(CACTACEAE): a comparative temporal evaluation using diverse bio-climatic earth system models and ensemble machine learning approach

A comprehensive evaluation of the habitat suitability across the India was conducted for the introduced species Opuntia ficus-indica. This assessment utilized a newly developed model called BioClimInd, takes into account five Earth System Models (ESMs). These ESMs consider two different emission scenarios known as Representative Concentration Pathways (RCP), specifically RCP 4.5 and RCP 8.5. Additionally, the assessment considered two future time frames: 2040-2079 (60) and 2060-2099 (80). Current study provided the threshold limit of different climatic variables in annual, quarter and monthly time slots like temperature annual range (26-30 °C), mean temperature of the driest quarter (25-28 °C); mean temperature of the coldest month (22-25 °C); minimum temperature of coldest month (13-17 °C); precipitation of the wettest month (250-500 mm); potential evapotranspiration Thronthwaite (1740-1800 mm). Predictive climatic habitat suitability posits that the introduction of this exotic species is deemed unsuitable in the Northern as well as the entirety of the cooler eastern areas of the country. The states of Rajasthan and Gujarat exhibit the highest degree of habitat suitability for this particular species. Niche hypervolumes and climatic variables affecting fundamental and realized niches were also assessed. This study proposes using multi-climatic exploration to evaluate habitats for introduced species to reduce modeling uncertainties.

Demystifying global climate models for use in the life sciences

For each assessment cycle of the Intergovernmental Panel on Climate Change (IPCC), researchers in the life sciences are called upon to provide evidence to policymakers planning for a changing future. This research increasingly relies on highly technical and complex outputs from climate models. The strengths and weaknesses of these data may not be fully appreciated beyond the climate modelling community; therefore, uninformed use of raw or preprocessed climate data could lead to overconfident or spurious conclusions. We provide an accessible introduction to climate model outputs that is intended to empower the life science community to robustly address questions about human and natural systems in a changing world.

Forest regeneration within Earth system models: current process representations and ways forward

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.

Simulating environmentally-sensitive tree recruitment in vegetation demographic models

Vegetation demographic models (VDMs) endeavor to predict how global forests will respond to climate change. This requires simulating which trees, if any, are able to recruit under changing environmental conditions. We present a new recruitment scheme for VDMs in which functional-type-specific recruitment rates are sensitive to light, soil moisture and the productivity of reproductive trees. We evaluate the scheme by predicting tree recruitment for four tropical tree functional types under varying meteorology and canopy structure at Barro Colorado Island, Panama. We compare predictions to those of a current VDM, quantitative observations and ecological expectations. We find that the scheme improves the magnitude and rank order of recruitment rates among functional types and captures recruitment limitations in response to variable understory light, soil moisture and precipitation regimes. Our results indicate that adopting this framework will improve VDM capacity to predict functional-type-specific tree recruitment in response to climate change, thereby improving predictions of future forest distribution, composition and function.

Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model

Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change. Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist's role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data-model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change, and broadening the impact of ecological research.

Country-level land carbon sink and its causing components by the middle of the twenty-first century

BACKGROUND

Countries have long been making efforts by reducing greenhouse-gas emissions to mitigate climate change. In the agreements of the United Nations Framework Convention on Climate Change, involved countries have committed to reduction targets. However, carbon (C) sink and its involving processes by natural ecosystems remain difficult to quantify.

METHODS

Using a transient traceability framework, we estimated country-level land C sink and its causing components by 2050 simulated by 12 Earth System Models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5) under RCP8.5.

RESULTS

The top 20 countries with highest C sink have the potential to sequester 62 Pg C in total, among which, Russia, Canada, USA, China, and Brazil sequester the most. This C sink consists of four components: production-driven change, turnover-driven change, change in instantaneous C storage potential, and interaction between production-driven change and turnover-driven change. The four components account for 49.5%, 28.1%, 14.5%, and 7.9% of the land C sink, respectively.

CONCLUSION

The model-based estimates highlight that land C sink potentially offsets a substantial proportion of greenhouse-gas emissions, especially for countries where net primary production (NPP) likely increases substantially and inherent residence time elongates.

Rubisco substitutions predicted to enhance crop performance through carbon uptake modelling

Improving the performance of the CO2-fixing enzyme Rubisco is among the targets for increasing crop yields. Here, Earth system model (ESM) representations of canopy C3 and C4 photosynthesis were combined with species-specific Rubisco parameters to quantify the consequences of bioengineering foreign Rubiscos into C3 and C4 crops under field conditions. The 'two big leaf' (sunlit/shaded) model for canopy photosynthesis was used together with species-specific Rubisco kinetic parameters including maximum rate (Kcat), Michaelis-Menten constant for CO2 at ambient atmospheric O2 (Kc21%O2), specificity for CO2 to O2 (Sc/o), and associated heat activation (Ha) values. Canopy-scale consequences of replacing native Rubiscos in wheat, maize, and sugar beet with foreign enzymes from 27 species were modelled using data from Ameriflux and Fluxnet databases. Variation among the included Rubisco kinetics differentially affected modelled carbon uptake rates, and Rubiscos from several species of C4 grasses showed the greatest potential of >50% carbon uptake improvement in wheat, and >25% improvement in sugar beet and maize. This study also reaffirms the need for data on fully characterized Rubiscos from more species, and for better parameterization of 'Vcmax' and temperature response of 'Jmax' in ESMs.

Annual precipitation explains variability in dryland vegetation greenness globally but not locally

Dryland vegetation productivity is strongly modulated by water availability. As precipitation patterns and variability are altered by climate change, there is a pressing need to better understand vegetation responses to precipitation variability in these ecologically fragile regions. Here we present a global analysis of dryland sensitivity to annual precipitation variations using long-term records of normalized difference vegetation index (NDVI). We show that while precipitation explains 66% of spatial gradients in NDVI across dryland regions, precipitation only accounts for <26% of temporal NDVI variability over most (>75%) dryland regions. We observed this weaker temporal relative to spatial relationship between NDVI and precipitation across all global drylands. We confirmed this result using three alternative water availability metrics that account for water loss to evaporation, and growing season and precipitation timing. This suggests that predicting vegetation responses to future rainfall using space-for-time substitution will strongly overestimate precipitation control on interannual variability in aboveground growth. We explore multiple mechanisms to explain the discrepancy between spatial and temporal responses and find contributions from multiple factors including local-scale vegetation characteristics, climate and soil properties. Earth system models (ESMs) from the latest Coupled Model Intercomparison Project overestimate the observed vegetation sensitivity to precipitation variability up to threefold, particularly during dry years. Given projections of increasing meteorological drought, ESMs are likely to overestimate the impacts of future drought on dryland vegetation with observations suggesting that dryland vegetation is more resistant to annual precipitation variations than ESMs project.

Lineage-based functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models

Process-based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass-dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C₃ grasses and (sub)tropical C₄ grasses. Incorporation of species-level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), situated between species-level trait data and PFT-level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.

Allometric constraints and competition enable the simulation of size structure and carbon fluxes in a dynamic vegetation model of tropical forests (LM3PPA-TV)

Tropical forests are a key determinant of the functioning of the Earth system, but remain a major source of uncertainty in carbon cycle models and climate change projections. In this study, we present an updated land model (LM3PPA-TV) to improve the representation of tropical forest structure and dynamics in Earth system models (ESMs). The development and parameterization of LM3PPA-TV drew on extensive datasets on tropical tree traits and long-term field censuses from Barro Colorado Island (BCI), Panama. The model defines a new plant functional type (PFT) based on the characteristics of shade-tolerant, tropical tree species, implements a new growth allocation scheme based on realistic tree allometries, incorporates hydraulic constraints on biomass accumulation, and features a new compartment for tree branches and branch fall dynamics. Simulation experiments reproduced observed diurnal and seasonal patterns in stand-level carbon and water fluxes, as well as mean canopy and understory tree growth rates, tree size distributions, and stand-level biomass on BCI. Simulations at multiple sites captured considerable variation in biomass and size structure across the tropical forest biome, including observed responses to precipitation and temperature. Model experiments suggested a major role of water limitation in controlling geographic variation forest biomass and structure. However, the failure to simulate tropical forests under extreme conditions and the systematic underestimation of forest biomass in Paleotropical locations highlighted the need to incorporate variation in hydraulic traits and multiple PFTs that capture the distinct floristic composition across tropical domains. The continued pressure on tropical forests from global change demands models which are able to simulate alternative successional pathways and their pace to recovery. LM3PPA-TV provides a tool to investigate geographic variation in tropical forests and a benchmark to continue improving the representation of tropical forests dynamics and their carbon storage potential in ESMs.

Time of Emergence and Large Ensemble Intercomparison for Ocean Biogeochemical Trends

Anthropogenically forced changes in ocean biogeochemistry are underway and critical for the ocean carbon sink and marine habitat. Detecting such changes in ocean biogeochemistry will require quantification of the magnitude of the change (anthropogenic signal) and the natural variability inherent to the climate system (noise). Here we use Large Ensemble (LE) experiments from four Earth system models (ESMs) with multiple emissions scenarios to estimate Time of Emergence (ToE) and partition projection uncertainty for anthropogenic signals in five biogeochemically important upper-ocean variables. We find ToEs are robust across ESMs for sea surface temperature and the invasion of anthropogenic carbon; emergence time scales are 20-30 yr. For the biological carbon pump, and sea surface chlorophyll and salinity, emergence time scales are longer (50+ yr), less robust across the ESMs, and more sensitive to the forcing scenario considered. We find internal variability uncertainty, and model differences in the internal variability uncertainty, can be consequential sources of uncertainty for projecting regional changes in ocean biogeochemistry over the coming decades. In combining structural, scenario, and internal variability uncertainty, this study represents the most comprehensive characterization of biogeochemical emergence time scales and uncertainty to date. Our findings delineate critical spatial and duration requirements for marine observing systems to robustly detect anthropogenic change.

Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests

Understanding the pronounced seasonal and spatial variation in leaf carboxylation capacity (V_c,max ) is critical for determining terrestrial carbon cycling in tropical forests. However, an efficient and scalable approach for predicting V_c,max is still lacking. Here the ability of leaf spectroscopy for rapid estimation of V_c,max was tested. V_c,max was estimated using traditional gas exchange methods, and measured reflectance spectra and leaf age in leaves sampled from tropical forests in Panama and Brazil. These data were used to build a model to predict V_c,max from leaf spectra. The results demonstrated that leaf spectroscopy accurately predicts V_c,max of mature leaves in Panamanian tropical forests (R²  = 0.90). However, this single-age model required recalibration when applied to broader leaf demographic classes (i.e. immature leaves). Combined use of spectroscopy models for V_c,max and leaf age enabled construction of the V_c,max -age relationship solely from leaf spectra, which agreed with field observations. This suggests that the spectroscopy technique can capture the seasonal variability in V_c,max , assuming sufficient sampling across diverse species, leaf ages and canopy environments. This finding will aid development of remote sensing approaches that can be used to characterize V_c,max in moist tropical forests and enable an efficient means to parameterize and evaluate terrestrial biosphere models.

A Wood Biology Agenda to Support Global Vegetation Modelling

Realistic forecasting of forest responses to climate change critically depends on key advancements in global vegetation modelling. Compared with traditional 'big-leaf' models that simulate forest stands, 'next-generation' vegetation models aim to track carbon-, light-, water-, and nutrient-limited growth of individual trees. Wood biology can play an important role in delivering the required knowledge at tissue-to-individual levels, at minute-to-century scales and for model parameterization and benchmarking. We propose a wood biology research agenda that contributes to filling six knowledge gaps: sink versus source limitation, drivers of intra-annual growth, drought impacts, functional wood traits, dynamic biomass allocation, and nutrient cycling. Executing this agenda will expedite model development and increase the ability of models to forecast global change impact on forest dynamics.

Assessing the Dynamic Versus Thermodynamic Origin of Climate Model Biases

Global climate models present systematic biases, among others, a tendency to overestimate hot and dry summers in midlatitude regions. Here we investigate the origin of such biases in the Community Earth System Model. To disentangle the contribution of dynamics and thermodynamics, we perform simulations that include nudging of horizontal wind and compare them to simulations with a free atmosphere. Prescribing the observed large-scale circulation improves the modeled weather patterns as well as many related fields. However, the larger part of the temperature and precipitation biases of the free atmosphere configuration remains after nudging, in particular, for extremes. Our results suggest that thermodynamical processes, including land-atmosphere coupling and atmospheric parameterizations, drive the errors present in Community Earth System Model. Our result may apply to other climate models and highlight the importance of distinguishing thermodynamic and dynamic sources of biases in present-day global climate models.

Models meet data: Challenges and opportunities in implementing land management in Earth system models

As the applications of Earth system models (ESMs) move from general climate projections toward questions of mitigation and adaptation, the inclusion of land management practices in these models becomes crucial. We carried out a survey among modeling groups to show an evolution from models able only to deal with land-cover change to more sophisticated approaches that allow also for the partial integration of land management changes. For the longer term a comprehensive land management representation can be anticipated for all major models. To guide the prioritization of implementation, we evaluate ten land management practices-forestry harvest, tree species selection, grazing and mowing harvest, crop harvest, crop species selection, irrigation, wetland drainage, fertilization, tillage, and fire-for (1) their importance on the Earth system, (2) the possibility of implementing them in state-of-the-art ESMs, and (3) availability of required input data. Matching these criteria, we identify "low-hanging fruits" for the inclusion in ESMs, such as basic implementations of crop and forestry harvest and fertilization. We also identify research requirements for specific communities to address the remaining land management practices. Data availability severely hampers modeling the most extensive land management practice, grazing and mowing harvest, and is a limiting factor for a comprehensive implementation of most other practices. Inadequate process understanding hampers even a basic assessment of crop species selection and tillage effects. The need for multiple advanced model structures will be the challenge for a comprehensive implementation of most practices but considerable synergy can be gained using the same structures for different practices. A continuous and closer collaboration of the modeling, Earth observation, and land system science communities is thus required to achieve the inclusion of land management in ESMs.

A lake classification concept for a more accurate global estimate of the dissolved inorganic carbon export from terrestrial ecosystems to inland waters

The magnitude of lateral dissolved inorganic carbon (DIC) export from terrestrial ecosystems to inland waters strongly influences the estimate of the global terrestrial carbon dioxide (CO₂) sink. At present, no reliable number of this export is available, and the few studies estimating the lateral DIC export assume that all lakes on Earth function similarly. However, lakes can function along a continuum from passive carbon transporters (passive open channels) to highly active carbon transformers with efficient in-lake CO₂ production and loss. We developed and applied a conceptual model to demonstrate how the assumed function of lakes in carbon cycling can affect calculations of the global lateral DIC export from terrestrial ecosystems to inland waters. Using global data on in-lake CO₂ production by mineralization as well as CO₂ loss by emission, primary production, and carbonate precipitation in lakes, we estimated that the global lateral DIC export can lie within the range of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {0.70}_{-0.31}^{+0.27} $$\end{document}0.70−0.31+0.27 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {1.52}_{-0.90}^{+1.09} $$\end{document}1.52−0.90+1.09 Pg C yr⁻¹ depending on the assumed function of lakes. Thus, the considered lake function has a large effect on the calculated lateral DIC export from terrestrial ecosystems to inland waters. We conclude that more robust estimates of CO₂ sinks and sources will require the classification of lakes into their predominant function. This functional lake classification concept becomes particularly important for the estimation of future CO₂ sinks and sources, since in-lake carbon transformation is predicted to be altered with climate change.

Detecting regional patterns of changing CO2 flux in Alaska

With rapid changes in climate and the seasonal amplitude of carbon dioxide (CO2) in the Arctic, it is critical that we detect and quantify the underlying processes controlling the changing amplitude of CO2 to better predict carbon cycle feedbacks in the Arctic climate system. We use satellite and airborne observations of atmospheric CO2 with climatically forced CO2 flux simulations to assess the detectability of Alaskan carbon cycle signals as future warming evolves. We find that current satellite remote sensing technologies can detect changing uptake accurately during the growing season but lack sufficient cold season coverage and near-surface sensitivity to constrain annual carbon balance changes at regional scale. Airborne strategies that target regular vertical profile measurements within continental interiors are more sensitive to regional flux deeper into the cold season but currently lack sufficient spatial coverage throughout the entire cold season. Thus, the current CO2 observing network is unlikely to detect potentially large CO2 sources associated with deep permafrost thaw and cold season respiration expected over the next 50 y. Although continuity of current observations is vital, strategies and technologies focused on cold season measurements (active remote sensing, aircraft, and tall towers) and systematic sampling of vertical profiles across continental interiors over the full annual cycle are required to detect the onset of carbon release from thawing permafrost.

Permafrost carbon-climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics

Permafrost soils contain enormous amounts of organic carbon whose stability is contingent on remaining frozen. With future warming, these soils may release carbon to the atmosphere and act as a positive feedback to climate change. Significant uncertainty remains on the postthaw carbon dynamics of permafrost-affected ecosystems, in particular since most of the carbon resides at depth where decomposition dynamics may differ from surface soils, and since nitrogen mineralized by decomposition may enhance plant growth. Here we show, using a carbon-nitrogen model that includes permafrost processes forced in an unmitigated warming scenario, that the future carbon balance of the permafrost region is highly sensitive to the decomposability of deeper carbon, with the net balance ranging from 21 Pg C to 164 Pg C losses by 2300. Increased soil nitrogen mineralization reduces nutrient limitations, but the impact of deep nitrogen on the carbon budget is small due to enhanced nitrogen availability from warming surface soils and seasonal asynchrony between deeper nitrogen availability and plant nitrogen demands. Although nitrogen dynamics are highly uncertain, the future carbon balance of this region is projected to hinge more on the rate and extent of permafrost thaw and soil decomposition than on enhanced nitrogen availability for vegetation growth resulting from permafrost thaw.