Assimilation of near real time GEOV Albedo and Leaf Area Index within the ECMWF modelling system

Slide 1

Assimilation of near real time GEOV Albedo and Leaf Area Index

within the ECMWF modelling system

Souhail Boussetta, Gianpaolo Balsamo, Emanuel Dutra, Anton Beljaars

Slide 1BG2.5 EGU, Vienna 02 May 2014. S. Boussetta

with the support and partnership of the EU FP7 project

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BG2.5 EGU, Vienna 02 May 2014. S. Boussetta

Slide 2

Why vegetation state is important?Vegetation was shown to be of critical importance under the NWP framework:

Evapotranspiration Boundary layer developement Cloud and precipitation ...

Vegetation directly affect the global carbon cycle

Earth System Models are evolving to better represent vegetation dynamic

Satellite observations with channels informative on the vegetation state are becoming more and more available and with higher accuracy & frequency

Assimilation of vegetation state products would allow:

Better represent land biogenic fluxes in interaction with the atmosphere Monitor present and past vegetation state and its dynamics Understand and adjust process development within models Ultimately to improve weather and earth systems prediction.

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Slide 3

The data:The Copernicus GEOV1 LAI/albedo product is based on observations from the VEGETATION sensor on board of SPOT satellite. Global coverage with 1km / 10 day space/time resolutionProduced in the framework of the Copernicus Initial Operation & IMAGINES and Freely available.

The model: The ECMWF LSM (CTESSEL) coupled within the Integrated Forecasting system IFS

The analysis system:The analysis procedure is an optimal combination of the satellite observations and derived climatology, depending on their associated errors σo and σc. Suitable for NWP framework and consistent with actual method used for slow evolving

variables.

clim

Analysed trajectoryobs

var

t

ccooa varvarvar

22

2

22

2

,co

oc

co

co and

where

How vegetation state is take into account?

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BG2.5 EGU, Vienna 02 May 2014. S. Boussetta Slide 4

Observation

Quality control + sf (albedo)

GEOV1 LAI/Alb

9-point Spatial smoothing

GEOV1 Observation + o

Analysis

ccooa CObsAna lim

22

2

22

2

,co

oc

co

co and

GEOV1 Analysis

Climatology(a-priori)

Quality control + sf (albedo)

GEOV1 LAI/Alb

Averaging => Gapped Climatology

Temporal Gap filling

Extraction of 10-day look-up table on land use type

GEOV1 Climatology + c

9-point Spatial smoothing

Spatial Gap filling

Vegetation state initialization

Slide 5Europe East China 2003droughts


Slide 5 NRT analysed LAI seems able to detect/monitor anomalous year The analysed LAI and albedo signal appear to be covariant mainly during wet year.

Horn of Africa 2010 drought

Australia drought recover also visible in albedo

LAI anomaly Albedo anomaly

Russian 2010 Heat wave

In which cases vegetation matters most?

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Slide 6

The surface-only simulation setup:To seek the impact of the NRT analysed data four experiments are performed

Period: 1999 to 2012 Coverage: Global Resolution: 40km

4 different experiments:Control: LAI+albedo climatology are usedNRT_ALB_LAI: LAI nrt data + albedo nrtNRT_LAI: LAI nrt data + albedo climatology NRT_ALB: LAI climatology + albedo nrt

Results evaluated on surface fluxes:Latent heat flux Sensible Heat fluxCarbon dioxide flux (Net Ecosystem Exchange of COt)

Which processes are affected by vegetation state?

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Slide 7

Clim

NRT_ALB - Clim NRT_LAI - Clim

NRT_ALB_LAI - Clim

Latent heat flux sensitivity

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Slide 8

Clim


NRT_ALB_LAI - Clim

Sensible heat flux sensitivity

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Slide 9

Clim


NRT_ALB_LAI - Clim

Carbon dioxide flux sensitivity

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Slide 10

The atmospheric coupled simulation setup:To seek the impact of the NRT analysed data four coupled experiments are performed

Period: 2010 Coverage: Global Resolution: 40km

4 different experiments:Control: LAI+albedo climatology are usedNRT_LAI: LAI nrt data + albedo climatology NRT_ALB: LAI climatology + albedo nrt NRT_ALB_LAI: LAI nrt data + albedo nrt

Results evaluated on weather forecasts for next day :2m temperature (36-hour forecast range)2m relative humidity (36-hour forecast range)

Is weather affected by vegetation state?

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Slide 11

Horn of Africa drought & Australia drought recover


Check the T 2m and RH on short term forecast fc+36 valid 12 UTC, Nov. 2010Sensitivity = (exp –ctl),

if >0 => warming/adding moisture, if <0 => cooling/removing moisture

And Impact = |ctl – analysis| - |exp – analysis| ,

if >0 => relative error reduction from the analysis (positive impact ) if <0 => relative error increase from the analysis (negative impact)

Is weather affected by vegetation state (II)?The focus is on:

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Slide 12

Coupled run: NRT_LAI+ALB Vs ClimSensitivity Impact

Weather forecasts sensitivity and impact

ΔRH2m

Red Warming

Blue Drying

Blue Better forecast

Blue Better forecast

ΔT2m ΔErr T2m

ΔErr RH2m

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Slide 13

Conclusions & OutlookThe proposed analysis procedure leads to smooth temporal evolution of LAI/Albedo, which is more appropriate for initialization of environmental prediction models.

The analysed NRT LAI is able to detect/monitor anomalous year. The analysed NRT albedo signal seems covariant with the NRT LAI mainly during wet year (compensation effect may occur between vegetation and bare-ground albedo).

GEOV1 NRT LAI/albedo showed potential for heat waves and drought monitoring but given the impact on energy and carbon fluxes these products are ECVs also for coupled experiments.

Introducing NRT LAI and Albedo in coupled runs is physically justified and has an overall neutral to positive impact on forecasted weather parameters, with the LAI signal being dominant and in some cases enhanced with the Albedo anomaly signal.

In future work, enhanced connections between albedo, LAI (and roughness) in Earth System Models will most likely increase the sensitivity to vegetation dynamics.

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Slide 14

Thank you for your attention

Contact: [email protected]

http://fp7-imagines.eu/

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Slide 15

Horn of Africa drought & Australia drought recover


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T2m Sensitivity Impact

Rh2m

Yellow Warming Blue Positive impact

Blue Positive impactBlue drying

Coupled run: NRT_LAI Vs Clim

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Coupled run: NRT_ALB Vs ClimT2m Sensitivity Impact

Rh2m

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2010 Russian Heat wave

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Latent Heat flux

Clim


NRT_ALB_LAI - Clim

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Sensible Heat flux

Clim


NRT_ALB_LAI - Clim

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Net Ecosystem Exchange

Clim


NRT_ALB_LAI - Clim

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Scores of GEOV1 LAI NRT against GEOV1 LAI climatology for JJA: a) 2m temperature sensitivity [K], b) 2m temperature impact, c) 2m relative humidity sensitivity [%], d) 2m relative humidity impact. An overall neutral to positive impact.

Assimilation of GEOV1 NRT LAI and its potential value (coupled runs)

ctlLAILAI XXXysensitivit _exp_)( ||||)( exp__ anLAIanctlLAI XXXXXimpact

T2m

RH

Blue cooling

Blue drying

Blue Positive impact

Blue Positive impact

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2003 Europe and East China drought


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Latent Heat flux

Clim


NRT_ALB_LAI - Clim

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Sensible Heat flux

Clim


NRT_ALB_LAI - Clim

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Net Ecosystem Exchange

Clim


NRT_ALB_LAI - Clim

Documents

Assimilation of near real time GEOV Albedo and Leaf Area Index within the ECMWF modelling system