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Jun Wei, M. T. Li, P. Malanotte-Rizzoli, A. L. Gordon, and D. X. Wang

Abstract

Based on a high-resolution (0.1° × 0.1°) regional ocean model covering the entire northern Pacific, this study investigated the seasonal and interannual variability of the Indonesian Throughflow (ITF) and the South China Sea Throughflow (SCSTF) as well as their interactions in the Sulawesi Sea. The model efficiency in simulating the general circulations of the western Pacific boundary currents and the ITF/SCSTF through the major Indonesian seas/straits was first validated against the International Nusantara Stratification and Transport (INSTANT) data, the OFES reanalysis, and results from previous studies. The model simulations of 2004–12 were then analyzed, corresponding to the period of the INSTANT program. The results showed that, derived from the North Equatorial Current (NEC)–Mindanao Current (MC)–Kuroshio variability, the Luzon–Mindoro–Sibutu flow and the Mindanao–Sulawesi flow demonstrate opposite variability before flowing into the Sulawesi Sea. Although the total transport of the Mindanao–Sulawesi flow is much larger than that of the Luzon–Mindoro–Sibutu flow, their variability amplitudes are comparable but out of phase and therefore counteract each other in the Sulawesi Sea. Budget analysis of the two major inflows revealed that the Luzon–Mindoro–Sibutu flow is enhanced southward during winter months and El Niño years, when more Kuroshio water intrudes into the SCS. This flow brings more buoyant SCS water into the western Sulawesi Sea through the Sibutu Strait, building up a west-to-east pressure head anomaly against the Mindanao–Sulawesi inflow and therefore resulting in a reduced outflow into the Makassar Strait. The situation is reversed in the summer months and La Niña years, and this process is shown to be more crucially important to modulate the Makassar ITF’s interannual variability than the Luzon–Karimata flow that is primarily driven by seasonal monsoons.

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Xidong Wang, Peter C. Chu, Guijun Han, Wei Li, Xuefeng Zhang, and Dong Li

Abstract

A new, fully conserved minimal adjustment scheme with temperature and salinity (T, S) coherency is presented for eliminating false static instability generated from analyzing and assimilating stable ocean (T, S) profiles data, that is, from generalized averaging over purely observed data (data analysis) or over modeled/observed data (data assimilation). This approach consists of a variational method with (a) fully (heat, salt, and potential energy) conserved conditions, (b) minimal adjustment, and (c) (T, S) coherency. Comparison with three existing schemes (minimal adjustment, conserved minimal adjustment, and convective adjustment) using observational profiles and a simple one-dimensional ocean mixed layer model shows the superiority of this new scheme.

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Christopher A. Davis, David A. Ahijevych, Wei Wang, and William C. Skamarock

Abstract

An evaluation of medium-range forecasts of tropical cyclones (TCs) is performed, covering the eastern North Pacific basin during the period 1 August–3 November 2014. Real-time forecasts from the Model for Prediction Across Scales (MPAS) and operational forecasts from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) are evaluated. A new TC-verification method is introduced that treats TC tracks as objects. The method identifies matching pairs of forecast and observed tracks, missed and false alarm tracks, and derives statistics using a multicategory contingency table methodology. The formalism includes track, intensity, and genesis.

Two configurations of MPAS, a uniform 15-km mesh and a variable-resolution mesh transitioning from 60 km globally to 15 km over the eastern Pacific, are compared with each other and with the operational GFS. The two configurations of MPAS reveal highly similar forecast skill and biases through at least day 7. This result supports the effectiveness of TC prediction using variable resolution.

Both MPAS and the GFS suffer from biases in predictions of genesis at longer time ranges; MPAS produces too many storms whereas the GFS produces too few. MPAS better discriminates hurricanes than does the GFS, but the false alarms in MPAS lower overall forecast skill in the medium range relative to GFS. The biases in MPAS forecasts are traced to errors in the parameterization of shallow convection south of the equator and the resulting erroneous invigoration of the ITCZ over the eastern North Pacific.

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Chun-Chieh Wu, Tzu-Hsiung Yen, Ying-Hwa Kuo, and Wei Wang

Abstract

In this study, a series of numerical experiments are performed to examine the ability of a high-resolution mesoscale model to predict the track, intensity change, and detailed mesoscale precipitation distributions associated with Typhoon Herb (1996), which made landfall over Taiwan. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), with a 2.2-km horizontal grid spacing, successfully simulates the mesoscale rainfall distribution associated with Herb, and the predicted maximum 24-h rainfall of 1199 mm accounts for about 70% of the observed amount of 1736 mm at Mount A-Li. It is shown that, with an accurate track simulation, the ability of the model to simulate successfully the observed rainfall is dependent on two factors: the model's horizontal grid spacing and its ability to describe the Taiwan terrain. The existence of the Central Mountain Range has only a minor impact on the storm track, but it plays a key role in substantially increasing the total rainfall amounts over Taiwan. The analysis presented here shows that the model and terrain resolutions play a nearly equivalent role in the heavy precipitation over Mount A-Li. The presence of maximum vertical motion and heating rate in the lower troposphere, above the upslope mountainous region, is a significant feature of forced lifting associated with the interaction of the typhoon's circulation and Taiwan's mountainous terrain. Overall, Typhoon Herb is a case in point to indicate the intimate relation between Taiwan's topography and the rainfall distribution associated with a typhoon at landfall.

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Steven M. Cavallo, Ryan D. Torn, Chris Snyder, Christopher Davis, Wei Wang, and James Done

Abstract

Real-time analyses and forecasts using an ensemble Kalman filter (EnKF) and the Advanced Hurricane Weather Research and Forecasting Model (AHW) are evaluated from the 2009 North Atlantic hurricane season. This data assimilation system involved cycling observations that included conventional in situ data, tropical cyclone (TC) position, and minimum SLP and synoptic dropsondes each 6 h using a 96-member ensemble on a 36-km domain for three months. Similar to past studies, observation assimilation systematically reduces the TC position and minimum SLP errors, except for strong TCs, which are characterized by large biases due to grid resolution. At 48 different initialization times, an AHW forecast on 12-, 4-, and 1.33-km grids is produced with initial conditions drawn from a single analysis member. Whereas TC track analyses and forecasts exhibit a pronounced northward bias, intensity forecast errors are similar to (lower than) the NWS Hurricane Weather Research Model (HWRF) and GFDL forecasts for forecast lead times ≤60 h (>60 h), with the largest track errors associated with the weakest systems, such as Tropical Storm (TS) Erika. Several shortcomings of the data assimilation system are addressed through postseason sensitivity tests, including using the maximum 800-hPa circulation to identify the TC position during assimilation and turning off the quality control for the TC minimum SLP observation when the initial intensity is far too weak. In addition, the improved forecast of TS Erika relative to HWRF is shown to be related to having initial conditions that are more representative of a sheared TC and not using a cumulus parameterization.

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Fuyao Wang, Yan Yu, Michael Notaro, Jiafu Mao, Xiaoying Shi, and Yaxing Wei

Abstract

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled control run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.

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Hainan Gong, Lin Wang, Wen Chen, Renguang Wu, Ke Wei, and Xuefeng Cui

Abstract

In this paper the model outputs from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) are used to examine the climatology and interannual variability of the East Asian winter monsoon (EAWM). The multimodel ensemble (MME) is able to reproduce reasonably well the circulation features of the EAWM. The simulated surface air temperature still suffers from a cold bias over East Asia, but this bias is reduced compared with CMIP phase 3 models. The intermodel spread is relatively small for the large-scale circulations, but is large for the lower-tropospheric meridional wind and precipitation along the East Asian coast. The interannual variability of the EAWM-related circulations can be captured by most of the models. A general bias is that the simulated variability is slightly weaker than in the observations. Based on a selected dynamic EAWM index, the patterns of the EAWM-related anomalies are well reproduced in MME although the simulated anomalies are slightly weaker than the observations. One general bias is that the northeasterly anomalies over East Asia cannot be captured to the south of 30°N. This bias may arise both from the inadequacies of the EAWM index and from the ability of models to capture the EAWM-related tropical–extratropical interactions. The ENSO–EAWM relationship is then evaluated and about half of the models can successfully capture the observed ENSO–EAWM relationship, including the significant negative correlation between Niño-3.4 and EAWM indices and the anomalous anticyclone (or cyclone) over the northwestern Pacific. The success of these models is attributed to the reasonable simulation of both ENSO’s spatial structure and its strength of interannual variability.

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Yan Yu, Michael Notaro, Fuyao Wang, Jiafu Mao, Xiaoying Shi, and Yaxing Wei

Abstract

Generalized equilibrium feedback assessment (GEFA) is a potentially valuable multivariate statistical tool for extracting vegetation feedbacks to the atmosphere in either observations or coupled Earth system models. The reliability of GEFA at capturing the terrestrial impacts on regional climate is demonstrated here using the National Center for Atmospheric Research Community Earth System Model (CESM), with focus on North Africa. The feedback is assessed statistically by applying GEFA to output from a fully coupled control run. To reduce the sampling error caused by short data records, the traditional or full GEFA is refined through stepwise GEFA by dropping unimportant forcings. Two ensembles of dynamical experiments are developed for the Sahel or West African monsoon region against which GEFA-based vegetation feedbacks are evaluated. In these dynamical experiments, regional leaf area index (LAI) is modified either alone or in conjunction with soil moisture, with the latter runs motivated by strong regional soil moisture–LAI coupling. Stepwise GEFA boasts higher consistency between statistically and dynamically assessed atmospheric responses to land surface anomalies than full GEFA, especially with short data records. GEFA-based atmospheric responses are more consistent with the coupled soil moisture–LAI experiments, indicating that GEFA is assessing the combined impacts of coupled vegetation and soil moisture. Both the statistical and dynamical assessments reveal a negative vegetation–rainfall feedback in the Sahel associated with an atmospheric stability mechanism in CESM versus a weaker positive feedback in the West African monsoon region associated with a moisture recycling mechanism in CESM.

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Morris L. Weisman, Christopher Davis, Wei Wang, Kevin W. Manning, and Joseph B. Klemp

Abstract

Herein, a summary of the authors’ experiences with 36-h real-time explicit (4 km) convective forecasts with the Advanced Research Weather Research and Forecasting Model (WRF-ARW) during the 2003–05 spring and summer seasons is presented. These forecasts are compared to guidance obtained from the 12-km operational Eta Model, which employed convective parameterization (e.g., Betts–Miller–Janjić). The results suggest significant value added for the high-resolution forecasts in representing the convective system mode (e.g., for squall lines, bow echoes, mesoscale convective vortices) as well as in representing the diurnal convective cycle. However, no improvement could be documented in the overall guidance as to the timing and location of significant convective outbreaks. Perhaps the most notable result is the overall strong correspondence between the Eta and WRF-ARW guidance, for both good and bad forecasts, suggesting the overriding influence of larger scales of forcing on convective development in the 24–36-h time frame. Sensitivities to PBL, land surface, microphysics, and resolution failed to account for the more significant forecast errors (e.g., completely missing or erroneous convective systems), suggesting that further research is needed to document the source of such errors at these time scales. A systematic bias is also noted with the Yonsei University (YSU) PBL scheme, emphasizing the continuing need to refine and improve physics packages for application to these forecast problems.

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Jianqiong Zhan, Wenyuan Chang, Wei Li, Yanming Wang, Liqi Chen, and Jinpei Yan

Abstract

Fujian Province in southeastern coastal China is a relatively clean region with low emissions, as its high altitude isolates it from the rest of the country. However, the region experienced haze episodes on 3–14 December 2013. The authors performed simulations using the Weather Research and Forecasting Model coupled with chemistry (WRF-Chem) to examine the impacts of meteorological conditions, aerosol radiative feedbacks (ARFs; including aerosol direct and nearly first indirect effect), and internal and external emissions reduction scenarios on particulate matter smaller than 2.5 μm (PM2.5) concentrations. To the best of the authors’ knowledge, this is the first time the WRF-Chem model has been used to study air quality in this region. The model reasonably reproduced the meteorological conditions and PM2.5 concentrations. The analysis demonstrated that the highest-PM2.5 event was associated with a cold surge that promoted the impingement of northern pollutants on the region, and PM2.5 concentrations were sensitive to the emissions from the Yangtze River delta (16.6%) and the North China Plain (12.1%). This suggests that efforts toward coastal air quality improvement require regional cooperation to reduce emissions. Noticeably, ARFs were unlikely to increase PM2.5 concentrations in the coastal region, which was in contrast to the case in northern China. ARFs induced strong clean wind anomalies in the coastal region and also lowered the inland planetary boundary layer, which enhanced the blocking of northern pollutants crossing the high terrain in the north of Fujian Province. This indicates that ARFs tend to weaken the haze intensity in the southeastern coastal region.

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