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Xudong Yin, Juanjuan Liu, and Bin Wang

Abstract

Model parameters can introduce significant uncertainties in climate simulations. Sensitivity analysis provides a way to quantify such uncertainties. Existing sensitivity analysis methods, however, cannot estimate the maximum sensitivity of the simulated climate to perturbations in multiple parameters. This study proposes the concept of nonlinear ensemble parameter perturbation (NEPP), which is independent of model initial conditions, to estimate the maximum effect of parameter perturbations on simulating Earth’s climate. The NEPP is obtained by solving a maximization problem, whose cost function is defined by the maximum deviation of a unique ensemble of short-term predictions with large enough members caused by parameter perturbations and whose optimal solution is obtained by an ensemble-based gradient approach. This method is used to investigate the effects of NEPP on the climate of the Lorenz-63 model and a complex climate model, the Grid-Point Atmospheric Model of IAP LASG, version 2 (GAMIL2). It is found that the NEPP is capable of estimating the maximum change in climate simulation caused by perturbations in multiple parameters when the Lorenz-63 model is used. With a low computational cost, the NEPP can cause remarkable changes in the climatology of GAMIL2. The results also illustrate that the effects of parameter perturbations on short-term weather predictions and those on long-term climate simulations are correlated.

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Susan Kemball-Cook, Bin Wang, and Xiouhua Fu

Abstract

Three 15-yr integrations were made with the ECHAM-4 atmospheric GCM (AGCM); in the first integration, the model lower boundary conditions were the observed monthly mean sea surface temperatures, and, in the second, the AGCM was coupled to the University of Hawaii 2.5-layer intermediate ocean model. In the third simulation, the SST climatology generated in the coupled run was used to create monthly mean SSTs, which were then used to drive the AGCM in an uncoupled configuration similar to the first run. The simulation of the intraseasonal oscillation (ISO) in these three runs was compared with data from the NCEP reanalysis and outgoing longwave radiation from NOAA polar-orbiting satellites, with particular emphasis on the boreal summer ISO.

The overall effect of coupling the AGCM to the ocean model is to improve the intraseasonal variability of the model. Upon coupling, the simulated boreal winter ISO becomes more spatially coherent and has a more realistic phase speed. In the May–June Asian monsoon season, the coupled run shows pronounced northward propagation of convection and circulation anomalies over the Indian Ocean, as in the observations, while northward propagation is absent in the uncoupled run. These improvements in the simulated ISO occur despite the fact that the coupled-run SST climatology has a substantial cold bias in both the Indian Ocean and the western Pacific warm pool. The improvement in the model ISO may be attributed to air–sea interaction whose mechanism is increased low-level convergence into the positive SST anomaly ahead of the convection anomaly.

The simulation of the August–October ISO is degraded upon coupling, however. The coupled-run basic state fails to produce the region of easterly vertical shear of the mean zonal wind, which lies on the equator during August–October. This region of easterly shear is critical for the emission of Rossby waves by equatorial convection associated with the ISO. In the absence of easterly shear, the observed northwestward propagation of convection is inhibited in both runs made using the coupled model basic state. The uncoupled AGCM run correctly locates the region of easterly shear and produces an August–October ISO that agrees well with observations.

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Rucong Yu, Bin Wang, and Tianjun Zhou

Abstract

Evidence is presented to show that the maximum annual mean cloud optical depth between 60°S and 60°N is located on the lee side of the Tibetan Plateau. This largest cloud optical depth is produced by persistent deep stratus clouds (primarily the nimbostratus and altostratus) during winter and spring. These deep stratus clouds are generated and maintained by the frictional and blocking effects of the Tibetan Plateau. The plateau slows down the overflow, inducing downstream midlevel divergence; meanwhile it forces the low-level flows to converge downstream, generating sustained large-scale lifting and stable stratification that maintain the thick stratus clouds.

These stratus clouds produce extremely strong cloud radiative forcing at the top of the atmosphere, which fundamentally influences the local energy balance and climate change. Analysis of the long-term meteorological station observations reveals that the monthly mean anomalous cloudiness and surface temperature vary in tandem. In addition, the surface warming leads to destabilization and desaturation in the boundary layer. This evidence suggests a positive feedback between the continental stratus clouds and surface temperature through changing lower-tropospheric relative humidity and stratification. It is shown that the positive feedback mechanism is more robust during the period of the surface cooling than during the surface warming. It is suggested that the positive climate feedback of the continental stratus cloud may be instrumental in understanding the long-term climatic trend and variations over East Asia.

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Fang Dong, Yangchun Li, and Bin Wang

Abstract

Responses of tropical Pacific air–sea CO2 flux (fCO2) to El Niño–Southern Oscillation (ENSO) events in 14 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are examined. The contributions of sea surface temperature (SST), dissolved inorganic carbon in surface seawater (DIC), and total alkalinity of surface seawater (TALK) to interannual variation of ln(pCO2sea) (instead of partial pressure of CO2 in surface seawater pCO2sea) are quantified based on standardized empirical orthogonal function (EOF) results. Results show that six of the models have poor responses because they fail to reproduce observed interannual variation of pCO2sea in the central-eastern tropical Pacific. These six models underestimate the contribution of DIC interannual variation to interannual variation of pCO2sea in the central-eastern tropical Pacific due to a weak relation between interannual variation of upwelling and ENSO events or a weak relation (including no relation) between interannual variation of upwelling and that of DIC. Furthermore, some models have biases in interannual variation of DIC, in terms of both location and period, that are associated with interannual variation of modeled precipitation. It is also found that two models produce unreasonable interannual variation of bioproductivity, which enlarges interannual variation of DIC in the central-eastern tropical Pacific; this may partly explain why the influence of upwelling on interannual variation of DIC is weak in these models, even when the relationship between interannual variation of DIC and ENSO index is reasonable.

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Bin Wang, Renguang Wu, and Roger Lukas

Abstract

In this paper the amplitude-phase characteristics of the annual adjustment of the thermocline in the entire tropical Pacific Ocean are described and numerical experiments with a tropical ocean model are performed to assess the roles of the major wind systems in controlling the annual variation of the thermocline.

In the region between about 8°N and 10°S, the annual adjustment of the thermocline is controlled by both the Ekman pumping and equatorial wave propagation. The local wind stress forcing plays a dominant role in the central North Pacific (3°–8°N, 170°–120°W) where the thermocline exhibits the largest amplitude due to the prominent annual variation of the wind stress curl south of the ITCZ. In the equatorial central Pacific (2°N–5°S, 170°–120°W), the annual cycle also exhibits a pronounced unimodal seasonal variation (deepest in December and shallowest in May–June). This distinctive annual cycle results primarily from the adjustment of the waves, which are excited around 4°N, 110°W by the annual march of the ITCZ and excited in the equatorial western Pacific by the monsoon flows. The December maximum and May–June minimum then propagate westward in the off-equatorial waveguides along 5°N (3°–7°N) and 6°S (3°–9°S) to the western boundary. These annual Rossby waves are reflected at the western ocean boundary. The bimodal annual variation in the equatorial western Pacific is caused by the combined effects of the annual Rossby wave reflection and the monsoon westerly forcing during transitional seasons. The bimodal variations in the equatorial far eastern Pacific are determined by the remote forcing through the eastward propagation of Kelvin waves.

The thermocline variations in the North Pacific poleward of 8°N and in the South Pacific poleward of 10°S form approximately an annual seesaw oscillation with maximum depth occurring in May–June (October–November) and minimum in December (April–May) in the North (South) Pacific. These regions are characterized by an Ekman regime.

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Chengsi Liu, Qingnong Xiao, and Bin Wang

Abstract

Applying a flow-dependent background error covariance (𝗕 matrix) in variational data assimilation has been a topic of interest among researchers in recent years. In this paper, an ensemble-based four-dimensional variational (En4DVAR) algorithm, designed by the authors, is presented that uses a flow-dependent background error covariance matrix constructed by ensemble forecasts and performs 4DVAR optimization to produce a balanced analysis. A great advantage of this En4DVAR design over standard 4DVAR methods is that the tangent linear and adjoint models can be avoided in its formulation and implementation. In addition, it can be easily incorporated into variational data assimilation systems that are already in use at operational centers and among the research community.

A one-dimensional shallow water model was used for preliminary tests of the En4DVAR scheme. Compared with standard 4DVAR, the En4DVAR converges well and can produce results that are as good as those with 4DVAR but with far less computation cost in its minimization. In addition, a comparison of the results from En4DVAR with those from other data assimilation schemes [e.g., 3DVAR and ensemble Kalman filter (EnKF)] is made. The results show that the En4DVAR yields an analysis that is comparable to the widely used variational or ensemble data assimilation schemes and can be a promising approach for real-time applications.

In addition, experiments were carried out to test the sensitivities of EnKF and En4DVAR, whose background error covariance is estimated from the same ensemble forecasts. The experiments indicated that En4DVAR obtained reasonably sound analysis even with larger observation error, higher observation frequency, and more unbalanced background field.

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Lijuan Li, Bin Wang, and Guang J. Zhang

Abstract

The weak response of surface shortwave cloud radiative forcing (SWCF) to El Niño over the equatorial Pacific remains a common problem in many contemporary climate models. This study shows that two versions of the Grid-Point Atmospheric Model of the Institute of Atmospheric Physics (IAP)/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) (GAMIL) produce distinctly different surface SWCF response to El Niño. The earlier version, GAMIL1, underestimates this response, whereas the latest version, GAMIL2, simulates it well. To understand the causes for the different SWCF responses between the two simulations, the authors analyze the underlying physical mechanisms. Results indicate the enhanced stratiform condensation and evaporation in GAMIL2 play a key role in improving the simulations of multiyear annual mean water vapor (or relative humidity), cloud fraction, and in-cloud liquid water path (ICLWP) and hence in reducing the biases of SWCF and rainfall responses to El Niño due to all of the improved dynamical (vertical velocity at 500 hPa), cloud amount, and liquid water path (LWP) responses. The largest contribution to the SWCF response improvement in GAMIL2 is from LWP in the Niño-4 region and from low-cloud cover and LWP in the Niño-3 region. Furthermore, as a crucial factor in the low-cloud response, the atmospheric stability change in the lower layers is significantly influenced by the nonconvective heating variation during La Niña.

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Tianjun Zhou, Bo Wu, and Bin Wang

Abstract

The authors evaluate the performances of 11 AGCMs that participated in the Atmospheric Model Intercomparison Project II (AMIP II) and that were run in an AGCM-alone way forced by historical sea surface temperature covering the period 1979–99 and their multimodel ensemble (MME) simulation of the interannual variability of the Asian–Australian monsoon (AAM). The authors explore to what extent these models can reproduce two observed major modes of AAM rainfall for the period 1979–99, which account for about 38% of the total interannual variances. It is shown that the MME SST-forced simulation of the seasonal rainfall anomalies reproduces the first two leading modes of variability with a skill that is comparable to the NCEP/Department of Energy Global Reanalysis 2 (NCEP-2) in terms of the spatial patterns and the corresponding temporal variations as well as their relationships with ENSO evolution. Both the biennial tendency and low-frequency components of the two leading modes are captured reasonably in MME. The skill of AMIP simulation is seasonally dependent. December–February (DJF) [July–August (JJA)] has the highest (lowest) skill. Over the extratropical western North Pacific and South China Sea, where ocean–atmosphere coupling may be critical for modeling the monsoon rainfall, the MME fails to demonstrate any skill in JJA, while the reanalysis has higher skills. The MME has deficiencies in simulating the seasonal phase of two anticyclones associated with the first mode, which are not in phase with ENSO forcing in observations but strictly match that of Niño-3.4 SST in MME. While the success of MME in capturing essential features of the first mode suggests the dominance of remote El Niño forcing in producing the predictable portion of AAM rainfall variability, the deficiency in capturing the seasonal phase implies the importance of local air–sea coupling effects. The first mode generally concurs with the turnabout of El Niño; meanwhile, the second mode is driven by La Niña at decaying stage. Multimodel intercomparison shows that there are good relationships between the simulated climatology and anomaly in terms of the degree of accuracy.

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Alejandro Ludert, Bin Wang, and Mark A. Merrifield

Abstract

The U.S.-Affiliated Pacific Islands (USAPIs), located in the tropical western Pacific, are very susceptible to severe drought. Dry season (December–May) rainfall anomalies have different relationships to ENSO for USAPIs north and south of 7°N. South of 7°N, rainfall exhibits a canonical negative correlation with the Oceanic Niño Index (ONI) (i.e., dry conditions during warm periods). To the north, the dry season falls into either “canonical” or “noncanonical” (positively correlated with ONI) regimes. Noncanonical years pose an important forecasting challenge as severe droughts have occurred during cool ONI conditions (referred to here as “cool dry” cases). Composite analysis of the two regimes shows that for noncanonical cool dry years, anticyclonic circulation anomalies over the tropical western North Pacific (TWNP), with a band of anomalous dry conditions extending from the central Pacific toward Micronesia, result in unexpected droughts. In contrast, canonical “cool wet” events show cyclonic TWNP circulation and increased rainfall over the northern USAPIs. Maximum SST anomalies are located near the date line during noncanonical years, and farther east during canonical years. While both regimes show negative rainfall and TWNP anticyclonic circulation anomalies before the onset of the December–May dry season, during the dry season these anomalies persist during noncanonical events but rapidly reverse sign during canonical events. SST anomalies in the noncanonical regime extend eastward from the central Pacific rather than intensify in place over the eastern Pacific in the canonical regime. Differences in the evolution of circulation, precipitation, and SST anomalies suggest distinct physical mechanisms governing the two ENSO regimes, with possible ramifications for seasonal forecasts.

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Chengsi Liu, Qingnong Xiao, and Bin Wang

Abstract

An ensemble-based four-dimensional variational data assimilation (En4DVAR) algorithm and its performance in a low-dimension space with a one-dimensional shallow-water model have been presented in Part I. This algorithm adopts the standard incremental approach and preconditioning in the variational algorithm but avoids the need for a tangent linear model and its adjoint so that it can be easily incorporated into variational assimilation systems. The current study explores techniques for En4DVAR application in real-dimension data assimilation. The EOF decomposed correlation function operator and analysis time tuning are formulated to reduce the impact of sampling errors in En4DVAR upon its analysis. With the Advanced Research Weather Research and Forecasting (ARW-WRF) model, Observing System Simulation Experiments (OSSEs) are designed and their performance in real-dimension data assimilation is examined. It is found that the designed En4DVAR localization techniques can effectively alleviate the impacts of sampling errors upon analysis. Most forecast errors and biases in ARW are reduced by En4DVAR compared to those in a control experiment. En3DVAR cycling experiments are used to compare the ensemble-based sequential algorithm with the ensemble-based retrospective algorithm. These experiments indicate that the ensemble-based retrospective assimilation, En4DVAR, produces an overall better analysis than the ensemble-based sequential algorithm, En3DVAR, cycling approach.

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