ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-47-2017Atmospheric CO2 observations and models suggest strong carbon uptake by
forests in New ZealandSteinkampKaykay.steinkamp@gmail.comMikaloff FletcherSara E.https://orcid.org/0000-0003-0741-0320BrailsfordGordonSmaleDanMooreStuartKellerElizabeth D.BaisdenW. Troyhttps://orcid.org/0000-0003-1814-1306MukaiHitoshiStephensBritton B.https://orcid.org/0000-0002-1966-6182National Institute of Water and Atmospheric Research,
Wellington, New ZealandGNS Science, Lower Hutt, New ZealandNational Institute for Environmental Studies, Tsukuba,
Ibaraki, JapanNational Center for Atmospheric Research, Boulder,
Colorado, USAKay Steinkamp (kay.steinkamp@gmail.com)2January2017171477622March201613April20165December20168December2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/47/2017/acp-17-47-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/47/2017/acp-17-47-2017.pdf
A regional atmospheric inversion method has been developed to determine the
spatial and temporal distribution of CO2 sinks and sources across New
Zealand for 2011–2013. This approach infers net air–sea and air–land
CO2 fluxes from measurement records, using back-trajectory simulations
from the Numerical Atmospheric dispersion Modelling Environment (NAME)
Lagrangian dispersion model, driven by meteorology from the New Zealand
Limited Area Model (NZLAM) weather prediction model. The inversion uses
in situ measurements from two fixed sites, Baring Head on the
southern tip of New Zealand's North Island (41.408∘ S,
174.871∘ E) and Lauder from the central South Island
(45.038∘ S, 169.684∘ E), and ship
board data from monthly cruises between Japan, New Zealand, and Australia. A
range of scenarios is used to assess the sensitivity of the inversion method
to underlying assumptions and to ensure robustness of the results. The
results indicate a strong seasonal cycle in terrestrial land fluxes from the
South Island of New Zealand, especially in western regions covered by
indigenous forest, suggesting higher photosynthetic and respiratory activity
than is evident in the current a priori land process model. On the
annual scale, the terrestrial biosphere in New Zealand is estimated to be a
net CO2 sink, removing 98 (±37) Tg CO2 yr-1 from the
atmosphere on average during 2011–2013. This sink is much larger than the
reported 27 Tg CO2 yr-1 from the national inventory for the same
time period. The difference can be partially reconciled when factors related
to forest and agricultural management and exports, fossil fuel emission
estimates, hydrologic fluxes, and soil carbon change are considered, but
some differences are likely to remain. Baseline uncertainty, model transport
uncertainty, and limited sensitivity to the northern half of the North Island
are the main contributors to flux uncertainty.
Introduction
The exchange of carbon between the atmosphere and the Earth's oceans and
terrestrial biospheres plays a crucial role in climate projections (IPCC,
2013; Friedlingstein et al., 2014). Predicting the future trajectories of
atmospheric CO2, temperature, and precipitation requires a solid
understanding of the magnitude of fluxes, their regional distribution, and
how and why they vary on seasonal to decadal timescales. National greenhouse
gas budgets are especially important in light of current policies regarding
climate change, such as the annual reporting requirements under the United
Nations Framework Convention on Climate Change (UNFCCC).
New Zealand's National Inventory Report (NIR) is compiled by the Ministry for
the Environment (MfE), and published annually. For 2013, the NIR puts New
Zealand's total CO2 emissions at about 35 Tg CO2 yr-1 and
it is estimated that the land-use and forestry sector acted as a sink for
carbon by removing three-quarters of that (27 Tg CO2 yr-1) from
the atmosphere (MfE, 2015). These estimates are based on measurements from a
network of forest plots throughout New Zealand, regularly updated land-use
maps, and models. Inventory methods give precise estimates of carbon uptake
and release of the locally present vegetation type, but they are often difficult
to scale up to regional or country scales due to heterogeneous biome
composition (Ciais et al., 2010). Independent methods are needed to verify
the reported carbon sink.
On very large, i.e. continental or global, scales both prognostic land
models and inverse atmospheric models have been used (Gurney et al., 2004;
Mikaloff Fletcher et al., 2007; Gruber et al., 2009; Le Quéré et
al., 2013; Steinkamp and Gruber, 2013; Friedlingstein et al., 2014). Global
inversions are a valuable top-down tool to estimate large-scale sinks and
sources by combining observations from a global network with atmospheric
circulation models. An inverse model interprets the observations to yield an
optimized carbon flux distribution that is most consistent with the
atmospheric data. In this approach, atmospheric model simulations relate
fluxes at the surface to concentration changes at the observation sites.
However, the number of available observation sites and the model resolution
are usually insufficient to constrain the exchange of trace gases like
CO2 on smaller, i.e. regional to country, scales.
To address those scales, regional atmospheric greenhouse gas inversions (Lin
et al., 2003; Stohl et al., 2009; Bergamaschi et al., 2010; Manning et al.,
2011), including CO2 inversions (Gerbig et al., 2003; Matross et al.,
2006; Lauvaux at al., 2008), have been developed and used to estimate the
carbon budgets of regions like Europe and the USA as well as individual
nations. They are complementary to bottom-up inventories and provide a means
to verify national inventories. Like their global counterparts, regional
inversion methods combine CO2 observations from surface sites with
modelled atmospheric circulation to derive the distribution of sinks and
sources over an area of Earth's surface (the inversion domain). In a
regional inversion, however, the sites are not distributed globally and the
domain is typically the size of a country or continent, which poses some
additional challenges (Manning, 2011). An accurate model of background
concentrations or sinks and sources from outside the inversion domain is
required, i.e. a baseline. There is also a need for adequate spatial and
temporal resolution for both the estimated fluxes and the circulation model
to account for topographic effects and local emission gradients and
hotspots. Many regional inversions use continuous in situ
observations from one to a few measurement stations to sample the whole
domain over the course of days to a few weeks. They use air from a
background sector to construct a baseline time series (Manning et al., 2011;
Uglietti et al., 2011). Due to chemical inertness of CO2, atmospheric
loss processes can be neglected once the gas has entered the domain, making
this approach viable as long as the measurement station is positioned so
that background air can be observed for significant fractions of time. It is
generally of advantage to use multiple stations, which are sensitive to a
larger surface area and allow for a better interpretation of spatial
gradients in atmospheric CO2.
In their inversion study, Stohl et al. (2009) estimate emissions for three
HFC and HCFC greenhouse gases on national to global scales for 2005–2007.
Their approach uses the FLEXPART Lagrangian model to describe the recent air
history arriving at nine observation stations distributed globally. They use
a priori emission maps and estimate both the baseline and the
regional emissions as part of the inverse modelling. Manning et al. (2011)
use 20 years of in situ CH4 and N2O observations from a
single station, Mace Head, on the west coast of Ireland. Mace Head regularly
receives air from the mid-latitude North Atlantic as well as from the UK and
continental Europe, which allows them to estimate both the baseline and
terrestrial emissions. Their emission estimates for the UK have been used to
complement those reported to the UNFCCC for the period 1990–2007. Matross et
al. (2006) derive regional-scale CO2 flux estimates for summer 2004 in
the north-eastern United States and southern Québec using the STILT Lagrangian
model in conjunction with aircraft and tower observations.
Here, we present the first regional inversion for New Zealand, which
leverages the country's unique characteristics. New Zealand is an isolated
country surrounded by approximately 2000 km of ocean on all sides. This
simplifies the construction of an accurate baseline model, as CO2
signals from other land masses, especially from Australia and the Northern
Hemisphere, will be significantly diluted and become part of the baseline
before reaching the country. The expected national carbon sink, which is
estimated by the inversion, is a large fraction of the fossil fuel emissions
(about three-quarters according to the NIR) which are prescribed. In
addition, New Zealand has multiple atmospheric CO2 measurement sites
across a relatively small country.
CO2 observations from BHD and LAU, twice-daily at 13:00–14:00
and 15:00–16:00 LT, through 2011–2013. The BHD southern baseline is shown
along with the weighted baseline used in the inversion. Gaps in the coloured
bars at the top indicate days when no observations are available.
Our inverse model utilizes in situ observations from two observing
stations in New Zealand for 2011–2013 and ship board measurements from a
regular transect between Australia, New Zealand, and Japan (conducted by
Japan's National Institute for Environmental Studies, NIES). The Numerical
Atmospheric dispersion Modelling Environment (NAME III) Lagrangian model
(Jones et al., 2007) was combined with meteorological output from the New
Zealand Limited Area Model (Davies et al., 2005) at ∼ 12 km
resolution (NZLAM-12), to model the pathway of air arriving at the stations.
Land model simulations from Biome-BGC (Thornton et al., 2005) are used as
a priori estimates. We compare our results to New Zealand's NIR
(MfE, 2015), point out differences and implications, and discuss the
regional distribution and seasonal cycle of CO2 sinks and sources
across the country.
Observations
We use in situ measurements of CO2 from two inter-calibrated stations in
New Zealand (Fig. 1). Baring Head (BHD) is located on the south coast of the
North Island, while Lauder (LAU) is located in the central South Island
(Fig. 2). Both stations are sensitive to different source regions of CO2
and complement each other to allow for a comprehensive regional coverage
spanning the Southern Ocean, Tasman Sea, the South Island, and – to a lesser
degree – the North Island and the subtropical South Pacific.
Air history map for 10 000 particles released at BHD during
15:00–16:00 LT on 19 May 2012 using the NAME III model. The particle back
trajectories show a southerly event locally at the station, though not a
baseline event as the air crosses parts of South Island. Also shown are the
locations of LAU and Kākaramea/Rainbow Mountain (KAK) stations.
The instruments used at LAU and BHD are operated with reference gases
traceable to the World Meteorological Organisations mole fraction scale as
maintained by the Central Calibration Laboratory (CCL) at the US National
Oceanic and Atmospheric Administration (NOAA). Both instruments share common
data processing code, improving data inter-comparability between the sites
(Brailsford et al., 2012). In addition, fine-scale instrumental biases were
assessed by using a suite of four transfer standard tanks with trace gas
concentrations defined by the CCL. These instrument-specific multiplicative
scalings were applied to the processed hourly data before the inversion. For
typical ambient mole fractions of CO2 (i.e. 380–410 ppm) these
adjustments were generally less than 0.07 and 0.1 ppm for BHD and LAU,
respectively.
Some of the elements of this study are prepared to include data from a third
station, Maunga Kākaramea/Rainbow Mountain, located in the northern half
of the North Island (Fig. 2). For example, the region definitions in
Sect. 5.2 include a local Maunga Kākaramea/Rainbow Mountain region.
Because the calibration and quality control processes are equivalent to BHD
and LAU, the station can be integrated seamlessly into the network. It is
ideally located to extend the sensitivity of the inverse model into the
north. However, it is not incorporated in this study because continuous
CO2 measurements from Maunga Kākaramea/Rainbow Mountain were not yet
available for the full 2011–2013 period.
For this study the hourly mean CO2 records from BHD and LAU covering
1 January 2011 to 31 December 2013 are used. Both stations measure in situ
with near-continuous observations throughout the day. The observations are
strongly influenced by local signals at night and under certain
meteorological conditions, e.g. a shallow planetary boundary layer (PBL) or
low wind speed (Stephens et al., 2013). Measurements at times with deep,
well-mixed PBL are better suited for inversion modelling as they are
sensitive to sinks and sources from a wider region and not subject to
localized processes or complex atmospheric structure. Similarly, because the
vertical resolution of the meteorological model NZLAM (Sect. 3) is not fine
enough to resolve the exact height of a station inlet, conditions with a
well-mixed PBL are preferable (see also Sect. 7.4). Afternoon observations
are, on average, well suited. Analysis of the diurnal cycle of CO2
concentrations at the various inlet heights of both BHD and LAU shows least
variability with altitude in the 13:00–16:00 afternoon hours. For the
inversion, two hourly average data
points per day are selected in the afternoon, 13:00–14:00 local time (LT)
and 15:00–16:00 LT. Local time represents NZST (NZST = UTC + 12 h)
in winter and NZDT (NZDT = UTC + 13 h) in summer. One potential risk
of using only afternoon data is the potential for misrepresentation of fluxes
with strong diurnal variations. Potential biases introduced by our sampling
strategy are investigated and quantified in Sect. 7.4.
For both stations, 1 standard deviation of a 5 min measurement interval is
taken as random data uncertainty for the hourly mean. This uncertainty is
generally much greater than the measurement precision, as it reflects real
atmospheric variability, and is instead intended to capture
representativeness errors such as different temporal resolutions of model and
data or the model failing to represent the specific conditions at an
individual location.
Baring Head
BHD station (Lowe et al., 1979) is located at 41.408∘ S,
174.871∘ E, 85 m a.m.s.l., approximately 10 km south-east of the
Wellington urban area (Fig. 2), close to the edge of a south-facing coastal
cliff. The surrounding land is sparsely populated and has primarily been used
for low density livestock farming. Wind speeds at the site regularly exceed
10 m s-1, reducing the influence of local sources. The wind directions
are primarily bimodal with the dominant wind from the north and the
secondary direction from the south (Stephens et al., 2013). Southerly air
arriving at the site has often been travelling over the Southern Ocean for at
least 4 days, sometimes weeks, without any contact to terrestrial sinks or
sources of CO2. The station is ideally situated to determine baseline
levels of atmospheric CO2 at latitudes up to 70∘ S during these
conditions. At other times, BHD measures air that has recently travelled over
Australia or New Zealand, carrying a terrestrial signal of CO2 sinks and
sources.
We use hourly averaged CO2 data from the non-dispersive infrared (NDIR)
analyser (Ultramat 3, Siemens) in situ observations during the 13:00–14:00
and 15:00–16:00 time windows (Fig. 1) from a 10 m air inlet height. For
more details on measurements, calibration, and data processing, we refer to
Brailsford et al. (2012); Stephens et al. (2013).
Lauder
LAU station is located at 45.038∘ S, 169.684∘ E,
370 m a.m.s.l., in a broad river valley on the South Island, approximately
35 km north of the township of Alexandra (population 5000). The surrounding
land is sparsely populated and largely used for low density livestock farming
and seasonal cropping. The local wind direction is predominantly ranging from
north-westerly to south-westerly. To the west lies a valley system in
mountainous terrain, behind which the north–south running mountain range of
the Southern Alps divides the island into a western coastal strip with a
humid maritime climate and the eastern part with a more continental climate
and relatively clear unclouded skies.
At LAU, CO2 has been measured in situ using a dual cell NDIR analyser
(LI-7000, LI-COR Inc.) since 2008 from a 10 m air inlet height. Unlike the
BHD measurements, where detailed descriptions and analyses are given by
Brailsford et al. (2012) and Stephens et al. (2013), the LAU measurements
have not been published before. Therefore, we provide an extended description
of the LAU in situ CO2 measurement system in Appendix A.
Model simulations
We use NAME III (Jones et al., 2007), a Lagrangian dispersion model developed
by the UK Met Office driven by three-dimensional meteorological fields
precomputed by a numerical weather prediction (NWP) model. While initially
developed more than 2 decades ago as an emergency response particle tool
for nuclear outfall, NAME has since evolved into a general purpose dispersion
model that is being used from local scales (a few hundred metres) to
mesoscales and global scales. Atmospheric turbulence is simulated using a
random walk technique (Morrison and Webster, 2005). CO2 is modelled as
an inert gas due to its long lifetime in the atmosphere (> 300 years), far
exceeding the 4-day periods used for the back trajectories.
In this work, meteorology from the NZLAM-12 NWP model was used to drive NAME.
NZLAM-12 is a local configuration of the UK Met Office Unified Model (Davies
et al., 2005) with a horizontal resolution of ∼ 12 km and 70 vertical
levels up to a ceiling height of 80 km. The meteorology is a sequence of
short, 6 h forecasts with hourly output that are produced from successive
NZLAM simulations and cover the period 2011–2013. At the beginning of each
simulation the available meteorological observations are assimilated into the
model to match the state of the NZLAM atmosphere to the measured atmosphere.
NAME uses the boundary layer depth (BLD) from NZLAM and applies a minimum and
maximum BLD of 50 and 4000 m, respectively. The maximum height in NAME is
30 km, corresponding to the first 59 levels of NZLAM. Both NZLAM and NAME
cover a domain ranging from 146.8 to 185.8∘ E in longitude and from
53.4 to 26.0∘ S in latitude (inversion domain is shown in Fig. 3).
Voyages of the Ship of Opportunity Trans Future 5. Ship tracks
have been slightly spread longitudinally to allow one to differentiate
individual cruises (more recent cruises are to the right). Colours give the
in situ CO2 concentration. Measurements made inside the open-ocean mask
were used in the northern baseline analysis. The inversion domain is outlined
in grey.
Air history and station footprints
The NAME model is run in backward mode to analyse the history of the air
travelling towards BHD and LAU over the preceding 4 days. Model particles are
released from both stations during a period of 1 h, twice per day in
2011–2013, at 13:00–14:00 and 15:00–16:00 LT. A simulation period of
4 days was found sufficiently long to allow all particles to leave the domain
during most meteorological conditions, except during extended periods of very
low wind speed. An air history map has been calculated for each release
(Fig. 2). We use model output that represents the 4-day integrated air
concentration (also called dosage, unit g s m-3) inside each grid box
on a regular 0.1∘× 0.1∘ grid, designed to be very
similar to the ∼ 12 km grid of NZLAM-12. During each release, the
dispersion of 10 000 particles is modelled and every particle registered
within the boundary layer at a given time contributes to the dosage of the
respective grid cell. Particles are simulated in three dimensions and do not
disappear when leaving the boundary layer as long as they remain below the
maximum model height of 30 km. Particles can leave the boundary layer
temporarily and descend back into it at a later time, in which case they
would again contribute to the dosage. An example of this can be seen in
Fig. 2, where many particles leave the boundary layer just south of the South
Island and later (from the point of view of the backward simulation) descend
again, visible as a weaker dosage (less strong colours) for some stretch of
the map.
Top panel: 2011–2013 mean footprints for BHD and LAU stations,
based on twice-daily air history maps at 13:00–14:00 and 15:00–16:00 LT.
Clusters of 4-day back trajectories are overlain. Percentages give the sizes
of clusters, i.e. the probability that a particle released on a random day
has followed that pathway. Lower panel: major atmospheric transport pathways
for both stations from cluster analysis of the back trajectories from
twice-daily particle release. Shades represent the geographical spread of
each pathway (1 standard deviation from cluster centroid in
latitude–longitude).
Average footprints for the BHD and LAU stations were computed by summing the
dosage maps for each day and release period in 2011–2013 and normalizing
them such that the domain integral equals one (Fig. 4). These footprints
represent the average sensitivity of a station to surface fluxes (sinks and
sources) of CO2 at a specific location. They have also been used to
inform the partitioning of the inversion domain into a set of regions for
which weekly surface fluxes are calculated (Sect. 5.2).
Transport matrix
For particle transport, the mass flux during each 1 h release period is
1 g CO2 s-1, amounting to a total emission of 3600 g CO2
over the period (0.36 g CO2 is assigned to each particle). The flux
strength is an arbitrary choice and does not affect the transport results due
to the implied linearity of transport. A transport matrix
Tg (unit s m-1) is formed by dividing the dosage by
the total emitted mass and multiplying by the area (m2) of each surface
grid cell. Thus, each element of Tg describes the
atmospheric transport of a continuous emission of
1 g CO2 m-2 s-1 from a given grid cell over the previous
4 days and subsequent contribution to the air concentration at the receptor
(BHD or LAU) during each 1 h period. With xg being a vector
containing all grid cells and c a vector containing the concentration
(unit g CO2 m-3) for all 1 h periods, this is written as
Tgxg=c. Given Tg
and the measured concentrations c, the aim is to solve for the
CO2 fluxes xg using a Bayesian inversion, i.e. a
statistical model that balances information from measurements with a priori
knowledge about the fluxes (Sect. 6).
Instead of solving on the grid scale, the fluxes in xg are
pre-aggregated into a set of regional fluxes (Sect. 5.2), x, and a
transport matrix T created by aggregating grid cells in
Tg to reflect the regions in x:
Tx=c.
The Bayesian inversion developed here solves for x. In addition, a
priori flux maps are taken into account for the terrestrial and oceanic
portions of the domain (Sect. 4).
A priori CO2 flux maps
The Bayesian approach in this study uses spatially distributed information
about CO2 sinks and sources as first-guess, or a priori, fluxes for
terrestrial and oceanic regions. These fluxes are optimized by the inversion
using the constraints imposed by the CO2 measurements at the stations
(Sect. 6). Fossil fuel and cement emissions are accounted for as well, though
unlike the natural fluxes they are prescribed and not optimized by the
inversion. Here we describe the datasets and flux maps used, while their
incorporation in the inversion is described in Sect. 5.
(a) Land-cover and land-use (LCLU) map with 10 categories,
based on the Land Cover Database (LCDB3) and the Land Use in Rural New
Zealand (LURNZ) basemap. (b) A priori CO2 flux distribution,
averaged over 2011–2013, from modelled NEP using Biome-BGC with the LCLU
categories. Both maps have been regridded to the NAME model grid.
(c) Monthly and annual contributions from each biome to the overall
a priori CO2 flux.
Terrestrial
First-guess land-to-air CO2 fluxes from the biosphere for every month in
2011–2013 are obtained from the Biome-BGC model (Thornton et al., 2005).
Biome-BGC (v4.2 final release) is an ecosystem process model that estimates
the storage and flux of carbon, nitrogen, and water (Thornton et al., 2002).
The model has been extensively tested and validated for North American and
European ecosystems and in addition was recently extended and applied to New
Zealand managed pasture systems (Keller et al., 2014). The adaptation of the
Biome-BGC model to New Zealand by Keller et al. (2014) is used in this study
to estimate net ecosystem production (NEP) for five biomes across New
Zealand: dairy pasture, sheep and beef pasture, shrub, evergreen broadleaf
forest (EBF), and evergreen needleleaf forest (ENF). The model is driven by
daily weather data from the NIWA virtual climate station network (VCSN). VCSN
data include numerous meteorological parameters on a regular (∼ 5 km)
grid covering the whole of New Zealand (Tait et al., 2006). The data are
based on the spatial interpolation of actual data observations made at
climate stations located around the country. Soil attributes are incorporated
from the fundamental soil layers database (Landcare Research, 2015).
Biome-BGC produces NEP maps for each biome covering the whole country; i.e.
it does not make assumptions about the actual distribution of biomes. In
order to partition the country into biomes approximating the five categories
available in Biome-BGC and then mask and sum the NEP contributions from each
biome, we produced a land-cover and land-use (LCLU) map. The LCLU map uses 10
categories based on a combination of the land-cover database (LCDB) for New
Zealand (Shepherd and Newsome, 2009; Dymond et al., 2012) and the Land-Use in
Rural New Zealand (LURNZ) model (Hendy et al., 2007; Timar, 2011; Kerr et
al., 2012).
Gridded fossil emissions of CO2 across the model domain and
averaged over 2011–2013, in kg CO2 m-2 yr-1. Emissions are
based on EDGAR v4.2, with 2011–2013 estimates extrapolated from the
2000–2010 trend in global total emissions.
The New Zealand LCDB is a thematic classification of land-cover and land-use
categories, created using satellite imagery and covering all of mainland New
Zealand. The dataset is polygon based and designed to be compatible in scale
and accuracy with Land Information New Zealand's 1 : 50 000 topographic
database. For the purpose of this study, the distribution of 33 land-cover
types from LCDB version 3 for 2008/2009 was used and rasterized on a
5 km × 5 km grid.
LURNZ is a dynamic partial equilibrium model that simulates changes in
private rural land use over time and space. It focuses on four key land uses
– dairy, sheep and beef, forestry (plantations), and scrub and shrubland. While
the model's primary focus is on simulating future changes in land use under
scenario projections of commodity prices in one of the four sectors, it also
provides a baseline of actual land use in 2008. This 2008 basemap is used in
this study to match the Biome-BGC dairy and sheep and beef pasture biomes;
however, LURNZ does not include native forests.
To account for all biomes in Biome-BGC, the LURNZ 2008 basemap and the LCDB
2008/2009 land-cover map are combined as follows. First the 33 LCDB
categories are aggregated into 7 – forest, scrub and shrubland, grassland,
cropland, water bodies, bare or lightly vegetated surfaces, and artificial
surfaces. The forest and grassland categories are then sub-divided into
plantations, “other forests”, dairy pasture, sheep and beef pasture, and
“other grasslands” using LURNZ, which results in the LCLU map in Fig. 5a.
The ENF biome is assumed to be well represented by the plantation forest
category, with plantations consisting primarily of pine trees, and the EBF
biome is assumed to be better represented by the “other forests” category.
The categories of artificial surfaces, bare and lightly vegetated surfaces, and
water bodies are assigned a zero flux, i.e. no exchange of CO2 with the
atmosphere. No flux estimates are made for cropland and “other grasslands”;
this does not affect results significantly, because these categories
represent only a small portion of the total land area. Figure 5b shows the
2011–2013 mean a priori land-to-air CO2 flux as estimated by matching
the LCLU and Biome-BGC biomes in this manner and summing their contributions
to the overall NEP. The monthly and annual contributions are shown in
Fig. 5c. Weekly first-guess CO2 flux maps are obtained by simple
interpolation of the monthly estimates throughout 2011–2013.
Simulated contributions to the observed CO2 anomaly, i.e.
concentration minus baseline, at BHD (top) and LAU (bottom) for each day in
2011–2013 averaged over both 13:00–14:00 and 15:00–16:00 LT release
periods. Contributions are calculated using NAME transport matrices with
EDGAR v4.2 fossil fuel emissions, prior oceanic CO2 flux from the
Takahashi pCO2 dataset, and prior terrestrial CO2 flux from the
Biome-BGC model. Note that scales vary due to stronger anomalies and seasonal
amplitude at LAU.
An uncertainty estimate is computed for the a priori CO2 flux from each
grid cell. Based on Keller et al. (2014) and personal communication with the
authors, we assign 10 % of the flux as uncertainty for pasture land. For
forests, we assign 10 % everywhere except in the Canterbury and Otago
regions in the South Island, where 56 and 36 % are used, respectively.
These are conservative estimates based on a comparison of the Biome-BGC ENF
modelled live stem carbon with the national exotic forest regional yield
tables (MPI, 2012). The Canterbury and Otago regions were assigned larger
uncertainties to reflect the larger discrepancy between the Biome-BGC model
and the MPI yields in these regions. The uncertainty is taken into account by
the Bayesian optimization (Sect. 5). We note that the EBF biome has not been
calibrated or tested under New Zealand conditions and might contain
additional uncertainty.
Oceanic
First-guess air–sea CO2 fluxes are calculated based on a global dataset
of surface ocean pCO2 (Takahashi et al., 2009a). The dataset contains
approximately 4.5 million measurements of surface water partial pressure of
CO2 (pCO2) obtained over the global oceans during 1968–2008,
approximately 90 000 of which were taken inside the model domain of this
study. A monthly climatology on a global 4 × 5 grid was derived by
Takahashi et al. (2009b), which also includes an estimate of air–sea CO2
flux derived from the difference of surface ocean and atmospheric CO2
and a gas exchange rate following Wanninkhof (1992).
An uncertainty estimate for the a priori ocean fluxes is computed as the root
mean square of two components reflecting the uncertain gas exchange rate and
the spatiotemporal coverage of measurements inside grid cells. For the first
component, we recalculated the CO2 flux using each of seven additional gas
transfer models (Ho et al., 2006; Sweeney et al., 2007) and used 1 standard
deviation from the eight-model mean as uncertainty. The second component applies
an uncertainty to grid-scale fluxes inversely proportional to the number of
measurements taken inside them for a given month of the climatology
(Steinkamp and Gruber, 2013). As with the terrestrial CO2 flux prior,
the uncertainty is accounted for in the Bayesian inversion.
Fossil emissions
A gridded map of CO2 emissions is derived from the Emission Database for
Global Atmospheric Research (EDGAR) version 4.2 (EC-JRC/PBL, 2011). EDGAR
contains global emission inventories for greenhouse gases and air pollutants
from sectors including energy, industrial processes, solvents and other
product use, agriculture, land-use change and forestry, and waste. Annual
emissions are available on a 0.1∘× 0.1∘ grid over
the globe up to the year 2010. Emissions for the 2011–2013 time period were
approximated by extrapolation using the trend in global total emissions over
2000–2010. The spatial distribution was assumed unchanged from 2010
(Fig. 6). Total emissions for the New Zealand mainland are
47.8 Tg CO2 yr-1 in 2011–2013.
Fossil CO2 emissions are not optimized by the inversion; instead, their
contribution to the CO2 signal at both stations (Fig. 7) is subtracted
from the actual measurements beforehand. That contribution is calculated
using the transport matrix from NAME, i.e. applying Eq. (1) with x
containing the emissions from every grid cell and week in 2011–2013. The
vector c then contains CO2 concentrations for the twice-daily
release periods at both stations that are caused by the emissions. To convert
concentrations into mole fractions (ppm) the atmospheric pressure and
temperature from the NAME model are used, which were interpolated to the BHD
and LAU site coordinates from the NZLAM-12 temperature and pressure fields.
Regional flux estimationBaseline analysis
Any regional CO2 inversion can only estimate sinks and sources within
the boundaries of the model domain. Sink and source processes from outside
the domain become part of the CO2 background concentrations (i.e.
baseline) seen by the regional inversion at the boundary. Therefore, an
accurate description of this baseline is needed. A common approach is the
background-sector method (Manning et al., 2011; Uglietti et al., 2011), where
air is classified as baseline if it originates from a certain wind sector and
fulfils site-specific meteorological criteria. A continuous baseline is
constructed using a gap-filling technique, which is subtracted from all other
measurements before the inversion. The inverse model then interprets the
remaining variability in the data, or anomalies, to find the optimal
distribution of sinks and sources within the model domain.
The background-sector method has been applied to the BHD CO2 record by
Brailsford et al. (2012) and Stephens et al. (2013). They use steady
background CO2 mole fractions during southerly wind conditions at BHD
and apply a multi-step filter to the BHD record to obtain a CO2 baseline
representative of a large region over the Southern Ocean. In short, the
filter selects measurements during extended periods of southerly winds at the
site, during which a maximum standard deviation of 0.1 ppm is achieved.
Additional meteorological conditions must be fulfilled to preclude the
influence of local sources and to ensure the air has not passed over the
South Island before arriving at BHD. After filtering the data for baseline
conditions, a continuous baseline is constructed using the seasonal time
series decomposition by Loess (STL) algorithm (Cleveland et al., 1990), which
can be sampled hourly, i.e. during the 13:00–14:00 and 15:00–16:00 LT
release periods. The baseline derived from the BHD record is shown in red in
Fig. 1 and will be called the southern baseline.
One disadvantage of a background-sector approach based on a single site is
that it may not capture variability in background concentrations from
different wind conditions, in particular along the latitudinal axis with its
gradient in atmospheric CO2, which could lead to errors in the flux
estimates within the domain. In our case, BHD's background sector is ideally
situated to obtain a CO2 baseline representative of a large region over
the Southern Ocean. However, observations made during northerly events are
not always well described using this baseline, as the air often originates
from the northern Tasman Sea or the subtropical South Pacific and carries a
contribution from northern hemispheric CO2 (Sect. 6, Fig. 4).
To alleviate this we augment the southern baseline with a second baseline
from ship data representative of the northern sector. This northern baseline
is based on in situ CO2 observations using a NDIR analyser on board the
Trans Future 5 (TF5), a ship of opportunity that cruised the triangle
Japan–Australia–New Zealand about once a month during the period 2011–2013
(Chierici et al., 2006). We filter the ship track data (Fig. 3) to select
open-ocean measurements and mask out observations taken close to the land,
especially near the Australian east coast as it is located upwind during
average south-westerly conditions and hosts large urban centres with
significant CO2 emissions. These selected data are then latitudinally
averaged between 26 and 27∘ S to produce a baseline representative of
the northern edge (26∘ S) of the inversion domain. A continuous
baseline is constructed using the same STL routine as for the southern
baseline.
For both baselines, uncertainty estimates are formed based on the monthly
standard deviations of the in situ data as well as differences between
measurements and the STL smoothed curve. A more detailed description of the
construction of both baselines is provided in Appendix B. A caveat of this
concept of baseline is that synoptic patterns of CO2 can travel over
large distances and cause variations in the background concentrations at the
regional boundary, which are not accounted for by the baseline derived here.
Given New Zealand's geographic isolation and location in the Southern
Hemisphere, the strength of synoptic variations is limited compared to
regions where inflow is characterized by strong variability due to
terrestrial signals from other areas. While an explicit treatment of synoptic
variability is beyond the scope of this study, a sensitivity experiment is
described in Sect. 5.4 to address potential biases in the baseline.
A combined CO2 baseline is constructed that takes into account where the
modelled trajectories originated for any given data point. The daily NAME
station footprints for the 13:00–14:00 and 15:00–16:00 LT windows are
integrated along the southern and northern edges of the domain to determine
the relative fraction of back trajectories leaving the domain to the south
and north. These fractions are then used to weight the two baselines and
create a baseline associated with each of the twice-daily data points.
Uncertainties are weighted in the same way. The combined baseline is shown in
green in Fig. 1. For the plot the 13:00–14:00 and 15:00–16:00 LT weighted
baselines were averaged, as they are visually almost indistinguishable, but
the individual baselines are used in the inversion.
Regional partitioning
CO2 fluxes are estimated for every week in 2011–2013 and for 25
geographic regions distributed across the inversion domain (Fig. 8). The
within-region pattern of the fluxes is prescribed using the a priori flux
maps for New Zealand and the surrounding oceans, while the inversion
estimates regional totals. The definition of the regional boundaries was
guided by several factors, including the distance from the measurement
stations, the gradient of the station footprint, local orography, and fossil
emission hotspots. For land regions in New Zealand, additional factors
include land-cover and land-use types as well as the expected (first-guess)
CO2 flux distribution.
Regional partitioning and indices in the inversion.
Due to its large distance from the stations, the portion of Australian land
inside the inversion domain is represented by a single region (no. 16). No a
priori information about natural CO2 fluxes from Australia is assumed,
i.e. they are set to zero with a very large uncertainty of
1000 Tg CO2 yr-1, so that the inversion is free to adjust them.
This is based on an analysis suggesting generally low sensitivity of CO2
measurements at our stations in New Zealand to fluxes from the Australian
region (Sect. 6, Fig. 7). The analysis uses fossil emissions from the
Australian region (Sect. 4.3) and investigates whether these emissions leave
a significant imprint on measured CO2 at BHD and LAU. Except for a few
days this imprint is negligible (Sect. 6).
The portions of the Southern Ocean, South Pacific and Tasman Sea that are
inside the model domain were divided into six open-ocean and three coastal regions
(17–25). Large open-ocean regions are chosen to make their regionally
integrated contribution to the CO2 signal become discernible at the
stations, although these contributions remain much smaller than those from
the land regions in New Zealand. Three northern and three southern open-ocean
regions allow for the difference in ocean biogeochemistry between Southern
Ocean and subtropical Pacific waters, with guidance from patterns of surface
ocean pCO2 from the a priori map. The coastal ocean regions were
included to separate the open ocean from the land explicitly and to account
for their stronger influence on the measured CO2 due to their relative
proximity to the stations compared to the open ocean. The coastal ocean was
defined as the combination of a 60 km coastal band around New Zealand and
the portion of ocean with a mean 2011–2013 BHD footprint value above a fixed
threshold. The threshold was chosen such that the integrated CO2 signal
at BHD from coastal regions is 25 % of that from all ocean areas.
It is generally important to separate regions that exhibit a strong
variability in sensitivity, as otherwise these within-region gradients can
skew the regional totals estimated by the inversion towards the most
sensitive areas inside the region. For land regions in New Zealand, we used
the spatial gradients of the 2011–2013 footprints as an estimate for this
variability, similar to the coastal regions, except that for the land, we use
the combined footprint of BHD and LAU (Fig. 9) and also account for
additional factors.
New Zealand was divided into 15 land regions (1–15) as follows. Three
small regions around BHD, LAU and Maunga Kākaramea/Rainbow Mountain were
defined, which have the largest contributions to the CO2 signal at the
respective stations. This separation of the highly influential local regions
from the rest of the country follows the same rationale as the separation of
the coastal from the open ocean, with the aim to prevent the inversion from
allocating local signals to regions further upwind. A separate region around
the urban areas of Auckland and Hamilton was defined to capture the strong
fossil emissions there. The remaining regions were defined with the aim to
minimize the footprint variability, the expected flux variability inside each
region, and the number of land-cover and land-use types within each region, while
accounting for topographic features.
Combined 2011–2013 footprint for both 13:00–14:00 and
15:00–16:00 LT release periods for BHD and LAU around New Zealand.
The resulting regional partitioning is shown in Fig. 8. A major feature is
the role of the Southern Alps as a dividing range between the humid west
coast of the South Island containing large patches of native forest, and the
dryer regions in the central and eastern parts, where pasture land is
predominant. On the North Island, the axial mountain ranges divide the land
into east and west as well, but the distribution of forests and pasture is
more complex. BHD and LAU have relatively low sensitivity to the northern
half of the North Island, which results in large uncertainties after the
inversion for individual regions. However, regionally aggregated results are
well constrained in that part of the country.
Inversion methodology
The aim of the inverse method is to estimate a net CO2 flux from every
region and for every week between 2011 and 2013 using a Bayesian approach
(Gurney et al., 2004; Tarantola, 2005; Steinkamp and Gruber, 2013). The
approach assimilates information from the twice-daily observations from both
stations (the “data”) and accounts for a priori fluxes (the “prior”) and
contributions from fossil emissions.
The data time series is constructed by subtracting the baseline from the
station measurements. The modelled CO2 signal from fossil emissions is
also subtracted. The resulting time series represents the part of the
observed CO2 signal that cannot be explained by background
concentrations or fossil emissions and is therefore due to the net effect of
sinks and sources of CO2 over the ocean and land portions inside the
model domain. The data for each station and each 1 h period are written as a
vector d.
The uncertainty applied to each data point is calculated as the quadrature
sum of the baseline uncertainty (Sect. 5.1) and the CO2 data uncertainty
(Sect. 2). A minimum uncertainty of 0.4 ppm is assumed to account for
uncertainties in the transport model as well as possible errors in the fossil
fuel emission estimates. The final uncertainty is multiplied by 2.9, a value
based on a goodness of fit analysis of the inverse model (reduced chi-squared
statistic, as described below). The resulting uncertainty is taken as the
root mean square (quadrature) of both components and has a minimum value of
1.16 ppm. The mean uncertainty is 1.91 ppm and 95 % of the values are
within the 1.16 to 4.56 ppm range. The square of the uncertainty populates
the main diagonal of the data covariance matrix Cd. We
assume no correlations between pairs of data points, so all off-diagonal
elements of Cd are set to zero.
The regional prior (denoted x0) is obtained by integrating the
weekly a priori terrestrial and oceanic flux maps over each region except the
Australian region. The prior uncertainty is similarly obtained by aggregating
the grid-scale uncertainty estimates. Since the within-region flux patterns
remain fixed, we assume full spatial correlation when propagating grid-scale
uncertainties to the regional scale. For land regions we added (via root mean
square) an additional uncertainty component of 50 % of the seasonal flux
amplitude. This allows the inversion to shift the seasonal cycle more freely;
otherwise, the seasonal turning points – i.e. the switch between net
CO2 uptake in the summer months and net release in the winter – would
essentially be fixed as the flux is near zero and the grid-scale uncertainty
estimates for the Biome-BGC model are proportional to the flux strength. The
diagonal prior covariance matrix C0 contains the regional
uncertainty.
The regional prior is linked to the data vector as in Eq. (1), except
x now contains fluxes on the regional instead of grid scale, and the
transport matrix T links regional total fluxes to the data time
series with baseline and fossil signal subtracted.
The inversion process seeks an optimal solution to the transport equation by
balancing the data and prior constraints (Tarantola, 2005), i.e. by
minimizing a Bayesian cost function J with respect to x:
J=12Tx-dTCd-1Tx-d+12x-x0TC0-1x-x0+12SxTCs-1Sx=12T̃x-d̃TC̃d-1T̃x-d̃+12x-x0TC0-1x-x0.
The first term in the equation evaluates the deviation of the modelled time
series from the data, with each data point weighted with the inverse
uncertainty. The second term evaluates the deviation of the optimized
regional fluxes from the prior fluxes. The last term is a Gaussian smoother,
which limits changes in week-to-week fluxes. The operator S forms
a vector whose elements correspond to the difference of each flux in
x and the flux of the following week. The diagonal matrix
Cs contains the squares of values representing the
strength of the smoother. We chose 5 kg CO2 m-2 yr-1 for
every grid cell, which is more than 10 times larger than the largest flux
from any grid cell of the a priori flux maps (Fig. 5); hence the smoother is
very weak. In fact the smoother was designed to have a negligible effect on
estimated CO2 fluxes and not interfere with the prior and data
constraints. Its role is merely to favour solutions with small week-to-week
changes in cases where a second solution with much larger week-to-week
changes would result in a very similar cost, J. Note that the smoothing
factors for individual regions differ slightly due to varying surface areas.
Due to their similar mathematical forms, the smoothing term can be absorbed
in the data term in Eq. (2) by appending S to T
(forming T̃), Cs to
Cd (forming C̃d), and a zero
vector of appropriate length to d (forming d̃). The
reduced chi-squared statistic χ2=2J/n is used to assess the fit of
the inverse model to the observations (other examples of how this statistic
can be used are described in e.g. Gurney et al., 2004; Baker et al., 2006),
where n is the number of observations. Computing the data uncertainty as
described above ensures χ2≈1, which means that the extent of
the match between observations and the model as well as between the a priori
and a posteriori sources are in accord with their respective uncertainties.
The cost function in Eq. (2) is minimized analytically (Enting, 2002;
Tarantola, 2005) to yield a posteriori fluxes x and covariance
matrix C:
x=CT̃TC̃d-1d̃+C0-1x0C=T̃TC̃d-1T̃+C0-1-1.
The square root of the diagonal elements of C is reported as
the uncertainty estimate for the a posteriori fluxes.
Sensitivity scenarios
Considerable effort has been undertaken to ensure e.g. the high quality of
available observations, the inter-comparability of measurements from BHD and
LAU, and the use of a state-of-the-art land process model to provide
meaningful first-guess estimates. However, there are a number of potential
sources for error that cannot be accounted for explicitly but could have a
significant influence on estimated land fluxes. These include (i) the
CO2 baseline, (ii) the flux distribution within each region, and
(iii) the ocean prior fluxes.
Sensitivity scenarios were designed to quantify each of these potential
errors, as described below. The results of these scenarios and analysis of
potential bias due to diurnal variability and atmospheric transport model
error are discussed in Sect. 7.4.
The inverse method assumes that air entering the domain is accurately
characterized by the baseline CO2 time series. While random noise in the
baseline concentration is accounted for (Sect. 5.1), there remains the
possibility of systematic errors. A positive (negative) bias in the baseline
would cause the inversion to estimate a total CO2 flux that is depressed
(elevated), in order to explain the measurements at the stations. Sensitivity
runs were conducted with both positive and negative biases. The baseline
mixing ratio is first decreased, then increased, by 1 standard deviation,
i.e. its uncertainty.
The inverse model optimizes total fluxes across large spatial regions,
but the geographic distribution of the CO2 fluxes within each region is
not adjusted by the inversion but prescribed based on the prior flux
distribution. This can lead to biases in the estimated flux if the region is
being unevenly sampled. That is, if a specific observation is sensitive to
only a small area inside the region, then the flux estimate for the entire
region will be biased towards that area, which may not be representative for
the region. This is why we took the geographic distribution of biomes into
account when defining the regions. The number of different biomes was
minimized and isolated patches of biomes avoided inside each region. However,
the region definition remained subjective, so we included a sensitivity case
where the within-region flux pattern is flat; i.e. the flux is constant
region-wide. Not all potential biases are removed this way, as that would
require solving the inverse problem at a much higher resolution, but it gives
an indication of the influence of a particular choice of pattern.
Estimates of terrestrial CO2 fluxes in New Zealand are influenced
by the ocean flux prior through atmospheric transport. After entering the
model domain at baseline levels, the air travels inevitably over a large
stretch of ocean and will arrive at the New Zealand coast carrying an oceanic
signal in its CO2 concentration. Errors in the oceanic prior flux
estimates can result in inaccurate terrestrial CO2 flux estimates. In a
sensitivity test, the oceanic prior uncertainty was raised to
108 Tg CO2 yr-1, effectively excluding the ocean prior.
Analysis of New Zealand's in situ CO2 observing sites
We conducted a clustering analysis using NAME III to characterize the
catchment areas of the BHD and LAU stations. The clustering was performed
using a convergent k means procedure, which is based on Kidson (1994), but
adjusted slightly to allow a larger number of trajectories to be clustered,
i.e. by using a smaller number of random seeds. This significantly boosts
the computation at the expense of likelihood to find the global minimum,
however, the reduced number of seeds appeared large enough to come
sufficiently close, as repeated computations with randomly different subsets
all produced very similar results.
A set of 1000 trajectories was used between 15:00 and 16:00 LT for every day in
2011–2013, resulting in approximately 1 million trajectories for each
station. The number of clusters was set to seven, because this number maximized
the distinctness of clusters with respect to each other as obtained from
their silhouette values. Cluster centroids and sizes are overlain on the
station footprints in Fig. 4, together with the geographical width of the
clusters.
In addition to the clustering analysis, we applied Eq. (1) to the a priori
flux maps for every day in 2011–2013. This allows us to calculate the
imprint of Australian and New Zealand fossil emissions as well as oceanic and
New Zealand terrestrial sinks and sources on the CO2 concentration
measured at BHD and LAU (Fig. 7).
Generally, CO2 measurements at BHD are most sensitive to sinks and
sources in the Southern Ocean (south of 55∘ S), the
Tasman Sea, and the South Island. Australia and the North Island influence
BHD CO2 to a lesser extent. Observations at LAU are strongly influenced
by local to regional terrestrial sinks and sources of CO2, enabling the
station to see air from a large portion of the southern South Island.
Further site-specific details are given in the subsections below.
The low sensitivity to Australia means it is infeasible to infer Australian
CO2 fluxes with our observational network most of the time. However, this underscores the isolation of New Zealand, where air is
received that largely contains background concentrations from the vast body
of surrounding ocean. This allows us to estimate terrestrial fluxes in New
Zealand with high sensitivity and little disturbing influence from
continental sources.
Baring Head
In 2011–2013, BHD sampled air that has travelled from the Southern Ocean
41 % of the time along two cluster pathways (Fig. 4), which correspond to
southerly wind conditions at the site. The more southerly cluster of the two
(16 %) contains trajectories that mostly have not seen land over at least
4 days and will carry Southern Ocean baseline CO2. Trajectories in the
more westerly cluster (25 %) have travelled across most of the South
Island after originating in the Southern Ocean and will carry a signal of the
terrestrial sinks and sources of CO2 there. Another 17 % of
trajectories are originating from the south-west but are associated with
slower wind speeds, so that within the 4-day time frame of the
back trajectories they have not yet left the domain. They correspond to a
local northerly wind at BHD associated with a common synoptic pattern
involving an anticyclone over the Tasman Sea. The Southern Alps on the South
Island strongly influence the south-westerly air flow and deflect it
northward along the west coast and then through Cook Strait, where it is
channelled into a northerly flow by local topography. Trajectories arriving
from Australia and the Tasman Sea occur 13 and 9 % of the time,
respectively; 10 % of the trajectories have crossed large parts of the
North Island before arriving at the station.
The application of Eq. (1) to the a priori flux maps shows that there are
only 4 days in 2011–2013 when a discernible (> 0.1 ppm) signal from
Australian fossil fuel emissions within the inversion domain was received at
BHD. The signal was always much smaller than 1.16 ppm, the minimum overall
uncertainty assumed in the modelling system.
During the winter and summer seasons, the New Zealand land area is the main
contributor to the BHD data series, with a seasonal pattern matching the
respiration and growing cycles. Assumed aseasonal fossil emissions from New
Zealand (mostly from the nearby city of Wellington) and seasonal
oceanic fluxes also play an important role at BHD.
Lauder
For LAU, there are two southernmost clusters representing a combined 40 %
percent of trajectories. These are very similar in size to the corresponding
clusters for BHD and have identical source areas in the Southern Ocean. While
similar to BHD's southern cluster, the air from the LAU southern cluster
would have travelled over a considerable stretch of land before arriving at
LAU. 14 % of the time, the air being sampled belongs to a south-western
cluster, which originates in the Tasman Sea. In addition, there is a western
cluster containing 15 % of trajectories that has crossed South Australia
and the Tasman Sea as well as two northern clusters representing air with
mixed origin from the northern Tasman Sea or the North Island. About 14 %
of trajectories are contained in a slow cluster whose origin is not very far
from LAU. These cases correspond to slow winds at the site and indicate that
the measurements are highly impacted by local and regional sources as the air
has been travelling over nearby land for the preceding 4 days.
The application of Eq. (1) to the a priori flux maps shows that LAU station
is dominated by terrestrial fluxes from New Zealand (particularly from the
South Island), with only minor contributions from the ocean, reflecting its
location further inland and shielded from the predominant westerly winds by
the Southern Alps. The seasonal amplitude in the CO2 signal at LAU is
about twice as large as at BHD, due to the more continental climate and more
pronounced growing seasons in the central South Island. Similar to BHD, there
are only 5 days in 2011–2013 when a larger than 0.1 ppm signal from
Australian fossil fuel emissions within the inversion domain was received at
LAU.
Weekly CO2 fluxes in 2011–2013 from selected regions, in
Tg CO2 yr-1. Prior flux estimates are shown in grey, and the
inversion results are shown in blue. Shaded areas represent flux uncertainty
(1σ). The cyan shade represents the extra uncertainty obtained from
the sensitivity cases (Sect. 7.4). Note that there is a one-off change in
scale of the flux axis for sub-island scale regions. A positive flux
indicates a net release of CO2 to the atmosphere, while a negative value
indicates uptake by the land biosphere. Regional indices in parentheses
correspond to Fig. 8.
Flux results and discussionSeasonal cycle
The inversion finds a much stronger seasonal cycle in terrestrial CO2
fluxes than the Biome-BGC model simulations used as a prior (Fig. 5c),
especially associated with enhanced CO2 uptake during the growing season
in (austral) summer (Fig. 10). There is very good agreement in the phasing of
the seasons with the land process model during all 3 years, which is
particularly encouraging in light of the weak constraints on the phasing
applied through the prior (Sect. 5.3). This strong seasonality is robust
within the estimated a posteriori uncertainty range and across the
sensitivity cases. Uncertainties for weekly fluxes were reduced significantly
compared to the prior, even when the range of sensitivity cases is added as
extra uncertainty.
The enhanced seasonal amplitude is assigned to the South Island almost
exclusively, with much stronger uptake during the growing season compared
with carbon uptake in Biome-BGC. The uncertainties associated with South
Island fluxes are generally smaller than on the country scale because of the
high sensitivity of the LAU and BHD stations to fluxes from much of the South
Island. In contrast, the North Island is estimated to have a weaker
seasonal cycle, in good agreement with the prior, which can be attributed to
widespread areas of summer soil water deficits, and the more marine climate
there, i.e. weaker seasonal temperature variations and milder winters.
Uncertainties for North Island fluxes, especially from the northern half of
the North Island, are generally larger due to the lower sensitivity of the
stations to that area. While northerly breezes are very common at BHD
(Fig. 4), they often correspond to a situation where south-westerly air was
deflected by the Southern Alps and channelled by local topography to turn
into a northerly at the station. Air that has travelled across the North
Island and picked up its terrestrial CO2 signal is therefore less often
sampled at BHD than local wind direction would suggest. At LAU, North Island
air can be sampled only about 8 % of the time, based on the NAME cluster
analysis. In a future study, the sensitivity to North Island fluxes can be
greatly enhanced by CO2 observations at the recently established Maunga
Kākaramea/Rainbow Mountain station in the central North Island.
When separating the South Island into parts east and west of the Southern
Alps, it becomes apparent that most of the enhanced seasonal cycle occurs, in
fact, in the west, despite the slightly smaller surface area
(86 173 km2 compared to 88 348 km2). Along the west coast, the
inversion estimates the seasonal amplitude to be more than twice as large as
suggested by the prior. Tracing the cause further to the individual regions
reveals that Fiordland (region 13) is the strongest contributor to the
signal. Fiordland is extremely sparsely populated and covered to a large
extent by indigenous temperate rainforest with southern beeches
(Nothofagus sp.), fern trees, and shrub. When forming the prior flux
map, these forests were categorized as EBF and
the respective module from Biome-BGC used. However, the EBF module had not
been optimized for New Zealand forests, so it is possible that the Fiordland
forests are not well described by that category. The inversion suggests much
stronger photosynthetic and respiratory activity in these forests than the
prior model.
Response to the 2012/2013 drought
In this section, we assess the ability of the inversion to detect the carbon
flux response to drought. The austral summer of 2013 was characterized by
unusually high temperatures and low precipitation over much of New Zealand
(Turner, 2013; Blunden and Arndt, 2014), with sustained periods of severe
drought in February–March 2013. The North Island and the west of the South
Island were the most strongly affected regions (Porteous and Mullan, 2013).
The Biome-BGC model is driven by detailed, reanalysed weather data and
clearly shows a positive flux anomaly, i.e. loss of CO2 to the
atmosphere, due to enhanced respiration and inhibited growth during that
period (Fig. 5c). The inversion sees this event in the observations as well,
suggesting even more CO2 release than Biome-BGC across the South Island.
Unfortunately, a prolonged data gap in the LAU time series during that period
caused by a lack of field standards (Fig. 1) leads to weaker constraints
from the atmospheric CO2 data and therefore larger uncertainty in the
flux estimates in the South Island.
A signal of excess CO2 release in February–March 2013 is seen by the
inversion across the North Island, too (Fig. 10). The limited coverage of
some areas in the North Island, especially in the north and east (Fig. 9),
leads to high annual mean flux uncertainty for individual regions and
prevents a robust analysis as to which regions responded the most strongly to
the drought. Eddy-covariance data from a dairy pasture site in the
north-western North Island during a ∼ 100 day drought in 2008 found a
temporary loss of CO2 to the atmosphere, but the ecosystem recovered to
become a net sink of CO2 for the year (Mudge et al., 2011).
Annual fluxes
The geographic air–land flux distribution averaged over 2011–2013 is shown
in Fig. 11, including flux gradients on the sub-regional level that were
prescribed in the inversion. A comparison to the a priori distribution in
Fig. 5b shows larger areas acting as a net carbon source. These include the
central and north-eastern parts of the South Island, which roughly correspond
to the Canterbury region and mostly contain pasture land, in particular sheep
and beef pasture (Fig. 5a). The inversion does not, however, resolve
ecosystem processes, but merely estimates net air–land fluxes, so it is not
possible to make a causal link between pasture and a net CO2 source. A
counterexample is the south-east of the South Island, which also contains
large areas of pasture but is estimated to be a net carbon sink. In general,
the inversion assigns much more of the total land sink to forested areas than
the Biome-BGC prior. This is particularly apparent along the western South
Island, as well as in the eastern half of the North Island. The strong flux
gradients seen in region no. 3 (north-western central North Island) are
likely to be the result of the very heterogeneous composition of LCLU types
there, combined with BHD and LAU having low sensitivity in the region
(Fig. 9), rather than a real signal. The inclusion of an additional station
with high sensitivity to the northern North Island, such as Maunga
Kākaramea/Rainbow Mountain, would be needed to improve flux estimates
there.
Geographic distribution of land-to-air CO2 flux, averaged over
2011–2013. Blue and red regions indicate net carbon uptake and release,
respectively. Per-area ocean fluxes are too small to show on this scale.
Fossil fuel emissions are included and reach up to
20 kg CO2 m-2 yr-1 in a few grid cells (Auckland area). The
colour scale is capped to focus on natural fluxes. Inset: annual mean results
compared to the National Greenhouse Gas Inventory Report.
In the inset of Fig. 11, we compare annual mean results from the inversion
with bottom-up estimates from the National Inventory Report (MfE, 2015), or
NIR. The inversion suggests a much larger net CO2 sink across the
country compared to the NIR. Particularly in the forest of the south-western
South Island, the inversion suggests both stronger photosynthetic and
respiratory activity than the prior model, with the overall balance towards a
larger CO2 sink over the course of a year. For example, Fiordland
appears to take up between 22 and 68 Tg CO2 each year in 2011–2013
(Table 1), which corresponds to per-area uptake rates of 614 and
1899 g CO2 m-2 yr-1, respectively. By comparison, the
Biome-BGC estimates range from 0 to 3 Tg CO2 yr-1.
The NIR estimates do not come with an overall uncertainty, but based on their
reporting of typical uncertainty for individual ecosystems, and personal
communication, an approximate figure of 50 % of the flux value was
identified. This implies statistical significance for the difference in
annual sink estimates, except in 2013, when both estimates agree within their
uncertainty range. Without ocean prior or by assuming a baseline bias of
0.1 ppm, the differences are reduced by up to a half (Sect. 7.4) but do not
disappear. How can these differences be explained? There are a number of
possible scenarios, which we explore in the following.
The accounting of fossil emissions differs between the NIR and the inversion.
The EDGAR emissions of 47.8 Tg CO2 yr-1 prescribed in the
inversion contain elements of land-use change and agriculture. The NIR gives
total emissions of 34.6 Tg CO2 yr-1 for 2013. The difference of
about 13 Tg CO2 yr-1 would appear in the inversion as an
additional sink of equal size.
The inversion and NIR estimates are not directly comparable due to
differences in the top-down vs. bottom-up viewpoints. While the inversion
sees the overall net CO2 exchange between the atmosphere and the land,
the NIR estimate represents the so-called LULUCF sector; i.e. it includes
contributions from land use, land-use change, and forestry. In the LULUCF
model, it is assumed that CO2 emissions from harvested wood products
occur at the location of the tree, a process particularly important to forest
plantations located in the central North Island and in the north of the South
Island (Fig. 5a). However, about 70 % of the biomass from forest
harvesting is exported before major processing, i.e. in the form of logs,
sawn timber, or manufactured wood products (Pike, 2014). Most of the CO2
release associated with harvesting will subsequently occur far away from New
Zealand, e.g. in China, with a 34 % share of New Zealand's forestry
exports in 2012. In a regional inversion these emissions cannot be seen,
leaving a larger net sink. Emissions from harvested wood products are
reported in the NIR at 10.3 Tg CO2 yr-1 in 2013, translating
into about 7 Tg CO2 yr-1 that cannot be seen by the inversion
when assuming a 70 % export rate. No emissions are reported for earlier
years because the harvested wood products category was introduced for the
first time in the 2015 report. The 2013 estimate is likely to be an upper
bound for the years 2011 and 2012 because the volume of harvested wood
products has increased steadily since 2009 (MfE, 2015). Other possible
discrepancies between the NIR methodology and the net CO2 fluxes for
forests include the variance in the timing of root carbon emission following
tree mortality (Kirschbaum et al., 2013). Large sinks observed but not
accounted for in NIR can result from applying steady state assumptions to
natural or pre-1990 forests when they are accumulating carbon in biomass
during recovery from past disturbance, with potential rates of biomass
accumulation by native species reaching
700–900 g CO2 m-2 yr-1 (Trotter et al., 2005).
For pastoral agriculture, a more complex set of differences applies to the
intercomparison of inversion results, a priori process-based model results
and the NIR methodology. Similar to forestry, agricultural exports (e.g. milk, meat, and wool) equated to 340 g CO2 m-2 yr-1 for a
dairy pasture (Mudge et al., 2011), and the 165 Gg of nitrogen estimated as
exported in produce (Parfitt et al., 2006) will equate to an apparent net
CO2 uptake of 5.8 Tg CO2 yr-1 across New Zealand. The
second-most important gap between methodologies results from the NIR
calculation that 6.3–6.5 % of the energy content of pasture consumed by
ruminants is converted to CH4 emissions. These CH4 emissions
represent a carbon flux to the atmosphere not observable as CO2 and
therefore require separate quantification. They have been calculated as
79 g CO2 m-2 yr-1 in a dairy pasture (Mudge et al., 2011)
and the carbon content of the NIR's 1137 Gg of CH4 emissions equates to
an unobserved 3.1 Tg CO2 yr-1. Several additional terms,
including leaching of dissolved carbon forms, and imports of feed and
fertiliser can also provide important corrections between NEP seen by inversions and eddy covariance and net ecosystem
carbon balance (NECB) (Mudge et al., 2011).
The NIR methodology also does not account for above or below-ground
grassland biomass nor does it account for soil carbon changes. The
process-based model Biome-BGC potentially accounts for both these flux
terms, but not in relation to intensive management. Therefore, both biomass
and soil carbon must be considered to explain additional CO2 uptake or
loss by pastures that might be seen by the inversion, but not by NIR or
Biome-BGC.
Biomass carbon is relatively small in New Zealand pastures (Tate et al.,
1997) but can be a significant component of seasonal net exchange (Mudge et
al., 2011; Rutledge et al., 2015; Hunt et al., 2016) as described in
Sect. 7.1. Repeated measurements of soil profiles suggest that soil carbon
changes can also be significant but uncertain due to limited sites available
for resampling. For example, a recent analysis of all sites available
nationally suggests that sites on flat pasture are losing soil carbon at
rates of ∼ 170 g CO2 m-2 yr-1, while sites in hill
country are gaining ∼ 770 g CO2 m-2 yr-1 (Schipper
et al., 2014). In addition to large areas of grazed pastures on both islands,
significant areas of tussock grasslands on the South Island could be gaining
biomass and soil carbon as they recover from historic overgrazing (Tate et
al., 1997). The extensive area of grasslands on both islands could result in
large net CO2 exchange fluxes usefully observed by inversion studies.
New Zealand's first process-based studies of net national ecosystem carbon
balance suggested large uncertainties in grasslands (Tate et al., 2000) and
later suggested grasslands were approximately carbon neutral in 2001 (Trotter
et al., 2004). Eddy-covariance studies remain limited in coverage across New
Zealand but tend to suggest potential for large negative NEP and near
neutral NECB. Rutledge et al. (2015) updated and extended the Mudge et
al. (2011) results to 4 years, yielding average NEP of
600 ± 180 g CO2 m-2 yr-1 and NECB of
220 ± 200 g CO2 m-2 yr-1. Hunt et al. (2016) also
report eddy-covariance carbon budgets for an irrigated intensively grazed
dairy pasture and an unirrigated winter-grazed pasture in Canterbury on the
South Island's east coast. Over 1 year, the unirrigated pasture was carbon
neutral (±80 g CO2 m-2 yr-1), while the
intensively managed and irrigated pasture displayed NEP of
1500 ± 140 g CO2 m-2 yr-1 and NECB of
380 (±150) g CO2 m-2 yr-1. The forest and grassland
studies described above suggest that large, negative flux anomalies estimated
by the inversion may be plausible when extrapolated across the large areas of
these LCLU categories.
Additional real land carbon balance terms may also contribute to large,
negative flux anomalies that differ from NIR and process-based models such as
Biome-BGC. These terms include areas of organic soil accumulation in wet
forests and bogs, typical of west-coast environments where the largest
negative flux anomalies are observed. Campbell et al. (2014) used eddy
covariance to find net ecosystem exchange (NEE) of
800–900 g CO2 m-2 yr-1 with a strong seasonal cycle.
Erosion and deposition can also create a net carbon sink that may be
unusually significant at active tectonic margins such as New Zealand (Tate et
al., 2000; Baisden and Manning, 2011). Small catchment and site studies have
estimated rates of net pasture soil carbon sequestration due to erosion and
burial, accounting for upland soil carbon recovery, of
220 g CO2 m-2 yr-1 (Page et al., 2004) and
370 g CO2 m-2 yr-1 (Parfitt et al., 2013). Scott et
al. (2006) have estimated the national delivery of eroded carbon to the coast
as 11 ± 4 Tg CO2 yr-1 and suggested that much of this
carbon is likely to be buried and replaced in uplands. Dymond (2010) attempts
to more fully and dynamically account for erosion, burial, and replacement,
suggesting a range of 4–20 Tg CO2 yr-1. Both studies suggest
the largest erosion-induced CO2 sinks occur in the Southern Alps in the
west of the South Island, where the Lauder station allows observation of a
strong sink, as well as in the North Island's east coast. These estimates may
partly be included in the hill country soil carbon accumulation estimated by
Schipper et al. (2014). Smith et al. (2015) suggest that fiords may also
create a strong carbon sink, with about 18 Mt of organic carbon being buried
in fiord sediments globally each year, yielding a rate of
198 g CO2 m-2 yr-1. Thus, a number of plausible mechanisms
have been documented in the literature to explain that the CO2 sink seen
by the inversion is stronger than estimated in the NIR.
Mismatch (residuals) of modelled vs. observed CO2 in multiples of
the prior data uncertainty at Baring Head (top) and Lauder (bottom). Vertical
dotted lines separate the years 2011, 2012, and 2013. Solid lines represent a
Loess fit with a 3-month window. Horizontal dotted lines mark the prior
uncertainty (1σ). The left column shows scatter plots for every day
and 1 h release period; the right column shows the mismatch distribution
over 2011–2013. Dashed lines with numbers give the bias.
Uncertainty and bias assessment
In addition to regional uncertainty, the posterior covariance matrix from the
Bayesian optimization also contains weekly error correlations between
regional flux estimates, which are used to inform flux uncertainties. Strong
negative correlations between two regions would indicate that the inversion
has difficulties distinguishing their individual flux components with the
available data, while their sum is better constrained. Similarly, positive
correlations indicate that the difference of flux components is constrained
better than each individually. An analysis of all regional error correlations
reveals that both negative and positive correlations are present; however,
only 0.13 % of all pairwise correlations have an absolute value greater
than 0.1. Hence, with the available data, the inversion appears able to
resolve weekly fluxes at the chosen regional level.
An analysis of the mismatch of modelled CO2 (i.e. the CO2 time
series obtained by propagating the posterior flux through the transport
model) and observed CO2 reveals differences between the BHD and LAU
stations (Fig. 12). At BHD, the mismatch distributions are very similar for
the 13:00–14:00 and 15:00–16:00 time series, have a positive bias (modelled
CO2 is higher than observations) of 11–13 % of the prior data
uncertainty, and show no discernible temporal pattern (smoothed, thick lines
in the figure). At LAU, the mismatch distribution is similar for the
13:00–14:00 time series, with an even smaller bias of 7 %, but for the
15:00–16:00 time series there is a much larger bias of 52 % of the data
uncertainty. This means the inversion has difficulties reproducing the low
CO2 concentrations in the LAU 15:00–16:00 observational record. The
temporal evolution indicates an alternating pattern of small mismatch during
the (austral) winter and larger mismatch during summer.
In an attempt to explain this mismatch, we looked at two possible sources of
bias: (1) a misrepresentation of the PBL depth in
the NZLAM model and (2) diurnal variability in the fluxes that is not
captured because we use afternoon data only and do not have diurnal
variability in our a priori fluxes.
Biases in the modelled planetary boundary layer
At a site like LAU, strong solar radiation during a clear summer day might
lead to a sudden deepening of the PBL in the afternoon between the two
release periods, which could prove difficult for NZLAM to model accurately.
Consequently, if the modelled PBL is deeper than in reality, any signal from
surface fluxes would be mixed in a larger volume of air and their effect on
modelled CO2 concentrations would be attenuated. This would require the
inversion to estimate stronger surface fluxes in order to explain measured
CO2 concentrations. This would lead to an overestimation of CO2
uptake during summer. The opposite is true if the modelled PBL is too shallow
and the measured concentrations can be explained with less CO2 uptake.
We compared the model PBL depth at 15:00–16:00 to radiosonde measurements
made at LAU (Fig. 13). The Heffter method (Heffter, 1980) was used to compute
PBL height from the radiosonde data. The comparison suggests that the
boundary layer is too shallow in the model during summer, thus enhancing the
effect of CO2 uptake on the modelled concentrations. Thus, smaller
fluxes should be required to match the uptake signals observed at LAU, and
therefore this process does not explain the observed mismatch. An equivalent
analysis with the 13:00–14:00 LT PBL data suggests a similar discrepancy,
yet these data can be explained by the inversion, further reducing the
likelihood that the PBL representation in the model causes the observed
mismatch. However, this analysis has caveats, because the radiosonde dataset
is preliminary, and only few measurements were taken during the right times
of day (13:00–14:00 and 15:00–16:00 LT, respectively).
Comparison of boundary layer depth at LAU in NAME at
15:00–16:00 LT and radiosonde observations made at the site (Heffter
method). The seasonal cycle has been made more visual using a robust Loess
smoother. Summer periods are highlighted in yellow, winter periods in grey.
Diurnal variability
Our method estimates surface fluxes on a weekly scale based on afternoon
measurements and does not resolve the diurnal cycle of the CO2 land
natural fluxes. The diurnal cycle is particularly pronounced in summer, with
strong uptake during the afternoon. This could cause the inversion to
overestimate the weekly sink and thus bias the annual sink towards more
uptake, in an attempt to match the low afternoon observations that are being
assimilated. This issue is of most concern near the stations, because
varying atmospheric transport integrates flux signals from more distant
regions across a larger range of times. We would therefore expect this
effect to be most pronounced in the local regions surrounding each station.
However, based on a synthetic data experiment described below, we find that
this is not the case; the sink in the two local regions is actually slightly
suppressed and does not reveal any discernible seasonal pattern.
Annual mean CO2 flux for selected aggregated regions, in
Tg CO2 yr-1. A negative sign indicates uptake by the land. In
parentheses the a posteriori uncertainty (1σ) is shown (excluding the
sensitivity cases).
Deviation of the posterior flux from the “true” flux (red lines)
in the synthetic data experiment with a diurnal flux cycle for the 15 land
regions in 2012. Patched areas give the posterior uncertainty (excluding
sensitivity cases). Green bands represent the annual mean deviation, with
their thickness corresponding to the annual uncertainty. Note there is a
change in scale of the flux axis for the three local regions (red font).
We conducted a synthetic data experiment in order to
assess the potential bias caused by
diurnal variations not represented in our inversion framework. A synthetic
dataset that included the atmospheric signature of diurnal variability was
generated using hourly footprints from the transport model (instead of the
4-day mean footprints described in Sect. 3.1) together with hourly Biome-BGC
flux maps for 2012. These flux maps were prepared by imposing a simplified
diurnal cycle on daily, gridded Biome-BGC outputs of gross primary production (GPP), net primary production (NPP), and heterotrophic respiration (HR). The diurnal
variation in GPP is directly proportional to the relative amount of solar
insolation, and HR is assumed to occur at a constant rate and spread evenly
throughout the day. There are a number of aspects of plant physiology and
ecosystem biogeochemistry that cause actual diurnal variation in NEE to be
more muted than the solar-radiation-driven pattern we have modelled. These
include reductions in photosynthesis during the middle of the day and
afternoon as stomata close due to drought or leaf temperature stress.
Similarly, reduced respiration can be expected at night due to cooler
temperatures. Synthetic CO2 concentrations were created for every day at
15:00–16:00 LT by propagating the hourly fluxes through the hourly model.
This set of synthetic mole fractions was then assimilated in an inversion
analogous to the reference inversion, whereby oceanic prior fluxes, fossil
emissions, and the data uncertainties were kept the same as in the reference
case. The weekly averages of the hourly fluxes then represent the “truth”
against which the posterior fluxes can be compared. The weekly averages of
the true fluxes are also used as a prior in the inversion, albeit with an
uncertainty twice as large as in the reference inversion, to limit its
influence on the results.
Unrepresented diurnal variability led to biases in the annual mean flux
estimates for some regions in our inversion, but these errors were much
smaller than our uncertainty estimates for most regions on an annual scale
(Fig. 14). Importantly, this experiment does not reveal a substantial
systematic bias in the annual mean across all regions. Two of the best
constrained regions (upwind of LAU during average wind conditions), regions
13 and 14 (south-western South Island and local LAU region), show a
suppressed, rather than overestimated, annual mean sink and this bias emerges
without a distinctive seasonal pattern. This is especially surprising in the
local region around the LAU station where the effect should be strongest.
This suggests that a diurnal bias is unlikely to be the cause of the observed
15:00–16:00 data mismatch, as the latter is characterized by a clear
seasonal cycle. Adding to this, the other local region (around BHD, region 8)
does not show any distinctive bias for both weekly and annual sinks. This is
less surprising as the BHD site is characterized by very high wind speeds for
much of the year (Stephens et al., 2013) and therefore less sensitive to
local effects. Overall, diurnal variability leads to an overestimation of the
annual mean land sink in the South Island of 3.5 Tg CO2 yr-1,
which is well within the uncertainty envelope of the reference inversion
(Table 1).
On a weekly timescale, estimates generally agree to within their
uncertainties for most regions, with the exception of the eastern South
Island (Regions 12 and 15), the southern central North Island (region 7), and
the local area region around Lauder (region 14). Regions 12 and 15 (eastern
and south-eastern South Island) show an increased sink late in the year (as
part of the 2012/2013 summer) but a smaller sink early in the year (as part
of the 2011/2012 summer), suggesting that there may be a seasonal bias in our
inverse methodology for the eastern South Island. Likewise, diurnal cycle
bias leads to significant weekly errors in the central North Island
(region 7), although with less seasonal coherence. The Lauder local region
(region 14) was created to capture local signals that are not well
represented in our inverse model, including diurnal variability, and prevent
them from biasing the inverse estimates on larger spatial scales. Thus the
larger errors for this region are expected.
Sensitivity scenarios
Results from the sensitivity scenarios (i)–(iii) are incorporated in the
figures as an additional uncertainty band on top of the Bayesian posterior
uncertainties from the default run (Fig. 10). That band represents the
maximum (minimum) value of the flux plus (minus) its uncertainty at every
point in time and across all runs, i.e. including the default and
sensitivity runs.
While the uncertainty range associated with the suite of sensitivity
scenarios is symmetrical around the reference case for most regions, the
sensitivity range is characterized by more positive flux estimates than the
reference case in the western South Island (Fig. 10), i.e. a slightly
smaller annual carbon sink. This can be attributed to sensitivity case (iii),
in which the inversion is allowed to adjust air–sea fluxes to any value
without penalty. Some of the terrestrial CO2 uptake is relocated to
upwind ocean regions, as this yields a lower Bayesian cost, because fluxes
from the western South Island are shifted towards the Biome-BGC prior
estimates. However, in order to offset a relatively small flux change on
land, the change in ocean flux has to be large due to the distance to the
stations and the dilution of CO2 concentrations on the way. This leads
to an ocean sink of 6 Pg CO2 in 2012 in our regional domain for this
sensitivity test, which is more than 10 times larger than estimates for the
whole Southern Ocean from global inversions, ocean carbon data, and ocean
biogeochemistry models (Gruber et al., 2009). Despite this unrealistic result
for the oceans in the sensitivity test, the conclusions about the seasonal
pattern in CO2 uptake and release and its spatial distribution in the
New Zealand land regions remain robust.
The inversion assumes an unbiased baseline CO2 record. Any positive
(negative) bias would be interpreted by the inversion as an additional sink
(source) of CO2. From the sensitivity runs we find that a constant bias
in the baseline of 0.1 ppm would cause the total CO2 flux of New
Zealand for each year in 2011–2013 to be offset by approximately
20 Tg CO2 yr-1. This corresponds to about 50 % of the flux
uncertainty from the default run (Table 1), thus underscoring the importance
of an accurate baseline in a regional inversion. An inversion such as ours
can always benefit from advances in air–sea flux datasets, such as
pCO2 measurements from a regional cruise network, as well as
well-characterized background air concentrations. However, similar to the
sensitivity case without ocean prior, the biased baseline has only a minor
influence on seasonal flux patterns over land.
Current plans to aid the baseline representation in future top-down studies
of the New Zealand region include the addition of in situ measurement sites
that observe inflow from the north-westerly and westerly sectors on the upper
North Island or west coast of the South Island, which will improve the
characterization of synoptic-scale variability in inflow that does not
originate from the Southern Ocean sector.
Conclusions
We present the first regional inversion estimates of air–land and air–sea
CO2 fluxes for the New Zealand region, which were estimated from two in
situ observing stations in New Zealand, ship-based measurements, and
Lagrangian model simulations using the NAME dispersion model driven by NZLAM
meteorology. The results imply a strong seasonal cycle, especially for fluxes
in the western South Island. Regions covered predominantly by indigenous
forest appear to have more pronounced photosynthetic and respiratory activity
than suggested by the land model's EBF category.
This is most apparent in Fiordland, which is a key contributor to the
seasonal cycle, as well as the annual mean sink, in the South Island. The
timing, magnitude, and regional distribution of seasonal flux patterns are
well constrained and robust across sensitivity cases, while uncertainties in
annual totals are more significant. Enhanced CO2 release from the
terrestrial biosphere in New Zealand is apparent in response to the 2012/2013
drought period. This response appears most prominent in the North Island and
western parts of the South Island, consistent with reports about these
regions being most severely affected.
The annual total CO2 sink in New Zealand is estimated to have decreased
over the 3-year period, at 132 ± 36, 97 ± 36, and
64 ± 40 Tg CO2 yr-1 in 2011, 2012, and 2013, respectively.
The New Zealand national inventory reports a much smaller sink of 28, 27, and
27 Tg CO2 yr-1 for the same years (with uncertainty around
50 %). About 7 Tg CO2 yr-1 of the discrepancy can be
attributed to emissions associated with forest harvesting, which are included
in the inventory but missed by the inversion due to forestry exports. Another
13 Tg CO2 yr-1 arise from different accounting of fossil
emissions between the inventory and the inversion. Additional factors
relating to the difference between NEP and NECB in pastures can account for
another 9 Tg CO2 yr-1. Other terms such as erosion, burial, and
soil carbon recovery may account for another 4–20 Tg CO2 yr-1.
These differences largely reconcile both results for 2013 but not
2011–2012. Carbon sequestration by grassland and soil carbon could also play
an important role in causing differences between the two methods, as these
processes are not included or fully resolved in inventory reporting but would
be seen by the inversion. Collectively, these factors are likely to reconcile
both results only partially, with some differences remaining.
Detailed sensitivity studies suggest that the most important causes of
uncertainty in the inverse estimates are uncertainties in the estimate of
baseline air entering the domain and air–sea fluxes from the ocean
surrounding New Zealand. These uncertainties could be reduced through higher
density of pCO2 measurements in the oceans around New Zealand, and
extending the ship-based atmospheric CO2 measurements presently used to
estimate the baseline air farther to the south and west. Another possibility
is to establish additional surface stations in strategic locations, i.e.
with footprints in areas where Lauder and Baring Head have low sensitivity.
While it was out of scope for this study, more detailed analyses of the
uncertainties associated with the atmospheric transport model are needed,
particularly the role of model resolution given the complex topography of New
Zealand.
The inversion methodology developed here is a powerful tool to validate net
regional CO2 sinks in the New Zealand national inventory report. It
offers an independent, top-down view on the national carbon budget. The
limited sensitivity to the northern half of the North Island, as well as
baseline errors, can lead to large uncertainties for annual mean flux
estimates in some regions. Improving on these factors in future studies can
further increase the usefulness of the top-down approach.
Data availability
The model code has been made publicly available in a
GitHub/Zenodo (10.5281/zenodo.215972) repository (Steinkamp et al.,
2016).
Lauder site descriptionThe Lauder station
The Lauder atmospheric research station (45.038∘ S,
169.684∘ E, 370 m a.m.s.l.) is located in the broad Manuherikia
river valley on the South Island of New Zealand. A semi-arid continental
climate predominates with an annual rainfall of 450 mm and mean annual
temperature of 9.7 ∘C. The prevailing wind is from the westerly
quarter (a mean daily wind run of approximately 300 km). Periodic southerly
frontal systems bring air masses from the Southern Ocean and Tasman Sea. The
station is surrounded by pastoral land dominated by non-intensive sheep and
cattle farming practices along with seasonal cropping. The valley is sparsely
populated. The land westward (on average upwind) of the valley consists of
numerous valley systems and mountainous terrain. The vast majority of this
land is undeveloped and is part of New Zealand's national park system. There
is no major industry present in the region.
Due to the relatively clear unclouded skies, low light pollution, and low
levels of local and regional anthropogenic emissions, “clean air”
ground-based remote sensing, balloon sonde, and in situ measurements are
routinely conducted at the station as part of NDACC (formerly known as NDSC)
(Kurylo, 1991), GAW (WMO-GAW, 2007), TCCON (Wunch et al., 2011), and GRUAN
(Seidel et al., 2009) activities.
Lauder in situ trace gas measurements
Long-term routine in situ measurements began at Lauder in 2003 with the
installation of a TEI-49C Ozone monitor (Zellweger et al., 2010). Previous to
this only sporadic short-term campaigns focusing on tropospheric nitrogen
dioxide had been undertaken (Johnston and McKenzie, 1984). Continuous in situ
measurements of carbon dioxide (CO2), methane (CH4), nitrous oxide
(N2O), and carbon monoxide (CO) began in March 2007 when a prototype FTIR
trace gas analyser was installed (Griffith et al., 2012; Sepúlveda et
al., 2014). In June 2008 a well-calibrated continuous CO2 NDIR
(differential, non-dispersive, infrared) analyser (LI-7000, manufactured by
LI-COR, Inc., USA, http://www.licor.com) was installed at Lauder. This
was followed by regular fortnightly flask samples analysed for CO2,
CH4, N2O, CO, and δ13C-CO2 concentrations, starting
in May 2009. An added advantage of employing the NDIR analysers at both sites
(Lauder and Baring Head) is that they share common data processing code and
calibration routines.
Air inlet system
The air inlet system consists of a permanent 10 m high metal NIWA
meteorological mast erected at a distance of 33 m to the north, of the
nearest building, housing the in situ instrumentation. Two sets of 60 m long
stainless steel (SS) tubing (ID 8.8 mm) are used to sample air from the mast
(inlet at 10 m) to two distribution manifolds; prior to 2012 these were
baked copper tube (ID 8.8 mm). The inlets are fitted with inverted funnels
with coarse mesh (0.7 mm) to provide rain and dust protection. For
electrical isolation of the instrumentation and the meteorological mast a
100 mm length of PFA 9.5 mm tubing is inserted between the sampling lines
and the manifolds. The manifolds are constructed from 25 mm SS diameter
tubing 200 mm in length (volume = 0.086 L). Each port consists of a SS
tube (OD 6.3 mm) welded perpendicular to the main body, these extend 15 mm
centrally into the main body and terminated with a 45∘ angle cut
facing the direction of flow. Sample air is drawn into the two 4-port
manifolds with a roughing pump (KNF Neuberger, N035 AN18) at
10–15 L min-1 giving an effective residence time of approximately
35 s and an associated pressure drop of 40 mbar. The roughing pump allows
sample air to be drawn at a higher flow rate and allows multiple instruments
to sample air without front end pressure coupling between co-sampling
instruments.
The LI-7000 draws air from the manifold via a diaphragm pump (KNF Neuberger,
KNF 86KNE, 2.6 L min-1). A set of four field standards, with a
calibration lineage to the mole fraction scale maintained by the CCL and a
target–archive tank, is connected to a valve manifold consisting of five
three-way (Parker B16DK1175) valves in a daisy chain configuration, along
with the dried air allowing selection of either field standards, target tank,
or sample air for the analyser. Gas regulators (Scott Marrin Inc.,
1-SS30-590-DAT) and 1.6 mm SS tubing are used to connect tanks to the gas
delivery system. On the outlet of the sample pump an overpressure is
maintained on the inlet to a Nafion drier (Perma Pure LLC, MD-110-144S-4)
with the excess flow vented at this point; this removes the bulk of the water
content from the sample flow. The air or selected calibration gas then passes
through a magnesium perchlorate trap to ensure all the same low water content
for gas being introduced to the analyser by a 100 sccm mass flow controller
(McMillian, 80SD-5). One of the field standards is also used in the reference
cell as a reference gas and is controlled using a similar mass flow
controller (McMillian, 80SD-3) at 10 sccm. The exhaust sample and reference
gas are joined together and then dried again on molecular sieve trap before
acting as the counter flow on the Nafion drier; in this way dew points of
-65 ∘C are consistently met.
The data acquisition system selects the calibration gas to measure and
monitors each field standard for stability to optimize the gas consumption.
When a field standard has a standard deviation of less than 0.015 ppm over a
minute it is defined as stable and the next gas is measured. Sample air is
continuously measured with 5 min averages collated and reported. Every
4–6 h the suite of four field standards is measured. A target–archive tank
is measured every 23 h. Each week the field standards and target tank are
measured as a separate aliquot multiple times. This sampling sequence is akin
to the calibration protocol employed by Brailsford et al. (2012) and Stephens
et al. (2011). Data processing is performed by Lauder LI-7000 specific
scripts adapted from those used by Stephens et al. (2011) and written in the
free statistical analysis software R.
Allan variance measurements (Allan, 1966) show the precision of the Lauder
LI-7000 system as 0.004 ppm (1σ in 5 min). Calibration of the
LI-7000 is obtained by fitting a third-order polynomial to the measurements of
the four field standards to characterize the concentration-dependent
non-linear response of the instrument every 4–6 h. This calibration curve is
then used to calibrate sample air measurements, putting the measurements on
the WMO X2007 scale. Thirty-minute zero offsets are calculated using the
interspersed individual field standard measurements. Instrument-dependent
artefacts (e.g instrument temperature and flushing times) are accounted for
in the processing code by calculating a linear fit of known field standard
concentrations and the parameter in question.
The field standards and the target tank are filled at BHD and characterized
at the NIWA GASLAB, Greta Point, Wellington, New Zealand. In preparing the
field standards mixtures are prepared to evenly span the typical air sample
concentration range encountered, including elevated nocturnal levels (typical
span of 380–450 ppm). The field standard CO2 concentrations are
calibrated to the WMO X2007 scale, along with δ13C–CO2 (VPDB
referenced to the WMO CCL scale) (Brailsford et al., 2012). Field standards
require changing every 12–18 months. The target tank requires changing every
6–12 months as in parallel it also functions as a target tank for the FTIR
trace gas analyser.
Meteorological sensors
Meteorological sensors were installed onto the sampling mast. Wind speed is
measured at three heights (2.8, 5.8, and 10.1 m) using Vector instruments
A100LK anemometers. A Vector instruments W200P wind vane mounted at a height
of 10.1 m is used to record wind direction. Relative humidity and
temperature are measured using Vaisala Humitter 50U/50Y sensors, placed at
heights of 2.6 and 9.9 m. In addition, a Vaisala PTB100 analog barometer was
installed adjacent to the in situ instruments (inside the building). All
these sensors are connected to a Campbell CR10X data logger and SDM-INT8
logger module. Ten-minute averages of all sensor output are recorded
independently of in situ gas measurement instrumentation output.
Baseline analysis
A CO2 baseline is constructed as a weighted average of a southern and
northern baseline, which takes into account whether the modelled
trajectories originated to the north or south of the inversion domain for a
given data point.
Southern baseline
The southern baseline represents a continuous record of steady background
CO2 mole fractions during southerly wind conditions at BHD. A multi-step
filter is applied to the BHD record to obtain a CO2 baseline
representative of a large region over the Southern Ocean, as described by
Brailsford et al. (2012) and Stephens et al. (2013). In short, the filter
selects measurements during extended periods of southerly winds at the site,
during which a maximum standard deviation of 0.1 ppm is achieved. Additional
meteorological conditions must be fulfilled to preclude the influence of
local sources and to ensure the air has not passed over the South Island
before arriving at BHD. The result of this filtering process is similar to
selecting observations from the southern cluster in Fig. 4. The southern
baseline based on the filtering is used in this study.
After filtering the data for baseline conditions, a continuous baseline is
constructed using the STL
algorithm (Cleveland et al., 1990), which allows estimation of a long-term
trend and interannually varying seasonal patterns. The STL algorithm uses two
time windows for the seasonal cycle and the trend, which are set by the user
and define the respective time periods over which variations in the data are
considered. The monthly averaged data fulfilling the baseline conditions are
used as input, and the algorithm is run first with a seasonal cycle window of
5 years and a trend window of 121 months to single out the decadal trend.
This trend is then removed before a second run with a trend window of 25
months to capture the interannual and seasonal patterns. Finally the decadal,
interannual, and seasonal time series are summed and the resulting baseline
subsampled at the 13:00–14:00 and 15:00–16:00 LT windows.
The remainder time series, which is the difference between the monthly
record and the sum of the seasonal and trend components from the STL
analysis, is used as uncertainty estimate for the southern baseline.
Northern baseline
The northern baseline is based on in situ CO2 observations using a NDIR
analyser on board the TF5, a ship of opportunity that cruised the triangle
Japan–Australia–New Zealand about once a month during the period 2011–2013
(Chierici et al., 2006). The cluster analysis showed that during northerly
events the air is usually coming from the northern Tasman Sea or the
subtropical waters to the north and only occasionally from the South Pacific
eastward of New Zealand. The layout of the TF5 cruises with legs crossing the
Tasman Sea as well as subtropical legs therefore offers the possibility to
characterize the CO2 concentrations in these regions with monthly
resolution. The TF5 dataset provides CO2 concentrations averaged over
10 min intervals along with the standard deviation of the high-frequency
measurements within these intervals.
We defined a regional mask to keep observations from the open ocean and avoid
observations taken close to the land, especially near the Australian east
coast as it is located upwind during average south-westerly conditions and
hosts large urban centres with significant CO2 emissions. The mask spans
the latitudes 39 to 24∘ S. The mask was then further partitioned
into bands spanning 1∘ of latitude and the data within each band
averaged for each month in 2011–2013. The uncertainty of the resulting
monthly record is taken as the quadrature sum of the standard deviation of
high-frequency data points measured during the ship's transit of the
respective latitude band and the standard deviation of the 10 min data about
the monthly mean.
The monthly record within each latitude band was analysed using the same STL
routine as for the southern baseline. An overall uncertainty estimate was
formed by root mean square combination of the monthly uncertainty and the
time series of the remainder from the STL analysis. The remainder time series
is the difference between the monthly record and the sum of the seasonal and
trend components from the STL analysis. Finally, the STL baseline for the two
latitude bands for 27–26∘ S were averaged to produce a baseline
representative of the northern edge of the inversion domain at
26∘ S.
Weighted baseline
A day-to-day baseline was constructed as a weighted superposition of the
southern and northern baselines, with weights depending on the proportional
latitude of air origin for the twice-daily measurements at BHD and LAU. The
daily NAME station footprints for the 13:00–14:00 and 15:00–16:00 LT
windows were integrated along the southern and northern edges of the domain
to determine the relative fraction of back trajectories leaving the domain to
the south and north. These fractions are then used to weigh the two baselines
and create a baseline associated with each of the twice-daily data points.
Uncertainties are weighed in the same way.
For most days the 13:00–14:00 and 15:00–16:00 LT footprints are similar
with regard to the origin of the air, so the weighted baselines for both time
windows are almost identical. For both stations on a typical day the region
where the air originates is either clearly in the north or the south, so that
the weights for the southern and northern baselines are close to zero and
one, or vice versa. However, middle cases can occur when the wind conditions
at a site rapidly changed during the 1-hour period over which measurements
are collected, which often results in two main branches of trajectories
originating in the north and south, respectively. In this case both baselines
are weighted proportionally to reflect the mixed origin of air during the 1-hour averaging period for the measurements. However, other days, or periods of
days, are characterized by slow wind speeds, sometimes slow enough that most
back trajectories end before reaching either the northern or southern edge of
the domain. In this case, the midpoint of the footprint is determined and its
latitude used to proportionally weigh the baselines. The same procedure
applies for days with trajectories leaving the domain predominantly to the
west or the east.
Acknowledgements
The author(s) wish to acknowledge the contribution of New Zealand eScience
Infrastructure (NeSI) to the results of this research. New Zealand's national
compute and analytics services and team are supported by the NeSI and funded
jointly by NeSI's collaborator institutions and through the Ministry of
Business, Innovation and Employment (http://www.nesi.org.nz). In
addition, Kay Steinkamp, Gordon Brailsford, Dan Smale, and
Sara E. Mikaloff Fletcher would like to acknowledge NIWA core funding through
the Greenhouse Gases, Emissions and Carbon Cycle Science Programme. We thank
our colleagues from New Zealand's Ministry for the Environment (MfE),
especially the LUCAS team, for very fruitful meetings. None of this work
could have been accomplished without the station operation teams at
Baring Head and Lauder. We thank Hinrich Schaefer for valuable discussions
about this paper. We are grateful for access to radiosonde data from Lauder,
and would like to thank Ben Liley for his PBL calculations. We would also
like to thank Paul Wennberg and his TCCON team for the generous loan of their
Li-7000 that is currently at Lauder. The National Center for Atmospheric
Research is sponsored by the National Science Foundation. Edited by: C. Gerbig Reviewed by: two
anonymous referees
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