Abstract
Organized systems of deep convective clouds are often associated with high-impact weather and changes in such systems may have implications for climate sensitivity. This has motivated the derivation of many organization indices that attempt to measure the level of deep convective aggregation in models and observations. Here we conduct a comprehensive review of existing methodologies and highlight some of their drawbacks, such as only measuring organization in a relative sense, being biased toward particular spatial scales, or being very sensitive to the details of the calculation algorithm. One widely used metric, I org, uses statistics of nearest-neighbor distances between convective storms to address the first of these concerns, but we show here that it is insensitive to organization beyond the meso-β scale and very contingent on the details of the implementation. We thus introduce a new and complementary metric, L org, based on all-pair convective storm distances, which is also an absolute metric that can discern regular, random, and clustered cloud scenes. It is linearly sensitive to spatial scale in most applications and robust to the implementation methodology. We also derive a discrete form suited to gridded data and provide corrections to account for cyclic boundary conditions and finite, open boundary domains of nonequal aspect ratios. We demonstrate the use of the metric with idealized synthetic configurations, as well as model output and satellite rainfall retrievals in the tropics. We claim that this new metric usefully supplements the existing family of indices that can help to understand convective organization across spatial scales.
Significance Statement
The clustering and organization of convection is associated with high-impact weather and changes could impact climate sensitivity, but no consensus exists on how to best measure organization. Here we suggest a new metric that is robust to the calculation details and can classify scenes as random, clustered, or regular. This new metric can therefore account for spacing of organized convective systems and convective storms on scales spanning tens of kilometers to the entire tropics. We suggest that the new metric L org addresses many shortcomings of existing measures and can act as a useful additional tool to further understanding of convective organization.
Abstract
Organized systems of deep convective clouds are often associated with high-impact weather and changes in such systems may have implications for climate sensitivity. This has motivated the derivation of many organization indices that attempt to measure the level of deep convective aggregation in models and observations. Here we conduct a comprehensive review of existing methodologies and highlight some of their drawbacks, such as only measuring organization in a relative sense, being biased toward particular spatial scales, or being very sensitive to the details of the calculation algorithm. One widely used metric, I org, uses statistics of nearest-neighbor distances between convective storms to address the first of these concerns, but we show here that it is insensitive to organization beyond the meso-β scale and very contingent on the details of the implementation. We thus introduce a new and complementary metric, L org, based on all-pair convective storm distances, which is also an absolute metric that can discern regular, random, and clustered cloud scenes. It is linearly sensitive to spatial scale in most applications and robust to the implementation methodology. We also derive a discrete form suited to gridded data and provide corrections to account for cyclic boundary conditions and finite, open boundary domains of nonequal aspect ratios. We demonstrate the use of the metric with idealized synthetic configurations, as well as model output and satellite rainfall retrievals in the tropics. We claim that this new metric usefully supplements the existing family of indices that can help to understand convective organization across spatial scales.
Significance Statement
The clustering and organization of convection is associated with high-impact weather and changes could impact climate sensitivity, but no consensus exists on how to best measure organization. Here we suggest a new metric that is robust to the calculation details and can classify scenes as random, clustered, or regular. This new metric can therefore account for spacing of organized convective systems and convective storms on scales spanning tens of kilometers to the entire tropics. We suggest that the new metric L org addresses many shortcomings of existing measures and can act as a useful additional tool to further understanding of convective organization.
Abstract
Stratocumulus occur in closed- or open-cell states, which tend to be associated with high or low cloud cover and the absence or presence of precipitation, respectively. Thus, the transition between these states has substantial implications for the role of this cloud type in Earth’s radiation budget. In this study, we analyze transitions between these states using an ensemble of 127 large-eddy simulations, covering a wide range of conditions. Our analysis is focused on the behavior of these clouds in a cloud fraction (fc ) scene albedo (A) phase space, which has been shown in previous studies to be a useful framework for interpreting system behavior. For the transition from closed to open cells, we find that precipitation creates narrower clouds and scavenges cloud droplets for all fc . However, precipitation decreases the cloud depth for fc > 0.8 only, causing a rapid decrease in A. For fc < 0.8, the cloud depth actually increases due to mesoscale organization of the cloud field. As the cloud deepening balances the effects of cloud droplet scavenging in terms of influence on A, changes in A are determined by the decreasing fc only, causing a linear decrease in A for fc < 0.8. For the transition from open to closed cells, we find that longwave radiative cooling drives the cloud development, with cloud widening dominating for fc < 0.5. For fc > 0.5, clouds begin to deepen gradually due to the decreasing efficiency of lateral expansion. The smooth switch between cloud widening and deepening leads to a more gentle change in A compared to the transitions under precipitating conditions.
Significance Statement
By reflecting a substantial fraction of solar shortwave radiation back to space, shallow clouds constitute a major cooling agent in Earth’s radiation budget. To constrain this effect, a profound understanding of cloud cover and cloud albedo is necessary. In this study, we analyze the processes that drive the variability in these cloud properties in stratocumulus clouds, a very common cloud type covering approximately 20% of the globe. For these clouds, we show that changes from low to high or high to low cloud cover are different due to the underlying cloud micro- and macrophysics, elucidating this crucial aspect of aerosol–cloud–climate interactions.
Abstract
Stratocumulus occur in closed- or open-cell states, which tend to be associated with high or low cloud cover and the absence or presence of precipitation, respectively. Thus, the transition between these states has substantial implications for the role of this cloud type in Earth’s radiation budget. In this study, we analyze transitions between these states using an ensemble of 127 large-eddy simulations, covering a wide range of conditions. Our analysis is focused on the behavior of these clouds in a cloud fraction (fc ) scene albedo (A) phase space, which has been shown in previous studies to be a useful framework for interpreting system behavior. For the transition from closed to open cells, we find that precipitation creates narrower clouds and scavenges cloud droplets for all fc . However, precipitation decreases the cloud depth for fc > 0.8 only, causing a rapid decrease in A. For fc < 0.8, the cloud depth actually increases due to mesoscale organization of the cloud field. As the cloud deepening balances the effects of cloud droplet scavenging in terms of influence on A, changes in A are determined by the decreasing fc only, causing a linear decrease in A for fc < 0.8. For the transition from open to closed cells, we find that longwave radiative cooling drives the cloud development, with cloud widening dominating for fc < 0.5. For fc > 0.5, clouds begin to deepen gradually due to the decreasing efficiency of lateral expansion. The smooth switch between cloud widening and deepening leads to a more gentle change in A compared to the transitions under precipitating conditions.
Significance Statement
By reflecting a substantial fraction of solar shortwave radiation back to space, shallow clouds constitute a major cooling agent in Earth’s radiation budget. To constrain this effect, a profound understanding of cloud cover and cloud albedo is necessary. In this study, we analyze the processes that drive the variability in these cloud properties in stratocumulus clouds, a very common cloud type covering approximately 20% of the globe. For these clouds, we show that changes from low to high or high to low cloud cover are different due to the underlying cloud micro- and macrophysics, elucidating this crucial aspect of aerosol–cloud–climate interactions.
Abstract
Severe floods and droughts, including their back-to-back occurrences (weather whiplash), have been increasing in frequency and severity around the world. Improved understanding of systematic changes in hydrological extremes is essential for preparation and adaptation. In this study, we identified and quantified extreme wet and dry events globally by applying a clustering algorithm to terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (FO). The most intense events, ranked using an intensity metric, often reflect impacts of large-scale oceanic oscillations such as El Niño–Southern Oscillation and consequences of climate change. The severity of both wet and dry events, represented by standardized TWS anomalies, increased significantly in most cases, likely associated with intensification of wet and dry weather regimes in a warmer world, and consequently, exhibited strongest correlation with global temperature. In the Dry climate, the number of wet events decreased while the number of dry events increased significantly, suggesting a drying trend that may be attributed to climate variability and possible increases in irrigation and reliance on groundwater. In the Continental climate where temperature has risen faster than global average, dry events increased significantly. Characteristics of extreme events often showed strong correlations with global temperature, especially when averaged over all climates. These results suggest changes in hydrological extremes and underscore the importance of quantifying total water storage changes when studying hydrological extremes. Extending the GRACE/FO record, which spans 2002 to the present, is essential to continuously tracking changes in TWS and hydrological extremes.
Abstract
Severe floods and droughts, including their back-to-back occurrences (weather whiplash), have been increasing in frequency and severity around the world. Improved understanding of systematic changes in hydrological extremes is essential for preparation and adaptation. In this study, we identified and quantified extreme wet and dry events globally by applying a clustering algorithm to terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (FO). The most intense events, ranked using an intensity metric, often reflect impacts of large-scale oceanic oscillations such as El Niño–Southern Oscillation and consequences of climate change. The severity of both wet and dry events, represented by standardized TWS anomalies, increased significantly in most cases, likely associated with intensification of wet and dry weather regimes in a warmer world, and consequently, exhibited strongest correlation with global temperature. In the Dry climate, the number of wet events decreased while the number of dry events increased significantly, suggesting a drying trend that may be attributed to climate variability and possible increases in irrigation and reliance on groundwater. In the Continental climate where temperature has risen faster than global average, dry events increased significantly. Characteristics of extreme events often showed strong correlations with global temperature, especially when averaged over all climates. These results suggest changes in hydrological extremes and underscore the importance of quantifying total water storage changes when studying hydrological extremes. Extending the GRACE/FO record, which spans 2002 to the present, is essential to continuously tracking changes in TWS and hydrological extremes.
Abstract
Typhoon Hagupit (2020), which formed unexpectedly close to land, posed great challenges for forecasters. During its genesis, there was a west-moving upper-tropospheric cold low (UTCL) to its north. This study investigated the impact of this UTCL on the genesis process using numerical simulations. In the semi-idealized experiment with this UTCL removed (run-Rcold), pre-Hagupit develops faster, but its track drifts southward in the later stage compared with the control experiment (run-cnl). In the experiment with enhanced UTCL (run-Ecold), the simulated track is similar to that in run-cnl, but pre-Hagupit does not develop into a tropical storm. In run-cnl and run-Ecold, the environmental vertical wind shear is larger than that in run-Rcold in the first two days, and the simulated pre-Hagupit experiences two prominent dry air intrusions in the middle and upper troposphere. At the second intrusion, when the weakened UTCL has moved within 2° of pre-Hagupit, the convection in both experiments decays significantly, and the development of the mid-level vortex begins to lag behind that in run-Rcold, and so does the vertical alignment of the low- and mid-level vortices. The UTCL influences the movement of pre-Hagupit by modifying the large-scale steering flows, especially those above 600 hPa. In run-Rcold, due to the absence of the northward component of wind fields related to the UTCL circulation, pre-Hagupit starts to move west-northwestwards instead of northwestwards as in run-cnl and run-Ecold.
Abstract
Typhoon Hagupit (2020), which formed unexpectedly close to land, posed great challenges for forecasters. During its genesis, there was a west-moving upper-tropospheric cold low (UTCL) to its north. This study investigated the impact of this UTCL on the genesis process using numerical simulations. In the semi-idealized experiment with this UTCL removed (run-Rcold), pre-Hagupit develops faster, but its track drifts southward in the later stage compared with the control experiment (run-cnl). In the experiment with enhanced UTCL (run-Ecold), the simulated track is similar to that in run-cnl, but pre-Hagupit does not develop into a tropical storm. In run-cnl and run-Ecold, the environmental vertical wind shear is larger than that in run-Rcold in the first two days, and the simulated pre-Hagupit experiences two prominent dry air intrusions in the middle and upper troposphere. At the second intrusion, when the weakened UTCL has moved within 2° of pre-Hagupit, the convection in both experiments decays significantly, and the development of the mid-level vortex begins to lag behind that in run-Rcold, and so does the vertical alignment of the low- and mid-level vortices. The UTCL influences the movement of pre-Hagupit by modifying the large-scale steering flows, especially those above 600 hPa. In run-Rcold, due to the absence of the northward component of wind fields related to the UTCL circulation, pre-Hagupit starts to move west-northwestwards instead of northwestwards as in run-cnl and run-Ecold.
Abstract
The Sahel is one of the most vulnerable regions to climate change. Robust estimation of future changes in the Sahel monsoon is therefore essential for effective climate change adaptation. Unfortunately, state-of-the-art climate models show large uncertainties in their projections of Sahel rainfall. In this study, we use 32 models from CMIP6 to identify the sources of this large inter-model spread of Sahel rainfall.
By using Maximum Covariance Analysis, we first highlight two new key drivers of this spread during boreal summer: the inter-hemispheric temperature gradient and equatorial Pacific Sea Surface Temperature (SST) changes. This contrasts with previous studies, which have focused mainly on the Northern Hemisphere rather than the global scale, and in which the Pacific Ocean has been neglected in favor of the Atlantic. Next, we unravel the physical mechanisms behind these statistical relationships. Firstly, the modulation of the inter-hemispheric temperature gradient across the models leads to varying latitudinal positions of the Inter Tropical Convergence Zone and, consequently, varying Sahel rainfall intensity. Secondly, models that exhibit less warming than the Multi-Model Mean in the equatorial Pacific, thereby projecting a less “El Niño-like” mean state, simulate enhanced precipitation over the central Sahel in the future through modulations of the Walker circulation, the Tropical Easterly Jet, the meridional tropospheric temperature gradient, and hence regional zonal wind shear. Finally, we show that these two indices collectively explain 62% of Sahel rainfall change uncertainty: 40% due to the inter-hemispheric temperature gradient and 22% through equatorial Pacific SST.
Abstract
The Sahel is one of the most vulnerable regions to climate change. Robust estimation of future changes in the Sahel monsoon is therefore essential for effective climate change adaptation. Unfortunately, state-of-the-art climate models show large uncertainties in their projections of Sahel rainfall. In this study, we use 32 models from CMIP6 to identify the sources of this large inter-model spread of Sahel rainfall.
By using Maximum Covariance Analysis, we first highlight two new key drivers of this spread during boreal summer: the inter-hemispheric temperature gradient and equatorial Pacific Sea Surface Temperature (SST) changes. This contrasts with previous studies, which have focused mainly on the Northern Hemisphere rather than the global scale, and in which the Pacific Ocean has been neglected in favor of the Atlantic. Next, we unravel the physical mechanisms behind these statistical relationships. Firstly, the modulation of the inter-hemispheric temperature gradient across the models leads to varying latitudinal positions of the Inter Tropical Convergence Zone and, consequently, varying Sahel rainfall intensity. Secondly, models that exhibit less warming than the Multi-Model Mean in the equatorial Pacific, thereby projecting a less “El Niño-like” mean state, simulate enhanced precipitation over the central Sahel in the future through modulations of the Walker circulation, the Tropical Easterly Jet, the meridional tropospheric temperature gradient, and hence regional zonal wind shear. Finally, we show that these two indices collectively explain 62% of Sahel rainfall change uncertainty: 40% due to the inter-hemispheric temperature gradient and 22% through equatorial Pacific SST.
Abstract
North Atlantic sea surface temperature (SST) variability plays a critical role in modulating the climate system. However, characterizing patterns of North Atlantic SST variability and diagnosing the associated mechanisms is challenging because they involve coupled atmosphere–ocean interactions with complex spatiotemporal relationships. Here we address these challenges by applying a time-evolving self-organizing map approach to a long preindustrial coupled control simulation and identify a variety of 10-yr spatiotemporal evolutions of winter SST anomalies, including but not limited to those associated with the North Atlantic Oscillation–Atlantic multidecadal variability (NAO–AMV)-like interactions. To assess mechanisms and atmospheric responses associated with various SST spatiotemporal evolutions, composites of atmospheric and oceanic variables associated with these evolutions are investigated. Results show that transient-eddy activities and atmospheric circulation responses exist in almost all the evolutions that are closely correlated to the details of the SST pattern. In terms of the mechanisms responsible for generating various SST evolutions, composites of ocean heat budget terms demonstrate that contributions to upper-ocean temperature tendency from resolved ocean advection and surface heat fluxes rarely oppose each other over 10-yr periods in the subpolar North Atlantic. We further explore the potential for predictability for some of these 10-yr SST evolutions that start with similar states but end with different states. However, we find that these are associated with abrupt changes in atmospheric variability and are unlikely to be predictable. In summary, this study broadly investigates the atmospheric responses to and the mechanisms governing the North Atlantic SST evolutions over 10-yr periods.
Significance Statement
Climate variability in the North Atlantic Ocean has wide-ranging impacts on global and regional climate. However, the processes involved include interactions between the ocean and atmosphere that vary across both space and time, making it challenging to characterize and predict. Using a novel machine learning approach, this study identifies various time evolutions of North Atlantic sea surface temperature patterns over 10-yr periods. This includes evolutions with similar start states but different trajectories, which have important implications for predictability. Furthermore, we investigate the mechanisms responsible for these evolutions and how different sea surface temperature patterns affect atmospheric circulation through small-scale atmospheric disturbances. These new insights into the complex ocean–atmosphere interactions over time are critical for improving decadal prediction skill.
Abstract
North Atlantic sea surface temperature (SST) variability plays a critical role in modulating the climate system. However, characterizing patterns of North Atlantic SST variability and diagnosing the associated mechanisms is challenging because they involve coupled atmosphere–ocean interactions with complex spatiotemporal relationships. Here we address these challenges by applying a time-evolving self-organizing map approach to a long preindustrial coupled control simulation and identify a variety of 10-yr spatiotemporal evolutions of winter SST anomalies, including but not limited to those associated with the North Atlantic Oscillation–Atlantic multidecadal variability (NAO–AMV)-like interactions. To assess mechanisms and atmospheric responses associated with various SST spatiotemporal evolutions, composites of atmospheric and oceanic variables associated with these evolutions are investigated. Results show that transient-eddy activities and atmospheric circulation responses exist in almost all the evolutions that are closely correlated to the details of the SST pattern. In terms of the mechanisms responsible for generating various SST evolutions, composites of ocean heat budget terms demonstrate that contributions to upper-ocean temperature tendency from resolved ocean advection and surface heat fluxes rarely oppose each other over 10-yr periods in the subpolar North Atlantic. We further explore the potential for predictability for some of these 10-yr SST evolutions that start with similar states but end with different states. However, we find that these are associated with abrupt changes in atmospheric variability and are unlikely to be predictable. In summary, this study broadly investigates the atmospheric responses to and the mechanisms governing the North Atlantic SST evolutions over 10-yr periods.
Significance Statement
Climate variability in the North Atlantic Ocean has wide-ranging impacts on global and regional climate. However, the processes involved include interactions between the ocean and atmosphere that vary across both space and time, making it challenging to characterize and predict. Using a novel machine learning approach, this study identifies various time evolutions of North Atlantic sea surface temperature patterns over 10-yr periods. This includes evolutions with similar start states but different trajectories, which have important implications for predictability. Furthermore, we investigate the mechanisms responsible for these evolutions and how different sea surface temperature patterns affect atmospheric circulation through small-scale atmospheric disturbances. These new insights into the complex ocean–atmosphere interactions over time are critical for improving decadal prediction skill.
Abstract
The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.
Abstract
The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.
Abstract
Wet bulb globe temperature (WBGT) is used to assess environmental heat stress and accounts for the influences of air temperature, humidity, wind speed, and radiation on heat stress. Measurements of WBGT are highly sensitive to slight changes in environmental conditions and can vary several degrees Celsius across small distances (10s to 100s of meters). Compared to observations with an ISO-compliant WBGT meter, this work assesses the accuracy of WBGT measurements made with a popular handheld meter (the Kestrel 5400 Heat Stress Tracker) and WBGT estimates. Measurements were made during the summers of 2019-2021 in a variety of suburban and urban environments in North Carolina, including three high school campuses. WBGT can be estimated from standard weather station variables, and many of these stations report cloud cover in lieu of solar radiation. Therefore, this work also evaluates the accuracy of clear-sky radiation estimates and adjustments to those estimates based on cloud cover. WBGT estimated with the method from Liljegren et al. (2008) from a weather station were on average 0.2°C warmer than observed WBGT, while the Kestrel 5400 WBGT was 0.7°C warmer. Large variations in WBGT were observed across surfaces and shade conditions, with differences of 0.9°C (0.3–1.4°C) between a tennis court and a neighboring grass field. The method for estimating clear-sky radiation in Ryan & Stolzenbach (1972) was most accurate and the clear-sky radiation modified by percentage cloud cover was found to be within 75 w/m2 of observations on average.
Abstract
Wet bulb globe temperature (WBGT) is used to assess environmental heat stress and accounts for the influences of air temperature, humidity, wind speed, and radiation on heat stress. Measurements of WBGT are highly sensitive to slight changes in environmental conditions and can vary several degrees Celsius across small distances (10s to 100s of meters). Compared to observations with an ISO-compliant WBGT meter, this work assesses the accuracy of WBGT measurements made with a popular handheld meter (the Kestrel 5400 Heat Stress Tracker) and WBGT estimates. Measurements were made during the summers of 2019-2021 in a variety of suburban and urban environments in North Carolina, including three high school campuses. WBGT can be estimated from standard weather station variables, and many of these stations report cloud cover in lieu of solar radiation. Therefore, this work also evaluates the accuracy of clear-sky radiation estimates and adjustments to those estimates based on cloud cover. WBGT estimated with the method from Liljegren et al. (2008) from a weather station were on average 0.2°C warmer than observed WBGT, while the Kestrel 5400 WBGT was 0.7°C warmer. Large variations in WBGT were observed across surfaces and shade conditions, with differences of 0.9°C (0.3–1.4°C) between a tennis court and a neighboring grass field. The method for estimating clear-sky radiation in Ryan & Stolzenbach (1972) was most accurate and the clear-sky radiation modified by percentage cloud cover was found to be within 75 w/m2 of observations on average.
Abstract
Previous studies have indicated that boreal winter-to-spring sea surface temperature anomalies (SSTA) over the tropical Atlantic or Indian Ocean can trigger the central-Pacific (CP) type of ENSO in the following winter due to winds over the western Pacific. Here, with the aid of observational data and CMIP5 model simulations, we demonstrate that the ability of the winter-to-spring north tropical Atlantic (NTA) SSTA or Indian Ocean Basin (IOB) mode to initiate CP ENSO events in the following winter may strongly depend on each other. Most warming events of the IOB and NTA, which are followed by CP La Niña events, are concomitant. The synergistic effect of the IOB and NTA SSTA may produce greater CP ENSO events in the subsequent winter via Walker circulation adjustments. The impacts between warming and cooling events of the IOB and NTA SSTA are asymmetric. IOB and NTA warmings appear to contribute to the subsequent CP La Niña development, which is much greater than IOB and NTA cooling contributing to CP El Niño. Overall, a combination of the IOB and NTA SSTA precursors may improve predictions of La Niña events.
Significance Statement
Although boreal winter-to-spring sea surface temperature anomalies over the tropical Atlantic or Indian Ocean can trigger central-Pacific (CP) ENSO in the following winter, it is not yet clear whether the effects of these two basins are independent. The purpose of this study is to better understand the joint effect of these two basins on CP ENSO events. We demonstrate that the ability of the north tropical Atlantic (NTA) SSTA to initiate CP ENSO events in the following winter may strongly depend on the state of the Indian Ocean Basin mode (IOB). The synergistic impact of these two basins may produce stronger CP ENSO events. These results highlight the role of three-ocean interactions in ENSO diversity and prediction.
Abstract
Previous studies have indicated that boreal winter-to-spring sea surface temperature anomalies (SSTA) over the tropical Atlantic or Indian Ocean can trigger the central-Pacific (CP) type of ENSO in the following winter due to winds over the western Pacific. Here, with the aid of observational data and CMIP5 model simulations, we demonstrate that the ability of the winter-to-spring north tropical Atlantic (NTA) SSTA or Indian Ocean Basin (IOB) mode to initiate CP ENSO events in the following winter may strongly depend on each other. Most warming events of the IOB and NTA, which are followed by CP La Niña events, are concomitant. The synergistic effect of the IOB and NTA SSTA may produce greater CP ENSO events in the subsequent winter via Walker circulation adjustments. The impacts between warming and cooling events of the IOB and NTA SSTA are asymmetric. IOB and NTA warmings appear to contribute to the subsequent CP La Niña development, which is much greater than IOB and NTA cooling contributing to CP El Niño. Overall, a combination of the IOB and NTA SSTA precursors may improve predictions of La Niña events.
Significance Statement
Although boreal winter-to-spring sea surface temperature anomalies over the tropical Atlantic or Indian Ocean can trigger central-Pacific (CP) ENSO in the following winter, it is not yet clear whether the effects of these two basins are independent. The purpose of this study is to better understand the joint effect of these two basins on CP ENSO events. We demonstrate that the ability of the north tropical Atlantic (NTA) SSTA to initiate CP ENSO events in the following winter may strongly depend on the state of the Indian Ocean Basin mode (IOB). The synergistic impact of these two basins may produce stronger CP ENSO events. These results highlight the role of three-ocean interactions in ENSO diversity and prediction.
Abstract
This study focuses on the application of two standard inflow turbulence generation methods for growing convective boundary layer (CBL) simulations: the recycle–rescale (R-R) and the digital filter–based (DF) methods, which are used in computational fluid dynamics. The primary objective of this study is to expand the applicability of the R-R method to simulations of thermally driven CBLs. This method is called the extended R-R method. However, in previous studies, the DF method has been extended to generate potential temperature perturbations. This study investigated whether the extended DF method can be applied to simulations of growing thermally driven CBLs. In this study, idealized simulations of growing thermally driven CBLs using the extended R-R and DF methods were performed. The results showed that both extended methods could capture the characteristics of thermally driven CBLs. The extended R-R method reproduced turbulence in thermally driven CBLs better than the extended DF method in the spectrum and histogram of vertical wind speed. However, the height of the thermally driven CBL was underestimated in about 100 m compared with the extended DF method. Sensitivity experiments were conducted on the parameters used in the extended DF and R-R methods. The results showed that underestimation of the length scale in the extended DF method causes a shortage of large-scale turbulence components. The other point suggested by the results of the sensitivity experiments is that the length of the driver region in the extended R-R method should be sufficient to reproduce the spanwise movement of the roll vortices.
Significance Statement
Inflow turbulence generation methods for large-eddy simulation (LES) models are crucial for the better downscaling of meteorological mesoscale models (RANS models) to microscale models (LES models). Various CFD methods have been developed, but few have been applied to simulations of thermally driven convective boundary layers (CBLs). To address this problem, we focused on a method that recycles turbulence [the recycle–rescale (R-R) method] and another method that synthetically generates turbulence [the digital filter–based (DF) method]. This study extends the R-R method to manage turbulence in thermally driven CBLs. In addition, this study investigated the applicability of the DF method to thermally driven CBL simulations. Both extended methods are effective for downscaling experiments and capture the characteristics of thermally driven CBLs.
Abstract
This study focuses on the application of two standard inflow turbulence generation methods for growing convective boundary layer (CBL) simulations: the recycle–rescale (R-R) and the digital filter–based (DF) methods, which are used in computational fluid dynamics. The primary objective of this study is to expand the applicability of the R-R method to simulations of thermally driven CBLs. This method is called the extended R-R method. However, in previous studies, the DF method has been extended to generate potential temperature perturbations. This study investigated whether the extended DF method can be applied to simulations of growing thermally driven CBLs. In this study, idealized simulations of growing thermally driven CBLs using the extended R-R and DF methods were performed. The results showed that both extended methods could capture the characteristics of thermally driven CBLs. The extended R-R method reproduced turbulence in thermally driven CBLs better than the extended DF method in the spectrum and histogram of vertical wind speed. However, the height of the thermally driven CBL was underestimated in about 100 m compared with the extended DF method. Sensitivity experiments were conducted on the parameters used in the extended DF and R-R methods. The results showed that underestimation of the length scale in the extended DF method causes a shortage of large-scale turbulence components. The other point suggested by the results of the sensitivity experiments is that the length of the driver region in the extended R-R method should be sufficient to reproduce the spanwise movement of the roll vortices.
Significance Statement
Inflow turbulence generation methods for large-eddy simulation (LES) models are crucial for the better downscaling of meteorological mesoscale models (RANS models) to microscale models (LES models). Various CFD methods have been developed, but few have been applied to simulations of thermally driven convective boundary layers (CBLs). To address this problem, we focused on a method that recycles turbulence [the recycle–rescale (R-R) method] and another method that synthetically generates turbulence [the digital filter–based (DF) method]. This study extends the R-R method to manage turbulence in thermally driven CBLs. In addition, this study investigated the applicability of the DF method to thermally driven CBL simulations. Both extended methods are effective for downscaling experiments and capture the characteristics of thermally driven CBLs.
