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SPACEOBSWP2: Coupling Simulations to DataWP2: Coupling Simulations to Data

Philippe Bousquet (LSCE)

Éric Buchlin (IAS)

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• Sun and Earth are large and complex systems which cannot be represented at all time scales and spatial locations by direct observations,

• Numerical models have to be developed to simulate their properties and evolution

• Models allow (among other things) to interpret observations (data analysis), to provide a 4D optimized view of relevant variables (data assimilation), and to access to forecast or parameter optimization capabilities.

SPACEOBS WP2 : Coupling simulations to observations

10 years of mid-tropospheric CH4 from IASI/MetOp A and

IASI/MetOp-B averaged from July 2007 to July 2016

Coronal Mass Ejection (CME) observed by the Solar Dynamics

Observatory (SDO) on 31 August 2012 at 17.1 and 30.4 nm.

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Models have to be correctly initiated in time (initial conditions) and space(boundary conditions), compared to observations (forward modelling), but alsooptimized against observations (using inversion methods).

satellitedata

Spaceweather


Initial & boundaryconditions

satellitedata

Numericalpredictions

Numericalmodel

Data

assimilation

data

Comparison to observations

In-situObs.

Otherdata

Inversion to optimize inputsData

assimilation

Forward process modelling

GHGcycles

3

satellitedata

Spaceweather


• Forward simulation of the solar coronal structure and evolution tostudy the emergence of solar events at the Sun’s surface, theirpropagation and interaction with the Earth

• Inverse simulation of the Earth carbon cycle using satellite data, as akey component of the ongoing climate change.

Objectives


satellitedata


Numericalmodel

Data

assimilation

data


In-situObs.

Otherdata


assimilation


GHGcycles

4

satellitedata

Spaceweather


• Developing synergies in data assimilation between space weatherand greenhouse gases (ideas, cross-fertilization, good practices)

• Results of simulations performed will be fed into the relevantdatabases of UPSay (MEDOC and CDS/ESPRI)

Objectives


satellitedata


Numericalmodel

Data

assimilation

data


In-situObs.

Otherdata


assimilation


GHGcycles

5

SPACEOBS WP2.1 : A contribution

to space weather science

Contributors: Sacha Brun, Tahar Amari, N. Aunai, E. Buchlin, R. Grappin, Miho Janvier, R. Modolo, R. Smets

Involved laboratories: AIM, CPhT, IAS, LATMOS, LPP

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• 11-yr solar activity cycle• Complex magnetic and

thermal structure

• Dynamical events: flares, Coronal Mass

SPACEOBS WP2.1 : A contribution to space weather science

Sun/heliosphere system: complex and variable

/EIT

flares, Coronal Mass Ejections (CME)…

• Propagation through the heliosphere, interaction with Earth environment

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SoH

O/E

ITSS

T

SDO/AIA, NAFE, MEDOC

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SoHO/EIT, SoHO/LASCO

• Models need to be able to reproducethese structures/events, which areat the origin of Space Weather

• Ultimate target: ability to forecast


Modelling of Sun/heliosphere

• Ultimate target: ability to forecast– emergence and triggering of solar events at the Sun– their propagation in the heliosphere– their interaction with the Earth environment

• Huge efforts made, but not easy!(large range of scales and physical processes)

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1. Multi-scale photospheric magnetic field maps

Solar observations of the magnetic field (B):• Full disk or synoptic maps of projected B• Vector B in SDO/HMI active regions• Processed and combined SoHO/MDI, Hinode/SOT, Canou 2011

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Result: multi-scale B maps (boundary condition for coronal models, and to be compared to magnetic field generation models)


2. 3D model of magnetic field generation and emergence

ASH code (anelastic MHD, AIM):

• Dynamo in the convective zone of the Sun• Complex interactions between compressible

turbulence and solar rotation

• Gives 11-yr cycle, sunspots/active regions…

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• Gives 11-yr cycle, sunspots/active regions…

Result: magnetic field maps close tothe solar surface (comparison toobservations, and boundary conditionsfor coronal models)

Pinto&Brun 2013


3a. Data-driven simulations of the corona and heliosphere

Am

ari e

t al

. 20

14

Stru

gare

ket

al.

20

15

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• 3D MHD (CPhT, AIM): static and dynamic, local and global, with/without currents, with different wind acceleration

• 1D hydrodynamic models (LPP) on B lines from magn. modelsResult: plasma and magnetic environment in the heliosphere; comparison with observations/measurements, or forecast.

Am

ari e

t al

. 20

14

Stru

gare

k


3b. Development of a a new massively parallel code

PHARE, a new hybrid Particle-In-Cell code:

• Adapted to the multi-scale physics of magnetic reconnection: better modelling of physical processes in the corona / heliosphere / magnetosphere

• Will allow obtaining better comparisons with

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• Will allow obtaining better comparisons with observations

Inn

oce

nti

et a

l.


4. Comparison between models and observations

Direct comparison of observable quantities:

• Produced by forward-modelling• Obtained by in-situ measurements and remote-sensing

observations of the heliosphere

Feedback on models and their parameters

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Feedback on models and their parameters

Turc

et a

l. 2

01

3


Summary

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• Optimize use of current space instruments• Prepare for next missions with strong UPSay involvement:

Solar Orbiter (ESA), Solar Probe Plus (NASA).

SPACEOBS WP2.2 : A

contribution to GHG science

Contributors: P. Ciais, P. Bousquet, F.-M. Bréon, F. Maignan, G. Broquet,F. Chevallier, R. Armante, V. Capelle, O. Chomette, C. Crevoisier, D. Edouart, F. Gibert, Y. Goulas, A. Ounis, S. Payan, C. Clerbaux, K. Soudani, E. Dufrene.

Involved laboratories: ESE, LATMOS, LMD, LSCE

Objectives:

• Investigate and predict the asset of different observing systems for the monitoring of GHG fluxes, including new spaceborne instrumental concepts.

• Main focus on CO2 but interest for CH4• Instrument concepts proposed by WP3 will be tested when available.

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Context : CO2 and CH4 are the main drivers of climate change

• CO2 concentrations have increasedby more than 35%

• CH4 concentrations have more thandoubled

• These two gases generate 80% of the greenhouse effect increase

SPACEOBS WP2.2 : A contribution to GHG science

The evolution of the Carbon cycle in a climate change context is very uncertain

• Currently, half of anthropogenic CO2 emission are removed from the atmosphere by natural sinks

• The precise location and processes that control these sinks are poorly known• Is the CO2 sink sustainable in the long term, or will it be affected by climate

change?

• Climate-sensitive CH4 sources remain largely uncertain

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LSCE and LMD lead two satellite missions for the monitoring of CO2 and CH4 columns

MICROCARB (CNES)• CO2 (possibly also CH4)• Passive (reflection of sun light at the surface)• UPSay Scientific contributions : LSCE, LMD• Phase B. Budget needs to be completed for next phases• Launch : 2020

MERLIN (CNES/DLR)

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MERLIN (CNES/DLR)• CH4 only• IPDA Lidar (innovative technology)• UPSAy Scientific contributions:

LSCE (PI, L4), LMD (L0 to L2)• Phase C (hardware starts !)• Launch : 2021

LMD and LSCE also participate to the ESA/FLEX mission recently selected by ESA to monitor vegetation fluorescence (proxy of photosynthesis)


Postdoc

• Development and management of the components of a simulation platform for GHG fluxes handling the chain between radiative transfer models LMD), flux inversion systems (LSCE), and land surface models (LSCE, LMD, ESE).

• Analysis and comparison of the potential of existing (e.g. GOSAT, OCO-2, TANSAT, IASI, …) and future missions (e.g. (Microcarb, Merlin, …) expected in this decade for CO2 and potentially CH4

• Labelisation of a PhD on fluorescence (application to IDI doctoral grants)

• GHG columns

• Surface proper es

Radia ve transfer models

Fluoresecence Land

surface model

GHG surface flu

x

e s

Chemistry transport

model

Satellite radiances

GHG global budgets

Photosynthesis Data assimila on

Process model

Product

Postdoc (Spaceobs)

PhD (IDI)

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• No Hardware responsability (CNES, DLR agencies)• Scientific lead and algorithm development• Chain of data:


Level 1

Radiances

Level 2

CO2 ann CH4 Columns

Level 4

Surface fluxes

Inversion of

Radiative

Inversion of

atmospheric

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Radiative

tranfert

atmospheric

transport

• LMD (LSCE) skills on Level 1 to Level 2• LSCE skills on Level 2 to Level 4• Postdoc :

• Improvement of Radiative transfert chain for Microcarb• Observing System Simulation Experiments (OSSE)

MICROCARB MICROCARBMERLIN

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