Search Results

You are looking at 81 - 90 of 94 items for

  • Author or Editor: Zhengyu Liu x
  • All content x
Clear All Modify Search
Zhengyu Liu, Steve Vavrus, Feng He, Na Wen, and Yafang Zhong

Abstract

The response of tropical Pacific SST to increased atmospheric CO2 concentration is reexamined with a new focus on the latitudinal SST gradient. Available evidence, mainly from climate models, suggests that an important tropical SST fingerprint to global warming is an enhanced equatorial warming relative to the subtropics. This enhanced equatorial warming provides a fingerprint of SST response more robust than the traditionally studied El Niño–like response, which is characterized by the zonal SST gradient. Most importantly, the mechanism of the enhanced equatorial warming differs fundamentally from the El Niño–like response; the former is associated with surface latent heat flux, shortwave cloud forcing, and surface ocean mixing, while the latter is associated with equatorial ocean upwelling and wind-upwelling dynamic ocean–atmosphere feedback.

Full access
Xuefeng Zhang, Shaoqing Zhang, Zhengyu Liu, Xinrong Wu, and Guijun Han

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.

Full access
Yuchu Zhao, Zhengyu Liu, Fei Zheng, and Yishuai Jin

Abstract

We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

Open access
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.

Restricted access
Xinrong Wu, Shaoqing Zhang, Zhengyu Liu, Anthony Rosati, Thomas L. Delworth, and Yun Liu

Abstract

Because of the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere–ocean–land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin-experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation, and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. The four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic-dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root-mean-square errors as 41%, 23%, 62%, and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature, and land surface temperature, respectively. Consistently, the new parameter optimization greatly improves the model predictability as a result of the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic-dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction.

Full access
Xiaodong Liu, Zhengyu Liu, John E. Kutzbach, Steven C. Clemens, and Warren L. Prell

Abstract

Insolation forcing related to the earth’s orbital parameters is known to play an important role in regulating variations of the South Asian monsoon on geological time scales. The influence of insolation forcing on the Indian Ocean and Asian monsoon is studied in this paper by isolating the Northern and Southern Hemispheric insolation changes in several numerical experiments with a coupled ocean–atmosphere model. The focus is on the response of South Asian summer rainfall (monsoon strength) with emphasis on impacts of the local versus remote forcing and possible mechanisms. The model results show that both Northern Hemisphere (NH) and Southern Hemisphere (SH) summer insolation changes affect the Indian Ocean and Asian monsoon as a local forcing (in the same hemisphere), but only the SH changes result in remote (in the other hemisphere) forcing. The NH insolation change has a local and immediate impact on NH summer monsoons from North Africa to South and East Asia, while the SH insolation change has a remote and seasonal-scale delayed effect on the South Asian summer monsoon rainfall. When the SH insolation is increased from December to April, the sea surface temperature (SST) in the southern tropical Indian Ocean remains high from January to July. The increased SST produces more atmospheric precipitable water over the southern tropical Indian Ocean by promoting evaporation from the ocean. The enhanced precipitable water over the southern Indian Ocean is transported northward to the South Asian monsoon region by the lower-tropospheric mean cross-equatorial flows with the onset of the Asian monsoon increasing precipitable water over South Asia, eventually leading to the increase of Indian summer monsoon precipitation. Thus, these model experiments, while idealized and not fully representing actual orbitally forced insolation changes, confirm the broadscale response of northern monsoons to NH summer insolation increases and also illustrate how SH summer insolation increases can have a delayed influence on the Indian summer monsoon.

Full access
Wei Liu, Francis P. Bretherton, Zhengyu Liu, Leslie Smith, Hao Lu, and Christopher J. Rutland

Abstract

The breaking of a monochromatic two-dimensional internal gravity wave is studied using a newly developed spectral/pseudospectral model. The model features vertical nonperiodic boundary conditions that ensure a realistic simulation of wave breaking during the wave propagation. Isopycnal overturning is induced at a local wave steepness of sc = 0.75–0.79, which is below the conventional threshold of s = 1. Isopycnal overturning is a sufficient condition for subsequent wave breaking by convective instability. When s = sc, little primary wave energy is being transferred to high-mode harmonics. Beyond s = 1, high-mode harmonics grow rapidly. Primary wave energy is more efficiently transferred by waves of lower frequency. A local gradient Richardson number is defined as Ri = −(g/ρ 0)(/dz)/ζ 2 to isolate convective instability (Ri ≤ 0) and wave-induced shear instability (0 < Ri < 0.25), where /dz is the local vertical density gradient and ζ is the horizontal vorticity. Consistent with linear wave theory, the probability density function (PDF) for occurrence of convective instability has a maximum at wave phase ϕ = π/2, where the wave-induced density perturbations to the background stratification are the greatest, whereas the wave-induced shear instability has maxima around ϕ = 0 (wave trough) and ϕ = π (wave crest). Nonlinearities in the wave-induced flow broaden the phase span in PDFs of both instabilities. Diapycnal mixing in numerical simulations may be compared with that in realistic oceanic flows in terms of the Cox number. In the numerical simulations, the Cox numbers increase from 1.5 (s = 0.78) to 21.5 (s = 1.1), and the latter is in the lower range of reported values for the ocean.

Full access
Xiaojie Zhu, Jilin Sun, Zhengyu Liu, Qinyu Liu, and Jonathan E. Martin

Abstract

An analysis of cyclone activity in winter associated with years of strong and weak Aleutian low in the North Pacific is presented. From 1958 to 2004, 10 winters with a strong Aleutian low are defined as the strong years, while 8 winters with a weak Aleutian low are defined as the weak years.

Employing a system-centered Lagrangian method, some characteristics of the cyclone activity in both sets of years are revealed. The cyclone frequency, duration, and intensity are nearly the same in both strong and weak years. The cyclone tracks in the strong years are more zonal than those in the weak years. More intense cyclone events and more large cyclone cases occur in strong years than in weak years and the deepening of cyclones in strong years is stronger than that in weak years. The analyses of geopotential height, wind, stationary Rossby wavenumber, and Eady growth rate index at 500 or 300 hPa reveal that conditions are favorable for more zonal tracks and greater cyclone growth in strong years than in weak years.

An estimation of the relative change of cyclone intensity and the relative change of Aleutian low intensity is made, which shows that the interannual change of cyclone intensity is about 73% of the interannual change of Aleutian low intensity. This result suggests that the evolution of individual cyclones may be a significant driver of changes in the Aleutian low.

Full access
Liang Ning, Kefan Chen, Jian Liu, Zhengyu Liu, Mi Yan, Weiyi Sun, Chunhan Jin, and Zhengguo Shi

Abstract

The influence and mechanism of volcanic eruptions on decadal megadroughts over eastern China during the last millennium were investigated using a control (CTRL) and five volcanic eruption sensitivity experiments (VOLC) from the Community Earth System Model (CESM) Last Millennium Ensemble (LME) archive. The decadal megadroughts associated with the failures of the East Asian summer monsoon (EASM) are associated with a meridional tripole of sea surface temperature anomalies (SSTAs) in the western Pacific from the equator to high latitudes, suggestive of a decadal-scale internal mode of variability that emerges from empirical orthogonal function (EOF) analysis. Composite analyses further showed that, on interannual time scales, within a decade after an eruption the megadrought was first enhanced but then weakened, due to the change from an El Niño state to a La Niña state. The impacts of volcanic eruptions on the magnitudes of megadroughts are superposed on internal variability. Therefore, the evolution of decadal megadroughts coinciding with strong volcanic eruptions demonstrate that the impacts of internal variability and external forcing can combine to influence hydroclimate.

Open access
Shan Li, Shaoqing Zhang, Zhengyu Liu, Xiaosong Yang, Anthony Rosati, Jean-Christophe Golaz, and Ming Zhao

Abstract

Uncertainty in cumulus convection parameterization is one of the most important causes of model climate drift through interactions between large-scale background and local convection that use empirically set parameters. Without addressing the large-scale feedback, the calibrated parameter values within a convection scheme are usually not optimal for a climate model. This study first designs a multiple-column atmospheric model that includes large-scale feedbacks for cumulus convection and then explores the role of large-scale feedbacks in cumulus convection parameter estimation using an ensemble filter. The performance of convection parameter estimation with or without the presence of large-scale feedback is examined. It is found that including large-scale feedbacks in cumulus convection parameter estimation can significantly improve the estimation quality. This is because large-scale feedbacks help transform local convection uncertainties into global climate sensitivities, and including these feedbacks enhances the statistical representation of the relationship between parameters and state variables. The results of this study provide insights for further understanding of climate drift induced from imperfect cumulus convection parameterization, which may help improve climate modeling.

Full access