The prediction of atmospheric CO2 concentrations is limited by the high interannual variability (IAV) in terrestrial gross primary
productivity (GPP). However, there are large uncertainties in the drivers of GPP IAV among Earth system models (ESMs). Here, we evaluate the impact
of these uncertainties on the predictability of atmospheric CO2 in six ESMs. We use regression analysis to determine the role of
environmental drivers in (i) the patterns of GPP IAV and (ii) the predictability of GPP. There are large uncertainties in the spatial distribution
of GPP IAV. Although all ESMs agree on the high IAV in the tropics, several ESMs have unique hotspots of GPP IAV. The main driver of GPP IAV is
temperature in the ESMs using the Community Land Model, whereas it is soil moisture in the ESM developed by the Institute Pierre Simon Laplace (IPSL-CM6A-LR) and in the low-resolution configuration of the Max Planck Earth System Model (MPI-ESM-LR), revealing underlying differences in the
source of GPP IAV among ESMs. Between 13 % and 24 % of the GPP IAV is predictable 1 year ahead, with four out of six ESMs showing values of between 19 %
and 24 %. Up to 32 % of the GPP IAV induced by soil moisture is predictable, whereas only 7 % to 13 % of the GPP IAV induced by
radiation is predictable. The results show that, while ESMs are fairly similar in their ability to predict their own carbon flux variability, these predicted contributions to the atmospheric CO2 variability originate from different regions and are caused by different drivers. A higher coherence in atmospheric
CO2 predictability could be achieved by reducing uncertainties in the GPP sensitivity to soil moisture and by accurate observational products
for GPP IAV.
European Commission4C - Climate-Carbon Interactions in the Current Century (821003)National Science FoundationOCE-1752724Introduction
Near-term predictions of atmospheric CO2 concentrations are an essential step towards the evaluation of climate mitigation efforts and the
development of carbon monitoring programmes . However, the high interannual variability (IAV) in land–atmosphere carbon
fluxes, specifically gross primary productivity (GPP), drives the variability in atmospheric CO2 and limits its predictability
. Therefore, the skilful prediction of GPP is a crucial step towards the real-time verification of anthropogenic carbon
emissions and the evaluation of mitigation efforts.
The usual approach to evaluate the predictability of an Earth system variable is to compare predictions to observed values. In the case of GPP, this
is complicated by the uncertainty in GPP observations . As an alternative to calculating the actual predictability that is based
on observations, the potential predictability can be assessed by evaluating how well the models can predict their own carbon flux variability. To do this, an ensemble of
simulations with an Earth system model (ESM) is initialized from quasi-identical conditions. In a system with little predictability, the spread among
the ensemble members increases quickly until all predictive capability is lost when the ensemble spread reaches the magnitude of the IAV. There
are, however, certain processes in the Earth system that provide predictability and hinder the divergence of the ensemble members. For example, the
El Niño–Southern Oscillation (ENSO) produces predictable climate anomalies that have a sustained impact on GPP . Other processes extend predictability by providing “memory” that maintains the initial conditions. Soils, for example, store
initial moisture anomalies by acting as a buffer between the atmosphere and the vegetation . Soil moisture anomalies
are further extended through land–atmosphere coupling, which creates a feedback loop that enhances the persistence of these anomalies
. The initial conditions of the simulations are maintained through the lagged response of plant growth to climatic
conditions. Slowly reacting vegetation can cause precipitation anomalies or prolonged drought . Given all of these mechanisms of predictability, we find that terrestrial carbon fluxes are predictable for 2 years
.
Although several ESMs reproduce the same predictability horizon for globally integrated terrestrial carbon fluxes , there are substantial differences in the spatial patterns of GPP IAV
. The reason for these differences lies in poorly constrained ecosystem processes that have
a large impact on GPP. One of these differences arises from the uncertainty in the sensitivity of GPP to environmental drivers
. The sensitivity of GPP to
temperature and precipitation varies among studies, leading to ongoing discussion concerning the dominant driver of global carbon fluxes
. The different sensitivity of GPP to precipitation across ESMs is further exacerbated by the large disagreement in water
storage anomalies . The simulated annual cycle of water storage anomalies of major river basins is between 0.1 and 2 times that of
the observed variability. These deviations in hydrological variability between models are likely to cause similar deviations in GPP IAV, especially in
semi-arid watersheds. Further differences in GPP IAV are due to variations in ecosystem boundaries and the related spatial distribution of plant
productivity. The Amazon rainforest, for instance, is a hotspot of land–atmosphere carbon fluxes and provides a large contribution to the
predictability of atmospheric CO2. However, the transition zone
between the wet tropical forest and semi-arid tropics within the Amazon Basin varies among the models due to differences in their representation of
land cover . Such differences in biome boundaries also modify the impact of ENSO on GPP
IAV. ENSO produces distinct spatial patterns of climatic anomalies that significantly influence the GPP on 32 % of the vegetated land area
. These ENSO-related climate patterns will have a different impact on GPP depending on the type of biomes under their
influence. In addition to the spatial variability, many ESMs struggle to reproduce the seasonal variability in carbon fluxes. This can be seen in the
large biases in phenology . Several models overestimate the seasonal amplitude of leaf area index (LAI) in the tropics and
mismatch the timing of the LAI maxima and minima .
All of these uncertainties suggest that there are large differences in the patterns of GPP IAV among the ESMs, but it is currently unclear how these
differences affect the predictability of GPP. With this study, we want to extend our understanding of GPP predictability by considering the different
patterns of GPP IAV among the ESMs. In a multi-model analysis, we investigate which processes drive the IAV in GPP and which processes allow the GPP
IAV to be predictable. Regression analysis is used to determine the role of three environmental variables (soil moisture, temperature, and radiation)
on GPP IAV and GPP predictability. We analyse the cause of differences in GPP predictability across ESMs, identify the areas of large discrepancies,
and determine the factors contributing to the attached uncertainties. The aim of this study is to reveal which factors of GPP representation are
limiting the predictability of atmospheric CO2.
MethodsData sources
We analyse model output from the Decadal Climate Prediction Project (DCPP; ). This protocol-driven multi-model approach aims
at studying the decadal predictability of the Earth system with hindcasts, quasi-real-time forecasts, and case studies on predictability
mechanisms. The hindcasts are initialized annually from 1960 to 2017 or 2019 with the starting dates between November and January and at least 10
ensemble members. Simulations are driven by Coupled Model Intercomparison Project Phase 5 (CMIP5) or Phase 6 (CMIP6) historical forcing and extended by Representative Concentration Pathway (RCP) 4.5 or Shared Socioeconomic Pathway (SSP) 2-4.5 afterwards. The DCPP framework does
not prescribe any specific initialization or data assimilation methods and leaves these details to be decided by the respective modelling centres.
We additionally use the Community Earth System Model 2 (CESM2) output from the Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system
. The SMYLE hindcasts ensembles are initialized four times per year with 20 ensemble members between 1970 and
2019. In this study, the November initializations are used to achieve the highest comparability with the DCPP hindcasts.
We compare the spatial GPP IAV patterns of the ESMs with observation-based GPP products. Because of the uncertainty among observations, we include
products based on three different sources. The Vegetation Photosynthesis Model (VPM; ) is a remote-sensing-based product that
uses a light use efficiency (LUE) model to calculate GPP. VPM uses satellite data from MODIS and an improved LUE algorithm that considers leaf
quality. The second data set is GOSIF which is based on data from MODIS and the Orbiting Carbon Observatory-2. GOSIF uses
solar-induced chlorophyll fluorescence, which is a more recent approach, to calculate GPP. Lastly, we use FLUXCOM (version RS–METEO, ERA5;
), which uses machine learning to upscale flux tower observations with meteorological and remote-sensing data. Because
FLUXCOM underestimates the IAV in GPP , it is recommended to scale the data so that the IAV of integrated FLUXCOM fluxes resembles observations . The VPM, GOSIF, and FLUXCOM data are linearly detrended before calculating
the IAV. Due to its long time span, FLUXCOM is detrended over two periods (1979–1999 and 2000–2018).
CanESM5
The Canadian Earth System Model version 5 (CanESM5; ) consists of the Canadian Land Surface Scheme (CLASS) and Canadian
Terrestrial Ecosystem Model (CTEM) with a T63 grid with an approximate resolution of 2.8∘. The atmosphere is realized with the Canadian
Atmospheric Model (CanAM5) with 49 vertical levels. Ocean physics is simulated with CanNEMO, on a tripolar grid with a resolution of 1 to
1/3∘ and 45 vertical levels, and ocean biogeochemistry is represented by the Canadian Model of Ocean Carbon (CMOC).
The CanESM5 hindcast simulations are initialized every January between 1960 and 2017 with 20 members. The 3D potential temperature and salinity of the
global oceans are nudged toward the monthly Ocean Reanalysis System 5 (ORAS5; ). Sea surface temperatures are nudged to the Extended
Reconstructed Sea Surface Temperature (ERSSTv3; ; ) until 1981 and to the Optimum
Interpolation Sea Surface Temperature (OISST; ) afterwards. Sea ice concentration is nudged to the Hadley Centre Sea Ice
and Sea Surface Temperature data set (HadISST.2; ), and sea ice thickness is nudged to monthly climatology until 1988 and to the SMv3
statistical model of afterwards. For the atmosphere, temperature, horizontal wind components, and specific humidity are
nudged to ERA-40 until 1978 and to 6-hourly ERA-Interim data afterwards.
CESM1-CAM5
The Community Earth System Model (CESM) version 1.1 is used to produce 40-member simulations in the Decadal Prediction
Large Ensemble (DPLE) project . The model components are the Community Land Model version 4 (CLM4;
) with a 1∘ resolution, the Community Atmosphere Model version 5 (CAM5) with 30 vertical levels, the
Parallel Ocean Program (POP2) with 60 vertical levels, and sea ice with the Community Ice Code (CICE4).
The CESM1-CAM5 hindcasts are initialized every November. There is no direct assimilation of observations to produce the initial conditions; instead,
ocean and sea ice are obtained from simulation runs forced by historic atmospheric surface fields . Initial conditions for
the land and atmosphere components are obtained from ensemble member no.34 of the CESM Large Ensemble .
CESM2
CESM version 2 runs on a 1∘ horizontal resolution for all components. The atmosphere is simulated by the
Community Atmosphere Model version 6 (CAM6) with 32 vertical levels. The ocean model is the Parallel Ocean Program version 2 (POP2) with 60 vertical
levels, with the biogeochemistry from the Marine Biogeochemistry Library and sea ice by CICE version 5.1.2 (CICE5) with 8 vertical layers. The land
component is simulated by the Community Land Model version 5 (CLM5; ), which has several updates to its predecessors
CLM4 and CLM4.5, leading to a better representation of the global carbon cycle in benchmarks .
Hindcasts are initialized on the first day of every November, February, May, and August, and they run for 24 months. Only the November initializations are used
in this analysis to increase comparability with the DCPP simulations. Initial conditions for the atmosphere, ocean, and sea ice stem from the Japanese
55-year Reanalysis – JRA-55 () and JRA55-do (). The land surface and biogeochemistry are
initialized from forced CLM5 simulations.
CMCC-CM2-SR5
The Euro-Mediterranean Centre on Climate Change coupled climate model (CMCC-CM2; ; ) is based on
CESM and consists of the Community Land Model (CLM4.5) with a 1∘ resolution and the atmospheric model CAM5.3 with 30 vertical levels. The
distinguishing element of CMCC-CM is the ocean, which is simulated by NEMO3.6, while sea ice is modelled by CICE4.
Workflow of the statistical analysis. In panel (a), lead years 5 to 10 of the hindcast simulations are used to train a regression model that calculates the components of GPP caused by the environmental drivers. In panel (b), the regression model is applied to lead years 5 to 10 to calculate the IAV in the GPP components (σGPP(IAV)) and to lead year 1 to calculate the mean ensemble spread of the GPP components (σGPP(LY1)).
The 10-member hindcast simulations are initialized every November . The ocean initial conditions are from CHOR
until 2010 and from CGLORSv7 afterwards. The atmosphere is initialized from ERA-40 until 1978
and from ERA-Interim afterwards. The land surface is initialized using the reanalysis with two different meteorological forcings. For this reason, only
ensemble members 1, 3, 5, 7, 8, and 9 are used, as the other members start from a different state and this would not allow for the quantification of
predictability by ensemble spread.
Because the CMCC-CM2-SR5 fields containing land–atmosphere carbon fluxes are not exported for the DCPP runs, the historical simulations are used to
infer the relationship between environmental drivers and GPP.
IPSL-CM6A-LR
The ESM developed by the Institute Pierre Simon Laplace (IPSL; ) uses the ORCHIDEE v2.0
land surface model (LSM) with an average resolution of 157 km. The atmosphere is simulated at the same resolution by LMDZ6 with 79 vertical
levels, the ocean is simulated with NEMO-OPA with a 1∘ resolution and 75 vertical levels, and ocean biogeochemistry is simulated with PISCESv2.
The hindcast simulations of IPSL-CM6A-LR come from the DCPP project. The 10-member ensembles start annually in January between 1960 and 2016. The
hindcasts are initiated from an assimilation run with EN4 sea surface temperatures and Atlantic sea surface salinity
. Subsurface ocean, sea ice, and atmosphere are not assimilated.
MPI-ESM-LR
MPI-ESM-LR is the Max Planck Earth System Model (MPI-ESM1.1; ) used in a low-resolution configuration. The land is simulated by JSBACH with dynamic vegetation . The ocean component is the Max Planck Institute for Meteorology Ocean Model (MPIOM) with a horizontal resolution of about
150 km and 40 vertical levels. The atmosphere is simulated by ECHAM at a T63 resolution with 47 vertical layers, and ocean biogeochemistry is
represented by the Hamburg Ocean Carbon Cycle (HAMOCC) model.
The utilized hindcast simulations of MPI-ESM-LR are conducted within the MiKlip project . The decadal prediction system comprises
10-member ensembles starting every January between 1961 and 2014. Ocean temperature and salinity are initialized from the Ocean Reanalysis System 4
(ORAS4; ), and the atmosphere is initialized from ERA-40 from 1960 to 1998 and from ERA-Interim from 1990 to 2014.
Statistical approachOverview
An overview of the statistical analysis is shown in Fig. . Every hindcast simulation is initialized from quasi-identical
conditions. With the increasing lead time, the variability within the hindcast ensemble (standard deviation across the ensemble members for a given
time) also increases until it reaches the IAV. Based on this assumption, the hindcast simulations are split into two groups by lead time: lead year
1 and lead years 5 to 10. For lead years 5 to 10, the effects of ocean and atmosphere initialization are assumed to be
negligible. These years are used to calculate the monthly mean climatology, which is removed from both groups to obtain the anomalies. The anomalies
of lead years 5 to 10 are used in a regression analysis to derive the sensitivity of GPP to the environmental drivers, i.e. soil moisture,
temperature, and radiation (Fig. a). The regression model is applied to the anomalies of lead years 5 to 10 to calculate the
IAV in all GPP components (σGPP(IAV)) and to the anomalies of lead year 1 to calculate the ensemble variability in all GPP
components (σGPP(LY1)) (Fig. b). We derive the predictability of GPP by comparing σGPP(LY1) to
σGPP(IAV). Because the hindcast simulations are not evaluated against observations, the calculated predictability reflects the
potential predictability.
Climatology and sensitivity
The monthly mean climatologies are calculated from lead years 5 to 10, with a 3-year moving-window approach for every calendar year. Because
the moving-window method is not applicable for the first decade of hindcast initializations, the monthly climatology for the 1960s (1970s for CESM2)
is calculated based on all lead years 5 to 10 within the 1960s (or 1970s). Anomalies of all input fields are calculated by subtracting the monthly
climatologies from the hindcast data. The obtained anomalies of lead years 5 to 10 make up a data set of n simulation years:
n=6 hindcast years ×no. ensemble members ×no. initializations.
A total of 10 to 40 ensemble members and 56 to 58 initializations result in sample sizes of 3330 to 13 680. Because the hindcast length is only 2 years
for the CESM2 simulations, a different approach is used here. Instead of lead years 5 to 10, only lead year 2 is selected and only five random
ensemble members are used from every hindcast to reduce the number of simulations with the same initial conditions. To offset the reduced number of
data points, five random simulations are also added from the hindcast simulations initialized in February, May, and August.
The resulting data set of lead year 5 to 10 anomalies is used to derive the sensitivity of GPP to the environmental drivers (ENV: soil
moisture, temperature, and radiation) by fitting a regression model for every grid cell and month of the year. The relationship between GPP and the
environmental drivers is frequently non-linear, sometimes due to specific breakpoints in the functional representation of GPP. For this reason,
segmented linear regression (SLR) is used to model GPP from the environmental drivers . SLR finds breakpoints in the data,
splitting them into multiple ranges and fitting an individual regression model to each of the data ranges. Here, a single breakpoint is determined for
each of the three predictor variables.
Because environmental drivers have some degree of collinearity, the regression analysis will not be able to fully attribute the GPP anomalies to their
specific causes. Therefore, the resulting sensitivities should be taken as a “contributive” and not a “true” effect of the environmental drivers
.
Variability and predictability
The SLR can now be applied to individual simulations to determine the component of GPP anomalies that can be attributed to each of the environmental
drivers:
ΔGPP≈ΔGPPSoilmoisture+ΔGPPTemerature+ΔGPPRadiation.
The three components of GPP anomalies (ΔGPPENV) are calculated for every simulation within the hindcast lead time 5 to 10. From
the results, we calculate the IAV in the components for every grid cell and month of the year (σGPP(IAV)ENV). Similarly, the SLR
is applied to the anomalies of lead year 1 to calculate the standard deviation for every month within the hindcast simulations. Averaging over the
standard deviations of every hindcast returns the ensemble variability in lead year 1 (σGPP(LY1)ENV).
Panel (a) presents the exemplary composition of GPP variability and predictability in a tropical forest. The components of GPP IAV are calculated from lead years 5 to 10 (green bars) and the ensemble variability is calculated from lead year 1 (red bars). In the exemplified region, most of the variability is caused by soil moisture and radiation, and GPP is not restricted by temperature. Predictability is exclusively provided through soil moisture. Panel (b) demonstrates the need for the two predictability metrics using a tropical savanna and an arid shrubland as examples. The predictable component (pc) is the absolute predictable IAV, and the predictable fraction (pf) is the pc scaled by the IAV. While the arid shrubland has a better potential to retain memory (as seen from the high pf), these ecosystems contribute little to the variability in atmospheric CO2, which can be better assessed using the pc.
The predictability is assessed by comparing σGPP(LY1) to σGPP(IAV). A high predictability of an input field
means that the ensemble variability is restricted for some time after the hindcast initialization and does not reach the IAV immediately
(Fig. a). In this study, we use two metrics to evaluate different aspects of predictability. We calculate the fraction of GPP IAV
that is predictable (the predictable fraction – pf) to assess the ability of a system to retain memory. Although this metric is useful for quantifying
the mechanisms that provide predictability at a local level, the pf is not suitable for assessing how GPP predictability affects the predictability
of atmospheric CO2. This is because the regions with a high pf do not necessarily contribute much to the global GPP fluxes. The
regions with the highest pf values are often in deserts with very low carbon fluxes . To assess the contribution of
GPP predictability to atmospheric CO2 predictability, we calculate the absolute portion of the IAV that can be predicted as the predictable
component (pc). The pc is the difference between IAV and ensemble variability and is generally higher in regions that contribute more to
CO2 IAV:
3pcENV=σGPP(IAV)ENV-σGPP(LY1)ENV,4pfENV=pcENVσGPP(IAV)ENV.
The use of the two predictability metrics is exemplified in Fig. b.
Results and discussionPatterns of GPP IAV
In order to understand what the models are predicting, we start by analysing the patterns of GPP IAV. There are differences in the overall magnitude
of GPP IAV among ESMs, with CanESM5, CMCC-CM2-SR5, and IPSL-CM6A-LR at the lower end of the IAV
spectrum, whereas CESM2 and MPI-ESM-LR are at the higher end of the IAV
spectrum. Factors that could explain some of the differences in the overall magnitude of IAV are the relatively weak ENSO teleconnection in CanESM5
or the low total GPP in CMCC-CM2-SR5 .
GPP IAV in three observational products (VPM, GOSIF, and FLUXCOM) and six ESMs. Panel (a) presents the spatial patterns of GPP IAV, with brighter colours representing higher values. The data are scaled across ESMs to highlight differences in patterns and not absolute differences. Panel (b) displays the spatial correlations between the products.
Because we focus on the spatial patterns of IAV rather than absolute differences, the GPP IAV patterns are scaled for better comparison
(Fig. ). We find agreement in the large-scale patterns of GPP IAV, with most of the IAV in the ESMs in the northern Amazon Basin and in
the semi-arid tropics like western South America, southern Africa, South Asia, Australia, and southern North America (detailed maps of the location of
the semi-arid regions in the ESMs are shown in Fig. ). A closer examination of GPP IAV reveals that the ESMs show less agreement in
the regions contributing most to the IAV, especially in the semi-arid tropics. Some ESMs have large hotspots of GPP IAV that cannot be found in
other ESMs. These unique hotspots are the western Amazon Basin (CanESM5), central South America (CESM2), southern Africa (MPI-ESM-LR and CanESM5), and
Australia (MPI-ESM-LR). We find the most consistency on the northern coast of South America, which is a high-IAV region in most ESMs. The spatial
patterns of IAV have an average correlation of 0.47 among the ESMs. The ESM with the lowest correlation values is CESM2, with an average of 0.29. CESM2
stands out with a very low IAV in the tropical rainforests of the Amazon and Congo basins and in Southeast Asia.
The correlation among the observational products is 0.65; although these products confirm most of the IAV patterns of the ESMs, we find stronger deviations
in South America. While many ESMs have IAV hotspots along the northern coast of South America, this is only reproduced in FLUXCOM. However, all
observational products show a high GPP IAV in western South America, which can not be found in the ESMs. The spatial patterns of GPP IAV revealed here
correspond to the literature, which suggests that the semi-arid tropics, tropical forests, grasslands, and croplands are the main drivers of global
GPP IAV . These studies also reflect the large uncertainty in the
contribution of the individual semi-arid regions to GPP IAV between the models, in particular the uncertain role of Australia. In an ensemble of
eight LSMs, Australia contributed 39 %, semi-arid tropical Africa contributed 32 %, and Southeast Asia contributed 10 % to global GPP IAV, whereas temperate South
America only contributed 2 % . Although Australia has the highest mean model IAV, the variability in IAV between the
models is also the largest, with GPP IAV ranging between 0.26 and 1.01 PgCyr-1. While the large role of the dry tropics in
driving GPP IAV is not disputed, it is likely that ESMs underestimate GPP IAV in wet tropical forests . This
results from the limited availability of observations due to few flux towers and from the fact that the quality of remote-sensing products is limited in tropical
forests due to saturation effects and a high cloud cover . In this study, the low GPP IAV in tropical forests is
especially evident for CESM2, as IAV increases abruptly outside the wet tropical forests.
The contribution of environmental drivers to GPP variability (σGPP(IAV)ENV). Colour intensity represents higher GPP IAV. The data are scaled across ESMs to highlight differences in patterns, not absolute differences. Bars represent the mean contribution of environmental drivers to global GPP IAV (kgC10-9m-2s-1).
The divergence in GPP IAV across different ESMs is largely caused by three factors: the sensitivity of carbon fluxes to climatic drivers
, as discussed in Sect. 3.2; phenology ; and meteorological input
. The role of phenology is crucial because the amount and quality of leaves determine the carbon fluxes between the
land and the atmosphere . Most LSMs tend to have a better representation of the growing season type and growing season
boundaries in the wet than in the semi-arid tropics. analysed the start and end months of growing seasons in eight LSMs
under the same climate forcing and found several regions with a wide range of simulated growing seasons. The largest uncertainties in the growing season
are in the semi-arid tropics, the same regions in which we find little agreement in GPP IAV. The start of the growing season ranges from February to October in
Australia and from March to October in southern Africa, while the end of the growing season ranges from March to September in Africa between 0 and
15∘ N. The vegetation types with the largest uncertainty in growing season timing are broadleaf, deciduous shrubs, which are mostly located in
northern Australia; Southern Hemisphere crops; broadleaf, evergreen trees; and grasses. The better-performing LSMs have a high number of plant
functional types or use more complex phenology schemes that depend on plant functional types, and they also use a larger number of environmental variables to
constrain phenology. Its complex phenological scheme puts ORCHIDEE among the better-performing LSMs and might also explain the high correlation of
IPSL-CM6A-LR with the GPP IAV for all three observational products.
A misrepresentation of phenology could also explain the overall high IAV in MPI-ESM-LR. JSBACH overestimates the seasonality of the LAI in the
tropics, and this becomes visible in the strong seasonal cycle of tropical LAI in MPI-ESM-LR . Consequentially, the area of the
evergreen tropics is underestimated in JSBACH . This leads to a larger fraction of semi-arid tropics with a higher GPP IAV. This
amplification of the equatorial dry season might lead to the high GPP IAV in the northern Amazon and contribute to the overall high IAV in MPI-ESM-LR
.
Drivers of GPP IAV
To determine the drivers of GPP IAV, we analysed the sensitivity of GPP to environmental drivers using regression analysis. The globally averaged
contribution of the drivers to GPP IAV is shown as the bars in Fig. . The CLM family and CanESM5 have similar patterns, with
temperature dominating the IAV or being on par with soil moisture. IPSL-CM6A-LR and MPI-EMS-LR have distinctly different patterns, with soil moisture
dominating the IAV and radiation contributing equally or more than temperature. A reason for the large contribution of soil moisture to GPP IAV in
IPSL-CM6A-LR and MPI-ESM-LR could be that both ESMs are at the high end of soil moisture IAV for deep soil layers in the Southern Hemisphere
, where many of the semi-arid ecosystems are located that contribute most to GPP IAV. Another explanation could be that out
of 11 ESMs, IPSL-CM6A-LR and MPI-ESM-LR have the lowest warm-season soil moisture . This increase in dryness can lead to
a larger extent of semi-arid ecosystems with a generally higher GPP IAV. Another effect of reduced warm-season soil moisture can be an increase in
the land–atmosphere coupling strength . Stronger land–atmosphere coupling would explain the higher correlation between
soil moisture and temperature in IPSL-CM6A-LR and MPI-ESM-LR and would make the regression coefficients shift towards the
stronger predictor, which is soil moisture.
The spatial drivers of GPP IAV show agreement in the wet and arid tropics, whereas there is little consistency in the semi-arid transition zones
(Fig. ). In many ESMs, the GPP IAV in the wet tropics and in eastern China is induced by radiation, while soil moisture becomes more
prevalent along the aridity gradient and is driving IAV in southern Africa, southern South America, and Australia. The IAV in the remaining land
surface is driven predominantly by soil moisture in IPSL-CM6A-LR and MPI-ESM-LR, whereas it is driven by a combination of temperature and soil moisture in the
remaining ESMs.
De Martonne aridity index of the analysed models.
Some of the differences in GPP sensitivity among the ESMs can be explained by differences in aridity. A higher sensitivity to soil moisture can result
from a dryer climate. The distribution of climate zones in the analysed models is shown in Fig. using the De Martonne aridity index
. MPI-ESM-LR and CanESM5 show an above-average extent of arid and semi-arid regions in Australia and southern
Africa. This could explain the high sensitivity of GPP to soil moisture in these regions. Differences in climate also explain some of the discovered
GPP patterns in the Amazon Basin. CESM2 is the model with the most humid climate in the Amazon Basin, which could be the reason for the low
sensitivity of GPP to soil moisture and the generally low GPP IAV in this region. In contrast, we find that CanESM5 is on the other side of the spectrum, with a relatively
dry Amazon Basin, leading to a higher sensitivity to soil moisture and a high GPP IAV. However, there are also differences in GPP sensitivity that
cannot be explained by differences in climate. IPSL-CM6A-LR is more or equally humid in Australia, southern Africa, and South America than the models
of the CLM family, despite having a high sensitivity of GPP to soil moisture in these regions. These differences are more likely to be caused by
differences in their land surface models than by climate.
The general patterns of GPP sensitivity agree with reported sensitivity patterns in the literature. Multi-model averages and observations of GPP
sensitivity agree with the larger role of temperature in tropical forests, radiation in western Amazonia, and the importance of precipitation in the
semi-arid tropics . However, the role of water on carbon fluxes increases when soil
moisture is used instead of precipitation in sensitivity studies . This can be observed in the sensitivity of net biome
productivity (NBP), which shows a more balanced contribution of soil moisture and temperature in the wet tropical forests . Although the comparison of GPP and NBP imposes limitations, GPP explains the majority of tropical NBP
. This suggests that the low water sensitivity of tropical GPP might explain the lower than expected GPP IAV in tropical
forests in ESMs.
Predictability of GPP
To analyse the role of GPP in the predictability of atmospheric CO2, we assessed GPP predictability using two metrics. The predictable
component (pc) is calculated as the difference between ensemble variability and IAV and provides a measure of absolute predictable
variability. The pc can be used to assess the predictability of GPP fluxes that contribute to CO2 variability. The predictable fraction
(pf) is the ratio of pc to IAV and illustrates how well memory is retained in the system. This metric can be used to compare the
predictive performance of different biomes, for example.
The contribution of environmental drivers to the predictable component (pc) of GPP. The contribution is calculated as the difference between the IAV and the ensemble variability within lead year 1 of the hindcast experiments. Values are scaled for each ESM. Bars represent the mean contribution of environmental drivers to the pc (Δσ GPP, in kgC10-9m-2s-1). Numbers on top of the bars show the predictable fraction (pf), which is the share of the pc to overall IAV. The correlation between GPP IAV and the pc is shown at the bottom of the plots.
There is relatively high consistency among the pf values of the environmental drivers across the models
(pfSoilmoisture>pfTemperature>pfRadiation; the numbers above the bars in Fig. ). This
pattern reflects the anticipated differences in predictability among the drivers. Atmospheric fields, as radiation, have a low persistence, leading to
a low predictability of 2 weeks for most regions . Soil hydrology, on the other hand, acts as a low-pass filter that removes
the unpredictable high-frequency variability in precipitation and allows a predictability of soil moisture of around 2 years
. Temperature gains most of its predictability through sea surface temperature (SST) forcing in the equatorial regions
and via land–atmosphere coupling in the semi-arid tropics .
The overall pf of CESM2, CMCC-CM2-SR5, and IPSL-CM6A-LR falls into a narrow window of 0.19 to 0.21. CESM1-CAM5 has the highest pf with a
value of 0.24. It is likely that this increased share of predictable IAV is not due to differences in model structure but rather due to the large number of ensemble members (40). Most other ESMs in this study have only 10 ensemble members, which is not enough to capture the difference between ensemble
variance and IAV; thus, an increase in ensemble members leads to an increase in prediction skill . However, despite
having 20 ensemble members, CanESM5 has the lowest pf among the models. A possible explanation could be the low IAV in deep-layer soil moisture
in CanESM5 . A limited ability to reproduce the full spectrum of soil moisture variability could mean that the soils have a
smaller buffering capacity. As a result, they are not able to simulate the observed persistence of soil moisture anomalies, leading to a reduction in
predictability. On the other hand, a high variability in soil moisture does not guarantee a high pf, as seen, for example, for MPI-ESM-LR. The
low pf of MPI-ESM-LR can be explained by the sensitivity of GPP to radiation. As
only 7 % to 12 % of the radiation-induced IAV is
predictable, a high share of σGPPRadiation reduces the predictability of GPP. This becomes evident in MPI-ESM-LR, in which the
share of σGPPRadiation is 20 % higher than in the other ESMs.
We find that the regions contributing to the predictability of atmospheric CO2 (pc) are highly related to the IAV patterns. The
spatial correlation between pc and IAV exceeds 0.79 in all models except CanESM5. Indeed, these high correlations between predictability and IAV
align with our understanding. Under a constant pf, pc would grow linearly with increasing IAV, leading to a perfect correlation. These
high correlations show that the differences in the predictability of atmospheric CO2 are determined more by the differences in GPP IAV than
the differences in the pf of GPP. While the pf values show that the ESMs have a similar degree of memory retention, there are few
overlaps in the spatial distribution of the pc, with an average correlation of 0.38 between the ESMs. For an alternative quantification of this
disagreement, we separate the high-predictability grid cells, which are the grid cells contributing to the top 20th quantile of pc. A total of 74 % of
these high-predictability grid cells are unique to only one ESM, and only 8 % of high-predictability grid cells can be found in three or more
ESMs.
Although the spatial patterns of the pc broadly resemble the patterns of GPP IAV, there are some slight differences between these fields. The
pc is relatively high along the northeastern coast of South America in most ESMs. This could be due to the high climate predictability caused
by slowly evolving Atlantic SST patterns . Other systematic differences can be explained by the differing pc of the
environmental drivers. The most evident is the difference between IAV and predictability in regions where GPP IAV is driven by radiation. This leads
to relatively low predictability in the tropical rainforests of the western Amazon Basin and the Congo Basin. An exception is the predictability
provided through radiation on the Southeast Asian islands in IPSL-CM6A-LR and CESM1-CAM5. High predictability in these regions could be explained by
the proximity to the ENSO SST region. Strong and predictable SST anomalies in the tropical Pacific that surround the islands can directly influence
the cloud cover over land. The predictable component is also higher over areas where IAV is driven by soil moisture rather than temperature. In many
ESMs, this leads to a high predictable component in the semi-arid regions of South America, Africa, and India.
Conclusions
We tested the ability of six ESMs to predict terrestrial GPP and determined their similarities and the sources of uncertainties. The ESMs are fairly
similar in their ability to retain memory in hindcast simulations and predict their own variability, with the pf values of four of the ESMs
falling between 19 % and 24 %. Most of the GPP pf is provided by soil moisture. Up to 32 % of the GPP IAV caused by soil moisture
is predictable, whereas this value is only 7 % to 12 % for the IAV caused by radiation. The differences in the pf values among ESMs are due to the ensemble
size and the sensitivity of GPP to radiation. Further sources of predictability that are not studied here are long-term vegetation
dynamics. Specifically, the large and structural changes like tree mortality and recruitment
. These processes only occur in extreme years and cause shifts in ecosystem states with long-lasting effects. The correct
representation of these processes in ESMs allows them to reproduce the low-frequency IAV in vegetation, thereby extending the pf of GPP.
Although ESMs are similar in the fraction of GPP IAV that they can predict, there are substantial differences in the patterns and drivers of GPP IAV. The
ESMs have distinct, non-overlapping hotspots of GPP IAV that drive the variability in atmospheric CO2. We find large disparities in the role
of Australia, southern Africa, and central South America in GPP IAV. The leading cause of the uncertainties in IAV patterns are differences in the
response of GPP to soil moisture and the capability of the ESMs to simulate soil hydrology accurately. These differences materialize through the
direct effect of soil moisture on photosynthesis and through the role of soil moisture on phenology. The inability of ESMs to reproduce GPP IAV also
means that there are regions where the potential predictability of GPP does not resemble the actual predictive skill.
This study shows that the predictability of atmospheric CO2 is currently not limited by the processes that provide predictability in the
Earth system but rather by the representation of carbon flux variability patterns. The mismatches in GPP IAV imply that the IAV in atmospheric
CO2 is caused by different regions and by different drivers across the ESMs. Consequentially, when ESMs predict the atmospheric
CO2, the GPP anomalies that constitute the predicted CO2 growth rate originate from different regions. Because the predicted
CO2 depends more on the distribution of GPP IAV hotspots than actual mechanisms that provide predictability, CO2 forecast skill is
not a suitable metric for studies on carbon flux predictability. An ESM with a high carbon flux predictability can be outperformed with respect to CO2
forecast skill by a model that has a better representation of IAV patterns. A more suitable measure to assess carbon flux predictability could be the
globally averaged anomaly correlation coefficient.
With the current uncertainties in GPP IAV patterns, the prediction of atmospheric CO2 relies less on the prediction of regional climate
anomalies and more on the predictable global climate patterns like ENSO. These global climate anomalies are able to balance out the regional
differences in GPP IAV patterns by affecting large parts of the land surface simultaneously. In order to utilize the benefits of regional climate
predictability for the predictability of CO2, further work ought to focus on constraining GPP IAV and not on the processes providing
predictability. The most limiting aspect in the use of ESMs to predict atmospheric CO2 is a better understanding of the drivers of carbon
flux variability. Whether GPP is limited by moisture, temperature, or radiation does not only affect variability patterns but also the predictability
of the fluxes. An overestimation of humidity in an ecosystem by an ESM would result in GPP being more controlled by radiation than soil moisture,
leading to an underestimation of predictability (or vice versa for systems that are too dry).
The findings of this study also suggest that previous estimations of ESM-based CO2 forecast skill are underestimating the predictive
capabilities of these systems. Various post-processing strategies could help to produce a CO2 forecast skill that is not obscured by
inaccurate IAV patterns but is a closer representation of the actual performance of ESM-based prediction systems. These strategies could include the
scaling of carbon flux IAV patterns to resemble the observed IAV patterns. As there are strong regional differences in the predictive performance
among the ESMs, another strategy would be to combine ESM predictions in a way that utilizes these differences. This could be done in a regionally
weighted multi-model approach.
The limiting factor to predicting atmospheric CO2 is the chaotic nature of weather and climate. However, our results show that we have not
reached this limitation yet and that we are instead constrained by our understanding of terrestrial carbon flux variability. The development of
observational products for terrestrial carbon fluxes, especially in the tropics, remains the main objective on the path of improving the
predictability of the global carbon cycle and atmospheric CO2.
Code and data availability
The data and code used to produce the figures shown in this study are available at
https://hdl.handle.net/21.11116/0000-000D-72B4-7.
Author contributions
The study was conceptualized by ID, TI, and VB; ID developed the methodology, ran the analysis, and wrote the original draft; NL, AC, VKA, TI, and VB reviewed and edited the manuscript; and VB and TI provided supervision.
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
The authors wish to thank the modelling groups that participated in the DCPP, SMYLE, and MiKlip projects. We are also grateful to Jürgen Bader for reviewing the manuscript.
Financial support
This project has received funding from the European Union's Horizon 2020 Research and Innovation programme (4C project, grant agreement no. 821003) and from the US National Science Foundation (grant no. OCE-1752724).The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by David Medvigy and reviewed by two anonymous referees.
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