Contributions of Initial Conditions and Meteorological Forecast to Subseasonal-to-Seasonal Hydrological Forecast Skill in Western Tropical South America.

Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March-May) using ensemble hindcasts for 2002-17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño-Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.

Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets.

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400-700 nm. However, ultra-violet (UV) radiation within 280-400 nm and far-red (FR) radiation within 700-750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. We explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year-1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

Spatial-temporal change of river thermal environment and anthropogenic impact in China.

River water temperature is important and closely related to river ecosystem, concerning fishery industry, human health, and the land-sea exchange of nutrients, especially for great powers with a good deal of heat emission from once-through cooling systems of thermal power plants. However, the changes in river water temperature under the joint action of climate change and human activity such as the heat emission have not been well investigated for rising powers, hampering environmental policy making for sustainable development. Therefore, we have taken advantage of a recently-developed land surface model including river water temperature calculation with anthropogenic thermal discharge and zonal statistics to quantitatively make out the river water temperature variation and the man-made influence over the past thirty years (1981-2010) in China for the first time. Results show that the estimated water temperature in major rivers is generally close to the observation with the r2 of 0.83, though the underestimation exists in some rivers. The river water in the Pearl River Basin was the warmest with the mean temperature of 19.2 °C and the others in order were in the Southeast Basin, the Huaihe River Basin, the Yangtze River Basin, the Haihe River Basin, the Yellow River Basin, the Southwest Basin, the Song-Liao River Basin, and the Continental Basin, ranging from 16.7 °C to 6.3 °C. The Huaihe River Basin had the fastest mean increase rate of ca. 0.27 °C decade-1, while the slowest mean increase rate of ca. 0.13 °C decade-1 existed in the Pearl River Basin. At the subbasin scale, a meridionally-distributed hot spot zone (along the 115°E) of the increasing water temperature has been identified, where the trends ranged from 0.2 °C decade-1 to 0.5 °C decade-1. Air temperature exerted a major control on the spatial pattern of climatological water temperature, while both air temperature and downwelling solar flux played a leading role in the distribution of water temperature change trends. Although anthropogenic thermal emission heated the rivers locally, the impacts in the Song-Liao River, the Haihe River, the Huaihe River, and the middle and lower reaches of Yellow River and Yangtze River had been raised up to ca. 4.5 °C, when comparing with those controlled by climate change only. In general, these results show an acceptable level of river water temperature simulation in the land surface model, and could provide a scientific reference for the assessment on riverine thermal environment under the climate change and social impact in China.

Research progress in parameterizing irrigation and fertilization in land surface model.

Under the context of global climate change and growing population, irrigation and fertilization have become important ways to ensure food production, with consequences on water cycling, energy flow, and materials cycling in terrestrial ecosystems. In the land surface model (LSM), coupling irrigation and fertilization schemes are of great importance for clearly understanding the land-atmosphere interactions to ensure food security. We reviewed the expression methods of three key parameters, namely, the applied method, usage, and time in the parameterization process of irrigation and fertilization (nitrogen fertilizer) in LSM. We found that the ways to irrigate and ferti-lize in LSM are different from the ways used in actual practice due to the limitation of the high resolution of spatio-temporal data, which makes it difficult to understand the actual influences of irrigation and fertilization on grain yield, environment, and local climate. Finally, we proposed future works: 1) taking the differences of crop water demand into account and making the different irrigation thresholds for different crops to properly evaluate the total and intensity of water consumption of different crops; 2) using the field records and the regional grid data of fertilization and irrigation developed in recent years to develop parameterized schemes that are more in line with actual agricultural operations, which can accurately reveal their economic, ecological, and climatic effects; 3) developing fertilization diagnosis scheme considering crop type, phenological stage, and soil basic fertility as the supplementary scheme in LSM, to improve the applicability and simulation accuracy of LSM in the areas without nitrogen fertilizer data.

Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation.

Land surface models (LSMs) have prominent advantages for exploring the best agricultural practices in terms of both economic and environmental benefits with regard to different climate scenarios. However, their applications to optimizing fertilization and irrigation have not been well discussed because of their relatively underdeveloped crop modules. We used a CLM5-Crop LSM to optimize fertilization and irrigation schedules that follow actual agricultural practices for the cultivation of maize and wheat, as well as to explore the most economic and environmental-friendly inputs of nitrogen fertilizer and irrigation (FI), in the North China Plain (NCP), which is a typical intensive farming area. The model used the indicators of crop yield, farm gross margin (FGM), nitrogen use efficiency (NUE), water use efficiency (WUE), and soil nitrogen leaching. The results showed that the total optimal FI inputs of FGM were the highest (230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize; 137.5 ± 25 kg N ha-1 and 362.5 ± 47.9 mm for wheat), followed by the FIs of yield, NUE, WUE, and soil nitrogen leaching. After multi-objective optimization, the optimal FIs were 230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize, and 137.5 ± 25 kg N ha-1 and 387.5 ± 85.4 mm for wheat. By comparing our model-based diagnostic results with the actual inputs of FIs in the NCP, we found excessive usage of nitrogen fertilizer and irrigation during the current cultivation period of maize and wheat. The scientific collocation of fertilizer and water resources should be seriously considered for economic and environmental benefits. Overall, the optimized inputs of the FIs were in reasonable ranges, as postulated by previous studies. This result hints at the potential applications of LSMs for guiding sustainable agricultural development.

Reduced global plant respiration due to the acclimation of leaf dark respiration coupled with photosynthesis.

Leaf dark respiration (Rd ) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that Rd and Rubisco carboxylation capacity (Vcmax ) at 25°C (Rd,25 , Vcmax,25 ) are coordinated so that Rd,25 variations support Vcmax,25 at a level allowing full light use, with Vcmax,25 reflecting daytime conditions (for photosynthesis), and Rd,25 /Vcmax,25 reflecting night-time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5-yr warming experiment, and spatially using an extensive field-measurement data set. We compared the results to three published alternatives: Rd,25 declines linearly with daily average prior temperature; Rd at average prior night temperatures tends towards a constant value; and Rd,25 /Vcmax,25 is constant. Our hypothesis accounted for more variation in observed Rd,25 over time (R2 = 0.74) and space (R2 = 0.68) than the alternatives. Night-time temperature dominated the seasonal time-course of Rd , with an apparent response time scale of c. 2 wk. Vcmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global Rd in response to rising CO2 and warming than is projected by the two of three alternative hypotheses, and by current models.

Climate-ecosystem modelling made easy: The Land Sites Platform.

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.

Evaluation of Terrestrial Water Storage Changes and Major Driving Factors Analysis in Inner Mongolia, China.

Quantitative assessment of the terrestrial water storage (TWS) changes and the major driving factors have been hindered by the lack of direct observations in Inner Mongolia, China. In this study, the spatial and temporal changes of TWS and groundwater storage (GWS) in Inner Mongolia during 2003-2021 were evaluated using the satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow On combined with data from land surface models. The results indicated that Inner Mongolia has experienced a widespread TWS loss of approximately 1.82 mm/yr from 2003-2021, with a more severe depletion rate of 4.15 mm/yr for GWS. Meteorological factors were the driving factors for water storage changes in northeastern and western regions. The abundant precipitation increased TWS in northeast regions at 2.36 mm/yr. Anthropogenic activities (agricultural irrigation and coal mining) were the driving factors for water resource decline in the middle and eastern regions (especially in the agropastoral transitional zone), where the decrease rates were 4.09 mm/yr and 3.69 mm/yr, respectively. In addition, the severities of hydrological drought events were identified based on water storage deficits, with average severity values of 17 mm, 18 mm, 24 mm, and 33 mm for the west, middle, east, and northeast regions, respectively. This study established a basic framework for water resource changes in Inner Mongolia and provided a scientific foundation for further water resources investigation.

High-quality reconstruction of China's natural streamflow.

Reconstruction of natural streamflow is fundamental to the sustainable management of water resources. In China, previous reconstructions from sparse and poor-quality gauge measurements have led to large biases in simulation of the interannual and seasonal variability of natural flows. Here we use a well-trained and tested land surface model coupled to a routing model with flow direction correction to reconstruct the first high-quality gauge-based natural streamflow dataset for China, covering all its 330 catchments during the period from 1961 to 2018. A stronger positive linear relationship holds between upstream routing cells and drainage areas, after flow direction correction to 330 catchments. We also introduce a parameter-uncertainty analysis framework including sensitivity analysis, optimization, and regionalization, which further minimizes biases between modeled and inferred natural streamflow from natural or near-natural gauges. The resulting behavior of the natural hydrological system is represented properly by the model which achieves high skill metric values of the monthly streamflow, with about 83% of the 330 catchments having Nash-Sutcliffe efficiency coefficient (NSE) > 0.7, and about 56% of the 330 catchments having Kling-Gupta efficiency coefficient (KGE) > 0.7. The proposed construction scheme has important implications for similar simulation studies in other regions, and the developed low bias long-term national datasets by statistical postprocessing should be useful in supporting river management activities in China.

Lowering water table reduces carbon sink strength and carbon stocks in northern peatlands.

Peatlands at high latitudes have accumulated >400 Pg carbon (C) because saturated soil and cold temperatures suppress C decomposition. This substantial amount of C in Arctic and Boreal peatlands is potentially subject to increased decomposition if the water table (WT) decreases due to climate change, including permafrost thaw-related drying. Here, we optimize a version of the Organizing Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE-PCH4) using site-specific observations to investigate changes in CO2 and CH4 fluxes as well as C stock responses to an experimentally manipulated decrease of WT at six northern peatlands. The unmanipulated control peatlands, with the WT <20 cm on average (seasonal max up to 45 cm) below the surface, currently act as C sinks in most years (58 ± 34 g C m-2  year-1 ; including 6 ± 7 g C-CH4 m-2  year-1 emission). We found, however, that lowering the WT by 10 cm reduced the CO2 sink by 13 ± 15 g C m-2  year-1 and decreased CH4 emission by 4 ± 4 g CH4 m-2  year-1 , thus accumulating less C over 100 years (0.2 ± 0.2 kg C m-2 ). Yet, the reduced emission of CH4 , which has a larger greenhouse warming potential, resulted in a net decrease in greenhouse gas balance by 310 ± 360 g CO2-eq  m-2  year-1 . Peatlands with the initial WT close to the soil surface were more vulnerable to C loss: Non-permafrost peatlands lost >2 kg C m-2 over 100 years when WT is lowered by 50 cm, while permafrost peatlands temporally switched from C sinks to sources. These results highlight that reductions in C storage capacity in response to drying of northern peatlands are offset in part by reduced CH4 emissions, thus slightly reducing the positive carbon climate feedbacks of peatlands under a warmer and drier future climate scenario.

The Terrestrial Biosphere Model Farm.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5.

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy-level is usually not well-represented. Here, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf-level fluorescence yield was simulated by a parametric simplification of the Soil Canopy-Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R 2 > 0.91, root-mean-square error <0.19 W⋅m-2⋅sr-1⋅µm-1), and captured the day-to-day variation of tower-measured SIF at temperate forest sites (R 2 > 0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite-observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

Towards species-level forecasts of drought-induced tree mortality risk.

Predicting species-level responses to drought at the landscape scale is critical to reducing uncertainty in future terrestrial carbon and water cycle projections. We embedded a stomatal optimisation model in the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and parameterised the model for 15 canopy dominant eucalypt tree species across South-Eastern Australia (mean annual precipitation range: 344-1424 mm yr-1 ). We conducted three experiments: applying CABLE to the 2017-2019 drought; a 20% drier drought; and a 20% drier drought with a doubling of atmospheric carbon dioxide (CO2 ). The severity of the drought was highlighted as for at least 25% of their distribution ranges, 60% of species experienced leaf water potentials beyond the water potential at which 50% of hydraulic conductivity is lost due to embolism. We identified areas of severe hydraulic stress within-species' ranges, but we also pinpointed resilience in species found in predominantly semiarid areas. The importance of the role of CO2 in ameliorating drought stress was consistent across species. Our results represent an important advance in our capacity to forecast the resilience of individual tree species, providing an evidence base for decision-making around the resilience of restoration plantings or net-zero emission strategies.

How does precipitation data influence the land surface data assimilation for drought monitoring?

Droughts are among the costliest natural hazards that occur annually worldwide. Their socioeconomic impacts are significant and widespread, affecting the sustainable development of human societies. This study investigates the influence of different forcing precipitation data in driving Land Surface Models (LSMs) and characterizing drought conditions. Here, we utilize our recently developed LSM data assimilation system for probabilistically monitoring drought over the Contiguous United States (CONUS). The Noah-MP LSM model is forced with two widely used precipitation data including IMERG (Integrated Multi-satellitE Retrievals for GPM) and NLDAS (North American Land Data Assimilation System). Soil moisture and evapotranspiration are known to have a strong relationship in the land-atmospheric interaction processes. Unlike other studies that attempted the individual assimilation of these variables, here we propose a multivariate data assimilation framework. Therefore, in both modeling scenarios, the data assimilation approach is used to integrate remotely sensed MODIS (Moderate Resolution Imaging Spectroradiometer) evapotranspiration and SMAP (Soil Moisture Active Passive) soil moisture observations into the Noah-MP LSM. The results of this study indicate that the source of precipitation data has a significant impact on the performance of LSM data assimilation system for drought monitoring. The findings revealed that NLDAS and IMERG precipitation can result in a significant difference in identifying drought severity depending on the region and time of the year. Furthermore, our analysis indicates that regardless of the precipitation forcing data product used in the land surface data assimilation system, our modeling framework can effectively detect the drought impacts on crop yield. Additionally, we calculated the drought probability based on the ensemble of soil moisture percentiles and found that there exist temporal and spatial discrepancies in drought probability maps generated from the NLDAS and IMERG precipitation forcings.

Estimating Heat-Related Exposures and Urban Heat Island Impacts: A Case Study for the 2012 Chicago Heatwave.

Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi-layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2-3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat-related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat-related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat-related health impacts.

Long-term ecosystem nitrogen limitation from foliar δ15 N data and a land surface model.

The effect of nutrient availability on plant growth and the terrestrial carbon sink under climate change and elevated CO2 remains one of the main uncertainties of the terrestrial carbon cycle. This is partially due to the difficulty of assessing nutrient limitation at large scales over long periods of time. Consistent declines in leaf nitrogen (N) content and leaf δ15 N have been used to suggest that nitrogen limitation has increased in recent decades, most likely due to the concurrent increase in atmospheric CO2 . However, such data sets are often not straightforward to interpret due to the complex factors that contribute to the spatial and temporal variation in leaf N and isotope concentration. We use the land surface model (LSM) QUINCY, which has the unique capacity to represent N isotopic processes, in conjunction with two large data sets of foliar N and N isotope content. We run the model with different scenarios to test whether foliar δ15 N isotopic data can be used to infer large-scale N limitation and if the observed trends are caused by increasing atmospheric CO2 , changes in climate or changes in sources and magnitude of anthropogenic N deposition. We show that while the model can capture the observed change in leaf N content and predict widespread increases in N limitation, it does not capture the pronounced, but very spatially heterogeneous, decrease in foliar δ15 N observed in the data across the globe. The addition of an observation-based temporal trend in isotopic composition of N deposition leads to a more pronounced decrease in simulated leaf δ15 N. Our results show that leaf δ15 N observations cannot, on their own, be used to assess global-scale N limitation and that using such a data set in conjunction with an LSM can reveal the drivers behind the observed patterns.

Spatio-Temporal Variations in Carbon Isotope Discrimination Predicted by the JULES Land Surface Model.

Stable carbon isotopes in plants can help evaluate and improve the representation of carbon and water cycles in land-surface models, increasing confidence in projections of vegetation response to climate change. Here, we evaluated the predictive skills of the Joint UK Land Environmental Simulator (JULES) to capture spatio-temporal variations in carbon isotope discrimination (Δ13C) reconstructed by tree rings at 12 sites in the United Kingdom over the period 1979-2016. Modeled and measured Δ13C time series were compared at each site and their relationships with local climate investigated. Modeled Δ13C time series were significantly correlated (p < 0.05) with tree-ring Δ13C at eight sites, but JULES underestimated mean Δ13C values at all sites, by up to 2.6‰. Differences in mean Δ13C may result from post-photosynthetic isotopic fractionations that were not considered in JULES. Inter-annual variability in Δ13C was also underestimated by JULES at all sites. While modeled Δ13C typically increased over time across the UK, tree-ring Δ13C values increased only at five sites located in the northern regions but decreased at the southern-most sites. Considering all sites together, JULES captured the overall influence of environmental drivers on Δ13C but failed to capture the direction of change in Δ13C caused by air temperature, atmospheric CO2 and vapor pressure deficit at some sites. Results indicate that the representation of carbon-water coupling in JULES could be improved to reproduce both the trend and magnitude of interannual variability in isotopic records, the influence of local climate on Δ13C, and to reduce uncertainties in predicting vegetation-environment interactions.

Disentangling the Impacts of Anthropogenic Aerosols on Terrestrial Carbon Cycle During 1850-2014.

Aerosols have a dimming and cooling effect and change hydrological regimes, thus affecting carbon fluxes, which are sensitive to climate. Aerosols also scatter sunlight, which increases the fraction of diffuse radiation, increasing photosynthesis. There remains no clear conclusion whether the impact of aerosols on land carbon fluxes is larger through diffuse radiation change than through changes in other climate variables. In this study, we quantified the overall physical impacts of anthropogenic aerosols on land C fluxes and explored the contribution from each factor using a set of factorial simulations driven by climate and aerosol data from the IPSL-CM6A-LR experiments during 1850-2014. A newly developed land surface model which distinguishes diffuse and direct radiation in canopy radiation transmission, ORCHIDEE_DF, was used. Specifically, a subgrid scheme was developed to distinguish the cloudy and clear sky conditions. We found that anthropogenic aerosol emissions since 1850 cumulatively enhanced the land C sink by 22.6 PgC. Seventy-eight percent of this C sink enhancement is contributed by aerosol-induced increase in the diffuse radiation fraction, much larger than the effect of the aerosol-induced dimming. The cooling of anthropogenic aerosols has different impacts in different latitudes but overall increases the global land C sink. The dominant role of diffuse radiation changes found in this study implies that future aerosol emissions may have a much stronger impacts on the C cycle through changing radiation quality than through changing climate alone. Earth system models need to consider the diffuse radiation fertilization effect to better evaluate the impacts of climate change mitigation scenarios.

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

A research framework for projecting ecosystem change in highly diverse tropical mountain ecosystems.

Tropical mountain ecosystems are threatened by climate and land-use changes. Their diversity and complexity make projections how they respond to environmental changes challenging. A suitable way are trait-based approaches, by distinguishing between response traits that determine the resistance of species to environmental changes and effect traits that are relevant for species' interactions, biotic processes, and ecosystem functions. The combination of those approaches with land surface models (LSM) linking the functional community composition to ecosystem functions provides new ways to project the response of ecosystems to environmental changes. With the interdisciplinary project RESPECT, we propose a research framework that uses a trait-based response-effect-framework (REF) to quantify relationships between abiotic conditions, the diversity of functional traits in communities, and associated biotic processes, informing a biodiversity-LSM. We apply the framework to a megadiverse tropical mountain forest. We use a plot design along an elevation and a land-use gradient to collect data on abiotic drivers, functional traits, and biotic processes. We integrate these data to build the biodiversity-LSM and illustrate how to test the model. REF results show that aboveground biomass production is not directly related to changing climatic conditions, but indirectly through associated changes in functional traits. Herbivory is directly related to changing abiotic conditions. The biodiversity-LSM informed by local functional trait and soil data improved the simulation of biomass production substantially. We conclude that local data, also derived from previous projects (platform Ecuador), are key elements of the research framework. We specify essential datasets to apply this framework to other mountain ecosystems.