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Feiyu Lu and Zhengyu Liu

Abstract

The extratropical influence on the observed events of El Niño–Southern Oscillation (ENSO) variability from 1948 to 2015 is assessed by constraining the extratropical atmospheric variability in a coupled general circulation model (CGCM) using the regional coupled data assimilation (RCDA) method. The ensemble-mean ENSO response to extratropical atmospheric forcing, which is systematically and quantitatively studied through a series of RCDA experiments, indicates robust extratropical influence on some observed ENSO events. Furthermore, an event-by-event quantitative analysis shows significant differences of the extratropical influence among the observed ENSO events, both in its own strength and in its relation to tropical precursors such as the equatorial Pacific heat content anomaly. This study provides the first dynamic quantitative assessment of the extratropical influence on observed ENSO variability on an event-by-event basis.

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Zhengyu Liu, Chengfei He, and Feiyu Lu

Abstract

We present a theoretical study on local and remote responses of atmosphere and ocean meridional heat transports (AHT and OHT, respectively) to climate forcing in a coupled energy balance model. We show that, in general, a surface heat flux forces opposite AHT and OHT responses in the so-called compensation response, while a net heat flux into the coupled system forces AHT and OHT responses of the same direction in the so-called collaboration response. Furthermore, unless the oceanic thermohaline circulation is significantly changed, a remote climate response far away from the forcing region tends to be dominated by the collaboration response, because of the effective propagation of a coupled ocean–atmosphere energy transport mode of collaboration structure. The relevance of our theory to previous CGCM experiments is also discussed. Our theoretical result provides a guideline for understanding of the response of heat transports and the associated climate changes.

Open access
Feiyu Lu, Zhengyu Liu, Shaoqing Zhang, Yun Liu, and Robert Jacob

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.

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Feiyu Lu, Zhengyu Liu, Shaoqing Zhang, and Yun Liu

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.

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Jingzhe Sun, Zhengyu Liu, Feiyu Lu, Weimin Zhang, and Shaoqing Zhang

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.

Free access
Lv Lu, Shaoqing Zhang, Stephen G. Yeager, Gokhan Danabasoglu, Ping Chang, Lixin Wu, Xiaopei Lin, Anthony Rosati, and Feiyu Lu

Abstract

The Atlantic meridional overturning circulation (AMOC) is of great importance in Earth’s climate system, and reconstructing its structure and variability by combining observations with a coupled model is a key step in understanding historical and future states of AMOC. However, models always have systematic errors called bias owing to imperfect numerical representation of the real world. Model bias and the sparse nature of ocean observations, particularly in deep oceans, make it difficult to generate a complete historical picture of AMOC structure and variability. Here, two coupled models that are biased with respect to each other are used to design “twin” experiments to systematically study the influence of model bias on AMOC reconstruction. One model is used to produce the “observations” that sample the “true” solution of the AMOC to be reconstructed, while the other model is used to incorporate the “observations” to reconstruct the “truth” through coupled data assimilation (CDA). The degree to which the “truth” is recovered by a CDA scheme assesses the critical role of coherent (both upper- and deep-ocean incorporate enough observations to mitigate stratification instability) ocean stratification on AMOC reconstruction. Results show that balancing restoration of climatology and assimilation of observations is vital to better reconstruct AMOC structure and variability, given that most ocean observations are only available in the upper 2000 m. The gained results serve as a guideline in ocean-state estimation with a balance of deep restoring and upper data constraint for climate prediction initialization, especially for decadal predictions.

Free access
Michael Winton, Mitchell Bushuk, Yongfei Zhang, Bill Hurlin, Liwei Jia, Nathaniel C. Johnson, and Feiyu Lu

Abstract

The continuing decline of the summertime sea ice cover has reduced the sea ice path that must be traversed to Arctic destinations and through the Arctic between the Atlantic and Pacific Oceans, stimulating interest in trans-Arctic Ocean routes. Seasonal prediction of the sea ice cover along these routes could support the increasing summertime ship traffic taking advantage of recent low ice conditions. We introduce the minimum Arctic sea ice path (MIP) between Atlantic and Pacific Oceans as a shipping-relevant metric amenable to multidecadal hindcast evaluation. We show, using 1992-2017 retrospective predictions, that bias correction is necessary for the GFDL SPEAR forecast system to improve upon damped persistence seasonal forecasts of summertime daily MIP between Atlantic and Pacific Oceans both east and west of Greenland, corresponding roughly to the Northeast and Northwest Passages. Without bias correction, only the NE MIP forecasts have lower error than a damped persistence forecast. Using the forecast ensemble spread to estimate a lower bound on forecast error, we find large opportunities for forecast error reduction, especially at lead-times less than two months. Most of the potential improvement remains after linear removal of climatological and trend biases, suggesting significant error reduction might come from improved initialization and simulation of sub-annual variability. Using a different passive microwave sea ice dataset for calculating error than was used for data assimilation increases the raw forecast errors but not trend anomaly forecast errors.

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Yong-Fei Zhang, Mitchell Bushuk, Michael Winton, Bill Hurlin, Thomas Delworth, Matthew Harrison, Liwei Jia, Feiyu Lu, Anthony Rosati, and Xiaosong Yang

Abstract

The current GFDL seasonal prediction system, the Seamless System for Prediction and EArth System Research (SPEAR), has shown skillful prediction of Arctic sea ice extent with atmosphere and ocean constrained by observations. In this study we present improvements in sub-seasonal and seasonal predictions of Arctic sea ice by directly assimilating sea ice observations. The sea ice initial conditions from a data assimilation (DA) system that assimilates satellite sea-ice concentration (SIC) observations are used to produce a set of reforecast experiments (IceDA) starting from the first day of each month from 1992 to 2017. Our evaluation of daily sea ice extent (SIE) prediction skill concludes that the SPEAR system generally outperforms the anomaly persistence forecast at lead times beyond 1 month. We primarily focus our analysis on daily grid-cell-level sea ice fields. SIC DA improves prediction skill of SIC forecasts prominently in the June, July, August, and September-initialized reforecasts. We evaluate two additional user-oriented metrics: the ice-free probability (IFP) and ice-free date (IFD). IFP is the probability of a grid cell experiencing ice-free conditions in a given year and IFD is the first date that a grid cell is ice-free. A combined analysis of IFP and IFD demonstrates that the SPEAR model can make skillful predictions of local ice melt as early as May, with modest improvements from SIC DA.

Restricted access
Liping Zhang, Thomas L. Delworth, Sarah Kapnick, Jie He, William Cooke, Andrew T. Wittenberg, Nathaniel C. Johnson, Anthony Rosati, Xiaosong Yang, Feiyu Lu, Mitchell Bushuk, Colleen McHugh, Hiroyuki Murakami, Fanrong Zeng, Liwei Jia, Kai-Chih Tseng, and Yushi Morioka

Abstract

One of the most puzzling observed features of recent climate has been a multidecadal surface cooling trend over the subpolar Southern Ocean (SO). In this study we use large ensembles of simulations with multiple climate models to study the role of the SO meridional overturning circulation (MOC) in these sea surface temperature (SST) trends. We find that multiple competing processes play prominent roles, consistent with multiple mechanisms proposed in the literature for the observed cooling. Early in the simulations (twentieth century and early twenty-first century) internal variability of the MOC can have a large impact, in part due to substantial simulated multidecadal variability of the MOC. Ensemble members with initially strong convection (and related surface warming due to convective mixing of subsurface warmth to the surface) tend to subsequently cool at the surface as convection associated with internal variability weakens. A second process occurs in the late-twentieth and twenty-first centuries, as weakening of oceanic convection associated with global warming and high-latitude freshening can contribute to the surface cooling trend by suppressing convection and associated vertical mixing of subsurface heat. As the simulations progress, the multidecadal SO variability is suppressed due to forced changes in the mean state and increased oceanic stratification. As a third process, the shallower mixed layers can then rapidly warm due to increasing forcing from greenhouse gas warming. Also, during this period the ensemble spread of SO SST trend partly arises from the spread of the wind-driven Deacon cell strength. Thus, different processes could conceivably have led to the observed cooling trend, consistent with the range of possibilities presented in the literature. To better understand the causes of the observed trend, it is important to better understand the characteristics of internal low-frequency variability in the SO and the response of that variability to global warming.

Restricted access
Youngji Joh, Thomas L. Delworth, Andrew T. Wittenberg, William F. Cooke, Xiaosong Yang, Fanrong Zeng, Liwei Jia, Feiyu Lu, Nathaniel Johnson, Sarah B. Kapnick, Anthony Rosati, Liping Zhang, and Colleen McHugh

Abstract

The Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in the northern midlatitudes. We explore the representation and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and Earth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: 1) restoring the coupled model’s SST and atmospheric variables toward existing reanalyses, or 2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation. Both systems use an ocean model with 1° resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be essential to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence and divergence, which forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022.

Open access