Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH

We calibrated the JSBACH model with six different stomatal conductance formulations using measurements from 10 FLUXNET coniferous evergreen sites in the boreal zone. The parameter posterior distributions were generated by the adaptive population importance sampler (APIS); then the optimal values were estimated by a simple stochastic optimisation algorithm. The model was constrained with in situ observations of evapotranspiration (ET) and gross primary production (GPP). We identified the key parameters in the calibration process. These parameters control the soil moisture stress function and the overall rate of carbon fixation. The JSBACH model was also modified to use a delayed effect of temperature for photosynthetic activity in spring. This modification enabled the model to correctly reproduce the springtime increase in GPP for all conifer sites used in this study. Overall, the calibration and model modifications improved the coefficient of determination and the model bias for GPP with all stomatal conductance formulations. However, only the coefficient of determination was clearly improved for ET. The optimisation resulted in best performance by the Bethy, Ball-Berry, and the Friend and Kiang stomatal conductance models. We also optimised the model during a drought event at a Finnish Scots pine forest site. This optimisation improved the model behaviour but resulted in significant changes to the parameter values except for the unified stomatal optimisation model (USO). Interestingly, the USO demonstrated the best performance during this event.Peer reviewe

C4MIP - The Coupled Climate-Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6

Coordinated experimental design and implementation has become a cornerstone of global climate modelling. Model Intercomparison Projects (MIPs) enable systematic and robust analysis of results across many models, by reducing the influence of ad hoc differences in model set-up or experimental boundary conditions. As it enters its 6th phase, the Coupled Model Intercomparison Project (CMIP6) has grown significantly in scope with the design and documentation of individual simulations delegated to individual climate science communities. The Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP) takes responsibility for design, documentation, and analysis of carbon cycle feedbacks and interactions in climate simulations. These feedbacks are potentially large and play a leading-order contribution in determining the atmospheric composition in response to human emissions of CO2 and in the setting of emissions targets to stabilize climate or avoid dangerous climate change. For over a decade, C4MIP has coordinated coupled climate-carbon cycle simulations, and in this paper we describe the C4MIP simulations that will be formally part of CMIP6. While the climate-carbon cycle community has created this experimental design, the simulations also fit within the wider CMIP activity, conform to some common standards including documentation and diagnostic requests, and are designed to complement the CMIP core experiments known as the Diagnostic, Evaluation and Characterization of Klima (DECK). C4MIP has three key strands of scientific motivation and the requested simulations are designed to satisfy their needs: (1) pre-industrial and historical simulations (formally part of the common set of CMIP6 experiments) to enable model evaluation, (2) idealized coupled and partially coupled simulations with 1% per year increases in CO2 to enable diagnosis of feedback strength and its components, (3) future scenario simulations to project how the Earth system will respond to anthropogenic activity over the 21st century and beyond. This paper documents in detail these simulations, explains their rationale and planned analysis, and describes how to set up and run the simulations. Particular attention is paid to boundary conditions, input data, and requested output diagnostics. It is important that modelling groups participating in C4MIP adhere as closely as possible to this experimental design

Assessing Model Predictions of Carbon Dynamics in Global Drylands

Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson’s r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson’s r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. We assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO$_2$ enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle

Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations

During the last decade, carbon cycle data assimilation systems (CCDAS) have focused on improving the simulation of seasonal and mean global carbon fluxes over a few years by simultaneous assimilation of multiple data streams. However, the ability of a CCDAS to predict longer-term trends and variability of the global carbon cycle and the constraint provided by the observations have not yet been assessed. Here, we evaluate two near-decade-long assimilation experiments of the Max Planck Institute-Carbon Cycle Data Assimilation System (MPI-CCDAS v1) using spaceborne estimates of the fraction of absorbed photosynthetic active radiation (FAPAR) and atmospheric CO2 concentrations from the global network of flask measurement sites from either 1982 to 1990 or 1990 to 2000. We contrast these simulations with independent observations from the period 1982-2010, as well as a third MPI-CCDAS assimilation run using data from the full 1982-2010 period, and an atmospheric inversion covering the same data and time. With 30 years of data, MPI-CCDAS is capable of representing land uptake to a sufficient degree to make it compatible with the atmospheric CO2 record. The long-term trend and seasonal amplitude of atmospheric CO2 concentrations at station level over the period 1982 to 2010 is considerably improved after assimilating only the first decade (1982-1990) of observations. After 15-19 years of prognostic simulation, the simulated CO2 mixing ratio in 2007-2010 diverges by only 2 +/- 1.3 ppm from the observations, the atmospheric inversion, and the MPI-CCDAS assimilation run using observations from the full period. The long-term trend, phenological seasonality, and interannual variability (IAV) of FAPAR in the Northern Hemisphere over the last 1 to 2 decades after the assimilation were also improved. Despite imperfections in the representation of the IAV in atmospheric CO2, model-data fusion for a decade of data can already contribute to the prognostic capacity of land carbon cycle models at relevant timescales.Peer reviewe

Projected changes in mineral soil carbon of European forests, 1990–2100

Forests are a major land use in Europe, and European forest soils contain about the same amount of carbon as is found in tree biomass. Changes in the size of the forest soil carbon pool could have significant impacts on the European carbon budget. We present the first assessment of future changes in European forest soil organic carbon (SOC) stocks using a dedicated process-based SOC model and state-of-the-art databases of driving variables. Soil carbon change was calculated for Europe using the Rothamsted Carbon model using climate data from four climate models, forced by four Intergovernmental Panel on Climate Change (IPCC) emissions scenarios (SRES). Changes in litter input to the soil due to forest management, projected changes in net primary production (NPP), forest age-class structure, and changes in forest area were taken into account. Results are presented for mineral soil only. Under some climate scenarios carbon in forest soils will increase slightly (0.1 to 4.6 Pg) in Europe over the 21st Century, whilst for one scenario, forest SOC stocks are predicted to decrease by 0.3 Pg. Different trends are seen in different regions. Climate change will tend to speed decomposition, whereas increases in litter input due to increasing NPP and changing age-class structure will slow the loss of SOC. Increases in forest area could further enhance the total soil carbon stock of European forests. Whilst climate change will be a key driver of change in forest soil carbon, changes in ageclass structure and land-use change are estimated to have greater effects

Projected changes in terrestrial carbon storage in Europe under climate and land-use change, 1990-2100

Changes in climate and land use, caused by socio-economic changes, greenhouse gas emissions, agricultural policies and other factors, are known to affect both natural and managed ecosystems, and will likely impact on the European terrestrial carbon balance during the coming decades. This study presents a comprehensive European Union wide (EU15 plus Norway and Switzerland, EU*) assessment of potential future changes in terrestrial carbon storage considering these effects based on four illustrative IPCC-SRES storylines (A1FI, A2, B1, B2). A process-based land vegetation model (LPJ-DGVM), adapted to include a generic representation of managed ecosystems, is forced with changing fields of land-use patterns from 1901 to 2100 to assess the effect of land-use and cover changes on the terrestrial carbon balance of Europe. The uncertainty in the future carbon balance associated with the choice of a climate change scenario is assessed by forcing LPJ-DGVM with output from four different climate models (GCMs: CGCM2, CSIRO2, HadCM3, PCM2) for the same SRES storyline. Decrease in agricultural areas and afforestation leads to simulated carbon sequestration for all land-use change scenarios with an average net uptake of 17-38 Tg C/year between 1990 and 2100, corresponding to 1.9-2.9% of the EU*s CO2 emissions over the same period. Soil carbon losses resulting from climate warming reduce or even offset carbon sequestration resulting from growth enhancement induced by climate change and increasing atmospheric CO2 concentrations in the second half of the twenty-first century. Differences in future climate change projections among GCMs are the main cause for uncertainty in the cumulative European terrestrial carbon uptake of 4.4-10.1 Pg C between 1990 and 2100

Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs

Leaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN) from the latest version (third generation) of GIMMS AVHRR NDVI data over the period 1986–2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees

Low phosphorus supply constrains plant responses to elevated CO2 : a meta-analysis

Phosphorus (P) is an essential macro-nutrient required for plant metabolism and growth. Low P availability could potentially limit plant responses to elevated carbon dioxide (eCO(2)), but consensus has yet to be reached on the extent of this limitation. Here, based on data from experiments that manipulated both CO(2)and P for young individuals of woody and non-woody species, we present a meta-analysis of P limitation impacts on plant growth, physiological, and morphological response to eCO(2). We show that low P availability attenuated plant photosynthetic response to eCO(2)by approximately one-quarter, leading to a reduced, but still positive photosynthetic response to eCO(2)compared to those under high P availability. Furthermore, low P limited plant aboveground, belowground, and total biomass responses to eCO(2), by 14.7%, 14.3%, and 12.4%, respectively, equivalent to an approximate halving of the eCO(2)responses observed under high P availability. In comparison, low P availability did not significantly alter the eCO(2)-induced changes in plant tissue nutrient concentration, suggesting tissue nutrient flexibility is an important mechanism allowing biomass response to eCO(2)under low P availability. Low P significantly reduced the eCO(2)-induced increase in leaf area by 14.3%, mirroring the aboveground biomass response, but low P did not affect the eCO(2)-induced increase in root length. Woody plants exhibited stronger attenuation effect of low P on aboveground biomass response to eCO(2)than non-woody plants, while plants with different mycorrhizal associations showed similar responses to low P and eCO(2)interaction. This meta-analysis highlights crucial data gaps in capturing plant responses to eCO(2)and low P availability. Field-based experiments with longer-term exposure of both CO(2)and P manipulations are critically needed to provide ecosystem-scale understanding. Taken together, our results provide a quantitative baseline to constrain model-based hypotheses of plant responses to eCO(2)under P limitation, thereby improving projections of future global change impacts

A representation of the phosphorus cycle for ORCHIDEE (revision 4520)

Paper contact with Daniel Goll: [email protected]ïments: S. Zaehle was supported by the QUINCY project of the European Research Council (ERC-2014-CoG-647204). We thank two anonymous referees and the editor H. Sato for their constructive comments. Further, we thank the TRY initiative and database, which is hosted, developed, and maintained by J. Kattge and G. Boenisch (Max Planck Institute for Biogeochemistry, Jena, DE), for additional leaf stoichiometric data on Metrosideros, Ying-Ping Wang and Ben Houlton for sharing their data compilation on the Hawaiian sites, and Sebastiaan Luysseart for the discussions related to biomass allocation.Land surface models rarely incorporate the terrestrial phosphorus cycle and its interactions with the carbon cycle, despite the extensive scientific debate about the importance of nitrogen and phosphorus supply for future land carbon uptake. We describe a representation of the terrestrial phosphorus cycle for the ORCHIDEE land surface model, and evaluate it with data from nutrient manipulation experiments along a soil formation chronosequence in Hawaii. ORCHIDEE accounts for the influence of the nutritional state of vegetation on tissue nutrient concentrations, photosynthesis, plant growth, biomass allocation, biochemical (phosphatase-mediated) mineralization, and biological nitrogen fixation. Changes in the nutrient content (quality) of litter affect the carbon use efficiency of decomposition and in return the nutrient availability to vegetation. The model explicitly accounts for root zone depletion of phosphorus as a function of root phosphorus uptake and phosphorus transport from the soil to the root surface. The model captures the observed differences in the foliage stoichiometry of vegetation between an early (300-year) and a late (4.1?Myr) stage of soil development. The contrasting sensitivities of net primary productivity to the addition of either nitrogen, phosphorus, or both among sites are in general reproduced by the model. As observed, the model simulates a preferential stimulation of leaf level productivity when nitrogen stress is alleviated, while leaf level productivity and leaf area index are stimulated equally when phosphorus stress is alleviated. The nutrient use efficiencies in the model are lower than observed primarily due to biases in the nutrient content and turnover of woody biomass. We conclude that ORCHIDEE is able to reproduce the shift from nitrogen to phosphorus limited net primary productivity along the soil development chronosequence, as well as the contrasting responses of net primary productivity to nutrient addition

Evaluating the predictability of terrestrial ecosystem carbon fluxes integrating long term eddy-covariance and biometric observations with multi-model ensembles

Discrepancies between future projections of land carbon fluxes originate from different process representations, but also from differences in model parameterization. Model parameters are typically drawn from disparate literature sources, individual site measurements, or expert judgment, allowing for large variability in the actual parameters used and thus model functional responses. In addition, differences in meteorological forcing datasets and modeling setups may contribute strongly to differences at inter-annual and longer time scales. Along with the known differences in the mean model behavior under future climate scenarios, there are likely also differences in model responses to increased climate variability, and extreme events, which have yet to be assessed. In this study we developed an in situ model data fusion experiment to explore the contribution of diverse long-term observations in addressing the divergence of modeled projections of ecosystem water and carbon fluxes until 2100, along with responses to climate variability and extreme events. We focus on two forest sites in France – Hesse and Le Bray – for which carbon and water fluxes have been observed for more than ten years using eddy covariance methodology. The consolidated set of eddy covariance observations and respective uncertainties is complemented with biometric information on aboveground biomass, biomass increments and soil carbon stocks. These datasets are simultaneously used as constraints in the inverse parameter optimization of an ensemble of terrestrial biogeochemical models ranging from specific forest models to generic land surface schemes, namely: BASFOR, FöBAAR, JSBACH, LPJ and ORCHIDEE. The experimental setup includes the harmonization of the optimization by forcing and constraining the models with the same observations, and through a common cost function. The set of multiple constraints ensures that the models simulate the responses of ecosystem fluxes to environmental conditions in agreement with ecosystem pools. In all models we observe significant improvements in modeling performance but modest improvements in estimating the interannual variability in carbon fluxes and pools. The divergence in long-term trends until 2100 between models is reduced in the carbon fluxes and pools after optimization. However, an increase in the variability of net ecosystem fluxes is observed, which results from the higher interannual variability in the climate scenarios, as well as the growing ecosystem carbon pools. These results suggest more frequent and amplified responses of ecosystem carbon cycle as present-day extreme conditions become more frequent. Overall, this study emphasizes the importance of long-term observations in assessing inter-model divergence and in addressing the future sensitivities of ecosystem carbon fluxes to changes in climate variability