Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios.

Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree-grass competition. Enhanced productivity due to CO2 fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.

Enhanced growth resistance but no decline in growth resilience under long-term extreme droughts.

The frequency, intensity, and duration of extreme droughts, with devastating impacts on tree growth and survival, have increased with climate change over the past decades. Assessing growth resistance and resilience to drought is a crucial prerequisite for understanding the responses of forest functioning to drought events. However, the responses of growth resistance and resilience to extreme droughts with different durations across different climatic zones remain unclear. Here, we investigated the spatiotemporal patterns in growth resistance and resilience in response to extreme droughts with different durations during 1901-2015, relying on tree-ring chronologies from 2389 forest stands over the mid- and high-latitudinal Northern Hemisphere, species-specific plant functional traits, and diverse climatic factors. The findings revealed that growth resistance and resilience under 1-year droughts were higher in humid regions than in arid regions. Significant higher growth resistance was observed under 2-year droughts than under 1-year droughts in both arid and humid regions, while growth resilience did not show a significant difference. Temporally, tree growth became less resistant and resilient to 1-year droughts in 1980-2015 than in 1901-1979 in both arid and humid regions. As drought duration lengthened, the predominant impacts of climatic factors on growth resistance and resilience weakened and instead foliar economic traits, plant hydraulic traits, and soil properties became much more important in both climatic regions; in addition, such trends were also observed temporally. Finally, we found that most of the Earth system models (ESMs) used in this study overestimated growth resistance and underestimated growth resilience under both 1-year and 2-year droughts. A comprehensive ecophysiological understanding of tree growth responses to longer and intensified drought events is urgently needed, and a specific emphasis should be placed on improving the performance of ESMs.

Two sub-annual timescales and coupling modes for terrestrial water and carbon cycles.

To bridge the knowledge gap between (a) our (instantaneous-to-seasonal-scale) process understanding of plants and water and (b) our projections of long-term coupled feedbacks between the terrestrial water and carbon cycles, we must uncover what the dominant dynamics are linking fluxes of water and carbon. This study uses the simplest empirical dynamical systems models-two-dimensional linear models-and observation-based data from satellites, eddy covariance towers, weather stations, and machine-learning-derived products to determine the dominant sub-annual timescales coupling carbon uptake and (normalized) evaporation fluxes. We find two dominant modes across the Contiguous United States: (1) a negative correlation timescale on the order of a few days during which landscapes dry after precipitation and plants increase their carbon uptake through photosynthetic upregulation. (2) A slow, seasonal-scale positive covariation through which landscape drying leads to decreased growth and carbon uptake. The slow (positively correlated) process dominates the joint distribution of local water and carbon variables, leading to similar behaviors across space, biomes, and climate regions. We propose that vegetation cover/leaf area variables link this behavior across space, leading to strong emergent spatial patterns of water/carbon coupling in the mean. The spatial pattern of local temporal dynamics-positively sloped tangent lines to a convex long-term mean-state curve-is surprisingly strong, and can serve as a benchmark for coupled Earth System Models. We show that many such models do not represent this emergent mean-state pattern, and hypothesize that this may be due to lack of water-carbon feedbacks at daily scales.

Anthropogenic climate change is worsening North American pollen seasons.

Airborne pollen has major respiratory health impacts and anthropogenic climate change may increase pollen concentrations and extend pollen seasons. While greenhouse and field studies indicate that pollen concentrations are correlated with temperature, a formal detection and attribution of the role of anthropogenic climate change in continental pollen seasons is urgently needed. Here, we use long-term pollen data from 60 North American stations from 1990 to 2018, spanning 821 site-years of data, and Earth system model simulations to quantify the role of human-caused climate change in continental patterns in pollen concentrations. We find widespread advances and lengthening of pollen seasons (+20 d) and increases in pollen concentrations (+21%) across North America, which are strongly coupled to observed warming. Human forcing of the climate system contributed ∼50% (interquartile range: 19-84%) of the trend in pollen seasons and ∼8% (4-14%) of the trend in pollen concentrations. Our results reveal that anthropogenic climate change has already exacerbated pollen seasons in the past three decades with attendant deleterious effects on respiratory health.

Massive and rapid predominantly volcanic CO2 emission during the end-Permian mass extinction.

The end-Permian mass extinction event (∼252 Mya) is associated with one of the largest global carbon cycle perturbations in the Phanerozoic and is thought to be triggered by the Siberian Traps volcanism. Sizable carbon isotope excursions (CIEs) have been found at numerous sites around the world, suggesting massive quantities of 13C-depleted CO2 input into the ocean and atmosphere system. The exact magnitude and cause of the CIEs, the pace of CO2 emission, and the total quantity of CO2, however, remain poorly known. Here, we quantify the CO2 emission in an Earth system model based on new compound-specific carbon isotope records from the Finnmark Platform and an astronomically tuned age model. By quantitatively comparing the modeled surface ocean pH and boron isotope pH proxy, a massive (∼36,000 Gt C) and rapid emission (∼5 Gt C yr-1) of largely volcanic CO2 source (∼-15%) is necessary to drive the observed pattern of CIE, the abrupt decline in surface ocean pH, and the extreme global temperature increase. This suggests that the massive amount of greenhouse gases may have pushed the Earth system toward a critical tipping point, beyond which extreme changes in ocean pH and temperature led to irreversible mass extinction. The comparatively amplified CIE observed in higher plant leaf waxes suggests that the surface waters of the Finnmark Platform were likely out of equilibrium with the initial massive centennial-scale release of carbon from the massive Siberian Traps volcanism, supporting the rapidity of carbon injection. Our modeling work reveals that carbon emission pulses are accompanied by organic carbon burial, facilitated by widespread ocean anoxia.

Responses of precipitation and water vapor budget on the Chinese Loess Plateau to global land cover change forcing.

There have been notable changes in precipitation patterns on the Loess Plateau (LP) of China in recent decades, and numerous attribution studies have focused on sea surface temperature anomalies and atmospheric circulation changes induced by aerosols and greenhouse gases emission. However, the influences of global land use and land cover change (LULCC) as an important forcing factor in the climate system on regional precipitation remains poorly understood. In this study, we quantified the impacts of LULCC on precipitation and the water vapor budget in the LP region, utilizing data from LULCC forcing experiments conducted by the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Although global LULCC forcing exerted a negative effect on long-term mean precipitation on the LP region from 1850 to 2014, the different response characteristics were detected during different time periods. The global LULCC caused a decrease of 14 mm in annual precipitation during the period of 1850-1960. Conversely, from 1961 to 2014, it led to an increase of 6.4 mm, which is largely attributed to the enhanced water vapor transport along the southern boundary and westerly belt of the LP region. Moreover, from the perspective of the net water vapor balance of the entire LP, although LULCC caused net water vapor export during both periods 1850-1960 and 1961-2014, the export during the latter period (0.20 × 104 kg s-1) was smaller than that during the former period (0.28 × 104 kg s-1), indicating that the global expansion of grassland and cropland, along with the continuous rise in the leaf area index from 1961 to 2014, contributed to retaining more water vapor within the LP, which in turn was more favorable for precipitation. These findings provide valuable insights into the reasons behind precipitation variations in the LP region, emphasizing that global vegetation restoration and greening play a significant role in improving precipitation in ecologically fragile areas.

High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change.

Despite recurrent emphasis on their ecological and economic roles, the importance of high trophic levels (HTLs) on ocean carbon dynamics, through passive (fecal pellet production, carcasses) and active (vertical migration) processes, is still largely unexplored, notably under climate change scenarios. In addition, HTLs impact the ecosystem dynamics through top-down effects on lower trophic levels, which might change under anthropogenic influence. Here we compare two simulations of a global biogeochemical-ecosystem model with and without feedbacks from large marine animals. We show that these large marine animals affect the evolution of low trophic level biomasses, hence net primary production and most certainly ecosystem equilibrium, but seem to have little influence on the 21st-century anthropogenic carbon uptake under the RCP8.5 scenario. These results provide new insights regarding the expectations for trophic amplification of climate change through the marine trophic chain and regarding the necessity to explicitly represent marine animals in Earth System Models.

Nonlinear interactions of land carbon cycle feedbacks in Earth System Models.

Carbon cycle feedbacks were often quantified through the carbon-concentration and carbon-climate feedbacks with the assumption of no significant interaction between the two feedbacks in most previous studies. Here we calculated the strength of the interactions between the two responses using simulations of models participated in the phase 6 of the Coupled Model Intercomparison Project (CMIP6). We found that the nonlinear interaction contributed 11% of the land-atmosphere carbon exchange on average with large intermodel variation (from -20% to +162%). This nonlinear interaction is largely driven by the pattern of net primary production (NPP), with shifts in heterotrophic respiration that dampen the overall positive interactions from NPP. Photosynthetic rate per unit leaf area alone cannot adequately explain a wide variation of interactions in global NPP simulated by CMIP6 models. Plant respiration and processes that regulate leaf area are also important contributors to the interactions. Dominant factors that induce carbon-concentration and carbon-climate interactions are highly variable among models. One of those dominant factors is nutrient limitation. Using additional simulations of ACCESS-ESM1.5 that include both nitrogen and phosphorus limitation, we found that the estimated interactions by ACCESS-ESM1.5 with or without nutrient limitations covered the large intermodel variations among the CMIP6 models. It remains largely unknown how nutrient limitation complicates ecosystem's responses to simultaneously CO2 fertilization and warming at the global scale. Our modeling results point to a potential important role of nutrients, especially phosphorus on the nonlinear interactions. Yet, more studies are needed on ecosystem responses to concurrent changes in nutrient availability, atmospheric CO2 concentration, and warming.

Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water.

Elevated atmospheric CO2 (eCO2 ) influences the carbon assimilation rate and stomatal conductance of plants, thereby affecting the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO2 in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO2 effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO2 effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process-based land surface model QUINCY to simulate the leaf-level effects of increasing atmospheric CO2 concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO2 effects. The eCO2 effect on gross primary productivity (GPP) emerges at relatively low CO2 increase (∆[CO2 ] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO2 effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO2 increase (∆[CO2 ] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO2 is detectable earlier for variables related to the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass-dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO2 effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.

Empirical and Earth system model estimates of boreal nitrogen fixation often differ: A pathway toward reconciliation.

The impacts of global environmental change on productivity in northern latitudes will be contingent on nitrogen (N) availability. In circumpolar boreal ecosystems, nonvascular plants (i.e., bryophytes) and associated N2 -fixing diazotrophs provide one of the largest known N inputs but are rarely accounted for in Earth system models. Instead, most models link N2 -fixation with the functioning of vascular plants. Neglecting nonvascular N2 -fixation may be contributing toward high uncertainty that currently hinders model predictions in northern latitudes, where nonvascular N2 -fixing plants are more common. Adequately accounting for nonvascular N2 -fixation and its drivers could subsequently improve predictions of future N availability and ultimately, productivity, in northern latitudes. Here, we review empirical evidence of boreal nonvascular N2 -fixation responses to global change factors (elevated CO2 , N deposition, warming, precipitation, and shading by vascular plants), and compare empirical findings with model predictions of N2 -fixation using nine Earth system models. The majority of empirical studies found positive effects of CO2 , warming, precipitation, or light on nonvascular N2 -fixation, but N deposition strongly downregulated N2 -fixation in most empirical studies. Furthermore, we found that the responses of N2 -fixation to elevated CO2 were generally consistent between models and very limited empirical data. In contrast, empirical-model comparisons suggest that all models we assessed, and particularly those that scale N2 -fixation with net primary productivity or evapotranspiration, may be overestimating N2 -fixation under increasing N deposition. Overestimations could generate erroneous predictions of future N stocks in boreal ecosystems unless models adequately account for the drivers of nonvascular N2 -fixation. Based on our comparisons, we recommend that models explicitly treat nonvascular N2 -fixation and that field studies include more targeted measurements to improve model structures and parameterization.

Interannual and Seasonal Drivers of Carbon Cycle Variability Represented by the Community Earth System Model (CESM2).

Earth system models are intended to make long-term projections, but they can be evaluated at interannual and seasonal time scales. Although the Community Earth System Model (CESM2) showed improvements in a number of terrestrial carbon cycle benchmarks, relative to its predecessor, our analysis suggests that the interannual variability (IAV) in net terrestrial carbon fluxes did not show similar improvements. The model simulated low IAV of net ecosystem production (NEP), resulting in a weaker than observed sensitivity of the carbon cycle to climate variability. Low IAV in net fluxes likely resulted from low variability in gross primary productivity (GPP)-especially in the tropics-and a high covariation between GPP and ecosystem respiration. Although lower than observed, the IAV of NEP had significant climate sensitivities, with positive NEP anomalies associated with warmer and drier conditions in high latitudes, and with wetter and cooler conditions in mid and low latitudes. We identified two dominant modes of seasonal variability in carbon cycle flux anomalies in our fully coupled CESM2 simulations that are characterized by seasonal amplification and redistribution of ecosystem fluxes. Seasonal amplification of net and gross carbon fluxes showed climate sensitivities mirroring those of annual fluxes. Seasonal redistribution of carbon fluxes is initiated by springtime temperature anomalies, but subsequently negative feedbacks in soil moisture during the summer and fall result in net annual carbon losses from land. These modes of variability are also seen in satellite proxies of GPP, suggesting that CESM2 appropriately represents regional sensitivities of photosynthesis to climate variability on seasonal time scales.

The Climate Response to Emissions Reductions Due to COVID-19: Initial Results From CovidMIP.

Many nations responded to the corona virus disease-2019 (COVID-19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate.

Present and future projections of heatwave hazard-risk over India: A regional earth system model assessment.

The heatwave is a disastrous hazard having significant impacts on health and society. This study analyses the heatwave hazards and risk for India's current and future scenarios using socioeconomic vulnerability and temperature datasets during the summer (April-June) season. The Census of India (CoI) 2011 datasets were considered to assess current vulnerability and projected from the SocioEconomic Data And Application Center (SEDAC) population at Shared Socioeconomic Pathway (SSP) 4 for future vulnerability. Whereas IMD temperature data used for hazard assessment for the present scenario (1958-2005) while projected temperature data from regional earth system model REMO-OASIS-MPIOM (ROM) were used for the future (2006-2099) scenario. The study exhibited the most hazardous, vulnerable, and risk-prone regions identified as the south-eastern coast and Indo-Gangetic plains and some populous districts with metropolitan regions (Mumbai, Delhi, and Kolkata) under the current scenario. The coupled model ROM has efficiently captured the critical districts with higher and lower risk, showing its future projection capability. The study highlighted that the heatwave hazard-risk would significantly worsen in future scenarios in all districts under enhanced global warming and largely affecting the districts in the eastern and middle Indo-Gangetic plains and Malabar region. The present study will provide sufficient insights into designing mitigation strategies and future adaptive planning for the heatwave risk, which is one of the targets under Sustainable Development Goal 13 (Goal 13: Climate Action).

Constraining estimates of terrestrial carbon uptake: new opportunities using long-term satellite observations and data assimilation.

The response of terrestrial carbon uptake to increasing atmospheric [CO2 ], that is the CO2 fertilization effect (CFE), remains a key area of uncertainty in carbon cycle science. Here we provide a perspective on how satellite observations could be better used to understand and constrain CFE. We then highlight data assimilation (DA) as an effective way to reconcile different satellite datasets and systematically constrain carbon uptake trends in Earth System Models. As a proof-of-concept, we show that joint DA of multiple independent satellite datasets reduced model ensemble error by better constraining unobservable processes and variables, including those directly impacted by CFE. DA of multiple satellite datasets offers a powerful technique that could improve understanding of CFE and enable more accurate forecasts of terrestrial carbon uptake.

Research challenges and opportunities for using big data in global change biology.

Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space-based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta-analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new data-driven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model-data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes.

Climate change impacts on mismatches between phytoplankton blooms and fish spawning phenology.

Substantial interannual variability in marine fish recruitment (i.e., the number of young fish entering a fishery each year) has been hypothesized to be related to whether the timing of fish spawning matches that of seasonal plankton blooms. Environmental processes that control the phenology of blooms, such as stratification, may differ from those that influence fish spawning, such as temperature-linked reproductive maturation. These different controlling mechanisms could cause the timing of these events to diverge under climate change with negative consequences for fisheries. We use an earth system model to examine the impact of a high-emissions, climate-warming scenario (RCP8.5) on the future spawning time of two classes of temperate, epipelagic fishes: "geographic spawners" whose spawning grounds are defined by fixed geographic features (e.g., rivers, estuaries, reefs) and "environmental spawners" whose spawning grounds move responding to variations in environmental properties, such as temperature. By the century's end, our results indicate that projections of increased stratification cause spring and summer phytoplankton blooms to start 16 days earlier on average (±0.05 days SE) at latitudes >40°N. The temperature-linked phenology of geographic spawners changes at a rate twice as fast as phytoplankton, causing these fishes to spawn before the bloom starts across >85% of this region. "Extreme events," defined here as seasonal mismatches >30 days that could lead to fish recruitment failure, increase 10-fold for geographic spawners in many areas under the RCP8.5 scenario. Mismatches between environmental spawners and phytoplankton were smaller and less widespread, although sizable mismatches still emerged in some regions. This indicates that range shifts undertaken by environmental spawners may increase the resiliency of fishes to climate change impacts associated with phenological mismatches, potentially buffering against declines in larval fish survival, recruitment, and fisheries. Our model results are supported by empirical evidence from ecosystems with multidecadal observations of both fish and phytoplankton phenology.

Effects of anthropogenic nitrogen discharge on dissolved inorganic nitrogen transport in global rivers.

Excess nutrients from fertilizer application, pollution discharge, and water regulations outflow through rivers from lands to oceans, seriously impacting coastal ecosystems. A reasonable representation of these processes in land surface models and River Transport Models (RTMs) is very important for understanding human-environment interactions. In this study, the schemes of riverine dissolved inorganic nitrogen (DIN) transport and human activities including nitrogen discharge and water regulation, were synchronously incorporated into a land surface model coupled with a RTM. The effects of anthropogenic nitrogen discharge on the DIN transport in rivers were studied based on simulations of the period 1991-2010 throughout the entire world, conducted using the developed model, which had a spatial resolution of about 1° for land processes and 0.5° for river transport, and data on fertilizer application, point source pollution, and water use. Our results showed that rivers in western Europe and eastern China were seriously polluted, on average, at a rate of 5,000-15,000 tons per year. In the Yangtze River Basin, the amount of point source pollution in 2010 was about four times more than that in 1991, while the amount of fertilizer used in 2010 doubled, which resulted in the increased riverine DIN levels. Further comparisons suggested that the riverine DIN in the USA was affected primarily by nitrogen fertilizer use, the changes in DIN flow rate in European rivers was dominated by point source pollution, and rivers in China were seriously polluted by both the two pollution sources. The total anthropogenic impact on the DIN exported to the Pacific Ocean has increased from 10% to 30%, more significantly than other oceans. In general, our results indicated that incorporating the schemes of nitrogen transport and human activities into land surface models could be an effective way to monitor global river water quality and diagnose the performance of the land surface modeling.

Changes in timing of seasonal peak photosynthetic activity in northern ecosystems.

Seasonality in photosynthetic activity is a critical component of seasonal carbon, water, and energy cycles in the Earth system. This characteristic is a consequence of plant's adaptive evolutionary processes to a given set of environmental conditions. Changing climate in northern lands (>30°N) alters the state of climatic constraints on plant growth, and therefore, changes in the seasonality and carbon accumulation are anticipated. However, how photosynthetic seasonality evolved to its current state, and what role climatic constraints and their variability played in this process and ultimately in carbon cycle is still poorly understood due to its complexity. Here, we take the "laws of minimum" as a basis and introduce a new framework where the timing (day of year) of peak photosynthetic activity (DOYPmax ) acts as a proxy for plant's adaptive state to climatic constraints on its growth. Our analyses confirm that spatial variations in DOYPmax reflect spatial gradients in climatic constraints as well as seasonal maximum and total productivity. We find a widespread warming-induced advance in DOYPmax (-1.66 ± 0.30 days/decade, p < 0.001) across northern lands, indicating a spatiotemporal dynamism of climatic constraints to plant growth. We show that the observed changes in DOYPmax are associated with an increase in total gross primary productivity through enhanced carbon assimilation early in the growing season, which leads to an earlier phase shift in land-atmosphere carbon fluxes and an increase in their amplitude. Such changes are expected to continue in the future based on our analysis of earth system model projections. Our study provides a simplified, yet realistic framework based on first principles for the complex mechanisms by which various climatic factors constrain plant growth in northern ecosystems.

Vegetation demographics in Earth System Models: A review of progress and priorities.

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.

Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems.

Climate change is lengthening the growing season of the Northern Hemisphere extratropical terrestrial ecosystems, but little is known regarding the timing and dynamics of the peak season of plant activity. Here, we use 34-year satellite normalized difference vegetation index (NDVI) observations and atmospheric CO2 concentration and δ13 C isotope measurements at Point Barrow (Alaska, USA, 71°N) to study the dynamics of the peak of season (POS) of plant activity. Averaged across extratropical (>23°N) non-evergreen-dominated pixels, NDVI data show that the POS has advanced by 1.2 ± 0.6 days per decade in response to the spring-ward shifts of the start (1.0 ± 0.8 days per decade) and end (1.5 ± 1.0 days per decade) of peak activity, and the earlier onset of the start of growing season (1.4 ± 0.8 days per decade), while POS maximum NDVI value increased by 7.8 ± 1.8% for 1982-2015. Similarly, the peak day of carbon uptake, based on calculations from atmospheric CO2 concentration and δ13 C data, is advancing by 2.5 ± 2.6 and 4.3 ± 2.9 days per decade, respectively. POS maximum NDVI value shows strong negative relationships (p < .01) with the earlier onset of the start of growing season and POS days. Given that the maximum solar irradiance and day length occur before the average POS day, the earlier occurrence of peak plant activity results in increased plant productivity. Both the advancing POS day and increasing POS vegetation greenness are consistent with the shifting peak productivity towards spring and the increasing annual maximum values of gross and net ecosystem productivity simulated by coupled Earth system models. Our results further indicate that the decline in autumn NDVI is contributing the most to the overall browning of the northern high latitudes (>50°N) since 2011. The spring-ward shift of peak season plant activity is expected to disrupt the synchrony of biotic interaction and exert strong biophysical feedbacks on climate by modifying the surface albedo and energy budget.