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Aug 15, 2023 239 likes •519 views

The world according to. The world according to. Modeling surface trace gas exchanges: Modeling surface trace gas exchanges: What are the main limitations? What are the main limitations? Laurens Ganzeveld Max-Planck

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SLIDE 1

“ “The world according to….” The world according to….”

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Modeling surface trace gas exchanges: Modeling surface trace gas exchanges: What are the main limitations? What are the main limitations?

Laurens Ganzeveld Max-Planck Institute for Chemistry, Mainz, Germany E-mail: ganzevl@mpch-mainz.mpg.de

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Outline Outline

  • Results of modelling of surface trace gas exchanges at

site- and global scale

  • What are the main limitations and how can we deal

with/get around these?

  • Future: scenario analyses of impact of land cover and

land use changes on atmospheric chemistry

  • Conclusion and outlook
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SLIDE 4

SO SO2

2 dry deposition velocity [cm s

dry deposition velocity [cm s-

  • 1

1]

]

0.02 1.15 0.75 0.35

Modeling surface trace gas exchanges Modeling surface trace gas exchanges

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SLIDE 5

emissions dry deposition turbulence

crown-layer ~ 0.5-15 m ECHAM/SCM surface layer ~ 68 m

chemistry

dc dt = ∂c ∂t

     

turb + ∂c ∂t

     

chem + ∂c ∂t

     

emiss + ∂c ∂t

     

dep

canopy-soil layer Vegetation model Vegetation and wet skin fraction

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SLIDE 6

Evaluation of multi Evaluation of multi-

  • layer model in SCM

layer model in SCM

Tropical rainforest, Tropical rainforest, Manaus Manaus (Brazil), LAI=7, canopy height = 30 m (Brazil), LAI=7, canopy height = 30 m

Model Model Measurements Measurements Rn [W m-2] 450 ~ 600 CC [0-1] 0.9 ~0.7 H [W m-2] 20 ~200 Tair [K] 297 – 300 (32 m) ~296-301 (~39 m) u [m s-1] 0.5 – 2 (~39 m) ~1.5 – 2.5 (~39 m) q [g kg-1] 19 (~53 m) 15 (~39 m)

  • 4
  • 3
  • 2
  • 1

4 8 12 16 20 24

Time [hr] O3 flux [1e11 molec. cm-2 s-1]

model

  • bserv.

model, 'dry day'

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SLIDE 7

Tropospheric Tropospheric NO NOx

x budgets

budgets

Annual Annual

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What are the limitations? What are the limitations?

  • Available measurements
  • Lack of understanding of controlling mechanisms
  • Scaling issue: upscaling of measurements to the model resolution
  • Characterization of surface properties and land use
  • Representation of micro- and PBL meteorology in models
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How to deal with /get around these limitations: How to deal with /get around these limitations:

  • Lack of measurements: biased towards vegetated surface and only a small

selection of gases (O3, SO2, NOx/y) have been measured on a ”routinely” bases

O3 soil resistance as a function of soil organic matter and soil moisture, July.

  • wait patiently and hopefully the scientific career will last long enough
  • offer help: join field campaigns!
  • use model results for feedback to experimentalists
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SLIDE 10

1 2 3 4 5 1 2 3 4 5

NO 2 dry deposition velocity versus the stomatal resistance [CNO2 = 41 ppbv], data by Johansson, 1987

V

d [mm s
  • 1]

Stomatal conductance K

s [mm s -1 ]
  • Lack of understanding of controlling mechanisms

Vegetation:

  • Stomatal vs cuticular uptake, what is the fate within the plant tissue? Effects?
  • What controls the internal N concentrations?
  • How are the VOC emissions related to the nitrogen and carbon cycle?
  • Surface wetness?

Soils:

  • What controls the uptake by soils? organic material, soil moisture, soil pH, etc.

Water:

  • Role of plankton in controlling uptake/release of trace gases like O3/DMS
  • Parameterisations become (too) complex and specific

Parameterisations become (too) complex and specific

Lehning at al., 2001: seasonal cycle in C5H8 emission factors based on a parameterization of isoprene synthase activity by fitting to observations.

  • Inconsistencies between parameterisations

Inconsistencies between parameterisations

VOC emissions versus dry deposition

  • Parameterisations are often scale dependent

Parameterisations are often scale dependent

  • Parameterisations are representative for present

Parameterisations are representative for present-

  • day

day conditions and might not be applicable to predict surface conditions and might not be applicable to predict surface exchanges for future environmental conditions exchanges for future environmental conditions

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More measurements in well constrained conditions (laboratory) to determine the role of the different substrates involved, e.g, soils versus vegetation . Include parameters in field campaigns that are complementary in identifying the mechanisms:

  • energy balance and turbulence
  • photosynthesis
  • O3 uptake
  • N uptake/release
  • VOC emissions

What is needed to develop process What is needed to develop process-

  • based models?

based models? We should get away from including too many different parameteris We should get away from including too many different parameterisations to ations to represent surface exchange processes of the different species in represent surface exchange processes of the different species in models. Instead,

  • models. Instead,

the development of more process the development of more process-

  • based models that link the C and N cycle to the

based models that link the C and N cycle to the uptake (and effects of the uptake) of gases such as O uptake (and effects of the uptake) of gases such as O3

3,

, SO SOx

x, etc., and VOC emissions

, etc., and VOC emissions should be pursued. should be pursued.

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SLIDE 12

10 January July

  • Characterization of surface properties and land use
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More collaborations with GIS and remote sensing specialists:

  • We:

What data are required for modeling? Soil properties, vegetation data, etc.

  • They: Data availability, domain (regional to global scale), resolution, and

UNCERTAINTIES UNCERTAINTIES! ! What is needed? What is needed?

  • 3
  • 2
  • 1

1 2 3 4 00:00 06:00 12:00 18:00 24:00

LAI=7 LAI=11 NOx flux [109 molec. cm-2 s-1] Time [hr:min]

LAI=7 LAI=11

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SLIDE 14
  • Representation of micro- and PBL meteorology in models

Sensible and latent heatfluxes, Harvard forest site.

  • 600
  • 500
  • 400
  • 300
  • 200
  • 100

100 1 10 19 28 37 46 55 64 73 82 91 100 109 118 Time [Hr] H F /LF [W m -2]

H, original soil moisture LE, original soil moisture H LE

  • Max. observed. LE
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200 400 600 800 1000 1200 1400 1600 0:00:00:00 0:06:00:00 0:12:00:00 0:18:00:00 1:00:00:00

Model resolved PBL height for a soil moisture

  • f 0.4 and 0.2 m, tropical rainforest, Manaus, April

ws=0.4 ws=0.2 PBL height [m] Time [day:hr:min:sec]

Ws=0.4 Ws=0.2

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Tropical rainforest

  • 4

1 6 11 16 1:03 1:06 1:10 1:14 1:17 1:21

Time [day:hr] NOx flux [1e9 molec. cm-2 s-1]

Leaf Area Index=8 LAI=4

  • comp. CNO2=0.5 ppbv

nocturnal mixing each 2 hr

  • Turbulence exchange:
  • counter-gradient transport
  • intermittency
  • stability effects in canopy versus surface layer, nocturnal free-convection
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Nudging of SCM for model evaluation Nudging of SCM for model evaluation

Friction velocity, BEWA site, July-august 2001

0.2 0.4 0.6 0.8 1 20.07.01 22.07.01 24.07.01 26.07.01 28.07.01 30.07.01 Time [dd.mm.yr] u* [m s-1]

measured model, Trelax=2hr model, Trelax=3hr

Air temperature, BEWA site, July-august 2001

10 15 20 25 30 27.07.01 27.07.01 28.07.01 28.07.01 29.07.01 29.07.01 30.07.01 Time [dd.mm.yr] Tair [°C]

Tair Ts, Trelax=2hr Ts, Trelax=3hr

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Air temperature, BEWA site, July-august 2001

5 10 15 20 25 30 10.07.01 15.07.01 20.07.01 25.07.01 30.07.01 Time [dd.mm.yr] Tair [°C]

  • bserved

model model, ws=wsmax

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Short-wave radiation, BEWA site, July-august 2001

200 400 600 800 1000

10.07.01 15.07.01 20.07.01 25.07.01 30.07.01

Time [dd.mm.yr] Rg [W m -2]

  • bserved

model model, albedo=0.12

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Simulated versus observed net radiation, BEWA site, July-August 2001.

y = 0.9394x + 5.2445 R2 = 0.8289 y = 1.0565x + 11.733 R2 = 0.8332

250 500 750 1000 250 500 750 1000 Simulated Rn [W m-2] Observed Rn [W m-2]

albedo=0.17 albedo=0.12 albedo=0.12 albedo=0.17

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  • Workshop with specialists from all kind of disciplines in which upscaling is

involved didn’t result in a major breakthrough.

  • Scaling issue: upscaling of measurements to the model resolution

Solutions? Nested models/model hierarchy, site-scale – regional – global scale applications

Example: SCM constrained with ECMWF analysed meteorology for interpretation of site

  • bservations using the process representation consistent with the global model ECHAM.

Using process-based models to avoid using scale-dependent parameterizations

Bottleneck: are the required input data available at the site- as well as larger scales? Accuracy? Example: DNDC model for N soil emissions based on soil-redox potential

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Land cover and land use changes Land cover and land use changes

L Large Scale

arge Scale B

Biosphere

iosphere-

  • atmosphere experiment in

atmosphere experiment in A

Amazonia

mazonia ( (LBA

LBA)

)

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Lagrangian Lagrangian experiment with SCM to study de experiment with SCM to study de-

  • forestation

forestation

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5 10 15 20 25 30 35 12 24 36 48 60 72 Time [hr] C5H8 flux [1e 11 m ole c. cm -2 s- 1] 'actual' dLAI, deforest C5H8 flux, deforest

deforest - 'actual'

50 100 150 200 12 24 36 48 60 72

Time [hr]

Rg [W m-2] 0.1 0.2 0.3 0.4 c(Cl-sun and Ct) Net radiation Cl-sun Ct

294 298 302 306 310 314 12 24 36 48 60 72

Time [hr] Tsurf [K]

'actual' deforest deforest, reduced ws

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Conclusions Conclusions

  • Characterization of the surface properties and land use, e.g., LAI, soil

properties, soil moisture etc., is critical for a fair model evaluation and limits to a large extent the quality of the calculations at large (global) scales.

  • Complexity of feedbacks in the chemistry-climate system requires the use of

mechanistic micrometeorological and biogeochemical models to assess the impact of land cover and land use changes on atmospheric chemistry.

  • The use of the nudging technique using ECMWF data seems to result in a

simulation of a more realistic meteorology in the SCM, which ensures a more fair comparison of simulated and observed trace gas exchanges.

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Outlook Outlook

  • Further evaluation of micro-meteorology and atmosphere-biosphere trace gas

exchanges in the SCM and ECHAM using observations of LBA-EUSTACH and CLAIRE/ECHO/BEWA

Tropical rainforest

  • 4

1 6 11 16 1:03 1:06 1:10 1:14 1:17 1:21 Time [day:hr] NO x flu x [1e 9 m o le c. cm -2 s -1]

LAI=8 LAI=4 CNO2=0.5 ppbv nocturnal mixing each 2 hr

  • dry deposition process, role of surface wetness,

compensation point

  • (nocturnal) turbulent exchange
  • land cover characterization

Site scale Site scale Global scale Global scale

  • Perform an ensemble of land use and land cover scenarios in the global

chemistry-GCM ECHAM5

  • Collect information on scenarios of future land cover and land use
  • Applying “observed” meteorological conditions (ECMWF/RAMS) to

constrain the SCM