TransCom Continuous Experiment CSU NASA PCTM Scott Denning, John Kleist

PCTM Simulations 1° x 1.25° lat/lon grid, 55 levels (we use 25) NASA GEOS4 reanalyzed winds, turbulence, and convective mass fluxes for Partial results only: –FF –Takahashi –SiB monthly –CASA monthly Doesn’t balance budget

Seasonal Cycles BRW MLO SMO LEF green = obs red = model

April, 2002 BRW MLO SMO LEF green = obs red = model

May, 2002 BRW MLO SMO LEF green = obs red = model

July, 2002 BRW MLO SMO LEF green = obs red = model

September, 2002 BRW MLO SMO LEF green = obs red = model

October, 2002 BRW MLO SMO LEF green = obs red = model

Source/Sink Inversions of Synthetic Satellite CO 2 with Errors Scott Denning Kevin Gurney Kathy Corbin Mick Christi TransCom3 Modelers

Inversions of Monthly Synthetic Data Generate global CO 2 from TransCom models Background fields plus G m post Interpolate all models onto common 4 x 5 grid Apply ISCCP cloud climatology to mask grid Invert for fluxes using all models’ response functions (standard T3 cyclostationary method) All results using flasks (with T3 error) plus satellite

Synthetic Satellite Retrievals TransCom models provide monthly mean 3D arrays of CO 2 mole fraction over 9 pressure-bounded layers To simulate satellite products from these fields, we need to treat –Vertical “weighting” of 9 layers corresponding to instrument retrieval (not the same as retrieval averaging kernel!) –An estimate of uncertainty in the retrieval –Averaging of many retrievals in each model grid column, with appropriate treatment of error reduction –Effects of clouds on number of retrievals in each model grid column, and therefore on aggregate uncertainty –Sampling biases (?) due to 1 PM Equatorial crossing time and measurement of only clearsky conditions

Vertical Weighting Sample synthetic data using two different vertical weighting: –Thermal IR (AIRS-like) sees mostly mid-to-upper troposphere –near-IR (OCO-like) sees column mean, with information all the way to surface

Monthly Column Uncertainty  =  0 exp(1.5 f c 3 ) Implemented by deweighting sat retrievals ppmv

Inversions of Flasks Plus AIRS Perfect transport on perfect data returns perfect fluxes Transport error makes satellite data pretty ineffective Even perfect model needs retrieval error about 1 ppm for significant improvement over flasks

Inversions of Flasks Plus OCO Significant improvements over flasks Fairly robust against terrible transport error! Caveats: monthly mean inversion! severe transport error!

Tropical America Flask constraint very weak Even poor transport beats flasks

Temperate North America Flask constraint already pretty good Need better transport to beat flask-only inversion aggregate uncertainty (ppmv)