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- Author or Editor: Shaoqing Zhang x
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Abstract
This paper studies a new leading averaged coupled covariance (LACC) method for the strongly coupled data assimilation (SCDA). The SCDA not only uses the coupled model to generate the forecast and assimilate observations into multiple model components like the weakly coupled version (WCDA), but also applies a cross update using the coupled covariance between variables from different model components. The cross update could potentially improve the balance and quality of the analysis, but its implementation has remained a great challenge in practice because of different time scales between model components. In a typical extratropical coupled system, the ocean–atmosphere correlation shows a strong asymmetry with the maximum correlation occurring when the atmosphere leads the ocean by about the decorrelation time of the atmosphere. The LACC method utilizes such asymmetric structure by using the leading forecasts and observations of the fast atmospheric variable for cross update, therefore, increasing the coupled correlation and enhancing the signal-to-noise ratio in calculating the coupled covariance. Here it is applied to a simple coupled model with the ensemble Kalman filter (EnKF). With the LACC method, the SCDA reduces the analysis error of the oceanic variable by over 20% compared to the WCDA and 10% compared to the SCDA using simultaneous coupled covariance. The advantage of the LACC method is more notable when the system contains larger errors, such as in the cases with smaller ensemble size, bigger time-scale difference, or model biases.
Abstract
This paper studies a new leading averaged coupled covariance (LACC) method for the strongly coupled data assimilation (SCDA). The SCDA not only uses the coupled model to generate the forecast and assimilate observations into multiple model components like the weakly coupled version (WCDA), but also applies a cross update using the coupled covariance between variables from different model components. The cross update could potentially improve the balance and quality of the analysis, but its implementation has remained a great challenge in practice because of different time scales between model components. In a typical extratropical coupled system, the ocean–atmosphere correlation shows a strong asymmetry with the maximum correlation occurring when the atmosphere leads the ocean by about the decorrelation time of the atmosphere. The LACC method utilizes such asymmetric structure by using the leading forecasts and observations of the fast atmospheric variable for cross update, therefore, increasing the coupled correlation and enhancing the signal-to-noise ratio in calculating the coupled covariance. Here it is applied to a simple coupled model with the ensemble Kalman filter (EnKF). With the LACC method, the SCDA reduces the analysis error of the oceanic variable by over 20% compared to the WCDA and 10% compared to the SCDA using simultaneous coupled covariance. The advantage of the LACC method is more notable when the system contains larger errors, such as in the cases with smaller ensemble size, bigger time-scale difference, or model biases.
Abstract
The deglacial change of Atlantic meridional overturning circulation (AMOC) since the Last Glacial Maximum (LGM; ~21 ka) has been studied extensively in both reconstructions and model simulations. While reconstructions suggest a shoaling of AMOC at the LGM, the strength of glacial AMOC relative to the modern day remains highly uncertain in both reconstructions and models. Using transient simulations of climate evolution forced by individual deglacial forcings since the LGM, this study shows that the uncertainties in glacial AMOC intensity can be caused by a competition between the elevated glacial Northern Hemisphere (NH) ice sheets and the reduced glacial greenhouse gases, in which the former tend to strengthen the AMOC while the latter play an opposite role. In spite of the dramatic difference of climate between the LGM and the present, the cancellation between the impacts of the two forcings leaves the strength of the glacial AMOC not too different from the modern day (0.5 Sv stronger in our study; 1 Sv ≡ 106 m3 s−1). Furthermore, consistent with theoretical analysis, the response of the AMOC return flow to either forcing is predominantly compensated by an interbasin exchange between the Indo-Pacific (including the Indo-Pacific sector of Southern Ocean) and Atlantic via the Agulhas Current.
Abstract
The deglacial change of Atlantic meridional overturning circulation (AMOC) since the Last Glacial Maximum (LGM; ~21 ka) has been studied extensively in both reconstructions and model simulations. While reconstructions suggest a shoaling of AMOC at the LGM, the strength of glacial AMOC relative to the modern day remains highly uncertain in both reconstructions and models. Using transient simulations of climate evolution forced by individual deglacial forcings since the LGM, this study shows that the uncertainties in glacial AMOC intensity can be caused by a competition between the elevated glacial Northern Hemisphere (NH) ice sheets and the reduced glacial greenhouse gases, in which the former tend to strengthen the AMOC while the latter play an opposite role. In spite of the dramatic difference of climate between the LGM and the present, the cancellation between the impacts of the two forcings leaves the strength of the glacial AMOC not too different from the modern day (0.5 Sv stronger in our study; 1 Sv ≡ 106 m3 s−1). Furthermore, consistent with theoretical analysis, the response of the AMOC return flow to either forcing is predominantly compensated by an interbasin exchange between the Indo-Pacific (including the Indo-Pacific sector of Southern Ocean) and Atlantic via the Agulhas Current.
Abstract
A reversal of zonal sea surface temperature (SST) gradient in the equatorial Atlantic is a common bias in climate models. Studies to investigate the origin of this bias mainly focused on the tropics itself. Applying the regional data assimilation method in the GFDL CM2.1 model, we investigate the combined and respective influences of the northern and southern extratropics on this bias. It is found that the reversed zonal SST gradient bias is caused to a considerable extent by the extratropical atmosphere, especially by the northern extratropics. This extratropical impact on the equator occurs mainly through influencing the Hadley circulation. Therefore, the ITCZ position in boreal spring in this model most likely determines the dominant role of northern extratropics in the spring equatorial westerly bias and additionally the zonal SST gradient bias. Due to the cold bias in the extratropical atmosphere, the northward shift of the ITCZ coupled with the increased meridional SST gradient caused by assimilating the northern extratropics strengthens the cross-equatorial southeasterly wind, thus correcting the spring equatorial westerly bias. The strengthened spring equatorial easterlies further steepen the thermocline slope and enhance the eastern upwelling, thus reproducing the summer cold tongue and finally improving the annual-mean zonal SST gradient bias.
Abstract
A reversal of zonal sea surface temperature (SST) gradient in the equatorial Atlantic is a common bias in climate models. Studies to investigate the origin of this bias mainly focused on the tropics itself. Applying the regional data assimilation method in the GFDL CM2.1 model, we investigate the combined and respective influences of the northern and southern extratropics on this bias. It is found that the reversed zonal SST gradient bias is caused to a considerable extent by the extratropical atmosphere, especially by the northern extratropics. This extratropical impact on the equator occurs mainly through influencing the Hadley circulation. Therefore, the ITCZ position in boreal spring in this model most likely determines the dominant role of northern extratropics in the spring equatorial westerly bias and additionally the zonal SST gradient bias. Due to the cold bias in the extratropical atmosphere, the northward shift of the ITCZ coupled with the increased meridional SST gradient caused by assimilating the northern extratropics strengthens the cross-equatorial southeasterly wind, thus correcting the spring equatorial westerly bias. The strengthened spring equatorial easterlies further steepen the thermocline slope and enhance the eastern upwelling, thus reproducing the summer cold tongue and finally improving the annual-mean zonal SST gradient bias.
Abstract
The double–intertropical convergence zone (ITCZ) bias has been an outstanding problem among climate models for two decades. However, it remains unclear how much of this tropical bias is attributed to the extratropics and tropics itself, respectively. Applying the regional data assimilation (RDA) method, we follow up a previous study with a more advanced model of GFDL CM2.1 to quantify the influence of extratropical atmosphere on the double-ITCZ bias. Our study reveals that this tropical bias is influenced to a large extent by the extratropics between 20° and 30°, with little impact from the extratropics poleward of 30°. This vital role of subtropics in the double-ITCZ bias is likely determined by the meridional extent of Hadley circulation from zonal-mean perspective. Besides, the vital role of subtropics is also supported by wind–evaporation–SST feedback in the subtropical southeastern Pacific from a regional perspective.
Abstract
The double–intertropical convergence zone (ITCZ) bias has been an outstanding problem among climate models for two decades. However, it remains unclear how much of this tropical bias is attributed to the extratropics and tropics itself, respectively. Applying the regional data assimilation (RDA) method, we follow up a previous study with a more advanced model of GFDL CM2.1 to quantify the influence of extratropical atmosphere on the double-ITCZ bias. Our study reveals that this tropical bias is influenced to a large extent by the extratropics between 20° and 30°, with little impact from the extratropics poleward of 30°. This vital role of subtropics in the double-ITCZ bias is likely determined by the meridional extent of Hadley circulation from zonal-mean perspective. Besides, the vital role of subtropics is also supported by wind–evaporation–SST feedback in the subtropical southeastern Pacific from a regional perspective.
Abstract
Recent studies proposed leading averaged coupled covariance (LACC) as an effective strongly coupled data assimilation (SCDA) method to improve the coupled state estimation over weakly coupled data assimilation (WCDA) in a coupled general circulation model (CGCM). This SCDA method, however, has been previously evaluated only in the perfect model scenario. Here, as a further step toward evaluating LACC for real world data assimilation, LACC is evaluated for the assimilation of reanalysis data in a CGCM. Several criteria are used to evaluate LACC against the benchmark WCDA. It is shown that despite significant model bias, LACC can improve the coupled state estimation over WCDA. Compared to WCDA, LACC increases the globally averaged anomaly correlation coefficients (ACCs) of sea surface temperature (SST) by 0.036 and atmosphere temperature at the bottom level (T s ) by 0.058. However, there also exist regions where WCDA outperforms LACC. Although the reduction in the anomaly root-mean-square error (RMSE) is not as consistently clear as the increase in ACC, LACC can largely correct the biased model climatology.
Abstract
Recent studies proposed leading averaged coupled covariance (LACC) as an effective strongly coupled data assimilation (SCDA) method to improve the coupled state estimation over weakly coupled data assimilation (WCDA) in a coupled general circulation model (CGCM). This SCDA method, however, has been previously evaluated only in the perfect model scenario. Here, as a further step toward evaluating LACC for real world data assimilation, LACC is evaluated for the assimilation of reanalysis data in a CGCM. Several criteria are used to evaluate LACC against the benchmark WCDA. It is shown that despite significant model bias, LACC can improve the coupled state estimation over WCDA. Compared to WCDA, LACC increases the globally averaged anomaly correlation coefficients (ACCs) of sea surface temperature (SST) by 0.036 and atmosphere temperature at the bottom level (T s ) by 0.058. However, there also exist regions where WCDA outperforms LACC. Although the reduction in the anomaly root-mean-square error (RMSE) is not as consistently clear as the increase in ACC, LACC can largely correct the biased model climatology.
Abstract
Imperfect physical parameterization schemes in a coupled climate model are an important source of model biases that adversely impact climate prediction. However, how observational information should be used to optimize physical parameterizations through parameter estimation has not been fully studied. Using an intermediate coupled ocean–atmosphere model, the authors investigate parameter optimization when the assimilation model contains biased physics within a biased assimilation experiment framework. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation model and the “truth” model that is used to generate simulated observations. While the stochastic physics, implemented by initially perturbing the physical parameters, can significantly enhance the ensemble spread and improve the representation of the model ensemble, the parameter estimation is able to mitigate the model biases induced by the biased physics. Furthermore, better results for climate estimation and prediction can be obtained when only the most influential physical parameters are optimized and allowed to vary geographically. In addition, the parameter optimization with the biased model physics improves the performance of the climate estimation and prediction in the deep ocean significantly, even if there is no direct observational constraint on the low-frequency component of the state variables. These results provide some insight into decadal predictions in a coupled ocean–atmosphere general circulation model that includes imperfect physical schemes that are initialized from the climate observing system.
Abstract
Imperfect physical parameterization schemes in a coupled climate model are an important source of model biases that adversely impact climate prediction. However, how observational information should be used to optimize physical parameterizations through parameter estimation has not been fully studied. Using an intermediate coupled ocean–atmosphere model, the authors investigate parameter optimization when the assimilation model contains biased physics within a biased assimilation experiment framework. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation model and the “truth” model that is used to generate simulated observations. While the stochastic physics, implemented by initially perturbing the physical parameters, can significantly enhance the ensemble spread and improve the representation of the model ensemble, the parameter estimation is able to mitigate the model biases induced by the biased physics. Furthermore, better results for climate estimation and prediction can be obtained when only the most influential physical parameters are optimized and allowed to vary geographically. In addition, the parameter optimization with the biased model physics improves the performance of the climate estimation and prediction in the deep ocean significantly, even if there is no direct observational constraint on the low-frequency component of the state variables. These results provide some insight into decadal predictions in a coupled ocean–atmosphere general circulation model that includes imperfect physical schemes that are initialized from the climate observing system.
Abstract
While fixed covariance localization can greatly increase the reliability of the background error covariance in filtering by suppressing the long-distance spurious correlations evaluated by a finite ensemble, it may degrade the assimilation quality in an ensemble Kalman filter (EnKF) as a result of restricted longwave information. Tuning an optimal cutoff distance is usually very expensive and time consuming, especially for a general circulation model (GCM). Here the authors present an approach to compensate the demerit in fixed localization. At each analysis step, after the standard EnKF is done, a multiple-scale analysis technique is used to extract longwave information from the observational residual (referred to the EnKF ensemble mean). Within a biased twin-experiment framework consisting of a global barotropical spectral model and an idealized observing system, the performance of the new method is examined. Compared to a standard EnKF, the hybrid method is superior when an overly small/large cutoff distance is used, and it has less dependence on cutoff distance. The new scheme is also able to improve short-term weather forecasts, especially when an overly large cutoff distance is used. Sensitivity studies show that caution should be taken when the new scheme is applied to a dense observing system with an overly small cutoff distance in filtering. In addition, the new scheme has a nearly equivalent computational cost to the standard EnKF; thus, it is particularly suitable for GCM applications.
Abstract
While fixed covariance localization can greatly increase the reliability of the background error covariance in filtering by suppressing the long-distance spurious correlations evaluated by a finite ensemble, it may degrade the assimilation quality in an ensemble Kalman filter (EnKF) as a result of restricted longwave information. Tuning an optimal cutoff distance is usually very expensive and time consuming, especially for a general circulation model (GCM). Here the authors present an approach to compensate the demerit in fixed localization. At each analysis step, after the standard EnKF is done, a multiple-scale analysis technique is used to extract longwave information from the observational residual (referred to the EnKF ensemble mean). Within a biased twin-experiment framework consisting of a global barotropical spectral model and an idealized observing system, the performance of the new method is examined. Compared to a standard EnKF, the hybrid method is superior when an overly small/large cutoff distance is used, and it has less dependence on cutoff distance. The new scheme is also able to improve short-term weather forecasts, especially when an overly large cutoff distance is used. Sensitivity studies show that caution should be taken when the new scheme is applied to a dense observing system with an overly small cutoff distance in filtering. In addition, the new scheme has a nearly equivalent computational cost to the standard EnKF; thus, it is particularly suitable for GCM applications.
Abstract
This paper uses a fully coupled general circulation model (CGCM) to study the leading averaged coupled covariance (LACC) method in a strongly coupled data assimilation (SCDA) system. The previous study in a simple coupled climate model has shown that, by calculating the coupled covariance using the leading averaged atmospheric states, the LACC method enhances the signal-to-noise ratio and improves the analysis quality of the slow model component compared to both the traditional weakly coupled data assimilation without cross-component adjustments (WCDA) and the regular SCDA using the simultaneous coupled covariance (SimCC).
Here in Part II, the LACC method is tested with a CGCM in a perfect-model framework. By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the SST error compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in the deep tropics. The improvement in SST analysis is a result of the enhanced signal-to-noise ratio in the LACC method, especially in the extratropical regions. The improved SST analysis also benefits the subsurface ocean temperature and low-level atmosphere temperature analyses through dynamic and statistical processes.
Abstract
This paper uses a fully coupled general circulation model (CGCM) to study the leading averaged coupled covariance (LACC) method in a strongly coupled data assimilation (SCDA) system. The previous study in a simple coupled climate model has shown that, by calculating the coupled covariance using the leading averaged atmospheric states, the LACC method enhances the signal-to-noise ratio and improves the analysis quality of the slow model component compared to both the traditional weakly coupled data assimilation without cross-component adjustments (WCDA) and the regular SCDA using the simultaneous coupled covariance (SimCC).
Here in Part II, the LACC method is tested with a CGCM in a perfect-model framework. By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the SST error compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in the deep tropics. The improvement in SST analysis is a result of the enhanced signal-to-noise ratio in the LACC method, especially in the extratropical regions. The improved SST analysis also benefits the subsurface ocean temperature and low-level atmosphere temperature analyses through dynamic and statistical processes.
Abstract
An ensemble filter data assimilation system is tested in a perfect model setting using a low resolution Held–Suarez configuration of an atmospheric GCM. The assimilation system is able to reconstruct details of the model’s state at all levels when only observations of surface pressure (PS) are available. The impacts of varying the spatial density and temporal frequency of PS observations are examined. The error of the ensemble mean assimilation prior estimate appears to saturate at some point as the number of PS observations available once every 24 h is increased. However, increasing the frequency with which PS observations are available from a fixed network of 1800 randomly located stations results in an apparently unbounded decrease in the assimilation’s prior error for both PS and all other model state variables. The error reduces smoothly as a function of observation frequency except for a band with observation periods around 4 h. Assimilated states are found to display enhanced amplitude high-frequency gravity wave oscillations when observations are taken once every few hours, and this adversely impacts the assimilation quality. Assimilations of only surface temperature and only surface wind components are also examined.
The results indicate that, in a perfect model context, ensemble filters are able to extract surprising amounts of information from observations of only a small portion of a model’s spatial domain. This suggests that most of the remaining challenges for ensemble filter assimilation are confined to problems such as model error, observation representativeness error, and unknown instrument error characteristics that are outside the scope of perfect model experiments. While it is dangerous to extrapolate from these simple experiments to operational atmospheric assimilation, the results also suggest that exploring the frequency with which observations are used for assimilation may lead to significant enhancements to assimilated state estimates.
Abstract
An ensemble filter data assimilation system is tested in a perfect model setting using a low resolution Held–Suarez configuration of an atmospheric GCM. The assimilation system is able to reconstruct details of the model’s state at all levels when only observations of surface pressure (PS) are available. The impacts of varying the spatial density and temporal frequency of PS observations are examined. The error of the ensemble mean assimilation prior estimate appears to saturate at some point as the number of PS observations available once every 24 h is increased. However, increasing the frequency with which PS observations are available from a fixed network of 1800 randomly located stations results in an apparently unbounded decrease in the assimilation’s prior error for both PS and all other model state variables. The error reduces smoothly as a function of observation frequency except for a band with observation periods around 4 h. Assimilated states are found to display enhanced amplitude high-frequency gravity wave oscillations when observations are taken once every few hours, and this adversely impacts the assimilation quality. Assimilations of only surface temperature and only surface wind components are also examined.
The results indicate that, in a perfect model context, ensemble filters are able to extract surprising amounts of information from observations of only a small portion of a model’s spatial domain. This suggests that most of the remaining challenges for ensemble filter assimilation are confined to problems such as model error, observation representativeness error, and unknown instrument error characteristics that are outside the scope of perfect model experiments. While it is dangerous to extrapolate from these simple experiments to operational atmospheric assimilation, the results also suggest that exploring the frequency with which observations are used for assimilation may lead to significant enhancements to assimilated state estimates.
