1. Diurnal Water and Energy Cycles over the Continental United States from three Reanalyses Alex Ruane John Roads Scripps Institution of Oceanography / UCSD

2. Introduction and Background –The Coordinated Enhanced Observing Period –Methodology Comparison with ARM SGP observations Diurnal Variations in Surface Energy Diurnal Variations in Water Outline

3. An exploration of the dominant balances, exchanges, and dynamics of the water and energy diurnal cycles over diverse geographic regions is lacking –What drives the regional consistency in diurnal behavior? –How do model parameterizations perform on the diurnal scale? –How does the resolution of data assimilation and reanalysis affect the simulation of the diurnal cycle? Currently, atmospheric models tend to focus on the mean at the expense of extreme events –Too much drizzle, not enough floods or droughts –Too many warm days, not enough heat waves This examination will help identify model error, as well as improve our ability to simulate the diurnal cycle and to calculate statistics at similar frequencies. Motivation

4. The Coordinated Enhanced Observing Period - CEOP takes advantage of ongoing continental- scale experiments and new Earth-observing satellites to examine our ability to understand and model the water cycle - 3½ year observation period (to be extended) - 35 highly-observed reference sites - 6 remote sensing centers - 10 modeling centers Source: CEOP Implementation Plan ECPC contributions to CEOP: 2 Model Versions: - NCEP/DOE Reanalysis-2 (RII) - ECPC’s Seasonal Forecast Model (SFM) reanalysis 2 Experiment Types: - 6-hr Analysis runs initialized at 00, 06, 12, and 18 UTC with 3-hr resolution - 36-hr Forecast runs initialized at 12 UTC with 3-hr resolution

5. Diurnal and semidiurnal harmonics least- squares fit onto 3-month seasons –Forms smooth reconstruction of diurnal cycle Global coverage and all seasons available Compare to North American Regional Reanalysis in Summertime (July- September, 2001-2003) –Summer features the strongest and most consistent diurnal cycle over the Continental United States Methodology

6. Grid point corresponding to ARM SGP –Experiments and observations agree on strong radiative forcing –Large disagreements between observed and simulated diurnal precipitation cycles –Where do the models diverge? ARM SGP Observations Latent Heat Flux –RII too high –SFM and NARR too low Sensible Heat Flux –RII too low –SFM and NARR too high 10-m Meridional Wind –Simulated wind too strong –Diurnal variations in core dynamics

7. Full region view reveals consistency of budget components’ diurnal cycles –Diurnal phases of these variables appear to be independent of location Strongly tied to the solar radiative forcing Geographic factors affect amplitude Are we missing surface interactions that would alter lags? Diurnal Cycle of Surface Energy 250 W/m2200 W/m2

8. Diurnal Cycle of Surface Water 10 mm/day 4 mm/day -Surface Water Tendency has large variation in NARR and RII –No significant diurnal cycle in SFM –NARR and RII surface water falls fastest with evaporation near local noon Unique regional patterns emerge from NARR assimilated rainfall as part of diurnal circulation centered over Northern Texas –Only half of the cycle is visible in SFM and RII –RII and SFM precipitation over the Great Plains is biased by parameterization error that makes for a consistent afternoon peak in precipitation

9. Diurnal Cycle of Atmospheric Water 10 mm/day 8 mm/day Precipitable Water Tendency and Water Vapor Convergence –Precipitable water tendency is significant term on diurnal scale –Water vapor convergence shows diurnal circulation pattern similar to diurnal precipitation Dynamics reasonable but parameterizations fail

10. The model experiments produce many interesting diurnal features –The surface energy cycle’s diurnal variation is dictated mostly by local thermodynamics –Column energy and water diurnal variations have regional influences Dynamics and reservoir terms are important in the atmosphere Many questions remain: –What causes lag between different budget components? –Is assimilation resolution the most important factor to improving simulation of the diurnal cycle? –How does parameterization error in the diurnal cycle affect the statistics of diurnal-order events? Future Work –Diurnal cycles in different global locations and/or seasons –Contrast analysis and forecast diurnal cycles to determine biases –Test the sensitivity and tuning of parameterizations to the diurnal forcing Conclusions