Scenarios that limit global warming to below 2 degrees Centigrade by 2100 assume significant land-use change to support large-scale carbon dioxide (CO2) removal from the atmosphere by ...afforestation/reforestation, avoided deforestation, and Biomass Energy with Carbon Capture and Storage (BECCS). The more ambitious mitigation scenarios require even greater land area for mitigation and/or earlier adoption of CO2 removal strategies. Here we show that additional land-use change to meet a 1.5 degrees Centigrade climate change target could result in net losses of carbon from the land. The effectiveness of BECCS strongly depends on several assumptions related to the choice of biomass, the fate of initial above ground biomass, and the fossil-fuel emissions offset in the energy system. Depending on these factors, carbon removed from the atmosphere through BECCS could easily be offset by losses due to land-use change. If BECCS involves replacing high-carbon content ecosystems with crops, then forest-based mitigation could be more efficient for atmospheric CO2 removal than BECCS.
We document the development of the first version of the U.K. Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2‐ES, with enhancements to all component models ...and new feedback mechanisms. These include a new core physical model with a well‐resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric‐stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane, and nitrous oxide; two‐moment, five‐species, modal aerosol; and ocean biogeochemistry with two‐way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land, and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall, the model performs well, with a stable pre‐industrial state and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger‐than‐observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealized simulations show a high climate sensitivity relative to previous generations of models: Equilibrium climate sensitivity is 5.4 K, transient climate response ranges from 2.68 to 2.85 K, and transient climate response to cumulative emissions is 2.49 to 2.66 K TtC−1.
Plain Language Summary
We describe the development and behavior of UKESM1, a novel climate model that includes improved representations of processes in the atmosphere, ocean, and on land. These processes are inter‐related: For example, dust is produced on the land and blown up into the atmosphere where it affects the amount of sunlight falling on Earth. Dust can also be dissolved in the ocean, where it affects marine life. This in turn changes both the amount of carbon dioxide absorbed by the ocean and the material emitted from the surface into the atmosphere, which has an affect on the formation of clouds. UKESM1 includes many processes and interactions such as these, giving it a high level of complexity. Ensuring realistic process behavior is a major challenge in the development of our model, and we have carefully tested this. UKESM1 performs well, correctly exhibiting stable results from a continuous pre‐industrial simulation (used to provide a reference for future experiments) and showing good agreement with observations toward the end of its historical simulations. Results for some properties—including the degree to which average surface temperature changes with increased amounts of carbon dioxide in the atmosphere—are examined in detail.
Key Points
UKESM1 represents a major advance over its predecessor HadGEM2‐ES, both in the complexity of its components and its internal coupling
The complex coupling presents challenges to the model development; we document the tuning process employed to obtain acceptable performance
UKESM1 performs well, having a stable pre‐industrial state and showing good agreement with observations in a wide variety of contexts
Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation ...responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.
In this research we evaluated the skill of 10 terrestrial ecosystem models in reproducing aboveground net primary productivity responses as measured in 10 rainfall manipulation experiments.See also the Commentary on this article by Xue Feng, 26, 3190–3192
Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space ...observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990–2009) from two observation‐based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon‐climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation‐based data that show little IAV and trend, while the process‐based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation‐based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.
Key Points
At global scale, direct measurements of GPP do not exist
Large uncertainties exist on terrestrial global GPP benchmarking
Models show large variability in mean global GPP estimates
Fires occurring over the peatlands in Indonesian Borneo accompanied by droughts have posed devastating impacts on human health, livelihoods, economy and the natural environment, and their prevention ...requires comprehensive understanding of climate‐associated risk. Although it is widely known that the droughts are associated with El Niño events, the onset process of El Niño and thus the drought precursors and their possible changes under the future climate are not clearly understood. Here, we use a causal network approach to quantify the strength of teleconnections to droughts at a seasonal timescale shown in observations and climate models. We portray two drivers of June‐July‐August (JJA) droughts identified through literature review and causal analysis, namely Niño 3.4 sea surface temperature (SST) in JJA (El Niño Southern Oscillation abbreviated as ENSO) and SST anomaly over the eastern North Pacific to the east of the Hawaiian Islands (abbreviated as Pacific SST) in March‐April‐May (MAM) period. We argue that the droughts are strongly linked to ENSO variability, with drier years corresponding to El Niño conditions. The droughts can be predicted with a lead time of 3 months based on their associations with Pacific SST, with higher SST preceding drier conditions. We find that under the SSP585 scenario, the Coupled Model Intercomparison Phase 6 (CMIP6) multi‐model ensembles show significant increase in both the maximum number of consecutive dry days in the Indonesian Borneo region in JJA (p = 0.006) and its linear association with Pacific SST in MAM (p = 0.001) from year 2061 to 2100 compared with the historical baseline. Some models are showing unrealistic amounts of JJA rainfall and underestimate drought risks in Indonesian Borneo and their teleconnections, owing to the underestimation of ENSO amplitude and overestimation of local convections. Our study strengthens the possibility of early warning triggers of fires and stresses the need for taking enhanced climate risk into consideration when formulating long‐term policies to eliminate fires.
We have identified some predictors (shown in the schematic diagram and equations) that are responsible for the drought and fire risks during boreal summer in Indonesian Borneo. We have also looked at climate model projections and detected the enhancing risks towards the future (shown in the boxplots). These findings are crucial for seasonal forecasting of fires, which enables early preventive actions, and foster long‐term resilience building to eradicate fire occurrences.
The length of time that carbon remains in forest biomass
is one of the largest uncertainties in the global carbon cycle, with both
recent historical baselines and future responses to environmental ...change
poorly constrained by available observations. In the absence of large-scale
observations, models used for global assessments tend to fall back on
simplified assumptions of the turnover rates of biomass and soil carbon
pools. In this study, the biomass carbon turnover times calculated by an
ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to
assess their current capability to accurately estimate biomass carbon
turnover times in forests and how these times are anticipated to change in
the future. Modelled baseline 1985–2014 global average forest biomass
turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in
phenological processes, which control allocation to, and turnover rate of,
leaves and fine roots, are as important as tree mortality with regard to
explaining the variation in total turnover among TBMs. The different
governing mechanisms exhibited by each TBM result in a wide range of
plausible turnover time projections for the end of the century. Based on
these simulations, it is not possible to draw robust conclusions regarding
likely future changes in turnover time, and thus biomass change, for
different regions. Both spatial and temporal uncertainty in turnover time
are strongly linked to model assumptions concerning plant functional type
distributions and their controls. Thirteen model-based hypotheses of
controls on turnover time are identified, along with recommendations for
pragmatic steps to test them using existing and novel observations. Efforts
to resolve uncertainty in turnover time, and thus its impacts on the future
evolution of biomass carbon stocks across the world's forests, will need to
address both mortality and establishment components of forest demography, as
well as allocation of carbon to woody versus non-woody biomass growth.
Plant temperature responses vary geographically, reflecting thermally contrasting habitats and long-term species adaptations to their climate of origin. Plants also can acclimate to fast temporal ...changes in temperature regime to mitigate stress. Although plant photosynthetic responses are known to acclimate to temperature, many global models used to predict future vegetation and climate–carbon interactions do not include this process.
We quantify the global and regional impacts of biogeographical variability and thermal acclimation of temperature response of photosynthetic capacity on the terrestrial carbon (C) cycle between 1860 and 2100 within a coupled climate–carbon cycle model, that emulates 22 global climate models.
Results indicate that inclusion of biogeographical variation in photosynthetic temperature response is most important for present-day and future C uptake, with increasing importance of thermal acclimation under future warming. Accounting for both effects narrows the range of predictions of the simulated global land C storage in 2100 across climate projections (29% and 43% globally and in the tropics, respectively).
Contrary to earlier studies, our results suggest that thermal acclimation of photosynthetic capacity makes tropical and temperate C less vulnerable to warming, but reduces the warming-induced C uptake in the boreal region under elevated CO2.
During the Quaternary, the Sahara desert was periodically colonized by vegetation, likely because of orbitally induced rainfall increases. However, the estimated hydrological change is not reproduced ...in climate model simulations, undermining confidence in projections of future rainfall. We evaluated the relationship between the qualitative information on past vegetation coverage and climate for the mid‐Holocene using three different dynamic vegetation models. Compared with two available vegetation reconstructions, the models require 500–800 mm of rainfall over 20°–25°N, which is significantly larger than inferred from pollen but largely in agreement with more recent leaf wax biomarker reconstructions. The magnitude of the response also suggests that required rainfall regime of the early to middle Holocene is far from being correctly represented in general circulation models. However, intermodel differences related to moisture stress parameterizations, biases in simulated present‐day vegetation, and uncertainties about paleosoil distributions introduce uncertainties, and these are also relevant to Earth system model simulations of African humid periods.
Key Points
First systematic dynamic vegetation model evaluation of the Green Sahara climate regime such as the mid‐Holocene and Last Interglacial
Three different dynamic vegetation models show a range of rainfall required to green the Sahara, largely caused by intermodel differences in moisture stress formulations
All models require that mid‐Holocene rainfall likely exceeded that inferred from pollen assemblages, averaging 500–800 mm over 20°–25°N
Land-atmosphere exchanges influence atmospheric CO
. Emphasis has been on describing photosynthetic CO
uptake, but less on respiration losses. New global datasets describe upper canopy dark ...respiration (R
) and temperature dependencies. This allows characterisation of baseline R
, instantaneous temperature responses and longer-term thermal acclimation effects. Here we show the global implications of these parameterisations with a global gridded land model. This model aggregates R
to whole-plant respiration R
, driven with meteorological forcings spanning uncertainty across climate change models. For pre-industrial estimates, new baseline R
increases R
and especially in the tropics. Compared to new baseline, revised instantaneous response decreases R
for mid-latitudes, while acclimation lowers this for the tropics with increases elsewhere. Under global warming, new R
estimates amplify modelled respiration increases, although partially lowered by acclimation. Future measurements will refine how R
aggregates to whole-plant respiration. Our analysis suggests R
could be around 30% higher than existing estimates.
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