As a contribution to phase 6 of the Coupled Model Intercomparison Project (CMIP6), the global climate simulated by an atmospheric general circulation model (GCM), the Seoul National University Atmosphere Model version 0 with a Unified Convection Scheme (SAM0-UNICON), is compared with observation and climates simulated by the Community Atmosphere Model version 5 (CAM5) and Community Earth System Model version 1 (CESM1), on which SAM0-UNICON is based. Both SAM0-UNICON and CESM1 successfully reproduce observed global warming after 1970. The global mean climate simulated by SAM0-UNICON is roughly similar to that of CAM5/CESM1. However, SAM0-UNICON improves the simulations of the double intertropical convergence zone, shortwave cloud forcing, near-surface air temperature, aerosol optical depth, sea ice fraction, and sea surface temperature (SST), but is slightly poorer for the simulation of tropical relative humidity, Pacific surface wind stress, and ocean rainfall. Two important biases in the simulated mean climate in both models are a set of horseshoe-shaped biases of SST, sea level pressure, precipitation, and cloud radiative forcings in the central equatorial Pacific and a higher sea ice fraction in the Arctic periphery and Southern Hemispheric circumpolar regions. Both SAM0-UNICON and CESM1 simulate the observed El Niño–Southern Oscillation (ENSO) reasonably well. However, compared with CAM5/CESM1, SAM0-UNICON performs better in simulating the Madden–Julian oscillation (MJO), diurnal cycle of precipitation, and tropical cyclones. The aerosol indirect effect (AIE) simulated by SAM0-UNICON is similar to that from CAM5 but the magnitudes of the individual shortwave and longwave AIEs are substantially reduced.

Purpose IBM Watson for Oncology (WFO) is a clinical decision-support computing system that provides oncologists with evidence-based treatment recommendations for a variety of cancer diagnoses. The evidence-based supported treatment recommendations are presented in three categories: Recommended, representing the Memorial Sloan Kettering Cancer Center (MSKCC) preferred approach; For Consideration, evidence-based alternative treatments; and Not Recommended, alternative therapies that may be unacceptable. We examined the absolute concordance of treatment options with that of the recommendations of a multidisciplinary team of oncologists from Gachon University, Gil Medical Centre, Incheon, South Korea. Methods We enrolled 656 patients with stage II, III, and IV colon cancer between 2009 and 2016. Cases were processed using WFO and, using retrospective clinical data, outputs were compared with the actual treatment the patient received. Absolute concordance was defined as an alignment of recommendation in the Recommended MSKCC preferred-approach category. Treatment recommendations that were represented in the For Consideration category were not the focus of this study. Results The absolute concordance between the WFO-derived MSKCC preferred approach and Gil Medical Centre treatment recommendations was 48.9%. The percentage of cases found to be acceptable was 65.8% (432 of 656) and the stage-specific concordance rate was 32.5% for patients with stage II disease who had risk factors and 58.8% for patients with stage III disease. Patients 70 years of age and older had a concordance rate of only 20.2%, whereas younger patients had a concordance rate of 63.8% ( P = .0001). Conclusion The main reasons attributed to the low concordance rate were age, reimbursement plan, omitting chemotherapy after liver resection, and not recommending biologic agents (ie, cetuximab and bevacizumab).

The deterministic prediction skill of the 10 operational models participating in the subseasonal‐to‐seasonal (S2S) prediction project is assessed for both the extratropical stratosphere and troposphere. Based on the mean squared skill score of 50‐ and 500‐hPa geopotential height forecasts, the overall prediction skill is on average 16 days in the stratosphere and 9 days in the troposphere. The high‐top models with a fully resolved stratosphere typically have a higher prediction skill than the low‐top models. Among them, the European Centre for Medium‐Range Weather Forecasts model shows the best performance in both hemispheres. The decomposition of model errors reveals that eddy errors are more important than zonal‐mean errors in both the stratosphere and troposphere. While the errors in the stratosphere are dominated by planetary‐scale eddies, those in the troposphere are equally influenced by planetary‐ and synoptic‐scale eddies. This result indicates that subseasonal‐to‐seasonal prediction could be improved by better representing planetary‐scale wave activities in the model.

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