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Abstract
Human influence has been robustly detected throughout many parts of the climate system. Pattern-based methods have been used extensively to estimate the strength of model-predicted “fingerprints,” both human and natural, in observational data. However, individual studies using different analysis methods and time periods yield inconsistent estimates of the magnitude of the influence of anthropogenic aerosols, depending on whether they examined the troposphere, surface, or ocean. Reducing the uncertainty of the impact of aerosols on the climate system is crucial for understanding past climate change and obtaining more reliable estimates of climate sensitivity. To reconcile divergent estimates of aerosol effects obtained in previous studies, we apply the same regression-based detection and attribution method to three different variables: mid-to-upper-tropospheric temperature, surface temperature, and ocean heat content. We find that quantitative estimates of human influence in observations are consistent across these three independently monitored components of the climate system. Combining the troposphere, surface, and ocean data into a single multivariate fingerprint results in a small (∼10%) reduction of uncertainty of the magnitude of the greenhouse gas fingerprint, but a large (∼40%) reduction for the anthropogenic aerosol fingerprint. This reduction in uncertainty results in a substantially earlier time of detection of the multivariate aerosol fingerprint when compared to aerosol fingerprint detection time in each of the three individual variables. Our results highlight the benefits of analyzing data across the troposphere, surface, and ocean in detection and attribution studies, and motivate future work to further constrain uncertainties in aerosol effects on climate.
Significance Statement
Fingerprints of human influence have been detected separately across the troposphere, surface, and ocean. Previous studies examining the different parts of the climate system are difficult to compare quantitatively, however, because they use different methods and cover differ timespans. Here we find consistent estimates of the human influence on the troposphere, surface, and ocean over recent decades when the same fingerprint method and analysis period is used. When we combine the three variables into a single fingerprint, the uncertainty of the influence of anthropogenic aerosols is substantially reduced and the signal is detectable considerably earlier in the observational record. Our results highlight the benefits of performing analysis across different variables instead of focusing on one variable only.
Abstract
Human influence has been robustly detected throughout many parts of the climate system. Pattern-based methods have been used extensively to estimate the strength of model-predicted “fingerprints,” both human and natural, in observational data. However, individual studies using different analysis methods and time periods yield inconsistent estimates of the magnitude of the influence of anthropogenic aerosols, depending on whether they examined the troposphere, surface, or ocean. Reducing the uncertainty of the impact of aerosols on the climate system is crucial for understanding past climate change and obtaining more reliable estimates of climate sensitivity. To reconcile divergent estimates of aerosol effects obtained in previous studies, we apply the same regression-based detection and attribution method to three different variables: mid-to-upper-tropospheric temperature, surface temperature, and ocean heat content. We find that quantitative estimates of human influence in observations are consistent across these three independently monitored components of the climate system. Combining the troposphere, surface, and ocean data into a single multivariate fingerprint results in a small (∼10%) reduction of uncertainty of the magnitude of the greenhouse gas fingerprint, but a large (∼40%) reduction for the anthropogenic aerosol fingerprint. This reduction in uncertainty results in a substantially earlier time of detection of the multivariate aerosol fingerprint when compared to aerosol fingerprint detection time in each of the three individual variables. Our results highlight the benefits of analyzing data across the troposphere, surface, and ocean in detection and attribution studies, and motivate future work to further constrain uncertainties in aerosol effects on climate.
Significance Statement
Fingerprints of human influence have been detected separately across the troposphere, surface, and ocean. Previous studies examining the different parts of the climate system are difficult to compare quantitatively, however, because they use different methods and cover differ timespans. Here we find consistent estimates of the human influence on the troposphere, surface, and ocean over recent decades when the same fingerprint method and analysis period is used. When we combine the three variables into a single fingerprint, the uncertainty of the influence of anthropogenic aerosols is substantially reduced and the signal is detectable considerably earlier in the observational record. Our results highlight the benefits of performing analysis across different variables instead of focusing on one variable only.
Abstract
Tropical Pacific quasi-decadal (TPQD) climate variability is characterized by quasi-decadal sea surface temperature (SST) variations in the central Pacific (CP). This low-frequency climate variability is suggested to influence extreme regional weather and substantially impact global climate patterns and associated socioeconomics through teleconnections. Previous studies mostly attributed the TPQD climate variability to basin-scale air–sea coupling processes. However, due to the coarse resolution of the majority of the observations and climate models, the role of subbasin-scale processes in modulating the TPQD climate variability is still unclear. Using a long-term high-resolution global climate model, we find that energetic small-scale motions with horizontal scales from tens to hundreds of kilometers (loosely referred to as equatorial submesoscale eddies) act as an important damping effect to retard the TPQD variability. During the positive TPQD events, compound increasing precipitation and warming SST in the equatorial Pacific intensifies the upper ocean stratification and weakens the temperature fronts along the Pacific cold tongue. This suppresses submesoscale eddy growth as well as their associated upward vertical heat transport by inhibiting baroclinic instability (BCI) and frontogenesis; conversely, during the negative TPQD events, the opposite is true. Using a series of coupled global climate models that participated in phase 6 of the Coupled Model Intercomparison Project with different oceanic resolutions, we show that the amplitude of the TPQD variability becomes smaller as the oceanic resolution becomes finer, providing evidence for the impacts of submesoscale eddies on damping the TPQD variability. Our study suggests that explicitly simulating equatorial submesoscale eddies is necessary for gaining a more robust understanding of low-frequency tropical climate variability.
Significance Statement
Submesoscale ocean eddies inhibit the development of quasi-decadal climate variability in the equatorial central Pacific, according to a high-resolution global climate simulation.
Abstract
Tropical Pacific quasi-decadal (TPQD) climate variability is characterized by quasi-decadal sea surface temperature (SST) variations in the central Pacific (CP). This low-frequency climate variability is suggested to influence extreme regional weather and substantially impact global climate patterns and associated socioeconomics through teleconnections. Previous studies mostly attributed the TPQD climate variability to basin-scale air–sea coupling processes. However, due to the coarse resolution of the majority of the observations and climate models, the role of subbasin-scale processes in modulating the TPQD climate variability is still unclear. Using a long-term high-resolution global climate model, we find that energetic small-scale motions with horizontal scales from tens to hundreds of kilometers (loosely referred to as equatorial submesoscale eddies) act as an important damping effect to retard the TPQD variability. During the positive TPQD events, compound increasing precipitation and warming SST in the equatorial Pacific intensifies the upper ocean stratification and weakens the temperature fronts along the Pacific cold tongue. This suppresses submesoscale eddy growth as well as their associated upward vertical heat transport by inhibiting baroclinic instability (BCI) and frontogenesis; conversely, during the negative TPQD events, the opposite is true. Using a series of coupled global climate models that participated in phase 6 of the Coupled Model Intercomparison Project with different oceanic resolutions, we show that the amplitude of the TPQD variability becomes smaller as the oceanic resolution becomes finer, providing evidence for the impacts of submesoscale eddies on damping the TPQD variability. Our study suggests that explicitly simulating equatorial submesoscale eddies is necessary for gaining a more robust understanding of low-frequency tropical climate variability.
Significance Statement
Submesoscale ocean eddies inhibit the development of quasi-decadal climate variability in the equatorial central Pacific, according to a high-resolution global climate simulation.
Abstract
This study investigates the dominant modes of the interannual variability of the northern Eurasian winter snowfall during 1982–2020 and explores their potential influencing factors and the associated physical processes. The first and second empirical orthogonal function (EOF) modes feature coherent snowfall anomalies over the high latitudes of Eurasia and western Siberia, respectively. Further analyses indicate that the anomalous atmospheric circulations play a major role in forming the snowfall variability, which could be further attributed to the influences of the atmospheric teleconnection patterns and Arctic sea ice variations. Specifically, the anomalous circulations related to the first EOF mode are mainly contributed by the effects of the teleconnections of the Polar–Eurasian and Scandinavian patterns. The formation of the second EOF mode has a close connection with the North Atlantic Oscillation and the Eurasian pattern. In addition, the sea ice variations over Baffin Bay exert a considerable influence on the snowfall anomalies related to the second EOF mode by exciting a wave train–like anomalous circulation. This effect is further verified by a numerical simulation. An empirical statistical model based on the above influencing factors can well explain the temporal evolutions of the two dominant modes, verifying the important value of our results to improve the understanding of interannual variability of northern Eurasian winter snowfall.
Abstract
This study investigates the dominant modes of the interannual variability of the northern Eurasian winter snowfall during 1982–2020 and explores their potential influencing factors and the associated physical processes. The first and second empirical orthogonal function (EOF) modes feature coherent snowfall anomalies over the high latitudes of Eurasia and western Siberia, respectively. Further analyses indicate that the anomalous atmospheric circulations play a major role in forming the snowfall variability, which could be further attributed to the influences of the atmospheric teleconnection patterns and Arctic sea ice variations. Specifically, the anomalous circulations related to the first EOF mode are mainly contributed by the effects of the teleconnections of the Polar–Eurasian and Scandinavian patterns. The formation of the second EOF mode has a close connection with the North Atlantic Oscillation and the Eurasian pattern. In addition, the sea ice variations over Baffin Bay exert a considerable influence on the snowfall anomalies related to the second EOF mode by exciting a wave train–like anomalous circulation. This effect is further verified by a numerical simulation. An empirical statistical model based on the above influencing factors can well explain the temporal evolutions of the two dominant modes, verifying the important value of our results to improve the understanding of interannual variability of northern Eurasian winter snowfall.
Abstract
The multiscale interaction and its role in the maintenance and propagation of the Madden–Julian oscillation (MJO) has been investigated using the newly developed multiscale window transform (MWT), the theory of canonical transfer, and the MWT-based multiscale energetics analysis (here particularly for this study, dry energetics analysis). The field variables are reconstructed/filtered with MWT onto three scale windows, namely a high-frequency window, intraseasonal window, and low-frequency window. Compositing the intraseasonal fields with respect to the real-time multivariate MJO (RMM) index unambiguously shows that the zonal extents of the easterlies and westerlies of MJO vary with the RMM phases, among which phases 4 and 2 are representative. In the former phase, the MJO has easterlies and westerlies within the same extent, while in the latter their extents are quite different. In phase 4, besides the previously discovered mechanisms such as pressure work and buoyancy conversion, the MJO is also energized by the canonical kinetic energy (KE) transfer from the low-frequency window to the intraseasonal window (signifying barotropic instability) on the west of its convection. But on the eastern side, MJO loses KE to the low-frequency window. The KE transport also functions like an energy sink. In phase 2, the MJO variabilities can be divided into an eastern part and a western part. The former is essentially the same as that in phase 4; for the latter, barotropic instability dominates. On the available potential energy (APE) budget, baroclinic instability and intraseasonal APE transport help produce and maintain the temperature anomalies. In contrast to previous energetics studies, our findings highlight the essential role played by multiscale interactions.
Abstract
The multiscale interaction and its role in the maintenance and propagation of the Madden–Julian oscillation (MJO) has been investigated using the newly developed multiscale window transform (MWT), the theory of canonical transfer, and the MWT-based multiscale energetics analysis (here particularly for this study, dry energetics analysis). The field variables are reconstructed/filtered with MWT onto three scale windows, namely a high-frequency window, intraseasonal window, and low-frequency window. Compositing the intraseasonal fields with respect to the real-time multivariate MJO (RMM) index unambiguously shows that the zonal extents of the easterlies and westerlies of MJO vary with the RMM phases, among which phases 4 and 2 are representative. In the former phase, the MJO has easterlies and westerlies within the same extent, while in the latter their extents are quite different. In phase 4, besides the previously discovered mechanisms such as pressure work and buoyancy conversion, the MJO is also energized by the canonical kinetic energy (KE) transfer from the low-frequency window to the intraseasonal window (signifying barotropic instability) on the west of its convection. But on the eastern side, MJO loses KE to the low-frequency window. The KE transport also functions like an energy sink. In phase 2, the MJO variabilities can be divided into an eastern part and a western part. The former is essentially the same as that in phase 4; for the latter, barotropic instability dominates. On the available potential energy (APE) budget, baroclinic instability and intraseasonal APE transport help produce and maintain the temperature anomalies. In contrast to previous energetics studies, our findings highlight the essential role played by multiscale interactions.
Abstract
This work explores the modulation of the Pacific decadal oscillation (PDO) on the relationship between the occurrence position of rapid intensification (RI) events of tropical cyclones (TCs) over the western North Pacific (WNP) in boreal autumn and El Niño–Southern Oscillation (ENSO). From the warm to cold phase of the PDO, the occurrence position of WNP RI events experiences a significant westward shift of 5.5° in El Niño years and a significant northward shift of 4.5° in La Niña years. The strengthening of thermodynamic conditions west of 160°N plays a dominant role in the westward shift of RI events in El Niño years, and the northward shift in La Niña years is associated with the expansion of areas with warm sea surface temperature, high tropical cyclone heat potential and midlevel relative humidity, strengthening of relative vorticity north of 20°N, and weakening of dynamic conditions within 10°–20°N. During the PDO cold phase, the descending branch of the Walker circulation over the western Pacific is weak and shifts west of 140°E in El Niño years, whereas it is much stronger in La Niña years. In addition, the Hadley circulation over the WNP shows little change during El Niño, but the ascending branch around 10°N expands to 20°N during La Niña. These trends reflect the changing responses of the WNP environment to ENSO variation and are consistent with the changing distribution of WNP RI events. Moreover, during the PDO cold phases, SST over the north Indian Ocean is much warmer, and anomalous anticyclonic circulation occurs in the WNP in boreal spring (summer and autumn) during the developing phase of El Niño (La Niña) years, which may also contribute to strengthening the thermodynamic conditions over the WNP.
Abstract
This work explores the modulation of the Pacific decadal oscillation (PDO) on the relationship between the occurrence position of rapid intensification (RI) events of tropical cyclones (TCs) over the western North Pacific (WNP) in boreal autumn and El Niño–Southern Oscillation (ENSO). From the warm to cold phase of the PDO, the occurrence position of WNP RI events experiences a significant westward shift of 5.5° in El Niño years and a significant northward shift of 4.5° in La Niña years. The strengthening of thermodynamic conditions west of 160°N plays a dominant role in the westward shift of RI events in El Niño years, and the northward shift in La Niña years is associated with the expansion of areas with warm sea surface temperature, high tropical cyclone heat potential and midlevel relative humidity, strengthening of relative vorticity north of 20°N, and weakening of dynamic conditions within 10°–20°N. During the PDO cold phase, the descending branch of the Walker circulation over the western Pacific is weak and shifts west of 140°E in El Niño years, whereas it is much stronger in La Niña years. In addition, the Hadley circulation over the WNP shows little change during El Niño, but the ascending branch around 10°N expands to 20°N during La Niña. These trends reflect the changing responses of the WNP environment to ENSO variation and are consistent with the changing distribution of WNP RI events. Moreover, during the PDO cold phases, SST over the north Indian Ocean is much warmer, and anomalous anticyclonic circulation occurs in the WNP in boreal spring (summer and autumn) during the developing phase of El Niño (La Niña) years, which may also contribute to strengthening the thermodynamic conditions over the WNP.
Abstract
In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic and dynamic (convergence, ridging, and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (∼20%) even in 2007 when dynamic loss was largest. Sea ice loss by compaction (within pack ice convergence) dominates the dynamic area loss, even in years when the export is largest. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that nonlinear effects between dynamic and thermodynamic area loss can be important for large negative anomalies in sea ice extent, in accord with previous modeling studies. A detailed analysis of two all-time record minimum years (2007 and 2012)—one with a semipermanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer—shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 whereas, in 2012, it was dominated by Ekman divergence amplified by sea–ice albedo feedback—together with an early melt onset. We argue that Ekman divergence from more intense summer storms when the sun is high above the horizon is a more likely mechanism for a “first-time” ice-free Arctic.
Abstract
In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic and dynamic (convergence, ridging, and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (∼20%) even in 2007 when dynamic loss was largest. Sea ice loss by compaction (within pack ice convergence) dominates the dynamic area loss, even in years when the export is largest. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that nonlinear effects between dynamic and thermodynamic area loss can be important for large negative anomalies in sea ice extent, in accord with previous modeling studies. A detailed analysis of two all-time record minimum years (2007 and 2012)—one with a semipermanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer—shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 whereas, in 2012, it was dominated by Ekman divergence amplified by sea–ice albedo feedback—together with an early melt onset. We argue that Ekman divergence from more intense summer storms when the sun is high above the horizon is a more likely mechanism for a “first-time” ice-free Arctic.
Abstract
Changes in the Madden–Julian oscillation (MJO) and its impacts on the South American monsoon season during different El Niño–Southern Oscillation (ENSO) states [El Niño (EN), La Niña (LN), neutral (NT)] are analyzed in the global context of the MJO-propagating anomalies of convection and circulation. The background ENSO-related changes influence several aspects of MJO (relative occurrence of phases, propagation, convection, and teleconnections), and therefore modify the MJO impacts on South America (SA), such as precipitation anomalies and frequency of extreme events, as well as their distribution throughout the MJO cycle. Among the changes are the following: 1) a delay in the teleconnection between the central-east Pacific and SA, from MJO phase 8 in LN (MJOLNphase8) to MJO phase1 in EN (MJOENphase1); 2) enhanced MJO convection in the central-east subtropical South Pacific in MJOLNphases7 + 8 and a little farther east in MJOENphases8 + 1, in a region efficient in generating teleconnections that produce rainfall anomalies over central-east SA (CESA), especially the South Atlantic convergence zone (SACZ), strongest one phase earlier in LN (MJOLNphase8) than in EN (MJOENphase1), and a little shifted east in the latter than in the former; 3) enhancement of the extratropical teleconnection and its impacts over the SACZ in both EN and LN (with regard to NT), indicating nonlinear effects on MJO impacts over SA; 4) predominant increase (or reduction) in the frequency of extreme events over SA regions where both ENSO and MJO contribute in the same direction, with the greatest increase over CESA during EN in MJOENphase1 and over Southeast SA (SESA) in MJOENphase3.
Significance Statement
The changes produced by different El Niño–Southern Oscillation background states (neutral, El Niño, La Niña) in the Madden–Julian oscillation (MJO) and its impacts on precipitation over South America (SA) are disclosed for the austral summer monsoon season, when MJO is strongest. The reliability of the results is enhanced by using observed rainfall data. The background states affect MJO propagation, circulation, and convection, producing significant differences in precipitation and extremes over densely populated regions, besides phase shifts of the strongest impacts. The results are relevant to subseasonal prediction, since MJO is a key predictability source, to validate models and describe realistically the impacts over SA in each MJO phase. Models’ skill in simulating these results is assessed in a follow-up work.
Abstract
Changes in the Madden–Julian oscillation (MJO) and its impacts on the South American monsoon season during different El Niño–Southern Oscillation (ENSO) states [El Niño (EN), La Niña (LN), neutral (NT)] are analyzed in the global context of the MJO-propagating anomalies of convection and circulation. The background ENSO-related changes influence several aspects of MJO (relative occurrence of phases, propagation, convection, and teleconnections), and therefore modify the MJO impacts on South America (SA), such as precipitation anomalies and frequency of extreme events, as well as their distribution throughout the MJO cycle. Among the changes are the following: 1) a delay in the teleconnection between the central-east Pacific and SA, from MJO phase 8 in LN (MJOLNphase8) to MJO phase1 in EN (MJOENphase1); 2) enhanced MJO convection in the central-east subtropical South Pacific in MJOLNphases7 + 8 and a little farther east in MJOENphases8 + 1, in a region efficient in generating teleconnections that produce rainfall anomalies over central-east SA (CESA), especially the South Atlantic convergence zone (SACZ), strongest one phase earlier in LN (MJOLNphase8) than in EN (MJOENphase1), and a little shifted east in the latter than in the former; 3) enhancement of the extratropical teleconnection and its impacts over the SACZ in both EN and LN (with regard to NT), indicating nonlinear effects on MJO impacts over SA; 4) predominant increase (or reduction) in the frequency of extreme events over SA regions where both ENSO and MJO contribute in the same direction, with the greatest increase over CESA during EN in MJOENphase1 and over Southeast SA (SESA) in MJOENphase3.
Significance Statement
The changes produced by different El Niño–Southern Oscillation background states (neutral, El Niño, La Niña) in the Madden–Julian oscillation (MJO) and its impacts on precipitation over South America (SA) are disclosed for the austral summer monsoon season, when MJO is strongest. The reliability of the results is enhanced by using observed rainfall data. The background states affect MJO propagation, circulation, and convection, producing significant differences in precipitation and extremes over densely populated regions, besides phase shifts of the strongest impacts. The results are relevant to subseasonal prediction, since MJO is a key predictability source, to validate models and describe realistically the impacts over SA in each MJO phase. Models’ skill in simulating these results is assessed in a follow-up work.
Abstract
Principal component analysis (PCA) is a widely used technique to identify modes of low-frequency variability of atmospheric circulation and their spatial changes. However, it turns out that PCA is highly sensitive to the period analyzed and the length of the time window used. Its results can vary considerably if the period is shifted by even 1 year. We present temporal variability of modes from the late nineteenth century using moving PCA of winter (DJF) monthly mean 500-hPa height anomalies for 20–50-yr moving periods with 1-yr step. We employ the congruence coefficient to compare spatial patterns of the modes and identify their substantial changes. Shorter moving periods are more susceptible to sudden fluctuations in mode patterns from one period to the next, while longer periods yield more stable results. We strongly recommend applying a moving PCA to detect spatial changes in modes of low-frequency variability, as it unveils any hidden sudden changes in the modes. These changes can be influenced by many aspects, such as data quality, sampling variability, and length of the analyzed period. Spatial patterns of the Atlantic–European modes are more stable across ensemble members than those over the Pacific and North America, especially before the 1920s. During this period, North Atlantic and European modes explain more variance in the ensemble mean than in ensemble members, while the reverse holds for Pacific and North American modes. In data-sparse regions, modes in ensemble members exhibit greater variability. The process of averaging then leads to weaker modes in the ensemble mean, explaining less variance compared to ensemble members.
Abstract
Principal component analysis (PCA) is a widely used technique to identify modes of low-frequency variability of atmospheric circulation and their spatial changes. However, it turns out that PCA is highly sensitive to the period analyzed and the length of the time window used. Its results can vary considerably if the period is shifted by even 1 year. We present temporal variability of modes from the late nineteenth century using moving PCA of winter (DJF) monthly mean 500-hPa height anomalies for 20–50-yr moving periods with 1-yr step. We employ the congruence coefficient to compare spatial patterns of the modes and identify their substantial changes. Shorter moving periods are more susceptible to sudden fluctuations in mode patterns from one period to the next, while longer periods yield more stable results. We strongly recommend applying a moving PCA to detect spatial changes in modes of low-frequency variability, as it unveils any hidden sudden changes in the modes. These changes can be influenced by many aspects, such as data quality, sampling variability, and length of the analyzed period. Spatial patterns of the Atlantic–European modes are more stable across ensemble members than those over the Pacific and North America, especially before the 1920s. During this period, North Atlantic and European modes explain more variance in the ensemble mean than in ensemble members, while the reverse holds for Pacific and North American modes. In data-sparse regions, modes in ensemble members exhibit greater variability. The process of averaging then leads to weaker modes in the ensemble mean, explaining less variance compared to ensemble members.
Abstract
A strong coherence of the summer (June–August) surface air temperature (SAT) anomalies exists over eastern Eurasia (EE) and central Eurasia (CE) in association with a remarkable intraseasonal (10–90-day) variability. The intraseasonal SAT over Eurasia shows a negative correlation between EE and CE, which is closely related to an intraseasonal wave train at the upper troposphere over the mid–high latitudes of Eurasia. The wave train propagates eastward and results in the intraseasonal variation of SAT in a seesaw pattern. The column-integrated temperature budget suggests that both the horizontal and vertical advection are important for the intraseasonal SAT dipole. More specifically, the climatological westerly, intraseasonal meridional wind anomaly, and the anomalous vertical motion play dominant roles in perturbing temperature over the CE and EE regions, while the diabatic heating is a negative feedback. The propagation of the Rossby wave train in the upper troposphere helps to maintain the circulation anomalies associated with the SAT seesaw. The leading modes of meridional wind anomalies at 200 hPa over the Eurasian high latitudes resemble the intraseasonal wave train, indicating that the wave train associated with the SAT seesaw is an atmospheric intrinsic mode over the mid–high latitudes. This study also investigates the impact of the intraseasonal SAT seesaw on regional heat wave events. It is suggested that the intraseasonal SAT seesaw has a major influence on the variability of heat wave events over both EE and CE, which may provide great implications for regional subseasonal forecasts of extreme weather events.
Abstract
A strong coherence of the summer (June–August) surface air temperature (SAT) anomalies exists over eastern Eurasia (EE) and central Eurasia (CE) in association with a remarkable intraseasonal (10–90-day) variability. The intraseasonal SAT over Eurasia shows a negative correlation between EE and CE, which is closely related to an intraseasonal wave train at the upper troposphere over the mid–high latitudes of Eurasia. The wave train propagates eastward and results in the intraseasonal variation of SAT in a seesaw pattern. The column-integrated temperature budget suggests that both the horizontal and vertical advection are important for the intraseasonal SAT dipole. More specifically, the climatological westerly, intraseasonal meridional wind anomaly, and the anomalous vertical motion play dominant roles in perturbing temperature over the CE and EE regions, while the diabatic heating is a negative feedback. The propagation of the Rossby wave train in the upper troposphere helps to maintain the circulation anomalies associated with the SAT seesaw. The leading modes of meridional wind anomalies at 200 hPa over the Eurasian high latitudes resemble the intraseasonal wave train, indicating that the wave train associated with the SAT seesaw is an atmospheric intrinsic mode over the mid–high latitudes. This study also investigates the impact of the intraseasonal SAT seesaw on regional heat wave events. It is suggested that the intraseasonal SAT seesaw has a major influence on the variability of heat wave events over both EE and CE, which may provide great implications for regional subseasonal forecasts of extreme weather events.
Abstract
The southern African easterly jet (AEJ-S) is an important midtropospheric feature critical to understanding the tropical convective system over central Africa and the aerosol–cloud interactions over the southeast Atlantic Ocean. However, it remains unclear how well models represent the AEJ-S and its influence on aerosol transport, clouds, and precipitation distribution. Here, we use ground- and satellite-based observations and reanalysis datasets to assess the representation of AEJ-S in the Coupled Model Intercomparison Project phase 6 (CMIP6) models between September and October during the peak of midtropospheric winds, aerosol transport, clouds, and precipitation. We find that most CMIP6 models have difficulty accurately simulating the strength, position, and spatial distribution of the AEJ-S. Specifically, the AEJ-S is relatively weaker and at a slightly lower altitude in the ensemble of CMIP6 models than represented by observation and reanalysis datasets. To assess the influence of the misrepresented the AEJ-S on CMIP6-simulated aerosol, clouds, and precipitation distributions, we performed composite analyses using models with low and high biases based on the estimates of their midtropospheric easterly wind speed. We find that the misrepresentation of the AEJ-S in CMIP6 models is associated with the overestimation of clouds and precipitation over central Africa, the underestimation of clouds over the southeast Atlantic Ocean, and the limitation of aerosol transport over the continent or the deviation of its spatial distribution from the typical zonal transport over the Atlantic Ocean. Because aerosols, clouds, and precipitation are important components of the regional climate system, we conclude that accurate representation of the AEJ-S is essential over central Africa and the southeast Atlantic Ocean.
Abstract
The southern African easterly jet (AEJ-S) is an important midtropospheric feature critical to understanding the tropical convective system over central Africa and the aerosol–cloud interactions over the southeast Atlantic Ocean. However, it remains unclear how well models represent the AEJ-S and its influence on aerosol transport, clouds, and precipitation distribution. Here, we use ground- and satellite-based observations and reanalysis datasets to assess the representation of AEJ-S in the Coupled Model Intercomparison Project phase 6 (CMIP6) models between September and October during the peak of midtropospheric winds, aerosol transport, clouds, and precipitation. We find that most CMIP6 models have difficulty accurately simulating the strength, position, and spatial distribution of the AEJ-S. Specifically, the AEJ-S is relatively weaker and at a slightly lower altitude in the ensemble of CMIP6 models than represented by observation and reanalysis datasets. To assess the influence of the misrepresented the AEJ-S on CMIP6-simulated aerosol, clouds, and precipitation distributions, we performed composite analyses using models with low and high biases based on the estimates of their midtropospheric easterly wind speed. We find that the misrepresentation of the AEJ-S in CMIP6 models is associated with the overestimation of clouds and precipitation over central Africa, the underestimation of clouds over the southeast Atlantic Ocean, and the limitation of aerosol transport over the continent or the deviation of its spatial distribution from the typical zonal transport over the Atlantic Ocean. Because aerosols, clouds, and precipitation are important components of the regional climate system, we conclude that accurate representation of the AEJ-S is essential over central Africa and the southeast Atlantic Ocean.
