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.

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.

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.

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 CO_2-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.

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.

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.

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.

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.

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.

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.

Simulating Erosion-Induced Soil and Carbon Delivery From Uplands to Rivers in a Global Land Surface Model

Global water erosion strongly affects the terrestrial carbon balance. However, this process is currently ignored by most global land surface models (LSMs) that are used to project the responses of terrestrial carbon storage to climate and land use changes. One of the main obstacles to implement erosion processes in LSMs is the high spatial resolution needed to accurately represent the effect of topography on soil erosion and sediment delivery to rivers. In this study, we present an upscaling scheme for including erosion-induced lateral soil organic carbon (SOC) movements into the ORCHIDEE LSM. This upscaling scheme integrates information from high-resolution (3″) topographic and soil erodibility data into a LSM forcing file at 0.5° spatial resolution. Evaluation of our model for the Rhine catchment indicates that it reproduces well the observed spatial and temporal (both seasonal and interannual) variations in river runoff and the sediment delivery from uplands to the river network. Although the average annual lateral SOC flux from uplands to the Rhine River network only amounts to 0.5% of the annual net primary production and 0.01% of the total SOC stock in the whole catchment, SOC loss caused by soil erosion over a long period (e.g., thousands of years) has the potential to cause a 12% reduction in the simulated equilibrium SOC stocks. Overall, this study presents a promising approach for including the erosion-induced lateral carbon flux from the land to aquatic systems into LSMs and highlights the important role of erosion processes in the terrestrial carbon balance.

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.

Weakened growth of cropland-N2 O emissions in China associated with nationwide policy interventions

China has experienced rapid agricultural development over recent decades, accompanied by increased fertilizer consumption in croplands; yet, the trend and drivers of the associated nitrous oxide (N₂ O) emissions remain uncertain. The primary sources of this uncertainty are the coarse spatial variation of activity data and the incomplete model representation of N₂ O emissions in response to agricultural management. Here, we provide new data-driven estimates of cropland-N₂ O emissions across China in 1990-2014, compiled using a global cropland-N₂ O flux observation dataset, nationwide survey-based reconstruction of N-fertilization and irrigation, and an updated nonlinear model. In addition, we have evaluated the drivers behind changing cropland-N₂ O patterns using an index decomposition analysis approach. We find that China's annual cropland-N₂ O emissions increased on average by 11.2 Gg N/year² (p < .001) from 1990 to 2003, after which emissions plateaued until 2014 (2.8 Gg N/year² , p = .02), consistent with the output from an ensemble of process-based terrestrial biosphere models. The slowdown of the increase in cropland-N₂ O emissions after 2003 was pervasive across two thirds of China's sowing areas. This change was mainly driven by the nationwide reduction in N-fertilizer applied per area, partially due to the prevalence of nationwide technological adoptions. This reduction has almost offset the N₂ O emissions induced by policy-driven expansion of sowing areas, particularly in the Northeast Plain and the lower Yangtze River Basin. Our results underline the importance of high-resolution activity data and adoption of nonlinear model of N₂ O emission for capturing cropland-N₂ O emission changes. Improving the representation of policy interventions is also recommended for future projections.

Soil Moisture Data Assimilation to Estimate Irrigation Water Use

Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm3 cm-3), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS-2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance.

Including Stable Carbon Isotopes to Evaluate the Dynamics of Soil Carbon in the Land-Surface Model ORCHIDEE

Soil organic carbon (SOC) is a crucial component of the terrestrial carbon cycle and its turnover time in models is a key source of uncertainty. Studies have highlighted the utility of δ13C measurements for benchmarking SOC turnover in global models. We used 13C as a tracer within a vertically discretized soil module of a land-surface model, Organising Carbon and Hydrology In Dynamic Ecosystems- Soil Organic Matter (ORCHIDEE-SOM). Our new module represents some of the processes that have been hypothesized to lead to a 13C enrichment with soil depth as follows: 1) the Suess effect and CO2 fertilization, 2) the relative 13C enrichment of roots compared to leaves, and 3) 13C discrimination associated with microbial activity. We tested if the upgraded soil module was able to reproduce the vertical profile of δ13C within the soil column at two temperate sites and the short-term change in the isotopic signal of soil after a shift in C3/C4 vegetation. We ran the model over Europe to test its performance at larger scale. The model was able to simulate a shift in the isotopic signal due to short-term changes in vegetation cover from C3 to C4; however, it was not able to reproduce the overall vertical profile in soil δ13C, which arises as a combination of short and long-term processes. At the European scale, the model ably reproduced soil CO2 fluxes and total SOC stock. These findings stress the importance of the long-term history of land cover for simulating vertical profiles of δ13C. This new soil module is an emerging tool for the diagnosis and improvement of global SOC models.

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.

Bridging Drought Experiment and Modeling: Representing the Differential Sensitivities of Leaf Gas Exchange to Drought

Global climate change is expected to increase drought duration and intensity in certain regions while increasing rainfall in others. The quantitative consequences of increased drought for ecosystems are not easy to predict. Process-based models must be informed by experiments to determine the resilience of plants and ecosystems from different climates. Here, we demonstrate what and how experimentally derived quantitative information can improve the representation of stomatal and non-stomatal photosynthetic responses to drought in large-scale vegetation models. In particular, we review literature on the answers to four key questions: (1) Which photosynthetic processes are affected under short-term drought? (2) How do the stomatal and non-stomatal responses to short-term drought vary among species originating from different hydro-climates? (3) Do plants acclimate to prolonged water stress, and do mesic and xeric species differ in their degree of acclimation? (4) Does inclusion of experimentally based plant functional type specific stomatal and non-stomatal response functions to drought help Land Surface Models to reproduce key features of ecosystem responses to drought? We highlighted the need for evaluating model representations of the fundamental eco-physiological processes under drought. Taking differential drought sensitivity of different vegetation into account is necessary for Land Surface Models to accurately model drought responses, or the drought impacts on vegetation in drier environments may be over-estimated.

Comparing optimal and empirical stomatal conductance models for application in Earth system models

Earth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, and CO₂ fluxes. This is achieved by scaling stomatal conductance, g_w , determined from physiological models developed for leaves. Traditionally, models for g_w have been semi-empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to model g_w in LSMs under the premise that it has a stronger grounding in physiological theory and might ultimately lead to improved predictive accuracy. However, this premise has not been thoroughly tested. Using original field data from contrasting forest systems, we compare a widely used empirical type and a more recently developed optimization-type g_w model, termed BB and MED, respectively. Overall, we find no difference between the two models when used to simulate g_w from photosynthesis data, or leaf gas exchange from a coupled photosynthesis-conductance model, or gross primary productivity and evapotranspiration for a FLUXNET tower site with the CLM5 community LSM. Field measurements reveal that the key fitted parameters for BB and MED, g_1B and g_1M, exhibit strong species specificity in magnitude and sensitivity to CO₂ , and CLM5 simulations reveal that failure to include this sensitivity can result in significant overestimates of evapotranspiration for high-CO₂ scenarios. Further, we show that g_1B and g_1M can be determined from mean c_i /c_a (ratio of leaf intercellular to ambient CO₂ concentration). Applying this relationship with c_i /c_a values derived from a leaf δ¹³ C database, we obtain a global distribution of g_1B and g_1M , and these values correlate significantly with mean annual precipitation. This provides a new methodology for global parameterization of the BB and MED models in LSMs, tied directly to leaf physiology but unconstrained by spatial boundaries separating designated biomes or plant functional types.

Impact of priming on global soil carbon stocks

Fresh carbon input (above and belowground) contributes to soil carbon sequestration, but also accelerates decomposition of soil organic matter through biological priming mechanisms. Currently, poor understanding precludes the incorporation of these priming mechanisms into the global carbon models used for future projections. Here, we show that priming can be incorporated based on a simple equation calibrated from incubation and verified against independent litter manipulation experiments in the global land surface model, ORCHIDEE. When incorporated into ORCHIDEE, priming improved the model's representation of global soil carbon stocks and decreased soil carbon sequestration by 51% (12 ± 3 Pg C) during the period 1901-2010. Future projections with the same model across the range of CO₂ and climate changes defined by the IPCC-RCP scenarios reveal that priming buffers the projected changes in soil carbon stocks - both the increases due to enhanced productivity and new input to the soil, and the decreases due to warming-induced accelerated decomposition. Including priming in Earth system models leads to different projections of soil carbon changes, which are challenging to verify at large spatial scales.

Increasing canopy photosynthesis in rice can be achieved without a large increase in water use-A model based on free-air CO2 enrichment

Achieving higher canopy photosynthesis rates is one of the keys to increasing future crop production; however, this typically requires additional water inputs because of increased water loss through the stomata. Lowland rice canopies presently consume a large amount of water, and any further increase in water usage may significantly impact local water resources. This situation is further complicated by changing the environmental conditions such as rising atmospheric CO₂ concentration ([CO₂ ]). Here, we modeled and compared evapotranspiration of fully developed rice canopies of a high-yielding rice cultivar (Oryza sativa L. cv. Takanari) with a common cultivar (cv. Koshihikari) under ambient and elevated [CO₂ ] (A-CO₂ and E-CO₂ , respectively) via leaf ecophysiological parameters derived from a free-air CO₂ enrichment (FACE) experiment. Takanari had 4%-5% higher evapotranspiration than Koshihikari under both A-CO₂ and E-CO₂ , and E-CO₂ decreased evapotranspiration of both varieties by 4%-6%. Therefore, if Takanari was cultivated under future [CO₂ ] conditions, the cost for water could be maintained at the same level as for cultivating Koshihikari at current [CO₂ ] with an increase in canopy photosynthesis by 36%. Sensitivity analyses determined that stomatal conductance was a significant physiological factor responsible for the greater canopy photosynthesis in Takanari over Koshihikari. Takanari had 30%-40% higher stomatal conductance than Koshihikari; however, the presence of high aerodynamic resistance in the natural field and lower canopy temperature of Takanari than Koshihikari resulted in the small difference in evapotranspiration. Despite the small difference in evapotranspiration between varieties, the model simulations showed that Takanari clearly decreased canopy and air temperatures within the planetary boundary layer compared to Koshihikari. Our results indicate that lowland rice varieties characterized by high-stomatal conductance can play a key role in enhancing productivity and moderating heat-induced damage to grain quality in the coming decades, without significantly increasing crop water use.

Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model

The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from -0.017 m for OL to -0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from -0.111 m for OL to -0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA.

Endurance of larch forest ecosystems in eastern Siberia under warming trends

The larch (Larix spp.) forest in eastern Siberia is the world's largest coniferous forest. Its persistence is considered to depend on near-surface permafrost, and thus, forecast warming over the 21st century and consequent degradation of near-surface permafrost is expected to affect the larch forest in Siberia. However, predictions of these effects vary greatly, and many uncertainties remain about land - atmosphere interactions within the ecosystem. We developed an integrated land surface model to analyze how the Siberian larch forest will react to current warming trends. This model analyzed interactions between vegetation dynamics and thermo-hydrology, although it does not consider many processes those are considered to affect productivity response to a changing climate (e.g., nitrogen limitation, waterlogged soil, heat stress, and change in species composition). The model showed that, under climatic conditions predicted under gradual and rapid warming, the annual net primary production of larch increased about 2 and 3 times, respectively, by the end of the 21st century compared with that in the previous century. Soil water content during the larch-growing season showed no obvious trend, even when surface permafrost was allowed to decay and result in subsurface runoff. A sensitivity test showed that the forecast temperature and precipitation trends extended larch leafing days and reduced water shortages during the growing season, thereby increasing productivity. The integrated model also satisfactorily reconstructed latitudinal gradients in permafrost presence, soil moisture, tree leaf area index, and biomass over the entire larch-dominated area in eastern Siberia. Projected changes to ecosystem hydrology and larch productivity at this geographical scale were consistent with those from site-level simulation. This study reduces the uncertainty surrounding the impact of current climate trends on this globally important carbon reservoir, and it demonstrates the need to consider complex ecological processes to make accurate predictions.

Endurance of larch forest ecosystems in eastern Siberia under warming trends

The larch (Larix spp.) forest in eastern Siberia is the world's largest coniferous forest. Its persistence is considered to depend on near‐surface permafrost, and thus, forecast warming over the 21st century and consequent degradation of near‐surface permafrost is expected to affect the larch forest in Siberia. However, predictions of these effects vary greatly, and many uncertainties remain about land – atmosphere interactions within the ecosystem. We developed an integrated land surface model to analyze how the Siberian larch forest will react to current warming trends. This model analyzed interactions between vegetation dynamics and thermo‐hydrology, although it does not consider many processes those are considered to affect productivity response to a changing climate (e.g., nitrogen limitation, waterlogged soil, heat stress, and change in species composition). The model showed that, under climatic conditions predicted under gradual and rapid warming, the annual net primary production of larch increased about 2 and 3 times, respectively, by the end of the 21st century compared with that in the previous century. Soil water content during the larch‐growing season showed no obvious trend, even when surface permafrost was allowed to decay and result in subsurface runoff. A sensitivity test showed that the forecast temperature and precipitation trends extended larch leafing days and reduced water shortages during the growing season, thereby increasing productivity. The integrated model also satisfactorily reconstructed latitudinal gradients in permafrost presence, soil moisture, tree leaf area index, and biomass over the entire larch‐dominated area in eastern Siberia. Projected changes to ecosystem hydrology and larch productivity at this geographical scale were consistent with those from site‐level simulation. This study reduces the uncertainty surrounding the impact of current climate trends on this globally important carbon reservoir, and it demonstrates the need to consider complex ecological processes to make accurate predictions.

Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4

Several studies have shown that satellite retrievals of solar-induced chlorophyll fluorescence (SIF) provide useful information on terrestrial photosynthesis or gross primary production (GPP). Here, we have incorporated equations coupling SIF to photosynthesis in a land surface model, the National Center for Atmospheric Research Community Land Model version 4 (NCAR CLM4), and have demonstrated its use as a diagnostic tool for evaluating the calculation of photosynthesis, a key process in a land surface model that strongly influences the carbon, water, and energy cycles. By comparing forward simulations of SIF, essentially as a byproduct of photosynthesis, in CLM4 with observations of actual SIF, it is possible to check whether the model is accurately representing photosynthesis and the processes coupled to it. We provide some background on how SIF is coupled to photosynthesis, describe how SIF was incorporated into CLM4, and demonstrate that our simulated relationship between SIF and GPP values are reasonable when compared with satellite (Greenhouse gases Observing SATellite; GOSAT) and in situ flux-tower measurements. CLM4 overestimates SIF in tropical forests, and we show that this error can be corrected by adjusting the maximum carboxylation rate (Vmax ) specified for tropical forests in CLM4. Our study confirms that SIF has the potential to improve photosynthesis simulation and thereby can play a critical role in improving land surface and carbon cycle models.

The relationship of leaf photosynthetic traits - V cmax and J max - to leaf nitrogen, leaf phosphorus, and specific leaf area: a meta-analysis and modeling study

Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. An important source of this uncertainty lies in the dependency of photosynthesis on the maximum rate of carboxylation (V_cmax) and the maximum rate of electron transport (J_max). Understanding and making accurate prediction of C fluxes thus requires accurate characterization of these rates and their relationship with plant nutrient status over large geographic scales. Plant nutrient status is indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Correlations between V_cmax and J_max and leaf nitrogen (N) are typically derived from local to global scales, while correlations with leaf phosphorus (P) and specific leaf area (SLA) have typically been derived at a local scale. Thus, there is no global-scale relationship between V_cmax and J_max and P or SLA limiting the ability of global-scale carbon flux models do not account for P or SLA. We gathered published data from 24 studies to reveal global relationships of V_cmax and J_max with leaf N, P, and SLA. V_cmax was strongly related to leaf N, and increasing leaf P substantially increased the sensitivity of V_cmax to leaf N. J_max was strongly related to V_cmax, and neither leaf N, P, or SLA had a substantial impact on the relationship. Although more data are needed to expand the applicability of the relationship, we show leaf P is a globally important determinant of photosynthetic rates. In a model of photosynthesis, we showed that at high leaf N (3 gm⁻²), increasing leaf P from 0.05 to 0.22 gm⁻² nearly doubled assimilation rates. Finally, we show that plants may employ a conservative strategy of J_max to V_cmax coordination that restricts photoinhibition when carboxylation is limiting at the expense of maximizing photosynthetic rates when light is limiting.

Climate-driven uncertainties in modeling terrestrial gross primary production: a site level to global-scale analysis

We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis-driven GPP bias was significantly positive with respect to the observed meteorology-driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m(-2)  yr(-1) across sites (ca. 15% of site level GPP). At the northern mid-/high-latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20-30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short-wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site-level reanalysis-driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate-driven uncertainties in carbon, energy, and water fluxes using a single modeling framework.