How to move the gravity-wave parameterization problem forward? Some thoughts

1. NCAR TIIMES Gravity-Wave Retreat, 2006 How to move the gravity-wave parameterization problem forward? Some thoughts Ted Shepherd Department of Physics University of Toronto

2. Observations

3. The “first principles” approach (Tim) • Need to test parameterizations against highly resolved simulations • Need to get reasonable results with reasonable parameters • Need to know that sensitivity to climate perturbations is realistic • Filtering effects are probably robust • Source changes are more of a challenge • The users’ approach….

4. We need to identify the aspects of GW parameterizations that matter the most for weather and climate simulation • Despite the number of different parameterizations — and the intensity of debate between some of their proponents — the choice of parameterization seems not to matter greatly • However most comparisons have not been well-constrained

5. Once the source spectrum is constrained, the only important parameter seems to be intermittency (McLandress & Scinocca 2005 JAS) • Determines the breaking height • This is not very surprising, in light of “downward control” • Some assumptions: • CLs always absorb • CLs always reflect • No horizontal propagation

6. The partitioning between nonlinear drag and critical-level drag depends on the scheme, but the net drag is the same From McLandress & Scinocca (2005)

7. In an “active” GCM such as CMAM, one can actually rely on critical-layer drag alone! But if the drag only sees the zonal-mean wind, then one needs nonlinear drag

8. Instantaneous snapshot of SKYHI • zonal winds for various altitudes, • during a model July • Increasing gravity-wave activity • with increasing altitude From Koshyk et al. (1999 JGR)

9. Horizontal wavenumber spectra (n = spherical harmonic index) of kinetic energy for SKYHI and CMAM Straight lines show -3 and -5/3 slopes Charney-Drazin filtering is evident Shallow spectra emerge with increasing altitude Figure courtesy of John Koshyk

10. This all confirms the suspicion that many have had for a long time: that the key issue is the source spectrum, and perhaps to a lesser extent intermittency • However it is also essential that parameterizations are implemented in a momentum-conserving way, and that there is no Rayleigh drag or zonal-mean sponge layer (Shaw & Shepherd JAS, in press) — otherwise robustness is lost • Remarkably, this is far from the case with climate models!

11. From the modelling side, we need to identify where it is that gravity wave parameterization is most important (for either climate or climate change), and assess the robustness of different model results in this respect • Polar vortex, esp. in the SH • Summer mesopause • MLT more generally: difficult • Tropical upwelling • SAO and QBO: difficult

12. First we need to identify the principal climate-change uncertainties associated with gravity-wave drag (insisting on momentum conservation!) • SPARC CCMVal is a good framework for this • And then we need to develop a better understanding of the sensitivities in well-constrained comparisons • Emerging SPARC initiative on dynamical processes should provide a good framework

13. Development of physically-based source parameterizations (which respond to climate change) is a very positive step • We need to assess their sensitivity (e.g. to climate variations) and identify their role in the simulations • Impact of changes in sources vs impact of changes in GW filtering • It’s not obvious that a predicted change in the source is better than no change!

14. In the extratropics, middle atmosphere data assimilation should provide very useful constraints on GWD • Planetary waves and zonal winds in the troposphere and stratosphere should be about right, hence the filtering of GW fluxes • This will also slave the large-scale mesospheric fields to a large extent • Increments from temperature observations will likely mainly reflect errors in GWD

15. In principle, if GW parameters are a control variable in the data assimilation, then they can be constrained by the temperature observations • This is a developing theme within the SPARC Data Assimilation Working Group • Can one use an instrument forward model “off-line”, acting on the parameterized GW spectrum, to predict GW variances observed by satellites? • Would likely need to be statistical

16. The large volume of satellite data relevant to GWs raises the prospect of performing reasonable statistical tests • Intermittency • Sensitivity of sources to local conditions • But how much of a constraint do these satellite observations place on the part of the GW spectrum that matters? • Need to relate what the satellites measure to the full spectrum (via mesoscale models, field experiments), and use the satellites to extrapolate to global fields