Atmospheric monitoring of CO2 emissions: an innovative ... Wu... · Atmospheric monitoring of CO2...

MRV full day workshopBonn, June 3, 2014

Atmospheric monitoring of CO2 emissions:an innovative solution for cities

Lin Wu, Grégoire Broquet, Felix Vogel, Philippe Ciais,Valentin Bellassen, François-Marie Breon, Irène Xueref-Remy and collaborators

LSCE – CEA/CNRS/UVSQ, Gif-sur-Yvette, FranceChaire BridGES ( UVSQ / CEA / CNRS / Thales Alenia Space / Veolia )

CDC-Climat

MRV GHG emissions under existing and developing carbon pricing mechanismsJune 3, 2014

1


Duren & Miller, Nature 2012

Dominant city CO2 emissionsIPCC working group 3: recommendation/challenges for city CO2 monitoring


Urbanisation decreases per capita emissions in developed countries

City CO2 emissions: challenge or chance?

MRV full day workshopBonn, June 3, 2014 4

Paris Climate Plan-30% GHG 2004-20-75% GHG 2004-50

Los Angeles:GreenLA & ClimateLA-35% GHG 1990-2030

Many others (US-EPA & C40)Secotral climate plan(energy, transport, building)

Cities in actions: political plans


Cities in actions

5

IPCC WG3: lack of comparable/reference methods/data for local/urban carbon accounting

• Vulantary reporting Networks & associations

C40 network, Carbon Disclosure Project


City CO2 MRV: down to sectors

6


MRV Sector: Regional & City Territorial Inventories

7

� Uncertainties (input data & emis. Factors)� Accuracy (uncertainties)

� Not obligatory , diverse voluntary tools� Needed standardized/harmonized protocal

Ian Cochran


City Fossil fuel CO 2 emission inventories

EDGAR10 AIRPARIF05 AIRPARIF08 IER05 Max - Min

Resolution 0.1°x 0.1° 1km x 1km 1km x 1km 1min x 1min

Annual budget of Ile-de-France (TgC)

Residential 6.75 5.65 7.80 5.36 31%

Road 8.50 3.63 3.37 6.03 60%

Industry 5.19 3.02 3.09 4.61 42%

Total 24.65 12.34 14.26 16.39 50%

50% difference at 25km when disaggregating into regions (Duren and Miller 2012)

TYPE 1 Existing inventories

• Research based tools (EDGAR, IER, PKU): top-

down, global coverage; uncertainty can be

high

• Simple budget methods (low price, no

assessment of the uncertainty)

• High quality inventories by local agencies (e.g.

AIRPARIF) ~20% monthly uncertainty

TYPE 2 Potential inventories if

incentive in the future

• MRV practice (e.g.CITEPA); verified

national uncertainty ~5%; following

IPCC guideline


Innovative approach: atmospheric inversion (constraint from conc. observations)

9

Bottom-up

Top-down

CityNetworkPlatform

Algorithm

• Observations linked with area emissions by numerical models

• Have been used for 15 years in scientific community to estimate natural fluxes at large scales

• Emergent regional/local applications: ability to estimate anthropogenic fluxes


Cities with monitoring/modeling/inversion projects

10

CO2MegacitiesParis London LA IndianapolisSalt lake city Houston Shenzhen …

Cities in actions: scientific attempts


In-situ network of 5 stations for Paris CO 2 monitoring

� High-precision, multi-species observations� Picarro(45k€ )+ add cost: 1 M€ / ~10 Picarro

Model G1302 (CO2/CO/H2O)

CO2 CO

Accuracy < 0.13 ppm <11 ppb

Precision < 0.1 ppm

(0.38 ppm for EIF)

<10.2 ppb

Irène Xueref-Remy et al, AGU 2013 & article in prep.

Fossil Fuel (FF) + Bio = Total

Prior CO 2 emission Posterior CO 2 emission

Data selection

Clipping out many data,but still have considerable uncertainty reductions

• Afternoon data• Wind > 3ms-1

• Obs gradient but not obs

Inversion results, Breon et al. 2014


Extending network with cost effective sensors?

Cost Comments

Sensors / site 2-45 k€

Infrastructure / site

• Container

• Inlet + R-Box + drying

• Calibration

• Others

8-30 k€

10 k€

5 k€

3-5 k€

1 k€

Mininum:

Inlet +

calib

= 8 k€

Annual

• Infrastructure / site

• 1 engineer: network

• 1 engineer: data

• 1 engineer: modeling

0-10 k€

60 k€

60 k€

60 k€

1 Number of sites 70

Cos

t (k€

)

• Cheap sensors• Using existing air quality network

Picarro + maximum cost / site

180

1000Table: detailed cost of monitoring network


Objectives and accuracies targets

13

Monte-Carlo simulations of a linear reduction model (5% annual accuracy in a 15-yr horizon)

Inversion objective: 5% annual uncertainty

• Same as (national) inventories for MRV use; thus an alternative/verifying approach• Ability to track graduately the 15-yr trend

• Sectorial verfications


All sectors: FF total

Waste

Air, rail, ship

Road transport

Production

Residential

…

Sectorial Inversion of CO 2 fluxes using data from extended networks

Zone1 Zone2 Zone3

Zone4 Zone5

CO2Megaparis

Ext

ende

d

10 sites

30 sites 50 sites

70 sites


y

?=x

15

Gaussian unbiased assumption� Obs: � Prior:

� Posterior:

Emis/factorTransport

Obs. Err.Obs

10 sites, number of assimilated gradients ~ 800

Data selectionAfternoon data; Wind > 3ms-1;choose one upwind station tocompute the concentration gradients

Obs uncertainties: transport error + aggreg. error + instru. Error

Control scaling factorsSpatio-temporal disaggregation (diurnal/zone)Monthly prior error budget: ~20%



• Reduction of uncertainties for total FF with very limited number of stations (~10 sites)

• More sites to reduce uncertainties for spatio-temporally distributed sectorial emissions

Aggregate monthly emissions to annual emissions: 10% 5 %


Public

Industry

Energy

Waste

CommercialResidentialInstitutional

Road

AirlineAgriculture

AIRPARIFInventory


Summary

17

• Results to be refined for large networks, for trend detection per sector, and to analyze the ability to solve for the spatial distribution

• Existing inversion framework: ability to monitor the city total emissions with high precision at a reasonable cost

• Near term inversion framework: ability to monitor the city sectorial budgets with high precision at a reasonable cost

� Atmospheric inversion and MRV systems:

• complementary to bottom-up approaches (Bayesian estimation)

• a solution for a fully independent budget verification method

• a robust sectorial monitoring method in a near term


The Forest Project

Courtesy of P. Bicheron and F. Maignan

Funded by the Climate KIC

An innovative monitoring service package for tropical forest carbon stocks and fluxes

(trusted, fully integrated, cost-effective) enabling forest carbon projects to achieve

carbon emission reduction and / or stock enhancement


� Target :� REDD, Afforestation/Reforestation projects, CDM, voluntary carbon market at national and project (local / regional) levels� prove positive impacts of forest projects linked or not to the carbon economy

Framework

Airbus DS satellites constellation – Spot, Pleiades, TerraSAR,

TanDEM-XBiomass production

based on a simulation of the ORCHIDEE model (gC m-2 yr-1)

� Based on fully optimized and reliable tools:� Observation from space, optical and radar (Astrium)� In situ measurements for calibration (AMAP, ONFI)� Carbon fluxes and stocks modeling (LSCE)� Quality Assurance, calibration, validation, accuracy assessment (NPL)� Cost effective capacities driven by market and end users (CDC-Climat, ONFI)


Continuous forest monitoring/mapping

Pleaides product – 50cm. Monitoring of a planting project -Brazil

Land cover classification with TanDEM-X –

Kalimantan, Indonesia

� Land cover / Land use change maps

� Forest base and degradation mapping

� Canopy texture features and forest structure(biomass, height)


Integration of satellite datastreamsUsing a process based forest model

� Estimates of sources and sinks due to forest management types, based on different scenarios over a 30 years time horizon (deforestation, degradation, and enhancement)

� Estimates of uncertainties (input data and model parameters)

� Model calibration against plot scale data in selected regions (Guyana, Gabon)

Evolution of circumference distribution (m) for a Tropical broadleaf evergreen grid box

> 22 - 1.81.6 - 1.81.4 - 1.61.2 - 1.41.0- 1.20.8 – 1.00.6 - 0.80.4 - 0.60.2 - 0.40.0 - 0.2

Diameter class

(m)


Products� Wall to wall quantification of tropical forest carbon stocks and fluxes

� Scale : from project to region and continent

� Accounts for :

� Effects of interannual variability and forest disturbance

� Net carbon balance

� Water and energy fluxes

� Possibility to integrate the calibrated model for future scenarios

Timeline� 2014 Model testing and calibration

� Mid 2015 Site level demonstrator

� End 2015 Prototype of regional applications

Atmospheric monitoring of CO2 emissions: an innovative ... Wu... · Atmospheric monitoring of CO2...

Documents

Transcript of Atmospheric monitoring of CO2 emissions: an innovative ... Wu... · Atmospheric monitoring of CO2...