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- Author or Editor: Dake Chen x
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Abstract
Two sets of mooring data were collected at two sites (MA and MB) along a cross-slope section on the northeastern continental slope in the South China Sea (SCS). These data are used to investigate evolution and energy decay of low-mode semidiurnal (M2) internal tides on a subcritical slope with respect to M2. At the deep portion of the slope (~1250 m; MA), the M2 internal tides show upward energy propagation, while vertically standing M2 internal tides are often observed at shallow MB (~845 m). A two-dimensional linear internal tide model with realistic topography and stratification reproduces the observations, suggesting that low-mode M2 internal tides incident on subcritical slopes evolve into vertically propagating internal waves due to topographic scattering, propagate upward to the boundary, and reflect from the sea surface. The reflection point largely depends on the phase between the modal components of the incoming flux. In the near-surface reflection region, two kinds of nonlinear effects are observed to decay energy of the incoming internal tides. One is the resonant parametric subharmonic instability which transfers M2 internal tides to diurnal subharmonics M1 (=M2/2), but the instability is found to mainly depend on the incident waves. The other one is the nonresonant wave–wave interaction, producing two higher-harmonic M4 (=2M2) rays with opposite vertical propagation. A strong westward mean flow is observed in the interacting region, with amplitude comparable to that of the incident waves. This mean flow also appears to be generated by the nonlinear reflection of the M2 internal tides.
Abstract
Two sets of mooring data were collected at two sites (MA and MB) along a cross-slope section on the northeastern continental slope in the South China Sea (SCS). These data are used to investigate evolution and energy decay of low-mode semidiurnal (M2) internal tides on a subcritical slope with respect to M2. At the deep portion of the slope (~1250 m; MA), the M2 internal tides show upward energy propagation, while vertically standing M2 internal tides are often observed at shallow MB (~845 m). A two-dimensional linear internal tide model with realistic topography and stratification reproduces the observations, suggesting that low-mode M2 internal tides incident on subcritical slopes evolve into vertically propagating internal waves due to topographic scattering, propagate upward to the boundary, and reflect from the sea surface. The reflection point largely depends on the phase between the modal components of the incoming flux. In the near-surface reflection region, two kinds of nonlinear effects are observed to decay energy of the incoming internal tides. One is the resonant parametric subharmonic instability which transfers M2 internal tides to diurnal subharmonics M1 (=M2/2), but the instability is found to mainly depend on the incident waves. The other one is the nonresonant wave–wave interaction, producing two higher-harmonic M4 (=2M2) rays with opposite vertical propagation. A strong westward mean flow is observed in the interacting region, with amplitude comparable to that of the incident waves. This mean flow also appears to be generated by the nonlinear reflection of the M2 internal tides.
Abstract
A linear Markov model has been developed to simulated and predict the short-term climate change in the Antarctic, with particular emphasis on sea ice variability. Seven atmospheric variables along with sea ice were chosen to define the state of the Antarctic climate, and the multivariate empirical orthogonal functions of these variables were used as the building blocks of the model. The predictive skill of the model was evaluated in a cross-validated fashion, and a series of sensitivity experiments was carried out. In both hindcast and forecast experiments, the model showed considerable skill in predicting the anomalous Antarctic sea ice concentration up to 1 yr in advance, especially in austral winter and in the Antarctic dipole regions. The success of the model is attributed to the domination of the Antarctic climate variability by a few distinctive modes in the coupled air–sea–ice system and to the model's ability to detect these modes. This model is presently being used for the experimental seasonal forecasting of Antarctic sea ice, and a current prediction example is presented.
Abstract
A linear Markov model has been developed to simulated and predict the short-term climate change in the Antarctic, with particular emphasis on sea ice variability. Seven atmospheric variables along with sea ice were chosen to define the state of the Antarctic climate, and the multivariate empirical orthogonal functions of these variables were used as the building blocks of the model. The predictive skill of the model was evaluated in a cross-validated fashion, and a series of sensitivity experiments was carried out. In both hindcast and forecast experiments, the model showed considerable skill in predicting the anomalous Antarctic sea ice concentration up to 1 yr in advance, especially in austral winter and in the Antarctic dipole regions. The success of the model is attributed to the domination of the Antarctic climate variability by a few distinctive modes in the coupled air–sea–ice system and to the model's ability to detect these modes. This model is presently being used for the experimental seasonal forecasting of Antarctic sea ice, and a current prediction example is presented.
Abstract
As an effective eigen method for phenomenon identification and space reduction, empirical orthogonal function (EOF) analysis is widely used in climate research. However, because of its orthorgonality constraint, EOF analysis has a tendency to produce unphysical modes. Previous studies have shown that the drawbacks of EOF analysis could be partly alleviated by rotated EOF (REOF) analysis, but such studies are always based on specific cases. This paper provides a thorough statistical evaluation of REOF analysis by comparing its ability with that of EOF analysis in reproducing a large number of randomly selected stationary modes of variability. The synthetic experiments indicate that REOF analysis is overwhelmingly a better choice in terms of accuracy and effectiveness, especially for picking up localized patterns. When applied to the tropical Pacific sea surface temperature variability, REOF and EOF analyses show obvious discrepancies, with the former making much better physical sense. This challenges the validity of the so-called sea surface temperature cooling mode and the spatial structure of “El Niño Modoki,” both of which are recently identified by EOF analysis. At any rate, one has to be cautious when claiming new discoveries of climate modes based on EOF analysis alone.
Abstract
As an effective eigen method for phenomenon identification and space reduction, empirical orthogonal function (EOF) analysis is widely used in climate research. However, because of its orthorgonality constraint, EOF analysis has a tendency to produce unphysical modes. Previous studies have shown that the drawbacks of EOF analysis could be partly alleviated by rotated EOF (REOF) analysis, but such studies are always based on specific cases. This paper provides a thorough statistical evaluation of REOF analysis by comparing its ability with that of EOF analysis in reproducing a large number of randomly selected stationary modes of variability. The synthetic experiments indicate that REOF analysis is overwhelmingly a better choice in terms of accuracy and effectiveness, especially for picking up localized patterns. When applied to the tropical Pacific sea surface temperature variability, REOF and EOF analyses show obvious discrepancies, with the former making much better physical sense. This challenges the validity of the so-called sea surface temperature cooling mode and the spatial structure of “El Niño Modoki,” both of which are recently identified by EOF analysis. At any rate, one has to be cautious when claiming new discoveries of climate modes based on EOF analysis alone.
Abstract
While both intrinsic low-frequency atmosphere–ocean interaction and multiplicative burst-like events affect the development of El Niño–Southern Oscillation (ENSO), the strong nonlinearity in ENSO dynamics has prevented us from separating their relative contributions. Here we propose an online filtering scheme to estimate the role of the westerly wind bursts (WWBs), a type of aperiodic burst-like atmospheric perturbation over the western-central tropical Pacific, in the genesis of the centennial extreme 1997/98 El Niño using the CESM coupled model. This scheme highlights the deterministic part of ENSO dynamics during model integration, and clearly demonstrates that the strong and long-lasting WWB in March 1997 was essential for generating the 1997/98 El Niño. Without this WWB, the intrinsic low-frequency coupling would have only produced a weak warm event in late 1997 similar to the 2014/15 El Niño.
Abstract
While both intrinsic low-frequency atmosphere–ocean interaction and multiplicative burst-like events affect the development of El Niño–Southern Oscillation (ENSO), the strong nonlinearity in ENSO dynamics has prevented us from separating their relative contributions. Here we propose an online filtering scheme to estimate the role of the westerly wind bursts (WWBs), a type of aperiodic burst-like atmospheric perturbation over the western-central tropical Pacific, in the genesis of the centennial extreme 1997/98 El Niño using the CESM coupled model. This scheme highlights the deterministic part of ENSO dynamics during model integration, and clearly demonstrates that the strong and long-lasting WWB in March 1997 was essential for generating the 1997/98 El Niño. Without this WWB, the intrinsic low-frequency coupling would have only produced a weak warm event in late 1997 similar to the 2014/15 El Niño.
Abstract
A linear Markov model has been developed to predict the short-term climate variability of the East Asian monsoon system, with emphasis on precipitation variability. Precipitation, sea level pressure, zonal and meridional winds at 850 mb, along with sea surface temperature and soil moisture, were chosen to define the state of the East Asian monsoon system, and the multivariate empirical orthogonal functions of these variables were used to construct the statistical Markov model. The forecast skill of the model was evaluated in a cross-validated fashion and a series of sensitivity experiments were conducted to further validate the model. In both hindcast and forecast experiments, the model showed considerable skill in predicting the precipitation anomaly a few months in advance, especially in boreal winter and spring. The prediction in boreal summer was relatively poor, though the model performance was better in an ENSO decaying summer than in an ENSO developing summer. Also, the prediction skill was better over the ocean than the land. The model's forecast ability is attributed to the domination of the East Asian monsoon climate variability by a few distinctive modes in the coupled atmosphere–ocean–land system, to the strong influence of ENSO on these modes, and to the Markov model's capability to capture these modes.
Abstract
A linear Markov model has been developed to predict the short-term climate variability of the East Asian monsoon system, with emphasis on precipitation variability. Precipitation, sea level pressure, zonal and meridional winds at 850 mb, along with sea surface temperature and soil moisture, were chosen to define the state of the East Asian monsoon system, and the multivariate empirical orthogonal functions of these variables were used to construct the statistical Markov model. The forecast skill of the model was evaluated in a cross-validated fashion and a series of sensitivity experiments were conducted to further validate the model. In both hindcast and forecast experiments, the model showed considerable skill in predicting the precipitation anomaly a few months in advance, especially in boreal winter and spring. The prediction in boreal summer was relatively poor, though the model performance was better in an ENSO decaying summer than in an ENSO developing summer. Also, the prediction skill was better over the ocean than the land. The model's forecast ability is attributed to the domination of the East Asian monsoon climate variability by a few distinctive modes in the coupled atmosphere–ocean–land system, to the strong influence of ENSO on these modes, and to the Markov model's capability to capture these modes.
Abstract
The relatively weak sea surface temperature bias in the tropical Indian Ocean (TIO) simulated in the coupled general circulation model (CGCM) from the recently released CMIP6 has been found to be important in model simulations of regional and global climate. However, the cause of the bias is debated because the bias is strongly model dependent and shows marked seasonality. In this study, we separate the bias in CGCMs into bias arising from oceanic GCMs (OGCMs) and bias that is independent of OGCMs using a set of CMIP6 and OMIP6 models. We found that OGCMs contribute little to mixed layer bias in the CGCMs. The OGCM-independent bias exhibits a large-scale cold mixed layer bias in the TIO throughout the year, with an unexpectedly high degree of model consistency. By conducting a set of OGCM experiments, we show that the OGCM-independent mixed layer bias is caused mainly by surface wind bias in the utilized CGCMs. About 89% of surface wind bias in the CGCMs is due to the inability of atmospheric GCMs (AGCMs), whereas atmosphere–ocean coupling in the CGCMs has only a minor influence on surface wind bias. The bias in surface wind is also found to be the cause of subsurface temperature bias besides the ocean dynamics such as vertical mixing and vertical shear in currents. Our results indicate that correcting TIO mixed layer bias in CGCMs requires improvement in the capability of AGCM in simulating the climatological surface winds.
Significance Statement
We aimed to discover the cause of temperature bias in the Indian Ocean in CMIP6 models. The bias was separated into oceanic model and ocean-model-independent bias to correspond exactly to bias caused by the oceanic model and by the atmospheric model and coupling, respectively. Oceanic bias contributes little to bias in CMIP6, but ocean-model-independent bias explains the CMIP6 bias throughout the year. We ran oceanic model experiments to show that surface wind bias causes ocean-model-independent temperature bias in the entire TIO and subsurface temperature bias in some areas of the Indian Ocean. We further found that 89% of surface wind bias originates from the atmospheric model. The results improve our understanding of the cause of the bias in the Indian Ocean and show that our method of bias separation is effective for attributing the source of bias to different proposed mechanisms.
Abstract
The relatively weak sea surface temperature bias in the tropical Indian Ocean (TIO) simulated in the coupled general circulation model (CGCM) from the recently released CMIP6 has been found to be important in model simulations of regional and global climate. However, the cause of the bias is debated because the bias is strongly model dependent and shows marked seasonality. In this study, we separate the bias in CGCMs into bias arising from oceanic GCMs (OGCMs) and bias that is independent of OGCMs using a set of CMIP6 and OMIP6 models. We found that OGCMs contribute little to mixed layer bias in the CGCMs. The OGCM-independent bias exhibits a large-scale cold mixed layer bias in the TIO throughout the year, with an unexpectedly high degree of model consistency. By conducting a set of OGCM experiments, we show that the OGCM-independent mixed layer bias is caused mainly by surface wind bias in the utilized CGCMs. About 89% of surface wind bias in the CGCMs is due to the inability of atmospheric GCMs (AGCMs), whereas atmosphere–ocean coupling in the CGCMs has only a minor influence on surface wind bias. The bias in surface wind is also found to be the cause of subsurface temperature bias besides the ocean dynamics such as vertical mixing and vertical shear in currents. Our results indicate that correcting TIO mixed layer bias in CGCMs requires improvement in the capability of AGCM in simulating the climatological surface winds.
Significance Statement
We aimed to discover the cause of temperature bias in the Indian Ocean in CMIP6 models. The bias was separated into oceanic model and ocean-model-independent bias to correspond exactly to bias caused by the oceanic model and by the atmospheric model and coupling, respectively. Oceanic bias contributes little to bias in CMIP6, but ocean-model-independent bias explains the CMIP6 bias throughout the year. We ran oceanic model experiments to show that surface wind bias causes ocean-model-independent temperature bias in the entire TIO and subsurface temperature bias in some areas of the Indian Ocean. We further found that 89% of surface wind bias originates from the atmospheric model. The results improve our understanding of the cause of the bias in the Indian Ocean and show that our method of bias separation is effective for attributing the source of bias to different proposed mechanisms.
Abstract
Generation of mesoscale eddies in the eastern South China Sea (SCS) in winters during August 1999 to July 2002 is studied with a reduced-gravity model. It is found that the orographic wind jets associated with the northeast winter monsoon and the gaps in the mountainous island chain along the eastern boundary of the SCS can spin up cyclonic and anticyclonic eddies over the SCS. Results suggest that direct wind forcing could be an important generation mechanism for the rich eddy activity in the SCS, and that to simulate this mechanism the resolution of the wind forcing has to be high enough to resolve the local wind jets induced by orographic effects.
Abstract
Generation of mesoscale eddies in the eastern South China Sea (SCS) in winters during August 1999 to July 2002 is studied with a reduced-gravity model. It is found that the orographic wind jets associated with the northeast winter monsoon and the gaps in the mountainous island chain along the eastern boundary of the SCS can spin up cyclonic and anticyclonic eddies over the SCS. Results suggest that direct wind forcing could be an important generation mechanism for the rich eddy activity in the SCS, and that to simulate this mechanism the resolution of the wind forcing has to be high enough to resolve the local wind jets induced by orographic effects.
Abstract
Using features based on correlation or noncausal dependence metrics can lead to false conclusions. However, recent research has shown that applying causal inference theory in conjunction with Bayesian networks to large-sample-size data can accurately attribute synoptic anomalies. Focusing on the East Asian summer monsoon (EASM), this study adopts a causal inference approach with model averaging to investigate causation of interannual climate variability. We attribute the EASM variability to five winter climate phenomena; our result shows that the eastern Pacific El Niño–Southern Oscillation has the largest causal effect. We also show that the causal precursors of the EASM variability are interpretable in terms of physics. Using linear regression, these precursors can predict the EASM one season ahead, outperforming correlation-based empirical models and three climate models. This study shows that even without large-sample-size data and substantial human intervention, even laymen can implement the causal inference approach to investigate the causes of climatic anomalies and construct reliable empirical models for prediction.
Significance Statement
We use causal inference theory to redesign the attribution procedure fundamentally and adjust a causal inference approach to commonly used climate research data. Our study shows that the causal inference approach can exhaustively reveal the causes of climatic anomalies with little human intervention, which is impossible for correlation-based studies. According to this attribution, one can construct models with a better predictive performance than the climate and correlation-based empirical models. Therefore, our causal inference approach will tremendously help both meteorologists and laymen (e.g., stakeholders and policymakers) accurately predict climate phenomena and reveal their interpretable causes. We recommend that it become a standard practice in attribution studies and operational prediction.
Abstract
Using features based on correlation or noncausal dependence metrics can lead to false conclusions. However, recent research has shown that applying causal inference theory in conjunction with Bayesian networks to large-sample-size data can accurately attribute synoptic anomalies. Focusing on the East Asian summer monsoon (EASM), this study adopts a causal inference approach with model averaging to investigate causation of interannual climate variability. We attribute the EASM variability to five winter climate phenomena; our result shows that the eastern Pacific El Niño–Southern Oscillation has the largest causal effect. We also show that the causal precursors of the EASM variability are interpretable in terms of physics. Using linear regression, these precursors can predict the EASM one season ahead, outperforming correlation-based empirical models and three climate models. This study shows that even without large-sample-size data and substantial human intervention, even laymen can implement the causal inference approach to investigate the causes of climatic anomalies and construct reliable empirical models for prediction.
Significance Statement
We use causal inference theory to redesign the attribution procedure fundamentally and adjust a causal inference approach to commonly used climate research data. Our study shows that the causal inference approach can exhaustively reveal the causes of climatic anomalies with little human intervention, which is impossible for correlation-based studies. According to this attribution, one can construct models with a better predictive performance than the climate and correlation-based empirical models. Therefore, our causal inference approach will tremendously help both meteorologists and laymen (e.g., stakeholders and policymakers) accurately predict climate phenomena and reveal their interpretable causes. We recommend that it become a standard practice in attribution studies and operational prediction.
Abstract
Focusing on ENSO seasonal phase locking, diversity in peak location, and propagation direction, as well as the El Niño–La Niña asymmetry in amplitude, duration, and transition, a set of empirical probabilistic diagnostics (EPD) is introduced to investigate how the ENSO behaviors reflected in SST may change in a warming climate.
EPD is first applied to estimate the natural variation of ENSO behaviors. In the observations El Niños and La Niñas mainly propagate westward and peak in boreal winter. El Niños occur more at the eastern Pacific whereas La Niñas prefer the central Pacific. In a preindustrial control simulation of the GFDL CM2.1 model, the El Niño–La Niña asymmetry is substantial. La Niña characteristics generally agree with observations but El Niño’s do not, typically propagating eastward and showing no obvious seasonal phase locking. So an alternative approach is using a stochastically forced simulation of a nonlinear data-driven model, which exhibits reasonably realistic ENSO behaviors and natural variation ranges.
EPD is then applied to assess the potential changes of ENSO behaviors in the twenty-first century using CMIP5 models. Other than the increasing SST climatology, projected changes in many aspects of ENSO reflected in SST anomalies are heavily model dependent and generally within the range of natural variation. Shifts favoring eastward-propagating El Niño and La Niña are the most robust. Given various model biases for the twentieth century and lack of sufficient model agreements for the twenty-first-century projection, whether the projected changes for ENSO behaviors would actually take place remains largely uncertain.
Abstract
Focusing on ENSO seasonal phase locking, diversity in peak location, and propagation direction, as well as the El Niño–La Niña asymmetry in amplitude, duration, and transition, a set of empirical probabilistic diagnostics (EPD) is introduced to investigate how the ENSO behaviors reflected in SST may change in a warming climate.
EPD is first applied to estimate the natural variation of ENSO behaviors. In the observations El Niños and La Niñas mainly propagate westward and peak in boreal winter. El Niños occur more at the eastern Pacific whereas La Niñas prefer the central Pacific. In a preindustrial control simulation of the GFDL CM2.1 model, the El Niño–La Niña asymmetry is substantial. La Niña characteristics generally agree with observations but El Niño’s do not, typically propagating eastward and showing no obvious seasonal phase locking. So an alternative approach is using a stochastically forced simulation of a nonlinear data-driven model, which exhibits reasonably realistic ENSO behaviors and natural variation ranges.
EPD is then applied to assess the potential changes of ENSO behaviors in the twenty-first century using CMIP5 models. Other than the increasing SST climatology, projected changes in many aspects of ENSO reflected in SST anomalies are heavily model dependent and generally within the range of natural variation. Shifts favoring eastward-propagating El Niño and La Niña are the most robust. Given various model biases for the twentieth century and lack of sufficient model agreements for the twenty-first-century projection, whether the projected changes for ENSO behaviors would actually take place remains largely uncertain.
Abstract
Mesoscale eddies are ubiquitous features of the global ocean circulation and play a key role in transporting ocean properties and modulating air–sea exchanges. Anticyclonic and cyclonic eddies are traditionally thought to be associated with anomalous warm and cold surface waters, respectively. Using satellite altimeter and microwave data, here we show that surface cold-core anticyclonic eddies (CAEs) and warm-core cyclonic eddies (WCEs) are surprisingly abundant in the global ocean—about 20% of the eddies inferred from altimeter data are CAEs and WCEs. Composite analysis using Argo float profiles reveals that the cold cores of CAEs and warm cores of WCEs are generally confined in the upper 50 m. Interestingly, CAEs and WCEs alter air–sea momentum and heat fluxes and modulate mixed layer depth and surface chlorophyll concentration in a way markedly different from the traditional warm-core anticyclonic and cold-core cyclonic eddies. Given their abundance, CAEs and WCEs need to be properly accounted for when assessing and parameterizing the role of ocean eddies in Earth’s climate system.
Abstract
Mesoscale eddies are ubiquitous features of the global ocean circulation and play a key role in transporting ocean properties and modulating air–sea exchanges. Anticyclonic and cyclonic eddies are traditionally thought to be associated with anomalous warm and cold surface waters, respectively. Using satellite altimeter and microwave data, here we show that surface cold-core anticyclonic eddies (CAEs) and warm-core cyclonic eddies (WCEs) are surprisingly abundant in the global ocean—about 20% of the eddies inferred from altimeter data are CAEs and WCEs. Composite analysis using Argo float profiles reveals that the cold cores of CAEs and warm cores of WCEs are generally confined in the upper 50 m. Interestingly, CAEs and WCEs alter air–sea momentum and heat fluxes and modulate mixed layer depth and surface chlorophyll concentration in a way markedly different from the traditional warm-core anticyclonic and cold-core cyclonic eddies. Given their abundance, CAEs and WCEs need to be properly accounted for when assessing and parameterizing the role of ocean eddies in Earth’s climate system.
