simulating two-way interactive vegetation-biophysical processes and atmospheric-mesoscale...

Simulating Two-way Interactive Simulating Two-way Interactive Vegetation-Biophysical Processes and Vegetation-Biophysical Processes and Atmospheric-Mesoscale Circulations Atmospheric-Mesoscale Circulations

During 2001 Santarem Field Campaign During 2001 Santarem Field Campaign Using SiB-RAMS ModelUsing SiB-RAMS Model

Lixin Lu1,2, Scott Denning1, Ian Baker1, Marek Uliasz1

1 Department of Atmospheric Science, Colorado State University,

Fort Collins, CO; and 2 ATOC/CIRES, University of Colorado, Boulder, CO

Collaborators: Marcos Longo, Maria A. da Silva-Dias, Pedro da Silva-Dias, Saulo Freitas

Tropical plays an important role in global carbon budget

Flux towers in “end-member” ecosystems (intact forest, selective logging, pasture site) measure local fluxes and help understand processes controlling them

Models can be tested against flux towers, and extrapolated to regional scales using remote sensing and other spatial data products

Regional up scaling and inverse modeling need to consider the unique “tower meteorology”

Amazon Carbon Balance

Objectives

Our numerical experiments aim to:

Evaluate SiBRAMS simulated surface fluxes against eddy flux tower observations.

Understand the mesoscale circulation patterns in Satarem region, and how they affect the regional carbon balance.

Investigate the potential impact of river CO2 effluxes on regional carbon balance.

Numerical Experiment Design MODEL: SiBRAMS,

Prognostic vegetation CO2 fluxes

3-D CO2 transport; Freitas (2000)

CONTROL EXPERIMENT:

No river water CO2 effluxes;

Sfc water CO2 flux = 5 μ mol m-2 s-1

MODELED PERIOD:

START: Aug 1, 2001

STOP: Aug 15, 2001

BOUNDARY FORCING:

CPTEC Analysis

GRID MESH: 4-level nested grids

Coarsest: 100 km

Finest: 1 km

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Topography and Vegetation Distribution

August 2001 Synoptic Situation

Horizontal wind at 500 mb

August 2001 Synoptic Situation

Observed accumulated precipitation 1 through 15 August 2001 from automatic weather stations located in western Para

Equivalent potential temperature from CPTEC analysis, at longitudi-nal cross section of 54.375W, and 700 mb level. The line corresponds to Santarem region.

3-4 days easterly wave oscillation

Runs Broadly Agree With Observations

Runs Broadly Agree With Observations

Runs Broadly Agree With Observations

Runs Broadly Agree With Observations

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0 3600 7200

time (s)

CO2 concentration measured by a continuous analyzer flown at 300 m elevation over the Amazon River (blue) and adjacent forest (green) on the morning of August 13. Two legs of over 100 km each were flown, one over the forest, and the other over the River.

Concentrations over the river were elevated by more than 10 ppm.

Turbulent variations of [CO2] were much greater over the forest.

River Forest

Aircraft Measured CO2 at 300 m

Time

Differences in Domain-averaged [CO2]

Strong diurnal cycle relate to CO2 uptake at the sfc and PBL meteorology

More impact during night

Strong PBL mixing during the day reduce the signal of the river CO2 efflux

Accumulation during night eludes tower observation due to calm condition and drainage flow

[CO2] Distribution Averaged Over Entire Simulation

With River CO2 Effluxes Without Differences

Increase Northwest-Southeast [CO2] gradient More of impact downwind (lee of the river) Drainage flows facilitate the accumulation of [CO2] at lowlands River CO2 efflux altered [CO2] distribution pattern

Domain-averaged, Vegetated Sfc CO2 Flux

Strong diurnal cycle relate

to CO2 uptake at the sfc

More impact during day when photosynthetic uptake in progress

Increased regional vegetation uptake of carbon.

Net effect of river CO2 effluxes on regional carbon balance increase carbon source.

Amazon continue to be a carbon sink after the river CO2 effluxes are accounted for.

River CO2 Efflux on Regional Carbon Budget

Area: River=27.1024%; Land=72.8967%

River CO2 effluxes = 5 umol/m2/s

Vegetated SFC:With River CO2 effluxes: -2.19287 umol/m2/sWithout River CO2 effluxes: -2.12198 umol/m2/s

Land and water: With River CO2 effluxes: -0.8378 umol/m2/sWithout River CO2 effluxes: -2.12198 umol/m2/s

Low-Level Convergence (LLC)

Low-level cumulus clouds appear persistently in the study area, and seem to favor the east bank of Tapajos River.

This cloud band is frequently observed both locally and in satellite imagery.

Previous research has stressed the role of the thermally driven “river breeze” effect (Silva Dias et al, 2003).

The asymmetry and persistence of LLC under strong trade wind conditions suggests that

other mechanisms may be involved

LLC Also Appear on GOES Imagery

Simulated Low-Level Convergence

8/1/2001, 10AM 8/3/2001, Noon 8/6/2001, 6 PM

Elevated topography block the easterly trade Sfc roughness length differences between water and land Channel the wind from Amazon to Tapajos River—northerly flow Slow down of sfc wind – convergence

Possible Mechanisms

Why is the low-level cumulus cloud often better organized on the east bank of the

Tapajos River? Wind Vector

Tapajos River

Amazon River

Elevated Topography

Wind Vector

Tap

ajos

Riv

er

These mechanically forced updrafts lead to increased cumulus clouds and sometimes precipitation.

Tower Observations Confirm Cloud Passing

Mid-day NEE dip frequently observed

Nearly constant Bowen ratio

at mid-day indicates cloud effects rather than water stress

The timing of simulated maximum surface convergences relate to the mid-day cloud passing

Observed net radiation from an nearby met station confirms the existence of midday cloudiness

Lagrangian Particle Model Coupled With SiB-RAMS

Transect of ten 60-m towers every 4 km

X = 143, 147, …, 171, 179 km, Y = -62 km

Virtual tower #8 corresponds to KM67 location

18,000 particles/hour at each tower, tracked backward in time; influence function was calculated for hourly concentration sampling at each tower

Time series of [CO2] coming to each tower from sfc fluxes (veg. photosynthesis and respiration, river CO2 efflux) within radius of 100-km

24 hour composite from 13 days Red line / Yellow - [CO2] at a tower Blue - fraction of [CO2] coming from water

X=143kmY=-62km

0 3 6 9 12 15 18 21 24

time of day [hours]

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CO2 [ppm]

Contribution of [CO2] Sources

X=147kmY=-62km

0 3 6 9 12 15 18 21 24

time of day [hours]

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-10

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CO2 [ppm]

Contribution of [CO2] Sources

X=151kmY=-62km

0 3 6 9 12 15 18 21 24

time of day [hours]

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CO2 [ppm]

Contribution of [CO2] Sources

X=155kmY=-62km

0 3 6 9 12 15 18 21 24

time of day [hours]

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-20

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0

10

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CO2 [ppm]

Contribution of [CO2] Sources

X=159kmY=-62km

0 3 6 9 12 15 18 21 24

time of day [hours]

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10

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CO2 [ppm]

Contribution of [CO2] Sources

X=163kmY=-62km

0 3 6 9 12 15 18 21 24

time of day [hours]

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10

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CO2 [ppm]

Contribution of [CO2] Sources

X=167kmY=-62km

0 3 6 9 12 15 18 21 24

time of day [hours]

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0

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CO2 [ppm]

Contribution of [CO2] Sources

X=171kmY=-62km

Location: Km 67Yellow: vegetation;

Blue: water CO2 efflux

0 3 6 9 12 15 18 21 24

time of day [hours]

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CO2 [ppm]

Contribution of [CO2] Sources

X=175kmY=-62km

0 3 6 9 12 15 18 21 24

time of day [hours]

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CO2 [ppm]

Contribution of [CO2] Sources

X=179kmY=-62km

0 3 6 9 12 15 18 21 24

time of day [hours]

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CO2 [ppm]

Contribution of [CO2] Sources

120 130 140 150 160 170 180 190x [km]

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1 river CO2 / land CO2

Relative Contribution in Tower Observed CO2

120 130 140 150 160 170 180 190x [km]

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river CO2 / total CO2

Relative Contribution in Tower Observed CO2

120 130 140 150 160 170 180 190x [km]

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land CO2 / total CO2

Relative Contribution in Tower Observed CO2

Summary and Conclusion Mesoscale simulation of the SMC reproduces many aspects of the

observed fluxes and [CO2] Topographic distribution, landscape feature, and heterogeneous

vegetation produce easily detectable spatial structure in simulated CO2 River CO2 efflux enhance vegetation carbon uptake, and modifies

regional carbon budget River CO2 efflux modifies [CO2] concentration distribution, especially

at night Low-level convergence is simulated on many days along the east bank

of the Tapajos River during strong, steady Trade Winds Cumulus clouds associated with this convergence line are frequently

observed by satellite imagery, and flux measurements often indicate reduced NEE due to resulting cloud shading

The mechanical forcings are likely the dominant factors to form LLC, while thermal forcing can be secondary during steady trade winds

Particle model can be used to quantify different sources of [CO2], and their contribution to tower-observed [CO2]

Flux tower data interpretation must consider LLC effects

Publications in Progress

Mesoscale circulations and atmospheric CO2 variations in the Tapajos Region, Para, Brazil. JGR-Atmosphere, 110, D21102, doi: 10.1029/2004JD005757.

Lu, L., et al. 2008: Simulating the two-way interactions between vegetation biophysical processes and mesoscale circulations during 2001 Santarem field campaign. JGR. To be submitted.

The potential influence of river CO2 efflux on regional carbon balance in the Tapajos region, Para, Brazil. GRL. In preparation.

Why the low-level cumulus clouds are often better organized on the east-side of Tapajos River – A mechanistic study. JAS. In Preparation.

Acknowledgments

We acknowledge NASA LBA-Ecology Grant, NCC5-707, for support of our research.

We thank Prof. Steven Wofsy, for his support of using KM 67 tower data for our analysis.

Additional Figures

NDVI distribution