Page 1
Biogeosciences, 19, 4671–4704, 2022
https://doi.org/10.5194/bg-19-4671-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research
ar
ticle
Evaluation of soil carbon simulation in CMIP6
Earth system models
Rebecca M. Varney
1
, Sarah E. Chadburn
1
, Eleanor J. Burke
2
, and Peter M. Cox
1
1
Department of Mathematics and Statistics, Faculty of Environment, Science and Economy,
University of Exeter, Laver Building, North Park Road, Exeter, EX4 4QE, UK
2
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
Correspondence:
Rebecca M. Varney (r.varney@exeter.ac.uk)
Received: 14 January 2022 – Discussion started: 24 January 2022
Revised: 29 July 2022 – Accepted: 18 August 2022 – Published: 5 October 2022
Abstract.
The response of soil carbon represents one of the
key uncertainties in future climate change. The ability of
Earth system models (ESMs) to simulate present-day soil
carbon is therefore vital for reliably estimating global carbon
budgets required for Paris Agreement targets. In this study
CMIP6 ESMs are evaluated against empirical datasets to as-
sess the ability of each model to simulate soil carbon and
related controls: net primary productivity (NPP) and soil car-
bon turnover time (
τ
s
). Comparing CMIP6 with the previ-
ous generation of models (CMIP5), a lack of consistency in
modelled soil carbon remains, particularly the underestima-
tion of northern high-latitude soil carbon stocks. There is a
robust improvement in the simulation of NPP in CMIP6 com-
pared with CMIP5; however, an unrealistically high correla-
tion with soil carbon stocks remains, suggesting the poten-
tial for an overestimation of the long-term terrestrial carbon
sink. Additionally, the same improvements are not seen in
the simulation of
τ
s
. These results suggest that much of the
uncertainty associated with modelled soil carbon stocks can
be attributed to the simulation of below-ground processes,
and greater emphasis is required on improving the represen-
tation of below-ground soil processes in future developments
of models. These improvements would help to reduce the un-
certainty in projected carbon release from global soils under
climate change and to increase confidence in the carbon bud-
gets associated with different levels of global warming.
1
Introduction
Soil carbon is the Earth’s largest terrestrial carbon store,
with a magnitude of at least 3 times the amount of car-
bon contained within the atmosphere (Jackson et al., 2017).
The response of soil carbon to CO
2
-induced global warm-
ing has the potential to provide a significant feedback to cli-
mate change, but this feedback is currently poorly known
(Friedlingstein et al., 2006; Gregory et al., 2009; Arora et al.,
2013; Friedlingstein et al., 2014; Arora et al., 2020; Song
et al., 2021). Carbon stored within the atmosphere and global
soils is exchanged via carbon fluxes as part of the global car-
bon cycle (Canadell et al., 2021). The Earth’s terrestrial sur-
face has acted as a carbon sink until now (Pan et al., 2011),
but there is a possibility of a switch to a source during the
21st century, which would accelerate climate change (Cox
et al., 2000; Crowther et al., 2016). Due to the significant
quantities of carbon stored in soils globally, understanding
and quantifying the potential release of carbon from soils is
vital if the existing Paris Agreement targets are to be met
(UNFCCC, 2015).
Earth system models (ESMs) are complex numerical mod-
els which simulate both climate and carbon cycle processes
and are used to make projections of climate change. The lat-
est generation of the Coupled Model Intercomparison Project
(CMIP), CMIP6 (Eyring et al., 2016), includes an ensemble
of ESMs, which are used in the most recent Intergovernmen-
tal Panel on Climate Change (IPCC) report (AR6) (IPCC,
2021). The relationships between carbon and environmental
drivers used in models help to determine the response of the
carbon cycle to climate change (Todd-Brown et al., 2013).
Published by Copernicus Publications on behalf of the European Geosciences Union.
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R. M. Varney et al.: Evaluation of soil carbon simulation in CMIP6
Therefore, representing present-day carbon stores and spa-
tial controls realistically within models is key for estimating
carbon emission cuts required for Paris Agreement targets
(Friedlingstein et al., 2022).
Present-day soil carbon can be approximately broken
down into above-ground and below-ground controls, which
influence the spatial distribution of soil carbon stocks (Koven
et al., 2015). The above-ground control of soil carbon can be
considered as the input flux of carbon into the soil from veg-
etation. Both the amount of carbon from plant and root litter
(known as litterfall) and the fraction of this that is converted
to longer-lived soil carbon pools will influence the storage of
soil carbon. Net primary productivity (NPP) can be used as a
proxy for the litterfall flux, where the fluxes are equal when
vegetation is in a steady state. The below-ground control of
soil carbon can be quantified simply in terms of the soil car-
bon turnover time (
τ
s
), which is defined as the time carbon re-
sides in the soil (Koven et al., 2017; Carvalhais et al., 2014).
τ
s
can be considered as a proxy for below-ground controls on
soil carbon storage (Koven et al., 2015).
In this study, the representation of late 20th century soil
carbon stores and these related controls (NPP and
τ
s
) is eval-
uated in CMIP6 ESMs. Previously, similar studies have been
conducted to evaluate soil carbon in the preceding genera-
tions of ESMs, for example, Anav et al. (2013) and Todd-
Brown et al. (2013) for CMIP5. There are some existing
CMIP6 soil-carbon-related studies; for example, Arora et al.
(2020) evaluated carbon-concentration and carbon-climate
feedbacks in 1 % CO
2
yr
−
1
forcing simulations, Burke et al.
(2020) evaluated the representation of permafrost in mod-
els, and Ito et al. (2020) investigated future soil carbon
stocks under specific land use conditions. This study is the
first to specifically focus on global and spatial soil carbon
and related controls in CMIP6, with a thorough evaluation
against empirical datasets and comparison against the pre-
ceding CMIP5 ensemble.
2
Methods
2.1
Earth system models
Soil carbon stores and related controls are examined in 11
CMIP6 ESMs (Eyring et al., 2016; Meehl et al., 2014), as
listed in Table 1. Throughout the study, comparisons are
made with 10 ESMs from the previous CMIP generation
(CMIP5, Taylor et al., 2012), as listed in Table 2. The ESMs
included in this study were chosen due to the availability
of the required data in the online repository at the time
of analysis (https://esgf-node.llnl.gov/search/cmip6/ (last ac-
cess: 8 April 2022) https://esgf-node.llnl.gov/search/cmip5/,
(last access: 12 April 2022)).
Tables 1 (CMIP6) and 2 (CMIP5) present information
about the included ESMs, specifically more details about
the associated land surface model (LSM). It should be
noted that there are similarities between some of the LSMs
– either advances from earlier models or even the same
LSM within different ESMs. For example, CESM2 and
NorESM2-LM both use the Community Land Model ver-
sion 5 (CLM5) (Arora et al., 2020). For some modelling
centres, both the CMIP5 and CMIP6 versions of the mod-
els are included, and in these cases direct comparisons can
be made to determine changes from CMIP5 to CMIP6.
These generationally related CMIP5 and CMIP6 models are
CanESM2 and CanESM5, CCSM4 and CESM2, GFDL-
ESM2G and GDFL-ESM4, IPSL-CM5A-LR and IPSL-
CM6A-LR, MIROC-ESM and MIROC-ES2L, MPI-ESM-
LR and MPI-ESM1.2-LR, NorESM1-M and NorESM2-
LM, and HadGEM2-ES and UKESM1-0-LL, respectively.
The models where only either the CMIP5 or CMIP6 ver-
sion from the modelling centre was included are BNU-
ESM and GISS-E2-R from CMIP5 and ACCESS-ESM1.5,
BCC-CSM2-MR, and CNRM-ESM2-1 from CMIP6. A key
general change to note is that CMIP6 has more models
that include an interactive nitrogen cycle compared with
CMIP5: ACCESS-ESM1.5, CESM2, MIROC-ES2L, MPI-
ESM1.2-LR, NorESM2-LM, and UKESM1-0-LL in CMIP6
compared with CCSM4 and NorESM1-M in CMIP5 (the
CMIP5 model BNU-ESM includes carbon–nitrogen interac-
tions; however, this process was turned off in CMIP5 simu-
lations; Ji et al., 2014). Additionally, an increased number
of soil carbon pools is seen in some CMIP6 models (e.g.
CLM5 has 29 carbon pools compared with 20 in CLM4).
Arora et al. (2020) include a comprehensive overview of the
updates seen in the individual CMIP6 models, which is pre-
sented in the “Model descriptions” section of the associated
Appendix.
Todd-Brown et al. (2013) include a summary of the tem-
perature and moisture dependencies of soil respiration/de-
composition as assumed in the CMIP5 models (see Table 1 in
Todd-Brown et al., 2013). The most common representation
of the temperature sensitivity of decomposition is the
Q
10
equation, which is defined by
f (T )
=
Q
(T
−
T
0
)/
10
10
, where
T
is temperature and
T
0
is a reference temperature. With the
Q
10
equation, decomposition increases exponentially with
temperature (Davidson and Janssens, 2006). The majority of
the other models used the Arrhenius equation to represent the
temperature sensitivity, where the main difference from the
Q
10
representation is that decomposition levels off at higher
temperature levels (Lloyd and Taylor, 1994). Of the remain-
ing models, the GFDL model simulates an increased decom-
position with temperature until some optimal temperature
above which it decreases (Shevliakova et al., 2009) (which
Todd-Brown et al., 2013, defined as a “hill” function) and
the GISS model implements a linear increase in respiration
to temperature up to a maximum value (Del Grosso et al.,
2005). The representation of the decomposition sensitivity
to soil moisture was found to be represented in two ways
amongst the CMIP5 models, where decomposition was as-
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Table 1.
The 11 CMIP6 Earth system models included in this study and relevant features of their land carbon cycle components (Arora et al.,
2020).
Earth system
Modelling
Land surface
Nitrogen
No. of live
No. of dead
Temperature
References
model
centre
model
cycle
carbon pools
carbon pools
and moisture
ACCESS-ESM1.5
CSIRO
CABLE2.4
Yes
3
6
Arrhenius
Ziehn et al. (2020)
+
CASA-CNP
and hill
Haverd et al. (2018)
Trudinger et al. (2016)
BCC-CSM2-MR
BCC
BCC-AVIM2
No
3
8
Hill
Wu et al. (2019)
and hill
Ji et al. (2008)
CanESM5
CCCma
CLASS-CTEM
No
3
2
Q
10
Swart et al. (2019)
and hill
Melton et al. (2020)
Seiler et al. (2021)
CESM2
CESM
CLM5
Yes
22
7
Arrhenius
Danabasoglu et al. (2020)
and increasing
Lawrence et al. (2019)
CNRM-ESM2-1
CNRM
ISBA-CTRIP
No
6
7
Q
10
Séférian et al. (2019)
and increasing
Delire et al. (2020)
GFDL-ESM4
GFDL
LM4.1
No
6
4
Hill
Dunne et al. (2020)
and increasing
Zhao et al. (2018)
IPSL-CM6A-LR
IPSL
ORCHIDEE
No
8
3
Q
10
Boucher et al. (2020)
branch 2.0
and increasing
Cheruy et al. (2020)
Guimberteau et al. (2018)
MIROC-ES2L
JAMSTEC
MATSIRO
Yes
3
6
Arrhenius
Hajima et al. (2020)
VISIT-s
and increasing
Ito and Oikawa (2002)
MPI-ESM1.2-LR
MPI
JSBACH3.2
Yes
3
18
Q
10
Mauritsen et al. (2019)
and increasing
Goll et al. (2017)
Goll et al. (2015)
NorESM2-LM
NCC
CLM5
Yes
22
7
Arrhenius
Seland et al. (2020)
and increasing
Lawrence et al. (2019)
UKESM1-0-LL
UK
JULES-ES-1.0
Yes
3
4
Q
10
Sellar et al. (2020)
and hill
Wiltshire et al. (2021)
sumed either to increase monotonically with increasing soil
moisture or less commonly to increase to some optimum
moisture level and then decrease (again described as a “hill”
function by Todd-Brown et al., 2013). In this study we note
that the representation of temperature and moisture functions
remains similar from CMIP5 to CMIP6. The
Q
10
equation
remains the most common representation of soil temperature
sensitivity in models, followed by the Arrhenius equation and
then “hill” functions. Similarly, the most common represen-
tation of the sensitivity of soil to moisture in CMIP6 is a
monotonically increasing function, followed by “hill” func-
tions of various sorts.
2.2
Defining soil carbon variables
CMIP defines common output variables (Meehl et al., 2000),
which allows for consistent comparison between the models
and for cleaner evaluation of models against observational
data. These common output variables also allow for con-
sistent comparison between model generations, in this case
between CMIP6 and CMIP5. This study focuses on evalu-
ation of near-present-day soil carbon and related controls.
Therefore the results presented in this study use the CMIP
standard historical simulation (CMIP scenario
historical
) for
both the CMIP6 and CMIP5 analyses. The historical simula-
tion runs from 1850 to 2015 in CMIP6 and from 1850 to 2005
in CMIP5, where the selected dates for each variable (stated
below) were chosen to allow for consistent comparison be-
tween CMIP5 and CMIP6 and to best match the modelled
data to the empirical data.
To evaluate soil carbon, this study uses “Soil Carbon”
(CMIP variable cSoil), which represents the carbon stored in
soils, and where applicable “Litter Carbon” (CMIP variable
cLitter), which represents carbon stored in the vegetation lit-
ter. Total soil carbon (
C
s
) is defined as the sum of these soil
carbon and litter carbon variables (cSoil
+
cLitter), where
for models that do not report a separate litter carbon pool,
the total soil carbon is taken to be simply the cSoil variable.
This allows for a more consistent comparison between the
models and between the models and empirical data due to
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R. M. Varney et al.: Evaluation of soil carbon simulation in CMIP6
Table 2.
The 10 CMIP5 Earth system models included in this study and relevant features of their land carbon cycle components (Arora et al.,
2013; Anav et al., 2013; Friedlingstein et al., 2014), including temperature and moisture functions presented in Todd-Brown et al. (2013).
Earth system
Modelling
Land surface
Nitrogen
No. of live and dead
Temperature
References
model
centre
model
cycle
carbon pools
and moisture
BNU-ESM
BNU
CoLM
+
Yes
–
Q
10
Ji et al. (2014)
BNU-DGVM
and increasing
Dai et al. (2003)
CCSM4
CCSM
CLM4
Yes
20
Arrhenius
Gent et al. (2011)
and increasing
Lawrence et al. (2011)
CanESM2
CCCma
CLASS2.7
No
5
Q
10
Arora et al. (2009)
+
CTEM1
and hill
Arora and Boer (2010)
GFDL-ESM2G
GFDL
LM3
No
10
Hill
Dunne et al. (2012)
and increasing
Dunne et al. (2013)
Shevliakova et al. (2009)
GISS-E2-R
NASA-GISS
YIBs,
No
12
Increasing
Schmidt et al. (2014)
version 1.0
and increasing
Yue and Unger (2015)
HadGEM2-ES
MOHC
JULES
+
No
7
Q
10
Jones et al. (2011)
TRIFFID
and hill
Best et al. (2011)
Clark et al. (2011)
IPSL-CM5A-LR
IPSL
ORCHIDEE
No
7
Q
10
Dufresne et al. (2013)
and increasing
Krinner et al. (2005)
MIROC-ESM
JAMSTEC
MATSIRO
No
6
Arrhenius
Watanabe et al. (2011)
+
SEIB-DGVM
and increasing
Ito and Oikawa (2002)
Sato et al. (2007)
MPI-ESM-LR
MPI
JSBACH
No
6
Q
10
Raddatz et al. (2007)
+
BETHY
and increasing
Knorr (2000)
NorESM1-M
NCC
CLM4
Yes
20
Arrhenius
Bentsen et al. (2013)
and increasing
Iversen et al. (2013)
Lawrence et al. (2011)
differences in how soil carbon and litter carbon are simulated
(Todd-Brown et al., 2013; Arora et al., 2020). Modelled
C
s
is
time-averaged between the years 1950 and 2000 of the his-
torical simulation and is considered spatially (kg m
−
2
) and
as global totals (PgC), where global totals are calculated as
an area-weighted sum using the model land surface fraction
(CMIP variable sftlf). To calculate northern latitude totals, a
sum between the latitudes 60 and 90
◦
N is considered.
The CMIP6 ESMs CESM2 and NorESM2-LM have two
different variables to represent soil carbon: (1) CMIP vari-
able cSoil, which represents the full vertical soil profile, and
(2) CMIP variable cSoilAbove1m, which represents soil car-
bon in the top 1 m of soil. This is due to the representation of
vertically resolved soil carbon in these models, which means
that there are separate carbon pools in the model that repre-
sent different soil depths (Lawrence et al., 2019). The CMIP
variable cSoilAbove1m is used throughout this study to rep-
resent soil carbon for the models CESM2 and NorESM2-
LM, unless otherwise stated. The use of this variable is to
enable a more consistent comparison with both the other
CMIP6 models and the CMIP5 models. Therefore, an as-
sumption of a 1 m depth of soil for modelled soil carbon al-
lows for the fairest evaluation, and evaluation is considered
against empirical datasets down to a depth of 1 m (see be-
low). However, comparisons with the cSoil variable for both
CESM2 and NorESM2-LM are included in Tables 4 and 6 of
the results.
In order to obtain a clean separation between above-
ground and below-ground drivers of soil carbon variations,
a quasi-equilibrium approximation is made. We begin with
the definition of the effective soil carbon turnover time (
τ
s
)
(Varney et al., 2020; Koven et al., 2017; Carvalhais et al.,
2014), which represents the average time carbon resides in
the soil:
τ
s
=
C
s
R
h
,
(1)
where
R
h
is the output flux of carbon from the soil known as
the heterotrophic respiration, which is described as the car-
bon loss from the decomposition by microbes. This defini-
tion of the turnover time implicitly neglects other processes
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which result in soil carbon release but which are not yet rou-
tinely included in ESMs (e.g. peat fires or dissolved organic
carbon fluxes).
The definition of the effective turnover time (Eq. 1) en-
sures that the soil carbon at any one time is given by
C
s
=
R
h
τ
s
. In an unperturbed steady state (i.e. neglecting distur-
bances from land use change, fires, insect outbreaks, etc.),
there is no net exchange of carbon between land and atmo-
sphere, and therefore
R
h
is equal to litterfall, known as fallen
organic material from plants. When vegetation and soil car-
bon are close to a steady state, litterfall and
R
h
are also ap-
proximately equal to NPP, where NPP is defined as the net
carbon assimilated by plants via photosynthesis minus loss
due to plant respiration. In the contemporary period consid-
ered in this study,
R
h
has been found to be well approximated
by NPP (Varney et al., 2020). This is because the difference
between NPP and
R
h
, which represents the net ecosystem
productivity (NEP), is a small fraction of the NPP over the
historical period (NPP
≈
60 PgC yr
−
1
; NEP
≈
3 PgC yr
−
1
).
Therefore the present-day soil carbon can be approximated
by
C
s
≈
NPP
τ
s
(2)
to a good accuracy. This allows for a clean separation of
soil carbon variation into the above (NPP) and below (
τ
s
)
ground drivers of soil carbon spatial patterns, following the
approach of previous published studies (Todd-Brown et al.,
2013; Koven et al., 2015).
To evaluate these soil carbon controls on
C
s
, NPP and
τ
s
are evaluated separately. This study uses modelled “net pri-
mary productivity” (CMIP variable npp), which is defined as
the mass flux of carbon out of the atmosphere due to NPP on
land. NPP is also considered spatially (kg m
−
2
yr
−
1
) and as
an area-weighted global total flux (PgC yr
−
1
). By definition,
τ
s
is defined by Eq. (1) and therefore is calculated by soil
carbon (as defined above) divided by
R
h
. For
R
h
, the vari-
able “heterotrophic respiration” (CMIP variable rh) is used,
which is defined as the mass flux of carbon into the atmo-
sphere due to heterotrophic respiration on land, primarily
due to the microbial respiration that occurs in the soil and
where the units of
R
h
are the same as those of NPP. The car-
bon fluxes (NPP and
R
h
) are time-averaged over the period
1995 to 2005 for consistency between the CMIP generations
and the empirical datasets.
τ
s
can be considered on a spatial
level or as an effective global
τ
s
, which is defined as the aver-
age
τ
s
=
mean(
C
s
)/mean(
R
h
) (where the mean represents an
area-weighted global average). The advantage of defining an
effective global
τ
s
is that it is not dominated by large spatial
outlying values. Using either method, the units for
τ
s
are in
years (yr) by definition.
The relationships of
C
s
, NPP, and
τ
s
with both tempera-
ture and soil moisture are also considered. For temperature,
the variable “near-surface air temperature” (CMIP variable
tas) representing atmospheric temperature at the surface is
considered, where the dates 1995 to 2005 were chosen to
be consistent with the carbon fluxes. The variable for atmo-
spheric temperature is considered as opposed to soil temper-
ature as equivalent global observational datasets are required
for the analysis. For soil moisture, the variable “moisture in
the upper portion of the soil column” (CMIP variable mrsos),
which is defined as the mass content of water in the soil layer
in the upper portion of the soil (0–10 cm depth), is consid-
ered, where the dates 1978 to 2000 were considered to match
the empirical data. The standard output mrsos is in units of
kg m
−
2
; however, in this study a volumetric soil moisture,
referred to as
θ
, is used to allow for consistent comparison
with the benchmark data.
θ
is calculated as mrsos divided
by the depth of the soil layer in millimetres, which in this
case is
θ
=
mrsos
/
100. The variable mrsos for soil mois-
ture was considered opposed to the full soil column moisture
(CMIP variable mrso) as this better matched the available
empirical dataset for soil moisture. It is noted that this rep-
resents surface soil moisture and does not match the depth
over which soil carbon is evaluated (0–1 m). This is due to
deeper soil moisture products not being as readily available
due to limitations of remote sensing methods in penetrating
deeper ground. It is expected that the surface soil moisture
will be related to deeper soil moisture to some extent but will
be influenced by different processes. For example, high sur-
face soil moisture after rainfall events could run off and thus
not always reach the deeper soil.
2.3
Empirical datasets
2.3.1
Soil carbon
Observational
C
s
to a depth of 1 m was obtained by com-
bining the empirical Harmonized World Soils Database
(HWSD) (FAO and ISRIC, 2012) and Northern Circumpolar
Soil Carbon Database (NCSCD) (Hugelius et al., 2013) soil
carbon datasets, where NCSCD was used where overlap of
the datasets occurs. This is a commonly used method when
considering empirical soil carbon and has been previously
used in multiple studies, such as Varney et al. (2020), Koven
et al. (2017), and Todd-Brown et al. (2013). This dataset is
referred to here as the “benchmark dataset”.
We use the 95 % confidence intervals given by Todd-
Brown et al. (2013) to derive standard deviations about the
global mean soil carbon. To do this, the constructed 95 %
confidence intervals were used to calculate upper and lower
bounds around the mean value. Then, assuming the data are
normally distributed, these derived 95 % confidence intervals
were halved to obtain confidence intervals equivalent to a
standard deviation error on the mean (1412
±
215 PgC). The
uncertainty analysis completed in Todd-Brown et al. (2013)
is used for the benchmark soil carbon dataset as no quantita-
tive uncertainty has been previously or since defined for the
HWSD and NCSCD datasets (Anav et al., 2013).
Additionally, the benchmark dataset was compared
with empirical estimates found in the literature to im-
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R. M. Varney et al.: Evaluation of soil carbon simulation in CMIP6
prove the robustness and reliability of the evaluation.
Todd-Brown et al. (2013) find that this derived uncertainty
is consistent with other empirical estimates of global soil
carbon, for example 1576 PgC in Eswaran et al. (1993),
1220 PgC in Sombroek et al. (1993), and 1502 PgC in Job-
bágy and Jackson (2000). This study further compares with
empirical estimates of 1395 PgC in Post et al. (1982) and
1515 PgC in Raich and Schlesinger (1992). These empirical
estimates are within 1 standard deviation of the global mean
soil carbon given by the benchmark dataset (Table 3).
Moreover, additional empirical datasets are considered to
improve the reliability of the benchmark dataset (Table 3).
These additional datasets include the following: (1) the
World Inventory of Soil property Estimates (WISE30sec)
dataset down to a depth of 2 m (Batjes, 2016), which includes
a given standard deviation on the global total soil carbon
consistent with our derived benchmark uncertainty, (2) the
named “S2017” from the Sanderman et al. (2017) soil car-
bon estimate (1 and 2 m), which uses a data-driven statisti-
cal model and the History Database of the Global Environ-
ment (HYDE) land use data, (3) the Global Soil Dataset for
use in Earth system models (GSDE), which provides esti-
mates for observational soil carbon down to a depth of up to
2.3 m (Shangguan et al., 2014), and (4) the Global Gridded
Surfaces of Selected Soil Characteristics (IGBP-DIS) esti-
mate of soil carbon to a depth of 1 m, derived by the Oak
Ridge National Laboratory Distributed Active Archive Cen-
tre (ORNL DAAC) (IGBP, 2000). These datasets were com-
bined to obtain a mean estimate for observational soil car-
bon down to a depth of 1 m, where a global total soil carbon
value of 1560
±
214 PgC was found. This estimate is con-
sistent with our benchmark dataset estimate and further im-
proves the confidence in our benchmark dataset.
Furthermore, the spatial correlation coefficients between
these additional datasets and our benchmark dataset are con-
sidered, where the following values correspond to the above
datasets: (1) 0.554, (2) 0.625, (3) 0.482, and (4) 0.622. Map
plots comparing the empirical soil carbon datasets are shown
in Fig. A1. The estimate for northern latitude total soil carbon
has greater uncertainties associated with it, where the stan-
dard deviation deduced by combining the empirical datasets
is
±
83 PgC. To account for this increased uncertainty in
these regions, the deduced standard deviation of
±
83 PgC
is used on the benchmark soil carbon throughout this study,
as opposed to the
±
61 PgC derived using the Todd-Brown
et al. (2013) uncertainty analysis.
2.3.2
Carbon fluxes
To estimate a benchmark NPP, the commonly used MODIS
NPP (2000–2010) dataset (Zhao et al., 2005) is used. The
MODIS NPP dataset does not have associated uncertainty
estimates, so this study estimates a standard deviation er-
ror on the benchmark NPP as derived by Ito (2011). The
MODIS NPP dataset is found to be consistent with 251
empirical present-day estimates of NPP found in the lit-
erature, which Ito (2011) used to estimate a global value
of 56.2
±
14.3 PgC yr
−
1
(compared with a derived MODIS
mean value of 56.6 yr
−
1
).
Moreover, due to the limited choice of observationally
derived NPP datasets (Harper et al., 2018), models can be
further evaluated using a benchmark dataset for
R
h
, where
R
h
is estimated using the CARDAMOM (2001–2010) het-
erotrophic respiration dataset (Bloom et al., 2015). The em-
pirical CARDAMOM
R
h
has associated estimates of er-
ror, which were used to derive a standard deviation uncer-
tainty on the empirical average
R
h
(51.7
±
21.8 PgC yr
−
1
).
This study includes map plots comparing the two empirical
datasets, which is shown in Fig. A2. Global totals for
R
h
are also considered for comparison against NPP, where the
CMIP6 and CMIP5 values are also shown in Appendix Ta-
bles A1 and A2, respectively.
2.3.3
Soil carbon turnover time
To estimate a benchmark
τ
s
, the estimates of observational
C
s
are divided by an estimate of
R
h
(see Eq. 1). To estimate
an uncertainty on the effective global
τ
s
, this study derived
upper (
τ
+
s
) and lower (
τ
−
s
) bounds based on the derived
C
s
and
R
h
uncertainty estimates. The upper bound was calcu-
lated using the following:
τ
+
s
=
C
+
s
/
R
−
h
, where
C
+
s
is equal
to the mean soil carbon plus 1 standard deviation and
R
−
h
is
equal to the mean heterotrophic respiration minus 1 standard
deviation. The lower bound was calculated using the follow-
ing:
τ
−
s
=
C
−
s
/
R
+
h
, where similarly
C
−
s
is equal to the mean
soil carbon minus 1 standard deviation and
R
+
h
is equal to the
mean heterotrophic respiration plus 1 standard deviation.
This method gives a large uncertainty bound around the
derived mean estimate (27.0
+
27
−
11
years), so the benchmark
data are further compared with empirical estimates. Raich
and Schlesinger (1992) derived an estimate of mean soil car-
bon turnover of 32 years, using estimates for mean soil car-
bon pools and mean soil respiration rates. More recently, Car-
valhais et al. (2014) derived an estimate for the mean global
ecosystem carbon turnover time of 23
+
7
−
4
years, which is a
spatially explicit and observation-based estimate. Ito et al.
(2020) derived an observational uncertainty range on soil
carbon turnover time of 18.5 to 45.8 years, which was de-
rived using similar empirical estimates found in the literature.
These estimates give more certainty on the values closer to
the derived empirical mean value for
τ
s
.
2.3.4
Soil moisture and air temperature
To estimate soil moisture (
θ
), the Copernicus Climate
Change Service (C3S) “Soil moisture gridded data from 1978
to present” dataset (published 25 October 2018) is used,
where the years 1978 to 2000 are considered. This dataset
is based on the ESA Climate Change Initiative soil mois-
ture and estimates global surface soil moisture from a large
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Table 3.
Table of global total and northern latitude total (northern latitudes defined as 60–90
◦
N) soil carbon estimates from multiple empirical
datasets, for varying soil depths where applicable.
Empirical
Depth
Global
Northern
Reference
dataset
total
latitude
C
s
(PgC)
total
C
s
(PgC)
HWSD
+
NCSCD
1 m
1412
±
215
401
±
61
FAO and ISRIC (2012)
Hugelius et al. (2013)
WISE30sec
1 m
1371
±
129
314
Batjes (2016)
2 m
1952
±
198
468
S2017
1 m
1966
515
Sanderman et al. (2017)
2 m
3141
893
GSDE
1 m
1682
526
Shangguan et al. (2014)
2.3 m
2593
849
IGBP DIS
1 m
1567
377
IGBP (2000)
set of satellite sensors (Copernicus Climate Change Service,
2021; Liu et al., 2011, 2012; Wagner et al., 2012; Gruber
et al., 2017; Dorigo et al., 2017). The WFDEI meteorological
forcing dataset is used to represent observational air temper-
atures (1995–2005) (Weedon et al., 2014), where dates are
chosen to allow for consistency between CMIP generations.
This study includes no uncertainty analysis of the soil mois-
ture and air temperature empirical datasets as these datasets
are only used to evaluate spatial correlations between vari-
ables and not to evaluate soil moisture and air temperature in
the models.
2.4
Regridding
To allow direct comparisons between the empirical data and
model output data, the model data were regridded to match
the observational grid. In this case, the observational grid is a
0.5
◦
by 0.5
◦
resolution, 720 longitude and 360 latitude grid.
The regridding was done using Iris – the community-driven
Python package for analysing and visualising Earth science
data (Met Office, 2010–2013). The regridding method as-
sumed conservation of mass and used linear extrapolation,
where extrapolation points will be calculated by extending
the gradient of the closest two points. Moreover, model land
masks are used to calculate the fraction of land in each
coastal grid cell (CMIP variable sftlf).
2.5
Statistical analysis
It is difficult to evaluate the spatial distributions of mod-
elled soil carbon and related spatial controls against empir-
ical data with a single metric, so the evaluation for both
CMIP6 and CMIP5 involves multiple methods. These in-
clude coefficients of variation, spatial standard deviations,
spatial Pearson correlation coefficients, and root mean square
errors (RMSEs). These methods can be combined to give a
more thorough evaluation of spatial soil carbon and associ-
ated controls in the CMIP6 models compared with the previ-
ous generation of CMIP5 models.
The coefficient of variation is defined as the ratio of the
ensemble standard deviation (SD) to the ensemble mean in
each grid cell. This is used to show the amount of variabil-
ity amongst the models in the ensemble scaled to the size of
the ensemble mean and so represents the variability spatially
in the ensemble and shows how much variation is present
across the ensemble in specific regions. It is presented as
hatching in a map figure (Fig. 3), where shaded “hatched”
regions show regions of high variability within the ensem-
ble. These regions show areas where there is disagreement
in the ensemble as there is a large spread compared with the
mean and was defined as being where SD
/
mean
>
0
.
75. The
regions where spatial
C
s
<
5 kg m
−
2
were discounted as low
values of soil carbon are present in these regions.
The spatial standard deviation is a measure of the spread
in the data across the globe compared with the mean value.
Pearson correlation coefficients (
r
values) were used as a spa-
tial measure of the linear correlation between the empirical
and modelled data, where a high
r
value (near 1 or
−
1) rep-
resents a high correlation in the data and a low
r
value (near
0) represents a negligible correlation. RMSEs were used as
an absolute measure of the difference between the modelled
and empirical data, where the lower the value, the lower the
difference error. The RMSE can be considered as the stan-
dard deviation of the difference, and it is a measure to show
the deviation of the modelled data in relation to the empirical
data. These statistical data, spatial standard deviations, Pear-
son correlation coefficients, and RMSEs can be presented us-
ing a Taylor diagram. A Taylor diagram is a graph used to
indicate the performance of a model compared with a bench-
mark, which in this case are the empirical datasets (Taylor,
2001).
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R. M. Varney et al.: Evaluation of soil carbon simulation in CMIP6
3
Results
3.1
Soil carbon stocks: northern latitude
underestimations remain in CMIP6
3.1.1
Global total evaluation
Global total soil carbon (in the top 1 m of soil) is shown to
vary amongst the ESMs in CMIP6, with a range of 1294 PgC
between the models with the lowest and highest values (Ta-
ble 4). The global total soil carbon for 2 (CanESM5 and
MIROC-ES2L) out of the 11 CMIP6 models falls within the
benchmark soil carbon uncertainty range, 1197–1627 PgC
(mean
±
stand deviation). The models with the largest global
total soil carbon are CNRM-ESM2-1 (1810 PgC), BCC-
CSM2-MR (1770 PgC), and UKESM1-0-LL (1760 PgC),
values greater than the benchmark dataset but not the addi-
tional empirical datasets (Table 3). The models GFDL-ESM4
(516 PgC) and IPSL-CM6A-LR (639 PgC) have the lowest
global total soil carbon values in the ensemble, with global
totals significantly lower (approximately 50 % less) than the
global totals seen in empirical data. It is noted that, if the full
soil carbon profile is considered for CESM2 and NorESM2-
LM opposed to a depth of 1 m, the global total soil carbon
values are increased to 1870 PgC from 991 PgC in CESM2
and to 2430 PgC from 969 PgC in NorESM2-LM.
Both the CMIP5 and CMIP6 ensemble mean global totals
fall within the benchmark uncertainty range (Tables 4 and
5). The ensemble mean global total soil carbon is found to
have decreased in CMIP6 from CMIP5 (1206
±
445 PgC vs.
1480
±
810 PgC). However, a significant reduction is seen
in the associated standard deviation of the ensemble mean
global totals in CMIP6 compared with CMIP5 (
±
445 PgC
in CMIP6 from
±
810 PgC in CMIP5) and a reduced range
of global total values (a range of 1294 PgC is seen in CMIP6
as opposed to 2493 PgC in CMIP5). This suggests that, al-
though a significant range in global soil carbon still exists
amongst the CMIP6 ESMs, there is an improved consistency
between the models seen in CMIP6 compared with the mod-
els in CMIP5, although it is noted that this may be a factor
of the selection of models included in each ensemble rather
than any change in process representation.
It is found from comparing the previous-generation mod-
els in CMIP5 with the updated CMIP6 equivalent that mul-
tiple models in CMIP6 have lower quantities of soil car-
bon than in CMIP5, such as GFDL-ESM4 from GFDL-
ESM2G, IPSL-CM6A-LR from IPSL-CM5A-LR, MIROC-
ES2L from MIRCO-ESM, and MPI-ESM1.2-LR from MPI-
ESM-LR. For example, the CMIP5 model MPI-ESM-LR is
reported to have the largest soil carbon magnitude amongst
the CMIP5 models, with a global total of 3000 PgC (Table 5),
whereas the updated CMIP6 model MPI-ESM1.2-LR has a
reduced global total soil carbon value of 970 PgC amongst
the lowest values reported in CMIP6 and below the observa-
tionally derived range (Table 4). Conversely, these reductions
are negated in the ensemble mean by the remaining mod-
els, which have greater quantities of soil carbon in CMIP6
compared with their CMIP5 equivalent, such as CanESM5
from CanESM2, CESM2 from CCSM4, NorESM2-LM from
NorESM1-M, and UKESM1-0-LL from HadGEM2-ES. For
example, the CMIP5 model NorESM1-M is amongst the
lowest soil carbon values presented in this ensemble at
538 PgC (Table 5), whereas the updated CMIP6 model
NorESM2-LM has an increased global total of 969 PgC
(down to 1 m) (Table 4).
3.1.2
Northern latitude total evaluation
Northern latitude soil carbon (down to a depth of 1 m and
where northern latitudes are defined as 60–90
◦
N) is found to
be underestimated in CMIP6, with 8 out of the 11 CMIP6
models having lower northern latitude soil carbon values
than the derived observational range (Table 4). A total of
2 of the 11 CMIP6 models (CNRM-ESM2-1 and MIROC-
ES2L) have northern latitude totals that fall within the uncer-
tainty range derived from the benchmark data, 318–484 PgC
(mean
±
stand deviation). The CMIP6 models with the great-
est northern latitude total soil carbon are BCC-CSM2-MR
(575 PgC), CNRM-ESM2-1 (440 PgC), and MIROC-ES2L
(347 PgC). The CMIP6 models with the lowest northern lat-
itude soil carbon are IPSL-CM6A-LR (66 PgC), ACCESS-
ESM1.5 (151 PgC), GFDL-ESM4 (163 PgC), MPI-ESM1.2-
LR (175 PgC), and UKESM1-0-LL (194 PgC), values signif-
icantly lower than the totals seen in empirical data.
The northern latitude soil carbon total was also under-
estimated in CMIP5, with 6 out of the 10 CMIP5 models
estimating northern latitude totals lower than the empirical
estimates (Table 5). The ensemble mean total northern lati-
tude soil carbon is lower in CMIP6 (266
±
139 PgC seen in
Table 4) than in CMIP5 (318
±
246 PgC seen in Table 5),
which is consistent with the global total results; however,
both the CMIP5 and CMIP6 mean values fall below the
benchmark range. Similarly, as with global soil carbon, a
smaller standard deviation on the mean is found for CMIP6
compared with CMIP5, and there is a reduced range in simu-
lated northern latitude total values amongst the CMIP6 mod-
els, where despite a large range seen (66 to 575 PgC), an even
greater range is seen in CMIP5 (28.1 to 742 PgC). More-
over, improvements are seen amongst models from CMIP5
to CMIP6. For example, the CMIP5 model NorESM1-M
had a northern latitude total soil carbon value of 31.0 PgC,
which is significantly lower than what is expected based
on the benchmark dataset (Table 5). However, the updated
CMIP6 version of this model, NorESM2-LM, has a northern
latitude total soil carbon value of 300 PgC, which is much
more in line with the expected observational values (Table 4).
An improved representation of northern latitude soil carbon
is also seen in CESM2 (compared with CCSM4), which
has the same land surface model as NorESM2-LM (CLM5
Lawrence et al., 2019).
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Table 4.
Table presenting global soil carbon values for the 11 CMIP6 models included in this study and the benchmark datasets, including
global total
C
s
in PgC, northern latitude total (90–60
◦
N)
C
s
in PgC, and the spatial mean value of
C
s
with the corresponding standard
deviation in kg m
−
2
.
Earth system model
Global total
C
s
(PgC)
Northern latitude total
C
s
(PgC)
Mean
C
s
±
SD (kg m
−
2
)
ACCESS-ESM1.5
900
151
5.86
±
5.35
BCC-CSM2-MR
1770
575
11.6
±
16.6
CanESM5
1500
218
3.87
±
6.52
CESM2 (cSoilAbove1m)
991
294
7.05
±
16.6
CESM2 (cSoil)
1870
1036
13.8
±
51.7
CNRM-ESM2-1
1810
440
12.2
±
9.98
GFDL-ESM4
516
163
1.36
±
3.43
IPSL-CM6A-LR
639
66.0
4.80
±
3.37
MIROC-ES2L
1460
347
9.31
±
10.7
MPI-ESM1.2-LR
970
175
6.68
±
5.23
NorESM2-LM (cSoilAbove1m)
969
300
2.61
±
6.97
NorESM2-LM (cSoil)
2430
1563
6.60
±
41.3
UKESM1-0-LL
1760
194
12.0
±
10.9
Ensemble mean
1206
±
445
266
±
139
2.80
±
5.15
Benchmark dataset
1412
±
215
401
±
83
10.7
±
9.28
Table 5.
Table presenting global soil carbon values for the 10 CMIP5 models included in this study and the benchmark datasets, including
global total
C
s
in PgC, northern latitude total (90–60
◦
N)
C
s
in PgC, and the spatial mean value of
C
s
with the corresponding standard
deviation in kg m
−
2
.
Earth system model
Global total
C
s
(PgC)
Northern latitude total
C
s
(PgC)
Mean
C
s
±
SD (kg m
−
2
)
BNU-ESM
681
135
5.31
±
4.55
CCSM4
507
28.1
4.03
±
3.24
CanESM2
1540
300
9.16
±
9.11
GFDL-ESM2G
1420
635
9.47
±
13.2
GISS-E2-R
2150
609
15.9
±
20.8
HadGEM2-ES
1080
148
8.19
±
6.24
IPSL-CM5A-LR
1350
346
9.77
±
7.64
MIROC-ESM
2550
742
20.5
±
15.1
MPI-ESM-LR
3000
204
23.5
±
14.8
NorESM1-M
538
31.0
3.61
±
3.34
Ensemble mean
1480
±
810
318
±
246
10.5
±
6.02
Benchmark dataset
1412
±
215
401
±
83
10.7
±
9.28
The CMIP6 models with the lowest global total values for
soil carbon do not always correspond to the lowest north-
ern latitude values for soil carbon. For example, UKESM1-
0-LL global total soil carbon is amongst the highest global
totals seen in CMIP6; however, low quantities of soil car-
bon are seen in the northern latitudes (approximately 10 %
of the global total). Conversely, BCC-CSM2-MR, CESM2,
GFDL-ESM4, and NorESM2-LM have approximately 30 %
of their global total stocks in the northern latitude region,
which is consistent with the ratio seen in the benchmark
dataset. This result suggests that representing global total soil
carbon stocks consistently with the benchmark soil carbon
does not imply consistency in the representation of north-
ern latitude soil carbon stocks, and these should be evaluated
separately. However, the large uncertainties associated with
the empirical datasets for the northern latitudes are noted (Ta-
ble 3).
3.1.3
Spatial evaluation
A lack of consistency in the simulation of soil carbon was
found amongst the CMIP5 models, which can be seen in
Fig. 1a, where differences between the empirical and mod-
elled data are shown. Northern latitude soil carbon was found
to be underestimated in CMIP5, where areas of blue can
be seen in the northern latitudes of the CMIP5 soil carbon
map in Fig. 1a. This underestimation of CMIP5 northern
latitude soil carbon is accompanied by significant overesti-
mations seen in mid-latitude soil carbon. Specifically, large
quantities of soil carbon which are inconsistent with our
benchmark dataset can be seen in the mid-latitude regions
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R. M. Varney et al.: Evaluation of soil carbon simulation in CMIP6
in the following CMIP5 models: CanESM2, GFDL-ESM2G,
GISS-E2-R, MIROC-ESM, and MPI-ESM-LR. Less signif-
icant overestimations are seen in HadGEM2-ES and IPSL-
CM5A-LR (Fig. A3). Systematic errors remain in the CMIP6
models; however, there are some improvements seen in the
spatial simulation of soil carbon from CMIP5. Soil carbon
is still underestimated in the northern latitudes, where the
areas of blue still remain in the northern latitudes of the
CMIP6 soil carbon map in Fig. 1a, though regions of over-
estimations in the northern latitudes are also seen amongst
the CMIP6 models in BCC-CSM2-MR, CESM2, CNRM-
ESM2-1, and NorESM2-LM (Fig. 2), but it is noted that
this representation might be more consistent with observa-
tions if a dataset including deeper soil carbon stocks was
considered. CMIP6 shows improvements in the representa-
tion of mid-latitude soil carbon, where less of an overesti-
mation is seen in CMIP6 compared with CMIP5 (Fig. 1a).
This overestimation can still be seen in 4 of the 11 CMIP6
models: ACCESS-ESM1.5, CanESM2, MIROC-ES2L, and
UKESM1-0-LL; however, the overestimations in CMIP6 are
less inconsistent than when compared with CMIP5, and the
number of models showing this limitation in CMIP6 has been
reduced (Fig. 2).
Despite the differences seen in the spatial representation of
soil carbon between the individual models in CMIP6, the en-
semble mean has more areas of agreement within the ensem-
ble compared with the ensemble mean in CMIP5. This can be
seen in Fig. 3a, where there is less hatching (where hatched
shaded areas represent regions of low agreement amongst
the models in the ensemble; see Methods) in the CMIP6
map compared with the CMIP5 map. Specifically, ensemble
mean soil carbon in CMIP6 has more areas of agreement in
the mid-latitude region compared with the CMIP5 ensemble
mean, where significant areas of disagreement are seen. This
disagreement is likely due to the overestimation which exists
in some of the CMIP5 models (Fig. A3). Also, a reduction
in the area of disagreement is seen in the northern latitudes
in CMIP6 compared with CMIP5; however, this remains the
region where the most disagreement exists across the gener-
ations. It is noted that this is a measure of agreement within
the ensemble and not between the models and empirical data
and so is dependent on the choice of ensemble members (see
Figs. A6 and A7 for individual model maps).
The inconsistency of the simulation of spatial soil car-
bon in CMIP6 is further evaluated using the spatial standard
deviations, the spatial Pearson correlation coefficients, and
RMSEs (see Methods), where the Taylor diagram (Fig. 4a)
presents all three statistical assessments. The spatial stan-
dard deviation for soil carbon is shown on the radial axis
between the standard
x
and
y
axes in Fig. 4a. The range of
spatial standard deviations amongst the CMIP6 models sees a
slight reduction from the range amongst the CMIP5 models,
though significant differences remain. The CMIP6 models
CNRM-ESM2-1, MIROC-ES2L, and UKESM1-0-LL best
match the spatial standard deviation derived from the bench-
mark dataset (Tables 4 and 5). It is found that the spatial rep-
resentation of modelled soil carbon in CMIP6 is poorly cor-
related with the empirical soil carbon, where the CMIP6 en-
semble spatial correlation coefficient with the empirical data
is found to be 0.250. The spatial correlation coefficients be-
tween the individual CMIP6 and CMIP5 models with the em-
pirical data can also be seen in Fig. 4a, where the low spatial
correlation coefficients are shown by the curved correlation
axis. The lowest spatial correlation coefficients amongst the
CMIP6 models were
r
values of 0.104 in IPSL-CM6A-LR
and 0.115 in UKESM1-0-LL. The CMIP6 model that was the
most spatially consistent with the empirical data is CNRM-
ESM2-1, with an
r
value of 0.630. The CMIP6 ensemble sees
a slight reduction in the RMSE compared with the CMIP5
ensemble, suggesting a slight improvement (Fig. 5a). Signif-
icant improvements in the RMSE are seen in MIROC-ES2L
from MIROC-ESM and MPI-ESM1.2-LR from MPI-ESM-
LR. These results suggest small improvements in the simu-
lation of soil carbon across this CMIP generation; however,
the low spatial correlation coefficients and variable RMSEs
seen across the models in CMIP6 suggest that inconsisten-
cies with the benchmark data remain.
3.2
Net primary productivity: improved in CMIP6
relative to CMIP5
3.2.1
Global total evaluation
Global total NPP amongst the CMIP6 models appears to be
consistent with the benchmark dataset (Table 6), where the
CMIP6 ensemble mean for NPP is approximately 95 % of
the benchmark mean. The CMIP6 ensemble mean global
total NPP (53.0
±
9.39 PgC yr
−
1
) is found to be slightly
lower than the derived mean benchmark value; however, it
is comfortably within the observational uncertainty range
(56.6
±
14.3 PgC yr
−
1
). The equivalent values for the CMIP5
models can be seen in Table 7, where the CMIP5 ensemble
total is also found to be within the observational uncertainty
range (56.3
±
15.4 PgC yr
−
1
).
The standard deviation surrounding the CMIP5 ensem-
ble mean is greater than in CMIP6. This reduced standard
deviation in CMIP6 is because several of the models have
a simulated global total NPP that more closely matches
the benchmark NPP global total value compared with the
previous CMIP5 generation: GFDL-ESM4 from GFDL-
ESM2G, IPSL-CM6A-LR from IPSL-CM5A-LR, MIROC-
ES2L from MIROC-ESM, MPI-ESM1.2-LR from MPI-
ESM1-M, and UKESM1-0-LL from HadGEM2-ES. The
majority of CMIP6 models see a reduction in NPP from the
CMIP5 equivalent model, which in general reduces the over-
estimation of NPP that was seen in the CMIP5 models (Ta-
bles 6 and 7). However, this was not the case for CanESM5
from CanESM2, which sees an increase in the magnitude
of NPP from CMIP5 to CMIP6, resulting in a consequent
overestimation compared with the benchmark data. A re-
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Figure 1.
Maps presenting the difference between the modelled and benchmark data for the CMIP5 and CMIP6 ensembles, for
(a)
C
s
(kg m
−
2
),
(b)
NPP (kg m
−
2
yr
−
1
), and
(c)
τ
s
(years).
Figure 2.
Maps of the difference in soil carbon (
C
s
) between the historical simulation of each CMIP6 model and the benchmark data.
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Figure 3.
Ensemble mean maps for
(a)
C
s
(kg m
−
2
),
(b)
NPP (kg m
−
2
yr
−
1
), and
(c)
τ
s
(years), presented for the CMIP6 ensemble,
the CMIP5 ensemble, and the benchmark datasets. The hatched areas are used to show regions of low agreement within the ensemble
(SD / mean
>
0
.
75) and where regions of low soil carbon (
<
5 kg m
−
2
) have been excluded. Equivalent maps for the individual CMIP6 and
CMIP5 models are shown within the Appendix: see Figs. A6 and A7 for
C
s
, Figs. A8 and A9 for NPP, and Figs. A10 and A11 for
τ
s
,
respectively.
Figure 4.
Taylor diagrams showing the spatial standard deviation (shown by the radial axis between standard
x
and
y
axes), the Pearson
correlation coefficients (shown by the curved correlation axis), and the RMSE (shown by the grey contours) for the ESMs in both CMIP5
and CMIP6 compared with the benchmark datasets, for
(a)
soil carbon (
C
s
),
(b)
NPP, and
(c)
soil carbon turnover time (
τ
s
).
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Table 6.
Table presenting global carbon fluxes and turnover time
values for the 11 CMIP6 models included in this study and the
benchmark datasets, including global total NPP (PgC yr
−
1
) and the
effective average soil carbon turnover time (years).
Earth system model
NPP (PgC yr
−
1
)
τ
s
(years)
ACCESS-ESM1.5
45.6
19.0
BCC-CSM2-MR
51.2
34.1
CanESM5
75.5
18.1
CESM2 (cSoilAbove1m)
43.9
25.8
CESM2 (cSoil)
–
50.4
CNRM-ESM2-1
45.6
41.5
GFDL-ESM4
52.6
11.2
IPSL-CM6A-LR
46.4
14.6
MIROC-ES2L
59.1
24.5
MPI-ESM1.2-LR
58.9
15.4
NorESM2-LM (cSoilAbove1m)
43.5
24.0
NorESM2-LM (cSoil)
–
60.8
UKESM1-0-LL
60.8
28.1
Ensemble mean
53.0
±
9.39
23.3
±
8.59
Benchmark datasets
56.6
±
14.3
27.0
+
27
−
11
Table 7.
Table presenting global carbon fluxes and turnover time
values for the 10 CMIP5 models included in this study and the
benchmark datasets, including global total NPP (PgC yr
−
1
) and the
effective average soil carbon turnover time (years).
Earth system model
NPP (PgC yr
−
1
)
τ
s
(years)
BNU-ESM
44.3
16.6
CCSM4
42.9
14.3
CanESM2
59.0
72.9
GFDL-ESM2G
74.4
57.3
GISS-E2-R
31.0
47.1
HadGEM2-ES
69.1
16.8
IPSL-CM5A-LR
76.6
19.4
MIROC-ESM
47.1
56.8
MPI-ESM-LR
73.5
42.7
NorESM1-M
45.0
34.5
Ensemble mean
56.3
±
15.4
37.8
±
19.7
Benchmark datasets
56.6
±
14.3
27.0
+
27
−
11
duced range of modelled global total NPP values is also seen
in CMIP6 from CMIP5, where the range is reduced from
48.5 PgC yr
−
1
in CMIP5 to 32.7 PgC yr
−
1
in CMIP6. These
results suggest that, overall, the representation of carbon
fluxes in CMIP6 ESMs is more consistent than in CMIP5.
3.2.2
Spatial evaluation
Modelled NPP in CMIP6 appears to be spatially more con-
sistent with the empirical data than in CMIP5. This is sug-
gested by Fig. 1b, where the difference between the mod-
elled and benchmark NPP is shown for both CMIP5 and
CMIP6. It can be seen in the CMIP5 map that NPP is over-
estimated in the tropical regions, specifically in Africa and
South-East Asia, and the equivalent CMIP6 difference map
shows a clear reduction in this overestimation. This tropi-
cal overestimation of NPP, prominent in CMIP5 (Fig. A4),
is still seen in the CMIP6 models: CanESM5, MPI-ESM1.2-
LR, and UKESM1-0-LL. However, this is not seen in the
CMIP6 ensemble mean as it is likely negated by underesti-
mations seen in CESM2, CNRM-ESM2-1, and NorESM2-
LM (Fig. 6). CMIP6 also sees more consistency with the
benchmark dataset in the northern and mid-latitude regions
compared with CMIP5, where more white areas are seen in
the CMIP6 map in Fig. 1b. An underestimation of NPP is
seen in both CMIP5 and CMIP6 on the western side of South
America, though unusually high NPP is seen in this region in
the MODIS NPP dataset (Fig. A2). Moreover, greater agree-
ment amongst the models within CMIP6 is seen compared
with the models in CMIP5. This can be seen in Fig. 3b, where
less hatching representing areas of disagreement within the
ensemble is seen in CMIP6 compared with CMIP5. Specifi-
cally, CMIP6 sees less hatching in the northern latitudes and
in the Middle East and south-eastern Europe as well as re-
gions in South America, southern Africa, and Australia (see
Figs. A8 and A9 for individual model maps).
The improved empirical consistency of modelled NPP in
CMIP6 is also suggested when further evaluated using the
same spatial metrics as with soil carbon. Despite a small
range remaining in the spatial standard deviations amongst
the CMIP6 models (shown by the radial axis in Fig. 4b),
robust improvements in the spatial correlation coefficients
(shown by the curved axis in Fig. 4b) and RMSEs are
seen across the ensemble compared with CMIP5 (Fig. 5b).
Notable improvements in the representation of NPP are
seen in GFDL-ESM4 compared with GFDL-ESM2G, IPSL-
CM6A-LR compared with IPSL-CM5A-LR, and UKESM1-
0-LL compared with HadGEM2-ES, with reduced RMSEs
seen in each updated model. A general improvement in
the spatial correlation coefficients is seen across all the
CMIP6 models, where the circle markers (CMIP6 models)
in Fig. 4b have higher correlation values than the cross mark-
ers (CMIP5 models). The general improvement has resulted
in the CMIP6 ensemble correlation coefficient (0.836) being
greater compared with the equivalent CMIP5 value (0.711).
The lowest correlations between modelled and observed NPP
amongst the CMIP5 models are GISS-E2-R (0.274) and
CanESM2 (0.469). The updated version CanESM5 remains
the lowest correlation seen in CMIP6 (0.655); however, an
improvement in the correlation is seen. The updated version
of the GISS model is not included in the CMIP6 ensemble
considered in this study, which could be a reason for the
increased ensemble mean correlation. However, this effect
does not take away from the improvements seen across the
CMIP6 models. HadGEM2-ES (0.764) and MPI-ESM-LR
(0.764) were the CMIP5 models with the highest correlation
with the benchmark NPP, and the updated CMIP6 equiva-
lents of these models remain the models with the greatest
correlations, but again improvements in the correlations are
seen (0.816 in UKESM1-0-LL and 0.785 in MPI-ESM1.2-
LR).
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Figure 5.
Bar charts comparing the root mean squared errors (RMSEs) in CMIP6 and CMIP5 for
(a)
soil carbon (
C
s
),
(b)
NPP, and
(c)
soil
carbon turnover time (
τ
s
).
Figure 6.
Maps of the difference in net primary production (NPP) between the historical simulation of each CMIP6 model and the benchmark
dataset.
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3.3
Soil carbon turnover time: no major improvements
in CMIP6 compared with CMIP5
3.3.1
Global evaluation
There are minor improvements suggested in the simu-
lated effective global
τ
s
amongst select CMIP6 models
(Table 6) compared with CMIP5 (Table 7). The ensem-
ble mean effective global
τ
s
was overestimated in CMIP5
(37.8
±
19.7 years) when compared with the derived mean
τ
s
using the benchmark datasets (27.0
+
27
−
11
years), which
is reduced to a less significant underestimation in CMIP6
(23.3
±
8.59 years), though both the CMIP5 and CMIP6
estimates fall within the observational uncertainty range.
The associated ensemble spread in effective mean
τ
s
is less
in CMIP6 compared with CMIP5, with an ensemble stan-
dard deviation of approximately 50 % less. A significant
range is seen in the effective global
τ
s
values amongst the
CMIP5 models, with a 5-fold difference between the low-
est and highest values (Table 7). This range is mostly due
to large overestimations seen amongst the CMIP5 models,
for example in CanESM2, GFDL-ESM2G, and MIROC-
ESM. A reduced range is seen amongst the models in
CMIP6; however, a 4-fold range still exists between the
lowest and highest values (Table 6). This reduced range
is partly due to reductions in the effective global
τ
s
val-
ues in CMIP6 models compared with the equivalent model
in CMIP5, specifically, CanESM5 from CanESM2, GFDL-
ESM4 from GFDL-ESM2G, MIROC-ES2L from MIROC-
ESM, and MPI-ESM1.2-LR from MPI-ESM-LR, though
overestimations do remain in CMIP6, for example in CNRM-
ESM2-1, where the slowest effective turnover time was seen.
Moreover, the range is also reduced due to improvements
seen in models which underestimated
τ
s
in CMIP5, such
as UKESM1-0-LL from HadGEM2-ES and CESM2 from
CCSM4.
3.3.2
Spatial evaluation
The comparison of spatial
τ
s
in CMIP6 with CMIP5 has more
varied results compared with simulated NPP. The CMIP5
ensemble showed an underestimation of
τ
s
in the northern
latitudes, which is replaced with an overestimation of
τ
s
in
CMIP6 when compared with the benchmark data (Fig. 1c).
This northern latitude overestimation in the CMIP6 ensem-
ble is a result of the overestimations of
τ
s
in CESM2 and
NorESM2-LM (Fig. 7), which dominate in the CMIP6 en-
semble mean. It is noted that this result may differ if deeper
soil carbon stocks were considered. The northern latitude un-
derestimation of
τ
s
is still seen within the CMIP6 models:
CanESM5, CNRM-ESM2-1, GFDL-ESM4, IPSL-CM6A-
LR, MIROC-ES2L, MPI-ESM1.2-LR, and UKESM1-0-LL
(Fig. 7). An overestimation of mid-latitude
τ
s
was seen in the
CMIP5 models MIROC-ESM and MPI-ESM-LR (Fig. A5),
which is no longer seen in the equivalent updated CMIP6
models MIROC-ES2L and MPI-ESM1-2-LR. However, an
overestimation of mid-latitude
τ
s
is seen in CMIP6 mod-
els BCC-CSM2-MR, CNRM-ESM2-1, and UKESM1-0-LL
(Fig. 7). The uncertainty in simulated northern latitude
τ
s
is
also apparent in Fig. 3c, where the hatching shows the lack of
agreement within the CMIP6 ensemble in this region. How-
ever, more agreement within the CMIP6 ensemble is seen
in the same figure in the mid-latitudes and in the tropical
regions compared with CMIP5 (see Figs. A10 and A11 for
individual model maps).
The simulation of spatial
τ
s
in CMIP6 is further evalu-
ated against the empirical data with the additional statisti-
cal metrics. Modelled
τ
s
is found to be poorly spatially cor-
related with empirical
τ
s
in both the CMIP5 and CMIP6
models (shown by the curved axis in Fig. 4c). A slight in-
crease in the ensemble mean spatial correlations is seen
from CMIP5 (0.188) to CMIP6 (0.267) due to increases seen
amongst individual models between CMIP5 and CMIP6:
CESM2 from CCSM4, MPI-ESM1.2-LR from MPI-ESM-
LR, and NorESM2-LM from NorESM1-M. However, the
consistency of modelled
τ
s
with the benchmark datasets re-
mains low. A particularly large range is seen in the spatial
standard deviations of
τ
s
amongst the CMIP6 models, which
is an increased range from CMIP5 (shown by the radial
axis in Fig. 4c). The CMIP6 models with the most extreme
overestimations of the spatial standard deviations compared
with the derived benchmark value (NorESM2-LM, CESM2,
and ACCESS-ESM1.5) are also found to have large RM-
SEs (Fig. 5c). Amongst the remaining CMIP6 models, the
RMSEs for modelled
τ
s
remain relatively consistent between
CMIP5 and CMIP6.
3.4
Drivers of soil carbon spatial patterns: soil carbon
spuriously highly correlated with NPP in CMIP5
and CMIP6
3.4.1
Global drivers
A negligible correlation (
≈
0) is found between the bench-
mark estimates of soil carbon and NPP, suggesting that soil
carbon is not spatially correlated with NPP in the real world.
On the other hand, soil carbon and NPP (
C
s
–NPP) were
found to be significantly correlated in the models in both
CMIP5 and CMIP6. The
C
s
–NPP spatial correlation was
found to be greater than 0.5 for 6 out of the 10 CMIP5
ESMs and 8 out of the 11 models in CMIP6 (Fig. 8a).
However, a low spatial correlation is found in the CMIP6
models CESM2 (0.134), NorESM2-LM (0.261), and BCC-
CSM2-MR (0.214), values most consistent with the bench-
mark datasets. The
C
s
–
τ
s
spatial correlations found in the
CMIP6 models tend to underestimate the positive correla-
tion seen in the benchmark datasets (Fig. 8a). The majority
of CMIP6 models see a negligible or slightly negative
C
s
–
τ
s
spatial correlation despite a low positive correlation pro-
duced by the benchmark datasets. The models BCC-CSM2-
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MR, MIROC-ES2L, and NorESM2-LM are most consistent
with the benchmark
C
s
–
τ
s
correlation.
The modelled NPP to temperature (NPP–
T
) spatial corre-
lations in CMIP6 are consistent with the positive relationship
seen in the benchmark datasets; however, the magnitude of
this positive correlation varies amongst the models (Fig. 8b).
The magnitude of the positive NPP–
T
correlation is under-
estimated in CanESM5, GFDL-ESM4, and NorESM2-LM
but is otherwise relatively consistent amongst the CMIP6
models. Nonetheless, a much greater range in the modelled
NPP–
T
correlations was seen amongst the CMIP5 models,
suggesting an improved representation of this relationship in
CMIP6. The variation in modelled NPP–
θ
correlations re-
mains in CMIP6, with models disagreeing on the sign and
magnitude of the correlation of NPP with soil moisture. The
modelled NPP–
θ
correlation is the most consistent with the
benchmark correlations in GFDL-ESM4, MPI-ESM1.2-LR,
and UKESM1-0-LL (Fig. 8b).
It is generally agreed across the models in CMIP6 and
CMIP5 that
τ
s
and temperature (
T
) are negatively correlated,
with the exception of MPI-ESM1.2-LR, where a slight pos-
itive correlation is seen (Fig. 8c). This is consistent with
the negative
τ
s
–
T
correlation derived with the benchmark
dataset. There is variation amongst the models in the mag-
nitude of the negative correlation, with a significant overes-
timation seen in CanESM5. A negative correlation is also
seen in the
τ
s
–
θ
correlation derived with the benchmark
datasets. Inconsistencies with this empirical relationship are
seen amongst the models in both CMIP5 and CMIP6, with
many negligible and positive correlations deduced (Fig. 8c).
The exception is again MPI-ESM1.2-LR, which in this case
is the model most consistent with the benchmark
τ
s
–
θ
corre-
lation.
3.4.2
Regional drivers
The spatial correlations of modelled
C
s
–NPP are shown to be
overestimated at every latitude in both CMIP6 and CMIP5
compared with the equivalent correlations derived from the
empirical datasets. It can be seen that the CMIP6 ensemble
mean
C
s
–NPP correlation has an even larger positive bias
compared with the benchmark correlation than in CMIP5.
The empirical data see a reduced
C
s
–NPP correlation in the
northern latitudes, whereas a slight but less significant reduc-
tion is seen in the models (Fig. 9a). The spatial correlation
between
C
s
and
τ
s
is shown to vary against latitude in the
empirical datasets, where a greater correlation is seen in the
tropical and northern latitude regions, and a negligible corre-
lation is seen at the mid-latitudes (Fig. 9b). The CMIP6 mod-
els simulate the negligible
C
s
and
τ
s
seen at the mid-latitudes
relatively consistently with the benchmark data, where an
improved consistency is seen from CMIP5. However, the
CMIP6 models do not simulate the tropical and northern lati-
tude positive
C
s
–
τ
s
correlations, where a negligible modelled
correlation remains in these regions. CMIP5 is more consis-
tent with the benchmark correlations than in CMIP6, where
a positive modelled correlation
C
s
–
τ
s
is seen (Fig. 9b).
The spatial correlation between modelled soil carbon and
soil moisture (
C
s
–
θ
) is consistent with the correlations seen
in the benchmark datasets at every latitude, with an improve-
ment seen in the tropical correlation patterns in CMIP6 com-
pared with CMIP5 (Fig. 9c). Both the CMIP5 and CMIP6 en-
sembles span the benchmark
C
s
–
θ
correlation, though large
model ranges in the
C
s
–
θ
sensitivity are seen across all lat-
itudes. However, there is a reduced ensemble spread in the
C
s
–
θ
correlation from CMIP5 to CMIP6 at low- and mid-
latitudes. An overestimation of the negative
C
s
–
T
correlation
seen in the benchmark datasets is present in both the CMIP5
and CMIP6 models, except the high latitudes (Fig. 9d). This
modelled
C
s
–
T
correlation is particularly underestimated at
the lower tropical latitudes, where a greater positive correla-
tion is seen here in the benchmark datasets. Figure 9d sug-
gests a slight improvement in the modelled tropical
C
s
–
T
correlation in CMIP6 and a worsening of modelled
C
s
–
T
at
the high latitudes than in CMIP5 when compared with the
C
s
–
T
correlations in the benchmark datasets.
4
Discussion
4.1
Soil carbon stocks
4.1.1
Global total soil carbon
Simulating global soil carbon stocks that are consistent with
empirical data is required to produce reliable projections
of future soil carbon storage and emission (Todd-Brown
et al., 2013). This study deduces a CMIP6 ensemble mean
global total soil carbon of 1206
±
445 PgC (Table 4), us-
ing regridded model resolutions (see Methods). It is noted
that Ito (2011) states a CMIP6 ensemble of 1553
±
672 PgC;
however, the full soil carbon profile is considered for CESM2
and NorESM2-LM, as opposed to a depth of 1 m consid-
ered in this study. Additionally, this study deduces a com-
parable CMIP5 ensemble mean global soil carbon value of
1480
±
810 PgC (Table 5) using equivalent dates in the his-
torical simulation (1950–2000). Todd-Brown et al. (2013)
state an ensemble mean soil carbon value of 1520
±
770 PgC
in CMIP5; however, the Todd-Brown et al. (2013) study in-
cludes the models BCC-CSM1.1, CESM1-CAM5, and INM-
CM4, which are missing from the analysis in this study due to
data availability. Anav et al. (2013) present a CMIP5 ensem-
ble mean soil carbon value of 1502
±
798 PgC, but this calcu-
lation includes multiple model versions (for example, LR and
MR) from the same modelling centre in their ensemble. A
caveat of this evaluation study is the non-independent nature
of CMIP ESMs, where for example CESM2 and NorESM2-
LM share the same LSM. Additionally, the ensembles in-
cluded here do not necessarily represent all models that ex-
ist within each CMIP generation. However, the evaluation
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Figure 7.
Maps of the difference in soil carbon turnover time (
τ
s
) between the historical simulation of each CMIP6 model and the benchmark
datasets.
Figure 8.
Scatter plots investigating the relationships between different Pearson correlation coefficients of climate variables,
(a)
C
s
–
τ
s
against
C
s
–NPP,
(b)
NPP–
T
against NPP–
θ
, and
(c)
τ
s
–
T
against
τ
s
–
θ
.
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Figure 9.
The latitudinal profiles of the Pearson correlation coefficients between soil carbon and
(a)
NPP (
C
s
–NPP),
(b)
soil carbon turnover
time (
C
s
–
τ
s
),
(c)
soil moisture (
C
s
–
θ
), and
(d)
temperature (
C
s
–
T
).
completed here allows for general improvements in the sim-
ulation of soil carbon stocks and fluxes between the CMIP5
and CMIP6 generations to be noted and key areas for future
model development to be highlighted.
Despite a suggestion of a reduced spread in model esti-
mates of global total soil carbon within CMIP6 relative to
CMIP5, discrepancies remain in the consistency of these es-
timates with the observations between the two CMIP gener-
ations. It should also be noted that CMIP6 does not simply
contain updated versions of every model in CMIP5: some
new models are included and some CMIP5 models not in-
cluded in CMIP6. These factors together with the uncertainty
associated with empirical datasets have resulted in no robust
conclusion being drawn on the improvement of soil carbon
simulation in CMIP6 compared with CMIP5. Due to the po-
tential significant feedback that exists between soil carbon
and global climate, this lack of consistency reduces our con-
fidence in future projections of climate change (Friedling-
stein et al., 2006; Gregory et al., 2009; Arora et al., 2013;
Friedlingstein et al., 2014).
4.1.2
Spatial soil carbon patterns
Modelled soil carbon was found to be poorly spatially corre-
lated with the empirical data amongst models in both CMIP5
and CMIP6 (Fig. 4a). An improvement in CMIP6 ESMs was
seen in the spatial patterns across the mid-latitudes, which
were generally overestimated in CMIP5. However, signifi-
cant underestimations of modelled soil carbon in the north-
ern latitudes still remain, which have a significant impact on
model predictions of global total soil carbon stocks (Fig. 1a).
This systematic underestimation was previously reported in
the literature as a limitation of the CMIP5 models, where
Todd-Brown et al. (2013) found northern latitude soil car-
bon to be less consistent with the empirical data than on a
global scale. This limitation remains amongst models in the
CMIP6 generation, where it was found that the majority of
CMIP6 models underestimate northern latitude soil carbon
stocks regardless of whether the global soil carbon stocks are
underestimated.
However, an exception to this northern latitude underes-
timation is seen within CMIP6 in the models CESM2 and
NorESM2-LM. These ESMs include the land surface model
(LSM) CLM5 (Lawrence et al., 2019), which is the first LSM
to include the representation of vertically resolved soil car-
bon in its CMIP simulations. This representation enables the
inclusion of separate carbon pools at varying depths in the
soil, which aims to more consistently simulate soil carbon
with the real world (Koven et al., 2013). This is of particu-
lar importance in the northern latitudes, where carbon stocks
are expected to exist at much greater depths than the 1 m
considered in this study (Tarnocai et al., 2009; Ran et al.,
2022). This can be seen in Table 3, where increased magni-
tudes of soil carbon stocks are shown when increased depths
are considered using the empirical datasets. A more thorough
evaluation of soil carbon in both CESM2 and NorESM2-LM
is suggested for future research, with a particular focus on
this improved northern latitude soil carbon stock simulation;
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however, this evaluation of deeper soil carbon stocks (below
1 m) is beyond the scope of this study.
Accurately simulating soil carbon in the northern latitude
regions is of particular importance as it is a major part of the
total global soil carbon pool (Jackson et al., 2017). Addition-
ally, much of the carbon stored in these soils is held within
permafrost, which is known to be particularly sensitive to cli-
mate change. Permafrost thaw under climate change has the
potential to release significant amounts of carbon into the at-
mosphere over a short period of time with increased warm-
ing (Schuur et al., 2015; Zimov et al., 2006; Burke et al.,
2017; Hugelius et al., 2020), representing a positive feedback
within the climate system. Permafrost dynamics are gener-
ally poorly represented in ESMs, where Burke et al. (2020)
found CMIP6 ESMs to have a similar representation com-
pared with CMIP5. Underestimating soil carbon in the north-
ern latitudes may result in underestimation of the impact of
this feedback in future climate change projections. Future
improvements are needed to improve the simulation of soil
carbon stocks globally, but particularly in the northern lati-
tudes.
4.2
Drivers of soil carbon change
To allow for a more in-depth understanding of the incon-
sistencies found between modelled and empirical soil car-
bon, the simulation of above- and below-ground controls of
soil carbon was also evaluated. Simulations of contemporary
soil carbon can be disaggregated into the effects of litterfall,
which is well approximated by plant NPP, and effective soil
carbon turnover time (
τ
s
), which is affected by both the tem-
perature and moisture of the soil (Koven et al., 2015). If mod-
els are to reliably simulate soil carbon in a way that is con-
sistent with empirical data, the spatial drivers of soil carbon,
NPP and
τ
s
, must also be simulated consistently with empir-
ical data. Isolating the effects of NPP and
τ
s
on soil carbon
helps us to break down the simulation of soil carbon to help
understand the limitations and inconsistencies seen amongst
the models.
4.2.1
NPP
An improved simulation of NPP is suggested in the ESMs in-
cluded from CMIP6 compared with the ESMs from CMIP5.
This conclusion is suggested by an increased number of mod-
els in our CMIP6 ensemble having global total NPP values
consistent with empirical data (Table 6), the overestimation
of tropical NPP amongst CMIP5 models being seen to be re-
duced amongst the CMIP6 models (Fig. 1b), and more agree-
ment being seen within CMIP6 relative to CMIP5 in the sim-
ulation of mid-latitude and northern latitude NPP (Fig. 3b).
Modelled NPP was found to be robustly more consistent
with the empirical data in our CMIP6 ensemble compared
with the CMIP5 ensemble in all statistical evaluation metrics.
Since CMIP5, multiple models have seen an addition of a
dynamic nitrogen cycle (Davies-Barnard et al., 2020), where
the models with nitrogen cycles are highlighted in Fig. 5 by
the shaded bars. The results suggest an improvement in the
simulation of NPP with the addition of dynamic nitrogen in
models. However, CMIP6 models that do not represent a ni-
trogen cycle also mostly see improvements in the simulation
of NPP, suggesting NPP is more constrained by observations
in the newest generation of models. CanESM5 is the only
ESM within CMIP6 included here to not see an overall im-
provement in the simulation of NPP, where NPP is found to
be overestimated compared with the benchmark dataset. It
is likely that the inclusion of a nitrogen cycle in this model
would limit this overestimated NPP and improve consistency
with the observations (Zhang et al., 2014; Exbrayat et al.,
2013).
Despite this apparent improved simulation of NPP in
CMIP6, the spatial correlation between modelled soil carbon
and NPP was found to be inconsistent with the equivalent
empirically derived relationship. This result was previously
shown for the CMIP5 models (Todd-Brown et al., 2013) and
has been more recently shown for the CMIP6 models (Geor-
giou et al., 2021), both agreeing with the results found here.
The majority of CMIP6 models were found to have positive
C
s
–NPP spatial correlations, as opposed to a negligible spa-
tial correlation found in the observations (Fig. 8a). Despite
NPP driving the spatial pattern of soil carbon stocks due to
carbon input from vegetation, a positive correlation was not
expected in the real world due to regions with high soil car-
bon not correlating with regions of high NPP. For example, in
the observationally derived data, soil carbon stocks are great-
est in the northern latitudes due to long turnover times in
these regions, whereas NPP is lower due to cold tempera-
tures in these regions limiting vegetation growth. The three
CMIP6 models which did not significantly overestimate this
correlation (CESM2, NorESM2-LM, and BCC-CSM2-MR)
are three of the models with the most empirically consistent
proportion of soil carbon stocks in the northern latitudes.
Conversely, the tropical regions see high NPP values, but
warmer temperatures result in faster turnover times and lower
soil carbon stocks. NPP is expected to increase in the future
under climate change (Kimball et al., 1993; Friedlingstein
et al., 1995; Amthor, 1995), which means an overly positive
correlation in models could result in a subsequent increase
in modelled projections of soil carbon stocks. An overesti-
mation of future soil carbon storage could result in an over-
estimation of the future carbon sink and an inaccurate global
carbon budget (Todd-Brown et al., 2013; Friedlingstein et al.,
2022).
4.2.2
Soil carbon turnover time
The systematic improvements suggested from the evaluation
of NPP simulation within our CMIP6 ensemble are not sug-
gested for the simulation of
τ
s
, where the simulation of
τ
s
appears to remain inconsistent with the empirical data in
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R. M. Varney et al.: Evaluation of soil carbon simulation in CMIP6
CMIP6 from CMIP5. Improvements are suggested within
CMIP6 relative to CMIP5, such as more agreement within
the ensemble at the mid-latitudes and in the tropical regions;
however, less agreement is seen in the northern latitudes
(Fig. 3c). Northern latitude
τ
s
is generally underestimated
in models, which corresponds to the underestimation of soil
carbon seen in these regions. This has been previously identi-
fied in ESMs, where it was found that the underestimation of
global
τ
s
amongst the CMIP5 models is primarily due to low
values in the northern latitudes (Wu et al., 2018). The reduced
agreement in CMIP6 is due to long
τ
s
values existing in
the northern latitudes of CESM2 and NorESM2-LM along-
side the general ensemble underestimations (Fig. 7). The in-
creased northern latitude
τ
s
values in CESM2 and NorESM2-
LM are likely to be due to the representation of vertically
resolved soil carbon pools, which allows for differential
τ
s
values for pools at varying depths. Despite these individual
improvements since CMIP5, large discrepancies exist within
the CMIP6 ensemble between modelled and empirical
τ
s
.
To simulate
τ
s
consistently with observations, the relation-
ship of
τ
s
with both temperature (
T
) and moisture (
θ
) must
also be simulated in a way that is consistent with observa-
tions. Generally, the
τ
s
–
T
relationship is consistently simu-
lated; however, there is variation in the modelled tempera-
ture sensitivity of
τ
s
across the ensemble. The
τ
s
–
θ
relation-
ship is less consistently represented, where the majority of
CMIP6 models do not match the empirically derived rela-
tionship. Despite a positive dependence of soil respiration on
soil moisture in the empirical data, many of the CMIP6 mod-
els display a contradictory positive
τ
s
–
θ
correlation (Fig. 8).
Many of the models use functions that increase respiration
with soil moisture (see Sect. 2.1), so the increase in
τ
s
with
increasing soil moisture indicated by positive
τ
s
–
θ
correla-
tions in the models is unexpected. We note that this effect
occurs most strongly in the models with a very strongly neg-
ative
τ
s
–
T
relationship (Fig. 8c), so it could in fact be an
artefact of a negative correlation between temperature and
soil moisture. In this context it is also important to consider
what soil moisture in LSMs represents (Koster et al., 2009).
The aim within the models is to act as the lower boundary
condition for atmospheric models, and therefore their soil pa-
rameters may historically have been tuned to give appropri-
ate evaporation rates and not necessarily to represent the soil
moisture itself in an accurate way, so it may be more relevant
to consider the large-scale emergent patterns of
τ
s
than the di-
rect relationships between soil moisture and respiration. It is
noted that the empirical relationship shows
τ
s
reducing with
higher soil moisture, which suggests that the observations are
picking up more on longer turnover times in dry areas rather
than in saturated areas such as peatlands. This may be due to
having only surface soil moisture information, whereas peat-
lands, while saturated at depth, typically have a water table
∼
10 cm below the surface and can be very dry at the sur-
face (Evans et al., 2021). Thus, while models do not include
the necessary processes for peat formation (Chadburn et al.,
2022), this is unlikely to be the cause of the discrepancy since
it would lead to even more of a positive
τ
s
–
θ
correlation in
the models.
Different processes control soil carbon formation in dif-
ferent ecosystems, including stabilization by clay particles,
transformation by microbes, and nitrogen and phosphorous
availability (Witzgall et al., 2021). In the present study, the
largest discrepancies in both soil carbon and turnover times
are seen in permafrost and peatland areas (see Figs. 2 and 7).
For example, the western Siberian peatland complex stands
out in the majority of the panels in these figures as an area
of high model error. This is partly because the soil carbon
turnover times and quantities of soil carbon are largest in
these regions but also partly because of the specific control-
ling processes in these ecosystems. A key part of soil carbon
development in permafrost regions is the fact that organic
material can be preserved in frozen soil, including via cry-
oturbation and yedoma deposits, which have not yet been
thoroughly represented in models (Beer, 2016; Zhu et al.,
2016). There are a variety of other factors, such as plants
storing significantly more of their carbon below ground in-
stead of above ground in cold climates and recalcitrant veg-
etation such as mosses, which are not represented in most
ESMs (Sulman et al., 2021). Peatland formation is con-
trolled primarily by waterlogging, which reduces the oxy-
gen available for decomposition, but there are a huge num-
ber of additional physical and biogeochemical feedbacks that
take place (Waddington et al., 2015). These kinds of small-
scale processes and inhomogeneities are difficult to resolve
in global models with
∼
100 km
2
grid cells, and this should
be weighed up against their relative impact on global car-
bon budgets when considering including these processes in
ESMs. However, it is suggested that the large-scale discrep-
ancies such as in the permafrost and large peatland areas can
and should be resolved in future model versions.
Our results suggest that much of the uncertainty associ-
ated with modelled soil carbon stocks can be attributed to
the simulation of below-ground processes. The apparent im-
proved consistency of NPP with empirical data suggests that
considerable efforts have been made to achieve an improved
representation of above-ground processes in CMIP6 ESMs
since the release of the CMIP5 ensemble. However, the same
improvements are not apparent in the simulation of
τ
s
as sys-
temic limitations remain in the new generation of ESMs con-
sidered in this study, suggesting that the same progress in
the model development of below-ground processes has not
been achieved between CMIP5 and CMIP6. Moreover, focus
on above-ground processes without consideration of below-
ground processes can result in inconsistencies of soil carbon
stocks. For example, the inclusion of a nitrogen cycle has
been shown to lead to a reduction in soil carbon in the model
(see Fig. 6 in Wiltshire et al., 2021), so tuning of the baseline
turnover rates is required to keep soil carbon stocks consis-
tent with observed values.
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The required improvement of soil carbon pool turnover
rates has previously been identified for the CMIP5 ensem-
ble (Nishina et al., 2014), and more recently, Ito et al.
(2020) found that the difference in turnover times amongst
the CMIP6 models is responsible for approximately 88 % of
the variation seen in global soil carbon stocks amongst the
models and stated that constraining key parameters which
control soil carbon turnover processes is a key area for fu-
ture model development. A key development seen in CMIP6
since CMIP5 is the representation of vertically resolved soil
carbon. Models which simulate non-vertically resolved soil
carbon typically turn over all the carbon based on the temper-
ature near the soil surface. This could lead to reduced quanti-
ties of soil carbon and an underestimation of northern latitude
soil carbon stocks, due to near-surface soil being warmer
than the deeper soil and as turnover is known to respond ex-
ponentially to temperature (Davidson and Janssens, 2006).
Overall, further improvements in the representation of soil
carbon turnover time, with a particular focus on the northern
latitudes, are identified as a key area for future model devel-
opment.
5
Conclusions
The ability of Earth system models (ESMs) to simulate
present-day soil carbon is vital to help predict reliable global
carbon budget estimates, which are required for Paris Agree-
ment targets. In this study, CMIP6 ESMs have been evalu-
ated against empirical datasets to assess their ability to rep-
resent soil carbon and related controls: net primary produc-
tivity (NPP) and the effective soil carbon turnover time (
τ
s
=
C
s
/
R
h
). The evaluation is completed by comparison with
the previous generation of CMIP5 ESMs to assess where
improvements have been made and to identify priorities for
future model development. Below the key conclusions from
this study are listed.
1. The spatial patterns of soil carbon in CMIP6 models
appear to be more in agreement with each other than
they were in CMIP5 and are more consistent with ob-
servations in the mid-latitudes, although caveats around
the uncertainty in observations and the ensemble design
make this conclusion uncertain. However, soil carbon
is still heavily underestimated in high northern latitudes
(with the exception of the two CMIP6 models that rep-
resent deep soil carbon).
2. Overall, we are not able to identify significant improve-
ments in the simulation of the observed spatial pat-
tern of soil carbon across the globe from the CMIP5 to
CMIP6 generation.
3. There is good evidence that spatial patterns of con-
temporary NPP are better simulated in CMIP6 than
in CMIP5 generation models when compared with
satellite-derived estimates.
4. However, spatial patterns of
τ
s
continue to be poorly
represented in CMIP6 models, in comparison with esti-
mates derived from observational datasets of soil carbon
and heterotrophic respiration (
R
h
).
5. Importantly, soil carbon simulations in both the CMIP5
and CMIP6 ESM generations seem to be spuriously
highly correlated with NPP, which may make soil car-
bon in these models over-responsive to future projected
changes in NPP.
Taken together, these conclusions point to a need for a much
greater emphasis on improving the representation of below-
ground soil processes in the next generation (CMIP7) of
ESMs.
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R. M. Varney et al.: Evaluation of soil carbon simulation in CMIP6
Appendix A
Figure A1.
Maps comparing empirical datasets of soil carbon (
C
s
). The benchmark dataset is a map plot showing
C
s
approximated to a
depth of 1 m by combining the Harmonized World Soils Database (HWSD) (FAO and ISRIC, 2012) and Northern Circumpolar Soil Carbon
Database (NCSCD) (Hugelius et al., 2013), where the NCSCD was used where overlap occurs. Additional map plots are shown for empirical
C
s
estimated by the World Inventory of Soil property Estimates (WISE30sec) (Batjes, 2016), the named “S2017” from Sanderman et al.
(2017), the Global Soil Dataset for use in Earth system models (GSDE) (Shangguan et al., 2014), and the Global Gridded Surfaces of
Selected Soil Characteristics (IGBP-DIS) (IGBP, 2000).
Figure A2.
Maps of empirical carbon flux datasets. Net primary production (NPP) is approximated using the MODIS NPP dataset (Zhao
et al., 2005), and heterotrophic respiration (
R
h
) is approximated using the CARDAMOM
R
h
dataset (Bloom et al., 2015).
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Figure A3.
Maps of the difference in soil carbon (
C
s
) between the historical simulation (1950–2000) for the CMIP5 models and the bench-
mark dataset.
Figure A4.
Maps of the difference in NPP between the historical simulation (1995–2005) for the CMIP5 models and the benchmark dataset.
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R. M. Varney et al.: Evaluation of soil carbon simulation in CMIP6
Figure A5.
Maps of the difference in
τ
s
between the historical simulation for the CMIP5 models and the benchmark datasets, where
τ
s
is
defined as the ratio of
C
s
(1950–2000) to
R
h
(1995–2005).
Figure A6.
Maps of soil carbon (
C
s
) in the historical simulation (1950–2000) for the CMIP6 models.
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Figure A7.
Maps of soil carbon (
C
s
) in the historical simulation (1950–2000) for the CMIP5 models.
Figure A8.
Maps of NPP in the historical simulation (1995–2005) for the CMIP6 models.
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R. M. Varney et al.: Evaluation of soil carbon simulation in CMIP6
Figure A9.
Maps of NPP in the historical simulation (1995–2005) for the CMIP5 models.
Figure A10.
Maps of soil carbon turnover times (
τ
s
) in the historical simulation for the CMIP6 models, where
τ
s
is defined as the ratio of
C
s
(1950–2000) to
R
h
(1995–2005).
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Figure A11.
Maps of soil carbon turnover times (
τ
s
) in the historical simulation for the CMIP5 models, where
τ
s
is defined as the ratio of
C
s
(1950–2000) to
R
h
(1995–2005).
Table A1.
Table presenting global carbon fluxes (PgC yr
−
1
), NPP, and
R
h
for the 11 CMIP6 models included in this study and the empirical
benchmark datasets.
Earth system model
NPP (PgC yr
−
1
)
R
h
(PgC yr
−
1
)
ACCESS-ESM1.5
45.6
45.1
BCC-CSM2-MR
51.2
48.9
CanESM5
75.5
75.0
CESM2
43.9
38.3
CNRM-ESM2-1
45.6
40.3
GFDL-ESM4
52.6
43.7
IPSL-CM6A-LR
46.4
39.9
MIROC-ES2L
59.1
52.7
MPI-ESM1.2-LR
58.9
53.4
NorESM2-LM
43.5
38.2
UKESM1-0-LL
60.8
57.5
Ensemble mean
53.0
±
9.39
48.4
±
10.5
Benchmark datasets
56.6
±
14.3
51.7
±
21.8
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Table A2.
Table presenting global carbon fluxes (PgC yr
−
1
), NPP, and
R
h
for the 10 CMIP5 models included in this study and the empirical
benchmark datasets.
Earth system model
NPP (PgC yr
−
1
)
R
h
(PgC yr
−
1
)
BNU-ESM
44.3
42.5
CCSM4
42.9
41.4
CanESM2
59.0
58.8
GFDL-ESM2G
74.4
62.7
GISS-E2-R
31.0
39.5
HadGEM2-ES
69.1
67.0
IPSL-CM5A-LR
76.6
62.4
MIROC-ESM
47.1
41.2
MPI-ESM-LR
73.5
59.9
NorESM1-M
45.0
41.3
Ensemble mean
56.3
±
15.4
52.8
±
10.7
Benchmark datasets
56.6
±
14.3
51.7
±
21.8
Data availability.
The datasets analysed during this study are
available online: CMIP5 model output (https://esgf-node.llnl.
gov/search/cmip5/, ESGF, 2022a), CMIP6 model output (https:
//esgf-node.llnl.gov/search/cmip6/, ESGF, 2022b), the Harmo-
nized
World
Soils
Database
(HWSD)
and
Northern
Cir-
cumpolar Soil Carbon Database (NCSCD) (https://github.com/
rebeccamayvarney/CMIP_soilcarbon_evaluation, FAO and IS-
RIC, 2012; Hugelius et al., 2013), the World Inventory
of Soil property Estimates (WISE30sec) (https://www.isric.org/
explore/wise-databases, ISRIC World Soil Information Service,
2022), Sanderman et al. (2017) soil carbon estimates (1
and 2 m) (https://doi.org/10.7910/DVN/QQQM8V, HARVARD,
2022), the Global Soil Dataset for use in Earth system
models (GSDE) (http://globalchange.bnu.edu.cn/research/soilw,
Land-Atmosphere Interaction Research Group at Sun Yat-
sen University, 2022), Global Gridded Surfaces of Selected
Soil Characteristics (IGBP-DIS) (https://daac.ornl.gov/cgi-bin/
dsviewer.pl?ds_id=569, ORNL-DAAC, 2022), MODIS net primary
production (https://modis.gsfc.nasa.gov/data/dataprod/mod17.php,
NASA, 2022), CARDAMOM heterotrophic respiration (https://
datashare.ed.ac.uk/handle/10283/875, CARDAMOM, 2022), the
Copernicus Climate Change Service (C3S) soil moisture gridded
dataset (https://doi.org/10.24381/cds.d7782f18, Copernicus, 2022),
and the WFDEI meteorological forcing data (https://rda.ucar.edu/
datasets/ds314.2/, NCAR, 2022).
Supplement.
The supplement related to this article is available on-
line at: https://doi.org/10.5194/bg-19-4671-2022-supplement.
Author contributions.
RMV, SEC, and PMC outlined the evalua-
tion and drafted the manuscript, and RMV completed the analysis
and produced the figures. EJB provided the empirical datasets and
gave helpful advice for addressing the empirical uncertainty. All the
co-authors provided guidance on the study at various times and sug-
gested edits to the draft manuscript.
Competing interests.
The contact author has declared that none of
the authors has any competing interests.
Disclaimer.
Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Acknowledgements.
We thank the World Climate Research Pro-
gramme’s Working Group on Coupled Modelling, which is respon-
sible for CMIP, and we thank the climate modelling groups for pro-
ducing and making their model output available to enable studies
such as this evaluation study. We also thank the providers of the em-
pirical datasets, which enabled us to complete the evaluation in this
study. The Taylor diagrams presented in this study were produced
using the code available at https://gist.github.com/ycopin/3342888
(last access: 14 January 2022).
Financial support.
This research has been supported by the Eu-
ropean Research Council, Emergent Constraints on Climate–Land
feedbacks in the Earth System project (ECCLES; grant no. 742472),
and the European Research Council, Climate–Carbon Interactions
in the Current Century (4C) project (grant no. 821003) (Rebecca
M. Varney and Peter M. Cox). Sarah E. Chadburn was supported by
a Natural Environment Research Council independent research fel-
lowship (grant no. NE/R015791/1). Eleanor J. Burke was supported
by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Pro-
gramme (grant no. GA01101).
Review statement.
This paper was edited by Alexey V. Eliseev and
reviewed by two anonymous referees.
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