Observation-based estimates of non-CO greenhouse gases · 2019. 3. 19. · HIMMELI...

Observation-based estimates of

non-CO2 greenhouse gasesRona Thompson (NILU), Tuula Aalto (FMI), Peter Bergamaschi (JRC),

Philippe Bousquet (LSCE), Dominik Brunner (EMPA), Sander Houweling

(VUA), Adrian Leip (JRC), Marilena Muntean (JRC), Pete Smith (UABDN),

Timo Vesala (UH), Pierre Regnier (ULB) & Jean-Matthieu Haussaire (EMPA)

General Objectives

➢ Deliver estimates of CH4 and N2O fluxes, including

anthropogenic as well as natural sources, and build this

capacity into a pre-operational system

➢ Improve the understanding of the processes driving

fluxes of CH4 and N2O, and reduce the uncertainties in

their budgets and trends at national, regional and

continental scales

13 March 2019 VERIFY-CHE GA, Reading 2

Sources of methane

Total global emission of 563 Tg/y

Wetlands

Termites

Ocean

Geological

Coal

Oil & Gas

Livestock

Rice

Waste

Biomass & Biofuel


Data from the Global Carbon Project

naturalanthropogenic

Sources of nitrous oxide


Total global emission of 17 Tg/y

Ocean

Natural soil

Agricultural soil

Industrial processes

Biomass burning

Manure management

Waste

Data from the Global Carbon Project

naturalanthropogenic

Uncertainties in BU estimates

Methane

• Microbial emissions which

depend strongly on local

conditions (temperature, soil

moisture etc.)

• Uncertainties on some sources

>100%

• Challenging to represent with

process-based models

Nitrous oxide

• Microbial emissions which

depend strongly on local

conditions (temperature, soil

moisture etc.)

• Emission factor uncertainty

range 30 to 300%

• Challenging to represent with

process-based models


Large uncertainties in BU methods, therefore, likely

that TD methods can contribute to improved estimates

Overview of the workflow


Bo

tto

m-u

p Agricultural

emissions of CH4

and N2O

Natural emissions

of CH4

Non-agricultural

anthrop. emissions

of CH4 and N2O

To

p-d

ow

nR

esearc

h &

develo

pm

en

t

Phase 1: Flux estimates using selected inversions

Phase 2: Flux estimates using CIF

DAS for

wetland

fluxes CH4

High-Res

satellite

data in

inversions

Suppl.

tracers in

inversions

High-Res

inversions

Pro

cess-b

ased a

nd

sta

tistical m

odels

Atm

ospheric

invers

ions


Bottom-up methods

for estimating CH4 and N2O emissions

Agricultural emissions: methods

• Process-based model: ECOSSE

• Statistical model: CAPRI


Sources Method/Model Notable Inputs

enteric fermentation (CH4) CAPRI

based on IPCC

Tier 1 approach

(emission factors)

crop areas, yield,

livestock densities,

nutrient inputsmanure management (CH4 + N2O)

direct + indirect emissions (CH4 + N2O)

soil emissions (N2O)

(cropland, grassland, forests)

ECOSSE

process-based

land surface

model

climate, land-use,

nutrient inputs, &

soil data

Agricultural emissions: N2O


avg.

N2O

[kg/h

a/y

r]

N2O emissions for cropland (annual average 2005-2015)

N2O emissions for grassland (annual average 2005-2015)

Preliminary results

Model approach is still under

development

Problem:

Emissions in Central (e.g. NL)

and Western Europe too low

Solution:

Fertilizer application needs to

be changed from fertilizer

demand to actual application

rate

For grasslands:

In the actual assumptions

management is not considered.

2.5

0.0

1.4

0.0

avg.

N2O

[kg/h

a/y

r]

Other anthropogenic emissions

• EDGAR – Emission Database for Greenhouse Gas Research: uses activity

data and IPCC Tier 1 approach. Estimates emissions for both agricultural and

non-agricultural anthropogenic sources

• Used for both CH4 and N2O for all countries

• EDGAR final products: emission time series and global maps at 0.1°×0.1°


Sources (for VERIFY) Method/Model Notable Inputs

industrial Based on EDGAR.v4.3.2

(1970-2012) and

extended forward in time

to 2015 (results

expected 2019)

Activity data from

international statistics

and satellite remote

sensing

residential

transport

energy



EDGARv4.3.2 CH4



EDGARv4.3.2 N2O



Uncertainties calculated by error propagation

Error bars reflect 95% confidence level

1B1: fugitive emissions from solid fuel

1B2: fugitive emissions from oil/gas

3A1: enteric fermentation

3A2: manure management

3C7: rice cultivation

4D: waste treatment

Soil and peatland emissions


water tabledepth

CATOTELM

ACROTELM

microbe processesproduction: CH4 CO2

aerobic respiration: CO2 O2

anaerobic respiration: CH4 CO2 O2

oxidation: CH4 CO2 O2

transport processesdiffusionplant transportebullition

Fluxes of CH4, CO2 and O2

Temp,

Prec, Rad

CRU-JRA

climate

data

0.5°×0.5°

resolution

JSBACH land

surface

model

Peat YASSO

soil carbon

model

WTD, LAI,

Resp, Temp

HIMMELI

JSBACH-YASSO-HIMMELI models

Soil and peatland emissions


Mineral soils both

emit and take up

methane

Peatland emissions

are predominantly

in northern Europe

Inland water body emissions


CH4 and N2O fluxes from inland water-systems• Computes fate of terrestrial-derived & in-situ produced C along the flow path

• Relies on estimates of river (Lauerwald et al., 2015), reservoir (GranD) and

lake (HydroLAKES) volumes and surface areas available for gas exchange

• Model inputs: Estimates of terrestrial C and nutrient inputs


Top-down methods

for estimating CH4 and N2O emissions

Inversion method


x0 xi

yobs

ymod = H(xi)

∆y = ymod–yobs

J′(xi+1) = B-1(xi+1 – x0) +

HadjR-1(∆y) = 0

prior

fluxes

best guess

estimate

modelled mixing

ratio

model errorobserved

mixing ratioiterate

xa

posterior

fluxes

end iteration once

reach certain criteria

H = transport and chemistry model

Hadj = adjoint transport model equiv. to (H′(x))T

B = prior error covariance matrix

R = obs. error covariance matrix

estimate xi+1 by solving:

Based on Bayesian statistics

Atmospheric network

Sites measuring CH4 and N2O in Europe


●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

●

●

●●● ●

●●

●●

●

●

●

●

●

●

●

40

50

60

−10 0 10 20 30

Type●

●

in−situ

flask

Emission estimates: CH4Results 1st year inversions


Preliminary results

▪ Model: TM5-4DVAR

▪ Global 6°×4°, Europe1°×1°

▪ Period: 2005-2016

▪ Priori estimate: GCP-CH4


Results 1st year inversions

Emission estimates: CH4

Preliminary results

▪ Model: FLEXPART-ExtKF

▪ Resolution: 0.5°×0.5°

▪ Period: 2005-2016 (shown for 2010)

▪ Priori estimate: based on EDGAR

15°W 5°W 5°E 15°E 25°E 35°E

33°N

43°N

53°N

63°N

73°N

0

20

40

60

80

100

15°W 5°W 5°E 15°E 25°E 35°E

33°N

43°N

53°N

63°N

73°N

0

20

40

60

80

100

A priori flux (mg m-2 day-1)

A posteriori flux (mg m-2 day-1) A posteriori – a priori flux (mg m-2 day-1)


Emission trends: CH4Preliminary results

▪ Model: TM5-4DVAR

▪ Global 6°×4°, Europe1°×1°

▪ Period: 2005-2016

▪ Priori estimate: GCP-CH4

All observations assimilated as available

(i.e. not continuous for all stations over

2005-2016 period)

Emission estimates: N2O

Results 1st year inversions


▪ Model: FLEXINVERT+

▪ Resolution: 0.5°×0.5°

▪ Period: 2005-2015

▪ Priori estimate: EDGAR-v4.32

15°W 5°W 5°E 15°E 25°E 35°E

30°N

40°N

50°N

60°N

70°N

0.000

0.005

0.010

0.015

0.020

0.025

0.030

15°W 5°W 5°E 15°E 25°E 35°E

30°N

40°N

50°N

60°N

70°N

0.000

0.005

0.010

0.015

0.020

0.025

0.030

Mean prior emissions (gN m-2 d-1) Mean posterior emissions (gN m-2 d-1)

National emissions: N2O


N2O

(G

gN

/y)

0

50

100

150

Iberia P

enn.

Fra

nce

Germ

any

Benelu

x

UK

Fennosc

andia

Italy

Aust

ria+S

wis

sPola

nd

Hung+C

zech

+S

lova

k

N2O

(G

gN

/y)

0

50

100

150

Iberia P

enn.

Fra

nce

Germ

any

Benelu

x

UK

Fennosc

andia

Italy

Aust

ria+S

wis

sPola

nd

Hung+C

zech

+S

lova

k

Mean annual emission

2005-2009 (GgN y-1)

Mean annual emission 2010-

2015 (GgN y-1)

prior

posterior

Detection of hotspots: N2O

• Only <1% land area responsible for 20% of

emissions

• Hotspots defined as >99.5 percentile emissions

• Hotspots largely from nitric and adipic acid

production (sector 2B)


0

500

1000

1500

2000

N2O

(G

gN

y-1) Total

prior

posterior

0

100

200

300

400

500

N2O

(G

gN

y-1)

2006 2008 2010 2012 2014

Hotspots

15°W 5°W 5°E 15°E 25°E 35°E

30°N

40°N

50°N

60°N

70°N

0.000

0.005

0.010

0.015

0.020

0.025

0.030

Total Land Emissions (GgN y-1)

Posterior total

15°W 5°W 5°E 15°E 25°E 35°E

30°N

40°N

50°N

60°N

70°N

0.000

0.005

0.010

0.015

0.020

0.025

0.030EDGAR 2B

Research & Development

• Using CH4 retrievals from satellite instrument TROPOMI aboard

Sentinel 5P (launched Oct-2017)

• High-resolution 7×7 km


Summary and conclusions

➢ Bottom-up methods have very large uncertainties on

some sources of CH4 and N2O leading to very large

overall uncertainties

➢ Potential for top-down methods to contribute to improved

emission estimate for these species

➢ Preliminary inversions show slightly higher CH4 and N2O

emissions for EU28 compared to UNFCCC reports

➢ Both CH4 and N2O show a small decreasing trend over

2005-2016


Observation-based estimates of non-CO greenhouse gases · 2019. 3. 19. · HIMMELI...

Documents

Transcript of Observation-based estimates of non-CO greenhouse gases · 2019. 3. 19. · HIMMELI...