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Yongjie Huang
,
Ming Xue
,
Xiao-Ming Hu
,
Elinor Martin
,
Hector Mayol Novoa
,
Renee A. McPherson
,
Andres Perez
, and
Isaac Yanqui Morales

Abstract

Regional climate dynamical downscaling at convection-permitting resolutions is now practical and has the potential to significantly improve over coarser-resolution simulations, but the former is not necessarily free of systematic biases. The evaluation and optimization of model configurations are therefore important. Twelve simulations at a grid spacing of 3 km using the WRF Model with different microphysics, planetary boundary layer (PBL), and land surface model (LSM) schemes are performed over the Peruvian central Andes during the austral summer, a region with particularly complex terrain. The simulated precipitation is evaluated using rain gauge data and three gridded precipitation datasets. All simulations correctly capture four precipitation hotspots associated with prevailing winds and terrain features along the east slope of the Andes, though they generally overestimate the precipitation intensity. The simulation using Thompson microphysics, Asymmetric Convection Model version 2 (ACM2) PBL, and Noah LSM schemes has the smallest bias. The simulated precipitation is most sensitive to PBL, followed by microphysics, and least sensitive to LSM schemes. The simulated precipitation is generally stronger in simulations using the YSU rather than the MYNN and ACM2 schemes. All simulations successfully capture the diurnal precipitation peak time mainly in the afternoon over the Peruvian central Andes and in the early morning along the east slope. However, there are significant differences over the western Amazon basin, where the precipitation peak occurs primarily in the late afternoon. Simulations using YSU exhibit a 4–8-h delay in the precipitation peak over the western Amazon basin, consistent with their stronger and more persistent low-level jets. These results provide guidance on the optimal configuration of a dynamical downscaling of global climate projections for the Peruvian central Andes.

Restricted access
Xiao-Ming Hu
,
Jun Park
,
Timothy Supinie
,
Nathan A. Snook
,
Ming Xue
,
Keith A. Brewster
,
Jerald Brotzge
, and
Jacob R. Carley

Abstract

During the winter of 2020/21 an ensemble of FV3-LAM forecasts was produced over the contiguous United States for the Winter Weather Experiment using five physics suites. These forecasts are evaluated with the goal of optimizing physics parameterizations within the future operational Rapid Refresh Forecast System (RRFS) in the Unified Forecast System (UFS) realm and for selecting suitable physics suites for a multiphysics RRFS ensemble. The five physics suites have different combinations of land surface models (LSMs), planetary boundary layer (PBL) parameterizations, and surface layer schemes, chosen from those used in current and possible future operational systems and likely to be supported in the operational UFS. Full-season evaluation reveals a persistent near-surface cold bias in the U.S. Northeast from one suite and a nighttime warm bias in the southern Great Plains in another suite, while other suites have smaller biases. A representative case is chosen to diagnose the cause for each of these biases using sensitivity simulations with different physics combinations or modified parameters and verified with additional mesonet observations. The cold bias in the Northeast is attributed to aspects of the Noah-MP LSM over snow cover, where Noah-MP simulates lower soil water content, and thus lower thermal conductivity than other LSMs, leading to less upward ground heat flux during nighttime and consequently lower surface temperature. The nighttime warm bias found in the southern Great Plains is attributed to overestimation of vertical mixing in the K-profile-based eddy-diffusivity mass-flux (K-EDMF) PBL scheme and insufficient land–atmospheric coupling from the GFS surface layer scheme over short vegetation. A few key parameters driving these systematic biases are identified.

Free access
Timothy A. Supinie
,
Jun Park
,
Nathan Snook
,
Xiao-Ming Hu
,
Keith A. Brewster
,
Ming Xue
, and
Jacob R. Carley

Abstract

To help inform physics configuration decisions and help design and optimize a multi-physics Rapid Refresh Forecasting System (RRFS) ensemble to be used operationally by the National Weather Service, five FV3-LAM-based convection allowing forecasts were run on 35 cases between October 2020 and March 2021. These forecasts used ∼3-km grid spacing on a CONUS domain with physics configurations including Thompson, NSSL, and Ferrier–Aligo microphysics schemes, Noah, RUC, and NoahMP land surface models, and MYNN-EDMF, K-EDMF, and TKE-EDMF PBL schemes. All forecasts were initialized from the 0000 UTC GFS analysis and run for 84 h. Also, a subset of 8 cases were run with 15 combinations of physics options, also including the Morrison–Gettelman microphysics and Shin–Hong PBL schemes, to help attribute behaviors to individual schemes and isolate the main contributors of forecast errors. Evaluations of both sets of forecasts find that the CONUS-wide 24-h precipitation > 1 mm is positively biased across all five forecasts. NSSL microphysics displays a low bias in QPF along the Gulf Coast. Analyses show that it produces smaller raindrops prone to evaporation. Additionally, TKE-EDMF PBL in combination with Thompson microphysics displays a positive bias in precipitation over the Great Lakes and in the ocean near Florida due to higher latent heat fluxes calculated over water. Furthermore, the K-EDMF PBL scheme produces temperature errors that result in a negative bias in snowfall over the southern Mountain West. Finally, recommendations for which physics schemes to use in future suites and the RRFS ensemble are discussed.

Free access
Shang-Min Long
,
Shang-Ping Xie
,
Yan Du
,
Qinyu Liu
,
Xiao-Tong Zheng
,
Gang Huang
,
Kai-Ming Hu
, and
Jun Ying

Abstract

The 2015 Paris Agreement proposed targets to limit global-mean surface temperature (GMST) rise well below 2°C relative to preindustrial level by 2100, requiring a cease in the radiative forcing (RF) increase in the near future. In response to changing RF, the deep ocean responds slowly (ocean slow response), in contrast to the fast ocean mixed layer adjustment. The role of the ocean slow response under low warming targets is investigated using representative concentration pathway (RCP) 2.6 simulations from phase 5 of the Coupled Model Intercomparison Project. In RCP2.6, the deep ocean continues to warm while RF decreases after reaching a peak. The deep ocean warming helps to shape the trajectories of GMST and fuels persistent thermosteric sea level rise. A diagnostic method is used to decompose further changes after the RF peak into a slow warming component under constant peak RF and a cooling component due to the decreasing RF. Specifically, the slow warming component amounts to 0.2°C (0.6°C) by 2100 (2300), raising the hurdle for achieving the low warming targets. When RF declines, the deep ocean warming takes place in all basins but is the most pronounced in the Southern Ocean and Atlantic Ocean where surface heat uptake is the largest. The climatology and change of meridional overturning circulation are both important for the deep ocean warming. To keep the GMST rise at a low level, substantial decrease in RF is required to offset the warming effect from the ocean slow response.

Free access
Xiaoyan Zhang
,
Jianping Huang
,
Gang Li
,
Yongwei Wang
,
Cheng Liu
,
Kaihui Zhao
,
Xinyu Tao
,
Xiao-Ming Hu
, and
Xuhui Lee

Abstract

The Weather Research and Forecasting (WRF) Model is used in large-eddy simulation (LES) mode to investigate a lake-breeze case occurring on 12 June 2012 over the Lake Taihu region of China. Observational data from 15 locations, wind profiler radar, and the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to evaluate the WRF nested-LES performance in simulating lake breezes. Results indicate that the simulated temporal and spatial variations of the lake breeze by WRF nested LES are consistent with observations. The simulations with high-resolution grid spacing and the LES scheme have a high correlation coefficient and low mean bias when evaluated against 2-m temperature, 10-m wind, and horizontal and vertical lake-breeze circulations. The atmospheric boundary layer (ABL) remains stable over the lake throughout the lake-breeze event, and the stability becomes even stronger as the lake breeze reaches its mature stage. The improved ABL simulation with LES at a grid spacing of 150 m indicates that the non-LES planetary boundary layer parameterization scheme does not adequately represent subgrid-scale turbulent motions. Running WRF fully coupled to a lake model improves lake-surface temperature and consequently the lake-breeze simulations. Allowing for additional model spinup results in a positive impact on lake-surface temperature prediction but is a heavy computational burden. Refinement of a water-property parameter used in the Community Land Model, version 4.5, within WRF and constraining the lake-surface temperature with observational data would further improve lake-breeze representation.

Full access
Jianping Guo
,
Xinyan Chen
,
Tianning Su
,
Lin Liu
,
Youtong Zheng
,
Dandan Chen
,
Jian Li
,
Hui Xu
,
Yanmin Lv
,
Bingfang He
,
Yuan Li
,
Xiao-Ming Hu
,
Aijun Ding
, and
Panmao Zhai

Abstract

The variability of the lower tropospheric temperature inversion (TI) across China remains poorly understood. Using seven years’ worth of high-resolution radiosonde measurements at 120 sites, we compile the climatology of lower tropospheric TI in terms of frequency, intensity, and depth during the period from 2011 to 2017. The TI generally exhibits strong seasonal and geographic dependencies. Particularly, the TI frequency is found to be high in winter and low in summer, likely due to the strong aerosol radiative effect in winter. The frequency of the surface-based inversion (SBI) exhibits a “west low, east high” pattern at 0800 Beijing time (BJT), which then switches to “west high, east low” at 2000 BJT. Both the summertime SBI and elevated inversion (EI) reach a peak at 0800 BJT and a trough at 1400 BJT. Interestingly, the maximum wintertime EI frequency occurs over Southeast China (SEC) rather than over the North China Plain (NCP), likely attributable to the combination of the heating effect of black carbon (BC) originating from the NCP, along with the strong subsidence and trade inversion in SEC. Correlation analyses between local meteorology and TI indicate that larger lower tropospheric stability (LTS) favors more frequent and stronger TIs, whereas the stronger EI under smaller LTS conditions (unstable atmosphere) is more associated with subsidence rather than BC. Overall, the spatial pattern of the lower tropospheric TI and its variability in China are mainly controlled by three factors: local meteorology, large-scale subsidence, and BC-induced heating. These findings help shed some light on the magnitude, spatial distribution, and underlying mechanisms of the lower tropospheric TI variation in China.

Open access