Midwest U.S. croplands determine model divergence in North American carbon fluxesWu Sun1, Yuanyuan Fang1,a, Xiangzhong (Remi) Luo2,3,b, Yoichi P. Shiga4, Arlyn E. Andrews5, Kirk W. Thoning5, Joshua B. Fisher6, Trevor F. Keenan2,3, Anna M. Michalak11Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA 5NOAA Global Monitoring Laboratory, Boulder, CO 80305, USA2Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA 6NASA Jet Propulsion Laboratory, Caltech, Pasadena, CA 91109, USA3Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA aPresent address: Bay Area Air Quality Management District, San Francisco, CA 94105, USA4NASA Ames Research Center / Universities Space Research Association, Mountain View, CA 94043, USA bPresent address: Department of Geography, National University of Singapore, Singapore

Motivation: Models diverge in simulated patterns of North American carbon fluxes

Large uncertainties exist in understanding the space-time variability of North American biosphericcarbon fluxes1. Terrestrial biosphere models (TBMs) disagree on whether croplands or temperateforests show the highest growing-season carbon uptake. Evidence from solar-induced chlorophyllfluorescence (SIF) and carbonyl sulfide observations has cast doubt on the “strong forest, weakcropland” carbon uptake patterns simulated by most TBMs2,3. To glean robust space-time patterns ofNorth American carbon fluxes from widely divergent model estimates, we need to leverageregional-scale (103–105 km2) constraints from atmospheric CO2 observations in model evaluation4,5,6.

Data and methods

Model estimates of gross primary productivity(GPP) and net ecosystem exchange (NEE) and SIFdata products are evaluated based on how muchspace-time variability in observed CO2 drawdownor enhancement they explain:

z = HXβ + ϵ

where▶ z: Biospheric drawdown or enhancement of

CO2 (ObsPack GlobalViewPlus and FFDAS v2)▶ H: Transport footprints from WRF-STILT▶ X: Covariates of the true unknown NEE; can

be monthly GPP, SIF, or NEE estimates▶ β : Linear coefficients, to be determined▶ ϵ: The sum of all errors and residuals

TowersTop 80% footprint sensitivity

Croplands (CRP)

Evergreen broadleafforests (EBF)Evergreen needleleafforests (ENF)Deciduous broadleaf &mixed forests (DBMF)Southern shrublands(SSHR)

Savannahs (SAV)

Grasslands (GRA)

Northern shrublands(NSHR)

CRP EBF ENF DBMF SSHR SAV GRA NSHR0

10

20

30

Perc

ent o

f are

a or

obse

rvat

ions

(%) Percent of observations

Percent of area

(a)

(b)

▶ ∼39000 three-hourly CO2 observations from44 towers during 2007–2010

Model fidelity to atmospheric CO2 benchmarks

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Fraction of explained variance (R2) in atmospheric CO2

References

Remote sensing

SIF

FluxCom

MsTMIP v2

TRENDY v6

APARGIM NEE (monthly)GIM NEE (three-hourly)

PRmodelP-modelVPMMODc55MODc6LUEoptBESSBEPSGOME-2AGOME-2A-krigedRSIFCSIFANNMARSRF

BIOME-BGCCLASS-CTEM-NCLM4VICCLM4DLEMGTECISAMJPL-HYLANDLPJ-wslORCHIDEE-LSCESiB3SiBCASATEM6TRIPLEX-GHGVEGASVISITCABLECLASS-CTEMCLM4.5DLEMISAMJULESLPJ-wslOCNORCHIDEE-MICTORCHIDEESDGVMVEGASVISIT

RS GPPSIFFluxCom GPPFluxCom NEEMsTMIP v2 GPPMsTMIP v2 NEETRENDY v6 GPPTRENDY v6 NEEAPARGIM NEE (monthly)GIM NEE (3 h)

▶ Absorbed photosynthetically active radiation(APAR): a GPP proxy and a lower reference

▶ Geostatistical inverse model estimates of NEE(GIM NEE): an upper reference

▶ GPP estimates from data-driven models and GPPproxies (SIF and APAR) explain a substantialfraction of variability in observed CO2.

▶ Terrestrial biosphere models (TBMs) divergestrongly in terms of how much observed CO2variability they explain. Only a minority of TBMscan outperform APAR.

▶ Models informed by remote sensing inputsresolve the regional-scale variability in carbonfluxes better than the rest.

Models with strong cropland uptake are more consistent with CO2 observations

(a) 82.1% (N= 20)

High R2

(b) 65.8% (N= 24)

Low R2

(c) 65.9% (N= 12) (d) 40.6% (N= 20)

0.0 0.2 0.4 0.6 0.8 1.0J-J-A mean of PC1, normalized

GPP

& SI

FNE

E

▶ The first principal component(PC1) pattern represents thedominant mode of seasonallyvarying spatial patterns sharedacross models and across years.

▶ Models that explain a higherfraction of observed CO2variability than does APAR(“high R2” groups) tend to bemore similar to each other.

▶ An emergent relationship: Modelperformance in explainingobserved CO2 variability islinked to the skill in simulatingthe growing-season carbonuptake in croplands.

Most terrestrial biosphere models misrepresent the seasonal cycles of carbon fluxes

0.0

0.1

0.2

0.3

0.4

0.5

0.6

R2 o

f GPP

and

SIF

(a) (b) (c) (d)

Regressionswith original

variables

0.0

0.1

0.2

0.3

0.4

0.5

0.6

R2 o

f NEE

(e)

Biome encodedregressions

(f)

Month encodedregressions

(g)

Biome–monthencoded

regressions

(h)

Model groups

RSSIFFluxComMsTMIP v2TRENDY v6Null models

What causes model underperformance?Hypothesis Expected outcome

H1: Misrepresentation of thedistribution of annual meanfluxes among biomes

→Biome-encoded regressionswill improve modelperformance substantially

H2: Misrepresentation ofseasonal cycles of fluxeswithin major biomes

→Month-encoded regressionswill improve modelperformance substantially

▶ Differences in model performance are mainlyattributable to models’ ability to representthe seasonal cycles of carbon fluxes (panels cand g), and only to a lesser extent to models’ability to represent the distribution of annualmean fluxes across biomes (panels b and f).

Acknowledgements

This study is funded by the NASA Terrestrial Ecology Interdisciplinary Science (IDS) Award no. NNH17AE86I. We thank alldata contributors and curators.

Model underperformance is linked to the seasonal cycles of cropland carbon uptake

J F M A M J J A S O N D−4

−3

−2

−1

0

1

2(a) NEE models of high R2

J F M A M J J A S O N D

(b) NEE models of low R2

Mul

ti-m

odel

mea

n, re

gres

sion-

adju

sted

NEE

in c

ropl

ands

(μm

ol m

−2 s

−1)

Original Biome-encoded Month-encoded regressions GIM NEE Month-encoded regressions attempt toadjust the seasonal cycles of NEEestimates with low explanatory power(panel b) to be more similar to those ofNEE estimates with high explanatorypower (panel a), shifting the timing ofpeak uptake from June to July.

Model bias in the timing of peak uptake in croplands is linked to phenology bias

0.0

0.1

0.2

0.3

0.4

0.5

0.6

R2 = 0.187p= 0.140

(a) North America

R2 = 0.625p= 0.001

(b) Croplands

0.0 0.2 0.4 0.60.0

0.1

0.2

0.3

0.4

0.5

0.6

R2 = 0.015p= 0.692

(c) Evergreen needleleaf forests

0.0 0.2 0.4 0.6

R2 = 0.244p= 0.086

(d) Deciduous broadleaf & mixed forests

Frac

tion

of v

aria

nce

in a

tmos

pher

ic CO

2ex

plai

ned

by M

sTM

IP v

2 GP

P (R

2 )

Pearson r2 between MsTMIP v2 leaf area indices (LAI) and MODIS LAI

▶ For croplands (panel b), the explanatorypower of model GPP estimates is linkedto how well model estimates of leafarea index (LAI) capture the variabilityin remotely sensed LAI.

▶ The link between model GPP skill andmodel LAI skill is not found over thewhole of North America (panel a), inevergreen needleleaf forests (panel c),or in deciduous broadleaf and mixedforests (panel d).

Conclusion

Atmospheric CO2 observations are consistent with strong growing-season carbon uptake in MidwestU.S. croplands. This salient feature of growing-season cropland carbon uptake is captured by modelsinformed by remotely sensed vegetation indices, but is either missed or represented with wrong timingin most TBMs. The phase bias in the simulated seasonal cycles from most TBMs is likely caused by themisrepresentation of leaf phenology, especially in croplands. Top-down constraints need to be usedmore widely in model evaluation to constrain regional-scale patterns of biospheric carbon fluxes.

References

[1] Huntzinger, D. N. et al. North American Carbon Program (NACP)regional interim synthesis: Terrestrial biospheric modelintercomparison. Ecological Modelling 232, 144–157 (2012).

[2] Guanter, L. et al. Global and time-resolved monitoring of cropphotosynthesis with chlorophyll fluorescence. Proceedings of theNational Academy of Sciences 111, E1327–E1333 (2014).

[3] Hilton, T. W. et al. Peak growing season gross uptake of carbon inNorth America is largest in the Midwest USA. Nature Climate Change7, 450–454 (2017).

[4] Fang, Y., Michalak, A. M., Shiga, Y. P. & Yadav, V. Using atmospheric

observations to evaluate the spatiotemporal variability of CO2 fluxessimulated by terrestrial biospheric models. Biogeosciences 11,6985–6997 (2014).

[5] Fang, Y. & Michalak, A. M. Atmospheric observations inform CO2flux responses to enviroclimatic drivers. Global Biogeochemical Cycles29, 555–566 (2015).

[6] Shiga, Y. P. et al. Atmospheric CO2 observations reveal strongcorrelation between regional net biospheric carbon uptake andsolar-induced chlorophyll fluorescence. Geophysical Research Letters45, 1122–1132 (2018).

Correspondence to: Wu Sun, Q wsun @ carnegiescience.edu The 7th North American Carbon Program Open Science Meeting, March 2021