Community Atmosphere Biosphere Land Exchange Model Research Articles

The effects of multiple environmental factors on global carbon allocation

BackgroundThe allocation of photosynthate among the parts of plants (e.g., leaves, wood tissues and roots) strongly regulates their growth, and this conditions the terrestrial carbon cycle. Recent studies have shown that atmospheric CO2 and climate change dominate the changes in carbon allocation in plants, but the magnitude and mechanism of its effects remain unclear.MethodsThe Community Atmosphere Biosphere Land Exchange (CABLE) model can accurately simulate the responses of carbon allocation to environmental changes. This study quantifies the contributions of four environmental factors—atmospheric CO2, temperature, precipitation, and radiation—on resource availability and carbon allocation from 1979 to 2014 by using the CABLE model.ResultsThe results of the CABLE model showed that rising CO2 significantly reduced carbon allocation to the leaves of plants at a global scale, but the other three environmental factors exhibited contrasting effects that dominated the rise in carbon allocation to the leaves. The increased precipitation and CO2 significantly reduced the light availability and increased carbon allocation to the wooden parts of plants. By contrast, the rising temperature reduced the water availability, resulting in a decrease in carbon allocation to the wooden parts. All four environmental factors consistently exhibited negative effects on carbon allocation to the roots, with rising precipitation causing the largest reduction in carbon allocation to them. Moreover, except for CO2, the effects of the other three environmental factors were heterogeneous owing to their variable interactions in different regions.ConclusionsThe CABLE model can accurately represent the mechanisms of response of resource availability and carbon allocation to environmental changes. Our study highlights the substantial environmental regulation of global carbon allocation. The responses of carbon allocation to global environmental changes need to be extensively studied through ecosystem models based on different hypotheses.

Improved water storage estimates within the North China Plain by assimilating GRACE data into the CABLE model

As one of the most important crop-producing bases, the North China Plain (NCP) has suffered serious groundwater depletion due to drying climate and intensive human activities. An accurate estimation of groundwater storage (GWS) is of great importance for the sustainable development of local society and region’s economy. This study investigates a methodology to determine improved GWS variation throughout the NCP based on assimilation of terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) data into the Community Atmosphere Biosphere Land Exchange (CABLE) model. We found that the CABLE model is not able to compute the prolonged decrease (and accelerated since 2013) in the GWS change around the southwestern region of the NCP where major cities are located. The model incorrectly characterizes the spatial patterns of interannual and annual changes of water storage variation, mainly caused by errors in the precipitation forcing data. The interannual TWS change is indicative of GWS, while the observed seasonal variation is primarily that of root-zone soil moisture. The GRACE data assimilation most effectively improves GWS computation. The GWS assimilation results were validated against a total of 156 in-situ groundwater level data in the NCP. Compared to the model computation, there was a significant improvement in terms of cross correlation, on average, from 0.12 (before assimilation) to 0.54 (after assimilation). This study demonstrates the effectiveness of GRACE data assimilation toward reliable estimation of ground water storage variation in the NCP, and its promise to quantify the potential implication of water supply from the South-to-North Water Transfer Project within the NCP.

An atmospheric inversion over the city of Cape Town: sensitivity analyses

Abstract. An atmospheric inversion was performed for the city of Cape Town for the period of March 2012 to June 2013, making use of in situ measurements of CO2 concentrations at temporary measurement sites located to the north-east and south-west of Cape Town. This paper presents results of sensitivity analyses that tested assumptions regarding the prior information and the uncertainty covariance matrices associated with the prior fluxes and with the observations. Alternative prior products were considered in the form of a carbon assessment analysis to provide biogenic fluxes and the ODIAC (Open-source Data Inventory for Anthropogenic CO2 product) fossil fuel product. These were used in place of the reference inversion's biogenic fluxes from CABLE (Community Atmosphere Biosphere Land Exchange model) and fossil fuel emissions from a bespoke inventory analysis carried out specifically for the Cape Town inversion. Our results confirmed that the inversion solution was strongly dependent on the prior information, but by using independent alternative prior products to run multiple inversions, we were able to infer limits for the true domain flux. Where the reference inversion had aggregated prior flux estimates that were made more positive by the inversion – suggesting that CABLE was overestimating the amount of CO2 biogenic uptake – the carbon assessment prior fluxes were made more negative by the inversion. As the posterior estimates tended towards the same point, we could infer that the best estimate was located somewhere between these two posterior fluxes. The inversion was shown to be sensitive to the spatial error correlation length in the biogenic fluxes – even a short correlation length – influencing the spatial distribution of the posterior fluxes, the size of the aggregated flux across the domain, and the uncertainty reduction achieved by the inversion. Taking advantage of expected spatial correlations in the fluxes is key to maximizing the use of a limited observation network. Changes to the temporal correlations in the observation errors had a very minor effect on the inversion. The control vector in the original version consisted of separate daytime and night-time weekly fluxes for fossil fuel and biogenic fluxes over a 4-week inversion period. When we considered solving for mean weekly fluxes over each 4-week period – i.e. assuming the flux remained constant over the month – larger changes to the prior fossil fuel and biogenic fluxes were possible, as well as further changes to the spatial distribution of the fluxes compared with the reference. The uncertainty reduction achieved in the estimation of the overall flux increased from 25.6 % for the reference inversion to 47.2 % for the mean weekly flux inversion. This demonstrates that if flux components that change slowly can be solved for separately in the inversion, where these fluxes are assumed to be constant over long periods of time, the posterior estimates of these fluxes substantially benefit from the additional observational constraint. In summary, estimates of Cape Town fluxes can be improved by using better and multiple prior information sources, and particularly on biogenic fluxes. Fossil fuel and biogenic fluxes should be broken down into components, building in knowledge of spatial and temporal consistency in these components into the control vector and uncertainties specified for the sources for the inversion. This would allow the limited observations to provide maximum constraint on the flux estimates.

Detection of groundwater storage variability based on GRACE and CABLE model in the Murray-Darling Basin

Monitoring groundwater storage is in great importance for economic and social development. In this paper, the monthly GRACE data from 2003 to 2015 is combined with the Community Atmosphere Biosphere Land Exchange (CABLE) model to estimate the variations of groundwater storage (GWS) in the Murray-Darling Basin (MDB). The results show that (1) the simulations of TWS from CABLE are more accurate than GLDAS over the MDB, and there is a higher correlation coefficient of 0.94 and a lower RMSE of 15.74 between CABLE and GRACE. (3) The spatial pattern of GWS trends shows decline trends in the southwest, east and south, and increasing trends in the north and south central (3) For the whole MDB, the average GWS has strong seasonality and shows an increasing trend with a rate of 1.19 0.41 mmyyear between 2003 and 2015.

Leaf area index identified as a major source of variability in modeled CO<sub>2</sub> fertilization

Abstract. The concentration–carbon feedback (β), also called the CO2 fertilization effect, is a key unknown in climate–carbon-cycle projections. A better understanding of model mechanisms that govern terrestrial ecosystem responses to elevated CO2 is urgently needed to enable a more accurate prediction of future terrestrial carbon sink. We conducted C-only, carbon–nitrogen (C–N) and carbon–nitrogen–phosphorus (C–N–P) simulations of the Community Atmosphere Biosphere Land Exchange model (CABLE) from 1901 to 2100 with fixed climate to identify the most critical model process that causes divergence in β. We calculated CO2 fertilization effects at various hierarchical levels from leaf biochemical reaction and leaf photosynthesis to canopy gross primary production (GPP), net primary production (NPP), and ecosystem carbon storage (cpool) for seven C3 plant functional types (PFTs) in response to increasing CO2 under the RCP 8.5 scenario. Our results show that β values at biochemical and leaf photosynthesis levels vary little across the seven PFTs, but greatly diverge at canopy and ecosystem levels in all simulations. The low variation of the leaf-level β is consistent with a theoretical analysis that leaf photosynthetic sensitivity to increasing CO2 concentration is almost an invariant function. In the CABLE model, the major jump in variation of β values from leaf levels to canopy and ecosystem levels results from divergence in modeled leaf area index (LAI) within and among PFTs. The correlation of βGPP, βNPP, or βcpool each with βLAI is very high in all simulations. Overall, our results indicate that modeled LAI is a key factor causing the divergence in β in the CABLE model. It is therefore urgent to constrain processes that regulate LAI dynamics in order to better represent the response of ecosystem productivity to increasing CO2 in Earth system models.

A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis

Abstract. The Community Atmosphere–Biosphere Land Exchange model (CABLE) is a land surface model (LSM) that can be applied stand-alone and provides the land surface–atmosphere exchange within the Australian Community Climate and Earth System Simulator (ACCESS). We describe new developments that extend the applicability of CABLE for regional and global carbon–climate simulations, accounting for vegetation responses to biophysical and anthropogenic forcings. A land use and land cover change module driven by gross land use transitions and wood harvest area was implemented, tailored to the needs of the Coupled Model Intercomparison Project 6 (CMIP6). Novel aspects include the treatment of secondary woody vegetation, which benefits from a tight coupling between the land use module and the Population Orders Physiology (POP) module for woody demography and disturbance-mediated landscape heterogeneity. Land use transitions and harvest associated with secondary forest tiles modify the annually resolved patch age distribution within secondary vegetated tiles, in turn affecting biomass accumulation and turnover rates and hence the magnitude of the secondary forest sink. Additionally, we implemented a novel approach to constrain modelled GPP consistent with the coordination hypothesis and predicted by evolutionary theory, which suggests that electron-transport- and Rubisco-limited rates adjust seasonally and across biomes to be co-limiting. We show that the default prior assumption – common to CABLE and other LSMs – of a fixed ratio of electron transport to carboxylation capacity at standard temperature (Jmax, 0∕Vcmax, 0) is at odds with this hypothesis; we implement an alternative algorithm for dynamic optimisation of this ratio such that coordination is achieved as an outcome of fitness maximisation. The results have significant implications for the magnitude of the simulated CO2 fertilisation effect on photosynthesis in comparison to alternative estimates and observational proxies. These new developments enhance CABLE's capability for use within an Earth system model and in stand-alone applications to attribute trends and variability in the terrestrial carbon cycle to regions, processes and drivers. Model evaluation shows that the new model version satisfies several key observational constraints: (i) trend and interannual variations in the global land carbon sink, including sensitivities of interannual variations to global precipitation and temperature anomalies; (ii) centennial trends in global GPP; (iii) coordination of Rubisco-limited and electron-transport-limited photosynthesis; (iv) spatial distributions of global ET, GPP, biomass and soil carbon; and (v) age-dependent rates of biomass accumulation in boreal, temperate and tropical secondary forests. CABLE simulations agree with recent independent assessments of the global land–atmosphere flux partition that use a combination of atmospheric inversions and bottom-up constraints. In particular, there is agreement that the strong CO2-driven sink in the tropics is largely cancelled by net deforestation and forest degradation emissions, leaving the Northern Hemisphere (NH) extratropics as the dominant contributor to the net land sink.

Incorporation of plant traits in a land surface model helps explain the global biogeographical distribution of major forest functional types

AbstractAimsContrasting leaf types with different leaf life spans represent different adaptive strategies in plants. Previous studies explained the adaptive advantages of different strategies on the basis of environmental climatic limits, but could not account for the observed co‐dominance of multiple plant functional types (PFTs) in many regions. Here we used a process‐based model to explore whether observed inter‐ and intra‐PFT variation in plant traits can explain global biogeographical variation in the dominance and co‐dominance of major forest types.LocationWorld‐wide.MethodsWe identified four important plant traits: leaf N concentration, leaf life span, fraction of net primary production (NPP) allocated to leaves and plant basal respiration rate. We incorporated means and variances of these plant traits from trait databases into the Community Atmosphere–Biosphere–Land Exchange model. We then predicted the dominant PFT or PFT mixture for global forested grid cells, using NPP as a proxy for growth rate, and considering three PFTs: evergreen needleleaf forests (ENF), evergreen broadleaf forests (EBF) and deciduous broadleaf forests (DBF). We compared results with: (1) those from simulations that did not account for trait variance; (2) results from an empirical model based purely on mean annual temperature; and (3) data from remotely sensed observations.ResultsOur estimates of the fractions of land area covered by major forest types were consistent with observation; i.e. ENFs dominate in boreal regions, EBFs dominate in tropical regions and DBFs are distributed widely across a broad range of environmental conditions. We also showed that co‐dominance of different forest PFTs cannot be reproduced without considering variation in plant traits.Main conclusionsGlobal trait data are useful for representing underlying plant strategies and functional diversity. Variation in key plant traits explains significant fractions of global biogeographical variation of three major forest types. Future developments in dynamic global vegetation modelling will benefit from the inclusion of plant trait variation.

The impact of changing the land surface scheme in ACCESS(v1.0/1.1) on the surface climatology

Abstract. The Community Atmosphere Biosphere Land Exchange (CABLE) model has been coupled to the UK Met Office Unified Model (UM) within the existing framework of the Australian Community Climate and Earth System Simulator (ACCESS), replacing the Met Office Surface Exchange Scheme (MOSES). Here we investigate how features of the CABLE model impact on present-day surface climate using ACCESS atmosphere-only simulations. The main differences attributed to CABLE include a warmer winter and a cooler summer in the Northern Hemisphere (NH), earlier NH spring runoff from snowmelt, and smaller seasonal and diurnal temperature ranges. The cooler NH summer temperatures in canopy-covered regions are more consistent with observations and are attributed to two factors. Firstly, CABLE accounts for aerodynamic and radiative interactions between the canopy and the ground below; this placement of the canopy above the ground eliminates the need for a separate bare ground tile in canopy-covered areas. Secondly, CABLE simulates larger evapotranspiration fluxes and a slightly larger daytime cloud cover fraction. Warmer NH winter temperatures result from the parameterization of cold climate processes in CABLE in snow-covered areas. In particular, prognostic snow density increases through the winter and lowers the diurnally resolved snow albedo; variable snow thermal conductivity prevents early winter heat loss but allows more heat to enter the ground as the snow season progresses; liquid precipitation freezing within the snowpack delays the building of the snowpack in autumn and accelerates snow melting in spring. Overall we find that the ACCESS simulation of surface air temperature benefits from the specific representation of the turbulent transport within and just above the canopy in the roughness sublayer as well as the more complex snow scheme in CABLE relative to MOSES.

On the potential application of land surface models for drought monitoring in China

The potential of using land surface models (LSMs) to monitor near-real-time drought has not been fully assessed in China yet. In this study, we analyze the performance of such a system with a land surface model (LSM) named the Australian Community Atmosphere Biosphere Land Exchange model (CABLE). The meteorological forcing datasets based on reanalysis products and corrected by observational data have been extended to near-real time for semi-operational trial. CABLE-simulated soil moisture (SM) anomalies are used to characterize drought spatial and temporal evolutions. One outstanding feature in our analysis is that with the same meteorological data, we have calculated a range of drought indices including Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI). We have assessed the similarity among these indices against observed SM over a number of regions in China. While precipitation is the dominant factor in the drought development, relationships between precipitation, evaporation, and soil moisture anomalies vary significantly under different climate regimes, resulting in different characteristics of droughts in China. The LSM-based trial system is further evaluated for the 1997/1998 drought in northern China and 2009/2010 drought in southwestern China. The system can capture the severities and temporal and spatial evolutions of these drought events well. The advantage of using a LSM-based drought monitoring system is further demonstrated by its potential to monitor other consequences of drought impacts in a more physically consistent manner.

Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia

Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with structural differences in representing spatial variability, alternate empirical methods, mathematical formulations and computational approach. These inherent differences in modeling approach, and associated variations in outputs make it difficult to compare and contrast land surface models in a straight-forward manner. While model intercomparison studies have been undertaken in the past, leading to significant progress on the improvement of land surface models, additional framework towards identification of model weakness is needed. Given that land surface models are increasingly being integrated with satellite based estimates to improve their prediction skill, it is practical to undertake model intercomparison on the basis of soil moisture data assimilation. Consequently, this study compares two land surface models: the Joint UK Land Environment Simulator (JULES) and the Community Atmosphere Biosphere Land Exchange (CABLE) for soil moisture estimation and associated assessment of model uncertainty. A retrieved soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission was assimilated into both models, with their updated estimates validated against in-situ soil moisture in the Yanco area, Australia. The findings show that the updated estimates from both models generally provided a more accurate estimate of soil moisture than the open loop estimate based on calibration alone. Moreover, the JULES output was found to provide a slightly better estimate of soil moisture than the CABLE output at both near-surface and deeper soil layers. An assessment of the updated membership in decision space also showed that the JULES model had a relatively stable, less sensitive, and more highly convergent internal dynamics than the CABLE model.

A stand‐alone tree demography and landscape structure module for Earth system models

AbstractWe propose and demonstrate a new approach for the simulation of woody ecosystem stand dynamics, demography, and disturbance‐mediated heterogeneity suitable for continental to global applications and designed for coupling to the terrestrial ecosystem component of any earth system model. The approach is encoded in a model called Populations‐Order‐Physiology (POP). We demonstrate the behavior and performance of POP coupled to the Community Atmosphere Biosphere Land Exchange model (CABLE) applied along the Northern Australian Tropical Transect, featuring gradients in rainfall and fire disturbance. The model is able to simultaneously reproduce observation‐based estimates of key functional and structural variables along the transect, namely gross primary production, tree foliage projective cover, basal area, and maximum tree height. Prospects for the use of POP to address current vegetation dynamic deficiencies in earth system modeling are discussed.

Evaluation of anomalies in GLDAS-1996 dataset

Global Land Data Assimilation System (GLDAS) data are widely used for land-surface flux simulations. Therefore, the simulation accuracy using GLDAS dataset is largely contingent upon the accuracy of the GLDAS dataset. It is found that GLDAS land-surface model simulated runoff exhibits strong anomalies for 1996. These anomalies are investigated by evaluating four GLDAS meteorological forcing data (precipitation, air temperature, downward shortwave radiation and downward longwave radiation) in six large basins across the world (Danube, Mississippi, Yangtze, Congo, Amazon and Murray-Darling basins). Precipitation data from the Global Precipitation Climatology Centre (GPCC) are also compared with GLDAS forcing precipitation data. Large errors and lack of monthly variability in GLDAS-1996 precipitation data are the main sources for the anomalies in the simulated runoff. The impact of the precipitation data on simulated runoff for 1996 is investigated with the Community Atmosphere Biosphere Land Exchange (CABLE) land-surface model in the Yangtze basin, for which area high-quality local precipitation data are obtained from the China Meteorological Administration (CMA). The CABLE model is driven by GLDAS daily precipitation data and CMA daily precipitation, respectively. The simulated daily and monthly runoffs obtained from CMA data are noticeably better than those obtained from GLDAS data, suggesting that GLDAS-1996 precipitation data are not so reliable for land-surface flux simulations.

Traceable components of terrestrial carbon storage capacity in biogeochemical models

Biogeochemical models have been developed to account for more and more processes, making their complex structures difficult to be understood and evaluated. Here, we introduce a framework to decompose a complex land model into traceable components based on mutually independent properties of modeled biogeochemical processes. The framework traces modeled ecosystem carbon storage capacity (Xss ) to (i) a product of net primary productivity (NPP) and ecosystem residence time (τE ). The latter τE can be further traced to (ii) baseline carbon residence times (τ'E ), which are usually preset in a model according to vegetation characteristics and soil types, (iii) environmental scalars (ξ), including temperature and water scalars, and (iv) environmental forcings. We applied the framework to the Australian Community Atmosphere Biosphere Land Exchange (CABLE) model to help understand differences in modeled carbon processes among biomes and as influenced by nitrogen processes. With the climate forcings of 1990, modeled evergreen broadleaf forest had the highest NPP among the nine biomes and moderate residence times, leading to a relatively high carbon storage capacity (31.5 kg cm(-2) ). Deciduous needle leaf forest had the longest residence time (163.3 years) and low NPP, leading to moderate carbon storage (18.3 kg cm(-2) ). The longest τE in deciduous needle leaf forest was ascribed to its longest τ'E (43.6 years) and small ξ (0.14 on litter/soil carbon decay rates). Incorporation of nitrogen processes into the CABLE model decreased Xss in all biomes via reduced NPP (e.g., -12.1% in shrub land) or decreased τE or both. The decreases in τE resulted from nitrogen-induced changes in τ'E (e.g., -26.7% in C3 grassland) through carbon allocation among plant pools and transfers from plant to litter and soil pools. Our framework can be used to facilitate data model comparisons and model intercomparisons via tracking a few traceable components for all terrestrial carbon cycle models. Nevertheless, more research is needed to develop tools to decompose NPP and transient dynamics of the modeled carbon cycle into traceable components for structural analysis of land models.