SCM key findings
Key finding #1
• For the strongly forced maritime case, the GFS-GF suite produces weaker convective tendencies and convective transport than GFS-SAS. This alters the relationship among the physics schemes within the suite, leading to the explicit microphysics scheme in GFS-GF to show a greater relative response to the forcing.
Key finding #2
• For the relatively weakly forced continental convection case, the convective tendencies produced by the GFS-GF suite were generally comparable to or greater than those produced by the GFS-SAS suite.
Key finding #3
• Use of the GFS-GF suite leads to higher moisture content in the boundary layer and generally produces a higher cloud fraction throughout the column, particularly in the lower-to-mid troposphere.
Key finding #4
• During the suppressed convection phase of the maritime convective case and two subperiods of the continental convective case, the GFS-GF suite alters the interaction with the PBL scheme, leading to the transport of PBL moisture higher in the column and occasionally spuriously large cloud fraction at the PBL top.
Key finding #5
• Although both suites produce approximately the same precipitation amounts for both cases, the GFS-GF suite produces a much lower convective precipitation ratio and lower temporal variability than the GFS-SAS suite.
Key finding #6
• During the maritime deep convective period, the forcing ensemble elicits greater variability from the GFS-GF suite than the GFS-SAS suite.
Global model key findings
Key finding #1
• There is little difference in results between the cold and cycled runs with GF.
Key finding #2
• For most variables and forecast lead times, regardless of global sub-region, GFS-SAS has less RMSE than GFS-GF. The fewest number of differences are noted in the SH, while the most are seen in the TROP region.
Key finding #3
• The profiles of temperature bias are different between the GFS-SAS and GFS-GF with the better performer depending strongly on sub-region; the GFS-GF is preferred over the NH and GFS-SAS is generally preferred for the TROP.
Key finding #4
• GFS-SAS is warmer than GFS-GF over the CONUS at 2m, and the two configurations have distinct diurnal cycle of errors: GFS-SAS warms up too quickly in the daytime, while GFS-GF maximum temperatures are below observed. A problem noted in a previous GMTB test using the FY16 GFS Physics suite, of CONUS 2-m temperatures increasing with forecast lead time in GFS-SAS runs, has not been seen in this test.
Key finding #5
• Wind biases are similar between the GFS-SAS and GFS-GF throughout the atmosphere in the NH and SH, but the GFS-GF has larger negative biases in the TROP sub-region, especially at upper levels.
Key finding #6
• Precipitation placement is better in GFS-SAS than GFS-GF.
Key finding #7
• The configuration that predicts better precipitation coverage depends on domain. Both configurations see an increase in precipitation coverage with forecast lead time.
Key finding #8
• GFS-GF is more cyclogenetic and produces more tropical cyclogenesis false alarms than GFS-SAS.
Global model key findings
Key finding #1
• GFS-GF produces extra precipitation in the tropics, especially between 5S and 5N.
Key finding #2
• Total precipitation, and its partition between convective and explicit components, is different between GFS-SAS and GFS-GF. Precipitation development occurs faster for the cycled runs. Comparing GFS-SAS with GFS-GF, the spin up time is shorter in GFS-SAS. Compared to CMORPH observations, model precipitation in GFS-GF is too light and frequent in rainfall intensity between 2-7 mm d-1.
Key finding #3
• GFS-GF has more low clouds in the SH and Tropics over the ocean, which leads to a substantially different radiation budget.
Key finding #4
• The terms of the water budget are different between the GFS-SAS and GFS-GF, with the GFS-GF displaying higher precipitable water.
