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Understanding the Influence of Biomass Burning on Tropospheric Ozone through Assimilation of TES data. Dylan Jones, Mark Parrington University of Toronto. Kevin Bowman, Helen Worden, John Worden, Greg Osterman Jet Propulsion Laboratory California Institute of Technology.
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Understanding the Influence of Biomass Burning on Tropospheric Ozone through Assimilation of TES data Dylan Jones, Mark ParringtonUniversity of Toronto Kevin Bowman, Helen Worden, John Worden, Greg Osterman Jet Propulsion LaboratoryCalifornia Institute of Technology Jennifer Logan Harvard University
Impact of Biomass Burning on Tropospheric O3 TES O3, 421 mb: Nov 5-17, 2004 TES CO, 421 mb: Nov 4-17, 2004 GEOS-Chem O3, 421 mb: Nov 4-17, 2004 GEOS-Chem CO 421 mb: Nov 4-17, 2004 ppb ppb Climatological emission inventory in the model underestimates the impact biomass burning on CO and O3 in the southern hemisphere Objective: Assess the potential of TES data to improve O3 in the model in a chemical data assimilation framework
Impact of Assimilation on CO and O3 (using a sequential sub-optimal Kalman filter with TES O3 and CO profile retrievals for Nov. 4-17, 2004) 24-hr averaged assimilated O3 at 7 km on Nov. 17 24-hr averaged assimilated CO at 7 km on Nov. 17 (ppb O3) (ppb CO) Change in CO at 7 km (assim. - without assim.) Change in O3 at 7 km (assim. - without assim.) percent percent • Assimilation increases CO throughout the southern hemisphere • Largest increases in O3 (20-50%) are over the Indian Ocean and the Indonesian/Australian region
Assimilation of TES O3 for 1 July 2005 - 1Jan. 2006 Mean GEOS-Chem O3 at 8 km between 20°S-equator and 180°W-180°E Assimilation Free running model O3 difference: assimilation - free running model data gaps • In early Sept 2005 the assimilation increases O3 by about 20% in upper troposphere • During the 2 week data gap in September the analysis reverts to the state of the free running model
Comparison with Ozonesonde Data at La Reunion Island (21°S, 55°E) 17 Oct 2005 12 Oct 2005 28 Oct 2005 2 Nov 2005 assimilation sonde free running model • The ozone tropopause in GEOS-Chem is too low in Austral spring 2005 compared to the sonde data • Assimilation of TES data reduces the bias in the model
Obs minus Forecast Obs minus Analysis Comparison of the O3 Analysiswith TES Observations (350 mb) • During October the assimilation reduces the bias in the model by about a factor of 2 • Despite the reduction in the bias, the residuals for the OmA are still large
Comparison of the CO Analysiswith TES Observations (350 mb) Obs minus Forecast Obs minus Analysis Assimilation extended through 1 Sept 2006 TES OB warm-up • Following the warm-up of the TES optical bench in Dec. 2005, the assimilation significantly reduced the bias in CO in the model • In contrast to the O3 analysis, the CO OmA residuals are small, reflecting the longer lifetime of CO
Latitudinal Dependence of the O3 Analysis Residuals (350 mb) Obs minus Forecast Obs minus Analysis 20°S-0° Larger OmA residuals in the tropics, reflecting the shorter O3 lifetime and a lower density of TES data 30°N-60°N The assimilation has less impact in summer 2006 because we are propagating the forecast error variance without accounting for forecast error growth by summer 2006 the forecast error is about 15% in the tropics and subtropics, compared to the assumed 50% error in July 2005
Assimilation of TES O3 data produces a much improved distribution of O3 in the model, which provides greater constraints on model parameters such as the lifetime of NOx a better constraint on the NOx lifetime will result in improved estimates of NOx emissions from lightning and of the export of NOy from continental source regions In contrast to the CO analysis, the residuals in the O3 assimilation are large, especially in the tropics, reflecting the shorter lifetime of O3 (and the low density of the TES data) assimilating trace gases that are more chemical active than CO will be a challenge A better approach for exploiting the satellite data would be to optimize the model parameters, such as the emissions, using adjoint techniques work is needed to characterize the forecast errors across the range of chemical timescales in the model Conclusions