| Session Details | |
| Section | AS - Atmospheric Sciences |
| Session Title | Predictability Problems and Systematic Errors in Numerical Weather and Climate Prediction: Theory, Modeling and Evaluation |
| Main Convener | Dr. Shaocheng Xie (Lawrence Livermore National Laboratory, United States) |
| Co-convener(s) | Prof. Wansuo Duan (Institute of Atmospheric Physics, Chinese Academy of Sciences, China) Dr. Kuan-Man Xu (NASA Langley Research Center, United States) Prof. Masahiro Watanabe (The University of Tokyo, Japan) Prof. Huang-Hsiung Hsu (Academia Sinica, Taiwan) Dr. Donghai Wang (State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China) Dr. Stéphane Vannitsem (Royal Meteorological Institute of Belgium, Belgium) Prof. Tieh-Yong Koh (Nanyang Technological University, Singapore) |
| Session Description | Accurately predicting weather and climate using general circulation models has been challenging. The difficulties and challenges may come from uncertainties in the initial conditions and/or errors in the forecast models. Nonlinear processes, leading to a chaotic behavior, may amplify sources of uncertainties and lead to the loss of predictability. Deficiencies in representing subgrid-scale physical processes could lead to large errors in their simulated weather and climate systems. Understanding the predictability of different weather and climate phenomena and the nature and causes of these errors is a critical step towards improving weather and climate models. This session invites presentations in the following two areas: (1) Predictability of various weather and climate phenomena from thunderstorms, torrential rains, tropical cyclones to inter-annual and -decadal variability such as ENSO, IOD, PDO, and AMO. The topics could include, but are not limited to, determining and/or testing the intrinsic limit of predictability by (a) Quantifying practical predictability properties of both deterministic and probabilistic approaches for weather and climate forecasts; (b) Characterizing key nonlinear physical processes controlling the chaotic behavior and hence limiting the predictability; and (c) Exploring novel approaches to reduce/control the above uncertainties such as data assimilation, ensemble forecast, and targeted (i.e., adaptive) observations. (2) Systematic errors in weather and climate models. The topics could include, but are not limited to, (a) Global and regional evaluation of weather and climate models such as those used in operational numerical weather prediction (NWP) centers, major climate centers, and major model intercomparison projects (e.g., CMIP5, YOTC and GASS); (b) Process studies that utilize single-column models, cloud-resolving models, and NWP techniques in climate models; and (c) Diagnostics and metrics that utilize both ground-based and satellite observations. Errors in the mean state and/or diurnal, seasonal, intraseasonal, and interannual variability, in atmosphere or land, in atmosphere-only or coupled atmosphere-ocean models, are all of interest. |