26 The Self-Aggregation of Convection in Idealized Numerical Simulations using Different Cumulus Parameterizations

Thursday, 2 July 2015
Salon A-3 & A-4 (Hilton Chicago)
Thomas J. Galarneau Jr., NCAR, Boulder, CO; and C. A. Davis, Y. H. Kuo, W. Wang, and D. A. Ahijevych

Handout (5.6 MB)

This study is motivated by the observation that the National Centers for Environmental Prediction Global Forecast System (GFS) systematically loses 10–15% of the total column precipitable water (PW) over the tropical western North Pacific (defined as the region 0–20°N and 120–180°E) in its medium-range (defined here as days 4–7) forecasts compared to the verifying GFS analysis. The loss of PW in the GFS forecast occurs during quiet regimes when a tropical cyclone (TC) is not present at the initial time. When a TC is present, the loss of PW does not occur in the subsequent GFS forecast, indicating that the GFS performs better when an incipient rotational feature is initialized well. The loss of PW appears to occur as convection gradually decreases and subsidence becomes more prevalent. Additionally, surface winds that are too weak contribute to reduced surface latent heat fluxes, which makes it difficult to replenish water vapor in the lower troposphere. The loss of PW contributes to TC “misses” in medium-range GFS forecasts, cases in which the GFS fails to develop an incipient tropical disturbance into a TC. Given that the PW loss worsens with increasing forecast lead, we hypothesize that it is driven by the model physics.

In the present study, we aim to use a simplified idealized framework to test the behavior and evolution of convection and atmospheric water vapor during long model integrations that employ different cumulus parameterizations. Specifically, we will compare a “GFS-like” simulation that uses the Simplified Arakawa-Schubert (SAS) cumulus parameterization to simulations that use the Tiedtke, Kain-Fritsch, and Grell-Freitas cumulus schemes. We use version 3.6.1 of the Weather Research and Forecasting (WRF)–Advanced Research WRF model, where we modified the two-dimensional sea breeze case to run in three-dimensions. We also remove the island in the sea breeze case, leaving an underlying ocean with a fixed sea surface temperature of 30°C. The simulations are initialized with the Rotunno-Emanuel radiative-convective equilibrium sounding (with no wind), with ±0.5°C thermal perturbations in the lowest 10 model levels, and integrated for 90 days with no coriolis and with doubly periodic boundary conditions. A uniform horizontal grid spacing of 15 km (67×67 grid points) with 44 vertical levels is used. A companion simulation run at 3 km horizontal grid spacing (334×334 grid points) with no cumulus parameterization is used for comparison against the coarser resolution simulations. The other physics options used are the Morrison double-moment microphysics, YSU boundary layer, and RRTMG radiation.

Preliminary results show that convection in the 3 km explicit simulation self-aggregates into a single mesoscale convective complex surrounded by extremely dry conditions (known as radiative-convective equilibrium; RCE) in about 40 days. The 15 km simulations that use the Kain-Fritsch and Grell-Freitas cumulus schemes behave similarly, while the simulation using the Tiedtke cumulus scheme reaches RCE in about 30 days. Unlike all other simulations, the “GFS-like” simulation that uses the SAS cumulus scheme reaches RCE in 10–15 days. As early as day 3, the SAS simulation has much lower domain-average PW compared to the other simulations. It appears that convective updrafts early in the model simulation are deeper and more intense in the SAS simulation. The compensating subsidence contributes to rapid drying throughout the rest of the domain, which enhances the rate of aggregation. The similar behavior of PW in the SAS simulation and the operational GFS over the tropical west Pacific during 2012–2014 suggests the possibility that modifying and testing the SAS scheme in the WRF framework may offer avenues to explore in modifying and improving the SAS scheme in the operational GFS.

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