Representation of atmospheric transport is a major source of error in the estimation of greenhouse gas sources and sinks by inverse modelling. Here we assess the impact on trace gas mole fractions of the new physical parameterizations recently implemented in the atmospheric global climate model LMDz to improve vertical diffusion, mesoscale mixing by thermal plumes in the planetary boundary layer (PBL), and deep convection in the troposphere. At the same time, the horizontal and vertical resolution of the model used in the inverse system has been increased. The aim of this paper is to evaluate the impact of these developments on the representation of trace gas transport and chemistry, and to anticipate the implications for inversions of greenhouse gas emissions using such an updated model.

Comparison of a one-dimensional version of LMDz with large eddy simulations shows that the thermal scheme simulates shallow convective tracer transport in the PBL over land very efficiently, and much better than previous versions of the model. This result is confirmed in three-dimensional simulations, by a much improved reproduction of the radon-222 diurnal cycle. However, the enhanced dynamics of tracer concentrations induces a stronger sensitivity of the new LMDz configuration to external meteorological forcings. At larger scales, the inter-hemispheric exchange is slightly slower when using the new version of the model, bringing them closer to observations. The increase in the vertical resolution (from 19 to 39 layers) significantly improves the representation of stratosphere/troposphere exchange. Furthermore, changes in atmospheric thermodynamic variables, such as temperature, due to changes in the PBL mixing modify chemical reaction rates, which perturb chemical equilibriums of reactive trace gases.

One implication of LMDz model developments for future inversions of greenhouse gas emissions is the ability of the updated system to assimilate a larger amount of high-frequency data sampled at high-variability stations. Others implications are discussed at the end of the paper.

A better knowledge of biogeochemical cycles is fundamental to improving our
understanding of processes and feedbacks involved in climate change. Carbon
dioxide (CO

One of the methods to derive greenhouse gas sources and sinks is to adapt the
inverse problem theory to the atmospheric sciences: from observations
(in situ measurements, satellite retrievals, etc.) and a prior knowledge of
emissions, it is possible to derive emissions of a given greenhouse gas using
a Bayesian formalism together with an atmospheric chemistry-transport model
(CTM), or with a global climate model (GCM)

The consistency of inverse estimates of regional emissions by inverse
modelling is mostly dependent on: (a) the number, accuracy and
spatio-temporal coverage of observations constraining the inversion, (b) the
ability of the CTM to simulate atmospheric processes

In the PYVAR (Python variational) variational inverse framework

Paths to improve the representation of trace gas concentrations may differ
for “online” (GCM) and “offline” (CTM) models. “Offline” models use
external meteorological fields (from weather forecast centres) for inputs
inducing a dependence on meteorological model performances. Consequently,
intensive and consistent pre-processing of the meteorological fields is
necessary to ensure the quality and comparability of results. Indeed,

“Online” models compute their own transport and meteorology, using physical
conservation laws, and parameterize sub-grid scale transport processes at the
same time. Physical, dynamical and chemical processes interact directly and
consistently as they do in the real world. Consequently, efforts to improve
the atmospheric transport of “online” models are generally focused on the
physical and/or dynamical schemes. The efficacy of these developments can
then be evaluated using trace gas observations

A new version of the LMDz physics was recently developed and implemented

The aim of this paper is to evaluate the ability of the modified LMDz model
to transport trace gases, with the ultimate goal to use this new version to
estimate trace gas sources and sinks with inverse modelling. In
Sect.

The LMDz GCM is the atmospheric
component of the Institut Pierre-Simon Laplace Coupled Model (IPSL-CM) used
for climate change projections in the 3rd

The LMDz version used in the inverse system of

With these issues in mind,

Description of the sub-grid scale schemes used in the three configurations of LMDz (TD, SP and NP).

Concerning the parameterization of deep convection,
the

Concerning model resolution in the inverse system, the current version of
LMDz has a grid of 96 cells in longitude by 72 cells in latitude (about
3.75

We henceforth use “NP” to refer to the new physical
parameterization package of LMDz: the

Atmospheric transport of an ideal tracer simulated by three different versions of a single-column configuration of LMDz model (SCM-TD (top left), SCM-SP (top right) and SCM-NP (bottom left)). These one-dimensional simulations are compared with a Large Eddy Simulation simulation (bottom right) of Meso-NH. The tracer is emitted at the surface and has a half-time life of 15 min in the atmosphere. This case represents an ideal shallow convection over land case at the Southern Great Plains of the Atmospheric Radiation Measurement site on 21 June 1997. Black contours represent the total cloud cover (in fraction of the grid cell). Contour values are 0.025, 0.05, 0.1 and 0.2 %.

In this section, we investigate the ability of the LMDz model to represent
transport of tracers in the planetary boundary layer, first in one dimension
(Sect.

In recent decades, several comparisons have been made between single column
models (SCM) and large eddy simulations (LES) to better understand physical
parameterizations

Here we compare three
single-column versions of LMDz with LES from the non-hydrostatic model
Meso-NH

The three versions of LMDz-SCM used are differentiated by their deep
convection and vertical diffusion parameterizations. Firstly, SCM-TD uses the

During the 9 h LES (Fig.

Deep convection trends (in kgTracer/kgAir/hour) simulated in SCM-TD
(top left) and SCM-SP (top right) configurations are compared with thermal
trends simulated by SCM-NP in an ideal shallow convection over land case.
Positive (Negative) trends are represented in shades of red (blue,
respectively). Black contours represent tracer trends in the LES. Contour
values are 2.0, 1.0, 0.5 and 0.1 kg kg

The three LMDz-SCM versions exhibit markedly different skills in their
attempt to reproduce the LES reference case. In the SCM-TD and SCM-SP
simulations, the tracer is confined within a 500 m layer near the surface
until 9 a.m. (SCM-TD) or 12 p.m. (SCM-SP). In SCM-TD, some tracer mass
is transported vertically after 9 p.m., but tracer concentrations in the
upper layers stay much lower than in the LES case. Indeed, tracer mixing
ratios are insignificant at 2000 m after 11 a.m., while they reach
3.5 kg kg

Moreover, we
notice that the cloud fraction (black contours) is underestimated in SCM-NP.
Indeed, SCM-NP simulates a cloud fraction of 5 % compared to 20 % in the
LES. It is known that cumulus cause a venting of the boundary-layer air as
shown in

Description of the different surface stations used for the evaluation of 3-D simulations of

Radon-

Radon-

We hereafter define trends as the change of mixing ratios (or activity for

After a promising one-dimensional evaluation (Sect.

Comparisons of

Figure

For most stations, the box width (representing the interquartile range) is relatively similar between observed and simulated values. However, large differences occur in the extreme values (black circles). The observed high concentration “outliers” correspond primarily to pre-dawn measurements on stable nights when PBL heights are lowest. A considerably better agreement is generally found between observed outliers and those simulated by NP, meaning that the NP simulation is better at representing mixing during the night associated with stable atmospheric conditions. On the other hand, nocturnal mixing in TD results in considerable underestimation of the outlier radon values.

More precisely, skills of TD and NP simulations may be very different
at some specific stations. For example, NP simulation gives remarkably good
results at Heidelberg (HEI) station where NP is able to reproduce the strong
diurnal cycle of

Here, we illustrate some typical LMDz behaviours in two case studies: at
Heidelberg (Germany) in April 2009 (Figs.

Time series of

Time series of advection (black), vertical diffusion (red),
convection (blue) and thermal (green) trends at Heidelberg station in the
beginning of April 2009 for NP simulation (top) and TD simulation (bottom).
The unit is Bq m

At Heidelberg in April 2009 (Fig.

To better understand the contrasting behaviour of the two LMDz versions here,
we compare time series of process trends for the first half of April 2009 at
Heidelberg (Fig.

For TD, the vertical diffusion trend is relatively similar to NP, although
the magnitude of its diurnal cycle is 60 % smaller. Indeed, maximum
(minimum) of the vertical diffusion trend does not exceed
0.4 Bq m

Time series of

It is also shown that thermals have no major impact on the amplitude of

Time series of advection (black), vertical diffusion (red),
convection (blue) and thermals (green) trends at Lutjewad station in the
beginning of February 2008 for NP simulation (top) and TD simulation
(bottom). The unit is Bq m

Despite the differences noted above,

We now investigate this issue with the example of radon time series at
Lutjewad in February 2008 (Fig.

We
have also looked at surface characteristics for the Heidelberg case
(Fig. S3). Both simulations (NP and TD) are able to reproduce the diurnal
cycle of

In this section, we investigate the ability of different LMDz versions to
represent large-scale (e.g. inter-hemispheric and troposphere/stratosphere)
trace gas exchanges. To do so, we focus on longer-lived species, such as the
inert sulfur hexafluoride (SF

SF

Here we employ monthly zonal averages of SF

First of all, we detail the general transport pathway
for the three configurations of LMDz. Vertical diffusion (including thermal
plumes in NP) mixes SF

Zonal and monthly average of SF

General characteristics of large-scale transport look similar between model
versions but we can wonder how the three configurations of the model (NP, TD
and SP) differ. Looking at the transport in the boundary layer, the main
difference is the height at which mixing due to vertical diffusion (and
thermals for NP configuration) transports SF

The inter-hemispheric (IH) exchange of trace gases simulated by CTMs is very
valuable indicators of how atmospheric transport performs at the global
scale. For example,

Annual means of SF

Knowing that the majority of
SF

SF

Overall, SF

Meridional transport may not be the only cause for IH gradient of trace
gases. Indeed, the so-called rectifier effect, introduced by

The Fig. 9b displays the impact of this rectifier effect on the
meridional CO

Furthermore, it is noticeable to see that the different configurations of
LMDz simulate oscillations in CO

Unfortunately, large uncertainties still exist on the knowledge of the
magnitude and of the spatial distribution of the actual rectifier effect.
Indeed, observations of covariance signals between CO

Finally, one can hardly conclude from these incomplete
pieces of information which version of LMDz better simulates the rectifier
effect. However, these differences in the representation of the rectifier
effect will definitely have large impacts on inverse estimates (see
Sect.

Exchanges between the stratosphere and troposphere may have large impacts on
atmospheric concentrations of trace gases

To investigate this statement, we present comparisons of the SF

Percentage (%) of the total atmospheric SF

First of all, it is found that differences between versions using different
vertical resolutions are much larger than differences between LMDz versions
using the same vertical resolution but different physical parameterizations.
Indeed, it is found that 10.9 % of total SF

Moreover, we have computed the tropopause height using the method described
in

Finally, these different results confirm that STE is slowed down in LMDz configurations using a finer vertical resolution (39 versus 19 vertical levels), which goes in the good direction. However, a validation of the new STE is necessary and will be performed in a future work.

Changes in parameterizations of atmospheric processes in GCMs can also affect
atmospheric chemistry

Time series of global-mean CH

Figure

We further investigate the
difference between TD/SP and NP CH

We have investigated impacts of new physical parameterizations for deep
convection

These results for the PBL are encouraging for future atmospheric inversions
of greenhouse gas emissions. When integrated in our inversion framework, this
new version of LMDz will allow us to assimilate a larger fraction of the
high-frequency data (daily, and maybe hourly) sampled at surface stations
located close to source areas, which often show large peaks of concentrations
on hourly to weekly timescales. Such data is not well simulated by the previous
version of LMDz-SACS and therefore are either removed or associated with a
very large uncertainty in the inverse procedure. However, the higher
sensitivity of NP version to the external forcing (see Sect.

The skill of LMDz to represent large-scale transport has also been studied
using a long-lived trace gas (SF

In global inversions, these changes in large-scale transport have large
impacts on the derived fluxes. For example, IH exchange is fundamental to
derive reasonable balance between Northern and Southern Hemisphere emissions.
In particular,

Moreover, assimilation of constraints on the vertical profile (aircraft profiles, total-column satellite data, etc.) should less suffer from any potential biases related to the modelling of high-tropospheric/low-stratospheric concentrations.

Changes in modelled atmospheric lifetimes exhibited by reactive species like
methane could lead to new biases in atmospheric concentrations, potentially
influencing flux estimates from inversion studies. One could consider that a
very slight nudging of temperature could reduce this bias. However, this
possibility may be moderated by the risk of partly removing convection mixing
when stabilizing the temperature profile and by the fact that the OH mean
value is also poorly known

Finally, this work has been driven by the need to reduce the impact of transport model errors in global inversions for estimation of greenhouse gas emissions and sinks. Such efforts are required in order to avoid a situation where CTM weaknesses become the main limiting factor in the reduction of uncertainties in the estimates of greenhouse gas emissions.

The Fortan code of LMDz model is available from the following website:

Robin Locatelli is supported by DGA (Direction Générale de l'Armement) and by CEA (Centre à l'Energie Atomique et aux Energies Alternatives). The LMDz simulations were done using computing resources provided by the CCRT/GENCI computer centre of the CEA. We would like to thank Rebecca Fisher, Ingeborg Levin, Juha Hatakka and Ernst Brunke for their contributions to produce measurements of radon-222 at Egham, Heidelberg, Pallas and Cape Point stations. We would also like to thank Frédéric Chevallier for fruitful discussions.Edited by: F. O'Connor