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2020 Vol. 37, No. 3

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Data Description Article
LICOM Model Datasets for the CMIP6 Ocean Model Intercomparison Project
Pengfei LIN, Zhipeng YU, Hailong LIU, Yongqiang YU, Yiwen LI, Jirong JIANG, Wei XUE, Kangjun CHEN, Qian YANG, Bowen ZHAO, Jilin WEI, Mengrong DING, Zhikuo SUN, Yaqi WANG, Yao MENG, Weipeng ZHENG, Jinfeng MA
2020, 37(3): 239-249. doi: 10.1007/s00376-019-9208-5
Abstract:
The datasets of two Ocean Model Intercomparison Project (OMIP) simulation experiments from the LASG/IAP Climate Ocean Model, version 3 (LICOM3), forced by two different sets of atmospheric surface data, are described in this paper. The experiment forced by CORE-II (Co-ordinated Ocean–Ice Reference Experiments, Phase II) data (1948–2009) is called OMIP1, and that forced by JRA55-do (surface dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis) data (1958–2018) is called OMIP2. First, the improvement of LICOM from CMIP5 to CMIP6 and the configurations of the two experiments are described. Second, the basic performances of the two experiments are validated using the climatological-mean and interannual time scales from observation. We find that the mean states, interannual variabilities, and long-term linear trends can be reproduced well by the two experiments. The differences between the two datasets are also discussed. Finally, the usage of these data is described. These datasets are helpful toward understanding the origin system bias of the fully coupled model.
Original Paper
Verification of Subseasonal-to-Seasonal Forecasts for Major Stratospheric Sudden Warmings in Northern Winter from 1998/99 to 2012/13
Masakazu TAGUCHI
2020, 37(3): 250-258. doi: 10.1007/s00376-019-9195-6
Abstract:
This study reports verification results of hindcast data of four systems in the subseasonal-to-seasonal (S2S) prediction project for major stratospheric sudden warmings (MSSWs) in northern winter from 1998/99 to 2012/13. This report deals with average features across all MSSWs, and possible differences between two MSSW types (vortex displacement and split types). Results for the average features show that stratospheric forecast verifications, when further averaged among the four systems, are judged to be successful for lead times around 10 d or shorter. All systems are skillful for lead times around 5 d, whereas the results vary among the systems for longer lead times. A comparison between the MSSW types overall suggests larger forecast errors or lower skill for MSSWs of the vortex split type, although the differences do not have strong statistical significance for almost all cases. This limitation is likely to at least partly reflect the small sample size of the MSSWs available.
Contributions to the Interannual Summer Rainfall Variability in the Mountainous Area of Central China and Their Decadal Changes
Kaiming HU, Yingxue LIU, Gang HUANG, Zhuoqi HE, Shang-Min LONG
2020, 37(3): 259-268. doi: 10.1007/s00376-019-9099-5
Abstract:
Using a high-resolution precipitation dataset, the present study detected that the mountainous area of central China (MACA) is a hotspot of ENSO’s impact on the summer rainfall variability. Further analysis suggests that both ENSO and atmospheric forcing make contributions to the summer rainfall variability in MACA. The dominant rainfall-related SST mode features as a seasonal transition from an El Niño-like warming in the preceding winter to a La Nina-like cooling in the following autumn, and it explains about 29% of the total variance of the rainfall during 1951–2018. It indicates that ENSO with a rapid phase transition is responsible for inducing summer rainfall anomalies in MACA. Besides, an upper-level circumglobal wave mode in the Northern Hemisphere during summer also explains about 29% of the summer rainfall variance. Contributions of both the SST and the atmospheric modes have experienced interdecadal changes. The influence of the SST mode gradually increases and plays a dominant role in the recent decades, suggesting that ENSO with a rapid phase transition becomes more important for rainfall prediction in MACA.
Comparison of Advanced Technology Microwave Sounder Biases Estimated Using Radio Occultation and Hurricane Florence (2018) Captured by NOAA-20 and S-NPP
Xiaoxu TIAN, Xiaolei ZOU
2020, 37(3): 269-277. doi: 10.1007/s00376-019-9119-5
Abstract:
The second Advanced Technology Microwave Sounder (ATMS) was onboard the National Oceanic and Atmospheric Administration (NOAA)-20 satellite when launched on 18 November 2017. Using nearly six months of the earliest NOAA-20 observations, the biases of the ATMS instrument were compared between NOAA-20 and the Suomi National Polar-Orbiting Partnership (S-NPP) satellite. The biases of ATMS channels 8 to 13 were estimated from the differences between antenna temperature observations and model simulations generated from Meteorological Operational (MetOp)-A and MetOp-B satellites’ Global Positioning System (GPS) radio occultation (RO) temperature and water vapor profiles. It was found that the ATMS onboard the NOAA-20 satellite has generally larger cold biases in the brightness temperature measurements at channels 8 to 13 and small standard deviations. The observations from ATMS on both S-NPP and NOAA-20 are shown to demonstrate an ability to capture a less than 1-h temporal evolution of Hurricane Florence (2018) due to the fact that the S-NPP orbits closely follow those of NOAA-20.
Uncertainty in Tropical Cyclone Intensity Predictions due to Uncertainty in Initial Conditions
Chenxi WANG, Zhihua ZENG, Ming YING
2020, 37(3): 278-290. doi: 10.1007/s00376-019-9126-6
Abstract:
Focusing on the role of initial condition uncertainty, we use WRF initial perturbation ensemble forecasts to investigate the uncertainty in intensity forecasts of Tropical Cyclone (TC) Rammasun (1409), which is the strongest TC to have made landfall in China during the past 50 years. Forecast results indicate that initial condition uncertainty leads to TC forecast uncertainty, particularly for TC intensity. This uncertainty increases with forecast time, with a more rapid and significant increase after 24 h. The predicted TC develops slowly before 24 h, and at this stage the TC in the member forecasting the strongest final TC is not the strongest among all members. However, after 24 h, the TC in this member strengthens much more than that the TC in other members. The variations in convective instability, precipitation, surface upward heat flux, and surface upward water vapor flux show similar characteristics to the variation in TC intensity, and there is a strong correlation between TC intensity and both the surface upward heat flux and the surface upward water vapor flux. The initial condition differences that result in the maximum intensity difference are smaller than the errors in the analysis system. Differences in initial humidity, and to a lesser extent initial temperature differences, at the surface and at lower heights are the key factors leading to differences in the forecasted TC intensity. These differences in initial humidity and temperature relate to both the overall values and distribution of these parameters.
Sensitivity to Tendency Perturbations of Tropical Cyclone Short-range Intensity Forecasts Generated by WRF
Xiaohao QIN, Wansuo DUAN, Hui XU
2020, 37(3): 291-306. doi: 10.1007/s00376-019-9187-6
Abstract:
The present study uses the nonlinear singular vector (NFSV) approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting (WRF) model for tropical cyclone (TC) intensity forecasts. For nine selected TC cases, the NFSV-tendency perturbations of the WRF model, including components of potential temperature and/or moisture, are calculated when TC intensities are forecasted with a 24-hour lead time, and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts. The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time, and their dominant energies concentrate in the middle layers of the atmosphere. Moreover, such structures do not depend on TC intensities and subsequent development of the TC. The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs, makes larger contributions to the TC intensity forecast uncertainty. Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model, even if using a WRF with coarse resolution.