Can nature help limit warming below 1.5°C?

Nature-based efforts could further climate mitigation and help limit warming to 1.5°C, given that proper and immediate solutions are implemented with similar ambition as in energy and industry sectors; however, omission of natural solutions or delays in overall climate action would substantially undermine the climate target of Paris Agreement.

Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century.

The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.

The global decline in the sensitivity of vegetation productivity to precipitation from 2001 to 2018.

The sensitivity of vegetation productivity to precipitation (Sppt ) is a key metric for understanding the variations in vegetation productivity under changing precipitation and predicting future changes in ecosystem functions. However, a comprehensive assessment of Sppt over all the global land is lacking. Here, we investigated spatial patterns and temporal changes of Sppt across the global land from 2001 to 2018 with multiple streams of satellite observations. We found consistent spatial patterns of Sppt with different satellite products: Sppt was highest in dry regions while low in humid regions. Grassland and shrubland showed the highest Sppt , and evergreen needle-leaf forest and wetland showed the lowest. Temporally, Sppt showed a generally declining trend over the past two decades (p < .05), yet with clear spatial heterogeneities. The decline in Sppt was especially noticeable in North America and Europe, likely due to the increase in precipitation. In central Russia and Australia, however, Sppt showed an increasing trend. Biome-wise, most ecosystem types exhibited significant decrease in Sppt , while grassland, evergreen broadleaf forest, and mixed forest showed slight increases or non-significant changes in Sppt . Our finding of the overall decline in Sppt implies a potential stabilization mechanism for ecosystem productivity under climate change. However, the revealed Sppt increase for some regions and ecosystem types, in particular global grasslands, suggests that grasslands might be increasingly vulnerable to climatic variability with continuing global climate change.

Tropical tall forests are more sensitive and vulnerable to drought than short forests.

Our limited understanding of the impacts of drought on tropical forests significantly impedes our ability in accurately predicting the impacts of climate change on this biome. Here, we investigated the impact of drought on the dynamics of forest canopies with different heights using time-series records of remotely sensed Ku-band vegetation optical depth (Ku-VOD), a proxy of top-canopy foliar mass and water content, and separated the signal of Ku-VOD changes into drought-induced reductions and subsequent non-drought gains. Both drought-induced reductions and non-drought increases in Ku-VOD varied significantly with canopy height. Taller tropical forests experienced greater relative Ku-VOD reductions during drought and larger non-drought increases than shorter forests, but the net effect of drought was more negative in the taller forests. Meta-analysis of in situ hydraulic traits supports the hypothesis that taller tropical forests are more vulnerable to drought stress due to smaller xylem-transport safety margins. Additionally, Ku-VOD of taller forests showed larger reductions due to increased atmospheric dryness, as assessed by vapor pressure deficit, and showed larger gains in response to enhanced water supply than shorter forests. Including the height-dependent variation of hydraulic transport in ecosystem models will improve the simulated response of tropical forests to drought.

Delayed impact of natural climate solutions.

To limit global temperature rise, scientists have proposed significant potentials for climate change mitigation from protecting and managing natural systems. However, depending on the time taken for technology deployment and natural carbon gain, actual mitigation can be dramatically delayed, and total mitigation by 2030 or 2050 can be more than halved compared to the estimated potential. Delayed or lack of action on implementation would push back the timeline to reduce greenhouse gas emissions, largely undermining the Paris goals. Launching actions now and learning from past experience can help deliver climate mitigation and sustainable development goals.

New insights on plant phenological response to temperature revealed from long-term widespread observations in China.

Constraints of temperature on spring plant phenology are closely related to plant growth, vegetation dynamics, and ecosystem carbon cycle. However, the effects of temperature on leaf onset, especially for winter chilling, are still not well understood. Using long-term, widespread in situ phenology observations collected over China for multiple plant species, this study analyzes the quantitative response of leaf onset to temperature, and compares empirical findings with existing theories and modeling approaches, as implemented in 18 phenology algorithms. Results show that the growing degree days (GDD) required for leaf onset vary distinctly among plant species and geographical locations as well as at organizational levels (species and community), pointing to diverse adaptation strategies. Chilling durations (CHD) needed for releasing bud dormancy decline monotonously from cold to warm areas with very limited interspecies variations. Results also reveal that winter chilling is a crucial component of phenology models, and its effect is better captured with an index that accounts for the inhomogeneous effectiveness of low temperature to chilling rate than with the conventional CHD index. The impact of spring warming on leaf onset is nonlinear, better represented by a logistical function of temperature than by the linear function currently implemented in biosphere models. The optimized base temperatures for thermal accumulation and the optimal chilling temperatures are species-dependent and average at 6.9 and 0.2°C, respectively. Overall, plants' chilling requirement is not a constant, and more chilling generally results in less requirement of thermal accumulation for leaf onset. Our results clearly demonstrate multiple deficiencies of the parameters (e.g., base temperature) and algorithms (e.g., method for calculating GDD) in conventional phenology models to represent leaf onset. Therefore, this study not only advances our mechanistic and quantitative understanding of temperature controls on leaf onset but also provides critical information for improving existing phenology models.

Joint structural and physiological control on the interannual variation in productivity in a temperate grassland: A data-model comparison.

Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data-model comparisons.

Contemporary evolution and scaling of 32 major cities in China.

Most of the planet's population currently lives in urban areas, and urban land expansion is one of the most dramatic forms of land conversion. Understanding how cities evolve temporally, spatially, and organizationally in a rapidly urbanizing world is critical for sustainable development. However, few studies have examined the coevolution of urban attributes in time and space simultaneously and the adequacy of power law scaling across cities and through time, particularly in countries that have experienced abrupt, widespread, political and economic changes. Here, we show the temporal coevolution of multiple physical, demographic, socioeconomic, and environmental attributes in individual cities, and the cross-city scaling of urban attributes at six time points (i.e., 1978, 1990, 1995, 2000, 2005, and 2010) in 32 major Chinese cities. We found that power law scaling could adequately characterize both the cross-city scaling of urban attributes across cities and the longitudinal scaling describing the temporal coevolution of urban attributes within individual cities. The cross-city scaling properties demonstrated substantial changes over time signifying evolved social and economic forces. A key finding was that the cross-city linear or superlinear scaling of urban area with population contradicts the theoretical sublinear power law scaling proposed between infrastructure and population. Furthermore, the cross-city scaling between area and population transitioned from linear to superlinear over time, and the superlinear scaling in recent times suggests decreased infrastructure efficiency. Our results demonstrate a diseconomy of scale in urban areal expansion that indicates a significant waste of land resources in the urbanization process. Future planning efforts should focus on policies that increase urban land use efficiency before continuing expansion.

Surface water areas significantly impacted 2014 dengue outbreaks in Guangzhou, China.

Dengue transmission in urban areas is strongly influenced by a range of biological and environmental factors, yet the key drivers still need further exploration. To better understand mechanisms of environment-mosquito-urban dengue transmission, we propose an empirical model parameterized and cross-validated from a unique dataset including viral gene sequences, vector dynamics and human dengue cases in Guangzhou, China, together with a 36-year urban environmental change maps investigated by spatiotemporal satellite image fusion. The dengue epidemics in Guangzhou are highly episodic and were not associated with annual rainfall over time. Our results indicate that urban environmental changes, especially variations in surface area covered by water in urban areas, can substantially alter the virus population and dengue transmission. The recent severe dengue outbreaks in Guangzhou may be due to the surge in an artificial lake construction, which could increase infection force between vector (mainly Aedes albopictus) and host when urban water area significantly increased. Impacts of urban environmental change on dengue dynamics may not have been thoroughly investigated in the past studies and more work needs to be done to better understand the consequences of urbanization processes in our changing world.

Developed and developing world responsibilities for historical climate change and CO2 mitigation.

At the United Nations Framework Convention on Climate Change Conference in Cancun, in November 2010, the Heads of State reached an agreement on the aim of limiting the global temperature rise to 2 °C relative to preindustrial levels. They recognized that long-term future warming is primarily constrained by cumulative anthropogenic greenhouse gas emissions, that deep cuts in global emissions are required, and that action based on equity must be taken to meet this objective. However, negotiations on emission reduction among countries are increasingly fraught with difficulty, partly because of arguments about the responsibility for the ongoing temperature rise. Simulations with two earth-system models (NCAR/CESM and BNU-ESM) demonstrate that developed countries had contributed about 60-80%, developing countries about 20-40%, to the global temperature rise, upper ocean warming, and sea-ice reduction by 2005. Enacting pledges made at Cancun with continuation to 2100 leads to a reduction in global temperature rise relative to business as usual with a 1/3-2/3 (CESM 33-67%, BNU-ESM 35-65%) contribution from developed and developing countries, respectively. To prevent a temperature rise by 2 °C or more in 2100, it is necessary to fill the gap with more ambitious mitigation efforts.

Linkages between stem vulnerability curves and tree demography and their implications for plant physiological modeling.

Vulnerability curves (VCs) have been measured extensively to describe the differences in plant vulnerability to cavitation. Although the roles of hydraulic conductivity (Ks,max) and hydraulic safety (P50, embolism resistance), both of which are parameters of VCs ('sigmoidal' type), in tree demography have been evaluated across different forests, the direct linkages between VCs and tree demography are rarely explored. In this study, we combined measured VCs and plot data of 16 tree species in Panamanian seasonal tropical forests to investigate the connections between VCs and tree mortality, recruitment and growth. We found that the mortality and recruitment rates of evergreen species were most significantly positively correlated with P50. However, the mortality and recruitment rates of deciduous species only exhibited significant positive correlations with parameter a, which describes the steepness of VCs and indicates the sensitivity of conductivity loss with water potential decline, but is often neglected. These differences among evergreen and deciduous species may contribute to the poor performance of existing quantitative relationships (such as the fitting relationships for all 16 species) in capturing tree mortality and recruitment dynamics. Additionally, evergreen species presented a significant positive relationship between relative growth rate (RGR) and Ks,max, while deciduous species did not display such relationship. The RGR of both evergreen and deciduous species also displayed no significant correlations with P50 and a. Further analysis demonstrated that species with steeper VCs tended to have high mortality and recruitment rates, while species with flatter VCs were usually those with low mortality and recruitment rates. Our results highlight the important role of parameter a in tree demography, especially for deciduous species. Given that VC is a key component of plant hydraulic models, integrating measured VC rather than optimizing its parameters will help improve the ability to simulate and predict forest response to water availability.

Large potential of strengthening the land carbon sink in China through anthropogenic interventions.

The terrestrial ecosystem in China mitigates 21%-45% of the national contemporary fossil fuel CO2 emissions every year. Maintaining and strengthening the land carbon sink is essential for reaching China's target of carbon neutrality. However, this sink is subject to large uncertainties due to the joint impacts of climate change, air pollution, and human activities. Here, we explore the potential of strengthening land carbon sink in China through anthropogenic interventions, including forestation, ozone reduction, and litter removal, taking advantage of a well-validated dynamic vegetation model and meteorological forcings from 16 climate models. Without anthropogenic interventions, considering Shared Socioeconomic Pathways (SSP) scenarios, the land sink is projected to be 0.26-0.56 Pg C a-1 at 2060, to which climate change contributes 0.06-0.13 Pg C a-1 and CO2 fertilization contributes 0.08-0.44 Pg C a-1 with the stronger effects for higher emission scenarios. With anthropogenic interventions, under a close-to-neutral emission scenario (SSP1-2.6), the land sink becomes 0.47-0.57 Pg C a-1 at 2060, including the contributions of 0.12 Pg C a-1 by conservative forestation, 0.07 Pg C a-1 by ozone pollution control, and 0.06-0.16 Pg C a-1 by 20% litter removal over planted forest. This sink can mitigate 90%-110% of the residue anthropogenic carbon emissions in 2060, providing a solid foundation for the carbon neutrality in China.

Four decades of full-scale nitrous oxide emission inventory in China.

China is among the top nitrous oxide (N2O)-emitting countries, but existing national inventories do not provide full-scale emissions including both natural and anthropogenic sources. We conducted a four-decade (1980-2020) of comprehensive quantification of Chinese N2O inventory using empirical emission factor method for anthropogenic sources and two up-to-date process-based models for natural sources. Total N2O emissions peaked at 2287.4 (1774.8-2799.9) Gg N2O yr-1 in 2018, and agriculture-developed regions, like the East, Northeast, and Central, were the top N2O-emitting regions. Agricultural N2O emissions have started to decrease after 2016 due to the decline of nitrogen fertilization applications, while, industrial and energetic sources have been dramatically increasing after 2005. N2O emissions from agriculture, industry, energy, and waste represented 49.3%, 26.4%, 17.5%, and 6.7% of the anthropogenic emissions in 2020, respectively, which revealed that it is imperative to prioritize N2O emission mitigation in agriculture, industry, and energy. Natural N2O sources, dominated by forests, have been steadily growing from 317.3 (290.3-344.1) Gg N2O yr-1 in 1980 to 376.2 (335.5-407.2) Gg N2O yr-1 in 2020. Our study produces a Full-scale Annual N2O dataset in China (FAN2020), providing emergent counting to refine the current national N2O inventories.

Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches.

Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties.

The carbon budget of China: 1980-2021.

As one of the world's largest emitters of greenhouse gases, China has set itself the ambitious goal of achieving carbon peaking and carbon neutrality. Therefore, it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide (CO2), and to monitor China's progress toward these goals. Using state-of-the-art datasets and models, this study comprehensively estimated the anthropogenic CO2 emissions from energy, industrial processes and product use, and waste along with natural sources and sinks of CO2 for all of China during 1980-2021. To recognize the differences among various methods of estimating greenhouse emissions, the estimates are compared with China's National Greenhouse Gas Inventories (NGHGIs) for 1994, 2005, 2010, 2012, and 2014. Anthropogenic CO2 emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO2 a-1 in 2021. While benefiting from ecological projects (e.g., Three Norths Shelter Forest System Project), the land carbon sink in China has reached 1.65 Gt CO2 a-1 averaged through 2010-2021, which is almost 15.81 times that of the carbon sink in the 1980s. On average, China's terrestrial ecosystems offset 14.69% ± 2.49% of anthropogenic CO2 emissions through 2010-2021. Two provincial-level administrative regions of China, Xizang and Qinghai, have achieved carbon neutrality according to our estimates, but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10% of anthropogenic CO2 emissions. This study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO2 emissions, but found notable differences for land carbon sinks. Future estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

Building soil to reduce climate change impacts on global crop yield.

Improving soil health and resilience is fundamental for sustainable food production, however the role of soil in maintaining or improving global crop productivity under climate warming is not well identified and quantified. Here, we examined the impact of soil on yield response to climate warming for four major crops (i.e., maize, wheat, rice and soybean), using global-scale datasets and random forest method. We found that each °C of warming reduced global yields of maize by 3.4%, wheat by 2.4%, rice by 0.3% and soybean by 5.0%, which were spatially heterogeneous with possible positive impacts. The random forest modeling analyses further showed that soil organic carbon (SOC), as an indicator of soil quality, dominantly explained the spatial heterogeneity of yield responses to warming and would regulate the negative warming responses. Improving SOC under the medium SOC sequestration scenario would reduce the warming-induced yield loss of maize, wheat, rice and soybean to 0.1% °C-1, 2.7% °C-1, 3.4% °C-1 and - 0.6% °C-1, respectively, avoiding an average of 3%-5% °C-1 of global yield loss. These yield benefits would occur on 53.2%, 67.8%, 51.8% and 71.6% of maize, wheat, rice and soybean planting areas, respectively, with particularly pronounced benefits in the regions with negative warming responses. With improved soil carbon, food systems are predicted to provide additional 20 to over 130 million tonnes of food that would otherwise lose due to future warming. Our findings highlight the critical role of soil in alleviating negative warming impacts on food security, especially for developing regions, given that sustainable actions on soil improvement could be taken broadly.

A twenty-year dataset of high-resolution maize distribution in China.

China is the world's second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great significance for regional and global food security and international cereals trade. However, it still lacks a long-term maize distribution dataset with fine spatial resolution, because the existing high spatial resolution satellite datasets suffer from data gaps caused by cloud cover, especially in humid and cloudy regions. This study aimed to produce a long-term, high-resolution maize distribution map for China (China Crop Dataset-Maize, CCD-Maize) identifying maize in 22 provinces and municipalities from 2001 to 2020. The map was produced using a high spatiotemporal resolution fused dataset and a phenology-based method called Time-Weighted Dynamic Time Warping. A validation based on 54,281 field survey samples with a 30-m resolution showed that the average user's accuracy and producer's accuracy of CCD-Maize were 77.32% and 80.98%, respectively, and the overall accuracy was 80.06% over all 22 provinces.

Dynamic multi-dimensional scaling of 30+ year evolution of Chinese urban systems: Patterns and performance.

Understanding the co-evolution and organizational dynamics of urban properties (i.e., urban scaling) is the science base for pursuing synergies toward sustainable cities and society. The generalization of urban scaling theory yet requires more studies from various developmental regimes and across time. Here, we extend the universality proposition by exploring the evolution of longitudinal and transversal scaling of Chinese urban attributes between 1987 and 2018 using a global artificial impervious area (GAIA) remotely sensed dataset, harmonized night light data (NTL), and socioeconomic data, and revealed agreements and disagreements with theories. The superlinear relationship of urban area and population often considered as an indicator of wasting land resources (challenging the universality theory ßc = 2/3), is in fact the powerful impetus (capital raising) behind the concurrent superlinear expansion of socio-economic metabolisms (e.g., GDP, total wage) in a rapidly urbanizing country that has not yet reached equilibrium. Similarly, infrastructural variables associated with public services, such as hospitals and educational institutions, exhibited some deviations as well and were scaled linearly. However, the temporal narrowing of spatial deviations, such as the decline in urban land diseconomies of scale and the stabilization of economic output, clearly indicates the Chinese government's effort in charting urban systems toward balanced and sustainable development across the country. More importantly, the transversal sublinear scaling of areal-based socio-economic variables was inconsistent with the theoretical concept of increasing returns to scale, thus validating the view that a single measurement cannot unravel the intricate web of diverse urban attributes and urbanization. Our dynamic urban scaling analysis across space and through time in China provides new insights into the evolving nexus of urbanization, socioeconomic development, and national policies.

Climate warming negatively affects plant water-use efficiency in a seasonal hydroperiod wetland.

Climate warming has substantial influences on plant water-use efficiency (PWUE), which is defined as the ratio of plant CO2 uptake to water loss and is central to the cycles of carbon and water in ecosystems. However, it remains uncertain how does climate warming affect PWUE in wetland ecosystems, especially those with seasonally alternating water availability during the growing season. In this study, we used a continuous 10-year (2011-2020) eddy covariance (EC) dataset from a seasonal hydroperiod wetland coupled with a 15-year (2003-2017) satellite-based dataset (called PML-V2) and an in situ warming experiment to examine the climate warming impacts on wetland PWUE. The 10-year EC observational results revealed that rising temperatures had significant negative impacts on the interannual variations in wetland PWUE, and increased transpiration (Et) rather than changes in gross primary productivity (GPP) dominated these negative impacts. Furthermore, the 15-year satellite-based evidence confirmed that, in the study region, climate warming had significant negative consequences for the interannual variations in wetland PWUE by enhancing wetland Et. Lastly, at the leaf-scale, the light response curves of leaf photosynthesis, leaf Et, and leaf-scale PWUE indicated that wetland plants need to consume more water during the photosynthesis process under warmer conditions. These findings provide a fresh perspective on how climate warming influences carbon and water cycles in wetland ecosystems.