CMIP5 Earth System Models Research Articles

Historical Simulation and Future Projection of Arctic‐Boreal Fire Carbon Emissions and Related Surface Climate by 17 CMIP6 ESMs

AbstractWildfire is a crucial factor in influencing the earth system. Wildfire activities in the Arctic‐boreal region have received increasing attention particularly with increasing frequency and intensity under rapid climate warming in recent years. In this study, the historical simulation and future projection of the Arctic‐boreal fire carbon emissions and associated surface climate conditions (surface air temperature and precipitation) are examined using 17 CMIP6 Earth System Models. For the historical period, more than half (11 out of 17) of the models underestimate ∼40% of the observed annual mean fire carbon emissions in the Arctic‐boreal region (0.20 PgC/yr) due to a wetter bias in the Arctic‐boreal regions. Spatially, there is common underestimation of the fire centers in the eastern part of Eurasian Continent and overestimation of that in Europe. With respect to the model spread, it mainly shows large spread for fire carbon emissions (surface air temperature) in Europe (high‐latitudes). For the future projection, the fire carbon emissions in the Arctic‐boreal region is projected to exceed 100% (0.5 PgC/yr until the end of the 21st century compared with ∼0.2 PgC/yr at present). The projected precipitation and temperature in the Arctic‐boreal region land also show an upward trend during the 21st century (∼31% for annual mean precipitation and ∼10°C for surface air temperature). There are considerable bias and intra‐model spread among different models in both historical simulation and future projection.

Emergent constraints on future Amazon climate change-induced carbon loss using past global warming trends

Reducing uncertainty in the response of the Amazon rainforest, a vital component of the Earth system, to future climate change is crucial for refining climate projections. Here we demonstrate an emergent constraint (EC) on the future response of the Amazon carbon cycle to climate change across CMIP6 Earth system models. Models that overestimate past global warming trends, tend to estimate hotter and drier future Amazon conditions, driven by northward shifts of the intertropical convergence zone over the Atlantic Ocean, causing greater Amazon carbon loss. The proposed EC changes the mean CMIP6 Amazon climate-induced carbon loss estimate (excluding CO2 fertilisation and land-use change impacts) from −0.27 (−0.59–0.05) to −0.16 (−0.42–0.10) GtC year−1 at 4.4 °C warming level, reducing the variance by 34%. This study implies that climate-induced carbon loss in the Amazon rainforest by 2100 is less than thought and that past global temperature trends can be used to refine regional carbon cycle projections.

Combining mathematical models and machine learning algorithms to predict the future regional-scale actual transpiration by maize

Plants on the land surface play a vital role in the hydrological water cycle as they transport soil water to the atmosphere through transpiration. Root water uptake (RWU) is considered a crucial step in this process as it is the first stage of transpiration, directly determining the actual transpiration (Ta) of plants. However, accurately measuring RWU or Ta in situ poses significant challenges. Here, we establish an overall approach of combining mathematical models and machine learning algorithms to obtain high-precision (500 m×500 m) regional-scale daily Ta maps for various future climate patterns. The Hydrus-1D and AquaCrop models were employed to calculate the total RWU fluxes across the entire root zone, aiming to achieve Ta at a point scale. A machine learning model was developed using the CatBoost algorithm and environmental covariates extracted from the Google Earth Engine (GEE) platform to upscale these point-scale Ta to the regional scale. Furthermore, a total of 22 CMIP6 Earth System Models (ESMs) were evaluated, and among them, ACCESS-CM2 and ACCESS-ESM1–5 were selected for simulating future climate scenarios. Based on the established machine learning model and selected ESMs, regional-scale Ta maps were generated from 2020 to 2100 for the SSP245 and SSP585 (Shared Socioeconomic Pathways) scenarios. The results indicate that near-surface specific humidity, mean near-surface air temperature, latitude, and surface downwelling shortwave radiation are the critical factors influencing regional-scale Ta. As greenhouse gas emissions intensify and temperatures rise, regional-scale Ta is enhanced, leading to an accelerated transfer of soil water to atmospheric water. Under the SSP245 scenario, Ta increases on average by 0.55–1.16 % every 20 years, with its incremental value ranging from 7.14∙10−4 to 8.65∙10−4 cm day−1, while under the SSP585 scenario, Ta increases more significantly, achieving an average increase of 0.64–1.81 % every 20 years, with its incremental value ranging from 1.595∙10−3 to 2.821∙10−3 cm day−1. This study provides a robust integrated approach to assess the future regional-scale Ta providing valuable insights into the underlying water cycle mechanisms and regional water requirements for future climate scenarios.

Spatially Resolved Temperature Response Functions to CO2 Emissions

AbstractCarbon dioxide (CO2) emissions affect local temperature; quantifying that local response is important for learning about the earth system, the impacts of mitigation, and adaptation needs. We assume the climate system can be represented as a time‐dependent linear system, diagnosing Green's Functions for the spatial temperature response to CO2 emissions based on CMIP6 earth system models. This allows us to emulate the linear component of the temperature response to CO2. This approach is sufficient to capture the spatial temperature response of CMIP6 experiments within one standard deviation of the multimodel spread across most regions, though accuracy is lower in the Southern Ocean and the Arctic. Our approach reveals where nonlinear feedbacks are important in current CMIP6 models, and where the local system response is well represented by a time‐dependent linear differential operator. It incorporates emissions path dependency and may be useful for evaluating large ensembles of emission scenarios.

Contrasting responses of vegetation productivity to intraseasonal rainfall in Earth system models

Abstract. Correctly representing the response of vegetation productivity to water availability in Earth system models (ESMs) is essential for accurately modelling the terrestrial carbon cycle and the evolution of the climate system. Previous studies evaluating gross primary productivity (GPP) in ESMs have focused on annual mean GPP and interannual variability, but physical processes at shorter timescales are important for determining vegetation–climate coupling. We evaluate GPP responses at the intraseasonal timescale in five CMIP6 ESMs by analysing changes in GPP after intraseasonal rainfall events with a timescale of approximately 25 d. We compare these responses to those found in a range of observation-based products. When composited around all intraseasonal rainfall events globally, both the amplitude and the timing of the GPP response show large inter-model differences, demonstrating discrepancies between models in their representation of water–carbon coupling processes. However, the responses calculated from the observational datasets also vary considerably, making it challenging to assess the realism of the modelled GPP responses. The models correctly capture the fact that larger increases in GPP at the regional scale are associated with larger increases in surface soil moisture and larger decreases in atmospheric vapour pressure deficit. However, the sensitivity of the GPP response to these drivers varies between models. The GPP in NorESM is insufficiently sensitive to vapour pressure deficit perturbations when compared all to other models and six out of seven observational GPP products tested. Most models produce a faster GPP response where the surface soil moisture perturbation is larger, but the observational evidence for this relationship is weak. This work demonstrates the need for a better understanding of the uncertainties in the representation of water–vegetation relationships in ESMs and highlights a requirement for future daily-resolution observations of GPP to provide a tighter constraint on global water–carbon coupling processes.

CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060

The “Future Global Aridity Index and PET Database” provides high-resolution (30 arc-seconds) average annual and monthly global estimates of potential evapotranspiration (PET) and aridity index (AI) for 22 CMIP6 Earth System Models for two future (2021-2041; 2041-2060) and two historical (1960-1990; 1970–2000) time periods, for each of four shared socio-economic pathways (SSP). Three multimodel ensemble averages are also provided (All; Majority Consensus, High Risk) with different level of risks linked to climate model uncertainty. An overview of the methodological approach, geospatial implementation and a technical evaluation of the results is provided. Historical results were compared for technical validation with weather station data (PET: r 2 = 0.72; AI: r 2 = 0.91) and the CRU_TS v 4.04 dataset (PET: r 2 = 0.67; AI: r 2 = 0.80). Within the context of projected significant change in the near- and medium-term, the “Future_Global_AI_PET Database” provides a set of data projections and tools available for a variety of scientific and practical applications, illustrating trends and magnitude of predicted climatic and eco-hydrological impacts on terrestrial ecosystems. The Future_Global_AI_PET Database is archived in the ScienceDB repository and available online at: https://doi.org/10.57760/sciencedb.nbsdc.00086

Snow cover duration delays spring green-up in the northern hemisphere the most for grasslands

Snow is an important factor controlling vegetation functions in high latitudes/altitudes. However, due to the lack of reliable in-situ measurements, the effects of snow on vegetation phenology remains poorly understood. Here, we examine the effects of snow cover duration (SCD) on the start of growing season (SOS) for different vegetation types. SOS and SCD were extracted from in-situ carbon flux and albedo data, respectively, at 51 eddy covariance flux sites in the northern mid-high latitudes. The effects of SCD on SOS vary substantially among different vegetation types. For grassland, preseason SCD outperforms other factors controlling grassland SOS. However, for forests and cropland, the preseason air temperature is the dominant factor in controlling SOS. Preseason SCD mainly influences the SOS by regulating preseason air and soil temperature rather than soil moisture. The CMIP6 Earth system models (ESMs) fail to capture the effect of SCD on SOS. Thus, Random Forest (RF) models were established to predict future SOS changing trends considering the effect of SCD. For grassland and evergreen needleleaf forest, the projected SOS advance rate is slower when SCD is considered. These findings can help us better understand impacts of snow on vegetation phenology and carbon-climate feedbacks in the warming world.

How Does Plant CO2 Physiological Forcing Amplify Amazon Warming in CMIP6 Earth System Models?

AbstractThe physiological response to increasing CO2 concentrations will lead to land surface warming through a redistribution of the energy balance. As the Amazon is one of the most plant‐rich regions, the increase in surface temperature, caused by plant CO2 physiological forcing, is particularly large compared to other regions. In this study, we analyze the outputs of the 11 models in the Coupled Model Intercomparison Project Phase 6 to find out how CO2 physiological forcing amplifies Amazonian warming under elevated CO2 levels. With the CO2 concentration increase from 285 to 823 ppm, the Amazon temperature increased by 0.48 ± 0.42 K as a result of plant physiological forcing. Moreover, we assess the contributions of each climate feedback to the surface warming due to physiological forcing by quantifying climate feedbacks based on radiative kernels. Lapse rate feedback and cloud feedback, analyzed as the primary contributors, accounted for 53% and 37% of Amazon warming, respectively. The warming contributions of these two feedbacks also exhibit a significant spread, which contributes to the predictive uncertainty. The surface warming due to reduced evapotranspiration is larger than the upper tropospheric warming in the Amazon, resulting in surface warming by lapse rate feedback. In addition, cloud cover in the Amazon region decreases due to the reduced evapotranspiration. Decreased cloud cover amplifies surface warming through the shortwave cloud feedback. Furthermore, differences in circulation and local convection caused by physiological effect contribute to the inter‐model spread of the cloud feedback.

Net primary production annual maxima in the North Atlantic projected to shift in the 21st century

Abstract. Shifts in the day of peak net primary production (NPP) were detected in different biogeochemical provinces of the North Atlantic (25–65° N). Most provinces displayed a shift toward earlier peak NPP, with the largest change points in the 21st century and in the northern parts of the domain. Furthermore, the occurrences of the first day with a mixed-layer depth (MLD) shallower than 40 m and the day of peak NPP are positively correlated over most of the domain. As was the case for the day of peak NPP, the largest change points for the day of MLD shallower than 40 m occur around or after the year 2000. Daily output from two fully coupled CMIP6 Earth system models, EC-Earth3-CC and NorESM2-LM, for the period 1750–2100 and under the SSP5-8.5 scenario, were used for the analysis. The ESM NPP data were compared with estimates derived from Carbon, Absorption and Fluorescence Euphotic-resolving (CAFE) satellite-based data. The ESMs showed significant differences from the CAFE model, though the timing of peak NPP was well captured for most provinces. The largest change points in the day of peak NPP occur earlier in EC-Earth3-CC than in NorESM2-LM. Although SSP5-8.5 is a scenario with very high warming, EC-Earth3-CC generates change points for most provinces in the early part of the 21st century, before the warming has deviated far from lower-emissions scenarios. NorESM2-LM displays the largest change points centered around the mid 21st century, with two out of eight provinces displaying the largest change point before the year 2050. The early timing of the detected shifts in some provinces in both ESMs suggests that similar shifts could already have been initiated or could start in the near future. This highlights the need for long-term monitoring campaigns in the North Atlantic.

Increase in the variability of terrestrial carbon uptake in response to enhanced future ENSO modulation

El Niño–Southern Oscillation (ENSO) is a major driver of climate change in middle and low latitudes and thus strongly influences the terrestrial carbon cycle through land–air interaction. Both the ENSO modulation and carbon flux variability are projected to increase in the future, but their connection still needs further investigation. To investigate the impact of future ENSO modulation on carbon flux variability, this study used 10 CMIP6 earth system models to analyze ENSO modulation and carbon flux variability in middle and low latitudes, and their relationship, under different scenarios simulated by CMIP6 models. The results show a high consistency in the simulations, with both ENSO modulation and carbon flux variability showing an increasing trend in the future. The higher the emissions scenario, especially SSP5-8.5 compared to SSP2-4.5, the greater the increase in variability. Carbon flux variability in the middle and low latitudes under SSP2-4.5 increases by 30.9% compared to historical levels during 1951–2000, while under SSP5-8.5 it increases by 58.2%. Further analysis suggests that ENSO influences mid- and low-latitude carbon flux variability primarily through temperature. This occurrence may potentially be attributed to the increased responsiveness of gross primary productivity towards regional temperature fluctuations, combined with the intensified influence of ENSO on land surface temperatures.摘要ENSO是中低纬度地区气候系统的主要驱动因素, 对陆地碳循环有重要影响. 本研究基于10个CMIP6地球系统模式, 分析了不同情景下ENSO变率与中低纬度地区总初级生产力变率的关系. 结果显示, 未来ENSO变率和总初级生产力变率在未来多数模式均显示为增加. 在未来情境下(2051-2100年), 中低纬度地区的总初级生产力变率较历史时期(1951–2000年)增加了30.9%(SSP2-4.5), 58.2%(SSP5-8.5). 进一步分析表明, ENSO主要通过温度影响中低纬度碳通量变率. 这种现象可能归因于总初级生产力对温度的响应增强, 以及ENSO对陆地表面温度的影响.

A scrutiny of plasticity management in irrigated wheat systems under CMIP6 earth system models (case study: Golestan Province, Iran)

A scrutiny of plasticity management in irrigated wheat systems under CMIP6 earth system models (case study: Golestan Province, Iran)

Global‐Scale Convergence Obscures Inconsistencies in Soil Carbon Change Predicted by Earth System Models

AbstractSoil carbon (C) responses to environmental change represent a major source of uncertainty in the global C cycle. Feedbacks between soil C stocks and climate drivers could impact atmospheric CO2 levels, further altering the climate. Here, we assessed the reliability of Earth system model (ESM) predictions of soil C change using the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6). ESMs predicted global soil C gains under the high emission scenario, with soils taking up 43.9 Pg (95% CI: 9.2–78.5 Pg) C on average during the 21st century. The variation in global soil C change declined significantly from CMIP5 (with average of 48.4 Pg [95% CI: 2.0–94.9 Pg] C) to CMIP6 models (with average of 39.3 Pg [95% CI: 23.9–54.7 Pg] C). For some models, a small C increase in all biomes contributed to this convergence. For other models, offsetting responses between cold and warm biomes contributed to convergence. Although soil C predictions appeared to converge in CMIP6, the dominant processes driving soil C change at global or biome scales differed among models and in many cases between earlier and later versions of the same model. Random Forest models, for soil carbon dynamics, accounted for more than 63% variation of the global soil C change predicted by CMIP5 ESMs, but only 36% for CMIP6 models. Although most CMIP6 models apparently agree on increased soil C storage during the 21st century, this consensus obscures substantial model disagreement on the mechanisms underlying soil C response, calling into question the reliability of model predictions.

Carbon cycle feedbacks in an idealized simulation and a scenario simulation of negative emissions in CMIP6 Earth system models

Abstract. Limiting global warming to well below 2 ∘C by the end of the century is an ambitious target that requires immediate and unprecedented emission reductions. In the absence of sufficient near-term mitigation, this target will only be achieved by carbon dioxide removal (CDR) from the atmosphere later during this century, which would entail a period of temperature overshoot. Aside from the socio-economic feasibility of large-scale CDR, which remains unclear, the effects on biogeochemical cycles and climate are key to assessing CDR as a mitigation option. Changes in atmospheric CO2 concentration and climate alter the CO2 exchange between the atmosphere and the underlying carbon reservoirs of the land and the ocean. Here, we investigate carbon cycle feedbacks under idealized and more realistic overshoot scenarios in an ensemble of Earth system models. The responses of oceanic and terrestrial carbon stocks to changes in atmospheric CO2 concentration and changes in surface climate (the carbon–concentration feedback and the carbon–climate feedback, quantified by the feedback metrics β and γ, respectively) show a large hysteresis. This hysteresis leads to growing absolute values of β and γ during phases of negative emissions. We find that this growth over time occurs such that the spatial patterns of feedbacks do not change significantly for individual models. We confirm that the β and γ feedback metrics are a relatively robust tool to characterize inter-model differences in feedback strength since the relative feedback strength remains largely stable between phases of positive and negative emissions and between different simulations, although exceptions exist. When the emissions become negative, we find that the model uncertainty (model disagreement) in β and γ increases more strongly than expected from the assumption that the uncertainties would accumulate linearly with time. This indicates that the model response to a change from increasing to decreasing forcing introduces an additional layer of uncertainty, at least in idealized simulations with a strong signal. We also briefly discuss the existing alternative definition of feedback metrics based on instantaneous carbon fluxes instead of carbon stocks and provide recommendations for the way forward and future model intercomparison projects.

Large spread in interannual variance of atmospheric CO2 concentration across CMIP6 Earth System Models

Numerical Earth System Models (ESMs) are our best tool to predict the evolution of atmospheric CO2 concentration and its effect on Global temperature. However, large uncertainties exist among ESMs in the variance of the year-to-year changes of atmospheric CO2 concentration. This prevents us from precisely understanding its past evolution and from accurately estimating its future evolution. Here we analyze various ESMs simulations from the 6th Coupled Model Intercomparison Projects (CMIP6) to understand the origins of the inter-model uncertainty in the interannual variability of the atmospheric CO2 concentration. Considering the observed period 1986-2013, we show that most of this uncertainty is coming from the simulation of the land CO2 flux internal variability. Although models agree that those variations are driven by El Niño Southern Oscillation (ENSO), similar ENSO-related surface temperature and precipitation teleconnections across models drive different land CO2 fluxes, pointing to the land vegetation models as the dominant source of the inter-model uncertainty.

Alkalinity biases in CMIP6 Earth system models and implications for simulated CO2 drawdown via artificial alkalinity enhancement

Abstract. The partitioning of CO2 between atmosphere and ocean depends to a large degree not only on the amount of dissolved inorganic carbon (DIC) but also on alkalinity in the surface ocean. That is also why ocean alkalinity enhancement (OAE) is discussed as one potential approach in the context of negative emission technologies. Although alkalinity is thus an important variable of the marine carbonate system, little knowledge exists on how its representation in models compares with measurements. We evaluated the large-scale alkalinity distribution in 14 CMIP6 Earth system models (ESMs) against the observational data set GLODAPv2 and show that most models, as well as the multi-model mean, underestimate alkalinity at the surface and in the upper ocean and overestimate it in the deeper ocean. The decomposition of the global mean alkalinity biases into contributions from (i) physical processes (preformed alkalinity), which include the physical redistribution of biased alkalinity originating from the soft tissue and carbonates pumps; (ii) remineralization; and (iii) carbonate formation and dissolution showed that the bias stemming from the physical redistribution of alkalinity is dominant. However, below the upper few hundred meters the bias from carbonate dissolution can gain similar importance to physical biases, while the contribution from remineralization processes is negligible. This highlights the critical need for better understanding and quantification of processes driving calcium carbonate dissolution in microenvironments above the saturation horizons and implementation of these processes into biogeochemical models. For the application of the models to assess the potential of OAE to increase ocean carbon uptake, a back-of-the-envelope calculation was conducted with each model's global mean surface alkalinity, DIC, and partial pressure of CO2 in seawater (pCO2) as input parameters. We evaluate the following two metrics: (1) the initial pCO2 reduction at the surface ocean after alkalinity addition and (2) the uptake efficiency (ηCO2) after air–sea equilibration is reached. The relative biases of alkalinity versus DIC at the surface affect the Revelle factor and therefore the initial pCO2 reduction after alkalinity addition. The global mean surface alkalinity bias relative to GLODAPv2 in the different models ranges from −85 mmol m−3 (−3.6 %) to +50 mmol m−3 (+2.1 %) (mean: −25 mmol m−3 or −1.1 %). For DIC the relative bias ranges from −55 mmol m−3 (−2.6 %) to 53 mmol m−3 (+2.5 %) (mean: −13 mmol m−3 or −0.6 %). All but two of the CMIP6 models evaluated here overestimate the Revelle factor at the surface by up to 3.4 % and thus overestimate the initial pCO2 reduction after alkalinity addition by up to 13 %. The uptake efficiency, ηCO2, then takes into account that a higher Revelle factor and a higher initial pCO2 reduction after alkalinity addition and equilibration mostly compensate for each other, meaning that resulting DIC differences in the models are small (−0.1 % to 1.1 %). The overestimation of the initial pCO2 reduction has to be taken into account when reporting on efficiencies of ocean alkalinity enhancement experiments using CMIP6 models, especially as long as the CO2 equilibrium is not reached.

Tropical Atlantic multidecadal variability is dominated by external forcing.

The tropical Atlantic climate is characterized by prominent and correlated multidecadal variability in Atlantic sea surface temperatures (SSTs), Sahel rainfall and hurricane activity1-4. Owing to uncertainties in both the models and the observations, the origin of the physical relationships among these systems has remained controversial3-7. Here we show that the cross-equatorial gradient in tropical Atlantic SSTs-largely driven by radiative perturbations associated with anthropogenic emissions and volcanic aerosols since 19503,7-is a key determinant of Atlantic hurricane formation and Sahel rainfall. The relationship is obscured in a large ensemble of CMIP6 Earth system models, because the models overestimate long-term trends for warming in the Northern Hemisphere relative to the Southern Hemisphere from around 1950 as well as associated changes in atmospheric circulation and rainfall. When the overestimated trends are removed, correlations between SSTs and Atlantic hurricane formation and Sahel rainfall emerge as a response to radiative forcing, especially since 1950 when anthropogenic aerosol forcing has been high. Our findings establish that the tropical Atlantic SST gradient is a stronger determinant of tropical impacts than SSTs across the entire North Atlantic, because the gradient is more physically connected to tropical impacts via local atmospheric circulations8. Our findings highlight that Atlantic hurricane activity and Sahel rainfall variations can be predicted from radiative forcing driven by anthropogenic emissions and volcanism, but firmer predictions are limited by the signal-to-noise paradox9-11 and uncertainty in future climate forcings.

Spatial Inhomogeneity of Atmospheric CO2 Concentration and Its Uncertainty in CMIP6 Earth System Models

Spatial Inhomogeneity of Atmospheric CO2 Concentration and Its Uncertainty in CMIP6 Earth System Models

Approaching a thermal tipping point in the Eurasian boreal forest at its southern margin

Climate change is increasing the intensity and frequency of extreme heat events. Ecological responses to extreme heat will depend on vegetation physiology and thermal tolerance. Here we report that Larix sibirica, a foundation species across boreal Eurasia, is vulnerable to extreme heat at its southern range margin due to its low thermal tolerance (Tcrit of photosynthesis: ~ 37–48 °C). Projections from CMIP6 Earth System Models (ESMs) suggest that leaf temperatures might exceed the 25th percentile of Larix sibirica’s Tcrit by two to three days per year within the next two to three decades (by 2050) under high emission scenarios (SSP3-7.0 and SSP5-8.5). This degree of warming will threaten the biome’s continued ability to assimilate and sequester carbon. This work highlights that under high emission trajectories we may approach an abrupt ecological tipping point in southern boreal Eurasian forests substantially sooner than ESM estimates that do not consider plant thermal tolerance traits.

Overestimated nitrogen loss from denitrification for natural terrestrial ecosystems in CMIP6 Earth System Models

Denitrification and leaching nitrogen (N) losses are poorly constrained in Earth System Models (ESMs). Here, we produce a global map of natural soil 15N abundance and quantify soil denitrification N loss for global natural ecosystems using an isotope-benchmarking method. We show an overestimation of denitrification by almost two times in the 13 ESMs of the Sixth Phase Coupled Model Intercomparison Project (CMIP6, 73 ± 31 Tg N yr−1), compared with our estimate of 38 ± 11 Tg N yr−1, which is rooted in isotope mass balance. Moreover, we find a negative correlation between the sensitivity of plant production to rising carbon dioxide (CO2) concentration and denitrification in boreal regions, revealing that overestimated denitrification in ESMs would translate to an exaggeration of N limitation on the responses of plant growth to elevated CO2. Our study highlights the need of improving the representation of the denitrification in ESMs and better assessing the effects of terrestrial ecosystems on CO2 mitigation.

CO2 fertilization enhances vegetation productivity and reduces ecological drought in India

Higher warming will affect more regions globally with intensified agricultural and ecological droughts. Higher CO2 concentration improves vegetation’s water use efficiency (WUE), but its potential to alleviate extreme agricultural and ecological droughts is unclear. India is the second-highest contributor to global greening, having two of the eight global hottest biodiversity hotspots. Here, for the first time, using the CMIP6 earth system models (ESMs), we found an increase in the net vegetation productivity in India at the rate of 10.552 TgC year−1 with 1% per year increase in atmospheric CO2 concentration from 285 ppm to 1140 ppm, contrary to global trends. The improved WUE resulting from carbon fertilization and higher rain under warming will supersede the increased evapotranspiration water loss due to radiative effects. We found that the substantial increase in vegetation productivity in India attributes to plant physiology, and such factor needs to be considered in the drought projections.