The importance of aerosols in numerical weather and climate prediction

Demerval S. Moreira demerval@fc.unesp.br

Universidade Estadual Paulista “Júlio de

Mesquita Filho” (UNESP), Brazil 2nd WCRP Summer School on Climate Model Development

21st -31th January 2018, Cachoeira Paulista, SP, Brazil

Urban: Industrial/traffic

Volcane

Dust

Biomass burning

MODIS TERRA 24 Aug 2010

Extensive area in South America

• Smoke reduces direct solar radiation reaching surface => cools up to 3°C, reduces sensible and latent heat fluxes => reduces plant respiration and thermal stress of the leaves.

• Smoke increases PAR diffuse fraction from 19% (clean atmosphere) up to 80% (heavy smoke) => diffuse radiation penetrates deeper into the canopy, increasing PAR radiation availability to the sub-canopy leaves => increases their rate of photosynthesis.

Yamasoe et al., (2006), Baldocchi, (1997),

Misson et al., 2005; Knohl and Baldocchi,

2008; Mercado et al, 2009; Doughty et al.,

2010).

Gross primary productivity (GPP) from JULES mdel vs. Radiation at flux sites with observations of diffuse radiation in the Amazon

Direct radiation regime

Rap et al. 2015

Diffuse radiation regime

So, plant photosynthesis tends to increase with irradiance

and under diffuse light conditions. (Mercado et al., Nature, 2009)

But, have a “optimum” amount of aerosol:

First we have to estimate the amount of aerosol that is released into the atmosphere

Burning points observed by the AVHRR sensor during September 2010

source: www.cptec.inpe.br/queimadas

Brazilian Biomass Burning Emission Model (3BEM) estimates the amount of aerosol that is released into the atmosphere (Longo et al., 2010). For each fire pixel detected, the mass of the emitted tracer is calculated by the expression: αveg = amount of biomass available for burning ; βveg = combustion factor; EFveg = emission factor; afire = burning area for each burning event.

Second we have to have a model that works with aerosol Example: BRAMS Model

BRAMS have:

CCATT (Coupled Chemistry-Aerosol-Tracer Transport) module

CCATT is an Eulerian transport model coupled online with BRAMS and developed to simulate the transport, dispersion, chemical transformation and removal processes associated with gases and aerosols (Freitas et al., 2009; Longo et al., 2013).

CCATT simulates the tracer transport online with the simulation of the atmospheric state by BRAMS.

The general mass continuity equation for tracers solved in the model is:

(I) Represents the 3-D advection, (II) is the sub-grid-scale diffusion in the PBL and terms (III) and (IV) are the sub-grid-scale transport by deep and shallow convection, respectively. Term (V) is the net production or loss by chemical reactions. Term (VI) is the wet removal, term (VII) refers to the dry deposition and, finally, (VIII) is the source term that includes the plume rise mechanism associated with vegetation fires.

JULES is able to simulate surface gases, energy fluxes, hydrological processes, photosynthesis, respiration, and vegetation and soil dynamics.

BRAMS provides to JULES: wind speed, air temperature, pressure, precipitation, downward radiation fluxes, water vapor and trace gases (including CO2).

JULES advances feeds back BRAMS with: sensible and latent heat, momentum surface fluxes, upward short-wave and long-wave radiation fluxes, and trace gases fluxes.

The photosynthesis radiation scheme in JULES accounts for the effects of diffuse radiation on canopy photosynthesis by splitting direct and diffuse radiation and sunlit and shaded leaves at each canopy layer.

JULES calculate photosynthesis at each canopy level.

BRAMS have:

JULES (Joint UK Land Environment Simulator) module

CARMA includes the aerosol radiation interaction with feedback to the model heating rates.

Moreira et al. (2013) included in CARMA a parameterization to calculate the diffuse fraction of solar irradiance specific to biomass burning aerosols Yamasoe et al. (2009):

BRAMS have:

CARMA (Community Aerosol and Radiation Model for Atmospheres) radiation scheme - and recently RRTMG

Parameters for a third-degree polynomial fit to the diffuse fraction of broadband solar irradiance reaching the surface as a function of AOD at 670 nm for distinct air mass intervals.

Diffuse fraction of solar irradiance

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Diffuse fraction: based on Yamasoe et al. (2009) measurements of solar radiation partitioning in Amazonia.

Model resolution:

• Grid domain over Amazon region with 20 × 20 km resolution.

• Vertical : dz = 100 to 1000 m (stretching grid)

Boundary conditions:

• Meteorology: NCEP/GFS analyses

• CO2 : CarbonTracker (3o × 2o)

• CO: Based on optimized fluxes as calculated by the 4D-var system using

IASI satellite data (Krol et al. 2013) (1o × 1o)

Emissions:

• Urban/industrial : EDGAR + South American inventory (Alonso et al. 2010).

• Biomass burning: 3BEM (Longo et al., 2010).

• CO2 biogenic fluxes from JULES surface scheme.

3-set of experiments for September 2010:

• NO_AER: no aerosol effect on radiation.

• DIR+DIF: direct aerosol effect + diffuse radiation.

• DIR_AER: direct aerosol effect.

BRAMS configuration

Model evaluation – AOD

Moreira et al. (2017), Atmos. Chem. Phys.

MODIS AQUA BRAMS

Monthly mean AOD at a 550 nm wavelength for September 2010 from the (a) MODIS Aqua retrieval and (b) from the model as simulated in the DIR+DIF experiment.

Moreira et al. (2017) Atmos. Chem. Phys.

Model evaluation – CO2

Santarém Alta Floresta

CO

2 [

pp

m]

CO

[p

pb

]

Apr Jul Oct Jan Apr Jul Oct 2010 2011

Apr Jul Oct Jan Apr Jul Oct 2010 2011

Apr Jul Oct Jan Apr Jul Oct 2010 2011

Apr Jul Oct Jan Apr Jul Oct 2010 2011

400

398

396

394

392

390

388

386

384

382

380

600

550

500

450

400

350

300

250

200

150

100

50

0

* Santarém

*Alta Floresta

BRAMS (18 UTC) Obs (17 UTC)

Alta Floresta Santarém

Model evaluation – CO2 and CO at ~2 km AGL

Moreira et al. (2017), Atmos. Chem. Phys.

Model evaluation - Accumulated precipitation ground station observation

TRMM

BRAMS

27

Mean PAR (µmolm-2s-1) at 16:00 UTC from DIR+DIF

Mean diffuse fraction of solar radiation at 16:00 from DIR+DIF

Shortwave irradiance at the surface at 16:00 UTC

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Mean from DIR+DIF

DIR+DIF - NO-AER

Difference in the 2 m temperature (◦C) DIR+DIF - NO-AER

Effect of diffuse radiation fraction on Gross Primary Productivity (GPP)

Diffu

se F

raction

GP

P[μ

mo

lCm

-2s-1

]

PAR [μmolm-2s-1]

• GPP increases with PAR, reaching a saturation regime with further decreasing.

• For the same PAR, GPP is higher for higher diffuse fraction

Individual contributions of diffuse radiation and direct aerosol effects to GPP enhancement using BRAMS

GPP Mean Midday September 2010

GPP [mmol m-2 s-1]

(Diffuse radiation effect on GPP via BBA)

(Direct aerosol effect on GPP: Reduction in radiation and associated effects on T & P)

Moreira et al. (2017), Atmos. Chem. Phys.

ΔGPPdir+diff ΔGPPdir

ΔGPPdir+diff = GPP (DIR+DIF) – GPP(NO_AER)

ΔGPPdir = GPP (DIR_AER) – GPP(NO_AER)

Aerosol effects on GPP [μmolCm-2s-1]

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ΔGPPdir+diff ΔGPPdir

Fore

st

Shru

b

C4

gra

ss

C3

gra

ss

ΔGPPdir+diff = GPP (DIR+DIF) – GPP(NO_AER)

ΔGPPdir = GPP (DIR_AER)

– GPP(NO_AER)

43%

39%

9%

36%

2%

10%

-6%

10%

Direct effect: GPP for all biomes, except C4G () because it does not saturate. Diffuse effect is dominant: GPP for all biomes

CO2 fluxes [μmolCm-2s-1]

Direct aerosol effect + diffuse rad

Direct aerosol effect

No aerosol effect

GPP

Resp. plant

Resp. soil

NEE

NO_AER DIR_AER DIR+DIF

GPP 4.6 4.7 6.3

respP 2.9 3.0 3.3

respS 2.5 2.4 2.4

NEE 0.8 0.7 -0.6

Mean CO2 fluxes (μmolCm-2s-1)

Model results indicate that aerosol effects invert the signal of NEE, changing the ecosystem from being a source to be a sink of CO2.

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Direct aerosol effect + diffuse rad

Direct aerosol effect

No aerosol effect

Mean GPP (μmolCm-2s-1)

NO_AER DIR_AER DIR+DIF

Forest 4.7 4.8 6.7

C3G 3.1 3.4 4.4

C4G 11.3 10.6 12.3

Shrub 1.4 1.6 1.9

Mean per month

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GPPDIR+DIF - GPPNO-AER GPPDIR-AER - GPPNO-AER

FluxDIR+DIF - FluxNO-AER FluxDIR-AER - FluxNO-AER

Mean diurnal cycle of the CO2 (ppmv) mixing ratio

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Conclusions • Model results indicate that biomass burning aerosol significantly

affects CO2 fluxes.

• The increase of AOD contributes to the increase of diffuse radiation fraction and reduces the total irradiance, cooling the surface.

• Each type of biome reacts differently to the increasing of AOD.

• The GPP of all biomes grows with the increase of diffuse fraction of radiation.

• The reduction in irradiance caused by the increase of AOD, typically increased GPP of forest, GC3 and shrub due to the reduction of the peak irradiance, however in a less extent than the diffuse effect.

• Model results are consistent with observation in Amazonia (Yamasoe, et al. 2006, Mercado et al., 2009).

• Model results pointed that aerosol effects even invert the signal of NEE, changing the ecosystem from being a source to be a sink of CO2.

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Thank you!

Contact: Demerval S. Moreira demerval@fc.unesp.br