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Trends in Terrestrial Carbon Sinks Driven by Hydroclimatic Change since 1948: Data-Driven Analysis using FLUXNET Christopher Schwalm , Christopher Williams, Kevin Schaefer, Kusum Naithani, Jingfeng Xiao. Ameriflux Science Meeting & 3rd NACP All-Investigators Meeting 2011

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Trends in Terrestrial Carbon Sinks Driven by Hydroclimatic Change since 1948: Data-Driven Analysis using FLUXNETChristopher Schwalm, Christopher Williams, Kevin Schaefer, Kusum Naithani, Jingfeng Xiao

Ameriflux Science Meeting & 3rd NACP

All-Investigators Meeting 2011

January 31 – February 4, New Orleans, LA

outline
Outline
  • We ask
    • What are the carbon consequences of hydrologic change?
  • We merge
    • Global monitoring network (FLUXNET)
    • LUH time-varying land cover (IPCC AR5)
    • NCEP/NCAR Reanalysis
  • We derive
    • Monthly time series (1948 – 2009)
    • 1° latitude/longitude resolution
    • Observationally-based estimates of carbon flux solely attributable to hydrologic change
global monitoring network
Global monitoring network

FLUXNET: Network of regional networks

Eddy covariance method: temporally dense in situ CO2 exchange including gross primary production and ecosystem respiration

Ancillary data: soil moisture, temperature, latent heat flux, LAI, etc.

mapping points to pixels

Carbon Flux

Evaporative Fraction

Mapping points to pixels

Extract relationship between hydrologic change and carbon flux

Aggregate FLUXNET sites by IGBP land cover class

Calculate sensitivity: change in carbon flux to a unit forcing in evaporative fraction (z-score)

Sensitivity: g C m-2 month-1σ-1

Map sensitivities to globe using

1) LUH [gridded land cover class]

2) NCEP/NCAR Reanalysis [gridded EF]

Schwalm et al. (2010) Global Change Biology

spatial scaling luh land cover
Spatial scaling: LUH land cover

“Points to pixels”

IGBP maps

18 IGBP land cover classes by pixel

1948

+

FLUXNET sensitivities

Vegetated classes – observed

Non-vegetated classes – set to zero

62 annual snapshots of land cover from Land Use Harmonization (LUH)

Crosswalk: LUH → IGBP

=

Pixel sensitivity [weighted average]

2009

Units: g C m-2 month-1σ-1

http://luh.unh.edu/

slide6

Temporal scaling: NCEP reanalysis

Example – Europe in June 1998

NEP sensitivity

(g C m-2 mon-1σ-1 )

EF (σ)

δNEP (g C m-2 mon-1)

global time series
Global time series

Sink (2000-2006) = +2.8 Canadell et al. (2007) PNAS

global trends
Global trends

Trend line (p > 0.44)

Visually the same as zero reference line

Grey envelope is ±2σ

continental trends nep

More uptake

Less uptake

Less uptake

Continental trends - δNEP

significant

not significant

More uptake

cumulative trend
Cumulative trend

outgassing

uptake

TNEP [g C m-2 62yr-1]

differential response case study
Differential response: Case study

Highest density of FLUXNET sites

relating trend to background flux
Relating trend to background flux

FLUXNET + LUH + NCEP

δR

δP

Does the trend overpower the mean?

What spatial features are present?

δNEP

R

P

NEP

MODIS + CARBONTRACKER

net effect on gross fluxes
Net effect on gross fluxes

|δP| > |δR| - color contrast

Median ratio 40% larger for |TP/P| than for |TR/R|

More clusters with |δP| > P

Fewer clusters with|δR| > R

Low productivity areas

net effect on source sink
Net effect on source/sink

Blue: source to sink [4%]

Red: sink to source [20%]

Green: enhanced uptake [18%]

Yellow: enhanced outgassing [12%]

summary
Summary
  • Observationally-based estimates of carbon cycling solely attributable to hydroclimatic variability
  • Range in del equals or exceeds terrestrial carbon sink magnitude or gross fluxes.
  • Hydroclimatic variability has acted to flip sources to sinks and vice versa (25%) over the 62-yr record → “key player”
net effect on gross fluxes1
Net effect on gross fluxes

Less assimilation

Trend < 0

More assimilation

Trend > 0

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