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Wei-Chyung Wang, William B. Rossow, Mao-Sung Yao, and Marilyn Wolfson

Abstract

We illustrate the potential complexity of the feedback between global mean cloud amount and global mean surface temperature when variations of the vertical cloud distribution are included by studying the behavior of a one-dimensional radiative–convective model with two types of cloud variation: 1) variable cloud cover with constant optical thickness and 2) variable optical thickness with constant cloud cover. The variable parameter is calculated assuming a correlation between cloud amount and precipitation or the vertical flux convergence of latent heat. Since the vertical latent heat flux is taken to be a fraction of the total heat flux, modeled by convective adjustment, we examine the sensitivity of the results to two different critical lapse rates, a constant 6.5 K km−1 lapse rate and a temperature-dependent, moist adiabatic lapse rate. The effects of the vertical structure of climate perturbations on the nature of the cloud feedback are examined using two cases: a 2% increase in the solar constant and a doubling of the atmospheric carbon dioxide concentration. The model results show that changes in the vertical cloud distribution and mean cloud optical thickness can be as important to climate variations as are changes in the total cloud cover. Further the variety and complexity of the feedbacks exhibited even by this simple model suggest that proper determination of cloud feedbacks must include the effects of varying vertical distribution.

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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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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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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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Thomas R. Karl, Wei-Chyung Wang, Michael E. Schlesinger, Richard W. Knight, and David Portman

Abstract

Important surface observations such as the daily maximum and minimum temperature, daily precipitation, and cloud ceilings often have localized characteristics that are difficult to reproduce with the current resolution and the physical parameterizations in state-of-the-art General Circulation climate Models (GCMs). Many of the difficulties can be partially attributed to mismatches in scale, local topography. regional geography and boundary conditions between models and surface-based observations. Here, we present a method, called climatological projection by model statistics (CPMS), to relate GCM grid-point flee-atmosphere statistics, the predictors, to these important local surface observations. The method can be viewed as a generalization of the model output statistics (MOS) and perfect prog (PP) procedures used in numerical weather prediction (NWP) models. It consists of the application of three statistical methods: 1) principle component analysis (FICA), 2) canonical correlation, and 3) inflated regression analysis. The PCA reduces the redundancy of the predictors The canonical correlation is used to develop simultaneous relationships between linear combinations of the predictors, the canonical variables, and the surface-based observations. Finally, inflated regression is used to relate the important canonical variables to each of the surface-based observed variables.

We demonstrate that even an early version of the Oregon State University two-level atmospheric GCM (with prescribed sea surface temperature) produces free-atmosphere statistics than can, when standardized using the model's internal means and variances (the MOS-like version of CPMS), closely approximate the observed local climate. When the model data are standardized by the observed free-atmosphere means and variances (the PP version of CPMS), however, the model does not reproduce the observed surface climate as well. Our results indicate that in the MOS-like version of CPMS the differences between the output of a ten-year GCM control run and the surface-based observations are often smaller than the differences between the observations of two ten-year periods. Such positive results suggest that GCMs may already contain important climatological information that can be used to infer the local climate.

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Guoxing Chen, Wei-Chyung Wang, Chao-Tzuen Cheng, and Huang-Hsiung Hsu

Abstract

Winter extreme snowstorm events along the coast of the northeast United States have significant impacts on social and economic activities, and their potential changes under global warming are of great concern. Here, we adopted the pseudo–global warming approach to investigate the responses of 93 events identified in our previous observational analysis. The study was conducted by contrasting two sets of WRF simulations for each event: the first set driven by the ERA-Interim reanalysis and the second set by that data superimposed with mean-climate changes simulated from HiRAM historical (1980–2004) and future (2075–99; RCP8.5) runs. Results reveal that the warming together with increased moisture tends to decrease the snowfall along the coast but increase the rainfall throughout the region. For example, the number of events having daily snow water equivalent larger than 10 mm day−1 at Boston, Massachusetts; New York City, New York; Philadelphia, Pennsylvania; and Washington, D.C., is decreased by 47%, 46%, 30%, and 33%, respectively. The compensating changes in snowfall and rainfall lead to a total-precipitation increase in the three more-southern cities but a decrease in Boston. In addition, the southwestward shift of regional precipitation distribution is coherent with the enhancement (reduction) of upward vertical motion in the south (north) and the movement of cyclone centers (westward in 58% of events and southward in 72%). Finally, perhaps more adversely, because of the northward retreat of the 0°C line and the expansion of the near-freezing zone, the number of events with mixed rain and snow and freezing precipitation in the north (especially the inland area) is increased.

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Chia-Chi Wang, Wei-Liang Lee, Yu-Luen Chen, and Huang-Hsiung Hsu

Abstract

The double intertropical convergence zone (ITCZ) bias in the eastern Pacific in the Community Earth System Model version 1 with Community Atmosphere Model version 5 (CESM1/CAM5) is diagnosed. In CAM5 standalone, the northern ITCZ is associated with inertial instability and the southern ITCZ is thermally forced. After air–sea coupling, the processes on both hemispheres are switched because the spatial pattern of sea surface temperature (SST) is changed.

Biases occur during boreal spring in both CAM5 and the ocean model. In CAM5 alone, weaker-than-observed equatorial easterly in the tropical eastern South Pacific leads to weaker evaporation and an increase in local SST. The shallow meridional circulation overly converges in the same region in the CAM5 standalone simulation, the planetary boundary layer and middle troposphere are too humid, and the large-scale subsidence is too weak at the middle levels. These biases may result from excessive shallow convection behavior in CAM5. The extra moisture would then fuel stronger convection and a higher precipitation rate in the southeastern Pacific.

In the ocean model, the South Equatorial Current is underestimated and the North Equatorial Countercurrent is located too close to the equator, causing a warm SST bias in the southeastern Pacific and a cold bias in the northeastern Pacific. These SST biases feed back to the atmosphere and further influence convection and the surface wind biases in the coupled simulation. When the convection in the tropical northeastern Pacific becomes thermally forced after coupling, the northern ITCZ is diminished due to colder SST, forming the so-called alternating ITCZ bias.

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Shou-Jun Chen, Ying-Hwa Kuo, Wei Wang, Zu-Yu Tao, and Bo Cui

Abstract

On 12–13 June 1991, a series of convective rainstorms (defined as mesoscale precipitation systems with rainfall rates exceeding 10 mm h−1) developed successively along the Mei-Yu front. During this event, new rainstorms formed to the east of preceding storms at an interval of approximately 300–400 km. The successive development and eastward propagation of these rainstorms produced heavy rainfall over the Jiang-Huai Basin in eastern China, with a maximum 24-h accumulation of 234 mm. This study presents the results of a numerical simulation of this heavy rainfall event using the Penn State–NCAR Mesoscale Model Version 5 (MM5) with a horizontal resolution of 54 km.

Despite the relatively coarse horizontal resolution, the MM5, using a moist physics package comprising an explicit scheme and the Grell cumulus parameterization, simulated the successive development of the rainstorms. The simulated rainstorms compared favorably with the observed systems in terms of size and intensity. An additional sensitivity experiment showed that latent heat release is crucial for the development of the rainstorms, the mesoscale low-level jet, the mesolow, the rapid spinup of vorticity, and the Mei-Yu frontogenesis. Without latent heat release, the maximum vertical motion associated with the rainstorm is reduced from 70 to 6 cm s−1.

Additional model sensitivity experiments using the Kain–Fritsch cumulus parameterization with grid sizes of 54 and 18 km produced results very similar to the 54-km control experiment with the Grell scheme. This suggests that the simulation of Mei-Yu rainstorms, the mesoscale low-level jet, and the mesolow is not highly sensitive to convective parameterization and grid resolution. In all the full-physics experiments, the model rainfall was dominated by the resolvable-scale precipitation. This is attributed to the high relative humidity and low convective available potential energy environment in the vicinity of the Mei-Yu front.

The modeling results suggest that there is strong interaction and positive feedback between the convective rainstorms embedded within the Mei-Yu front and the Mei-Yu front itself. The front provides a favorable environment for such rainstorms to develop, and the rainstorms intensify the Mei-Yu front.

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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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Shuo Ma, Wei Yan, Yunxian Huang, Jun Jiang, Shensen Hu, and Yingqiang Wang

Abstract

Many quantitative uses of the nighttime imagery provided by low-light sensors, such as the day–night band (DNB) on board the Suomi–National Polar-Orbiting Partnership (SNPP), have emerged recently. Owing to the low nighttime radiance, low-light calibration at night must be investigated in detail. Traditional vicarious calibration methods are based on some targets with nearly invariant surface properties under lunar illumination. However, the relatively stable light emissions may also be used to realize the radiometric calibration under low light. This paper presents a low-light calibration method based on bridge lights, and Visible Infrared Imaging Radiometer Suite (VIIRS) DNB data are used to assess the proposed method. A comparison of DNB high-gain-stage (HGS) radiances over a 2-yr period from August 2012 to July 2014 demonstrates that the predictions are consistent with the observations, and the agreement between the predictions and the observations is on the order of −2.9% with an uncertainty of 9.3% (1σ) for the Hangzhou Bay Bridge and −3.9% with an uncertainty of 7.2% (1σ) for the Donghai Bridge. Such a calibration method based on stable light emissions has a wide application prospect for the calibration of low-light sensors at night.

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