CO2 Fertilization Effect Research Articles

The behavioral dimension of CO2 fertilization effect: Evidence from US harvesting records

AbstractThe CO2 fertilization effect has been demonstrated to boost crop production by enhancing photosynthesis and reducing drought stress. In addition to this biological channel, there is also a behavioral margin for the CO2 fertilization effect that was previously unnoticed. I find evidence that an additional ppm of average CO2 concentration leads to increases in the corn and soybean harvested ratios of .57 and .75 percentage points, respectively. My predictions suggest that, without considering the adjustment in harvesting decision, the crop production benefits from CO2 fertilization effect would be biasedly estimated for both corn and soybeans, which broadens the understanding of farmers’ behavioral responses to environmental stimuli and extends the knowledge on how CO2 would benefit crop production.

The Fate of Deep Permafrost Carbon in Northern High Latitudes in the 21st Century: A Process‐Based Modeling Analysis

AbstractWarming in permafrost regions stimulates carbon (C) release through decomposition, but increasing atmospheric CO2 and available soil nitrogen enhance plant productivity at the same time. To date, a large uncertainty in the regional C dynamics still remains. Here we use a process‐based biogeochemical model by considering C exposure from thawed permafrost and observational data to quantify permafrost C emissions and ecosystem C budget in northern high latitudes in the 21st century. Permafrost degradation will make 119.3 Pg and 251.6 Pg C available for decomposition by 2100 under the Shared Socioeconomic Pathway (SSP)126 and SSP585, respectively. However, only 4–8% of the newly thawed permafrost C is expected to be released into the atmosphere by 2100. Cumulatively, permafrost degradation will reduce ecosystem C stocks by 3.37 Pg and 15.37 Pg under the SSP126 and SSP585, respectively. Additionally, CO2 fertilization effects would stimulate plant productivity and increase ecosystem C stocks substantially. The combined effects of climate change, CO2 fertilization, and permafrost degradation on C fluxes are typically more profound than any single factor, emphasizing the intricate interplay between these elements in shaping permafrost C‐climate feedbacks. Our study suggests that the majority of the thawed C will remain sequestered in previously frozen layers in this century, posing a significant challenge to climate change mitigation efforts once any process accelerates the decomposition of this huge amount of thawed C.

Projecting impacts of extreme weather events on crop yields using LASSO regression

Extreme weather events are recognized as major drivers of crop yield losses, which threaten food security and farmers’ incomes. Given the increasing frequency and intensity of extreme weather under climate change, it is crucial to quantify the related future yield damages of important crops to inform prospective climate change adaptation planning. In this study, we present a statistical modeling approach to project the changes in crop yields under climate change for eight majorly cultivated field crops in Germany, estimating the impacts of nine types of extreme weather events. To select the most relevant predictors, we apply the least absolute shrinkage and selection operator (LASSO) regression to district-level yield data.The LASSO models select, on average, 62% of the features, which align with well-known biophysical impacts on crops, suggesting that different extremes at various growth stages are relevant for yield prediction. We project on average 2.5-times more severe impacts on summer crops than on winter crops. Under RCP8.5, crop yields experience a mean change from -2.53% to -8.63% in the far future (2069-98) for summer crops and from -0.80% to -2.88% for winter crops, without accounting for CO2 fertilization effects. Heat impacts are identified as the primary driver of yield losses across all crops for 2069-98, while shifting precipitation patterns exacerbate winter and spring waterlogging, and summer and fall drought.Our findings underscore the utility of LASSO regression in identifying relevant drivers for projecting changes in crop yields across multiple crops, crucial for guiding agricultural adaptation. While the present analysis can identify empirical relationships, replicating these findings in biophysical models could provide new insights into the underlying processes.

Anomalous wet summers and rising atmospheric CO2 concentrations increase the CO2 sink in a poorly drained forest on permafrost

At the northern high latitudes, rapid warming, associated changes in the hydrological cycle, and rising atmospheric CO2 concentrations, [CO2], are observed at present. Under rapid environmental changes, it is important to understand the current and future trajectories of the CO2 budget in high-latitude ecosystems. In this study, we present the importance of anomalous wet conditions and rising [CO2] on the long-term CO2 budget based on two decades (2003-2022) of quasicontinuous measurements of CO2 flux at a poorly drained black spruce forest on permafrost peat in interior Alaska. The long-term CO2 budget for the black spruce forest was a small sink of -53 ± 63 g C m-2 y-1. The CO2 sink increased from 49 g C m-2 y-1 for the first decade to 58 g C m-2 y-1 for the second decade. The increased CO2 sink was attributed to an 11.3% increase in gross primary productivity (GPP) among which 9% increase in GPP was explained by a recent increase in precipitation. Furthermore, a 3% increase in GPP in response to a 37-ppm increase in [CO2] was estimated from the data-model fusion. Our study shows that understanding the coupling between hydrological and carbon cycles and the CO2 fertilization effect is important for understanding the current and future carbon budgets of high-latitude ecosystems in permafrost regions.

Assessing the ecological complexity and uncertainty of predicting forest ecosystem services under climate change

Climate change affects Central European forest ecosystems in different ways and, consequently, these changes result in different feedbacks on the provision of forest ecosystem services. Regarding the complexity and the variability in climate-forest interactions outcome, forest decision makers necessitate reliable information about changes in the forest ecosystem services for planning and adaptation purposes. However, forest productivity predictions incorporate multiple levels of uncertainty that have to be regarded to ensure building realistic expectations in forest decision-making. Besides the chosen forest simulation model, uncertainties come from the climate change data represented by a set of representative concentration pathways (RCP), within the underlying ensemble of global circulation and regional climate models (GCM-RCM), and further in the treatment of the CO2-fertilization effect. We considered the mentioned uncertainties in a framework on simulating forest growth and water services for two forest sites, a Sessile oak and a Scots pine stand in Rhineland-Palatine, Germany. The framework revealed a high variability in future forest ecosystem services. Particularly, the variability among the selected GCM-RCM models within the same Representative Concentration Pathway (RCP) was higher than the variability among different RCPs (RCP2.6 and RCP8.5 representing the low and high CO2-emission scenarios, respectively). Sessile oak productivity increased under all scenarios, whereas Scots pine growth declined in the lower end of the RCP8.5 scenario. Water services remained mostly stable at both sites. Moreover, we applied a panel data model to estimate what climate indices caused changes in the forest ecosystem services. We found that Scots pine is more sensitive to a multitude of climate indices, such as temperature changes and Sessile oak showed strong response to the CO2-fertilization. We propose applying this framework to evaluate forest management options under climate change.

Atmospheric CO2 fertilization effect on cereal yields in Morocco using the CARAIB dynamic vegetation model

Climate change and rising atmospheric CO2 levels are critical factors influencing agricultural productivity, particularly in Morocco, where cereal crops are essential for food security. The primary objective of this study is to evaluate the combined effects of atmospheric CO2 variations and climatic changes on cereal yields up to 2099 using the CARAIB dynamic vegetation model. This evaluation is driven by four future scenarios based on the Euro-CORDEX initiative's regional climate models under the Representative Concentration Pathway 8.5. As part of this evaluation, the study also validates the CARAIB model for Morocco’s major cereal crops: soft wheat, durum wheat, and barley, in order to ensure the model's accuracy in simulating crop responses under projected environmental conditions. The CARAIB model effectively simulated historical cereal yield trajectories across major farming regions in Morocco from 2000 to 2016, demonstrating its robust predictive capability. Our future projections suggest that elevated CO2 levels might initially sustain cereal yields at approximately 90 % of current levels until 2050. This trend indicates that the increase in atmospheric CO2 may exert a moderating influence on the negative impacts of other environmental stressors on crop yields. However, despite this initial buffering effect, the overall yield trend from the present until 2099 indicates a decrease for most combinations of crop, zone, and climate model, even with the CO2 fertilization effect, except in some cases, the model exhibits slight increases or stabilization in yields. Additionally, the CARAIB model predicts potential yield shocks of 10–35 % below current levels from the 2080 s onwards, primarily due to periodic droughts. This variation underscores the complexity of the interplay between CO2 fertilization and climatic changes, emphasizing the urgent need for Morocco to develop adaptive agricultural strategies for long-term sustainability in the face of climatic challenges.

Effects of genetic and environmental trends from 1970 to 2020 on farm efficiency estimated with a whole farm modeling system

The objectives of this study were to evaluate associations of genetic, cow management and nutrition, inbreeding, and crop yields from 1970 to 2020 with measures of production and economic efficiency according to a whole farm model, and to evaluate effects of genetic change in individual traits on economic efficiency in comparison to expectations from economic selection indexes. Genetic and phenotypic performance metrics for Holsteins from 1970 and 2020 were retrieved and input into the Integrated Farm System Model (IFSM) for a 7000 cow TX herd and a 50 cow PA grazing herd. Crop yields estimates from 1970 and 2020 were retrieved and farm hectarage was altered so that forage and energy concentrate requirements were met through farm production; likewise, scenarios evaluating effects of atmospheric CO2 fertilization (CO2F) on crop yield were evaluated by altering farm hectarage. For single traits that could be dynamically modeled by IFSM, performance shifts and resulting change in product prices or management expenses were added to 1970 base models. Economic efficiency was evaluated as the per cow return to management and unpaid factors as compared with 1970 base models. As averaged across state scenarios, gains in economic efficiency were +$945 and -$76 for additive genetic and inbreeding effects, respectively, for a total gain from genetic change of +$869. Genetic gain in fat yield (+$549) and protein yield (+$524) were responsible for most of the genetic gain, whereas milk yield (-$128) and increased cow BW (-$129) depressed economic efficiency. Genetic change in productive life had a smaller effect (+$44) than predicted unless heifers were purchased and at double the default value. Gains due to cow management and nutrition increased efficiency by +$666 and crop yield increased efficiency by +$711, of which +$371 was attributed to CO2F across scenarios. Whole farm dry matter efficiencies derived as the ratio of fat and protein corrected milk yield to dry matter intake increased from 0.82 (PA) and 0.97 (TX) in 1970 to 1.20 in 2020 and could be higher if farms reduce the size of their replacement herd by producing beef calves. The landmass required in 2020 was 63% and 78% of the 1970 requirement for TX and PA, respectively. Changes in cow genetic merit, management and nutrition, and crop yields have all increased the economic and environmental sustainability of milk production, and systems such as IFSM could be a useful tool to help inform economic selection indices.

Game analysis of future rice yield changes in China based on explainable machine-learning and planting date optimization

ContextGlobal warming's escalating severity necessitates sophisticated approaches for predicting rice yield. Research QuestionCombining crop models with data-driven techniques, such as machine learning, can more effectively grasp the complex interplay of variables influencing crop growth. It remains a significant challenge to balance accuracy and interpretability in such hybrid models. MethodsThe research integrated the Decision Support System for Agrotechnology Transfer (DSSAT) with statistical and machine learning models respectively, to assess rice yield changes in China under four future Shared Socio-economic Pathway (SSP). SSPs are scenarios that integrate socioeconomic trends with greenhouse gas emissions and radiative forcing pathways, which affect the phenology and yield of rice. The Shapley Additive Explanation (SHAP) method was employed to interpret the model, effectively determining the interplay among variables influenced rice yields. Mitigated the negative impacts of climate change on rice yield through the planting date optimization. ResultsProjections indicate significant rice yield losses in China without CO2, worsening with increased radiative forcing (p < 0.001). Considering rising CO2, single-season rice yields are projected to increase by 0.1–3.6 %, early rice by 4.6–9.5 %, while late rice yields are still decrease by 2.3–8.8 %. The rising CO2 can offset yield losses for single and early rice but not for late rice. The hybrid approach which combined the Random Forest (RF) with the DSSAT performed best in predicting rice yield. Studies showed that rising temperatures caused rice yield losses in China, yet we found that Growing Degree Days (GDD) exerted a more negative impact (p < 0.001). In high-precipitation regions, deep soil moisture is more influential than shallow soil moisture, whereas the reverse was true in drier areas (p < 0.001). Advancing planting dates for early and single rice and delaying for late rice can increase yields (p < 0.001). Adjusting to optimal planting dates, single-season rice yields increased by 3.3–6.3 %, early rice increased by 9.7–18.3 %, while late rice still decreased by 1.0–4.7 %. ConclusionsWithout considering the impact of CO2, significant rice yield losses in China are projected. Even with the fertilization effect of CO2, rice yields remain negatively impacted by climate change. However, implementing appropriate measures, such as optimizing planting dates, can help Chinese rice production benefit under changing climate. ImplicationsThis study offers insights into balancing accuracy and interpretability in hybrid models and provides guidance for local policymakers to address future climate change.

Improved water use efficiency of vegetation due to carbon fertilization not translating to increased soil moisture in India

Soil moisture (SM) plays a critical role in land–atmosphere interaction. Literature shows that the CO2 fertilization effect (CFE) governs vegetation dynamics and its interactions with the atmosphere. However, the impacts of changing vegetation properties due to CFE, on SM, specifically in India, remains unexplored. Comparing Land-hist (observed CO2 concentration) and Land-cCO2 (constant preindustrial CO2 concentration) simulations with the same meteorological forcings from the Land Use Model Inter-comparison Project (LUMIP), we found that changing vegetation due to CFE impacts SM following two pathways. Enhanced water use efficiency (WUE) due to CFE seeks to ameliorate SM by regulating transpiration. Conversely, elevated CO2 increases leaf area index and plant water use, ultimately leading to a decrease in SM due to the combined impacts of CFE in India. This reduction is most prominent in arid regions, where we observe a decline in SM particularly during dry periods. Our study highlights the need to consider plant physiology in land surface modeling under increased CO2 concentration.

Rising atmospheric carbon dioxide concentrations increase gaps of rice yields between low- and middle-to-high-income countries.

The rising carbon dioxide concentrations are expected to increase future rice yields. However, variations in the CO2 fertilization effect (CFE) between rice subspecies and the influence of concurrent global warming introduce uncertainty in future global rice yield projections. Here we conducted a meta-analysis of rising carbon dioxide field experiments and employed crop modelling to assess future global rice yields for the top 14 rice producing countries. We found a robust parabolic relationship between rice CFE and temperature, with significant variations between rice subspecies. Our projections indicate that global rice production in the 2050s is expected to increase by 50.32 million tonnes (7.6%) due to CFE compared with historical production. Because low-income countries will experience higher temperatures, the gaps (difference of Δyield) between middle-to-high-income and low-income countries are projected to widen from the 2030s to the 2090s under elevated carbon dioxide. These findings underscore the critical role of CFE and emphasize the necessity to increase investments in research and technology for rice producing systems in low-income countries.

Enhanced woody biomass production in a mature temperate forest under elevated CO2

Enhanced CO2 assimilation by forests as atmospheric CO2 concentration rises could slow the rate of CO2 increase if the assimilated carbon is allocated to long-lived biomass. Experiments in young tree plantations support a CO2 fertilization effect as atmospheric CO2 continues to increase. Uncertainty exists, however, as to whether older, more mature forests retain the capacity to respond to elevated CO2. Here, aided by tree-ring analysis and canopy laser scanning, we show that a 180-year-old Quercus robur L. woodland in central England increased the production of woody biomass when exposed to free-air CO2 enrichment (FACE) for 7 years. Further, elevated CO2 increased exudation of carbon from fine roots into the soil with likely effects on nutrient cycles. The increase in tree growth and allocation to long-lived woody biomass demonstrated here substantiates the major role for mature temperate forests in climate change mitigation.

Reduction of uncertainties in rice yield response to elevated CO2 by experiment-model integration: A case study in East China

Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors, of which the most important one is the increasing carbon dioxide (CO2) concentrations. Estimates of CO2 fertilization effect (CFE) on rice, however, still had large uncertainties. Therefore, using the rice planting areas in East China as the study area, we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models, and found that the CFE predicted by these models had significant differences. We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province. Using CFE measurements from a field experiment as benchmark, we have developed an experiment–model integration approach aiming to reduce this variation. This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty, which is beneficial for the accurate prediction of future global rice yield in the context of climate change.

Trigger thresholds and their dynamics of vegetation production loss under different atmospheric and soil drought conditions

Many evidences have shown that both atmospheric and soil droughts can constrain vegetation growth and further threaten its ability to sequester carbon. However, the trigger thresholds of vegetation production loss under different atmospheric and soil drought conditions are still unknown. In this study, we proposed a Copula and Bayesian equations-based framework to investigate trigger thresholds of various vegetation production losses under different atmospheric and soil drought conditions. The trigger thresholds dynamics and their possible causes were also investigated. To achieve this goal, we first simulated the gross primary production, soil moisture, and vapor pressure deficit over China during 1961–2018 using an individual-based, spatially explicit dynamic global vegetation model. The main drivers of the dynamic change in trigger thresholds were then explored by Random Forest model. We found that soil drought caused greater stress on gross primary production loss than atmospheric drought, with a larger impact area and higher probability of damage. In terms of spatial distribution, the risk probability of gross primary production loss was higher in eastern China than in western China, and the drought trigger threshold was also smaller in eastern China. In addition, the trigger thresholds for atmospheric and soil drought in most regions exhibited a decreasing trend from 1961 to 2018, while the CO2 fertilization enhanced the drought tolerance of vegetation. The reduction in CO2 fertilization effect slowed down the downward trend of trigger threshold for soil drought, while the increase in temperature exacerbated the downward trend of trigger threshold for atmospheric drought. This study highlighted the larger effect of soil drought on vegetation production loss than atmospheric drought and implied that climate change can modulate the trigger threshold of vegetation production losses under drought conditions. These findings provide scientific guidance for managing the increasing risk of drought on vegetation and optimizing watershed water allocation.

Accelerate Senescence Reversed CO2‐Fertilization Effect under Elevated CO2 in Potato: A Weak Relationship with Nitrogen Acquisition

ABSTRACTAccelerated senescence under elevated CO2 (eCO2) has not received sufficient attention, and its impact on the effect of CO2‐fertilization is unclear. To investigate the relationship between plant senescence and CO2 concentration, a pot experiment was conducted in four potato genotypes under low CO2 (LC), medium CO2 (MC) and high CO2 (HC) conditions. Nitrogen (N) uptake and cumulative transpiration were analysed to clarify whether eCO2‐induced senescence could be explained by low N uptake due to reduced transpiration. Compared to LC, the lifespan of potato plants under MC and HC was reduced by 3%–6% and 12%–32%, respectively, depending on the genotype. Biomass accumulation at full senescence was reduced when lifespan was shortened by approximately 5% and 10% under MC and HC, respectively. Cumulative transpiration was less affected by eCO2 during early developmental stages but decreased under eCO2 as plants aged. Plant water use decreased with a shortened lifespan under eCO2, but there was no reduction in N uptake, which was attributed to the high N uptake per unit of water used. The results of this study indicate that senescence in potato genotypes is non‐linearly related to CO2 concentration and cannot be explained by reduced N acquisition via reduced transpiration. The positive effect of CO2 fertilization can be reversed by accelerated senescence under eCO2.

Earth system model’s capability of predicting drought-induced crop failure

We used the Meteorological Research Institute Earth System Model to investigate land precipitation and net primary production (NPP, proxy for agricultural production here) in terms of food and water security. In the preindustrial state, the largest decrease in Southeast Asian precipitation (> 20%) occurs along with the largest El Niño and positive phase of the Atlantic multidecadal oscillation, consistent with the 1876-78 Great Drought, leading to the largest decrease in NPP (~ 20%); >2 standard deviations, extreme dryness and crop failure. In relatively arid regions (e.g., India and northeastern Brazil), the largest decreases in precipitation and NPP reach ≥ 50%, particularly 80 ~ 90% in Deccan, India. In the 21st century warming projection (Shared Socioeconomic Pathways 5-8.5, the largest CO2 emission scenario), the interannual variability of precipitation and NPP in Monsoon Asia increase under the influence of enhanced El Niño-Southern Oscillation. Although the increased precipitation variability indicates more frequent severer droughts than in the preindustrial state, this severity does not influence NPP so much: the increased NPP variability is negligible compared to the significant increase in normal NPP by CO2 fertilization effect, which indicates usually rich harvests. Compared to a recent severe crop failure (− 25% in Japan in 1993), the simulated preindustrial drought-induced NPP decreases of ≥ 50% in parts of low latitudes are extraordinarily large enough to correspond to devastating crop failures. The model indicates that great famines before the 20th century were natural in origin, and that manmade factors, such as harsh taxes and hoarding and export of grain, made the famines more devastating.

Estimating the CO2 Fertilization Effect on Extratropical Forest Productivity From Flux‐Tower Observations

AbstractThe land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO2 fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO2 fertilization effect from other long‐term confounding drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO2 fertilization effect on photosynthetic carbon uptake using eddy covariance (EC) records across 38 extratropical forest sites. We find the median stimulation rate of GPP to be 3.2 ± 0.9 gC m−2 yr−1 ppm−1 (or 16.4 ± 4.2% per 100 ppm) under increasing atmospheric CO2 across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process‐based land surface models. We address additional factors, including nitrogen deposition and land management, that may impact plant productivity, potentially confounding the attribution to the CO2 fertilization effect. Assuming these site‐specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO2 fertilization effect. However, disentanglement of these long‐term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long‐term CO2 fertilization across multiple EC sites. Such observation‐based quantification is key to constraining the long‐standing uncertainties in the land carbon cycle under rising CO2 concentrations.

An Improved Gross Primary Production Model Considering Atmospheric CO2 Fertilization: The Qinghai–Tibet Plateau as a Case Study

Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau.

Rain use efficiency gradients across Australian ecosystems

Rain use efficiency (RUE) quantifies the ecosystem's capacity to use precipitation water to assimilate atmospheric CO2. The spatial distribution of RUE and its drivers across the Australian continent is largely unknown. This knowledge gap limits our understanding of the possible contribution of Australian ecosystems to global carbon assimilation. This study investigates the spatial distribution of RUE across diverse terrestrial ecosystems in Australia. The results show that RUE ranges from 0.43 (1st percentile) to 3.10 (99th percentile) g C m−2 mm−1 with a continental mean of 1.19 g C m−2 mm−1. About 68 % of the spatiotemporal variability of RUE can be explained by a multiple linear regression model primarily contributed by climatic predictors. Benchmarked by the model estimation, drainage-diverging/converging landscapes tend to have reduced/increased RUE. The model also revealed the impact of increasing atmospheric CO2 concentration on RUE. The continental mean RUE would increase by between 29.3 and 64.8 % by the end of this century under the SSP5–8.5 scenario in which the CO2 concentration is projected to double from the present level. This increase in projected RUE is attributed to the assumed greening effect of increasing CO2 concentration, which does not consider the saturation of CO2 fertilisation effect and the warming effect on increasing wildfire occurrence. Under the SSP1–2.6 scenario, RUE would decrease by about 7 %. This study provides baseline RUEs of various ecosystems in Australia for investigating the impacts of human interferences and climate change on the capacity of Australian vegetation to assimilate atmospheric CO2 under given precipitation.

Global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit.

Atmospheric vapor pressure deficit (VPD) increases with climate warming and may limit plant growth. However, gross primary production (GPP) responses to VPD remain a mystery, offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics. In this study, in-situ measurements, satellite-derived data, and Earth System Models (ESMs) simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD: first increasing and then declining. When VPD exceeds these thresholds, atmospheric drought stress reduces soil moisture and stomatal conductance, thereby decreasing the productivity of terrestrial ecosystems. Current ESMs underscore CO2 fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds. These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand. Incorporating VPD, soil moisture, and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

An emergent constraint on the thermal sensitivity of photosynthesis and greenness in the high latitude northern forests

Despite the general consensus that the warming over the high latitudes northern forests (HLNF) has led to enhanced photosynthetic activity and contributed to the greening trend, isolating the impact of temperature increase on photosynthesis and greenness has been difficult due to the concurring influence of the CO2 fertilization effect. Here, using an ensemble of simulations from biogeochemical models that have contributed to the Trends in Net Land Atmosphere Carbon Exchange project (TRENDY), we identify an emergent relationship between the simulation of the climate-driven temporal changes in both gross primary productivity (GPP) and greenness (Leaf Area Index, LAI) and the model’s spatial sensitivity of these quantities to growing-season (GS) temperature. Combined with spatially-resolved observations of LAI and GPP, we estimate that GS-LAI and GS-GPP increase by 17.0 ± 2.4% and 24.0 ± 3.0% per degree of warming, respectively. The observationally-derived sensitivities of LAI and GPP to temperature are about 40% and 71% higher, respectively, than the mean of the ensemble of simulations from TRENDY, primarily due to the model underestimation of the sensitivity of light use efficiency to temperature. We estimate that the regional mean GS-GPP increased 28.2 ± 5.1% between 1983–1986 and 2013–2016, much larger than the 5.8 ± 1.4% increase from the CO2 fertilization effect implied by Wenzel et al. This suggests that warming, not CO2 fertilization, is primarily responsible for the observed dramatic changes in the HLNF biosphere over the last century.