BOG Report

BOG 3 – AFOLU GHG

IPCC TFI Expert Meeting on Use of Atmospheric
Observation Data in Emission Inventories
5-7 September 2022
WMO HQ, Geneva - Switzerland

IPCC TFI TSUGeneral Comments
 Focused on atmospheric observations that are used in inversion
frameworks to estimate fluxes Verification of inventory using inversion data is not necessarily
leading to direct changes in the emissions reporting (i.e., directly
using the inversion results for reporting), but rather as a starting pointfor further investigation of the inventory methods as well as theinversion framework
 Goal is to reduce uncertainty in the GHG inventory by further
investigating and improving the underlying activity data and otherdriver data, as well as emission rates
 Inventories may also be useful for improving atmospheric inversions

General Comments
 In general, developing country parties to the UNFCCC face morechallenges to use atmospheric observations for evaluating inventories

 Global inversions may provide some insights into key categories
 Not clear that inversions can be used directly in evaluating inventories indeveloping countries, particularly smaller countries

 Questions that can be asked in these comparisons: Is the inventory accurate?
 Is the inversion accurate?
 What are the drivers of the stock changes or GHG fluxes?
 Are trends and spatial patterns consistent between the inventory andinversion?

Discussion Question #1
 Assess and critique recent estimation techniques that utilise
atmospheric observations as well as operational systems, platforms,instruments/sensors and methods/models for their potential to beused for the verification of national inventory sectoral emission
estimates, consistent with the guidance provided in the 2019Refinement

Discussion Question #1
 Overarching conclusion: Need to gain more experience using
atmospheric observations and inversions to evaluate AFOLU sectoral
GHG inventories.
 Best examples from evaluation of CH4 and N2O emissions in AFOLU sector

Considerations for conducting an informative analysis based on recent
inversion and inventory comparisons Scale of inversion is important

 Regional inversions critical for small countries, and to evaluate subnational
fluxes in larger countries
 Global inversions may be adequate for large countries
 Regional inversions need to be consistent with the global inversions

Discussion Question #1
Considerations
 Prior fluxes for inversions is a large source of uncertainty

 Could use the inventory as a prior if adequate for purposes but will likely
need data beyond the inventory to estimate the full flux, particularly for
CO2
 In AFOLU, lateral fluxes associated with harvested woody products, crop
harvest and transport, indirect N2O emissions, and hydrological flows
complicate the estimation of the prior Transport is another large source of uncertainty in inversion
frameworks
 May contribute to mismatch in inversion fluxes compared to inventory Depends on topography and complexity of atmospheric structure

Discussion Question #1
Considerations
 Atmospheric observations of concentrations have considerably lessuncertainty, but quality and quantity of atmospheric observations areanother consideration

 Tall tower and aircraft are a more direct inference on the concentrations, andneed to be expanded in some regions (initiative led by WMO) Satellite data provide additional information on atmospheric concentrations tosupport the inversions
 In turn, satellite data are providing opportunities for inversion analysis in regions
where this was not possible in the past

 Inventory compilers need to be actively involved in these comparisons Providing context about the inventory analysis
 Expressing needs for improving the inventory Evaluating differences and possibly surprises from the comparison

Discussion Question #2
 Assess and evaluate successful examples of: comparisons between atmospheric observations and national
inventories that are consistent with good practice provided in
the 2019 Refinement that have led to implemented or planned
improvements in national inventories;
 Several examples on next few slides

 available examples where emission factors derived fromatmospheric observations have been incorporated into abottom-up inventory framework No known example of this latter case in AFOLU reporting to the UNFCCC

Discussion Question #2
 Comparison 1: New Zealand Example

 Inversion system applied to evaluate CO2
 Inversion System: Surface measurements and Langragian Bayesian
inversion system Finding C sink in the inversion analysis that has not been identified inthe inventory along coastal areas
 May be associated with lateral flows of sediment and/or water New studies initiated to investigate these possibilities Publications: Steinkamp et al. 2017, ACP; Geddes et al. 2020, Reportto Ministry of Primary Industries

Discussion Question #2
 Comparison 2: Australia Example

 Inversion system applied to evaluate CO2
 Inversion System: OCO-2 Satellite data with Data Assimilation
 Finding a sink in rangelands that has not been identified in theinventory
 Maybe associated with precipitation patterns, woody encroachment, and/ormanagement
 Planning to further investigate these differences with inventory team

 Publications: Villalobos et al. 2020, ACP; Villalobos et al. 2021, ACP;
Villalobos et al. 2022, ACP

Discussion Question #2
 Comparison 3: United States Example

 Inversion system applied to investigate impact of freeze-thaw process
on soil N2O emissions
 System: Tower observations with Carbontracker Lagrangian BayesianInversion
 Findings showed that freeze-thaw periods coinciding with largeemission pulses consistent with the small set of experimental data.
 Inversion informed model development leading to an improvement in
the inventory that has been incorporated into UNFCCC reporting Publication: Nevison et al. 2018, GBC; Del Grosso et al. 2022, PNAS

Discussion Question #2
 Comparison 3: Subnational Example from US State ofCalifornia

 Inversion system applied to emissions from dairy operations
 Inversion System: Lagrangian Bayesian Inversion System
 Inversion suggested that they were under-estimated Mobile and airborne measurements confirmed that the inventory
emission factors were accurate and the inversion was not accurate inthis case.
 Publication: Amini et al. 2022, ES&T

Discussion Question #3
 Assess the possibility that emerging datasets from atmosphericobservations could be used to test and verify particular IPCC
default values (emission factors) and associated uncertainties

Discussion Question #3
 Tier 1 Emission Factor Evaluation: Soil N2O emissions

 Inversion system applied to estimate N2O fluxes in Brazil, UnitedStates, China, South Asia, and Europe
 Estimated implied emission factors based on N inputs (i.e., dividing
the emissions by the inputs) combining direct and indirect emissions
 Found that Tier 1 factors from the 2006 GL were lower than impliedemission factors from the inversion analysis
 Note: 2019 Refinement has updated these factors so could be furtherinvestigation
 Regardless, example of how atmospheric data could be used toevaluate default factors
 Publication: Thompson et al. 2019, Nature Climate Change

Discussion Question #4
 Discuss the use of gridding (spatial and temporal) of NGHGIs toallow comparison with atmospheric observation data

Discussion Question #4
 Inventory compiler needs sufficient resources and time toparticipate in the gridding of inventory data Compiler has the detailed understanding of these data and should be involvedin this process even if the compiler is not gridding the data Need to determine the appropriate spatial and temporal scales forcomparing the inversion and inventory before gridding theinventory data e.g., comparisons at finer scales may not be informative if uncertainties arelarge Need to determine where uptake and release are occurring,particularly the lateral flows of materials that may lead to spatial
separation in processes driving uptake and release of the GHGs

Discussion Question #5
 Discuss terminology and classifications of sources/sinks andassociated natural and anthropogenic GHG fluxes to find acommon understanding of consistency and differences inatmospheric observation data and GHG inventory estimates

Discussion Question #5
 Anthropogenic GHG Emissions

 Managed land proxy leads to inconsistency between inversion and inventory
estimates Differences in application of inventory methods

 Particularly differences among inventory Tier methods Tier 1 v. Tier 3 for soil N2O has a different level of estimation and can lead toinconsistencies

 Missing sources in the inventory

 Some sources may not be estimated depending on policy relevance but leads
to inconsistencies with the inversion
 e.g., Herbaceous biomass and dead organic matter

Discussion Question #5
 Attribution of inversion flux to individual source categories Fluxes are based on gases but source categories are often further subdivisionof gases into categories such as direct and indirect soil N2O emissions
 Inversions may provide some information on emissions if separation ofactivities across the country (e.g., pixels dominated by forest, cropland etc.,
rice fields, may provide some insight on sources categories)
 However, more heterogeneous land use patterns will be difficult todisaggregate and also evaluating livestock systems in this sector which are
more mobile across the landscape Interannual variability

 Not always addressed in the inventory whereas this is part of the inversion,leading to inconsistencies
 Longer term climate change is imposed on these patterns, but may not beaddressed in the inventory
 May be able to extract interannual variability out of the atmospheric data as
the time series increase in lengthThank you