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Data assimilation method for control analysis: 3D Var method for oceanic analysis and nudging technique for atmospheric analysis used in BCC Coordinated Initialization System (CIS). The CIS is to use BCC-CSM1.2 integrating a period time before forecast time to create a coordinated initial state in each component of BCC-CSM1.2 under the forcing of the NCEP air temperature and U- and V-velocity reanalyses, BCC merged precipitation observations, and BCC Global Ocean Data Assimilation System analyses.
Resolution of model used to generate Control Analysis: T106L40 resolution for atmospheric model component, and 1°/3-1° horizontal resolution and 40 vertical levels for oceanic model component
Ensemble initial perturbation strategy: LAF perturbations added to control analysis
Horizontal and vertical resolution of perturbations: same as the control analysis
Perturbations in +/- pairs: No
Initialization of land surface:
1. What is the land surface model (LSM) and version used in the forecast model, and what are the current/relevant references for the model? BCC_AVIM2 land surface model was used in the forecast model. It was originated from the Atmosphere and Vegetation Interaction Model version 2 (AVIM2, Ji, 1995; Ji, et al. 2008) and the NCAR Community Land Model version 3.0 (CLM3, Oleson et al., 2004). An overview on the development of this model is given in Wu et al. (2013, 2014).
Are there any significant changes/deviations in the operational version of the LSM from the documentation of the LSM? There are no changes in the operational version of the LSM.
2. How is soil moisture initialized in the forecasts? (climatology / realistic / other): Soil moisture is not directly initialized using the climatology or realistic analysis in the forecasts. Nevertheless, we have utilized high-level and near-surface atmospheric analysis and ocean analysis to force the air-sea-land-ice coupled model in a long-term integration, and the land initial conditions are produced during this process.
Is there horizontal and/or vertical interpolation of initialization data onto the forecast model grid? If so, please give original data resolution(s). No initialization data about soil moisture is interpolated onto the model grid.
Does the LSM differentiate between liquid and ice content of the soil? If so, how are each initialized? Yes, liquid and ice content of soil are different in BCC_AVIM model, but they were not initialized in the forecasts.
If all model soil layers are not initialized in the same way or from the same source, please describe. No, all soil layers are treated in same way.
3. How is snow initialized in the forecasts? (climatology / realistic / other)It is similar as the above mentioned for question 2.
Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) No initialization data about snow is interpolated onto the model grid.
Are snow mass, snow depth or both initialized? What about snow age, albedo, or other snow properties? They were not directly initialized in the forecasts. The initial conditions are from a balance state produced by a long-term air-sea initialization integration. The method is similar as the above mentioned for question 2.
4. How is soil temperature initialized in the forecasts? (climatology / realistic / other) It is similar as the above mentioned for question 2.
Is the soil temperature initialized consistently with soil moisture (frozen soil water where soil temperature ≤0°C) and snow cover (top layer soil temperature ≤0°C under snow)? These variables are not initialized directly and they are connected with each other by model physics.
Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s)No initialization data about soil temperature is interpolated onto the model grid.
If all model soil layers are not initialized in the same way or from the same source, please describe. No, all soil layers are treated in same way.
5. How are time-varying vegetation properties represented in the LSM? Is phenology predicted by the LSM? If so, how is it initialized? If not, what is the source of vegetation parameters used by the LSM? Which time-varying vegetation parameters are specified (e.g., LAI, greenness, vegetation cover fraction) and how (e.g., near-real-time satellite observations? Mean annual cycle climatology? Monthly, weekly or other interval?) The phenology (LAI) was predicted by the LSM. It is also not directly initialized in forecasts. The initial value is given by a long-term air-sea initialization integration.The vegetation parameters such as vegetation type, vegetation cover fraction and vegetation height are used by the LSM. They are all monthly climatology values.
6. What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM? The soil properties in BCC_AVIM are same as those in NCAR CLM3.0 model (Bonan, 2002). The soil texture (percent sand and clay) varies with depth according to the IGBP soil dataset (Global Soil Data Task 2000).
7. If the initialization of the LSM for re-forecasts deviates from the procedure for forecasts, please describe the differences. The initialization of the LSM in reforecasts is similar as that in forecasts.
4. Model Uncertainties perturbations
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- Liu X., Wu T., Yang S., et al., 2016: MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center. Climate Dynamics, DOI: 10.1007/s00382-016-3264-7
- Wu T. et al., 2014: An overview of BCC climate system model development and application for climate change studies. J. Meteor. Res., 28(1), 34-56;
- Wu T. et al., 2013: Global carbon budgets simulated by the Beijing climate center climate system model for the last century. J Geophys Res Atmos, 118, 4326-4347
- Wu T., R. Yu, F. Zhang, Z. Wang, M. Dong, L. Wang, X.Jin, D. Chen, L. Li, 2010：The Beijing Climate Center atmospheric general circulation model: description and its performance for the present-day climate, Climate Dynamics, 34, 123-147, DOI 10.1007/s00382-008-0487-2.
- Wu T., R. Yu, F. Zhang, 2008: A modified dynamic framework for atmospheric spectral model and its application, J. Atmos.Sci., 65, 2235-2253
- Wu T., 2012: A Mass-Flux Cumulus Parameterization Scheme for Large-scale Models: Description and Test with Observations, Clim. Dyn., 38:725–744, DOI: 10.1007/ s00382-011-0995-3.
- Jie W., T. Wu, J. Wang, W. Li, T. Polivka, 2015: Using a Deterministic Time-Lagged Ensemble Forecast with a Probabilistic Threshold for Improving 6-15 Day Summer Precipitation Prediction in China, Atmospheric Research, 156: 142–159
- Liu X., T. Wu, S. Yang, W. Jie, S. Nie, Q. Li, Y. Cheng, X. Liang, 2015: Performance of the Seasonal Forecasting of the Asian Summer Monsoon by BCC_CSM1.1(m). Adv. Atmos. Sci.,doi: 10.1007/s00376-015-4194-8,in press.
- Liu X., T. Wu, S. Yang, Q. Li, Y. Cheng, X. Liang, Y. Fang, W. Jie, S. Nie. 2014. Relationships between interannual and intraseasonal variations of the Asian - western Pacific summer monsoon hindcasted by the BCC_CSM1.1(m). Adv. Atmos. Sci., 31, 1051–1064.
- Jie W., T. Wu, J. Wang, W. Li, X. Liu, 2014: Improvement of 6–15 day precipitation forecasts using a time-lagged ensemble method. Adv. Atmos. Sci., 31(2), 293–304, doi: 10.1007/s00376-013-3037-8.
- Huang A., Y. Zhang, Z. Wang, T. Wu, D. Huang, Y. Zhou, Y. Zhao, Y. Huang, X. Kuang, L. Zhang, Y. Fang, Y. Guo, 2013: Extended range simulations of the extreme snow storms over southern China in early 2008 with the BCC_AGCM2.1 model,J. Geophys. Res. Atmos., 118, 8253–8273, doi:10.1002/jgrd.50638.
Bonan G B, 2002: The Land Surface Climatology of the NCAR Land Surface Model Coupled to the NCAR Community Climate Model*[J]. Journal of Climate, 15(22):3123–3149.
Ji, J., 1995: A climate-vegetation interaction model: Simulating physical and biological processes at the surface. J Biogeogr, 22: 2063–2069
Ji, J., 1995: A climate-vegetation interaction model: Simulating physical and biological processes at the surface. J Biogeogr, 22: 2063–2069
Oleson, K.W., Y. Dai, and Coauthors, 2004: Technical description of the Community Land Model (CLM). NCAR Tech. Note TN-461+STR, 174 pp.
Wu, T., W. Li, J. Ji et al, 2013: Global carbon budgets simulated by the Beijing Climate Center climate system model for the last century. J Geophys Res Atmos 118:1–22
Wu, T., L. Song, W. Li et al, 2014: An overview of BCC climate system model development and application for climate change studies. J Meteor Res 28:34–56
http://forecast.bcccsm.cma.gov.cn/htm/.
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