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Keqi Zhang, Yuepeng Li, Huiqing Liu, Jamie Rhome, and Cristina Forbes

Abstract

The operational forecast demands and constraints of the National Hurricane Center require that a storm surge model in research mode be tested against a benchmark model such as Sea, Lake, and Overland Surges from Hurricanes (SLOSH) for accuracy, computation time, and numerical stability before the model is used for operational forecasts. Additionally, the simulated results must be in a geographic information system format to facilitate the usage of computed storm surge for various applications. This paper presents results from a demonstration project to explore the pathway for the transition of the Coastal and Estuarine Storm Tide (CEST) model to an operational forecast model by testing CEST over SLOSH basins in Florida. The performance and stability of CEST were examined by conducting simulations for Hurricane Andrew (1992) and more than 100 000 synthetic hurricanes for nine SLOSH basins covering the Florida coast and Lake Okeechobee. The results show that CEST produces peak surge heights similar to those from SLOSH. Additionally, CEST has proven to be numerically stable against all synthetic hurricanes and the computation time of CEST is comparable to that of SLOSH. Therefore, CEST has the potential to be used for operational forecasts of storm surge. The potential of producing more detailed real-time surge inundation forecasts was also investigated through the simulations of Andrew's surge on various grids with different cell sizes. The results indicate that CEST can produce 48-h forecasts using a single processor in about 40 min over a grid generated by reducing the cell edge size of the SLOSH grid by 4 times.

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Wenjun Zhang, Fei-Fei Jin, Jing-Xia Zhao, and Jianping Li

Abstract

The fidelity of coupled climate models simulating El Niño–Southern Oscillation (ENSO) patterns has been widely examined. Nevertheless, a systematical narrow bias in the simulated meridional width of the sea surface temperature anomaly (SSTA) of ENSO has been largely overlooked. Utilizing the preindustrial control simulations of 11 coupled climate models from phase 3 of the Coupled Model Intercomparison Project (CMIP3), it was shown that the simulated width of the ENSO SSTA is only about two-thirds of what is observed. Through a heat budget analysis based on simulations and ocean reanalysis datasets, it is demonstrated that the SSTA outside of the equatorial strip is predominantly controlled by the anomalous meridional advection by climatological currents and heat-flux damping. The authors thus propose a simple damped-advective conceptual model to describe ENSO width. The simple model indicates that this width is primarily determined by three factors: meridional current, ENSO period, and thermal damping rate. When the meridional current is weak, it spreads the equatorial SSTA away from the equator less effectively and the ENSO width thus tends to be narrow. A short ENSO period allows less time to transport the equatorial SSTA toward the off-equatorial region, and strong damping prevents expansion of the SSTA away from the equator, both of which lead to the meridional width becoming narrow. The narrow bias of the simulated ENSO width is mainly due to a systematical bias in weak trade winds that lead to weak ocean meridional currents, and partly due to a bias toward short ENSO periods.

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Jie Peng, Zhanqing Li, Hua Zhang, Jianjun Liu, and Maureen Cribb

Abstract

It has been widely recognized that aerosols can modify cloud properties, but it remains uncertain how much the changes and associated variations in cloud radiative forcing are related to aerosol loading. Using 4 yr of A-Train satellite products generated from CloudSat, the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite, and the Aqua satellite, the authors investigated the systematic changes of deep cloud properties and cloud radiative forcing (CRF) with respect to changes in aerosol loading over the entire tropics. Distinct correlations between CRF and aerosol loading were found. Systematic variations in both shortwave and longwave CRF with increasing aerosol index over oceans and aerosol optical depth over land for mixed-phase clouds were identified, but little change was seen in liquid clouds. The systematic changes are consistent with the microphysical effect and the aerosol invigoration effect. Although this study cannot fully exclude the influence of other factors, attempts were made to explore various possibilities to the extent that observation data available can offer. Assuming that the systematic dependence originates from aerosol effects, changes in CRF with respect to aerosol loading were examined using satellite retrievals. Mean changes in shortwave and longwave CRF from very clean to polluted conditions ranged from −192.84 to −296.63 W m−2 and from 18.95 to 46.12 W m−2 over land, respectively, and from −156.12 to −170.30 W m−2 and from 6.76 to 11.67 W m−2 over oceans, respectively.

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Min Wen, Tim Li, Renhe Zhang, and Yanjun Qi

Abstract

The structure and evolution features of the quasi-biweekly (10–20 day) oscillation (QBWO) in boreal spring over the tropical Indian Ocean (IO) are investigated using 27-yr daily outgoing longwave radiation (OLR) and the National Centers for Environment Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data. It is found that a convective disturbance is initiated over the western IO and moves slowly eastward. After passing the central IO, it abruptly jumps into the eastern IO. Meanwhile, the preexisting suppressed convective anomaly in the eastern IO moves poleward in the form of double-cell Rossby gyres. The analysis of vertical circulation shows that a few days prior to the onset of local convection in the eastern equatorial IO an ascending motion appears in the boundary layer.

Based on the diagnosis of the zonal momentum equation, a possible boundary layer–triggering mechanism over the eastern equatorial IO is proposed. The cause of the boundary layer convergence and vertical motion is attributed to the free-atmospheric divergence in association with the development of the barotropic wind. It is the downward transport of the background mean easterly momentum by perturbation vertical motion during the suppressed convective phase of the QBWO that leads to the generation of a barotropic easterly—the latter of which further causes the free-atmospheric divergence and, thus, the boundary layer convergence. The result suggests that the local process, rather than the eastward propagation of the disturbance from the western IO, is essential for the phase transition of the QBWO convection over the eastern equatorial IO.

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Zongjian Ke, Peiqun Zhang, Wenjie Dong, and Laurent Li

Abstract

Seasonal climate prediction, in general, can achieve excellent results with a multimodel system. A relevant calibration of individual models and an optimal combination of individual models are the key elements leading to this success. However, this commonly used approach appears to be insufficient to remove the intermodel systematic errors (IMSE), which represent similar error properties in individual models after their calibration. A new postprocessing method is proposed to correct the IMSE and to increase the prediction skill. The first step consists of carrying out a diagnosis on the calibrated errors before constructing the multimodel ensemble. In contrast to previous studies, the calibrated errors here are treated directly as the investigation target, and temporal correlation coefficients between the calibrated errors and other meteorological variables are calculated. In the second stage, mathematical and statistical tools are applied in an effort to forecast the IMSE in individual models. Then, the IMSE are removed from the calibrated results and the new corrected data are used to construct the multimodel ensemble. The hindcast of the European Union–funded Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel system is used to test the method. The simulated Southern Oscillation index is used to diagnose and to correct the calibrated errors of the simulated precipitation. The prediction qualities of the corrected data are assessed and compared with those of the uncorrected dataset. The results show that it is feasible to improve seasonal precipitation prediction by forecasting and correcting the IMSE. This improvement is visible not only for the individual models, but also for the multimodel ensemble.

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Robert J. Kuligowski, Yaping Li, Yan Hao, and Yu Zhang

Abstract

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar–gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%.

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Qiaohong Sun, Francis Zwiers, Xuebin Zhang, and Guilong Li

Abstract

Long-term changes in extreme daily and subdaily precipitation simulated by climate models are often compared with corresponding temperature changes to estimate the sensitivity of extreme precipitation to warming. Such “trend scaling” rates are difficult to estimate from observations, however, because of limited data availability and high background variability. Intra-annual temperature scaling (here called binning scaling), which relates extreme precipitation to temperature at or near the time of occurrence, has been suggested as a possible substitute for trend scaling. We use a large ensemble simulation of the Canadian regional climate model (CanRCM4) to assess this possibility, considering both daily near-surface air temperature and daily dewpoint temperature as scaling variables. We find that binning curves that are based on precipitation data for the whole year generally look like the composite of binning curves for winter and summer, with the lower temperature portion similar to winter and the higher temperature portion similar to summer, indicating that binning curves reflect seasonal changes in the relationship between temperature and extreme precipitation. The magnitude and spatial pattern of binning and trend scaling rates are also quantitatively different, with little spatial correlation between them, regardless of precipitation duration or choice of temperature variable. The evidence therefore suggests that binning scaling with temperature is not a reliable predictor for future changes in precipitation extremes in the climate simulated by CanRCM4. Nevertheless, external forcing does have a discernable influence on binning curves, which are seen to shift upward and to the right in some regions, consistent with a general increase in extreme precipitation.

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Liwei Zou, Tianjun Zhou, Laurent Li, and Jie Zhang

Abstract

A variable-grid atmospheric general circulation model, namely, Laboratoire de Météorologie Dynamique-zoom, version 4 (LMDz4), with a local zoom over eastern China, is driven by 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data and is used as a downscaling tool of summer rainfall variability for the period 1958–2000. During the integration, the model temperature and wind were nudged to the ERA-40 data through a relaxation procedure. The performance of the LMDz4 in simulating the regional rainfall features is thoroughly assessed through a comparison to both rain gauge data and the reanalysis product. The dynamical downscaling improves not only the climatology of the monsoon major rainband but also the interannual variability modes of rainfall over eastern China in comparison with that of the ERA-40 data. The added values of LMDz4 are evident in both the spatial patterns of dominant rainfall variability modes and the associated temporal variations. A comparison of rainfall averaged over several typical regions shows improvement as a better-matched variability and a reduced root-mean-square error, except for the region over the lower reaches of the Yellow River valley, where the model shows bias because of the northward shift of the monsoon rainband. This rainband shift is caused by the stronger low-level southerlies and the lower specific humidity over southern China. The stronger southwestern wind transports excessive water vapor northward, and the underestimation of specific humidity implies that air masses need to go farther north to reach condensation. Both favor a northward shift of the major rainband. The analysis demonstrates that a variable-resolution AGCM can be a useful tool for the dynamical downscaling of rainfall variability over eastern China, although the rainband bias remains evident as with many other regional climate models.

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Lei Zhang, Weiqing Han, Yuanlong Li, and Toshiaki Shinoda

Abstract

Generation and development mechanisms of the Ningaloo Niño are investigated using ocean and atmospheric general circulation model experiments. Consistent with previous studies, northerly wind anomalies off the West Australian coast are critical in generating warm sea surface temperature (SST) anomalies of the Ningaloo Niño, which induce SST warming through reduced turbulent heat loss toward the atmosphere (by decreasing surface wind speed), enhanced Leeuwin Current heat transport, and weakened coastal upwelling. Our results further reveal that northerly wind anomalies suppress the cold dry air transport from the Southern Ocean to the Ningaloo Niño region, which also contributes to the reduced turbulent heat loss. A positive cloud–radiation feedback is also found to play a role. Low stratiform cloud is reduced by the underlying warm SSTAs and the weakened air subsidence, which further enhances the SST warming by increasing downward solar radiation. The enhanced Indonesian Throughflow also contributes to the Ningaloo Niño, but only when La Niña co-occurs. Further analysis show that northerly wind anomalies along the West Australian coast can be generated by both remote forcing from the Pacific Ocean (i.e., La Niña) and internal processes of the Indian Ocean, such as the positive Indian Ocean dipole (IOD). Approximately 40% of the Ningaloo Niño events during 1950–2010 co-occurred with La Niña, and 30% co-occurred with positive IOD. There are also ~30% of the events independent of La Niña and positive IOD, suggesting the importance of other processes in triggering the Ningaloo Niño.

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Wei Zhang, Bing Fu, Melinda S. Peng, and Tim Li

Abstract

This study investigates the classification of developing and nondeveloping tropical disturbances in the western North Pacific (WNP) through the C4.5 algorithm. A decision tree is built based on this algorithm and can be used as a tool to predict future tropical cyclone (TC) genesis events. The results show that the maximum 800-hPa relative vorticity, SST, precipitation rate, divergence averaged between 1000- and 500-hPa levels, and 300-hPa air temperature anomaly are the five most important variables for separating the developing and nondeveloping tropical disturbances. This algorithm also unravels the thresholds of the five variables (i.e., 4.2 × 10−5 s−1 for maximum 800-hPa relative vorticity, 28.2°C for SST, 0.1 mm h−1 for precipitation rate, −0.7 × 10−6 s−1 for vertically averaged convergence, and 0.5°C for 300-hPa air temperature anomaly). Six rules are derived from the decision tree. The classification accuracy of this decision tree is 81.7% for the 2004–10 cases. The hindcast accuracy for the 2011–13 dataset is 84.6%.

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