P. K. Patra*, R. M. Law, W. Peters, C. Rodenbeck et al.

*Frontier Research Center for Global Change/JAMSTECYokohama, Japan

Woulter Peters at Paris, 2005

2003

Summer 2006: Prabir ‘suckered into’

TransCom Continuous ExperimentTransport model simulations of CO2, SF6, Radon-

222 for the period 2002-2003Prescribed surface fluxes for CO2

Fossil fuel emission for 1998 (EDGAR)Oceanic exchange (Takahashi-2002, updated)Terrestrial biosphere

CASA 3-hourly, monthly mean SiB hourly, daily, monthly

SF6 emission from EDGAR 1995 with growth rateRadon-222; land: 1.66x10-20 mol m-2 s-1 (60oS-

60oN); Ocean: 8.3x20-23 mol m-2 s-1; half-life: 3.8 days

List of modelsand model variantsparticipating in TransComcontinuousexperiment (20 global, 3 regional)

Stations with hourly observations (approx. 37)

Extraction of seasonal cycle and synoptic variations: anything between 0-~10 days is defined as Synoptic

Low bias in seasonal cycle estimation using digital filtering, and thus Synoptic variations

A station with large diurnal amplitude: effect of PM/ALL hourly data selection

Seasonal cycle simulation depends on flux amplitudes and time resolutionSynoptic variations are less so

Vertical profiles: Park Falls, WI (LEF) observations (Source: NOAA/ESRL)

Synoptic scale variations in nighttime CO2 are more consistent with meteorology!

Vertical profiles: Afternoon vs. Nighttime selection at Park Falls (comparison with all/21 models)

Synoptic variation correlations between observation and models at stations (in ascending order w.r.t. AM2)

Worse seasonal cycle simulations at SH stations caused by error in flux

Synoptic variation correlations: Effect of resolution and model versions

Synoptic variation correlations: statistical significance and physically meaningful?

Co

rrelation

s >0.15 are statistically sig

nifican

t (N~

300; P=

0.01)

Synoptic variation correlations: relation to representation/sampling error

Correlations increases with closer model grids to station locations

Taylor diagrams: All data (a), Winter (b), Summer (c)

Averaged over all stations are shown, i.e., one symbol per flux per model

COMET

REMO

STAGN

ConclusionsSynoptic scale variations can be robustly

estimated from hourly/daily data and mode simulations

The modeled variations are statistically significantly correlated with observed variations at most stations and aren’t random

Taylor diagrams suggest that SiB-hourly fluxes are better suited for sub-daily CO2 simulations (compared to CASA-3hr)

Spatial representation error is still a major problem in multi-model analysis

Recommendations

Analysis using models at two spatial resolution indicates higher resolution is better!!!A result of lesser spatial representation error

and better meteorologyInterpolation to observation grid is an

approximate solution (M. Krol)?

Plots using observations and model simulations at LEF suggest nighttime CO2 variability is reasonably well simulated Should those be used in inversions?

Woulter Peters at Paris, 2005

?~

extras

Seasonal cycle at Southern ocean stations

Patra et al., ACPD, 2006

Chi2