ESDEarth System DynamicsESDEarth Syst. Dynam.2190-4987Copernicus GmbHGöttingen, Germany10.5194/esd-6-435-2015Decomposing uncertainties in the future terrestrial carbon budget
associated with emission scenarios, climate projections, and ecosystem
simulations using the ISI-MIP resultsNishinaK.nishina.kazuya@nies.go.jpItoA.https://orcid.org/0000-0001-5265-0791FalloonP.FriendA. D.https://orcid.org/0000-0002-9029-1045BeerlingD. J.CiaisP.ClarkD. B.https://orcid.org/0000-0003-1348-7922KahanaR.KatoE.https://orcid.org/0000-0001-8814-804XLuchtW.LomasM.PavlickR.SchaphoffS.WarszawaskiL.YokohataT.https://orcid.org/0000-0001-7346-7988National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki, JapanMet Office Hadley Centre, FitzRoy Road, Exeter, Devon, EX1 3PB, UKDepartment of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN, UKDepartment of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UKLaboratoire des Sciences du Climat et de l'Environment, Joint Unit of CEA-CNRS-UVSQ, Gif-sur-Yvette, FranceCentre for Ecology and Hydrology, Wallingford, OX10 8BB, UKPotsdam Institute for Climate Impact Research, Telegraphenberg A 31, 14473, Potsdam, GermanyMax Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745 Jena, GermanyInstitute of Applied Energy, 105-0003 Tokyo, JapanK. Nishina (nishina.kazuya@nies.go.jp)13July20156243544516September201410October201422May201522June2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.earth-syst-dynam.net/6/435/2015/esd-6-435-2015.htmlThe full text article is available as a PDF file from https://www.earth-syst-dynam.net/6/435/2015/esd-6-435-2015.pdf
We examined the changes to global net primary production (NPP), vegetation
biomass carbon (VegC), and soil organic carbon (SOC) estimated by six global
vegetation models (GVMs) obtained from the Inter-Sectoral Impact Model
Intercomparison Project. Simulation results were obtained using five
global climate models (GCMs) forced with four representative concentration
pathway (RCP) scenarios. To clarify which component (i.e., emission
scenarios, climate projections, or global vegetation models) contributes the
most to uncertainties in projected global terrestrial C cycling by 2100,
analysis of variance (ANOVA) and wavelet clustering were applied to 70
projected simulation sets. At the end of the simulation period, changes from
the year 2000 in all three variables varied considerably from net negative to
positive values. ANOVA revealed that the main sources of uncertainty are
different among variables and depend on the projection period. We determined
that in the global VegC and SOC projections, GVMs are the main influence on
uncertainties (60 % and 90 %, respectively) rather than
climate-driving scenarios (RCPs and GCMs). Moreover, the divergence of changes in
vegetation carbon residence times is dominated by GVM uncertainty,
particularly in the latter half of the 21st century. In addition, we found
that the contribution of each uncertainty source is spatiotemporally
heterogeneous and it differs among the GVM variables. The dominant uncertainty
source for changes in NPP and VegC varies along the climatic gradient. The
contribution of GVM to the uncertainty decreases as the climate division
becomes cooler (from ca. 80 % in the equatorial division to
40 % in the snow division). Our results suggest that to assess
climate change impacts on global ecosystem C cycling among each RCP scenario,
the long-term C dynamics within the ecosystems (i.e., vegetation turnover and
soil decomposition) are more critical factors than photosynthetic
processes. The different trends in the contribution of uncertainty sources in each
variable among climate divisions indicate that improvement of GVMs based on
climate division or biome type will be effective. On the other hand, in dry
regions, GCMs are the dominant uncertainty source in climate impact
assessments of vegetation and soil C dynamics.
Introduction
Terrestrial ecosystems play important roles in the C cycling of climate
systems and provide various ecosystem services (e.g., water supply and wild
habitats for biodiversity); however, these ecosystem functions are threatened
by climate change . Previous model intercomparison
studies (e.g., VEMAP , dynamic global vegetation models (DGVMs)
, Coupled Carbon Cycle Climate Model Intercomparison Project (C4MIP)
, and The fifth phase of the Coupled Model Intercomparison Project (CMIP5) ) have
demonstrated a lack of coherence in future projections of terrestrial C
cycling for different global land models because of differences in their
representations of system processes. For climate change impact assessments,
the cascade of uncertainty sources must be considered . Greenhouse gas concentrations, temperature, and
precipitation are critical factors in determining the feedback of terrestrial
ecosystems in response to atmospheric carbon dioxide (CO2)
. These factors could become more important for
terrestrial ecosystem C cycling under future higher CO2 concentrations and
climate change conditions . The recent
International Panel on Climate Change assessments (AR5) took anthropogenic
CO2 emission uncertainties into account in a representative concentration
pathway (RCP) scenario . Future
projected changes in temperature and precipitation have large spatial and
temporal uncertainties even for the same radiative forcing levels because of
the different structures and parameters used in global climate models (GCMs)
. These differences could affect the global C
budget of terrestrial ecosystems. Global vegetation models (GVMs) such as
(DGVMs and components of earth system
models also have inherently large uncertainties because of differences in
model structures and parameters e.g.,. Thus, various sources of uncertainty may cause
divergence in projected C cycling.
For climate impact assessments and adaptations, different levels of
uncertainty sources should be considered in order to manage climate change
risks. Such information in impact assessments may benefit from experience
gained in the climate-modeling community and vice versa
. For example, recently, the likelihood of the
occurrence of large Amazon dieback in this century has become lower in
simulation studies because of the reduction of uncertainties in the projected
precipitation in Amazon regions among GCMs . However, the improvement of vegetation
processes in this region could result in the improvement of local
vegetation–climate feedbacks, which might contribute to changes in
temperature and precipitation in this region
. At the global scale, in earth system
models in the CMIP5 study, the sensitivities in global land climate–carbon
feedback varied considerably . The reduction of C
budget uncertainties in ecosystem models could serve to reduce climate change
uncertainties, particularly regarding the climate sensitivity of earth system
models. In addition, determining which uncertainty source is dominant in the
projection is an important aspect of recognizing the limitations of ecosystem
C cycling projections and climate impact assessments via GVM and GCM.
However, to date, how each uncertainty source (CO2 concentration, GCM, and
GVM) is important in regions and periods affected by climate change still
remains to be clarified in climate impact research.
In ecosystem climate impact assessments, how the uncertainties of climate
impacts matter is still a challenging issue, in part due to the lack of
standardized impact evaluation protocols. The Inter-Sectoral Impact Model
Intercomparison Project (ISI-MIP) is the first attempt to apply ensembles of
both impact and climate models to obtain robust future assessments
. In assessments of climate impacts on ecosystem
functions, regionality is extremely important for the severity and timing of
impacts owing to the different types of climate change in each region and the
presence of different ecosystem types in different areas
. For comprehensive climate
impact assessments on ecosystems, it is necessary to possess spatiotemporal
information for which uncertainty sources can be chosen or ignored, for which
some processes contributed to uncertainty, and for which it is known how the contribution
of each uncertainty source changed with time. Separation of the
different sources of uncertainty in projections of ecosystem models in
various aspects can be used to comprehend the uncertainties and risks in
climate impacts on ecosystem conditions and C cycling.
General properties of biome models. *PFT indicates plant functional type.
Global annual NPP, VegC stock, SOC stock, and VegC
residence time changes. The boxplot summarizes the values at the end of the
simulation period. Open circles represent outliers if the largest (or
smallest) value is greater (or less) than 1.5 times the box length from the
75 % percentile (or 25 % percentile).
In this study, we examined the C dynamics in six GVMs obtained from the
ISI-MIP. In the ISI-MIP, these GVMs were simulated using five GCMs forced
with four newly developed climate scenarios, i.e., RCP in the CMIP5
experiments . In this model intercomparison project, an orthogonal
experimental design with RCP, GCM, and GVM was adopted. In total, 70
independent simulation sets were used in this study, which enabled us to
evaluate the relative contributions to total uncertainty of the projection
factors (emission scenarios, climate projections, and GVMs) in terrestrial C
cycling. Our objective was to explore the comprehensive uncertainties in
future global and regional terrestrial C projections by decomposing the
uncertainty sources in terms of time, space, and processes.
Data and methodsModel and simulation protocol
We examined the global annual changes in net primary production (NPP),
vegetation biomass carbon stocks (VegC), and soil organic carbon (SOC) using
six GVMs obtained from the ISI-MIP . In addition, we
calculated the annual VegC residence time from annual mean VegC divided by
annual NPP, which is an index of the turnover rates of plant parts including
the mortality rates of individuals, processes modeled using baseline rates,
climate sensitivities (including fire), and competitively induced mortality,
and are affected indirectly through shifts in vegetation composition
.
The GVMs used were HYBRID4 , JeDi
, JULES , LPJmL
, SDGVM , and VISIT
, which conducts model simulations under multiple GCMs and
RCPs in the ISI-MIP. HYBRID4, JeDi, LPJmL, and JULES are DGVMs, and a fixed
land cover map was used for the other models in this study. The general
properties of the participating ecosystem models are summarized in Table 1.
More detailed information on each model can be found in
and .
These models were simulated partly in five GCMs with four RCP scenarios.
HadGEM2-ES (HadGEM), IPSL-CM5A-LR (IPSL), MIROC-ESM-CHEM (MIROC), GFDL-ESM2M
(GFDL), and NorESM1-M (NorESM) are the GCMs from a CMIP5 experiment
with bias correction for temperature and
precipitation performed by . In this study, to focus on
climate change impacts on terrestrial ecosystem C cycling, anthropogenic
land-use changes were not considered in the simulation. Every GVM was used
for a separate spin-up for each GCM, with the aim of bringing the carbon and
water pools into equilibrium using detrended and bias-corrected daily climate
inputs for 3 consecutive decades spanning 1951–1980. The number of
simulations for each GVM–GCM–RCP combination is
summarized in the Supplement (Table S2). The global climate
variables (atmospheric CO2 concentration, global mean temperature anomaly
ΔT (∘C), and global precipitation anomaly ΔP (%)) in each RCP scenario for all GCMs are summarized in the
Supplement (Fig. S1). All the simulation
results and bias-corrected climate data are available at the Earth System
Grid Federation (ESGF) portal (http://esg.pik-potsdam.de/).
Statistical analysis
We used three-way analysis of variance (ANOVA) for global ΔNPP,
ΔVegC, ΔSOC, and ΔVegC in each year as factors for RCP,
GCM, and GVM and determined their interactions in order to decompose total
variance in all ensembles into each factor . For this
analysis, we used only the simulations for the RCP2.6 and 8.5 scenarios due
to the fact that incomplete samples were simulated.
To avoid internal variability of GCMs, we used decadal-averaged values for
ΔNPP, ΔVegC, ΔSOC, and ΔVegC. Subsequently, we
calculated the Type II sums of squares in ANOVA using R . In
this study, the overall uncertainty, denoted as variance (SSoverall),
can be expressed as follows:
SSoverallit=SSRCPit+SSGCMit+SSGVMit+SSRCP×GCMit+SSRCP×GVMit+SSGCM×GVMit+SSRCP×GCM×GVMit,
in which i indicates each variable (i.e., ΔNPP, ΔVegC,
ΔSOC, and ΔVegC) and t indicates decadal time steps from the
2000s to the 2090s. SSoverallit is the total sum of
squares, and the other SS terms indicate the sums of squares for each main
effect and each interaction effect.
For grid-based assessment, we conducted additional ANOVA for ΔNPP,
ΔVegC, and ΔSOC in each grid for two projection periods (2055
and 2099). For simplicity, we did not consider the interaction terms (i.e.,
SSRCP×GCM, SSRCP×GVM,
SSGCM×GVM,
SSRCP×GCM×GVM) in the grid-based
assessment. We used only the main effects to calculate the relative
importance of each uncertainty source as follows:
SSmainit=SSRCPit+SSGCMit+SSGVMit.
The relative fractions of uncertainty are expressed as Sit for each
main effect divided by SSmainit.
In addition, using the grid-based maps, we compiled the dominant uncertainty
in each grid source on the basis of the observation-based present-day
Köppen–Geiger climatic divisions . The five major
climate types are equatorial (A), arid (B), warm-temperature (C), snowy (D),
and polar (E). In this analysis, we selected the dominant uncertainty source
in each grid and expressed them as fractions of the total grid numbers in
each climatic division.
ResultsGlobal NPP, VegC, SOC, and VegC residence time changes during 1970–2099
At the end of the simulation period, ΔNPP ranged from -7.0 to
54.3 Pg C year-1, ΔVegC ranged from -27 to
543 Pg C, and ΔSOC ranged from -195 to 471 Pg C in
the entire simulation set. The variance of ΔNPP increased with time
and was highest in RCP8.5. This was true for the other variables
(ΔVegC and ΔSOC). NPP increased in RCP8.5, except in the
HYBRID4 model. NPP in HYBRID4 forced with two GCMs (HadGEM and MIROC) showed
negative values by 2099. Global VegC stocks increased in almost all RCPs and
GVMs compared with global VegC in 2000. However, the global Veg stocks in
LPJmL peaked at ca. 2050 and then declined toward 2100. In the projection
period (2000–2099), the SOC stock in the five models (except for HYBRID4)
increased in all RCPs compared with that in 2000.
ΔVegC residence time at the global scale showed increased divergence
in scenarios with higher radiative forcing. In spite of radiative forcing,
ΔVegC declines residence time increased in HYBRID4 and decreased in
LPJmL. In RCP2.6, the median value of ΔVegC residence time was
positive. Conversely, in RCP8.5, the median ΔVegC residence time was
almost 0 within a considerable range from -2.8 to 9.0 years. In SDGVM,
ΔVegC residence time remained fairly constant in all RCPs under all
GCMs.
Fraction of variance derived from the emission scenarios
(RCPs), GCMs, and GVMs for annual NPP, VegC, SOC, and VegC residence time
changes. The variances were estimated by three-way ANOVA. The fractions in
interactions include the sum of variations of interaction terms
(RCP × GCM, RCP × GVM, and GCM × GVM).
Geographic distribution of the relative importance of
the uncertainty derived from the emission scenarios (RCPs), GCMs, and GVMs
for annual NPP, VegC, SOC, and VegC residence time changes from 2000 to 2050
and 2099 in each grid cell. The variances were estimated by one-way ANOVA.
The fraction of dominant uncertainty source in each
Köppen climatic divisions in ΔNPP (a), ΔVegC
(b), ΔSOC (c), ΔVegC residence time
(d) in the 2090s, and Köppen climate classification map for the
period 1951 to 2000 from the Climatic Research Unit (CRU) (e). In panels (a–c), the colors
indicate each uncertainty source as in Fig. 2 (i.e., orange indicates RCP,
yellow indicates GCM, and blue indicates GVM).
Contribution of each uncertainty source to Global ΔNPP, ΔVegC, and ΔSOC
Figure 2 shows the fraction of uncertainty for each variable. For NPP, the
GCM uncertainty dominated before the year 2020, and the RCP uncertainty
increased and dominated after 2040. The GVM uncertainties were approximately
20 % for most of the simulation period. For VegC, the RCP
uncertainty also increased gradually after 2020 and became approximately
40 % of the total variance by 2100. The GVM uncertainty was most
prominent for most of the projection period; however, it decreased after 2040
by 40 % of the total variance. For SOC, the GVM uncertainty
dominated throughout the projection period, with an average value of
92 % of the total variance. For ΔVegC residence time, GVM
contribution gradually increased after the 2010s and reached 74 % in the
2090s. Conversely, the contribution of GCM to ΔVegC residence time
decreased from 80 % in the 2000s to 2 % in the 2090s. Although RCP formed
a considerable part of VegC and NPP uncertainties in the latter half of the
21st century, an RCP contribution to the global ΔVegC residence time
of 5 % was observed in the 2090s.
Spatial heterogeneity of the contribution of each uncertainty source
The strength of each uncertainty source relative to total variance showed
geographical heterogeneity for each variable (Fig. ). For
ΔNPP, GCM had a considerable contribution to total variance in many
parts of the world in the 2050s. In the 2090s, variance mainly explained by
GCM was observed in limited regions, e.g., the Sahara and central Australia.
RCP-dominant uncertainty source regions were present in part of the tropics
(Southeast Asia) to cool temperate regions (North America) in the 2090s for
ΔNPP. For ΔVegC, GCM had a large contribution to each grid
total variance in most regions at both times. For ΔSOC, GVM was the
major uncertainty source for each grid total variance in most regions in both
periods. GCM was observed to be the largest uncertainty source in some
regions such as the southwestern USA and the Sahara region for ΔSOC.
For ΔVegC residence time, GCM dominated more and its contribution was
scattered across different parts of the globe at both periods
(Fig. ). In northern Arctic regions, GVM was dominant over a wide
area from high- to low-latitude regions.
In terms of climatic divisions, the dominant uncertainty source clearly
showed different patterns in ΔNPP and ΔVegC from equatorial
climate (A) to snowy climate (D) (Fig. ). The contribution of GVM
to ΔNPP variance decreased as the climate became cooler in NPP
(Fig. a). In each major climatic division, the seasonally drier
divisions (m, s, w) tended to show a higher contribution of GCM compared with
the division with fully humid seasons (f). Similarly, in arid climates (BW
and BS), the contribution of GCM to the uncertainties of all variables was
relatively high (Fig. a–c). Unlike global ΔNPP and global
ΔVegC, GVM was dominant in tropical climates (Af–Aw), whereas RCP was
not dominant in these regions, even in 2100. In Cf, Ds, Dw, and ET, RCP was
the largest or second-largest source of uncertainty (from 30 to
50 % area) in each climatic division. For ΔSOC, GVM was
dominant in a broad area of all climate divisions, as shown in the results
for global ΔSOC. Furthermore, there were negligible areas where RCP
dominated the uncertainty in ΔSOC for all climatic divisions. The
contributions of each uncertainty source showed similar patterns to the
climatic gradients between ΔVegC and ΔVegC residence time. The
contributions of GVM in ΔVegC residence time in tropical to arid
regions (Af to BW) were larger than those in ΔVegC, which ranged from
21 to 42 %.
Discussion
For the historical period (1970–2000), the models simulated similar
historical NPP, VegC, and SOC trends for different GCMs (Fig. ).
However, at the end of the projection period, there were marked differences
for all variables (Fig. ). In particular, NPP and SOC varied from a
net sink to a net source in the highest baseline emission scenario (RCP8.5).
In higher emission scenarios, the total uncertainties for all variables
increased to a greater extent. The total uncertainties for each variable in
this study were comparable with or greater than those for projected C cycling
in a previous model intercomparison study even with a smaller number of GVMs.
Compared with previous model intercomparison studies of terrestrial C
cycling, the ISI-MIP study has an important simulation protocol advantage,
i.e., it is a partial factorial experiment with three independent treatments
for CO2 emission scenarios (RCP), GCM, and GVM. Therefore, uncertainty can
be decomposed into the sum of interclass variance (σRCP2,
σGCM2, σGVM2, and their interactions) and
within-class variance (σresid2). The ANOVA results revealed
that each source made quite a different contribution to the total
uncertainty, which varied with projection period (Fig. ). Whereas
GCMs were the dominant sources of uncertainty for NPP early in the projection
period (2000–2040), RCP dominated later in the projection period
(2050–2100) (Fig. ). This trend of increasing RCP importance is
similar to that of VegC (Fig. ). This may be attributed to the
enlargement of CO2 concentration differences among RCPs for this period.
The interaction terms as a source of uncertainty were significant (p<0.05
level, not described) and contributed considerably to total uncertainties (up
to 20 %) in NPP. This result indicates that there were different
sensitivities to the CO2 fertilization effect on vegetation processes
among the GVMs that also contributed to projection
uncertainties.
Uniqueness in the HYBRID4 model projection was observed in the ΔVegC
residence time (Fig. ). This is partially due to HYBRID4 having
strong stomatal responses to elevated vapor pressure deficits, and thus
simulated negative ΔNPP between 2080 and 2100 even in higher CO2
conditions . In addition, GVM had a contribution of
less than 20 % to global ΔNPP (Fig. ); however, there were
large fractional uncertainties in the ΔVegC residence time (over
60 % at the end of the 21st century). The ΔVegC residence time
represents the turnover rates of plant parts and the mortality rates of
individuals, processes modeled using baseline rates, climate sensitivities
(including fire), and competitively induced mortality. So ΔVegC
residence time is affected indirectly through shifts in vegetation
composition . The interaction terms in VegC residence
time changes dominated at about 20 % during the entire simulation period,
indicating that GVM has a different response to individual GCMs and RCPs. For
example, the HYBRID4 model notably showed high sensitivity to GVMs
(Fig. ). This term constitutes a non-negligible fraction compared
with the main effects of each uncertainty source.
pointed out that the humidity term in the vapor pressure deficit is a
critical factor to differentiate the projected NPP among GVMs in the ISI-MIP.
This is because the adoption of a response function to the vapor pressure
deficit is critical for responses to warmer climate conditions
. Furthermore, in this study, only
HYBRID4 incorporated a fully coupled N cycle; therefore, besides CO2
fertilization effects, implementation of the N cycle in more models is
required for more plausible modeling of effects of CO2 fertilization in
terrestrial C projections .
Humidity data for GCMs were not adjusted to the bias-corrected air
temperature and precipitation in the ISI-MIP study
. This might be another potential source of
uncertainty and bias for ecosystem projections and for
evapotranspiration in global hydrological water models
. Our results suggested that an essential factor
to reduce uncertainties in the climate assessment of ecosystems is improved
understanding of C dynamics after photosynthesis rather than reduction of
uncertainties in the exchange of C between the atmosphere and vegetation. In
fact, the representations of these processes are quite different among GVMs
.
The uncertainties in SOC changes driven by GVM were significantly large and
were dominant over the entire simulation period (Fig. ), possibly
suggesting that SOC processes are not well constrained by the observational
data or consistent between models, suggesting that the uncertainties derived
from the GVMs overwhelmed those derived from the climate scenarios. In
addition, a previous study showed that VegC dynamics did not correlate strongly
with that for SOC , i.e., SOC processes contributed
considerably to GVM-driven clustering in the SOC dendrogram. Another ISI-MIP
study demonstrated that the sensitivity of global SOC decomposition to
increasing global mean temperature varied significantly among GVMs
. Moreover, differences in the initial SOC stock
resulting from different spin-up procedures among GVMs critically contributed
to the incoherence in SOC dynamics. In a CMIP5 study,
demonstrated that microbial decomposition processes are a dominant factor
determining the amount of global SOC stock rather than C input from
photosynthetic products. Determination of the initial SOC stock is important
for future soil carbon stock and land surface fluxes
. In our results, there was no regional and
ecosystem-type (climatic divisions) dependency on GVM contributions to
uncertainty in SOC changes. Therefore, to reduce GVM uncertainties in SOC
projection, improvement of spin-up procedures and microbial decomposition
will be effective for reduction of SOC uncertainties at both local and global
scale.
Considering the geographic distribution, we determined that the contributions of
each uncertainty source to each grid variance were spatially heterogenous
(Fig. ), although the total contributions of each uncertainty
source in the grid-based assessment (Fig. ) were roughly in
agreement with Fig. for each period (2050 and 2099). These
heterogeneities could be linked with climatic divisions
(Fig. ). For example, in ΔSOC, GVMs are also a main
contributor in most regions in both periods (2050 and 2099). However, the
grid-based assessment revealed geographically distinct regions for each
uncertainty source. Although GCM was not a large contributor to global SOC
dynamics (Figs. and ), GCM had a significant effect on
uncertainty in arid (BW) to semi-arid (BS) regions (e.g., sub-Saharan Africa,
the southwestern USA, South America (pampa), Central Asia, and Australia) for
all variables. In a CMIP5 study, reported that
changes in precipitation patterns in their regions showed the low degree of
coincidence among GCMs. These results suggest that the projection of
precipitation patterns among GCMs is critically important to evaluate the
impact of climate change on ecosystem conditions and C stocks in these
regions (as shown in the Supplement). Although the carbon stocks and
changes in these regions are not large, it is important to predict local
climate condition uncertainties in order to obtain local climate predictions
of ecosystem changes during climate change. In NPP and VegC in the 2090s, GVM
is the dominant source in semitropical to tropical climate zones (especially
in Southeast Asia, Latin America, and central Africa), whereas GVM is not
dominant for global ΔNPP during this period. This implies that
modification of tropical rainforest C cycling is critical for reducing
uncertainties in global NPP. In broad terms, the contribution of GVM as an
uncertainty source in ΔNPP becomes smaller in cooler climatic regions
(C–D); however, those of GVM to ΔVegC were larger in cooler climatic
regions (Fig. ). This inconsistency can be explained by the large
differences between GVMs in the vegetation turnover rate in northern
ecosystems because of the different representations of vegetation dynamic
processes (e.g., forest fires, N cycling, and senescence)
. These results highlight that model improvement on
the basis of plant functional type (corresponding to climate divisions) could
be important for the effective reduction of uncertainty in climate impact
assessments.
Our results do not mean that GCMs are not important for the uncertainties in
VegC and SOC projection from the viewpoint of global C stocks. For example,
under RCP8.5, the HYBRID4 model simulation showed that VegC diverged
considerably among GCMs by 2100 (from 162 to 547 Pg C). Moreover, in
, one DGVM forced with 10 different GCMs showed
a difference of approximately 500 Pg C among projections of changes
in global terrestrial C stock (VegC and SOC) by 2100. Furthermore, the
numbers of GCMs and impact models used in this study likely affected the
results. Hence, our results indicate a smaller contribution by GCM to total
uncertainties than a lack of inter-GVM constraints owing to insufficient
validation for the SOC and VegC processes from global observations. In the
case of RCP2.6, the model projections were comparable for ΔNPP;
however, the results for ΔVegC and ΔSOC differed significantly.
This implies that internal ecosystem processes such as photosynthate
partitioning and mortality were poorly constrained in the GVMs. Moreover,
process uncertainties considerably affect SOC dynamics as a C source via
litter inputs. More observation-based model intercomparison (e.g., MsTMIP,
) for each component is required for GVMs to
reduce the overall uncertainty. For SOC dynamics, empirical estimations using
observation-based heterotrophic respiration are available for validation of SOC decomposition
processes. In addition to each model modification, in future, multiple
land-use scenarios should be considered in projections to understand
additional potential uncertainties (σlanduse2) in the
global terrestrial C budget. Furthermore, the use of bias-corrected GCM forcing
data will probably affect C dynamics as well as the projections in
hydrological models ;
however, there is still a lack of validation for the effect of various
bias-correction methods on C cycling projections and their relative
uncertainty.
Conclusions
In conclusion, by combining multiple GVMs, GCMs, and RCP scenarios, we
determined the different contributions of each factor to total uncertainty,
which is highly dependent on the variables (NPP, VegC, SOC, and VegC
residence time), projection periods, and regions. The contribution of each
source of uncertainty in these variables showed different patterns compared
with the hydrological variables simulated by global hydrological models from
another ISI-MIP study . At the global scale, by the middle of
the 21st century, GCM is the dominant uncertainty source in most regions for
NPP, VegC, and VegC residence time. However, GVM largely remains the major
uncertainty in the impact models in most regions, particularly at the end of
the 21st century.
Although RCP can differentiate NPP in temperate and cool climate regions, the
uncertainties of VegC and VegC residence time are dominated by GVM. These
results suggest that the fate of photosynthetic carbon over the long term is
an important uncertainty process for GVM models in climate impact
assessments. Thus, our findings indicate that model improvement on the basis
of plant functional type (corresponding to the climate divisions) could be
important for the effective reduction of uncertainty in climate impact
assessments.
For global SOC projections, the uncertainty driven by GVM was greater than
that of the climate scenarios, i.e., RCPs and GCMs. This SOC uncertainty
might be attributable mainly to the variety of SOC processes among GVMs and a
lack of constraints for spin-up procedures. The uncertainties associated with
SOC projections are significantly high, and the global SOC stocks by 2099
shift from net CO2 sources to net sinks (from -195 to 471 Pg C).
Because of the magnitude of the uncertainty range in projected global SOC
stock, the reduction of SOC uncertainties in GVM could be important for the
terrestrial C budget.
Particularly in arid to dry climate regions, GCM was the dominant uncertainty
source for all compartments and fluxes of ecosystem models even at the end of
the 21st century because NPP in these regions is strongly subjected to
water-use limitation. The CO2 emission scenario (RCP) as an uncertainty
source is important for the late projection period for both NPP and VegC.
Moreover, the CO2 fertilization sensitivity of vegetation processes is
quantitatively important for future C projection uncertainties.
The Supplement related to this article is available online at doi:10.5194/esd-6-435-2015-supplement.
Acknowledgements
We wish to thank the ISI-MIP coordination team of the Potsdam Institute for
Climate Impact Research. We also acknowledge the World Climate Research
Programme's Working Group on Coupled Modelling, which is responsible for
CMIP, and we thank the climate modeling groups for producing and making their
model output available. The ISI-MIP Fast Track project was funded by the
German Federal Ministry of Education and Research (BMBF), project funding
reference no. 01LS1201A. K. Nishina, A. Ito, E. Kato, and T. Yokohata were
supported by the Environment Research and Technology Development Fund (S-10)
of the Ministry of the Environment, Japan.Edited by: D. Lapola
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