify nonlinear relationships, particularly when there are clusters and blank areas (high density and low density) in joint probability distributions between source and target variables (e.g., detected between soil moisture conditions in eastern Montana from mid-February to mid-August and summer precipitation in the SGP). The linear and nonlinear information are found to be sometimes mixed and rather convoluted with time, for instance, in the subtropical Pacific of the Southern Hemisphere, revealing relationships that cannot be fully detected by either NMI or correlation alone. Therefore, this joint approach is a potentially powerful tool to reveal complex and heretofore undetected relationships.
Abstract
Convectively coupled waves (CCWs) over the Western Hemisphere are classified based on their governing thermodynamics. It is found that only the tropical depressions (TDs; TD waves) satisfy the criteria necessary to be considered a moisture mode, as in the Rossby-like wave found in an earlier study. In this wave, water vapor fluctuations play a much greater role in the thermodynamics than temperature fluctuations. Only in the eastward-propagating inertio-gravity (EIG) wave does temperature govern the thermodynamics. Temperature and moisture play comparable roles in all the other waves, including the Madden–Julian oscillation over the Western Hemisphere (MJO-W). The moist static energy (MSE) budget of CCWs is investigated by analyzing ERA5 data and data from the 2014/15 observations and modeling of the Green Ocean Amazon (GoAmazon 2014/15) field campaign. Results reveal that vertical advection of MSE acts as a primary driver of the propagation of column MSE in westward inertio-gravity (WIG) wave, Kelvin wave, and MJO-W, while horizontal advection plays a central role in the mixed Rossby gravity (MRG) and TD wave. Results also suggest that cloud radiative heating and the horizontal MSE advection govern the maintenance of most of the CCWs. Major disagreements are found between ERA5 and GoAmazon. In GoAmazon, convection is more tightly coupled to variations in column MSE, and vertical MSE advection plays a more prominent role in the MSE tendency. These results along with substantial budget residuals found in ERA5 data suggest that CCWs over the tropical Western Hemisphere are not represented adequately in the reanalysis.
Significance Statement
In comparison to other regions of the globe, the weather systems that affect precipitation in the tropical Western Hemisphere have received little attention. In this study, we investigate the structure, propagation, and thermodynamics of convectively coupled waves that impact precipitation in this region. We found that slowly evolving tropical systems are “moisture modes,” i.e., moving regions of high humidity and precipitation that are maintained by interactions between clouds and radiation. The faster waves are systems that exhibit relatively larger fluctuations in temperature. Vertical motions are more important for the movement of rainfall in these waves. Last, we found that reanalysis and observations disagree over the importance of different processes in the waves that occurred over the Amazon region, hinting at potential deficiencies on how the reanalysis represents clouds in this region.
Abstract
Convectively coupled waves (CCWs) over the Western Hemisphere are classified based on their governing thermodynamics. It is found that only the tropical depressions (TDs; TD waves) satisfy the criteria necessary to be considered a moisture mode, as in the Rossby-like wave found in an earlier study. In this wave, water vapor fluctuations play a much greater role in the thermodynamics than temperature fluctuations. Only in the eastward-propagating inertio-gravity (EIG) wave does temperature govern the thermodynamics. Temperature and moisture play comparable roles in all the other waves, including the Madden–Julian oscillation over the Western Hemisphere (MJO-W). The moist static energy (MSE) budget of CCWs is investigated by analyzing ERA5 data and data from the 2014/15 observations and modeling of the Green Ocean Amazon (GoAmazon 2014/15) field campaign. Results reveal that vertical advection of MSE acts as a primary driver of the propagation of column MSE in westward inertio-gravity (WIG) wave, Kelvin wave, and MJO-W, while horizontal advection plays a central role in the mixed Rossby gravity (MRG) and TD wave. Results also suggest that cloud radiative heating and the horizontal MSE advection govern the maintenance of most of the CCWs. Major disagreements are found between ERA5 and GoAmazon. In GoAmazon, convection is more tightly coupled to variations in column MSE, and vertical MSE advection plays a more prominent role in the MSE tendency. These results along with substantial budget residuals found in ERA5 data suggest that CCWs over the tropical Western Hemisphere are not represented adequately in the reanalysis.
Significance Statement
In comparison to other regions of the globe, the weather systems that affect precipitation in the tropical Western Hemisphere have received little attention. In this study, we investigate the structure, propagation, and thermodynamics of convectively coupled waves that impact precipitation in this region. We found that slowly evolving tropical systems are “moisture modes,” i.e., moving regions of high humidity and precipitation that are maintained by interactions between clouds and radiation. The faster waves are systems that exhibit relatively larger fluctuations in temperature. Vertical motions are more important for the movement of rainfall in these waves. Last, we found that reanalysis and observations disagree over the importance of different processes in the waves that occurred over the Amazon region, hinting at potential deficiencies on how the reanalysis represents clouds in this region.
Abstract
This study quantifies the potential effect of the lee vortex on the fine particulate matter (PM2.5) pollution deterioration under the complex topography in Taiwan using observational data. We select the lee-vortex days that favor the development of the lee vortices in northwestern Taiwan under the southeasterly synoptic winds. We then define the enhancement index that discerns the areas with the high occurrence frequencies of the PM2.5 enhancement under the flow regime relative to the seasonal background concentrations. Under the lee-vortex days, the center of western Taiwan exhibits enhancement indices higher than 0.65. In addition, during the consecutive lee-vortex days, the index characterizes a northward shift in the PM2.5-enhanced areas under the subtle transition of the background wind directions. The areas with indices higher than 0.65 expand on the second day in northwestern Taiwan; the number of stations exhibiting indices higher than 0.8 increases by threefold from the first to the second day. The idealized numerical simulations using the Taiwan vector vorticity equation cloud-resolving model (TaiwanVVM) explicitly resolve the structures of leeside circulations and the associated pollutant transport, and their evolutions are highly sensitive to the background winds.
Significance Statement
Our study investigates the challenging question of local circulation patterns affected by mountain orography and the associated pollutant transport. We analyzed long-term balloon sounding and ground station observations to select the weather regime favoring the formation of lee vortices on Taiwan island. We then quantified the areas with a frequent enhancement of particulate pollutants. The long-term trend of the lee-vortex days exhibited a significant increase in the past decades. The pollution enhancement areas are highly consistent with the regions dominated by leeside local circulation. Together with the idealized high-resolution simulations, we identified that the detailed evolution of the lee vortices is highly sensitive to the subtle changes in background wind direction and hence the redistribution of local pollution.
Abstract
This study quantifies the potential effect of the lee vortex on the fine particulate matter (PM2.5) pollution deterioration under the complex topography in Taiwan using observational data. We select the lee-vortex days that favor the development of the lee vortices in northwestern Taiwan under the southeasterly synoptic winds. We then define the enhancement index that discerns the areas with the high occurrence frequencies of the PM2.5 enhancement under the flow regime relative to the seasonal background concentrations. Under the lee-vortex days, the center of western Taiwan exhibits enhancement indices higher than 0.65. In addition, during the consecutive lee-vortex days, the index characterizes a northward shift in the PM2.5-enhanced areas under the subtle transition of the background wind directions. The areas with indices higher than 0.65 expand on the second day in northwestern Taiwan; the number of stations exhibiting indices higher than 0.8 increases by threefold from the first to the second day. The idealized numerical simulations using the Taiwan vector vorticity equation cloud-resolving model (TaiwanVVM) explicitly resolve the structures of leeside circulations and the associated pollutant transport, and their evolutions are highly sensitive to the background winds.
Significance Statement
Our study investigates the challenging question of local circulation patterns affected by mountain orography and the associated pollutant transport. We analyzed long-term balloon sounding and ground station observations to select the weather regime favoring the formation of lee vortices on Taiwan island. We then quantified the areas with a frequent enhancement of particulate pollutants. The long-term trend of the lee-vortex days exhibited a significant increase in the past decades. The pollution enhancement areas are highly consistent with the regions dominated by leeside local circulation. Together with the idealized high-resolution simulations, we identified that the detailed evolution of the lee vortices is highly sensitive to the subtle changes in background wind direction and hence the redistribution of local pollution.
Abstract
To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilize when constructing warnings, it is important to compare systems in their ability to forecast the occurrence and severity of high-impact weather events. However, evaluating forecasts for particular outcomes is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realization that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasized during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce conditional probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate probabilistic forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).
Abstract
To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilize when constructing warnings, it is important to compare systems in their ability to forecast the occurrence and severity of high-impact weather events. However, evaluating forecasts for particular outcomes is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realization that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasized during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce conditional probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate probabilistic forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).
Abstract
The North Pacific Subtropical Fronts (STFs), accompanied by the eastward-flowing subtropical countercurrent, stretch from the western Pacific Ocean to the north of Hawaii. Previous work has detected different trends of the frontal position and strength between the western STF (WSTF; west of 180°) and the eastern STF (ESTF; east of 180°) in the past 40 years. However, whether the basin-scale STFs have zonally asymmetric variability on multidecadal time scales and what drives that change remain to be quantified. Our recent work has shown that the multidecadal variability of the WSTF is controlled by the Atlantic multidecadal oscillation via the subtropical mode water variability. The present study proposes that the variability of ESTF is modulated by the Pacific decadal oscillation (PDO) via the central mode water (CMW) variability, quasi synchronously on multidecadal time scales. During a PDO positive phase, the enhanced midlatitude westerly winds in the central North Pacific increase the local surface buoyancy loss and deepen the winter mixed layer, which enlarges the CMW formation and thus increases its volume. Meanwhile, accompanied by the southward-migrated outcropping zone, the main body of CMW shifts equatorward. In response to such CMW changes, the ESTF strengthens and shifts equatorward correspondingly. Conversely, during a PDO negative phase, the weakened midlatitude westerly winds in the central North Pacific decrease the local surface buoyancy loss and shallow the winter mixed layer, which reduces the CMW formation and thus decreases its volume. Meanwhile, accompanied by northward-migrated outcropping zone, the main body of CMW shifts poleward. In response to such CMW changes, the ESTF weakens and shifts poleward correspondingly. Our results reveal that the dominant factor controlling the low-frequency variability of the WSTF and ESTF is different, which renews the conventional picture that all of the STFs behave symmetrically, with important implications for the North Pacific climate variability.
Abstract
The North Pacific Subtropical Fronts (STFs), accompanied by the eastward-flowing subtropical countercurrent, stretch from the western Pacific Ocean to the north of Hawaii. Previous work has detected different trends of the frontal position and strength between the western STF (WSTF; west of 180°) and the eastern STF (ESTF; east of 180°) in the past 40 years. However, whether the basin-scale STFs have zonally asymmetric variability on multidecadal time scales and what drives that change remain to be quantified. Our recent work has shown that the multidecadal variability of the WSTF is controlled by the Atlantic multidecadal oscillation via the subtropical mode water variability. The present study proposes that the variability of ESTF is modulated by the Pacific decadal oscillation (PDO) via the central mode water (CMW) variability, quasi synchronously on multidecadal time scales. During a PDO positive phase, the enhanced midlatitude westerly winds in the central North Pacific increase the local surface buoyancy loss and deepen the winter mixed layer, which enlarges the CMW formation and thus increases its volume. Meanwhile, accompanied by the southward-migrated outcropping zone, the main body of CMW shifts equatorward. In response to such CMW changes, the ESTF strengthens and shifts equatorward correspondingly. Conversely, during a PDO negative phase, the weakened midlatitude westerly winds in the central North Pacific decrease the local surface buoyancy loss and shallow the winter mixed layer, which reduces the CMW formation and thus decreases its volume. Meanwhile, accompanied by northward-migrated outcropping zone, the main body of CMW shifts poleward. In response to such CMW changes, the ESTF weakens and shifts poleward correspondingly. Our results reveal that the dominant factor controlling the low-frequency variability of the WSTF and ESTF is different, which renews the conventional picture that all of the STFs behave symmetrically, with important implications for the North Pacific climate variability.
Abstract
Submesoscale baroclinic instabilities have been shown to restratify the surface mixed layer and to seasonally energize submesoscale turbulence in the upper ocean. But do these instabilities also affect the large-scale circulation and stratification of the upper thermocline? This question is addressed for the North Atlantic Subtropical Mode Water region with a series of numerical simulations at varying horizontal grid spacings (16, 8, 4, and 2 km). These simulations are realistically forced and integrated long enough for the thermocline to adjust to the presence or absence of submesoscales. Linear stability analysis indicates that a 2-km grid spacing is sufficient to resolve the most unstable mode of the wintertime mixed layer instability. As the resolution is increased, spectral slopes of horizontal kinetic energy flatten and vertical velocities increase in magnitude, consistent with previous regional and short-time simulations. The equilibrium stratification of the thermocline changes drastically as the grid spacing is refined from 16 to 8 km and mesoscale eddies are fully resolved. The thermocline stratification remains largely unchanged, however, between the 8-, 4-, and 2-km runs. This robustness is argued to arise from a mesoscale constraint on the buoyancy variance budget. Once mesoscale processes are resolved, the rate of mesoscale variance production is largely fixed. This constrains the variance destruction by submesoscale vertical buoyancy fluxes, which thus remain invariant across resolutions. The bulk impact of mixed layer instabilities on upper-ocean stratification in the Subtropical Mode Water region through an enhanced vertical buoyancy flux is therefore captured at 8-km grid spacing, even though the instabilities are severely underresolved.
Abstract
Submesoscale baroclinic instabilities have been shown to restratify the surface mixed layer and to seasonally energize submesoscale turbulence in the upper ocean. But do these instabilities also affect the large-scale circulation and stratification of the upper thermocline? This question is addressed for the North Atlantic Subtropical Mode Water region with a series of numerical simulations at varying horizontal grid spacings (16, 8, 4, and 2 km). These simulations are realistically forced and integrated long enough for the thermocline to adjust to the presence or absence of submesoscales. Linear stability analysis indicates that a 2-km grid spacing is sufficient to resolve the most unstable mode of the wintertime mixed layer instability. As the resolution is increased, spectral slopes of horizontal kinetic energy flatten and vertical velocities increase in magnitude, consistent with previous regional and short-time simulations. The equilibrium stratification of the thermocline changes drastically as the grid spacing is refined from 16 to 8 km and mesoscale eddies are fully resolved. The thermocline stratification remains largely unchanged, however, between the 8-, 4-, and 2-km runs. This robustness is argued to arise from a mesoscale constraint on the buoyancy variance budget. Once mesoscale processes are resolved, the rate of mesoscale variance production is largely fixed. This constrains the variance destruction by submesoscale vertical buoyancy fluxes, which thus remain invariant across resolutions. The bulk impact of mixed layer instabilities on upper-ocean stratification in the Subtropical Mode Water region through an enhanced vertical buoyancy flux is therefore captured at 8-km grid spacing, even though the instabilities are severely underresolved.
