Global GPP Research Articles

Improving MODIS Gross Primary Productivity by Bridging Big‐Leaf and Two‐Leaf Light Use Efficiency Models

AbstractGross primary productivity (GPP) is an important component of the terrestrial carbon cycle in climate change research. The global GPP product derived using Moderate Resolution Imaging Spectroradiometer (MODIS) data is perhaps the most widely used. Unfortunately, many studies have indicated evident error patterns in the MODIS GPP product. One of the main reasons for this is that the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. In this study, we developed a model for correcting the errors in the MODIS GPP product by bridging BL and two‐leaf (TL) light use efficiency (LUE) models (CTL‐MOD17). With the available MODIS GPP product, which considers environmental stress factors, the CTL‐MOD17 model only needs to reuse the two inputs of the leaf area index (LAI) and incoming radiation. The CTL‐MOD17 model was calibrated and validated at 153 global FLUXNET eddy covariance (EC) sites. The results indicate that the modeled GPP obtained with the correction model matches better with the EC GPP than the original MODIS GPP product at different time scales, with an improvement of 0.07 in R2 and a reduction in root‐mean‐square error (RMSE) of 117.08 g C m−2 year−1. The improvements are more significant in the green season when the contribution of shaded leaves is larger. In terms of the global spatial pattern, the obvious underestimation in the regions with high LAI and the overestimation in the low LAI regions of the MODIS GPP product is effectively corrected by the CTL‐MOD17 model. This paper not only bridges the BL and TL LUE models, but also provides a new and simple method to obtain accurate GPP through reusing two inputs used in producing the MODIS GPP product.

Serious underestimation of reduced carbon uptake due to vegetation compound droughts

Compound droughts with low soil moisture (SM) and high vapor pressure deficit (VPD) pose significant threats to terrestrial carbon sink and agricultural production. However, the frequency and intensity of compound droughts and their adverse impacts on the carbon cycle remain highly uncertain. Here, we define and identify vegetation compound droughts (VCDs) when low SM and high VPD severely limit and adversely affect vegetation carbon uptake. We find frequent and severe VCDs and their considerable adverse impacts on carbon uptake in mid- and low-latitude regions, particularly in drylands. Risks of VCDs have been greatly underestimated as the widely adopted quantile-based approach identifies only 11% of VCDs and 26% of global GPP anomalies due to VCDs. The frequency and intensity of VCDs and their adverse impacts on carbon uptake are projected to increase further, irrespective of whether the CO2 fertilization effect on vegetation growth and photosynthesis is considered or not. These findings improve our understanding of current and future risks of VCDs and underline the importance of adaptation measures to cope with the adverse impacts of ever-increasing compound droughts.

Large latitudinal difference in soil nitrogen limitation on global vegetation response to elevated CO2

This study is the first to evaluate latitudinal characteristics of the coupled impacts of atmospheric-CO2 fertilization and soil nitrogen limitation on global plant photosynthesis for 2001–2019 by using a remote sensing-based light use efficiency model (DTECM-l-CN). It predicted that the global GPP should have increased by 0.40 Pg C a − 2 (PMK < 0.001) from 2001 to 2019. Leaf area index change contributed 55.0% to this predicted increase in the global GPP trend, followed by coupled CO2 and soil nitrogen effects (32.5%) and the effects from the climate forcing (12.5%). Without CO2 fertilization effects, the global GPP trend was predicted lower by 32.5%. With CO2 fertilization effects included but without a nitrogen constraint, the model would predict global GPP trend higher by 67.5%. These contrasting changes strongly imply the need to consider all these effects simultaneously to estimate future GPP. Comparing the annual mean CO2 fertilization factor with and without soil nitrogen stress showed that the fertilization factor under soil nitrogen stress was only 62.5% of that with no soil nitrogen stress.As a result of soil nitrogen stress, annual mean CO2 response metric (RCO2), indicating the CO2 fertilization effect, declined from 2002 to 2019. Soil nitrogen stress was found to attenuate the positive effect of CO2 fertilization on GPP trend with a large latitudinal difference, i.e., the tropics had a weaker CO2 fertilization effect than the extratropics. This study highlights that soil nitrogen has progressively become limiting on plant response to CO2 fertilization with a large latitudinal divergence.

The rising impact of urbanization-caused CO2 emissions on terrestrial vegetation

Vegetation photosynthesis is largely determined by CO2 concentrations, which have significantly accumulated by urbanization. Thus, quantifying urbanization-caused CO2 concentrations’ impact on the terrestrial ecosystem, especially for vegetation photosynthesis (represented by GPP, NPP, NEP, and SIF), is vital. In this study, we firstly examined the effectiveness of the observation minus reanalysis (OMR) method in quantifying urbanization-caused CO2 concentrations and the effect of urbanization on CO2 accumulation from 2009 to 2018. Then, the effect of urbanization-caused CO2 on global vegetation GPP, NPP, NEP, and SIF was calculated and analyzed using terrestrial vegetation radiative transfer and prognostic models. Lastly, the impacts of urbanization-caused CO2 increase in different continents and vegetation types were analyzed, separately. The results demonstrated that: 1) Urbanization-caused CO2 concentrations accumulated for 3.93 ppm during 2009–2018. For carbon fluxes, urbanization-caused GPP, NPP and NEP accumulation increased 1.24 PgC, 0.64 PgC, 0.17 PgC, respectively, while urbanization-caused SIF accumulated 0.07 PWum−1sr−1. 2) From a continental perspective, South America accounted for the most significant urbanization caused GPP, NPP, NEP, and SIF accumulation, because the tropical rainforest climate dramatically assists photosynthesis in Amazon Rainforests. 3) Forests accounted for the most significant urbanization-caused GPP, NPP, NEP, and SIF accumulation (over 35% of global vegetation). 4) SIF is more sensitive to the autumn postponement of photosynthetic activities than GPP, NPP, and NEP. ΔOMR(GPP), ΔOMR(NPP), ΔOMR(NEP) showed the most significant urbanization impact in summertime, but the North Hemisphere’s photosynthesis accumulated dramatically in autumntime shown as rising ΔSIF_OMR, partly due to the delay of the end of the growing season which happens in most northern ecosystems. Thus, this study will be helpful for us to further understand the terrestrial carbon cycle dynamics and the potential for alleviating global climate change.

Uncertainty analysis of multiple terrestrial gross primary productivity products

AbstractAimGross primary productivity (GPP) of vegetation is an important component of the carbon balance of terrestrial ecosystems. With the development of science and technology, there has been a proliferation of multiple GPP products, yet large uncertainties exist in the global GPP estimated by different GPP products. The objectives of this study were to assess the uncertainty of 45 GPP products in 4 different categories (machine learning, Fluxnet, light use efficiency, land surface model) and to perform an attribution analysis.LocationGlobal.Time period2003–2010.Major taxa studiedTerrestrial ecosystems.MethodsThe three‐cornered hat (TCH) method was used to assess GPP uncertainty because it does not require a priori knowledge of the true GPP values. We also used generalized additive models to explore the contribution of different factors to GPP uncertainty.ResultsThe results show that the magnitude of absolute uncertainty (AU) is generally higher in the GPP high‐value region and also indicate large spatial variability of relative uncertainty (RU) among products. Deciduous broadleaf forest (20.2 g C/m2/year) and deciduous needleleaf forest (27.2 g C/m2/ year) have lower uncertainty compared to that of woody savanna (128.4 g C/m2/ year) and savanna (128.7 g C/m2/year). The RU in machine learning (ML) products overall is low (average value = 14.3%), with ORCHIDEE‐CNP (weight = 4.19%), being more recognized by GPP products are more consistent in the magnitude of spatial variability (similar in the standard deviations) and more uncertain in spatial distribution patterns. Excluding the combined effects of other factors, soil moisture (15.2%) and precipitation (8.3%) are the two factors that contribute most to GPP uncertainty, indicating that focusing on water constraints is a guarantee of GPP product quality.Main conclusionsThe TCH approach quantifies the uncertainty of GPP products well and the spatial distribution pattern variability contributes more uncertainty compared to the magnitude of spatial variability. Future GPP estimates could consider combining the advantages of different GPP products to limit uncertainty.

Global assessment of lagged and cumulative effects of drought on grassland gross primary production

Drought has large impacts on the vegetation growth of global terrestrial ecosystems, particularly grasslands. Extensive in-situ studies have shown that the impact of drought on vegetation growth has lagged and cumulative effects, but it is not well known how grassland productivity (gross primary production or GPP) responds to drought over time at large scales. Here, we investigated the spatiotemporal patterns of lagged and cumulative effects of drought on global grassland GPP using an OCO-2 solar-induced chlorophyll fluorescence (SIF) based GPP product (GOSIF GPP) and the Standardized Precipitation Evapotranspiration Index (SPEI). The results showed that globally, 88.37% of the grassland had a lagged response to drought, with a lagged time scale of mainly 1 month. With the increase in soil water availability, the magnitude of the lagged effect gradually weakened. Grasslands in semiarid and semi humid conditions that were adaptable to periodic drought responded fastest to drought. Globally, more than three quarters (78.55%) of the grassland was affected by the cumulative effect of drought. The accumulated months occurred at longer time scales (8 ∼ 10 months) for the region at 40°N–55°N, which is relatively arid. The cumulative effect in relatively arid areas was stronger than that in relatively humid areas, indicating that grasslands in more arid areas had stronger tolerance to drought. More importantly, we found that the cumulative effect of drought on grassland GPP was stronger than the lagged effect. This study highlights the great importance of considering the legacy effect, especially the cumulative effect, of drought on grassland productivity, which would advance our understanding of the impact of climate change on the carbon and water cycles of terrestrial ecosystems.

Projections of changes in ecosystem productivity under 1.5 °C and 2 °C global warming

The ecosystem plays an important role in mitigating global warming through carbon assimilation. In addition, climate change has both positive and negative impacts on ecosystem productivity. To combat climate change, the Paris Agreement has set the goal of limiting global warming well below 2 °C with an ultimate target of 1.5 °C above preindustrial levels. It remains unclear how ecosystem productivity will respond to these warming targets. Here, we project global changes in gross (GPP) and net (NPP) primary productivity under 1.5 °C/2 °C warming using a dynamic vegetation model coupled with a state-of-art climate model. We use site-level observations to validate the present-day simulations and the multimodel ensemble to validate the future projections. We identify the main drivers of ecosystem changes by distinguishing between the impacts of CO 2 fertilization, fast climatic responses to CO 2 radiative perturbation, and slow climatic feedbacks to ocean warming due to the large heat capacity. Compared with the present day, global GPP increases by 11.5% (20.9%) and NPP increases by 14.8% (27.1%) under the 1.5 °C (2 °C) warming scenario with similar magnitudes of change in the tropics and subtropics. Changes in the leaf area index mainly follow those of GPP/NPP but with more significant enhancement in boreal regions. CO 2 fertilization causes the dominant contributions to GPP changes over 83% of land areas. The slow responses to ocean warming cause negative changes in GPP/NPP and partly offset regional CO 2 fertilization over the tropics, where the climate is projected to be drier due to atmosphere-ocean feedback. The fast climatic responses to CO 2 radiative perturbation further decrease GPP/NPP but with a moderate magnitude. The projection suggests that ecosystem productivity will increase in a warmer climate but with large spatial heterogeneity. • Global GPP increases by 11.5% under 1.5 °C and 20.9% under a 2 °C warming world. • Fast responses to CO 2 fertilization cause dominant and uniform GPP enhancement. • Slow responses to the warming of sea surface temperature reduce tropical GPP.

Drought Risk of Global Terrestrial Gross Primary Productivity Over the Last 40 Years Detected by a Remote Sensing‐Driven Process Model

AbstractGross primary productivity (GPP) is the largest flux in the global terrestrial carbon cycle. Drought has significantly impacted global terrestrial GPP in recent decades, and has been projected to occur with increasing frequency and intensity. However, the drought risk of global terrestrial GPP has not been well investigated. In this study, global terrestrial GPP during 1981–2016 was simulated with the process‐based Boreal Ecosystem Productivity Simulator model. Then, the drought risk of GPP was quantified as the product of drought probability and reduction of GPP caused by drought, which was determined using the standardized precipitation evapotranspiration index. During the study period, the drought risk of GPP was high in the southeastern United States, most of South America, southern Europe, central and eastern Africa, eastern and southeastern Asia, and eastern Australia. It was low at some high latitudes of the Northern Hemisphere and in part of tropical South America, where terrestrial GPP increased slightly in drought years. The drought risk of terrestrial GPP was greater during 2000–2016 than during 1981–1999 in 21 out of 24 climatic zones. The global mean drought risk of GPP increased from 13.6 g C m−2 yr−1 during 1981–1999 to 19.3 g C m−2 yr−1 during 2000–2016. The increase in drought risk of GPP was mainly caused by the increase in drought vulnerability. Simulation experiments indicated that the drought vulnerability of GPP was mainly induced by climatic variability. This study advances our understanding on the impact of drought on GPP over the globe.

How to reconstruct aerosol-induced diffuse radiation scenario for simulating GPP in land surface models? An evaluation of reconstruction methods with ORCHIDEE_DFv1.0_DFforc

Abstract. The impact of diffuse radiation on photosynthesis has been widely documented in field measurements. This impact may have evolved over time during the last century due to changes in cloudiness, increased anthropogenic aerosol loads over polluted regions, and to sporadic volcanic eruptions curtaining the stratosphere with sulfate aerosols. The effects of those changes in diffuse light on large-scale photosynthesis (GPP) are difficult to quantify, and land surface models have been designed to simulate them. Investigating how anthropogenic aerosols have impacted GPP through diffuse light in those models requires carefully designed factorial simulations and a reconstruction of background diffuse light levels during the preindustrial period. Currently, it remains poorly understood how diffuse radiation reconstruction methods can affect GPP estimation and what fraction of GPP changes can be attributed to aerosols. In this study, we investigate different methods to reconstruct spatiotemporal distribution of the fraction of diffuse radiation (Fdf) under preindustrial aerosol emission conditions using a land surface model with a two-stream canopy light transmission scheme that resolves diffuse light effects on photosynthesis in a multi-layered canopy, ORCHIDEE_DF. We show that using a climatologically averaged monthly Fdf, as has been done by earlier studies, can bias the global GPP by up to 13 PgC yr−1 because this reconstruction method dampens the variability of Fdf and produces Fdf that is inconsistent with shortwave incoming surface radiation. In order to correctly simulate preindustrial GPP modulated by diffuse light, we thus recommend that the Fdf forcing field should be calculated consistently with synoptic, monthly, and inter-annual aerosol and cloud variability for preindustrial years. In the absence of aerosol and cloud data, alternative reconstructions need to retain the full variability in Fdf. Our results highlight the importance of keeping consistent Fdf and radiation for land surface models in future experimental designs that seek to investigate the impacts of diffuse radiation on GPP and other carbon fluxes.

Climate-Driven Variability and Trends in Plant Productivity Over Recent Decades Based on Three Global Products.

Variability in climate exerts a strong influence on vegetation productivity (gross primary productivity; GPP), and therefore has a large impact on the land carbon sink. However, no direct observations of global GPP exist, and estimates rely on models that are constrained by observations at various spatial and temporal scales. Here, we assess the consistency in GPP from global products which extend for more than three decades; two observation‐based approaches, the upscaling of FLUXNET site observations (FLUXCOM) and a remote sensing derived light use efficiency model (RS‐LUE), and from a suite of terrestrial biosphere models (TRENDYv6). At local scales, we find high correlations in annual GPP among the products, with exceptions in tropical and high northern latitudes. On longer time scales, the products agree on the direction of trends over 58% of the land, with large increases across northern latitudes driven by warming trends. Further, tropical regions exhibit the largest interannual variability in GPP, with both rainforests and savannas contributing substantially. Variability in savanna GPP is likely predominantly driven by water availability, although temperature could play a role via soil moisture‐atmosphere feedbacks. There is, however, no consensus on the magnitude and driver of variability of tropical forests, which suggest uncertainties in process representations and underlying observations remain. These results emphasize the need for more direct long‐term observations of GPP along with an extension of in situ networks in underrepresented regions (e.g., tropical forests). Such capabilities would support efforts to better validate relevant processes in models, to more accurately estimate GPP.

Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data

Terrestrial vegetation absorbs approximately 30% of the anthropogenic carbon dioxide (CO2) emitted into the atmosphere through photosynthesis (represented by gross primary productivity, GPP) and thus effectively mitigates global warming. However, large uncertainties still remain in the global GPP estimations and their long-term trends. Here we used the satellite-based near-infrared reflectance (NIRv) as the proxy of GPP and generated a global long-term (1982–2018) GPP datasets (hereafter GPPNIRv). Analysis at the site-level showed that NIRv could accurately capture both the monthly and annual variations in GPP (R2 = 0.71 and 0.74 respectively) at 104 flux sites. Upscaling the relationships between NIRv and GPP to the global scale, the global annual GPP was estimated to be 128.3 ± 4.0 Pg C yr−1 during the last four decades, which fell between the estimations from the machine-learning upscaling approach, light-use-efficiency (LUE) models and processed-based models. The seasonal variation of GPPNIRv was also consistent with those from flux sites and models. More importantly, the inter-annual trends in GPPNIRv during the last four decades were consistent with those from processed-based models across latitudes, which outperformed the other GPP products. This evidence suggested that the long-term GPP datasets derived from NIRv have better abilities to capture the seasonal and inter-annual variations of terrestrial GPP at the global scale. The long-term GPPNIRv product could be beneficial for the estimation of terrestrial carbon fluxes and for the projection of future climates.

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production

AbstractGross primary production (GPP) has been identified as the largest terrestrial carbon flux and the major driver of the growing biosphere uptake of carbon. Factorial simulations using several biosphere models have been used to estimate the effects of long‐term (&gt;50 years) climate change on global terrestrial GPP. However, no study has integrated large‐ensemble climate simulation data into a biosphere model to realistically estimate global terrestrial GPP with associated uncertainty. Here we present a novel approach to estimate the global terrestrial long‐term GPP with associated climate data‐induced uncertainty that combines a diagnostic‐type biosphere model with a large‐ensemble climate simulation data set. We distinguish the effects of recent anthropogenic activity on global GPP (the anthropogenic GPP effect) from the effects of interannual climate variability (the natural GPP effect) by comparing GPP model estimates forced by historical and “nonwarming” climate data. We provide evidence for an increasing anthropogenic effect on global terrestrial GPP. The anthropogenic GPP effect is driven by CO2 fertilization, which is projected to weaken or saturate by 2050–2150, depending on the representative concentration pathway scenario used. Model results suggest that shortwave radiation couples with El Niño–Southern Oscillation conditions and volcanic eruptions to drive the natural GPP effect. Because shortwave radiation at the surface is related to cloud cover, we encourage future studies to focus on cloud‐radiation feedbacks on the carbon cycle.

Accounting for carbon and nitrogen interactions in the global terrestrial ecosystem model ORCHIDEE (trunk version, rev 4999): multi-scale evaluation of gross primary production

Abstract. Nitrogen is an essential element controlling ecosystem carbon (C) productivity and its response to climate change and atmospheric [CO2] increase. This study presents the evaluation – focussing on gross primary production (GPP) – of a new version of the ORCHIDEE model that gathers the representation of the nitrogen cycle and of its interactions with the carbon cycle from the OCN model and the most recent developments from the ORCHIDEE trunk version. We quantify the model skills at 78 FLUXNET sites by simulating the observed mean seasonal cycle, daily mean flux variations, and annual mean average GPP flux for grasslands and forests. Accounting for carbon–nitrogen interactions does not substantially change the main skills of ORCHIDEE, except for the site-to-site annual mean GPP variations, for which the version with carbon–nitrogen interactions is in better agreement with observations. However, the simulated GPP response to idealised [CO2] enrichment simulations is highly sensitive to whether or not carbon–nitrogen interactions are accounted for. Doubling of the atmospheric [CO2] induces an increase in the GPP, but the site-averaged GPP response to a CO2 increase projected by the model version with carbon–nitrogen interactions is half of the increase projected by the version without carbon–nitrogen interactions. This model's differentiated response has important consequences for the transpiration rate, which is on average 50 mm yr−1 lower with the version with carbon–nitrogen interactions. Simulated annual GPP for northern, tropical and southern latitudes shows good agreement with the observation-based MTE-GPP (model tree ensemble gross primary production) product for present-day conditions. An attribution experiment making use of this new version of ORCHIDEE for the time period 1860–2016 suggests that global GPP has increased by 50 %, the main driver being the enrichment of land in reactive nitrogen (through deposition and fertilisation), followed by the [CO2] increase. Based on our factorial experiment and sensitivity analysis, we conclude that if carbon–nitrogen interactions are accounted for, the functional responses of ORCHIDEE r4999 better agree with the current understanding of photosynthesis than when the carbon–nitrogen interactions are not accounted for and that carbon–nitrogen interactions are essential in understanding global terrestrial ecosystem productivity.

Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model

Abstract. This paper presents the assimilation of solar-induced chlorophyll fluorescence (SIF) into a terrestrial biosphere model to estimate the gross uptake of carbon through photosynthesis (GPP). We use the BETHY-SCOPE model to simulate both GPP and SIF using a process-based formulation, going beyond a simple linear scaling between the two. We then use satellite SIF data from the Orbiting Carbon Observatory-2 (OCO-2) for 2015 in the data assimilation system to constrain model biophysical parameters and GPP. The assimilation results in considerable improvement in the fit between model and observed SIF, despite a limited capability to fit regions with large seasonal variability in SIF. The SIF assimilation increases global GPP by 31 % to 167±5 Pg C yr−1 and shows an improvement in the global distribution of productivity relative to independent estimates, but a large difference in magnitude. This change in global GPP is driven by an overall increase in photosynthetic light-use efficiency across almost all biomes and more minor, regionally distinct changes in APAR. This process-based data assimilation opens up new pathways to the effective utilization of satellite SIF data to improve our understanding of the global carbon cycle.

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.

Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data

Accurate terrestrial biosphere model (TBM) simulations of gross carbon uptake (gross primary productivity – GPP) are essential for reliable future terrestrial carbon sink projections. However, uncertainties in TBM GPP estimates remain. Newly-available satellite-derived sun-induced chlorophyll fluorescence (SIF) data offer a promising direction for addressing this issue by constraining regional-to-global scale modelled GPP. Here, we use monthly 0.5° GOME-2 SIF data from 2007 to 2011 to optimise GPP parameters of the ORCHIDEE TBM. The optimisation reduces GPP magnitude across all vegetation types except C4 plants. Global mean annual GPP therefore decreases from 194 ± 57 PgCyr−1 to 166 ± 10 PgCyr−1, bringing the model more in line with an up-scaled flux tower estimate of 133 PgCyr−1. Strongest reductions in GPP are seen in boreal forests: the result is a shift in global GPP distribution, with a ~50% increase in the tropical to boreal productivity ratio. The optimisation resulted in a greater reduction in GPP than similar ORCHIDEE parameter optimisation studies using satellite-derived NDVI from MODIS and eddy covariance measurements of net CO2 fluxes from the FLUXNET network. Our study shows that SIF data will be instrumental in constraining TBM GPP estimates, with a consequent improvement in global carbon cycle projections.

Response of ecosystem productivity to dry/wet conditions indicated by different drought indices

Various climatic and hydrological variables such as precipitation, soil moisture, stream flow, and water level can be used to assess drought conditions, however, the response of ecosystem productivity to such metrics is not very clear. In this study, we examined the sensitivity of GPP anomalies to five drought indicators: the Standardized Precipitation Index (SPI), the Standardized Precipitation–Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), deficit of soil moisture (DSM), and the difference between precipitation (P) and evapotranspiration (ET) (D(P-ET)). The global spatial distributions of drying and wetting trends from 2000 to 2014 determined by these five indices were similar. Additionally, the percent of drought-impacted areas decreased over the study period, indicating a reduction in drought conditions. GPP increased over the study period in the Northern Hemisphere (NH) but decreased in the Southern Hemisphere (SH), resulting in a net increase in global GPP. GPP anomalies were more sensitive to drought indices in the SH than in the NH. Among the five indices, GPP anomalies were most closely correlated with SPI in the NH (R=0.60, P<0.05) and SPEI in the SH (R=0.93, P<0.01). Regionally speaking, annual and seasonal GPP anomalies were most sensitive to DSM and PDSI, highlighting the importance of soil moisture observations to regional drought monitoring and assessment. The results of this study are important for evaluating the impacts of drought on ecosystem production and the global carbon cycle.

Photosynthetic productivity and its efficiencies in ISIMIP2a biome models: benchmarking for impact assessment studies

Simulating vegetation photosynthetic productivity (or gross primary production, GPP) is a critical feature of the biome models used for impact assessments of climate change. We conducted a benchmarking of global GPP simulated by eight biome models participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a) with four meteorological forcing datasets (30 simulations), using independent GPP estimates and recent satellite data of solar-induced chlorophyll fluorescence as a proxy of GPP. The simulated global terrestrial GPP ranged from 98 to 141 Pg C yr−1 (1981–2000 mean); considerable inter-model and inter-data differences were found. Major features of spatial distribution and seasonal change of GPP were captured by each model, showing good agreement with the benchmarking data. All simulations showed incremental trends of annual GPP, seasonal-cycle amplitude, radiation-use efficiency, and water-use efficiency, mainly caused by the CO2 fertilization effect. The incremental slopes were higher than those obtained by remote sensing studies, but comparable with those by recent atmospheric observation. Apparent differences were found in the relationship between GPP and incoming solar radiation, for which forcing data differed considerably. The simulated GPP trends co-varied with a vegetation structural parameter, leaf area index, at model-dependent strengths, implying the importance of constraining canopy properties. In terms of extreme events, GPP anomalies associated with a historical El Niño event and large volcanic eruption were not consistently simulated in the model experiments due to deficiencies in both forcing data and parameterized environmental responsiveness. Although the benchmarking demonstrated the overall advancement of contemporary biome models, further refinements are required, for example, for solar radiation data and vegetation canopy schemes.

Improved representation of plant functional types and physiology in the Joint UK Land Environment Simulator (JULES v4.2) using plant trait information

Abstract. Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle–climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes – the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year−1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage.