Use of Targeted Orographic Smoothing in Very High Resolution Simulations of a Downslope Windstorm and Rotor in a Sub-tropical Highland Location

Nested simulations of a downslope windstorm over Cangshan mountain,Yunnan,China,have been used to demonstrate a method of topographic smoothing that preserves a relatively large amount of terrain detail compared to typical smoothing procedures required for models with terrain-following grids to run stably.The simulations were carried out using the Met Office Unified Model(MetUM)to investigate downslope winds.The smoothing method seamlessly blends two terrain datasets to which uniform smoothing has been applied—one with a minimum of smoothing,the other smoothed more heavily to remove gradients that would cause model instabilities.The latter dataset dominates the blend where the steepest slopes exist,but this is localised and recedes outside these areas.As a result,increased detail is starkly apparent in depictions of flow simulated using the blend,compared to one using the default approach.This includes qualitative flow details that were absent in the latter,such as narrow shooting flows emerging from roughly 1-2 km wide leeside channels.Flow separation is more common due to steeper lee slopes.The use of targeted smoothing also results in increased lee side temporal variability at a given point during the windstorm,including over flat areas.Low-/high-pass filtering of the wind perturbation field reveals that relative spatial variability above 30 km in scale(reflecting the background flow)is similar whether or not targeting is used.Beneath this scale,when smoothing is targeted,relative flow variability decreases at the larger scales,and increases at lower scales.This seems linked to fast smaller scale flows disturbing more coherent flows(notably an along-valley current over Erhai Lake).Spatial variability of winds in the model is unsurprisingly weaker at key times than is observed across a local network sampling mesoscale variation,but results are compromised due to relatively few observation locations sampling the windstorm.Only when targeted smoothing is applied does the model capture the downslope windstorm's extension over the city of Dali at the mountain's foot,and the peak mean absolute wind.

Spatial Inhomogeneity of Atmospheric CO_(2) Concentration and Its Uncertainty in CMIP6 Earth System Models

This paper provides a systematic evaluation of the ability of 12 Earth System Models(ESMs)participating in the Coupled Model Intercomparison Project Phase 6(CMIP6)to simulate the spatial inhomogeneity of the atmospheric carbon dioxide(CO_(2))concentration.The multi-model ensemble mean(MME)can reasonably simulate the increasing trend of CO_(2) concentration from 1850 to 2014,compared with the observation data from the Scripps CO_(2) Program and CMIP6 prescribed data,and improves upon the CMIP5 MME CO_(2) concentration(which is overestimated after 1950).The growth rate of CO_(2) concentration in the northern hemisphere(NH)is higher than that in the southern hemisphere(SH),with the highest growth rate in the mid-latitudes of the NH.The MME can also reasonably simulate the seasonal amplitude of CO_(2) concentration,which is larger in the NH than in the SH and grows in amplitude after the 1950s(especially in the NH).Although the results of the MME are reasonable,there is a large spread among ESMs,and the difference between the ESMs increases with time.The MME results show that regions with relatively large CO_(2) concentrations(such as northern Russia,eastern China,Southeast Asia,the eastern United States,northern South America,and southern Africa)have greater seasonal variability and also exhibit a larger inter-model spread.Compared with CMIP5,the CMIP6 MME simulates an average spatial distribution of CO_(2) concentration that is much closer to the site observations,but the CMIP6-inter-model spread is larger.The inter-model differences of the annual means and seasonal cycles of atmospheric CO_(2) concentration are both attributed to the differences in natural sources and sinks of CO_(2) between the simulations.

BCC-ESM1 Model Datasets for the CMIP6 Aerosol Chemistry Model Intercomparison Project (AerChemMIP)

BCC-ESM1 is the first version of the Beijing Climate Center’s Earth System Model,and is participating in phase 6 of the Coupled Model Intercomparison Project(CMIP6).The Aerosol Chemistry Model Intercomparison Project(AerChemMIP)is the only CMIP6-endorsed MIP in which BCC-ESM1 is involved.All AerChemMIP experiments in priority 1 and seven experiments in priorities 2 and 3 have been conducted.The DECK(Diagnostic,Evaluation and Characterization of Klima)and CMIP historical simulations have also been run as the entry card of CMIP6.The AerChemMIP outputs from BCC-ESM1 have been widely used in recent atmospheric chemistry studies.To facilitate the use of the BCC-ESM1 datasets,this study describes the experiment settings and summarizes the model outputs in detail.Preliminary evaluations of BCC-ESM1 are also presented,revealing that:the climate sensitivities of BCC-ESM1 are well within the likely ranges suggested by IPCC AR5;the spatial structures of annual mean surface air temperature and precipitation can be reasonably captured,despite some common precipitation biases as in CMIP5 and CMIP6 models;a spurious cooling bias from the 1960s to 1990s is evident in BCC-ESM1,as in most other ESMs;and the mean states of surface sulfate concentrations can also be reasonably reproduced,as well as their temporal evolution at regional scales.These datasets have been archived on the Earth System Grid Federation(ESGF)node for atmospheric chemistry studies.

Variation of Dust in Northern China and Its Reproduction in BCC-ESM1 since 1980

In this paper,we explore the possible causes and mechanisms for the variation of dust in northern China from 1980to 2014 using the Modern-Era Retrospective analysis for Research and Applications version 2(MERRA-2)data,observational data,and BCC-ESM1(Beijing Climate Center Earth System Model version 1)simulation data.Two important dust centers are identified in China:one in the Taklamakan Desert in southern Xinjiang Region and the other in the Badain Jaran Desert in western Inner Mongolia Plateau.Both centers display distinct seasonal variations,with high dust concentration in spring and summer and low in autumn and winter.BCC-ESM1 is able to generally capture the main spatial and temporal characteristics of dust in northern China.Both the MERRA-2 reanalysis data and BCCESM1 simulation data show a decreasing trend in spring dust,which is evident during 1980–2000 and 2001–2014.The analysis based on daily mean dust loads and wind fields from MERRA-2 and BCC-ESM1 indicates that dusty weather in North China may be mainly caused by transport of the dust,especially that from the central and western Inner Mongolia Plateau during the prior 0–2 days,through the westerly winds from the upstream“dust core”region(38°–45°N,90°–105°E).This is one of the important paths for dust to move into North China.The weakened westerly wind in the lower troposphere in this“dust core”region may be responsible for the reduction of spring dust in North China.

Science and prediction of monsoon heavy rainfall

With the increasing incidence of heavy rainfall events,particularly over the monsoon regions,the highly dense populations are more vulnerable[1].Research initiatives on observation,modeling,and prediction of monsoon heavy rainfall have been promoted actively by World Weather Research Programme's(WWRP)Working Group on Tropical Meteorology Research(WGTMR)of the World Meteorological Organization(WMO)since 2010.Series of monsoon-heavy-rainfall workshops were held in Beijing(2011),Petaling Jaya(2012),and New Delhi(2015)to benefit scientists worldwide and forecasters from the National Meteorological and Hydrological Services.An international Research and Development Project,namely,the Southern China Monsoon Rainfall Experiment(SCMREX)[2]was established in 2013 to coordinate field campaign experiments and to conduct scientific research on presummer(April-June)heavy rainfall processes in southern China.

Present-Day PM_(2.5)over Asia:Simulation and Uncertainty in CMIP6 ESMs

This study assesses the ability of 10 Earth System Models(ESMs)that participated in the Coupled Model Intercomparison Project Phase 6(CMIP6)to reproduce the present-day inhalable particles with diameters less than 2.5 micrometers(PM_(2.5))over Asia and discusses the uncertainty.PM_(2.5)accounts for more than 30%of the surface total aerosol(fine and coarse)concentration over Asia,except for central Asia.The simulated spatial distributions of PM_(2.5)and its components,averaged from 2005 to 2020,are consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2(MERRA-2)reanalysis.They are characterized by the high PM_(2.5)concentrations in eastern China and northern India where anthropogenic components such as sulfate and organic aerosol dominate,and in northwestern China where the mineral dust in PM_(2.5)fine particles(PM_(2.5)DU)dominates.The present-day multimodel mean(MME)PM_(2.5)concentrations slightly underestimate ground-based observations in the same period of 2014–2019,although observations are affected by the limited coverage of observation sites and the urban areas.Those model biases partly come from other aerosols(such as nitrate and ammonium)not involved in our analyses,and also are contributed by large uncertainty in PM_(2.5)simulations on local scale among ESMs.The model uncertainties over East Asia are mainly attributed to sulfate and PM_(2.5)DU;over South Asia,they are attributed to sulfate,organic aerosol,and PM_(2.5)DU;over Southeast Asia,they are attributed to sea salt in PM_(2.5)fine particles(PM_(2.5)SS);and over central Asia,they are attributed to PM_(2.5)DU.They are mainly caused by the different representations of aerosols within individual ESMs including the representation of aerosol size distributions,dynamic transport,and physical and chemistry mechanisms.