CO2 variability simulated with daily fluxes

1. CO2 variability simulated with daily fluxes Shamil Maksyutov, Misa Ishizawa Frontier Research System for Global Change, Yokohama Japan Transcom workshop Tsukuba 2004

2. Should one simulate the high frequency variability • Pro • Inverse models relying on snapshot observations (flask data) have to deal with observation noise translated directly into noise in fluxes (interannual or monthly).  Perfect forward model simulation is supposed to reduce this source of noise • Compared to monthly average data use, more uncertainty reduction can be expected with the same data, by applying different weight (observation error) to each measurement, avoiding conservative error estimate for aggregated data. Kind of “data aggregation” error or bias

3. Should one simulate the high frequency variability

4. Should one simulate the high frequency variability • Contra • Forward model simulation errors are even more evident in high frequency time series, including: • Transport model error (limited model resolution in time and space, numerical diffusion, imperfect physics, etc) • Emission field errors • Terrestrial ecosystem flux simulation with ecosystem models – daily flux simulation often fails even at flux tower sites with well known hydrology, phenology, vegetation type, when global/regional gridded meteo data sets are used as forcing for process based models.

5. Multiyear simulation of atmospheric CO2 variability in global scale • Atmospheric tracer transport: NIES transport model with NCEP wind interpolated to 2.5 or 1 degree resolution. Modification – increased tropospheric mixing by prescribed turbulence. • Fossil fuel emission (CDIAC) • Oceanic flux (Takahashi, 1999, 2002), as in T3 protocol • Terrestrial biosphere • a) CASA Randerson 1997 (Transcom) • b) Biome BGC (Fujita et al 2003)

6. Biospheric model fluxes at daily resolution • Biome-BGC model (S. Running, P. Thornton) v. 4.12 • Ecosystem type map (derived from Matthews by R. Hunt, 1996) 1x1 deg. • 1x1 deg Zobler soil data set (% clay, sand, silt for water holding capacity simulation) • 1.8 deg 6hourly NCEP reanalysis data set (tmin, tmax, temp, precip, short wave radiation) interpolated to 1x1 deg. • 2 versions of SWR algorithm tried: a) - Mtclim processor, b) – NCEP reanalysis

7. Model vs. observations: simulating spikes

8. Model vs. observations: simulating spikes

9. Simulating continuous observations at Hateruma • Observations: hourly data interpolated to daily average. Hourly data show much larger variability as compared to model simulation • Model simulation (6hourly output) performed with CASA (monthly) and Biome-BGC (daily) fluxes. • Surprisingly, monthly CASA fluxes are sufficient in many cases for simulating the synoptic scale variability, while dailiy Biome-BGC fluxes do not have much advantage.

10. Short term variability at marine site: Hateruma

11. Extracting short term variability • Seasonal cycle fit curve is subtracted from both observations and simulations respectively.

12. Short term variability at marine site: Hateruma Comparison shows better correlation in winter

13. Short term variability at marine site: Hateruma Breakdown into components show anti-correlation of biospheric and fossil fuel components in summer – may be a reason for difficulty to simulate In winter – similar magnitude and sign.

14. Short term variability at land site: ITN 1997-98

15. Short term variability – breakdown into components: ITN In summer: dominated by biospheric flux contribution In winter: biospheric and fossil fuel are of similar order of magnitude 1997-98

16. Data selection issue for land site • Preferable treatment is to select well mixed (usually afternoon condition) • Selecting afternoon values at low towers may still lead to significant difference vs 500m level in winter • For tall towers (500 m LEF, ITN) difference between daily and afternoon is small, compared to short term variability.

17. Short term variability – breakdown into components: LEF 1995-96

18. Correlation as a measure of the forward model performance (LEF) • Better correlation in winter, CASA vs Biome BGC

19. Few formulas • Objective function for optimization • Data uncertainty – in Globalview based analysis defined as observation error (variability plus analysis error including calibration offset uncertainty) • In Bayesian inversion (as in Tarantola) flux is a linear function of (observation-model) mismatch, so it makes sense reducing mismatch at synoptic scale too. However posterior flux uncertainty is not influenced by the mismatch.

20. Summary • Taking into account high frequency variability may improve inverse modeling performance, reduce actual flux noise. • Even simple model setup with relatively crude resolution shows good correlation at time scales of 3-5 days most of the year • Improvements in the inverse model theory are needed to incorporate model error and observation-model mismatch into the flux error estimate.