co2 variability simulated with daily fluxes

CO2 variability simulated CO2 variability simulated with daily fluxes with daily fluxes

Shamil Maksyutov, Misa IshizawaShamil Maksyutov, Misa Ishizawa

Frontier Research System for Global Frontier Research System for Global Change, Yokohama JapanChange, Yokohama Japan

Transcom workshop Tsukuba 2004

Should one simulate the high Should one simulate the high frequency variabilityfrequency variability

ProPro Inverse models relying on snapshot observations (flask Inverse models relying on snapshot observations (flask

data) have to deal with observation noise translated data) have to deal with observation noise translated directly into noise in fluxes (interannual or monthly). directly into noise in fluxes (interannual or monthly). Perfect forward model simulation is supposed to reduce Perfect forward model simulation is supposed to reduce this source of noisethis source of noise

Compared to monthly average data use, more Compared to monthly average data use, more uncertainty reduction can be expected with the same uncertainty reduction can be expected with the same data, by applying different weight (observation error) to data, by applying different weight (observation error) to each measurement, avoiding conservative error each measurement, avoiding conservative error estimate for aggregated data. Kind of “data estimate for aggregated data. Kind of “data aggregation” error or biasaggregation” error or bias

Should one simulate the high frequency Should one simulate the high frequency variabilityvariability

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Should one simulate the high Should one simulate the high frequency variabilityfrequency variability

ContraContra Forward model simulation errors are even more evident Forward model simulation errors are even more evident

in high frequency time series, including:in high frequency time series, including: Transport model error (limited model resolution in time Transport model error (limited model resolution in time

and space, numerical diffusion, imperfect physics, etc)and space, numerical diffusion, imperfect physics, etc) Emission field errorsEmission field errors Terrestrial ecosystem flux simulation with ecosystem Terrestrial ecosystem flux simulation with ecosystem

models – daily flux simulation often fails even at flux models – daily flux simulation often fails even at flux tower sites with well known hydrology, phenology, tower sites with well known hydrology, phenology, vegetation type, when global/regional gridded meteo vegetation type, when global/regional gridded meteo data sets are used as forcing for process based models.data sets are used as forcing for process based models.

Multiyear simulation of atmospheric Multiyear simulation of atmospheric CO2 variability in global scaleCO2 variability in global scale

Atmospheric tracer transport: NIES transport model with Atmospheric tracer transport: NIES transport model with NCEP wind interpolated to 2.5 or 1 degree resolution. NCEP wind interpolated to 2.5 or 1 degree resolution. Modification – increased tropospheric mixing by Modification – increased tropospheric mixing by prescribed turbulence.prescribed turbulence.

Fossil fuel emission (CDIAC)Fossil fuel emission (CDIAC) Oceanic flux (Takahashi, 1999, 2002), as in T3 protocolOceanic flux (Takahashi, 1999, 2002), as in T3 protocol Terrestrial biosphere Terrestrial biosphere a) CASA Randerson 1997 (Transcom)a) CASA Randerson 1997 (Transcom) b) Biome BGC (Fujita et al 2003) b) Biome BGC (Fujita et al 2003)

Biospheric model fluxes at daily Biospheric model fluxes at daily resolutionresolution

Biome-BGC model (S. Running, P. Thornton) v. 4.12Biome-BGC model (S. Running, P. Thornton) v. 4.12 Ecosystem type map (derived from Matthews by R. Hunt, Ecosystem type map (derived from Matthews by R. Hunt,

1996) 1x1 deg.1996) 1x1 deg. 1x1 deg Zobler soil data set (% clay, sand, silt for water 1x1 deg Zobler soil data set (% clay, sand, silt for water

holding capacity simulation)holding capacity simulation) 1.8 deg 6hourly NCEP reanalysis data set (tmin, tmax, 1.8 deg 6hourly NCEP reanalysis data set (tmin, tmax,

temp, precip, short wave radiation) interpolated to 1x1 temp, precip, short wave radiation) interpolated to 1x1 deg.deg.

2 versions of SWR algorithm tried: a) - Mtclim processor, 2 versions of SWR algorithm tried: a) - Mtclim processor, b) – NCEP reanalysisb) – NCEP reanalysis

Model vs. observations: simulating spikesModel vs. observations: simulating spikes

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Model vs. observations: simulating spikesModel vs. observations: simulating spikes

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Simulating continuous observations Simulating continuous observations at Haterumaat Hateruma

Observations: hourly data interpolated to daily average. Observations: hourly data interpolated to daily average. Hourly data show much larger variability as compared to Hourly data show much larger variability as compared to model simulationmodel simulation

Model simulation (6hourly output) performed with CASA Model simulation (6hourly output) performed with CASA (monthly) and Biome-BGC (daily) fluxes. (monthly) and Biome-BGC (daily) fluxes.

Surprisingly, monthly CASA fluxes are sufficient in many Surprisingly, monthly CASA fluxes are sufficient in many cases for simulating the synoptic scale variability, while cases for simulating the synoptic scale variability, while dailiy Biome-BGC fluxes do not have much advantage.dailiy Biome-BGC fluxes do not have much advantage.

Short term variability at marine site: HaterumaShort term variability at marine site: Hateruma

Extracting short term variabilityExtracting short term variability

Seasonal cycle fit curve is subtracted from both Seasonal cycle fit curve is subtracted from both observations and simulations respectively.observations and simulations respectively.

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Comparison shows better correlation in winter

Short term variability at marine site: Short term variability at marine site: HaterumaHateruma

Breakdown into components show anti-correlation of biospheric and fossil fuel components in summer – may be a reason for difficulty to simulateIn winter – similar magnitude and sign.

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Short term variability at land site: ITNShort term variability at land site: ITN

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Short term variability – breakdown into Short term variability – breakdown into components: ITNcomponents: ITN

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In summer: dominated by biospheric flux contributionIn winter: biospheric and fossil fuel are of similar order of magnitude

Data selection issue for land siteData selection issue for land site

Preferable treatment is to select well Preferable treatment is to select well mixed (usually afternoon condition)mixed (usually afternoon condition)

Selecting afternoon values at low towers Selecting afternoon values at low towers may still lead to significant difference vs may still lead to significant difference vs 500m level in winter500m level in winter

For tall towers (500 m LEF, ITN) difference For tall towers (500 m LEF, ITN) difference between daily and afternoon is small, between daily and afternoon is small, compared to short term variability.compared to short term variability.

Short term variability – breakdown into Short term variability – breakdown into components: LEFcomponents: LEF

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Correlation as a measure of the Correlation as a measure of the forward model performance (LEF)forward model performance (LEF)

Better correlation in winter, CASA vs Biome BGCBetter correlation in winter, CASA vs Biome BGC

Monthly correlations model vs observed at LEF

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Few formulasFew formulas Objective function for optimizationObjective function for optimization

Data uncertainty – in Globalview based analysis defined Data uncertainty – in Globalview based analysis defined as observation error (variability plus analysis error as observation error (variability plus analysis error including calibration offset uncertainty) including calibration offset uncertainty)

In Bayesian inversion (as in Tarantola) flux is a linear In Bayesian inversion (as in Tarantola) flux is a linear function of (observation-model) mismatch, so it makes function of (observation-model) mismatch, so it makes sense reducing mismatch at synoptic scale too. However sense reducing mismatch at synoptic scale too. However posterior flux uncertainty is not influenced by the posterior flux uncertainty is not influenced by the mismatch.mismatch.

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SummarySummary

Taking into account high frequency variability may Taking into account high frequency variability may improve inverse modeling performance, reduce actual improve inverse modeling performance, reduce actual flux noise.flux noise.

Even simple model setup with relatively crude resolution Even simple model setup with relatively crude resolution shows good correlation at time scales of 3-5 days most shows good correlation at time scales of 3-5 days most of the yearof the year

Improvements in the inverse model theory are needed Improvements in the inverse model theory are needed to incorporate model error and observation-model to incorporate model error and observation-model mismatch into the flux error estimate.mismatch into the flux error estimate.

co2 variability simulated with daily fluxes

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