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Abstract
Air–sea coupling system in the southwestern Indian Ocean (SWIO; 35°–55°S, 40°–75°E) exhibits predominant multidecadal variability that is the strongest during austral summer. It is characterized by an equivalent barotropic atmospheric high (low) pressure over warm (cold) sea surface temperature (SST) anomalies and a poleward (equatorward) shift of the westerlies during the positive (negative) phase. In this study, physical processes of this multidecadal variability are investigated by using observations/reanalysis and CMIP6 model simulations. Results suggest that the multidecadal fluctuation can be explained by the modulation of the Atlantic meridional overturning circulation (AMOC) and the local air–sea positive feedback in the SWIO. In both observations/reanalysis and CMIP6 model simulations, the AMOC fluctuation presents a significantly negative correlation with the multidecadal SST variation in the SWIO when the AMOC is leading by about a decade. The mechanisms are that the preceding AMOC variation can cause an interhemispheric dipolar pattern of SST anomalies in the Atlantic Ocean. Subsequently, the SST anomalies in the midlatitudes of the South Atlantic can propagate to the SWIO by the oceanic Rossby wave under the influence of the Antarctic Circumpolar Current (ACC). Once the SST anomalies reach the SWIO, these SST anomalies in the oceanic front can affect the baroclinicity in the lower troposphere to influence the synoptic transient eddy and then cause the atmospheric circulation anomaly via the eddy–mean flow interaction. Subsequently, the anomalous atmospheric circulation over the SWIO can significantly strengthen the SST anomalies through modifying the oceanic meridional temperature advection and latent and sensible heat flux.
Abstract
Air–sea coupling system in the southwestern Indian Ocean (SWIO; 35°–55°S, 40°–75°E) exhibits predominant multidecadal variability that is the strongest during austral summer. It is characterized by an equivalent barotropic atmospheric high (low) pressure over warm (cold) sea surface temperature (SST) anomalies and a poleward (equatorward) shift of the westerlies during the positive (negative) phase. In this study, physical processes of this multidecadal variability are investigated by using observations/reanalysis and CMIP6 model simulations. Results suggest that the multidecadal fluctuation can be explained by the modulation of the Atlantic meridional overturning circulation (AMOC) and the local air–sea positive feedback in the SWIO. In both observations/reanalysis and CMIP6 model simulations, the AMOC fluctuation presents a significantly negative correlation with the multidecadal SST variation in the SWIO when the AMOC is leading by about a decade. The mechanisms are that the preceding AMOC variation can cause an interhemispheric dipolar pattern of SST anomalies in the Atlantic Ocean. Subsequently, the SST anomalies in the midlatitudes of the South Atlantic can propagate to the SWIO by the oceanic Rossby wave under the influence of the Antarctic Circumpolar Current (ACC). Once the SST anomalies reach the SWIO, these SST anomalies in the oceanic front can affect the baroclinicity in the lower troposphere to influence the synoptic transient eddy and then cause the atmospheric circulation anomaly via the eddy–mean flow interaction. Subsequently, the anomalous atmospheric circulation over the SWIO can significantly strengthen the SST anomalies through modifying the oceanic meridional temperature advection and latent and sensible heat flux.
Abstract
Radiative feedbacks over interannual time scales can be potentially useful for global warming estimation. However, the diversity of the lead–lag relationships in global mean surface temperature (GMST) and net radiation flux at the top of the atmosphere (GMTOA) create uncertainty during the estimation of radiative feedbacks. In this study, key physical processes controlling lead–lag relationships were elucidated by categorizing preindustrial control simulations of CMIP6 into three groups based on cross correlation values of GMTOA against GMST at lag 0 and lag +1 year. The diversity in the lead–lag relationships was primarily caused by the climatological state difference of the atmosphere over the equatorial Pacific, which modulated the strength of convective activity and sensitivity of low-level clouds. Diminished atmospheric stability caused enhanced convective activity, more efficient energy release, and smaller lags. In addition, enhanced stability in the lower atmosphere rendered the low-level clouds more sensitive to sea surface temperature changes and considerably delayed the radiative response. The climatological state difference of the atmosphere resulted from model-inherent atmospheric conditions. These findings suggest that the diversity of lead–lag relationships of GMST and GMTOA over interannual time scales could represent the characteristics of general atmospheric circulation models and possible solutions of the actual atmosphere, which could also affect long-term feedback features.
Abstract
Radiative feedbacks over interannual time scales can be potentially useful for global warming estimation. However, the diversity of the lead–lag relationships in global mean surface temperature (GMST) and net radiation flux at the top of the atmosphere (GMTOA) create uncertainty during the estimation of radiative feedbacks. In this study, key physical processes controlling lead–lag relationships were elucidated by categorizing preindustrial control simulations of CMIP6 into three groups based on cross correlation values of GMTOA against GMST at lag 0 and lag +1 year. The diversity in the lead–lag relationships was primarily caused by the climatological state difference of the atmosphere over the equatorial Pacific, which modulated the strength of convective activity and sensitivity of low-level clouds. Diminished atmospheric stability caused enhanced convective activity, more efficient energy release, and smaller lags. In addition, enhanced stability in the lower atmosphere rendered the low-level clouds more sensitive to sea surface temperature changes and considerably delayed the radiative response. The climatological state difference of the atmosphere resulted from model-inherent atmospheric conditions. These findings suggest that the diversity of lead–lag relationships of GMST and GMTOA over interannual time scales could represent the characteristics of general atmospheric circulation models and possible solutions of the actual atmosphere, which could also affect long-term feedback features.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations.
Significance Statement
High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations.
Significance Statement
High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.
Abstract
The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes’s theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes’s theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.
Significance Statement
Atmospheric circulation regimes are recurrent and persistent patterns that characterize the atmospheric circulation on time scales of 1–3 weeks. They are relevant for predictability on these time scales as mediators of weather. In this study we propose a novel approach to assigning atmospheric states to six predefined wintertime circulation regimes over the North Atlantic and Europe, which can be applied in real time. This approach introduces a probabilistic, instead of deterministic, regime assignment and uses prior knowledge on the regime dynamics. It allows us to better identify the regime persistence and indicates when a state does not clearly belong to one regime. Making use of an ensemble of model simulations, we can identify more pronounced interannual variability by using the full ensemble to inform prior knowledge on the regimes.
Abstract
The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes’s theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes’s theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.
Significance Statement
Atmospheric circulation regimes are recurrent and persistent patterns that characterize the atmospheric circulation on time scales of 1–3 weeks. They are relevant for predictability on these time scales as mediators of weather. In this study we propose a novel approach to assigning atmospheric states to six predefined wintertime circulation regimes over the North Atlantic and Europe, which can be applied in real time. This approach introduces a probabilistic, instead of deterministic, regime assignment and uses prior knowledge on the regime dynamics. It allows us to better identify the regime persistence and indicates when a state does not clearly belong to one regime. Making use of an ensemble of model simulations, we can identify more pronounced interannual variability by using the full ensemble to inform prior knowledge on the regimes.
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
This study investigates the combined impacts of the Madden–Julian oscillation (MJO) and extratropical anticyclonic Rossby wave breaking (AWB) on subseasonal Atlantic tropical cyclone (TC) activity and their physical connections. Our results show that during MJO phases 2–3 (enhanced Indian Ocean convection) and 6–7 (enhanced tropical Pacific convection), there are significant changes in basinwide TC activity. The MJO and AWB collaborate to suppress basinwide TC activity during phases 6–7 but not during phases 2–3. During phases 6–7, when AWB occurs, various TC metrics including hurricanes, accumulated cyclone energy, and rapid intensification probability decrease by ∼50%–80%. Simultaneously, large-scale environmental variables, like vertical wind shear, precipitable water, and sea surface temperatures become extremely unfavorable for TC formation and intensification, compared to periods characterized by suppressed AWB activity during the same MJO phases. Further investigation reveals that AWB events during phases 6–7 occur in concert with the development of a stronger anticyclone in the lower troposphere, which transports more dry, stable extratropical air equatorward, and drives enhanced tropical SST cooling. As a result, individual AWB events in phases 6–7 can disturb the development of surrounding TCs to a greater extent than their phases 2–3 counterparts. The influence of the MJO on AWB over the western subtropical Atlantic can be attributed to the modulation of the convectively forced Rossby wave source over the tropical eastern Pacific. A significant number of Rossby waves initiating from this region during phases 5–6 propagate into the subtropical North Atlantic, preceding the occurrence of AWB events in phases 6–7.
Abstract
This study investigates the combined impacts of the Madden–Julian oscillation (MJO) and extratropical anticyclonic Rossby wave breaking (AWB) on subseasonal Atlantic tropical cyclone (TC) activity and their physical connections. Our results show that during MJO phases 2–3 (enhanced Indian Ocean convection) and 6–7 (enhanced tropical Pacific convection), there are significant changes in basinwide TC activity. The MJO and AWB collaborate to suppress basinwide TC activity during phases 6–7 but not during phases 2–3. During phases 6–7, when AWB occurs, various TC metrics including hurricanes, accumulated cyclone energy, and rapid intensification probability decrease by ∼50%–80%. Simultaneously, large-scale environmental variables, like vertical wind shear, precipitable water, and sea surface temperatures become extremely unfavorable for TC formation and intensification, compared to periods characterized by suppressed AWB activity during the same MJO phases. Further investigation reveals that AWB events during phases 6–7 occur in concert with the development of a stronger anticyclone in the lower troposphere, which transports more dry, stable extratropical air equatorward, and drives enhanced tropical SST cooling. As a result, individual AWB events in phases 6–7 can disturb the development of surrounding TCs to a greater extent than their phases 2–3 counterparts. The influence of the MJO on AWB over the western subtropical Atlantic can be attributed to the modulation of the convectively forced Rossby wave source over the tropical eastern Pacific. A significant number of Rossby waves initiating from this region during phases 5–6 propagate into the subtropical North Atlantic, preceding the occurrence of AWB events in phases 6–7.
Abstract
The northeastern Pacific climate system features an extensive low-cloud deck off California on the southeastern flank of the subtropical high that accompanies intense northeasterly trades and relatively low sea surface temperatures (SSTs). This study assesses climatological impacts of the low-cloud deck and their seasonal differences by regionally turning on and off the low-cloud radiative effect in a fully coupled atmosphere–ocean model. The simulations demonstrate that the cloud radiative effect causes a local SST decrease of up to 3°C on an annual average with the response extending southwestward with intensified trade winds, indicative of the wind–evaporation–SST (WES) feedback. This nonlocal wind response is strong in summer, when the SST decrease peaks due to increased shortwave cooling, and persists into autumn. In these seasons when the background SST is high, the lowered SST suppresses deep-convective precipitation that would otherwise occur in the absence of the low-cloud deck. The resultant anomalous diabatic cooling induces a surface anticyclonic response with the intensified trades that promote the WES feedback. Such seasonal enhancement of the atmospheric response does not occur without air–sea couplings. The enhanced trades accompany intensified upper-tropospheric westerlies, strengthening the vertical wind shear that, together with the lowered SST, acts to shield Hawaii from powerful hurricanes. On the basin scale, the anticyclonic surface wind response accelerates the North Pacific subtropical ocean gyre to speed up the Kuroshio by as much as 30%. SST thereby increases along the Kuroshio and its extension, intensifying upward turbulent heat fluxes from the ocean to increase precipitation.
Abstract
The northeastern Pacific climate system features an extensive low-cloud deck off California on the southeastern flank of the subtropical high that accompanies intense northeasterly trades and relatively low sea surface temperatures (SSTs). This study assesses climatological impacts of the low-cloud deck and their seasonal differences by regionally turning on and off the low-cloud radiative effect in a fully coupled atmosphere–ocean model. The simulations demonstrate that the cloud radiative effect causes a local SST decrease of up to 3°C on an annual average with the response extending southwestward with intensified trade winds, indicative of the wind–evaporation–SST (WES) feedback. This nonlocal wind response is strong in summer, when the SST decrease peaks due to increased shortwave cooling, and persists into autumn. In these seasons when the background SST is high, the lowered SST suppresses deep-convective precipitation that would otherwise occur in the absence of the low-cloud deck. The resultant anomalous diabatic cooling induces a surface anticyclonic response with the intensified trades that promote the WES feedback. Such seasonal enhancement of the atmospheric response does not occur without air–sea couplings. The enhanced trades accompany intensified upper-tropospheric westerlies, strengthening the vertical wind shear that, together with the lowered SST, acts to shield Hawaii from powerful hurricanes. On the basin scale, the anticyclonic surface wind response accelerates the North Pacific subtropical ocean gyre to speed up the Kuroshio by as much as 30%. SST thereby increases along the Kuroshio and its extension, intensifying upward turbulent heat fluxes from the ocean to increase precipitation.
Abstract
The causes of historical changes in the Southern Hemisphere (SH) monsoon are less understood than the Northern Hemisphere (NH) counterpart. Unlike the decline in the NH monsoon during 1901–2014, we found that the SH land monsoon precipitation significantly increased during 1901–2014 in observation, reanalysis, and most historical simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The observed increase in SH land monsoon precipitation is dominated by the Australian and South American monsoons. Moisture budget analysis suggests that half of the wettening is attributable to the strengthening of monsoon circulation, and only one-fifth is caused by atmospheric moistening. The SH monsoon circulation change is mainly affected by the sea surface temperature (SST) gradient between the Indo-Pacific and the eastern Pacific. It enhances the tropical zonal circulation that redistributes the moisture from tropical oceans to land monsoon regions by strengthening the lower-tropospheric convergence and convection. The CMIP6 models, which successfully reproduced the SST contrast between the Indo-Pacific and eastern Pacific, simulate the wettening of the SH monsoon during the historical period; otherwise, the SH monsoon is weakened. In a meridional sense, reanalysis and CMIP6 simulations both demonstrated that the strengthening of SH monsoon convection plays a vital role in the long-term change of zonal mean Hadley circulation, albeit the monsoon band only accounts for 1/3 of the global longitudinal area. Results from this study are useful for constraining the future projection of SH monsoon and understanding the long-term change of Hadley circulation.
Abstract
The causes of historical changes in the Southern Hemisphere (SH) monsoon are less understood than the Northern Hemisphere (NH) counterpart. Unlike the decline in the NH monsoon during 1901–2014, we found that the SH land monsoon precipitation significantly increased during 1901–2014 in observation, reanalysis, and most historical simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The observed increase in SH land monsoon precipitation is dominated by the Australian and South American monsoons. Moisture budget analysis suggests that half of the wettening is attributable to the strengthening of monsoon circulation, and only one-fifth is caused by atmospheric moistening. The SH monsoon circulation change is mainly affected by the sea surface temperature (SST) gradient between the Indo-Pacific and the eastern Pacific. It enhances the tropical zonal circulation that redistributes the moisture from tropical oceans to land monsoon regions by strengthening the lower-tropospheric convergence and convection. The CMIP6 models, which successfully reproduced the SST contrast between the Indo-Pacific and eastern Pacific, simulate the wettening of the SH monsoon during the historical period; otherwise, the SH monsoon is weakened. In a meridional sense, reanalysis and CMIP6 simulations both demonstrated that the strengthening of SH monsoon convection plays a vital role in the long-term change of zonal mean Hadley circulation, albeit the monsoon band only accounts for 1/3 of the global longitudinal area. Results from this study are useful for constraining the future projection of SH monsoon and understanding the long-term change of Hadley circulation.
Abstract
North Atlantic atmosphere–ocean variability is assessed in climate model simulations from HighResMIP that have low resolution (LR) or high resolution (HR) in their atmosphere and ocean model components. It is found that some of the LR simulations overestimate the low-frequency variability of subpolar sea surface temperature (SST) anomalies and underestimate its correlation with the NAO compared to ERA5. These deficiencies are significantly reduced in the HR simulations, and it is shown that the improvements are related to a reduction of intrinsic (non-NAO-driven) variability of the subpolar ocean circulation. To understand the cause of the overestimated intrinsic subpolar ocean variability in the LR simulations, a link is demonstrated between the amplitude of the subpolar ocean variability and the mean state of the Labrador–Irminger Seas. Supporting previous studies, the Labrador–Irminger Seas tend to be colder and fresher in the LR simulations compared to the HR simulations and oceanic observations from EN4. This promotes upper-ocean density anomalies in this region to be more salinity-controlled in the LR simulations versus more temperature-controlled in the HR simulations and EN4 observations. It is argued that this causes the excessive subpolar ocean variability in the LR simulations by favoring a positive feedback between subpolar upper-ocean salinity and Atlantic meridional overturning circulation (AMOC) anomalies, rather than a negative feedback between subpolar SST and AMOC anomalies as in the HR simulations. The findings overall suggest that the subpolar ocean mean state impacts the variability of the ocean circulation and SSTs, including their relationship with the atmospheric circulation, in the extratropical North Atlantic.
Abstract
North Atlantic atmosphere–ocean variability is assessed in climate model simulations from HighResMIP that have low resolution (LR) or high resolution (HR) in their atmosphere and ocean model components. It is found that some of the LR simulations overestimate the low-frequency variability of subpolar sea surface temperature (SST) anomalies and underestimate its correlation with the NAO compared to ERA5. These deficiencies are significantly reduced in the HR simulations, and it is shown that the improvements are related to a reduction of intrinsic (non-NAO-driven) variability of the subpolar ocean circulation. To understand the cause of the overestimated intrinsic subpolar ocean variability in the LR simulations, a link is demonstrated between the amplitude of the subpolar ocean variability and the mean state of the Labrador–Irminger Seas. Supporting previous studies, the Labrador–Irminger Seas tend to be colder and fresher in the LR simulations compared to the HR simulations and oceanic observations from EN4. This promotes upper-ocean density anomalies in this region to be more salinity-controlled in the LR simulations versus more temperature-controlled in the HR simulations and EN4 observations. It is argued that this causes the excessive subpolar ocean variability in the LR simulations by favoring a positive feedback between subpolar upper-ocean salinity and Atlantic meridional overturning circulation (AMOC) anomalies, rather than a negative feedback between subpolar SST and AMOC anomalies as in the HR simulations. The findings overall suggest that the subpolar ocean mean state impacts the variability of the ocean circulation and SSTs, including their relationship with the atmospheric circulation, in the extratropical North Atlantic.
Abstract
Prediction of El Niño–Southern Oscillation (ENSO) is hindered by a spring predictability barrier (SPB). In this paper, we investigate the effects of the Indian Ocean (IO) on the SPB. Using a seasonally varying extended IO–ENSO recharge oscillator model, we find that the SPB is much weakened when IO is coupled with ENSO. To gauge the relative role of the Indian Ocean dipole (IOD) and the Indian Ocean Basin (IOB) modes in weakening ENSO SPB, we develop an empirical dynamical model, the linear inverse model (LIM). By coupling/decoupling the IOB or IOD mode with ENSO, we show that the IOB significantly weakens eastern Pacific and central Pacific ENSO SPBs, while the IOD plays a weaker role. The evolution of the optimum initial structures also illustrates the importance of the IOB in ENSO SPB. Moreover, the IOB strongly influences the forecast skill of La Niña SPB rather than El Niño SPB. This point is also identified through six coupled models from the North American multimodel ensemble. It may be related to the role of the IO in the asymmetry in the duration of El Niño and La Niña. The IOB-induced easterly wind anomalies are conducive to the development of La Niña and thus the prediction of La Niña events, whereas these anomalous easterlies are less important during the development of El Niño and the related forecast of El Niño events.
Abstract
Prediction of El Niño–Southern Oscillation (ENSO) is hindered by a spring predictability barrier (SPB). In this paper, we investigate the effects of the Indian Ocean (IO) on the SPB. Using a seasonally varying extended IO–ENSO recharge oscillator model, we find that the SPB is much weakened when IO is coupled with ENSO. To gauge the relative role of the Indian Ocean dipole (IOD) and the Indian Ocean Basin (IOB) modes in weakening ENSO SPB, we develop an empirical dynamical model, the linear inverse model (LIM). By coupling/decoupling the IOB or IOD mode with ENSO, we show that the IOB significantly weakens eastern Pacific and central Pacific ENSO SPBs, while the IOD plays a weaker role. The evolution of the optimum initial structures also illustrates the importance of the IOB in ENSO SPB. Moreover, the IOB strongly influences the forecast skill of La Niña SPB rather than El Niño SPB. This point is also identified through six coupled models from the North American multimodel ensemble. It may be related to the role of the IO in the asymmetry in the duration of El Niño and La Niña. The IOB-induced easterly wind anomalies are conducive to the development of La Niña and thus the prediction of La Niña events, whereas these anomalous easterlies are less important during the development of El Niño and the related forecast of El Niño events.
