Bibliography - Feiyu Lu

1. Liu, Zhengyu, C He, and Feiyu Lu, August 2018: Local and Remote Responses of Atmospheric and Oceanic Heat Transports to Climate Forcing: Compensation versus Collaboration. Journal of Climate, 31(16), DOI:10.1175/JCLI-D-17-0675.1 .
   [ 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.

2. Lu, Feiyu, and Zhengyu Liu, November 2018: Assessing Extratropical Influence on Observed El Nino-Southern Oscillation Events Using Regional Coupled Data Assimilation. Journal of Climate, 31(21), DOI:10.1175/JCLI-D-17-0849.1 .
   [ 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.

3. Lu, Feiyu, Zhengyu Liu, Y Liu, Shaoqing Zhang, and R Jacob, May 2017: Understanding the control of extratropical atmospheric variability on ENSO using a coupled data assimilation approach. Climate Dynamics, 48(9), DOI:10.1007/s00382-016-3256-7 .
   [ Abstract ]

   The control of extratropical atmospheric variability on ENSO variability is studied in a coupled general circulation model (CGCM) utilizing an ensemble-based coupled data assimilation (CDA) method in the perfect-model framework. Assimilation is limited to the desired model components (e.g. atmosphere) and spatial areas (e.g. the extratropics) to study the ensemble-mean model response (e.g. tropical response to “observed” extratropical atmospheric variability). The CDA provides continuously “corrected” extratropical atmospheric forcing and boundary conditions for the tropics and the use of ensemble optimizes the observational forcing signal over internal variability in the model component or region without assimilation. The experiments demonstrate significant control of extratropical atmospheric forcing on ENSO variability in the CGCM. When atmospheric “observations” are assimilated only poleward of 20° in both hemispheres, most ENSO events in the “observation” are reproduced and the error of the Nino3.4 index is reduced by over 40 % compared to the ensemble control experiment that does not assimilate any observations. Further experiments with the assimilation in each hemisphere show that the forced ENSO variability is contributed roughly equally and independently by the Southern and Northern Hemisphere extratropical atmosphere. Further analyses of the ENSO events in the southern hemisphere forcing experiment reveal robust precursors in both the extratropical atmosphere over southeastern Pacific and equatorial Pacific thermocline, consistent with previous studies of the South Pacific Meridional Mode and the discharge-recharge paradigm, respectively. However, composite analyses based on each precursor show that neither precursor alone is sufficient to trigger ENSO onset by itself and therefore neither alone could serve as a reliable predictor. Additional experiments with northern hemisphere forcing, ocean assimilation or different latitudes are also performed.

4. Liu, H, Feiyu Lu, Zhengyu Liu, Y Liu, and Shaoqing Zhang, June 2016: Assimilating atmosphere reanalysis in coupled data assimilation. Journal of Meteorological Research, 30(4), DOI:10.1007/s13351-016-6014-1 .
   [ Abstract ]

   This paper tests the idea of substituting the atmospheric observations with atmospheric reanalysis when setting up a coupled data assimilation system. The paper focuses on the quantification of the effects on the oceanic analysis resulted from this substitution and designs four different assimilation schemes for such a substitution. A coupled Lorenz96 system is constructed and an ensemble Kalman filter is adopted. The atmospheric reanalysis and oceanic observations are assimilated into the system and the analysis quality is compared to a benchmark experiment where both atmospheric and oceanic observations are assimilated. Four schemes are designed for assimilating the reanalysis and they differ in the generation of the perturbed observation ensemble and the representation of the error covariance matrix. The results show that when the reanalysis is assimilated directly as independent observations, the root-mean-square error increase of oceanic analysis relative to the benchmark is less than 16% in the perfect model framework; in the biased model case, the increase is less than 22%. This result is robust with sufficient ensemble size and reasonable atmospheric observation quality (e.g., frequency, noisiness, and density). If the observation is overly noisy, infrequent, sparse, or the ensemble size is insufficiently small, the analysis deterioration caused by the substitution is less severe since the analysis quality of the benchmark also deteriorates significantly due to worse observations and undersampling. The results from different assimilation schemes highlight the importance of two factors: accurate representation of the error covariance of the reanalysis and the temporal coherence along each ensemble member, which are crucial for the analysis quality of the substitution experiment.

5. Lu, Feiyu, Zhengyu Liu, Shaoqing Zhang, and Y Liu, September 2015: Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study. Monthly Weather Review, 143(9), DOI:10.1175/MWR-D-14-00322.1 .
   [ 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 assimilates observations into multiple model components like the weakly coupled version (WCDA), but also applies 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 due to different timescales between model components. In a typical extra-tropical 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 timescale difference or model biases.

6. Lu, Feiyu, Zhengyu Liu, Shaoqing Zhang, Y Liu, and R Jacob, November 2015: Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part II: CGCM experiments. Monthly Weather Review, 143(11), DOI:10.1175/MWR-D-15-0088.1 .
   [ 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. Our previous study in a simple coupled climate model (Lu et al. 2015) 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, we test the LACC method 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 extra-tropical 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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