Modeling Global Carbon Costs of Plant Nitrogen and Phosphorus Acquisition

Most Earth system models (ESMs) do not explicitly represent the carbon (C) costs of plant nutrient acquisition, which leads to uncertainty in predictions of the current and future constraints to the land C sink. We integrate a plant productivity-optimizing nitrogen (N) and phosphorus (P) acquisition model (fixation & uptake of nutrients, FUN) into the energy exascale Earth system (E3SM) land model (ELM). Global plant N and P uptake are dynamically simulated by ELM-FUN based on the C costs of nutrient acquisition from mycorrhizae, direct root uptake, retranslocation from senescing leaves, and biological N fixation. We benchmarked ELM-FUN with three classes of products: ILAMB, a remotely sensed nutrient limitation product, and CMIP6 models; we found significant improvements in C cycle variables, although the lack of more observed nutrient data prevents a comprehensive level of benchmarking. Overall, we found N and P co-limitation for 80% of land area, with the remaining 20% being either predominantly N or P limited. Globally, the new model predicts that plants invested 4.1 Pg C yr-1 to acquire 841.8 Tg N yr-1 and 48.1 Tg P yr-1 (1994-2005), leading to significant downregulation of global net primary production (NPP). Global NPP is reduced by 20% with C costs of N and 50% with C costs of NP. Modeled and observed nutrient limitation agreement increases when N and P are considered together (r 2 from 0.73 to 0.83).

Identifying areas at risk of drought-induced tree mortality across South-Eastern Australia

South-East Australia has recently been subjected to two of the worst droughts in the historical record (Millennium Drought, 2000-2009 and Big Dry, 2017-2019). Unfortunately, a lack of forest monitoring has made it difficult to determine whether widespread tree mortality has resulted from these droughts. Anecdotal observations suggest the Big Dry may have led to more significant tree mortality than the Millennium drought. Critically, to be able to robustly project future expected climate change effects on Australian vegetation, we need to assess the vulnerability of Australian trees to drought. Here we implemented a model of plant hydraulics into the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. We parameterized the drought response behaviour of five broad vegetation types, based on a common garden dry-down experiment with species originating across a rainfall gradient (188-1,125 mm/year) across South-East Australia. The new hydraulics model significantly improved (~35%-45% reduction in root mean square error) CABLE's previous predictions of latent heat fluxes during periods of water stress at two eddy covariance sites in Australia. Landscape-scale predictions of the greatest percentage loss of hydraulic conductivity (PLC) of about 40%-60%, were broadly consistent with satellite estimates of regions of the greatest change in both droughts. In neither drought did CABLE predict that trees would have reached critical PLC in widespread areas (i.e. it projected a low mortality risk), although the model highlighted critical levels near the desert regions of South-East Australia where few trees live. Overall, our experimentally constrained model results imply significant resilience to drought conferred by hydraulic function, but also highlight critical data and scientific gaps. Our approach presents a promising avenue to integrate experimental data and make regional-scale predictions of potential drought-induced hydraulic failure.

Predicting Thermal Adaptation by Looking Into Populations' Genomic Past

Molecular evolution offers an insightful theory to interpret the genomic consequences of thermal adaptation to previous events of climate change beyond range shifts. However, disentangling often mixed footprints of selective and demographic processes from those due to lineage sorting, recombination rate variation, and genomic constrains is not trivial. Therefore, here we condense current and historical population genomic tools to study thermal adaptation and outline key developments (genomic prediction, machine learning) that might assist their utilization for improving forecasts of populations' responses to thermal variation. We start by summarizing how recent thermal-driven selective and demographic responses can be inferred by coalescent methods and in turn how quantitative genetic theory offers suitable multi-trait predictions over a few generations via the breeder's equation. We later assume that enough generations have passed as to display genomic signatures of divergent selection to thermal variation and describe how these footprints can be reconstructed using genome-wide association and selection scans or, alternatively, may be used for forward prediction over multiple generations under an infinitesimal genomic prediction model. Finally, we move deeper in time to comprehend the genomic consequences of thermal shifts at an evolutionary time scale by relying on phylogeographic approaches that allow for reticulate evolution and ecological parapatric speciation, and end by envisioning the potential of modern machine learning techniques to better inform long-term predictions. We conclude that foreseeing future thermal adaptive responses requires bridging the multiple spatial scales of historical and predictive environmental change research under modern cohesive approaches such as genomic prediction and machine learning frameworks.

Higher than expected CO2 fertilization inferred from leaf to global observations

Several lines of evidence point to an increase in the activity of the terrestrial biosphere over recent decades, impacting the global net land carbon sink (NLS) and its control on the growth of atmospheric carbon dioxide (ca ). Global terrestrial gross primary production (GPP)-the rate of carbon fixation by photosynthesis-is estimated to have risen by (31 ± 5)% since 1900, but the relative contributions of different putative drivers to this increase are not well known. Here we identify the rising atmospheric CO2 concentration as the dominant driver. We reconcile leaf-level and global atmospheric constraints on trends in modeled biospheric activity to reveal a global CO2 fertilization effect on photosynthesis of 30% since 1900, or 47% for a doubling of ca above the pre-industrial level. Our historic value is nearly twice as high as current estimates (17 ± 4)% that do not use the full range of available constraints. Consequently, under a future low-emission scenario, we project a land carbon sink (174 PgC, 2006-2099) that is 57 PgC larger than if a lower CO2 fertilization effect comparable with current estimates is assumed. These findings suggest a larger beneficial role of the land carbon sink in modulating future excess anthropogenic CO2 consistent with the target of the Paris Agreement to stay below 2°C warming, and underscore the importance of preserving terrestrial carbon sinks.

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

Effect of climate dataset selection on simulations of terrestrial GPP: Highest uncertainty for tropical regions

Biogeochemical models use meteorological forcing data derived with different approaches (e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulate ecosystem processes such as gross primary productivity (GPP). This study assesses the impact of different widely used climate datasets on simulated gross primary productivity and evaluates the suitability of them for reproducing the global and regional carbon cycle as mapped from independent GPP data. We simulate GPP with the biogeochemical model LPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP, PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-based GPP product derived from eddy covariance measurements in combination with remotely sensed data. Our results show that all datasets tested produce relatively similar GPP simulations at a global scale, corresponding fairly well to the observation-based data with a difference between simulations and observations ranging from -50 to 60 g m^-2 yr^-1. However, all simulations also show a strong underestimation of GPP (ranging from -533 to -870 g m^-2 yr^-1) and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwave radiation for tropical areas was found to have the highest uncertainty in the analyzed historical climate datasets, we test whether simulation results could be improved by a correction of the tested shortwave radiation for tropical areas using a new radiation product from the International Satellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) in simulated GPP magnitude was observed with bias corrected shortwave radiation, as well as an increase in spatio-temporal agreement between the simulated GPP and observation-based GPP. This study conducts a spatial inter-comparison and quantification of the performances of climate datasets and can thereby facilitate the selection of climate forcing data over any given study area for modelling purposes.

A Photosynthesis-based Two-leaf Canopy Stomatal Conductance Model for Meteorology and Air Quality Modeling with WRF/CMAQ PX LSM

A coupled photosynthesis-stomatal conductance model with single layer sunlit and shaded leaf canopy scaling is developed for the Pleim-Xiu land surface model (LSM) option in the meteorology and air quality modeling system - WRF/CMAQ (Weather Research and Forecast model and Community Multiscale Air Quality model). The photosynthesis-based model for the PX LSM (PX PSN) is implemented and evaluated in a diagnostic box model that has evapotranspiration and ozone deposition components taken directly from WRF/CMAQ. We evaluate PX PSN for latent heat (LH) estimation at four FLUXNET sites with different vegetation types and landscape characteristics and at one FLUXNET site with ozone flux measurements against the simple Jarvis approach used in the current PX LSM. Overall, the PX PSN simulates LH as well as the PX Jarvis approach. The PX PSN, however, shows distinct advantages over the PX Jarvis approach on grassland that likely results from its treatment of C₃ and C₄ plants for CO₂ assimilation estimation. Simulations using Moderate Resolution Imaging Spectroradiometer (MODIS) LAI rather than LAI observations assess how the model would perform with the grid averaged data available in the Eulerian grid model (WRF/CMAQ). While MODIS LAI generally follows the seasonality of the observed LAI, it cannot capture the extreme highs and lows of the site measurements. MODIS LAI estimates degrade model performance at all sites but one site having old and tall trees. Ozone deposition velocity and ozone flux along with LH are simulated especially well by PX PSN as compared to significant PX Jarvis overestimation.

Data-driven Critical Zone science: A new paradigm

This paper uses examples from Australia to argue for a new approach to integrative research in the Earth's near surface environment where the pedosphere, atmosphere, hydrosphere, and biosphere interact, the so-called 'Critical Zone'. In Australia, for around 25years, environmental data layers presented through Geographical Information Systems software have been combined with field-based measurements and observations to produce spatially explicit predictive models for digitally mapping soils and soil properties. The availability of spatially extensive datasets representing different factors of landscape evolution and their exploration with machine learning and rule induction techniques also allow the evaluation of emergent patterns against existing domain knowledge, which in turn can lead to new insights and can facilitate their extrapolation over large areas. Thus the data-driven approach is complementary to the hypothesis-driven scientific inquiry in Critical Zone observatories.

Biogeographic variation in evergreen conifer needle longevity and impacts on boreal forest carbon cycle projections

Leaf life span is an important plant trait associated with interspecific variation in leaf, organismal, and ecosystem processes. We hypothesized that intraspecific variation in gymnosperm needle traits with latitude reflects both selection and acclimation for traits adaptive to the associated temperature and moisture gradient. This hypothesis was supported, because across 127 sites along a 2,160-km gradient in North America individuals of Picea glauca, Picea mariana, Pinus banksiana, and Abies balsamea had longer needle life span and lower tissue nitrogen concentration with decreasing mean annual temperature. Similar patterns were noted for Pinus sylvestris across a north-south gradient in Europe. These differences highlight needle longevity as an adaptive feature important to ecological success of boreal conifers across broad climatic ranges. Additionally, differences in leaf life span directly affect annual foliage turnover rate, which along with needle physiology partially regulates carbon cycling through effects on gross primary production and net canopy carbon export. However, most, if not all, global land surface models parameterize needle longevity of boreal evergreen forests as if it were a constant. We incorporated temperature-dependent needle longevity and %nitrogen, and biomass allocation, into a land surface model, Community Atmosphere Biosphere Land Exchange, to assess their impacts on carbon cycling processes. Incorporating realistic parameterization of these variables improved predictions of canopy leaf area index and gross primary production compared with observations from flux sites. Finally, increasingly low foliage turnover and biomass fraction toward the cold far north indicate that a surprisingly small fraction of new biomass is allocated to foliage under such conditions.