1. mesoscale climate dynamics
PACLIM 2006
Sebastien Conil
Greg Masi
Alex Hall
Mimi Hughes
Southern California February 9, 2002 MISR

2. Experimental Design model: MM5
boundary conditions: eta model reanalysis
resolution: domain 1, 54 km, domain 2, 18 km, domain 3, 6 km
time period: to present.
One can think of this as a reconstruction of weather conditions over this time period consistent with three constraints: (1) our best guess of the large-scale conditions, (2) the physics of the MM5 model, and (3) the prescribed topography, consistent with model resolution.

4. VALIDATION
We evaluate the simulation’s fidelity by comparing available observations with model output from the grid points nearest observation stations.

5. DIURNAL CYCLES
(Hughes et al. 2006)

6. (deg C)
The simulated climatological diurnal cycle amplitude exhibits significant spatial structure, with small values at tops of mountain complexes and within km of the coast, and values 2-3 times as large elsewhere.
VALIDATION The spatial correlation between diurnal cycle amplitudes at 30 observation stations and nearest model grid points is 0.83.

7. EOF analysis of diurnal cycles: surface air temperature
The dark blue curve shows the time series associated with the top pattern. This is the first EOF of the diurnal variation of temperature, peaking in the late afternoon, and reaching a minimum just at sunrise. The pattern accounts for 96% of the variance in diurnal variability of surface air temperature.

8. EOF analysis of diurnal cycles: winds
The light blue curve shows the time series associated with the top pattern. Note the flow crossing the coastline and converging at mountaintops. Note that the light and dark blue curves overlap almost perfectly: This mode of wind variability, accounting for more than half the diurnal variance, coincides almost exactly with the principal mode of surface air temperature diurnal variations. This represents the land/sea breeze and diurnal mountain winds. These same flows are found in the observation network.

9. Links between diurnal cycles of surface air temperature and wind.
The diurnal winds can account for the geographical structure of the surface air temperature diurnal cycle amplitude. Near the coast, the land/sea breeze clearly crosses isentropes, moderating diurnal cycle amplitude . An analogous phenomenon takes place in conjunction with the topographically-forced winds, since the atmosphere is generally stratified in Southern California.

10. The S. California atmosphere is usually stratified.
WARM
During the daytime, converging upslope flows bring cold air to the highest elevations, reducing daily maximum temperature.
At night, diverging downslope flows draw warm air from higher altitudes to the mountaintop.
The S. California atmosphere is usually stratified.
CONVERGING COLD AIR
increasing potential temperature
COLD

11. CIRCULATION VARIABILITY
(Conil and Hall 2006)

12. A view of the Santa Anas from space, taken by the Multi-angle Imaging SpectroRadiometer (MISR) on February 9, 2002.

13. The winds simulated by the model during the Santa Ana event of February 9-12, 2002.
Note the intense flow, reaching speeds on the order of 10 meters per second, being channeled through mountain passes.

14. VALIDATION
This shows the correlation of daily-mean wind speed and direction between model and observations at 18 locations. Both wind speed and direction are reasonably well-correlated at all locations, in spite of many possible reasons for disagreement, including systematic and random measurement error. This gives confidence that the timing and magnitude of circulation anomalies are correct in the simulation.

15. Cluster Analysis
To classify the regimes of wind variability in Southern California, we performed a probabalistic cluster analysis algorithm (Smyth et al. 1999) on the October to March daily-mean winds.
The clustering technique provides an quantitative means of defining preferred modes of wind variability.
We chose to focus on the wet season because of the interesting combination of phenomena (Santa Ana events and precipitation) during this period.
We found that the wind regimes can be well-described in terms of three clusters, which together account for 82% of the days.

16. The Santa Ana regime is characterized by intense offshore flow with significant spatial structure. The composite wind pattern is shown above, with winds in m/s. Its average duration is 1.7 days and it accounts for 13% of the total days in the analysis.

17. The Santa Ana regime is also associated with extreme dryness in the zone between the mountains and the coast, as illustrated by this composite relative humidity (%) map.

18. APPLICATION TO FIRE

19. Moritz et al (2004) undertook a fire frequency analysis of these 9 fire-prone landscapes in Southern California. They quantified the probability that a given area will burn as a function of fuel age.

20. They found that all areas but one exhibit very little increase in fire hazard with fuel age.

21. This is the region that exhibits dependence of fire hazard on fuel age.

22. It’s also a region where downslope flow speeds are very low during Santa Ana events…
This is the region that exhibits dependence of fire hazard on fuel age.

23. It’s also a region where downslope flow speeds are very low during Santa Ana events…
This is the region that exhibits dependence of fire hazard on fuel age.
…and relative humidity anomalies during Santa Ana events are relatively small

24. The importance of mesoscale climate dynamics
Relying on the examples of
(1) The amplitude of the diurnal cycle of surface air temperature
(2) The Santa Ana wind regime
we’ve shown that there exist significant gradients in climatological variables on scales of tens of km. This has been objectively determined through classic statistical techniques of climate analysis. The simulated phenomena have been validated by comparing relevant model quantities to a network of observations.
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These spatial gradients have implications for interpretation of point measurements, including paleoclimate records, as well as the use of climate data by the applications community and for interdisciplinary research.
The big question: What are the characteristic spatial scales we need to be concerned about to understand climate and its interactions with other elements of the earth system?