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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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Fei Liu, Bin Wang, and In-Sik Kang

Abstract

Both observational data analysis and model simulations suggest that convective momentum transport (CMT) by cumulus convection may play a significant role in the intraseasonal oscillations (ISO) by redistributing atmospheric momentum vertically through fast convective mixing process. The authors present a simple theoretical model for the ISO by parameterizing the cumulus momentum transport process in which the CMT tends to produce barotropic wind anomalies that will affect the frictional planetary boundary layer (PBL). In the model with equatorial easterly vertical wind shear (VWS), it is found that the barotropic CMT tends to select most unstable planetary-scale waves because CMT suppresses the equatorial Ekman pumping of short waves, which reduces the shortwave instability from the PBL moisture convergence and accelerates the shortwave propagation. The model with subtropical easterly VWS has behavior that can be qualitatively different from the model with equatorial easterly VWS and has robust northward propagation. The basic mechanism of this northward propagation is that the CMT accelerates the barotropic cyclonic wind to the north of ISO, which will enhance the precipitation by PBL Ekman pumping and favor the northward propagation. The simulated northward propagation is sensitive to the strength and location of the seasonal-mean easterly VWS. These results suggest that accurate simulation of the climatological-mean state is critical for reproducing the realistic ISO in general circulation models.

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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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Ying Zhao, Bin Wang, and Juanjuan Liu

Abstract

In this study, a new data assimilation system based on a dimension-reduced projection (DRP) technique was developed for the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) modeling system. As an initial step to test the newly developed system, observing system simulation experiments (OSSEs) were conducted using a simulated sea level pressure (SLP) field as “observations” and assimilation experiments using a specified SLP field to evaluate the effects of the new DRP–four-dimensional variational data assimilation (4DVar) method, initialization, and simulation of a tropical storm—Typhoon Bilis (2006) over the western North Pacific. In the OSSEs, the “nature” run, which was assumed to represent the “true” atmosphere, was simulated by the MM5 model, which was initialized with the 1.0° × 1.0° NCEP final global tropospheric analyses and integrated for 120 h. The simulated SLP field was then used as the observations in the data assimilation. It is shown that the MM5 DRP–4DVar system can successfully assimilate the (simulated) model output (used as observations) because the OSSEs resulted in improved storm-track forecasts. In addition, compared with an experiment that assimilated the SLP data fixed at the end of a 6-h assimilation window, the experiment that assimilated the SLP data every 3 min in a 30-min assimilation window further improved the typhoon-track forecasts, especially in terms of the initial vortex location and landfall location. Finally, the assimilation experiments with a specified SLP field have demonstrated the effectiveness of the new method.

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Sihua Huang, Bin Wang, and Zhiping Wen

Abstract

The upper-level tropical easterly jet (TEJ) is a crucial component of the summer monsoon system and tropical general circulation. The simulation and projection of the TEJ, however, have not been assessed. Here we evaluate models’ fidelity and assess the future change of the TEJ by utilizing 16 models that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most of the models can reproduce the TEJ reasonably well in terms of climatology, seasonal evolution, and interannual variability. Nevertheless, underestimation of the TEJ’s intensity and extent is identified, with the maximum bias occurring in the jet centers over the tropical Indian Ocean (IO) and the tropical eastern Pacific (EP). Under the shared socioeconomic pathway 5–8.5, the multimodel ensemble projects a remarkable reduction in the central TEJ intensity by about 18% over the IO and 77% over the EP toward the end of the twenty-first century. The mean intensity of TEJ will weaken by about 11%, and the extent will reduce by 6%, suggesting a significantly weakened upper-level monsoon circulation in the future climate. The projected El Niño–like warming pattern over the tropical Pacific may play a critical role in the future weakening of the TEJ via inducing suppressed rainfall over the tropical eastern IO and Central America. The model uncertainties in the projected TEJ changes may arise from the uncertainties in the models’ projected tropical EP warming. The sensitivity of future projections to model selection is also examined. Results show that the selection of models based on different physical considerations does not yield a significantly different projection.

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Qingnong Xiao, Xiaolei Zou, and Bin Wang

Abstract

The bogus data assimilation (BDA) scheme designed by Zou and Xiao to specify initial structures of tropical cyclones was tested further on the simulation of a landfalling hurricane—Hurricane Fran (1996). The sensitivity of the simulated hurricane track and intensity to the specified radius of maximum wind of the bogus vortex, the resolution of the BDA assimilation model, and the bogus variables specified in the BDA are studied. In addition, the simulated hurricane structures are compared with the available observations, including the surface wind analysis and the radar reflectivity captured onshore during Fran’s landfall.

The sensitivity study of the BDA scheme showed that the simulations of the hurricane track and intensity were sensitive to the size of the specified bogus vortex. Hurricanes with a larger radius of maximum sea level pressure gradient are prone to a more westward propagation. The larger the radius, the weaker the predicted hurricane. Results of the hurricane initial structures and prediction were also sensitive to the bogus variables specified in the BDA. Fitting the model to the bogused pressure data reproduced the hurricane structure of all model variables more efficiently than when fitting it to bogused wind data. Examining the initial conditions produced by the BDA, it is found that the generation and intensity of hurricane warm-core structure in the model initial state was a key factor for the hurricane intensity prediction.

Initialized with the initial conditions obtained by the BDA scheme, the model successfully simulated Hurricane Fran’s landfall, the intensity change, and some inner-core structures. Verified against the surface wind analysis, the model reproduced the distribution of the maximum wind streaks reasonably well. The model-predicted reflectivity field during the landfall of Hurricane Fran resembled the observed radar reflectivity image onshore.

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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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Xiouhua Fu, Bin Wang, and Tim Li

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Atmosphere–ocean coupling was found to play a critical role in simulating the mean Asian summer monsoon and its climatological intraseasonal oscillation (CISO) in comparisons of the results from a stand-alone ECHAM4 atmospheric general circulation model (AGCM) and a coupled ECHAM4–ocean [Wang–Li–Fu (WLF)] model. The stand-alone simulation considerably overestimates the equatorial Indian Ocean rainfall and underestimates monsoon rainfall near 15°N, particularly over the eastern Arabian Sea and the Bay of Bengal. Upon coupling with an ocean model, the simulated monsoon rainfall becomes more realistic with the rainbelt near 15°N (near the equator) intensified (reduced). These two rainbelts are connected by the northward-propagating CISOs that are significantly enhanced by the air–sea interactions.

Both local and remote air–sea interactions in the tropical Indian and Pacific Oceans contribute to better simulation of the Asian summer monsoon. The local impact is primarily due to negative feedback between SST and convection. The excessive rainfall near the equatorial Indian Ocean reduces (increases) the downward solar radiation (upward latent heat flux). These changes of surface heat fluxes cool the sea surface upon coupling, thus reducing local rainfall. The cooling of the equatorial Indian Ocean drives an anticyclonic Rossby wave response and enhances the meridional land–sea thermal contrast. Both strengthen the westerly monsoon flow and monsoon rainfall around 15°N. The local negative feedback also diminishes the excessive CISO variability in the equatorial Indian Ocean that appeared in the stand-alone atmospheric run. The remote impact stems from the reduced rainfall in the western Pacific Ocean. The overestimated rainfall (easterly wind) in the western North (equatorial) Pacific cools the sea surface upon coupling, thus reducing rainfall in the tropical western Pacific. This reduced rainfall further enhances the Indian monsoon rainfall by strengthening the Indian–Pacific Walker circulation. These results suggest that coupling an atmospheric model with an ocean model can better simulate Asian summer monsoon climatology.

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Bin Wang, Chunhan Jin, and Jian Liu

Abstract

Projecting future change of monsoon rainfall is essential for water resource management, food security, disaster mitigation, and infrastructure planning. Here we assess the future change and explore the causes of the changes using 15 models that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The multimodel ensemble projects that, under the shared socioeconomic pathway (SSP) 2–4.5, the total land monsoon rainfall will likely increase in the Northern Hemisphere (NH) by about 2.8% per one degree Celsius of global warming (2.8% °C−1) in contrast to little change in the Southern Hemisphere (SH; −0.3% °C−1). In addition, in the future the Asian–northern African monsoon likely becomes wetter while the North American monsoon becomes drier. Since the humidity increase is nearly uniform in all summer monsoon regions, the dynamic processes must play a fundamental role in shaping the spatial patterns of the global monsoon changes. Greenhouse gas (GHG) radiative forcing induces a “NH-warmer-than-SH” pattern, which favors increasing the NH monsoon rainfall and prolonging the NH monsoon rainy season while reducing the SH monsoon rainfall and shortening the SH monsoon rainy season. The GHG forcing induces a “land-warmer-than-ocean” pattern, which enhances Asian monsoon low pressure and increases Asian and northern African monsoon rainfall, and an El Niño–like warming, which reduces North American monsoon rainfall. The uncertainties in the projected monsoon precipitation changes are significantly related to the models’ projected hemispheric and land–ocean thermal contrasts as well as to the eastern Pacific Ocean warming. The CMIP6 models’ common biases and the processes by which convective heating drives monsoon circulation are also discussed.

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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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