Download presentation
Presentation is loading. Please wait.
1
Investigating Representation Errors in Inversions of Satellite CO 2 Retrievals K.D. Corbin, A.S. Denning, N.C. Parazoo Department of Atmospheric Science Colorado State University Transcom Meeting - Purdue University April 24-27, 2007
2
Transcom Meeting - Purdue University April 24-27, 2007 Motivation OCO and GOSAT will retrieve total column X CO2 measurements beginning late 2008 Inverse modelers will use these measurements to help identify CO 2 sources and sinks Using space borne X CO2 to represent a transport model grid-cell may introduce sampling errors into inversions X CO2 measurements will only represent clear conditions Investigate sampling errors using continuous data, a regional cloud-resolving model (SiB-RAMS), and a global transport model (PCTM)
3
Transcom Meeting - Purdue University April 24-27, 2007 CO 2 continuous data from 3 towers: WLEF: 1995-2003 Harvard Forest: 1993-2002 Tapajos (km67): 2002-2005 Mid-day means from 1100-1600 LST Created clear-sky subset Ranked PAR measurements from all years at each site Selected 20% of days per month with highest PAR Fit separate harmonic functions to clear-sky subset and entire time-series Subtracted fits: CO 2 fit CLEAR - CO 2 fit TOTAL Calculating Clear-Sky Errors at CO 2 Towers
4
Transcom Meeting - Purdue University April 24-27, 2007 Clear-Sky Bias at Continuous CO 2 Tower Sites CO 2 lower on clear days than on average Temperate sites have greatest bias in winter Tropical site shows biggest difference in rainy season [Corbin and Denning, 2006]
5
Transcom Meeting - Purdue University April 24-27, 2007 NEE Clear-Sky Bias at Tower Sites [Corbin and Denning, 2006] In mid-lats, enhanced uptake on clear days during summer, but negligible winter errors In tropics, enhanced uptake year-round on clear days NEE bias cannot account for CO 2 errors
6
Transcom Meeting - Purdue University April 24-27, 2007 SiB2-RAMS Case Descriptions North AmericaSouth America 97 KM 450 KM August 11-21, 2001 3 frontal passages August 1-16, 2001 Dry season - calm conditions Bulk microphysical parameterization to simulate clouds and precipitation explicitly Emulated satellite track
7
Transcom Meeting - Purdue University April 24-27, 2007 Spatial Representation Errors using SiB-RAMS [Corbin et al., 2007] Errors are unbiased and generally less than 0.5 ppm Spatial errors increase with domain heterogeneity and size
8
Transcom Meeting - Purdue University April 24-27, 2007 Clear-Sky Temporal Errors using SiB-RAMS [Corbin et al., 2007] Large errors at both sites Biased errors at temperate site due to CO 2 anomalies associated with frontal systems that are masked by clouds
9
Transcom Meeting - Purdue University April 24-27, 2007 Clear-Sky Errors using PCTM Global 2003 simulation 1.25 o longitude x 1.0 o latitude Calculated clear-sky CO 2 error for each land grid-cell Used daytime mean total column CO 2 concentrations Created clear-sky subset using downward shortwave radiation Fit 2 harmonics to clear-sky and total data Subtracted Fit CLEAR - Fit TOTAL WLEF Tapajos
10
Transcom Meeting - Purdue University April 24-27, 2007 2003 PCTM Comparisons to Observations Clear-sky bias from PCTM at tower locations match observed errors reasonably well
11
Transcom Meeting - Purdue University April 24-27, 2007 Annual Mean Clear-Sky Errors in PCTM Errors vary regionally with spatially coherent patterns Underestimation of CO 2 in South America and Alaska Overestimation of CO 2 in Asia
12
Transcom Meeting - Purdue University April 24-27, 2007 Annual Mean Clear-Sky Errors by Latitude Clear-sky errors larger in NH Underestimation of mean in sub-tropics
13
Transcom Meeting - Purdue University April 24-27, 2007 Seasonal Clear-Sky Errors in PCTM Magnitude of errors varies with season
14
Transcom Meeting - Purdue University April 24-27, 2007 Seasonal Clear-Sky Errors by Latitude Large underestimation of CO 2 in NH land during winter Overestimation of CO 2 in NH summer Underestimation of CO 2 in SH spring
15
Transcom Meeting - Purdue University April 24-27, 2007 Conclusions Spatial representation errors are small (< 0.5 ppm) Clear-sky (temporal) sampling errors are large and vary seasonally and regionally Satellite X CO2 cannot be used to represent temporal averages Transport must be modeled accurately and sampled at same time/location as satellite
16
Transcom Meeting - Purdue University April 24-27, 2007 Acknowledgements Thanks to Steve Wofsy for the Tapajos Forest (km67) and Harvard Forest tower data and Ken Davis for the data from WLEF Funding by NASA Earth System Science Fellowship 53-1970, NASA Contract NNG04GQ15H SUPP2, and NASA Subcontract (via Purdue University) 521-0438-01
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.