New constraints on Northern Hemisphere growing season net flux

Z. Yang,

R. A. Washenfelder,

2,3

G. Keppel-Aleks,

N. Y. Krakauer,

1,4

J. T. Randerson,

P. P. Tans,

C. Sweeney,

and P. O. Wennberg

1,2

Received 5 March 2007; revised 27 April 2007; accepted 1 May 2007; published 23 June 2007.

[

] Observations of the column-averaged dry molar mixing

ratio of CO

above both Park Falls, Wisconsin and Kitt Peak,

Arizona, together with partial columns derived from aircraft

profiles over Eurasia and North America are used to estimate

the seasonal integral of net ecosystem exchange (NEE)

between the atmosphere and the terrestrial biosphere in the

Northern Hemisphere. We find that NEE is

25% larger than

predicted by the Carnegie Ames Stanford Approach

(CASA) model. We show that the estimates of NEE

may have been biased low by too weak vertical mixing in

the transport models used to infer seasonal changes in

Northern Hemisphere CO

mass from the surface

measurements of CO

mixing ratio.

Citation:

Yang, Z.,

R. A. Washenfelder, G. Keppel-Aleks, N. Y. Krakauer, J. T.

Randerson, P. P. Tans, C. Sweeney, and P. O. Wennberg (2007),

New constraints on Northern Hemisphere growing season net flux,

Geophys. Res. Lett.

, L12807, doi:10.1029/2007GL029742.

1. Introduction

[

] Forecasting CO

levels in the atmosphere is needed to

predict future climate. Accurate forecasts require an im-

proved understanding of carbon sources and sinks [

Inter-

governmental Panel on Climate Change

, 2001]. During the

1990s, fossil fuel combustion and cement production added

approximately 6 Pg C yr

to the atmosphere. These fluxes

are well constrained spatially and temporally [

Andres et al.

1996]. From the observed atmospheric increase and the

known anthropogenic emissions, the combined ocean and

terrestrial biosphere carbon sinks must have been close to 3

Pg C yr

[

Intergovernmental Panel on Climate Change

2001].

[

] To estimate the spatial and temporal distribution of

these carbon sinks, inverse methods have been used to infer

carbon fluxes from geographically sparse observations of

atmospheric CO

mixing ratio, typically measured at the

surface [e.g.,

Tans et al.

, 1990]. In these methods, surface

fluxes are scaled within the framework of an atmospheric

transport model to minimize the difference between the

observed and simulated spatial and temporal gradients of

atmospheric CO

mixing ratio [

Enting et al.

, 1995;

Kaminski

et al.

, 1999;

Rayner et al.

, 1999;

Bousquet et al.

, 2000;

Krakauer et al.

, 2004;

Baker et al.

, 2006]. Estimates of both

net ecosystem exchange (NEE) and the geographical distri-

bution of fossil fuel carbon sinks vary substantially due in

large part to errors in the atmospheric transport models used

in these inversions [e.g.,

Gurney et al.

, 2004]. This is quite

understandable; estimation of fluxes (i.e., mass m

)on

large geographical scales requires knowledge of temporal

and spatial gradients in CO

column abundance (i.e., mass

) in the atmosphere. These gradients in CO

column can

be inferred from gradients in the observed mixing ratio at

the surface only if the vertical structure of atmospheric CO

is well known. Proper simulation of the vertical structure

requires accurate simulation of the exchange between the

planetary boundary layer (PBL) and the free troposphere: a

difficult requirement and an area of active research in the

atmospheric dynamics community.

[

] In this study, we use newly available observations of

the column and vertical profile dry air CO

molar mixing

ratios above eight sites (Table 1) to estimate the seasonally-

varying carbon flux (NEE) in the northern hemisphere.

Because these observations are of the column or partial

column abundance, they come close to directly representing

a measure of atmospheric CO

mass per unit area. As

a result, our estimate of NEE are less sensitive to errors in

the vertical transport than estimates based solely on surface

mixing ratio observations. Our analysis suggests that the

seasonally-varying fluxes are substantially larger than the

NEE fluxes from the CASA model used in the TransCom 3

studies. We further show, using vertically resolved observa-

tions of CO

obtained at several sites in Eurasia and North

America, that the TransCom models underestimate the

seasonally-varying fluxes because they underestimate the

efficiency of CO

mixing throughout the free troposphere.

2. Measurements and Models

[

] Measurements of column-averaged dry mixing ratio

of CO

were obtained at Park Falls, Wisconsin beginning in

2004. Using an automated solar observatory, direct solar

spectra were acquired continuously during clear-sky, day-

time conditions. These spectra were used to determine

vertically integrated CO

with high precision (0.1%) and

accuracy (0.3%) [

Washenfelder et al.

, 2006]. The 337 days

of measurements were taken during May 2004 to November

2006 and have been averaged daily. We also included

similar but much infrequent (only 96 days during the two

periods: Jan 1979 to Dec 1985 and Mar 1989 to Mar 1995)

column measurements obtained at the Kitt Peak solar obser-

vatory, Arizona [

Yang et al.

, 2002]. In addition to the ground-

GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L12807, doi:10.1029/2007GL029742, 2007

Division of Geological and Planetary Sciences, California Institute of

Technology, Pasadena, California, USA.

Division of Engineering and Applied Science, California Institute of

Technology, Pasadena, California, USA.

Now at Chemical Sciences Division, Earth System Research

Laboratory, NOAA, Boulder, Colorado, USA.

Now at Department of Earth and Planetary Science, University of

California, Berkeley, California, USA.

Department of Earth System Science, University of California, Irvine,

California, USA.

Earth System Research Laboratory, NOAA, Boulder, Colorado, USA.

Cooperative Institute for Research in Environmental Sciences,

University of Colorado, Boulder, Colorado, USA.

L12807

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based total columns, multi-level aircraft CO

measurements

were available at six sites in North America and Eurasia

during 2003–2004 (Table 1). Discrete CO

samples were

acquired biweekly or monthly during aircraft profiles up to

7500 m above the surface [e.g.,

Levin et al.

, 2002]. In our

analysis, we used the interpolations of these measurements at

fixed temporal (48 per year) and spatial (every 500 m in

altitude) intervals [

GLOBALVIEW-CO

, 2006].

[

] To compare with the observations, we used the twelve

TransCom 3 experiment models that differ in spatial reso-

lution, advection scheme, driving winds, and sub-grid scale

parameterizations [

Gurney et al.

, 2003]. Monthly terrestrial

biosphere exchange (1

°

°

) was derived from the Carne-

gie-Ames-Stanford Approach (CASA) terrestrial biosphere

model [

Randerson et al.

, 1997], and is annually balanced at

each grid cell.

3. Methods

[

] In our analysis, we compare the observed amplitude

and phase of the atmospheric CO

seasonal cycles to

simulations obtained from propagating seasonal surface

fluxes from a terrestrial biosphere model (CASA) with

annually-balanced fluxes through the twelve different trans-

port models. Since the same fluxes are used, differences in

the simulated atmospheric CO

seasonal cycles at different

altitudes and locations result only from the differences in

transport in the models. To quantify the differences between

the observations and the simulations, we use a simple least

squares fit, assuming the observed seasonal cycle

(t) can

be expressed as a function of the simulated CASA bio-

sphere model response

(t), adjusted by scale factor

, time

delay

, and offset

ðÞ¼

ðÞþ

[

] Focusing on the shape of seasonal cycle, we report

and

but not offset

. The

and

parameters can also be

thought of as two spatially uniform adjustments to all

CASA surface fluxes because of the linear relationship

between these fluxes and

(t). Besides the simulations from

the twelve models, the mean of all these models’ simula-

tions is considered as our ‘‘best’’ estimate and included in

the comparison. The fitting rms (

) for the all-model mean

simulation is reported to measure the goodness of the fit,

and to derive a weighted mean CASA scale factor (but not

time delay) for

different sites:

P

=

P

=

[

] To compare the observations with the neutral bio-

sphere simulations, the measurements were detrended and

offset by the annual mean value. The interannual trend for

the Park Falls column CO

was empirically determined as

1.80 ppm yr

during 2004 to 2006. For Kitt Peak, the

trends were 1.41 ppm yr

during 1979 to 1985 and

0.83 ppm yr

during 1989 to 1995. For the temporally

evenly spaced GlobalView assimilations, their seasonal

cycles were directly decomposed using the empirical mode

decomposition method [

Huang et al.

, 1998] and folded into

one year.

4. Results and Discussion

[

] The comparison between the Park Falls CO

season-

al cycle of column-averaged observation and the TransCom

simulations is shown in Figure 1. The observed seasonal

cycle amplitude is larger than any model simulation. A

best fit was obtained by increasing the CASA fluxes by

34%. Models also underestimated the CO

seasonal cycle at

Kitt Peak and at all the other six aircraft sites. The average

difference across all the column and partial column sites

was 28% (Table 1). Because these vertically-integrated

observations sample a substantial fraction of the northern

hemisphere landmass, they provide a measure of CO

variations that is not highly sensitive to error in the transport

fields. As a group, the seasonal cycle in column CO

is most

sensitive to the seasonal fluxes themselves. This is sup-

ported by the relatively small variation in the model

simulations of the columns illustrated for Park Falls in

Table 1.

Column and Profile Observation Sites and the CO

Seasonal Cycle Amplitude Comparison With Model Simulations

Site Name (Code)

Location

Altitude Range

Above

Surface, m

Mean Scale

Factor

of the 12

Models

a,b

Phase Shift

of 12

Models,

days

Scale Factor

for the

Mean Response

of the 12

Models

RMS in Fitting

the Mean Response

of the 12

Models, ppm

Poker Flats, AK (PFA)

65.07

°

N, 147.29

°

W 1500–7500 1.20 ± 0.11

16.8 ± 4.5

1.21

0.71

Zotino, Russia (ZOT)

60.75

°

N, 89.38

°

500–3500 1.44 ± 0.21

18.4 ± 2.8

1.42

1.70

Estevan Point, Canada (ESP)

49.38

°

N, 126.55

°

W 500–5500 1.20 ± 0.12

16.0 ± 3.2

1.20

0.54

Orleans, France (ORL)

47.80

°

N, 2.50

°

500–3500 1.39 ± 0.18

19.6 ± 3.1

1.38

0.43

Park Falls, WI (LEF)

45.93

°

N, 90.27

°

W Total column 1.34 ± 0.14

7.3 ± 4.0

1.34

0.43

Harvard Forest, MA (HFM)

42.54

°

N, 72.17

°

500–7500 1.38 ± 0.09

16.0 ± 2.7

1.38

0.67

Carr, CO (CAR)

40.90

°

N, 104.80

°

W 1500–6500 1.20 ± 0.11

7.6 ± 7.3

1.21

0.56

Kitt Peak, AZ

(KTP)

31.90

°

N, 111.60

°

W Total column 1.11 ± 0.07 15.3 ± 4.6

1.12

0.56

Mean

1.28

14.5 ± 5.0

1.28

0.70

Mean of 35 surface sites

in 30

°

°

1.12

11.9 ± 9.8

1.11

1.07

For LEF and KTP, total CO

columns were simulated for the comparison. For the aircraft sites, only partial columns with measurements were simulated.

The scale factor

and phase shift

here are described by equation (1).

The names of the models are CSU.gurney, GISS.prather, GISS.prather2, GISS.prather3, JMA-CDTM.maki, MATCH.bruhwiler, MATCH.chen,

MATCH.law, RPN.yuen, SKYHI.fan, TM3.heimann, GCTM.baker. For more detail refer to TransCom website (http://www.purdue.edu/transcom/) and

Gurney et al.

[2003].

The Kitt Peak observations were taken from Jan 1979 to Mar 1995, for more detail refer to

Yang et al.

[2002].

Excluding Kitt Peak due to different observation time period.

These surface sites are part of the

Globalview-CO

[2006] network, for a detailed list see auxiliary material.

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YANG ET AL.: LARGER NORTH HEMISPHERE NET ECOSYSTEM EXCHANGE

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Figure 1 and for the other sites in the accompanying

auxiliary material

. Our column-based optimization implies

that the true growing season net flux (GSNF) in the northern

hemisphere is approximately 28% greater than that pre-

dicted by CASA. North of 30

°

N, this corresponds to a

GSNF of 7.9 Pg C/yr.

[

] The results shown here are not sensitive to the

seasonally-varying fossil fuel fluxes. We repeated our

analysis to investigate the impact of seasonally-varying

fossil fuel emissions (in 1995) estimated by A. L. Brenkert

(Carbon dioxide emission estimates from fossil-fuel burn-

ing, hydraulic cement production, and gas flaring for 1995

on a one degree grid cell basis, 1998 (data available at http://

cdiac.esd.ornl.gov/epubs/ndp/ndp058a/ndp058a.html)) on

our estimate of the seasonal cycle of carbon dioxide due to

terrestrial processes. Including this estimate of fossil fuel

emissions increases our estimate of the terrestrial fluxes

obtained from the column data by

1% in average and

decreases the estimate obtained from the surface observations

by a similar amount (see auxiliary material).

[

] The NEE from CASA was derived from 1990

satellite observations, and so the observed 0.66% yr

increase rate of CO

seasonal-signal amplitude between

1981 to 1995 [

Randerson et al.

, 1997] may explain some,

but clearly not all, of the differences between the observa-

tions and simulations of the CO

seasonal cycle amplitude.

In addition, the phase analysis of CO

seasonal cycles

shown in Table 1 shows that for all sites except Kitt Peak,

CASA fluxes needed to be shifted earlier by one to three

weeks, which may, in part, be explained by advances in the

timing of spring thaw since 1988 [

Smith et al.

, 2004]. More

generally, although changes in the seasonality of terrestrial

fluxes and the annual mean flux are probably linked by

means of long-term trends in photosynthesis and respiration,

this relationship is complex and not necessarily predictable

without more information about the underlying drivers

[

Randerson et al.

, 1999]. For example, if the 2.3 Pg C/yr

Northern Hemisphere terrestrial sink inferred by

Gurney et

al.

[2002] were caused by long-term gains in carbon during

spring or fall, it might cause the seasonal cycle of atmo-

spheric carbon dioxide to decrease, whereas gains during

mid-summer may have the opposite effect. Further, rela-

tively large carbon sinks may be sustained by very small

long-term increases in net primary production (much less

than 1% yr

)[

Friedlingstein et al.

, 1995] that would have

a small influence on the observed season cycle of CO

[

] In contrast to the column results, comparison of the

simulations of the seasonal cycle with CO

observations

obtained at the surface (GLOBALVIEW-CO

flasks)

between 30

°

Nto70

°

N shows a much smaller under-

estimation of seasonal cycle (

12%, Table 1) and a smaller

phase delay (

surface

11.9 days;

column

14.5 days).

Both the amplitude and phase differences between the

estimates from surface and column observations suggest

that the TransCom models as a group do not mix the surface

fluxes into the free troposphere quickly enough.

[

] The vertical propagation of the seasonal cycle of

from its source at the surface into the interior of the

atmosphere is sensitive to the efficiency of vertical ex-

change. To investigate the accuracy of the TRANSCOM

model simulations of this propagation, we analyzed the CO

vertical profiles at the six sites sampled by the aircraft.

Directly comparing the simulations and observations for

these sites using the same analysis method as described

above was, however, hampered by large differences in the

shape of the CO

seasonal cycle at some sites (e.g., ZOT in

Figure 2). (The results of such an analysis are shown in the

online supplement

; the retrieved CASA scale factors

increase with altitude, but the increase is not statistically

significant.) To minimize the impact of this mismatch, we

analyzed the simulations (using the a priori CASA fluxes)

and the atmospheric observation separately. At each site, we

defined a reference height (3500 m) and fit the seasonal

cycles

(t) at all other heights (H) in both the observations

and simulations by:

ðÞ¼

3500

ðÞþ

Figure 1.

(a) Atmospheric column-average CO

mole

fractions at Park Falls for May 2004–March 2006. (b) The

monthly mean of observations (closed circles) compared

with the TransCom simulations (grey shade shows range of

12-model predictions; thin solid line represents average).

Each of the 12 models underpredicts the seasonal cycle

observed in the column measurements. The best match to the

observations is achieved by scale the model-mean simula-

tions by 1.34 and shift them 7 days earlier (thick solid line).

Auxiliary material data sets are available at ftp://ftp.agu.org/apend/gl/

2007gl029742. Other auxiliary material files are in the HTML.

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YANG ET AL.: LARGER NORTH HEMISPHERE NET ECOSYSTEM EXCHANGE

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where

represents the time delay,

is the scaled

amplitude and

is a seasonally-invariant offset. The

comparison for each site is shown in Figure 2 and the

retrieved values of

and

are listed in Table 2. In

the model simulations, the scale factors monotonically

decrease with altitude at all sites, while the time delays

monotonically increase. In contrast, in the observations at or

above 2500 m, all sites except ESP showed smaller

decreases or even increases in the amplitude scale factor

with altitude as well as shorter delay, and even advance (at

PFA) in the seasonal cycle phase. For levels below 2500 m,

the observations showed mixed trends from site to site,

again possibly influenced by strong PBL variation. The

observation-model differences above 2500 m strongly

suggest that the atmospheric vertical and/or meridional

mixing within the free troposphere is faster than the

TransCom simulations.

5. Summary and Implications

[

] Comparison of the column-averaged CO

dry volume

mixing ratio measurements and the TransCom models

implies that GSNF north of 30

°

Nis

7.9 Pg C/yr, approx-

imately 28% larger than that predicted by CASA. Using

multi-level observed CO

from the Northern hemisphere to

diagnose the model performance at different altitudes, we

identify substantial underestimation of free troposphere

vertical mixing rates by TransCom models. While the

mixing between the PBL and the free troposphere has

been a major focus of carbon flux inversion experiments

(i.e. TransCom), this analysis suggests that equally large

Figure 2.

Comparison of the CO

seasonal cycles at different levels, for both (left) the aircraft observations and (right)

the TransCom 12-model mean simulation. Each altitude level is represented by a different line and each row represents

one site respectively. The range of model simulations for 3500 meters altitude is also shown in the left panel as the

shaded area.

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YANG ET AL.: LARGER NORTH HEMISPHERE NET ECOSYSTEM EXCHANGE

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4of6

errors exist in the rate of vertical mixing throughout the

free troposphere.

[

] The weak vertical exchange of the TransCom models

will have impacts beyond the estimation of seasonal CO

exchange between the biosphere and atmosphere.

Gurney et

al.

[2004] have shown, for example, that the inferred uptake

of fossil fuel carbon by land in the Northern Hemisphere by

the various TransCom models (from 0.0 to 4.0 Pg C/yr

depending on which transport model is used) is correlated

with their estimate of the CO

seasonal cycle produced by

the biosphere fluxes. Gurney et al. suggest that this corre-

lation is consistent with errors in parameterization of the

seasonal mixing efficiency between the planetary boundary

layer (PBL) and the free troposphere (FT), which co-varies

in time with the surface carbon exchange direction and

strength [

Denning et al.

, 1995]. Our finding suggests that as

a group, the TransCom models may have too little vertical

mixing in the free troposphere and so may overestimate the

size of the Northern Hemisphere land sink. The validity of

this inference, however, depends in part on the how the

transport errors vary seasonally, something this study has

not addressed.

[

] The analysis described in this letter illustrates the

utility of having information about the vertical distribution

of CO

from aircraft. In addition, the total column measure-

ments allow a more continuous record of CO

mass. The

Total Carbon Column Observing Network (TCCON) is

being established to expand the number of sites where

columns are measured (data available at http://

www.tccon.caltech.edu). TCCON will include a number

of sites in both the Northern and Southern Hemispheres.

These observations should provide an improved measure of

the gradient in CO

mass between two hemispheres. Based

on the findings of this study, we expect that the N–S

gradient will be larger than predicted by the TransCom

inversions tied to surface observations.

[

]

Acknowledgments.

We thank the TransCom 3 modeling com-

munity (K. Gurney, M. Prather, T. Maki, L. Bruhwiler, Y. Chen, R. Law,

C. Yuen, S. Fan, M. Heimann, and D. Baker) for making the results of

their simulations publicly available. This work was supported by a NASA

grant NNG05GD07G. N. Y. Krakauer was supported by Graduate Fellow-

ships from both NASA Earth and Space Science and the Betty and

Gordon Moore Foundation.

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Table 2.

Optimal Values of Scale Factor,

, and Time Delay in Days,

, Applied to 3500-m-Level Seasonal CO

Cycle for Best

Matching the Other Levels (Equation (3))

Altitude

Optimal Values for Scale Factor

PFA

ZOT

ESP

ORL

HFM

CAR

Obs.

Mod.

Obs.

Mod.

Obs.

Mod.

Obs.

Mod.

Obs.

Mod.

Obs.

Mod.

7500 m

0.88

0.80

0.78

0.74

6500 m

0.93

0.85

0.87

0.78

1.05

0.90

5500 m

0.89

0.90

0.74

0.91

0.83

0.84

1.00

0.94

4500 m

0.92

0.95

0.89

0.95

0.85

0.91

1.05

0.98

3500 m

1.00

2500 m

1.03

1.12

1.18

1.20

1.07

1.03

1.06

1.25

1.23

0.98

1.12

1500 m

1.33

1.30

1.32

1.51

1.24

1.16

1.41

1.12

1.67

1.57

0.96

1.26

Optimal Values for Time Delay

, days

Altitude

Obs.

Mod.

Obs.

Mod.

Obs.

Mod.

Obs.

Mod.

Obs.

Mod.

Obs.

Mod.

7500 m

2.0

7.0

13.0

6500 m

4.0

6.0

11.0

10.0

3.0

12.0

5500 m

1.0

4.0

8.0

1.0

10.0

8.0

2.0

10.0

4500 m

0.0

2.0

3.0

1.0

6.0

4.0

8.0

3500 m

0.0

2500 m

5.0

4.0

5.0

4.0

1.0

5.0

3.0

6.0

4.0

8.0

1500 m

7.0

10.0

4.0

2.0

12.0

7.0

21.0

13.0

4.0

16.0

For each site, the left column is for the observations and the right column is for the 12-model mean simulations.

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G. Keppel-Aleks and P. O. Wennberg, Division of Engineering and

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N. Y. Krakauer, Department of Earth and Planetary Science, University of

California, Berkeley, CA 94720, USA. (niryk@berkeley.edu)

J. T. Randerson, Department of Earth System Science, University of

California, Irvine, CA 92697, USA. (jranders@uci.edu)

C. Sweeney, Cooperative Institute for Research in Environmental Sciences,

University of Colorado, Boulder, CO 80309, USA. (colm.sweeney@noaa.

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P. P. Tans, Earth System Research Laboratory, National Oceanic and

Atmospheric Administration, Boulder, CO 80305, USA. (pieter.tans@

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R. A. Washenfelder, Chemical Sciences Division, Earth System Research

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Z. Yang, Division of Geological and Planetary Sciences, California

Institute of Technology, Pasadena, CA 91125, USA. (yangzh@caltech.edu)

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