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- Author or Editor: Rong Zhang x
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Abstract
The tropical thermocline plays an important role in regulating equatorial sea surface temperature (SST); at present, it is still poorly simulated in the state-of-the-art climate models. In this paper, thermocline biases in the tropical North Pacific are investigated using the newly released CMIP6 historical simulations. It is found that CMIP6 models tend to produce an overly shallow thermocline in the northwestern tropics, accompanied by a deep thermocline in the northeastern tropics. A pronounced thermocline strength bias arises in the tropical northeastern Pacific, demonstrating a dipole structure with a sign change at about 8°N. These thermocline biases are accompanied with biases in the simulations of oceanic circulations, including a too weak North Equatorial Countercurrent (NECC), a reduction in water exchanges between the subtropics and the equatorial regions, and an eastward extension of the equatorward interior water transport. The causes of these thermocline biases are further analyzed. The thermocline bias is primarily caused by the model deficiency in simulating the surface wind stress curl, which can be further attributed to the longstanding double-ITCZ bias in the tropical North Pacific. Besides, thermocline strength bias can be partly attributed to the poor prescription of oceanic background diffusivity. By constraining the diffusivity to match observations, the thermocline strength in the tropical northeastern Pacific is greatly increased.
Abstract
The tropical thermocline plays an important role in regulating equatorial sea surface temperature (SST); at present, it is still poorly simulated in the state-of-the-art climate models. In this paper, thermocline biases in the tropical North Pacific are investigated using the newly released CMIP6 historical simulations. It is found that CMIP6 models tend to produce an overly shallow thermocline in the northwestern tropics, accompanied by a deep thermocline in the northeastern tropics. A pronounced thermocline strength bias arises in the tropical northeastern Pacific, demonstrating a dipole structure with a sign change at about 8°N. These thermocline biases are accompanied with biases in the simulations of oceanic circulations, including a too weak North Equatorial Countercurrent (NECC), a reduction in water exchanges between the subtropics and the equatorial regions, and an eastward extension of the equatorward interior water transport. The causes of these thermocline biases are further analyzed. The thermocline bias is primarily caused by the model deficiency in simulating the surface wind stress curl, which can be further attributed to the longstanding double-ITCZ bias in the tropical North Pacific. Besides, thermocline strength bias can be partly attributed to the poor prescription of oceanic background diffusivity. By constraining the diffusivity to match observations, the thermocline strength in the tropical northeastern Pacific is greatly increased.
Abstract
A variety of observational and modeling studies show that changes in the Atlantic meridional overturning circulation (AMOC) can induce rapid global-scale climate change. In particular, a substantially weakened AMOC leads to a southward shift of the intertropical convergence zone (ITCZ) in both the Atlantic and the Pacific Oceans. However, the simulated amplitudes of the AMOC-induced tropical climate change differ substantially among different models. In this paper, the sensitivity to cloud feedback of the climate response to a change in the AMOC is studied using a coupled ocean–atmosphere model [the GFDL Coupled Model, version 2.1 (CM2.1)]. Without cloud feedback, the simulated AMOC-induced climate change in this model is weakened substantially. Low-cloud feedback has a strong amplifying impact on the tropical ITCZ shift in this model, whereas the effects of high-cloud feedback are weaker. It is concluded that cloud feedback is an important contributor to the uncertainty in the global response to AMOC changes.
Abstract
A variety of observational and modeling studies show that changes in the Atlantic meridional overturning circulation (AMOC) can induce rapid global-scale climate change. In particular, a substantially weakened AMOC leads to a southward shift of the intertropical convergence zone (ITCZ) in both the Atlantic and the Pacific Oceans. However, the simulated amplitudes of the AMOC-induced tropical climate change differ substantially among different models. In this paper, the sensitivity to cloud feedback of the climate response to a change in the AMOC is studied using a coupled ocean–atmosphere model [the GFDL Coupled Model, version 2.1 (CM2.1)]. Without cloud feedback, the simulated AMOC-induced climate change in this model is weakened substantially. Low-cloud feedback has a strong amplifying impact on the tropical ITCZ shift in this model, whereas the effects of high-cloud feedback are weaker. It is concluded that cloud feedback is an important contributor to the uncertainty in the global response to AMOC changes.
Abstract
In this study, an improved sea surface temperature (SST) anomaly (SSTA) solution for the tropical Pacific is presented by explicitly embedding into a layer ocean general circulation model (OGCM) a separate SSTA submodel with an empirical parameterization for the temperature of subsurface water entrained into the ocean mixed layer (Te ). Instead of using subsurface temperature directly from the OGCM, Te anomalies for the embedded SSTA submodel are calculated from a historical data-based empirical procedure in terms of sea level (SL) anomalies simulated from the OGCM. An inverse modeling approach is first adopted to estimate Te anomalies from the SSTA equation using observed SST and simulated upper-ocean currents from the OGCM. A relationship between Te and SL anomalies is then obtained by utilizing an empirical orthogonal function (EOF) analysis technique. The empirical Te parameterization optimally leads to a better balanced depiction of the subsurface effect on SST variability by the mean upwelling of anomalous subsurface temperature and vertical mixing in the equatorial Pacific. As compared with a standard OGCM simulation, SSTA simulations from the embedded submodel exhibit more realistic variability, with significantly increased correlation and reduced SSTA errors due to the optimized empirical Te parameterization. In the Niño-3 region (5°S–5°N, 150°–90°W), the anomaly correlation and root-mean-square (RMS) error of the simulated SST anomalies for the period 1963–96 from the standard OGCM are 0.74° and 0.58°C, while from the embedded SSTA submodel they are 0.94° and 0.29°C in the Te -dependent experiment, and 0.86° and 0.41°C in the experiment with one-dependent-year data excluded, respectively. Cross validation and sensitivity experiments to training periods for building the Te parameterization are made to illustrate the robustness and effectiveness of the approach. Moreover, the impact on simulations of SST anomalies and El Niño are examined in hybrid coupled atmosphere–ocean models (HCMs) consisting of the OGCM and a statistical atmospheric wind stress anomaly model that is constructed from a singular value decomposition (SVD) analysis. Results from coupled runs with and without embedding the SSTA submodel are compared. It is demonstrated that incorporating the embedded SSTA submodel in the context of an OGCM has a significant impact on performance of the HCMs and the behavior of the coupled system, with more realistic simulations of interannual SST anomalies (e.g., the amplitude and structure) in the tropical Pacific.
Abstract
In this study, an improved sea surface temperature (SST) anomaly (SSTA) solution for the tropical Pacific is presented by explicitly embedding into a layer ocean general circulation model (OGCM) a separate SSTA submodel with an empirical parameterization for the temperature of subsurface water entrained into the ocean mixed layer (Te ). Instead of using subsurface temperature directly from the OGCM, Te anomalies for the embedded SSTA submodel are calculated from a historical data-based empirical procedure in terms of sea level (SL) anomalies simulated from the OGCM. An inverse modeling approach is first adopted to estimate Te anomalies from the SSTA equation using observed SST and simulated upper-ocean currents from the OGCM. A relationship between Te and SL anomalies is then obtained by utilizing an empirical orthogonal function (EOF) analysis technique. The empirical Te parameterization optimally leads to a better balanced depiction of the subsurface effect on SST variability by the mean upwelling of anomalous subsurface temperature and vertical mixing in the equatorial Pacific. As compared with a standard OGCM simulation, SSTA simulations from the embedded submodel exhibit more realistic variability, with significantly increased correlation and reduced SSTA errors due to the optimized empirical Te parameterization. In the Niño-3 region (5°S–5°N, 150°–90°W), the anomaly correlation and root-mean-square (RMS) error of the simulated SST anomalies for the period 1963–96 from the standard OGCM are 0.74° and 0.58°C, while from the embedded SSTA submodel they are 0.94° and 0.29°C in the Te -dependent experiment, and 0.86° and 0.41°C in the experiment with one-dependent-year data excluded, respectively. Cross validation and sensitivity experiments to training periods for building the Te parameterization are made to illustrate the robustness and effectiveness of the approach. Moreover, the impact on simulations of SST anomalies and El Niño are examined in hybrid coupled atmosphere–ocean models (HCMs) consisting of the OGCM and a statistical atmospheric wind stress anomaly model that is constructed from a singular value decomposition (SVD) analysis. Results from coupled runs with and without embedding the SSTA submodel are compared. It is demonstrated that incorporating the embedded SSTA submodel in the context of an OGCM has a significant impact on performance of the HCMs and the behavior of the coupled system, with more realistic simulations of interannual SST anomalies (e.g., the amplitude and structure) in the tropical Pacific.
Abstract
The relationship between the North Atlantic Oscillation (NAO) and Atlantic sea surface temperature (SST) variability is investigated using models and observations. Coupled climate models are used in which the ocean component is either a fully dynamic ocean or a slab ocean with no resolved ocean heat transport. On time scales less than 10 yr, NAO variations drive a tripole pattern of SST anomalies in both observations and models. This SST pattern is a direct response of the ocean mixed layer to turbulent surface heat flux anomalies associated with the NAO. On time scales longer than 10 yr, a similar relationship exists between the NAO and the tripole pattern of SST anomalies in models with a slab ocean. A different relationship exists both for the observations and for models with a dynamic ocean. In these models, a positive (negative) NAO anomaly leads, after a decadal-scale lag, to a monopole pattern of warming (cooling) that resembles the Atlantic multidecadal oscillation (AMO), although with smaller-than-observed amplitudes of tropical SST anomalies. Ocean dynamics are critical to this decadal-scale response in the models. The simulated Atlantic meridional overturning circulation (AMOC) strengthens (weakens) in response to a prolonged positive (negative) phase of the NAO, thereby enhancing (decreasing) poleward heat transport, leading to broad-scale warming (cooling). Additional simulations are used in which heat flux anomalies derived from observed NAO variations from 1901 to 2014 are applied to the ocean component of coupled models. It is shown that ocean dynamics allow models to reproduce important aspects of the observed AMO, mainly in the Subpolar Gyre.
Abstract
The relationship between the North Atlantic Oscillation (NAO) and Atlantic sea surface temperature (SST) variability is investigated using models and observations. Coupled climate models are used in which the ocean component is either a fully dynamic ocean or a slab ocean with no resolved ocean heat transport. On time scales less than 10 yr, NAO variations drive a tripole pattern of SST anomalies in both observations and models. This SST pattern is a direct response of the ocean mixed layer to turbulent surface heat flux anomalies associated with the NAO. On time scales longer than 10 yr, a similar relationship exists between the NAO and the tripole pattern of SST anomalies in models with a slab ocean. A different relationship exists both for the observations and for models with a dynamic ocean. In these models, a positive (negative) NAO anomaly leads, after a decadal-scale lag, to a monopole pattern of warming (cooling) that resembles the Atlantic multidecadal oscillation (AMO), although with smaller-than-observed amplitudes of tropical SST anomalies. Ocean dynamics are critical to this decadal-scale response in the models. The simulated Atlantic meridional overturning circulation (AMOC) strengthens (weakens) in response to a prolonged positive (negative) phase of the NAO, thereby enhancing (decreasing) poleward heat transport, leading to broad-scale warming (cooling). Additional simulations are used in which heat flux anomalies derived from observed NAO variations from 1901 to 2014 are applied to the ocean component of coupled models. It is shown that ocean dynamics allow models to reproduce important aspects of the observed AMO, mainly in the Subpolar Gyre.
Abstract
After the quick decaying of the 2015 super El Niño, the predicted La Niña unexpectedly failed to materialize to the anticipated standard in 2016. Diagnostic analyses, as well as numerical experiments, showed that this ENSO evolution of the 2015 super El Niño and the hindered 2016 La Niña may be essentially caused by sea surface temperature anomalies (SSTAs) in the subtropical Pacific. The self-sustaining SSTAs in the subtropical Pacific tend to weaken the trade winds during boreal spring–summer, leading to anomalous westerlies along the equatorial region over a period of more than one season. Such long-lasting wind anomalies provide an essential requirement for ENSO formation, particularly before a positive Bjerknes feedback is thoroughly built up between the oceanic and atmospheric states. Besides the 2015 super El Niño and the hindered La Niña in 2016, there were several other El Niño and La Niña events that cannot be explained only by the oceanic heat content in the equatorial Pacific. However, the questions related to those eccentric El Niño and La Niña events can be well explained by suitable SSTAs in the subtropical Pacific. Thus, the leading SSTAs in the subtropical Pacific can be treated as an independent indicator for ENSO prediction, on the basis of the oceanic heat content inherent in the equatorial region. Because ENSO events have become more uncertain under the background of global warming and the Pacific decadal oscillation during recent decades, thorough investigation of the role of the subtropical Pacific in ENSO formation is urgently needed.
Abstract
After the quick decaying of the 2015 super El Niño, the predicted La Niña unexpectedly failed to materialize to the anticipated standard in 2016. Diagnostic analyses, as well as numerical experiments, showed that this ENSO evolution of the 2015 super El Niño and the hindered 2016 La Niña may be essentially caused by sea surface temperature anomalies (SSTAs) in the subtropical Pacific. The self-sustaining SSTAs in the subtropical Pacific tend to weaken the trade winds during boreal spring–summer, leading to anomalous westerlies along the equatorial region over a period of more than one season. Such long-lasting wind anomalies provide an essential requirement for ENSO formation, particularly before a positive Bjerknes feedback is thoroughly built up between the oceanic and atmospheric states. Besides the 2015 super El Niño and the hindered La Niña in 2016, there were several other El Niño and La Niña events that cannot be explained only by the oceanic heat content in the equatorial Pacific. However, the questions related to those eccentric El Niño and La Niña events can be well explained by suitable SSTAs in the subtropical Pacific. Thus, the leading SSTAs in the subtropical Pacific can be treated as an independent indicator for ENSO prediction, on the basis of the oceanic heat content inherent in the equatorial region. Because ENSO events have become more uncertain under the background of global warming and the Pacific decadal oscillation during recent decades, thorough investigation of the role of the subtropical Pacific in ENSO formation is urgently needed.
Abstract
This paper is about the statistical correction of systematic errors in dynamical sea surface temperature (SST) prediction systems using linear regression approaches. The typically short histories of model forecasts create difficulties in developing regression-based corrections. The roles of sample size, predictive skill, and systematic error are examined in evaluating the benefit of a linear correction. It is found that with the typical 20 yr of available model SST forecast data, corrections are worth performing when there are substantial deviations in forecast amplitude from that determined by correlation with observations. The closer the amplitude of the uncorrected forecasts is to the optimum squared error-minimizing amplitude, the less likely is a correction to improve skill. In addition to there being less “room for improvement,” this rule is related to the expected degradation in out-of-sample skill caused by sampling error in the estimate of the regression coefficient underlying the correction.
Application of multivariate [canonical correlation analysis (CCA)] correction to three dynamical SST prediction models having 20 yr of data demonstrates improvement in the cross-validated skills of tropical Pacific SST forecasts through reduction of systematic errors in pattern structure. Additional beneficial correction of errors orthogonal to the CCA modes is achieved on a per-gridpoint basis for features having smaller spatial scale. Until such time that dynamical models become freer of systematic errors, statistical corrections such as those shown here can make dynamical SST predictions more skillful, retaining their nonlinear physics while also calibrating their outputs to more closely match observations.
Abstract
This paper is about the statistical correction of systematic errors in dynamical sea surface temperature (SST) prediction systems using linear regression approaches. The typically short histories of model forecasts create difficulties in developing regression-based corrections. The roles of sample size, predictive skill, and systematic error are examined in evaluating the benefit of a linear correction. It is found that with the typical 20 yr of available model SST forecast data, corrections are worth performing when there are substantial deviations in forecast amplitude from that determined by correlation with observations. The closer the amplitude of the uncorrected forecasts is to the optimum squared error-minimizing amplitude, the less likely is a correction to improve skill. In addition to there being less “room for improvement,” this rule is related to the expected degradation in out-of-sample skill caused by sampling error in the estimate of the regression coefficient underlying the correction.
Application of multivariate [canonical correlation analysis (CCA)] correction to three dynamical SST prediction models having 20 yr of data demonstrates improvement in the cross-validated skills of tropical Pacific SST forecasts through reduction of systematic errors in pattern structure. Additional beneficial correction of errors orthogonal to the CCA modes is achieved on a per-gridpoint basis for features having smaller spatial scale. Until such time that dynamical models become freer of systematic errors, statistical corrections such as those shown here can make dynamical SST predictions more skillful, retaining their nonlinear physics while also calibrating their outputs to more closely match observations.
Abstract
An empirical model for the temperature of subsurface water entrained into the ocean mixed layer (Te ) is presented and evaluated to improve sea surface temperature anomaly (SSTA) simulations in an intermediate ocean model (IOM) of the tropical Pacific. An inverse modeling approach is adopted to estimate Te from an SSTA equation using observed SST and simulated upper-ocean currents. A relationship between Te and sea surface height (SSH) anomalies is then obtained by utilizing a singular value decomposition (SVD) of their covariance. This empirical scheme is able to better parameterize Te anomalies than other local schemes and quite realistically depicts interannual variability of Te , including a nonlocal phase lag relation of Te variations relative to SSH anomalies over the central equatorial Pacific. An improved Te parameterization naturally leads to better depiction of the subsurface effect on SST variability by the mean upwelling of subsurface temperature anomalies. As a result, SSTA simulations are significantly improved in the equatorial Pacific; a comparison with other schemes indicates that systematic errors of the simulated SSTAs are significantly small—apparently due to the optimized empirical Te parameterization. Cross validation and comparisons with other model simulations are made to illustrate the robustness and effectiveness of the scheme. In particular it is demonstrated that the empirical Te model constructed from one historical period can be successfully used to improve SSTA simulations in another.
Abstract
An empirical model for the temperature of subsurface water entrained into the ocean mixed layer (Te ) is presented and evaluated to improve sea surface temperature anomaly (SSTA) simulations in an intermediate ocean model (IOM) of the tropical Pacific. An inverse modeling approach is adopted to estimate Te from an SSTA equation using observed SST and simulated upper-ocean currents. A relationship between Te and sea surface height (SSH) anomalies is then obtained by utilizing a singular value decomposition (SVD) of their covariance. This empirical scheme is able to better parameterize Te anomalies than other local schemes and quite realistically depicts interannual variability of Te , including a nonlocal phase lag relation of Te variations relative to SSH anomalies over the central equatorial Pacific. An improved Te parameterization naturally leads to better depiction of the subsurface effect on SST variability by the mean upwelling of subsurface temperature anomalies. As a result, SSTA simulations are significantly improved in the equatorial Pacific; a comparison with other schemes indicates that systematic errors of the simulated SSTAs are significantly small—apparently due to the optimized empirical Te parameterization. Cross validation and comparisons with other model simulations are made to illustrate the robustness and effectiveness of the scheme. In particular it is demonstrated that the empirical Te model constructed from one historical period can be successfully used to improve SSTA simulations in another.
Abstract
Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parameter estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Overall, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.
Abstract
Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parameter estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Overall, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.
Abstract
Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. Here, an adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.
Abstract
Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. Here, an adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.
