In Press

Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes/issues, but are citable by Digital Object Identifier (DOI).
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Original Paper
Dynamic scaling of the generalized complementary relationship (GCR) improves long-term tendency estimates in land evaporation
jozsef szilagyi, Richard Crago, Ning Ma
, Available online   , Manuscript accepted  28 June 2020, doi: 10.1007/s00376-020-0079-6
Most large-scale evapotranspiration (ET) estimation methods require detailed information of land use, land cover, and/or soil type on top of various atmospheric measurements. The complementary relationship of evaporation (CR) takes advantage of the inherent dynamic feedback mechanisms found in the soil-vegetation-atmosphere interface for its estimation of ET rates without the need of such bio-geo-physical data. Evapotranspiration estimates over the conterminous United States by a new, globally calibrated, static scaling (GCR-stat) of the generalized complementary relationship (GCR) of evaporation were compared to similar estimates of an existing, calibration-free version (GCR-dyn) of the GCR that employs a temporally varying dynamic scaling. Simplified annual water-balances of 327 medium and 18 large watersheds served as ground-truth ET values. With long-term monthly mean forcing, GCR-stat (also utilizing precipitation measurements) outperforms GCR-dyn as the latter cannot fully take advantage of its dynamic scaling with such data of reduced temporal variability. However, in a continuous monthly simulation, GCR-dyn is on a par with GCR-stat, and especially excels in reproducing long-term tendencies in annual catchment ET rates even though it does not require precipitation information. The same GCR-dyn estimates were also compared to similar estimates of eight other popular ET products and generally outperform all of them. For this reason, a dynamic scaling of the GCR is recommended over a static one for modeling long-term behavior of terrestrial ET.
Analysis of Short-term Cloud Feedback in East Asia Using Cloud Radiative Kernels
Fei Wang, Hua Zhang, Qi Chen, Min Zhao, Ting You
, Available online   , Manuscript accepted  28 June 2020, doi: 10.1007/s00376-020-9281-9
First, cloud radiative kernels were built by BCC_RAD radiative transfer code in this work. Then, short-term cloud feedback and its mechanisms in East Asia (-0.5–60.5°N, 69.5–150.5°E) were analyzed quantitatively using the kernels combined with satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua satellite from 2002.7 to 2018.6. According to the surface and monsoon types, four subregions in East Asia, the Tibetan Plateau (TP), northwest (NW), temperate monsoon (TM), and subtropical monsoon (SM), were selected. The average longwave (LW), shortwave (SW), and net cloud feedbacks in East Asia are -0.68 ± 1.20, 1.34 ± 1.08, and 0.66 ± 0.40 W m<sup>-2</sup> K<sup>-1</sup> (±2σ), respectively. Of which, the net feedback is dominated by the positive SW feedback. Positive feedback in SM is the strongest of all subregions, mainly due to the contributions of nimbostratus and stratus. In East Asia, short-term feedback in spring is primarily caused by marine stratus in SM, in summer is primarily driven by deep convective cloud in TM, in autumn is mainly caused by land nimbostratus in SM, and in winter is mainly driven by land stratus in SM. Cloud feedback in East Asia is chiefly driven by decreases in mid-level and low cloud fraction owing to the changes in relative humidity, and a decrease in low cloud optical thickness due to the changes in cloud water content.
The Simulation and Improvements of Oceanic Circulation and Sea Ice by a Coupled Climate System Model FGOALS-f3-L
Yuyang Guo, Yongqiang Yu, Pengfei LIN, Hailong LIU, Bian He, Qing Bao, Bo An, Shuwen Zhao, Lijuan Hua
, Available online   , Manuscript accepted  24 June 2020, doi: 10.1007/s00376-020-0006-x
This study s simulated oceanic circulations and sea ice by a coupled climate system model FGOALS-f3-L developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (LASG/IAP, CAS) under historical forcing from the Coupled Model Inter-comparison Project Phase 6 (CMIP6). FGOALS-f3-L reproduces the fundamental features of global oceanic circulations, such as sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), vertical temperature and salinity, and meridional overturning circulations (MOCs). There are notable improvements compared with the previous version FGOALS-s2, such as a reduction in warm SST biases near the western and eastern boundaries of oceans and salty SSS biases in the tropical western Atlantic and eastern boundaries, and a mitigation of deep MLD biases at high latitudes. However, several obvious biases remain, the most significant biases include cold SST biases in northwestern Pacific (over 4◦C), freshwater SSS biases and deep MLD biases in the subtropics, and temperature and salinity biases in deep ocean at high latitudes. The simulated sea ice shows a reasonable distribution but stronger seasonal cycle than observed. The spatial patterns of sea ice are more realistic in FGOALS-f3-L than its previous version because the latitude-longitude grid is replaced with tripolar grid in the ocean and sea ice model. The most significant biases are the overestimated sea ice and underestimated sea surface salinity in the Labrador Sea and Barents Sea, which are related to the shallower MLD and weaker vertical mixing.
CAS FGOALS-g3 Model Datasets for the CMIP6 Scenario Model Intercomparison Project (ScenarioMIP)
Ye Pu, Hongbo Liu, Ruojing Yan, Hao Yang, Kun Xia, Yiyuan Li, Li Dong, Lijuan Li, He Wang, Yan Nie, Mirong SONG, Jinbo Xie, Shuwen Zhao, Kangjun Chen, Bin Wang, jianghao lee, Ling Zuo
, Available online   , Manuscript accepted  18 June 2020, doi: 10.1007/s00376-020-2032-0
This paper describes the datasets from the Scenario Model Intercomparison Project (ScenarioMIP) simulation experiments run with the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Model grid-point version 3 (CAS FGOALS-g3). FGOALS-g3 is driven by eight shared socio-economic pathways (SSPs) with different sets of future emission, concentration, and land-use scenarios. All Tier 1 and 2 experiments were carried out and were initialized using historical runs. A branch run method was used for the ensemble simulations. Model outputs were three-hourly, six-hourly, daily, and/or monthly mean values for the primary variables of the four component models. An evaluation and analysis of the simulations is also presented. The present results are expected to aid research into future climate change and socio-economic development.
Comparison of ten potential evapotranspiration models and their attribution analyses for ten Chinese drainage basins
Ruiheng Xie, Aihui Wang
, Available online   , Manuscript accepted  18 June 2020, doi: 10.1007/s00376-020-2105-0
Potential evapotranspiration (PET) is usually calculated by empirical methods from surface meteorological variables, such as temperature, radiation and wind speed. The in situ measured pan evaporation (ETpan) can also be used as a proxy PET. In this study, PET values computed from ten models are compared with observed ETpan data in ten Chinese river basins for the 1961-2013 period. The daily observed meteorological variables at 2267 stations are used as the input to those models, and a ranking scheme is applied to rank the statistical quantities (ratio of standard deviations, correlation coefficient and ratio of trends) between ETpan and modeled PETs in different river basins. There are large deviations between the modeled PETs and the ETpan in both the magnitude and the annual trend at most stations. In 8 of the basins (except for Southeast and Southwest China), ETpan shows decreasing trends with magnitudes ranging between -0.01 mmday-1 yr-1 and -0.03 mmday-1 yr-1 while the decreasing trends in modeled PETs are less than -0.01 mmday-1 yr-1. Inter-comparisons among different models in different river basins suggest that, PETHam1 is the best model in the Pearl River basin, PETHam2 outperforms other models in the Huai, Yangtze and Yellow River basins, and PETFAO is the best model for the remaining basins. Sensitivity analyses reveal that wind speed and sunshine duration are two important factors for decreasing PET in most basins except in Southeast and Southwest China. The increasing PET trend in Southeast China is mainly attributed to the reduced relative humidity.
FGOALS-g3 Model Datasets for CMIP6 Flux-Anomaly-Forced Model Intercomparison Project
Yaqi Wang, Zipeng Yu, Pengfei LIN, Hailong LIU, Jiangbo Jin, Lijuan Li, Yanli Tang, Li Dong, Kangjun Chen, yiwen Li, Qian Yang, Mengrong Ding, Yao Meng, Bowen Zhao, Jilin Wei, Jinfeng Ma, Zhikuo Sun
, Available online   , Manuscript accepted  18 June 2020, doi: 10.1007/s00376-020-2045-8
The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) is an endorsed Model Intercomparison Project (MIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The goal of FAFMIP is to investigate the spread in the Atmosphere-Ocean General Circulation Model (AOGCM) projections of ocean climate change forced by CO2 increase, including the uncertainties in the simulations of ocean heat uptake, global mean sea level rise (GMSLR) due to ocean thermal expansion and dynamic sea level (DSL) change due to ocean circulation and density changes. The FAFMIP experiments have already been conducted with Grid-Point Version 3 of the Flexible Global Ocean-Atmosphere-Land System Model (FGOALS-g3). The model datasets have been submitted to the Earth System Grid (ESG) node. Here, the details of the experiments, the output variables and some baseline results are presented. Compared with the preliminary results of other models, the evolutions of global mean variables can be reproduced well by FGOALS-g3. The simulations of spatial patterns are also consistent with those of other models in most regions except the North Atlantic and the Southern Ocean, indicating large uncertainties in the regional sea level projections of these two regions.
Potential Vorticity Diagnostic Analysison the Impact of the Easterlies Vortex on the Short-term Movement of the Subtropical Anticyclone over the Western Pacific in the Meiyu Period
Xiuping Yao, Qin Zhang, Xiao Zhang
, Available online   , Manuscript accepted  16 June 2020, doi: 10.1007/s00376-020-9271-y
By employing the NCEP/NCAR reanalysis datasets, the mechanism of the easterlies vortex (EV) affecting the short-term movement of the subtropical anticyclone over the Western Pacific (WPSA) in the Meiyu period is studied based on the potential vorticity thinking. The results show that the EV and the westerlies vortex (WV) manifest as the downward transport of potential vorticity (PV) in the upper troposphere, and the variation of the corresponding high-value regions of PV significantly reflects the intensity changes of EV and WV. The meridional propagation of PV causes the intensity change of EV. The vertical movement on both sides of EV is related to the position of EV relative to WPSA and the South Asia High (SAH). Affected by the unique spatial configuration of PV surfaces and isentropic surfaces at the eastern margin of SAH, the WV develops towards the troposphere at low latitudes. When the high PV in the easterlies and westerlies arrive at the same longitude in the meridional direction, the special circulation pattern would lower the position of PV isolines at the ridge line of WPSA. Thus, the cyclonic circulation at the lower level would be strengthened, causing the abnormally eastward retreat of WPSA. Analysis of the PV equation at 348-K isentropic surface indicates that when the positive PV variation west of EV intensifies, it connects with the positive PV variation east of WV, forming a positive PV band and making the WPSA retreat abnormally. The local variation of PV is related with its horizontal advection, vertical advection and the spatial non-uniform heating effect.

Revisiting the concentration observations and source apportionment of atmospheric ammonia

Yuepeng Pan, Mengna Gu, Yuexin He, Dianming Wu, Chunyan Liu, Linlin Song, Shili Tian, Xuemei Lü, Yang Sun, Tao Song, Wendell W.Walters, Xuejun Liu, Nicholas A. Martin, Qianqian Zhang, Yunting Fang, Valerio Ferracci, Yuesi Wang
, Available online   , Manuscript accepted  15 June 2020, doi: 10.1007/s00376-020-2111-2
While China's Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in east China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provides evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on whether agricultural or non-agricultural emissions dominate the urban ammonia budget. Identifying the ammonia source by nitrogen isotope helps in designing a mitigation strategy for policy makers, but existing methods have not been well validated. Revisiting the concentration measurement and identifying source apportionment of atmospheric ammonia is thus an essential step towards reducing ammonia emissions.
Quantitative comparison of predictabilities of warm and cold events using the backward nonlinear local Lyapunov exponent method
Xuan LI, Ruiqiang Ding, Jianping Li
, Available online   , Manuscript accepted  12 June 2020, doi: 10.1007/s00376-020-2100-5
The backward nonlinear local Lyapunov exponent method (BNLLE) is applied to quantify the predictability of warm and cold events in the Lorenz model. Results show that the maximum prediction lead times of warm and cold events present obvious layered structures in phase space. The maximum prediction lead times of each warm (cold) event on individual circles concentric with the distribution of warm (cold) regime events are roughly the same, whereas the maximum prediction lead time of events on other circles are different. Statistical results show that warm events are more predictable than cold events.
How different between CMIP6 and CMIP5 models in simulating climate over China and East Asian monsoon?
Dabang Jiang, Dan Hu, Zhiping Tian, Xian-Mei Lang
, Available online   , Manuscript accepted  09 June 2020, doi: 10.1007/s00376-020-2034-y
We compare the ability of coupled global climate models from the Coupled Model Intercomparison Project Phases 5 (CMIP5) to 6 (CMIP6) in simulating the temperature and precipitation climatology and interannual variability over China for the period 1961–2005 and the climatological East Asian monsoon for the period 1979–2005. All 92 models are able to simulate the geographical distribution of the above variables reasonably well. Compared with earlier CMIP5 models, current CMIP6 models have nationally weaker cold biases, a weaker nationwide overestimation of precipitation and a weaker underestimation of the southeast–northwest precipitation gradient, a comparable overestimation of the spatial variability of the interannual variability, and a similar underestimation of the strength of winter monsoon over northern Asia. Pairwise comparison indicates that models have improved from CMIP5 to CMIP6 for climatological temperature and precipitation and winter monsoon, but display little improvement for the interannual temperature and precipitation variability and summer monsoon. The ability of models relates to their horizontal resolutions in certain aspects. Both the multi-model arithmetic mean and median display similar skills and outperform most of individual models in all considered aspects.
An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization
Mingkui Li, Shaoqing Zhang, Lixin Wu, Xiaopei Lin, Ping Chang, Gokhan Danabasoglu, Zhiqiang Wei, Xiaolin Yu, Huiqin Hu, Xiaohui Ma, Weiwei Ma, Haoran Zhao, Dongning Jia, Xin Liu, Kai Mao, Youwei Ma, Yingjing Jiang, Xue Wang, Guangliang Liu, Yuhu Chen
, Available online   , Manuscript accepted  04 June 2020, doi: 10.1007/s00376-020-9220-9
Predicting tropical cyclone (TC) genesis is of great societal importance but scientifically challenging. It requires fine resolution coupled models that properly represent air–sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks, with aids from coherent coupled initialization. This study uses two sets of high-resolution regional coupled models (RCMs) covering the Asia–Pacific (AP) region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific. The AP-RCMs consist of two set resolutions of Weather Research and Forecast–Regional Ocean Model System (WRF-ROMS): 27-km WRF with 9-km ROMS, and 9-km WRF with 3-km ROMS. In this study, a 9-km WRF with 9-km ROMS coupled model system is also used in case test for the predictability of TC genesis. Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved, the enhanced-resolution coupled model tends to improve the predictability of TC genesis, which could be further improved by improving planetary boundary-layer physics thus resolving better air–sea and air–land interactions.
Evaluation of two Initialization Schemes for Simulating the Rapid Intensification of Typhoon Lekima (2019)
Donglei Shi, Guanghua Chen, Ke Wang, Xinxin Bi, Kexin Chen
, Available online   , Manuscript accepted  04 June 2020, doi: 10.1007/s00376-020-2038-7
Two different initialization schemes for tropical cyclone (TC) prediction in numerical models are evaluated based on a case study of Typhoon Lekima (2019). The first is a dynamical initialization (DI) scheme where the axisymmetric TC vortex in the initial conditions is spun up through the 6-h cycle runs before the initial forecast time. The second scheme is a bogussing scheme where the analysis TC vortex is replaced by a synthetic Rankine vortex. Results show that although both initialization schemes can help improve the simulated rapid intensification (RI) of Lekima, the simulation employing DI scheme (DIS) reproduces better the RI onset and intensification rate than that employing the bogussing scheme (BOG). Further analyses show the cycle runs of DI help establish a realistic TC structure with stronger secondary circulation than those in control run and BOG, leading to fast vortex spinup and contraction of the radius of maximum wind (RMW). The resultant strong inner-core primary circulation favors precession of the midlevel vortex under the moderate vertical wind shear (VWS) and thus helps vortex alignment, contributing to an earlier RI onset. Afterwards, the decreased vertical shear and the stronger convection inside the RMW support the persistent RI of Lekima in DIS. In contrast, the reduced VWS is not well captured and the inner-core convection is weaker and resides farther away from the TC center in BOG, leading to slower intensification. The results imply that the DI effectively improves the prediction of the inner-core process, which is crucial to the RI forecast.
Insights into Convective-scale Predictability in East China: Error Growth dynamics and Associated Impact on Precipitation of Warm-Season Convective Events
Xiaoran Zhuang, Jinzhong Min, Liu Zhang, Shizhang Wang, Naigeng Wu, Zhu Haonan
, Available online   , Manuscript accepted  01 June 2020, doi: 10.1007/s00376-020-9269-5
This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basins (YHRB) of East China. The scale-dependent error growth (ensemble variability) and associated impact on precipitation forecasts (precipitation uncertainties) were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong-forcing and weak-forcing. The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale, while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak-forcing. This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales. The lateral boundary condition errors exert quasi-linear increase to error growth with time at the larger-scale, suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties, especially for the strong-forcing regime. Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors. Specifically, small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong-forcing. While larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at meso-β-scale. Consequently, these results can be used to explain under-dispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.
The roles of wind stress and subsurface cold water in the second-year cooling of the 2017/18 La Niña event
Licheng Feng, Rong-Hua Zhang, Bo Yu, Xue Han
, Available online   , Manuscript accepted  28 May 2020, doi: 10.1007/s00376-020-0028-4
After the strong 2015-16 El Niño event, cold conditions prevailed in the tropical Pacific, with the second-year cooling of the 2017/18 La Niña event. Many coupled models failed to predict the cold SST anomalies (SSTAs) in 2017. By using the ERA5 and Global Ocean Data Assimilation System (GODAS) products, atmospheric and oceanic factors are examined that can be responsible for the second-year cooling, including surface wind and subsurface thermal state. A chronicle sequence is described to demonstrate how the cold SSTAs are produced in the central-eastern tropical Pacific in late 2017. Since July 2017, easterly anomalies strengthened in the central Pacific; in the meantime, wind stress divergence anomalies emerged in the far eastern region, which strengthened during the following months and propagated westward, contributing to the development of the second-year cooling in 2017. At the subsurface, weak negative temperature anomalies are were accompanied with upwelling in the eastern equatorial Pacific, which provided the cold water source for the sea surface. Thereafter, both the cold anomalies and upwelling were enhanced and extended westward in the central-eastern equatorial Pacific. These changes were associated with the seasonally weakened EUC and strengthened SEC, which were in favor for more cold waters being accumulated in the central-equatorial Pacific. Then, the subsurface cold waters stretched upward with the convergence of the horizontal currents and eventually outcropped to the surface. The subsurface–induced SSTAs acted to stimulate local coupled air-sea interactions which generated atmospheric-oceanic anomalies developing and evolving into the second-year cooling in the fall of 2017.
Insights of the FY-3D Microwave Instruments: Data Quality and Assimilation in the Met Office NWP System
Fabien Carminati, Nigel Atkinson, Brett Candy, Qifeng Lu
, Available online   , Manuscript accepted  28 May 2020, doi: 10.1007/s00376-020-0010-1
This work presents the evaluation of the microwave instruments on board the latest Chinese polar platform, FY-3D. Comparing 3 months of observations from the Microwave Temperature Sounder 2 (MWTS-2), the Microwave Humidity Sounder 2 (MWHS-2), and the Microwave radiation Imager (MWRI) to the Met Office short range forecasts, we have characterised instrumental biases, show how those biases have changed with respect to their predecessors on board FY-3C, and how they compare to the Advanced Technology Microwave Sounder (ATMS) on board NOAA-20 and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). MWTS-2 global bias is much reduced with respect to its predecessor and compared well to ATMS at equivalent channel frequencies, differing only by 0.36 ± 0.28 K (1σ) on average. A suboptimal averaging of raw digital counts is found to cause an increase in striping noise and an ascending-descending bias. MWHS-2 benefits from a new calibration method improving the 183 GHz humidity channels with respect to its predecessor and biases for these channels are within ± 1.9 K to ATMS. MWRI presents the largest improvements with reduced global bias and standard deviation with respect to the FY-3C version, although spurious, seemingly transient, brightness temperatures have been detected in the observations at 36.5 GHz (vertical polarisation). The strong solar-dependent bias that affects the instrument on FY-3C is reduced to less than 0.2 K on average for FY-3D MWRI.
Introduction to the Regional Coupled Model WRF4-LICOM: Performance and Model Intercomparison over the Western North Pacific
Liwei ZOU, Tianjun ZHOU, Jianping TANG, Hailong LIU
, Available online   , Manuscript accepted  26 May 2020, doi: 10.1007/s00376-020-9268-6
Regional coupled modeling is one of the frontiers of regional climate modeling, but intercomparison has not been well coordinated. In this study, a community regional climate model, WRF4, with a resolution of 15 km, was coupled with a high-resolution (0.1°) North Pacific Ocean model (LICOM_np). The performance of the regional coupled model, WRF4_LICOM, was compared to that of another regional coupled model, RegCM4_LICOM, which was a coupling of version 4 of the Regional Climate Model (RegCM4) with LICOM_np. The analysis focused on the 2005 western North Pacific summer monsoon rainfall. The results showed that the regional coupled models with either RegCM4 or WRF4 as their atmospheric model component simulated the broad features over the WNP reasonably well. Quantitative intercomparison of the regional coupled simulations exhibited different biases for different climate variables. RegCM4_LICOM exhibited smaller biases in its simulation of the averaged June–July–August SST and rainfall, while WRF4_LICOM better captured the tropical cyclone (TC) intensity, the percentage contributions of rainfall induced by TCs to the total rainfall, and the diurnal cycle of rainfall and stratiform percentages, especially over land areas. The different behaviors in rainfall simulated by the two models were partly ascribed to the behaviors in the simulated western North Pacific subtropical high (WNPSH). The stronger (weaker) WNPSH in WRF4_LICOM (RegCM4_LICOM) was driven by overestimated (underestimated) diabatic heating, which peaked at approximately 450 hPa over the region around the Philippines in association with different condensation–radiation processes. Coupling of WRF4 with LIOCM is a crucial step towards the development of the next generation of regional earth system models at the Chinese Academy of Sciences.
Impacts of Multigrid NLS-4DVar-based Doppler Radar Observation Assimilation on Numerical Simulations of Landfalling Typhoon Haikui (2012)
Lu Zhang, Xiangjun Tian, Hongqin Zhang, Feng Chen
, Available online   , Manuscript accepted  26 May 2020, doi: 10.1007/s00376-020-9274-8
We applied the multigrid nonlinear least-squares four-dimensional variational assimilation (MG-NLS4DVar) method in data assimilation and prediction experiments for Typhoon Haikui (2012) using the Weather Research and Forecasting (WRF) model. Observation data included radial velocity (Vr) and reflectivity (Z) data from a single Doppler radar, quality controlled prior to assimilation. Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods. Compared with a forecast that began with the National Centers for Environmental Prediction (NCEP) analysis, our radar data assimilation results were clearly improved in terms of structure, intensity, track, and precipitation prediction for Typhoon Haikui. The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method but that the latter was more efficient. The assimilation of Vr alone and Z alone each improved predictions of typhoon intensity, track, and precipitation; however, the impacts of Vr data were significantly greater that those of Z data. Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.
CAS-FGOALS Datasets for the Two Interglacial Epochs of the Holocene and the Last Interglacial in PMIP4
Weipeng ZHENG, Yongqiang YU, Yihua LUAN, Shuwen ZHAO, Bian HE, Li DONG, Mirong SONG, Pengfei LIN, Hailong LIU
, Available online   , Manuscript accepted  20 May 2020, doi: 10.1007/s00376-020-9290-8
Two versions of the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System model (CAS-FGOALS), version f3-L and g3, are used to simulate the two interglacial epochs of the mid-Holocene and the Last Interglacial in phase 4 of the Paleoclimate Modelling Intercomparison Project (PMIP4), which aims to study the impact of changes in orbital parameters on the Earth’s climate. Following the PMIP4 experimental protocols, four simulations for the mid-Holocene and two simulations for the Last Interglacial have been completed, and all the data, including monthly and daily outputs for the atmospheric, oceanic, land and sea-ice components, have been released on the Earth System Grid Federation (ESGF) node. These datasets contribute to PMIP4 and CMIP6 (phase 6 of the Coupled Model Intercomparison Project) by providing the variables necessary for the two interglacial periods. In this paper, the basic information of the CAS-FGOALS models and the protocols for the two interglacials are briefly described, and the datasets are validated using proxy records. Results suggest that the CAS-FGOALS models capture the large-scale changes in the climate system in response to changes in solar insolation during the interglacial epochs, including warming in mid-to-high latitudes, changes in the hydrological cycle, the seasonal variation in the extent of sea ice, and the damping of interannual variabilities in the tropical Pacific. Meanwhile, disagreements within and between the models and the proxy data are also presented. These datasets will help the modeling and the proxy data communities with better understanding of model performance and biases in paleoclimate simulations.
Automatic Identification of Clear-Air Echoes based on Millimeter-wave Cloud Radar Measurements
Ling YANG, Yun WANG, Zhongke WANG, Qian Yang, Xingang FAN, Fa TAO, Xiaoqiong ZHEN, Zhipeng YANG
, Available online   , Manuscript accepted  09 May 2020, doi: 10.1007/s00376-020-9270-z
Millimeter-wave cloud radar (MMCR) provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds. Therefore, MMCR has been widely applied in cloud observations. However, due to the influence of non-meteorological factors such as insects, the cloud observations are often contaminated by non-meteorological echoes in the clear air, known as clear-air echoes. It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background. The characteristics of clear-air echoes are studied here by combining data from four devices: an MMCR, a laser-ceilometer, an L-band radiosonde, and an all-sky camera. In addition, a new algorithm, which includes feature extraction, feature selection, and classification, is proposed to achieve the automatic identification of clear-air echoes. The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions. The recognition accuracy can reach up to 95.86% for the simple cases when cloud echoes and clear-air echoes are separate, and 88.38% for the complicated cases when low cloud echoes and clear-air echoes are mixed.
Investigating Lightning Characteristics through a Supercell Storm by Comprehensive Coordinated Observations over North China
Dongxia LIU, Xiushu QIE, Yichen CHEN, Zhuling SUN, Shanfeng YUAN
, Available online   , Manuscript accepted  09 May 2020, doi: 10.1007/s00376-020-9264-x
Electrical characteristics of an isolated supercell storm observed on 13 June 2014 over Beijing were investigated using lightning data obtained from the Beijing Lightning Network, radar reflectivity, and hydrometeor retrievals during the 6-h lifetime. Positive cloud-to-ground (+CG) lightning took a high percentage of CG lightning. Before and during a hail event, +CG lightning was more frequent than negative cloud-to-ground (−CG) lightning, except that +CG lightning took a high percentage at the beginning and in the dissipating stage. After the hail event ended, −CG lightning dominated and reached its maximum value. An analysis of hydrometeors retrieved by X-band polarimetric radar revealed that the discharge concentrated in the convective region with graupel particles and hailstones, whereas graupel, snow and ice crystals in the stratiform region. Lightning radiation sources were located mainly in the convective region, some of which were distributed along a gradient of radar reflectivity from the convective region to the stratiform region. The indication is that the supercell demonstrated an inverted tripole charge structure before the hail event, which converted to a normal tripole structure after the hail event.
FIO-ESM v2.0 Outputs for the CMIP6 Global Monsoons Model Intercomparison Project Experiments
Yajuan SONG, Xinfang LI, Ying BAO, Zhenya SONG, Meng WEI, Qi SHU, Xiaodan YANG
, Available online   , Manuscript accepted  06 May 2020, doi: 10.1007/s00376-020-9288-2
Three tiers of experiments in the Global Monsoons Model Intercomparison Project (GMMIP), one of the endorsed model intercomparison projects of phase 6 of the Coupled Model Intercomparison Project (CMIP6), are implemented by the First Institute of Oceanography Earth System Model, version 2 (FIO-ESM v2.0), following the GMMIP protocols. Evaluation of global mean surface air temperature from 1870 to 2014 and climatological precipitation (1979–2014) in tier-1 shows that the atmosphere model of FIO-ESM v2.0 can reproduce the basic observed atmospheric features. In tier-2, the internal variability is reproduced well by the coupled model, with the SST restoring to the model climatology plus the observed anomalies in the tropical Pacific and North Atlantic. Simulation of the Northern Hemisphere summer monsoon circulation is significantly improved by the SST restoration in the North Atlantic. In tier-3, five orographic perturbation experiments are conducted covering the period 1979–2014 by modifying the surface elevation or vertical heating in the prescribed region. In particular, the strength of the South Asian summer monsoon is reduced by removing the topography or thermal forcing above 500 m over the Asian continent. Monthly and daily simulated outputs of FIO-ESM v2.0 are provided through the Earth System Grid Federation (ESGF) node to understand the physical processes of the global monsoon system.
Does CMIP6 Inspire More Confidence in Simulating Climate Extremes over China?
Huanhuan ZHU, Zhihong JIANG, Juan LI, Wei LI, Cenxiao SUN, Laurent LI
, Available online   , Manuscript accepted  06 May 2020, doi: 10.1007/s00376-020-9289-1
Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during 1961–2005, the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), and 30 models from phase 5 of CMIP (CMIP5), are assessed in terms of spatial distribution and interannual variability. The CMIP6 multi-model ensemble mean (CMIP6-MME) can simulate well the spatial pattern of annual mean temperature, maximum daily maximum temperature, and minimum daily minimum temperature. However, CMIP6-MME has difficulties in reproducing cold nights and warm days, and has large cold biases over the Tibetan Plateau. Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices. Compared to CMIP5, CMIP6 models show improvements in the simulation of climate indices over China. This is particularly true for precipitation indices for both the climatological pattern and the interannual variation, except for the consecutive dry days. The areal-mean bias for total precipitation has been reduced from 127% (CMIP5-MME) to 79% (CMIP6-MME). The most striking feature is that the dry biases in southern China, very persistent and general in CMIP5-MME, are largely reduced in CMIP6-MME. Stronger ascent together with more abundant moisture can explain this reduction in dry biases. Wet biases for total precipitation, heavy precipitation, and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME, but smaller, compared to CMIP5-MME.
Diurnal Variation in the Vertical Profile of the Raindrop Size Distribution for Stratiform Rain as Inferred from Micro Rain Radar Observations in Sumatra
, Available online   , Manuscript accepted  29 April 2020, doi: 10.1007/s00376-020-9176-9
The diurnal variation in the vertical structure of the raindrop size distribution (RSD) associated with stratiform rain at Kototabang, West Sumatra (0.20°S, 100.32°E), was investigated using micro rain radar (MRR) observations from January 2012 to August 2016. Along with the MRR data, the RSD from an optical disdrometer and vertical profile of precipitation from the Tropical Rainfall Measuring Mission were used to establish the microphysical characteristics of diurnal rainfall. Rainfall during 0000–0600 LST and 1800–2400 LST had a lower concentration of small drops and a higher concentration of large drops when compared to rainfall during the daytime (0600–1800 LST). The RSD stratified on the basis of rain rate (R) showed a lower total concentration of drops and higher mass-weighted mean diameter in 0000–0600 LST and 1800–2400 LST than in the daytime. During the daytime, the RSD is likely governed by a riming process that can be seen from a weak bright band (BB). On the other hand, during 0000–0600 LST and 1800–2400 LST, the BB was stronger and the rainfall was associated with a higher concentration of midsize and large drops, which could be attributed to more active aggregation right above the melting layer with minimal breakup. Diurnal variation in the vertical profile of RSD led to a different radar reflectivity (Z)–R relationship in the rain column, in which Z during the periods 0000–0600 LST and 1800–2400 LST was larger than at the other times, for the same R.
Model Uncertainty Representation for a Convection-Allowing Ensemble Prediction System Based on CNOP-P
Lu WANG, Xueshun SHEN, Juanjuan LIU, Bin WANG
, Available online   , Manuscript accepted  29 April 2020, doi: 10.1007/s00376-020-9262-z
Formulating model uncertainties for a convection-allowing ensemble prediction system (CAEPS) is a much more challenging problem compared to well-utilized approaches in synoptic weather forecasting. A new approach is proposed and tested through assuming that the model uncertainty should reasonably describe the fast nonlinear error growth of the convection-allowing model, due to the fast developing character and strong nonlinearity of convective events. The Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) is applied in this study. Also, an ensemble approach is adopted to solve the CNOP-P problem. By using five locally developed strong convective events that occurred in pre-rainy season of South China, the most sensitive parameters were detected based on CNOP-P, which resulted in the maximum variations in precipitation. A formulation of model uncertainty is designed by adding stochastic perturbations into these sensitive parameters. Through comparison ensemble experiments by using all the 13 heavy rainfall cases that occurred in the flood season of South China in 2017, the advantages of the CNOP-P-based method are examined and verified by comparing with the well-utilized stochastically perturbed physics tendencies (SPPT) scheme. The results indicate that the CNOP-P-based method has potential in improving the under-dispersive problem of the current CAEPS.
Predictability of ensemble forecasting estimated using the Kullback–Leibler divergence in the Lorenz model
Ruiqiang Ding, Baojia Liu, Bin Gu, Jianping Li, xuan li
, Available online   , Manuscript accepted  30 April 2019
A new method to quantify the predictability limit of ensemble forecasting is presented using the Kullback–Leibler (KL) divergence (also called the relative entropy), which provides a measure of the difference between the probability distributions of ensemble forecasts and local reference (true) states. The KL divergence is applicable to a nonnormal distribution of ensemble forecasts, which is a substantial improvement over the previous method using the ensemble spread. An example from the three-variable Lorenz model illustrates the effectiveness of the KL divergence, which can effectively quantify the predictability limit of ensemble forecasting. On this basis, the KL divergence is used to investigate the dependence of the predictability limit of ensemble forecasting on the initial states and the magnitude of initial errors. The local predictability limit of ensemble forecasting varies considerably with the initial states as well as with the magnitude of initial errors. Further research is needed to examine the real-world applications of the KL divergence in measuring the predictability of ensemble weather forecasts.
News & Views
Machine-learning-based weather support for 2022 Winter Olympics
Jiangjiang Xia, Haochen Li, Yanyan Kang, Chen Yu, Lei Ji, Lue Wu, Xiao Lou, Guangxiang Zhu, Zaiwen Wang, Zhongwei Yan, Li Zhi Wang, Jiang Zhu, Pingwen Zhang, Min Chen, Yingxin Zhang, Lihao Gao, Jiarui Han
, Available online   , Manuscript accepted  28 May 2020, doi: 10.1007/s00376-020-0043-5
Success of the 2022 Winter Olympics will greatly depend on weather conditions at the outdoor competition venues. The current forecasting techniques (numerical weather prediction, i.e. NWP models) are incapable of capturing the complex mountain weather variations around some venues. Aiming at providing high-quality mountain weather forecast for the Winter Olympics, an interdisciplinary data science team was organized. This team proposed NWP model (ECMWF model) output machine learning (MOML) method, which is an integration of machine learning techniques and conventional physical simulation, to improve the objective forecasting capability. MOML method increased the forecasting accuracy by more than 10%, compared with conventional model output statistics (MOS) and the ECMWF model results. Further work will focus on carrying out three tasks to improve machine-learning-based forecast accuracy (especially for the wind forecast). First, to apply MOML with refined numerical model prediction results; Second, to integrate semi-intelligent forecast with forecasters’ experience and the data augmentation techniques; Finally, to apply multi-model integrated probability prediction. In addition, beyond serving the requirements of the Winter Olympics, views of developing objective meteorological prediction by using machine learning techniques are given. For example, multimodal machine learning could be used to make predictions for multiple tasks in a uniform framework; machine learning based model emulation is of potential for reproducing a far more expensive simulation. The 2022 Winter Olympics offer a good opportunity for developing and training a multidisciplinary group of young researchers in meteorology, computer science, mathematics and operational forecasting to promote the development of machine-learning-based weather forecast.
News & Views
The Forgotten Nutrient—The Role of Nitrogen in Permafrost Soils of Northern China
Elisabeth RAMM, Chunyan LIU, Xianwei WANG, Hongyu YUE, Wei ZHANG, Yuepeng PAN, Michael SCHLOTER, Silvia GSCHWENDTNER, Carsten W. MUELLER, Bin HU, Heinz RENNENBERG, Michael DANNENMANN
, Available online   , Manuscript accepted  15 April 2020, doi: 10.1007/s00376-020-0027-5
Data Description Article
Overview of the CMIP6 Historical Experiment Datasets with the Climate System Model CAS FGOALS-f3-L
Yuyang GUO, Yongqiang YU, Pengfei LIN, Hailong LIU, Bian HE, Qing BAO, Shuwen ZHAO, Xiaowei WANG
, Available online   , Manuscript accepted  20 March 2020, doi: 10.1007/s00376-020-2004-4
The three-member historical simulations by the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System model, version f3-L (CAS FGOALS-f3-L), which is contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6), are described in this study. The details of the CAS FGOALS-f3-L model, experiment settings and output datasets are briefly introduced. The datasets include monthly and daily outputs from the atmospheric, oceanic, land and sea-ice component models of CAS FGOALS-f3-L, and all these data have been published online in the Earth System Grid Federation (ESGF, The three ensembles are initialized from the 600th, 650th and 700th model year of the preindustrial experiment (piControl) and forced by the same historical forcing provided by CMIP6 from 1850 to 2014. The performance of the coupled model is validated in comparison with some recent observed atmospheric and oceanic datasets. It is shown that CAS FGOALS-f3-L is able to reproduce the main features of the modern climate, including the climatology of air surface temperature and precipitation, the long-term changes in global mean surface air temperature, ocean heat content and sea surface steric height, and the horizontal and vertical distribution of temperature in the ocean and atmosphere. Meanwhile, like other state-of-the-art coupled GCMs, there are still some obvious biases in the historical simulations, which are also illustrated. This paper can help users to better understand the advantages and biases of the model and the datasets.
Meeting Summary
The First Fengyun Satellite International User Conference
Di XIAN, Peng ZHANG, Meng FANG, Chang LIU, Xu JIA
, Available online   , Manuscript accepted  09 March 2020, doi: 10.1007/s00376-020-2011-5
Erratum to: Estimate of Hydrofluorocarbon Emissions for 2012–16 in the Yangtze River Delta, China
Jingjiao PU, Honghui XU, Bo YAO, Yan YU, Yujun JIANG, Qianli MA, Liqu CHEN
, Available online   , doi: 10.1007/s00376-020-2007-1