On what scales can GOSAT flux inversions constrain anomalies in terrestrial ecosystems?

This is the final version. Available on open access from European Geosciences Union via the DOI in this recordData availability. CarbonTracker CT2016 results were provided by NOAA ESRL, Boulder, Colorado, USA, from the website at https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/ (National Oceanic and Atmospheric Administration (NOAA) Earth System Laboratory (ESRL), 2019a). CASA GFED 4.1 and CASA CMS NEE fluxes were also downloaded from the CT2016 website. The GOSAT L4 product and VISIT NEE were downloaded from the GOSAT Data Archive Service (https://data2.gosat.nies.go.jp; NIES, 2019). The Dai Global Palmer Drought Severity Index was downloaded from the Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory (https://doi.org/10.5065/D6QF8R93; Dai, 2017). NASA GOME-2 SIF products were obtained from the Aura Validation Data Center (https://avdc.gsfc.nasa.gov/; Aura Validation Data Center, 2019). FLUXCOM products were obtained from the data portal of the Max Planck Institute for Biochemistry (https://www.bgc-jena.mpg.de/geodb/projects/Home.php.; Max Plank Institue for Biogeochemistry, 2019). MERRA-2 products were downloaded from MDISC (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/; Global Modeling and Assimilation Office, 2019), managed by the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The GEOS-Chem forward and adjoint models are freely available to the public. Instructions for downloading and running the models can be found at http://wiki.seas.harvard.edu/geos-chem (Atmospheric Chemistry Modeling Group at Harvard University , 2019). ACOS GOSAT lite files were obtained from the CO2 Virtual Science Data Environment (https://co2.jpl.nasa.gov/; Jet Propulsion Laboratory, California Institute of Technology, 2019). The SST anomalies were downloaded from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) website (https://www.esrl.noaa.gov; National Oceanic and Atmospheric Administration (NOAA) Earth System Laboratory (ESRL), 2019b).Interannual variations in temperature and precipitation impact the carbon balance of terrestrial ecosystems, leaving an imprint in atmospheric CO2. Quantifying the impact of climate anomalies on the net ecosystem exchange (NEE) of terrestrial ecosystems can provide a constraint to evaluate terrestrial biosphere models against and may provide an emergent constraint on the response of terrestrial ecosystems to climate change. We investigate the spatial scales over which interannual variability in NEE can be constrained using atmospheric CO2 observations from the Greenhouse Gases Observing Satellite (GOSAT). NEE anomalies are calculated by performing a series of inversion analyses using the GEOS-Chem adjoint model to assimilate GOSAT observations. Monthly NEE anomalies are compared to "proxies", variables that are associated with anomalies in the terrestrial carbon cycle, and to upscaled NEE estimates from FLUXCOM. Statistically significant correlations (P<0.05) are obtained between posterior NEE anomalies and anomalies in soil temperature and FLUXCOM NEE on continental and larger scales in the tropics, as well as in the northern extratropics on subcontinental scales during the summer (R2≥0.49), suggesting that GOSAT measurements provide a constraint on NEE interannual variability (IAV) on these spatial scales. Furthermore, we show that GOSAT flux inversions are generally better correlated with the environmental proxies and FLUXCOM NEE than NEE anomalies produced by a set of terrestrial biosphere models (TBMs), suggesting that GOSAT flux inversions could be used to evaluate TBM NEE fluxes.Environment and Climate Change CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Space Agenc

Surface-Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest

This is the final verison. Available from American Geophysical Union (AGU) via the DOI in this record.The K83 observational data are available from AmeriFlux (ameriflux.lbl.gov), NCEP Reanalysis data provided by NOAA/ESRL/PSD, Boulder, Colorado, USA, from the http://www.cdc.noaa.gov/ website. Model code and output is stored at GitLab (gitlab.com). This project is password protected, and the password can be obtained from the corresponding author at [email protected] upon request.Tropical South America plays a central role in global climate. Bowen ratio teleconnects to circulation and precipitation processes far afield, and the global CO2 growth rate is strongly influenced by carbon cycle processes in South America. However, quantification of basin-wide seasonality of flux partitioning between latent and sensible heat, the response to anomalies around climatic norms, and understanding of the processes and mechanisms that control the carbon cycle remains elusive. Here, we investigate simulated surface-atmosphere interaction at a single site in Brazil, using models with different representations of precipitation and cloud processes, as well as differences in scale of coupling between the surface and atmosphere. We find that the model with parameterized clouds/precipitation has a tendency toward unrealistic perpetual light precipitation, while models with explicit treatment of clouds produce more intense and less frequent rain. Models that couple the surface to the atmosphere on the scale of kilometers, as opposed to tens or hundreds of kilometers, produce even more realistic distributions of rainfall. Rainfall intensity has direct consequences for the “fate of water,” or the pathway that a hydrometeor follows once it interacts with the surface. We find that the model with explicit treatment of cloud processes, coupled to the surface at small scales, is the most realistic when compared to observations. These results have implications for simulations of global climate, as the use of models with explicit (as opposed to parameterized) cloud representations becomes more widespread.National Aeronautics and Space Administration (NASA)National Science Foundation (NSF)National Science Foundation (NSF)U.S. Department of Energy (DOE

Simulating increased permafrost peatland plant productivity in response to belowground fertilisation using the JULES land surface model

This is the final version. Available from MDPI via the DOI in this record. Data Availability Statement: Abisko meteorological data and snow depth data are available on request from the Abisko Scientific Research Station (https://polar.se/en/research-in-abisko) (last access: 17 April 2022), and the soil temperature data are archived in the GTN-P database (https: //gtnp.arcticportal.org/) (last access: 17 April 2022). Soil carbon profiles are available from the following Zenodo repository: https://zenodo.org/record/5818180#.YmmBftrMKUk (last access: 17 April 2022).Several experimental studies have shown that climate-warming-induced permafrost thaw releases previously unavailable nitrogen which can lower nitrogen limitation, increase plant productivity, and counteract some of the carbon released from thawing permafrost. The net effect of this belowground fertilisation effect remains debated and is yet to be included in Earth System models. Here, we included the impact of thaw-related nitrogen fertilisation on vegetation in the Joint UK Land Environment Simulator (JULES) land surface model for the first time. We evaluated its ability to replicate a three-year belowground fertilisation experiment in which JULES was generally able to simulate belowground fertilisation in accordance with the observations. We also ran simulations under future climate to investigate how belowground nitrogen fertilisation affects the carbon cycle. These simulations indicate an increase in plant-available inorganic nitrogen at the thaw front by the end of the century with only the productivity of deep-rooting plants increasing in response. This suggests that deep-rooting species will have a competitive advantage under future climate warming. Our results also illustrate the capacity to simulate belowground nitrogen fertilisation at the thaw front in a global land surface model, leading towards a more complete representation of coupled carbon and nitrogen dynamics in the northern high latitudes.European Union’s Horizon 2020e Joint UK BEIS/Defra Met Office Hadley Centre Climate ProgrammeNatural Environment Research Council (NERC

Future fire risk under climate change and deforestation scenarios in tropical Borneo

This is the final version. Available from IOP Publishing via the DOI in this record. Data availability statement: The data that support the findings of this study are available upon reasonable request from the authors.Fire in the tropical peatland forests of Borneo is an environmental issue with interactions with climate change and deforestation, and the consequences have local and global implications. While research has shown that fire severity and frequency are expected to increase with climate change, there is conflicting model and observational data as to the effect of deforestation on precipitation, which is a key metric for fire risk. To better understand the changes in fire risk from deforestation and climate change we ran simulations of the climate scenario RCP8.5 with and without total deforestation using regional climate model RegCM4. The output was then used for calculations of the Fire Weather Index. We find that annual temperature change from deforestation at elevations above 500m is 53% of the change over the 21st Century in RCP8.5. Fire risk is significantly affected by both climate change and deforestation, despite some increases in precipitation from deforestation. While the multi model dry season (June-August) mean increases in fire risk are larger from elevated atmospheric carbon dioxide, the increases in maximum fire risk are larger from deforestation. The altitude is a good predictor of fire risk change, with larger increases at more densely populated lower elevations where the peatlands are concentrated and smaller increases at higher elevations. Therefore, while deforestation generally causes a smaller increase in climate-related fire risk than climate change, its local control and heterogeneous effects compared to global carbon emissions makes it critical for climate mitigation policy. These high-resolution simulations provide a guide to the most vulnerable areas of Borneo from climatic increases in fire risk.Natural Environment Research Council (NERC

Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: Datasets for this research are openly available in the in-text citations. For observational estimates (ERA5, CHIRPS, Hadley SST, etc.), readers are referred to Tables S1 and S2 for details. Climate model data used in this study are available from the CMIP6 archive at https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/Fires occurring over the peatlands in Indonesian Borneo accompanied by droughts have posed devastating impacts on human health, livelihoods, economy and the natural environment, and their prevention requires comprehensive understanding of climate-associated risk. Although it is widely known that the droughts are associated with El Niño events, the onset process of El Niño and thus the drought precursors and their possible changes under the future climate are not clearly understood. Here, we use a causal network approach to quantify the strength of teleconnections to droughts at a seasonal timescale shown in observations and climate models. We portray two drivers of June-July-August (JJA) droughts identified through literature review and causal analysis, namely Niño 3.4 sea surface temperature (SST) in JJA (El Niño Southern Oscillation [abbreviated as ENSO]) and SST anomaly over the eastern North Pacific to the east of the Hawaiian Islands (abbreviated as Pacific SST) in March-April-May (MAM) period. We argue that the droughts are strongly linked to ENSO variability, with drier years corresponding to El Niño conditions. The droughts can be predicted with a lead time of 3 months based on their associations with Pacific SST, with higher SST preceding drier conditions. We find that under the SSP585 scenario, the Coupled Model Intercomparison Phase 6 (CMIP6) multi-model ensembles show significant increase in both the maximum number of consecutive dry days in the Indonesian Borneo region in JJA (p = 0.006) and its linear association with Pacific SST in MAM (p = 0.001) from year 2061 to 2100 compared with the historical baseline. Some models are showing unrealistic amounts of JJA rainfall and underestimate drought risks in Indonesian Borneo and their teleconnections, owing to the underestimation of ENSO amplitude and overestimation of local convections. Our study strengthens the possibility of early warning triggers of fires and stresses the need for taking enhanced climate risk into consideration when formulating long-term policies to eliminate fires.Engineering and Physical Sciences Research Council (EPSRC

The Impact of a Simple Representation of Non-Structural Carbohydrates on the Simulated Response of Tropical Forests to Drought

This is the final version. Available on open access from European Geosciences Union via the DOI in this recordCode availability. A model example of SUGAR for a single site and set-up to run at Caxiuanã using output from JULES is available at https://doi.org/10.5281/zenodo.3547613 (Jones, 2019). For further information or code please contact [email protected] representing the response of ecosystems to environmental change in land surface models (LSMs) is crucial to making accurate predictions of future climate. Many LSMs do not correctly capture plant respiration and growth fluxes, particularly in response to extreme climatic events. This is in part due to the unrealistic assumption that total plant carbon expenditure (PCE) is always equal to gross carbon accumulation by photosynthesis. We present and evaluate a simple model of labile carbon storage and utilisation (SUGAR) designed to be integrated into an LSM, which allows simulated plant respiration and growth to vary independent of photosynthesis. SUGAR buffers simulated PCE against seasonal variation in photosynthesis, producing more constant (less variable) predictions of plant growth and respiration relative to an LSM that does not represent labile carbon storage. This allows the model to more accurately capture observed carbon fluxes at a large-scale drought experiment in a tropical moist forest in the Amazon, relative to the Joint UK Land Environment Simulator LSM (JULES). SUGAR is designed to improve the representation of carbon storage in LSMs and provides a simple framework that allows new processes to be integrated as the empirical understanding of carbon storage in plants improves. The study highlights the need for future research into carbon storage and allocation in plants, particularly in response to extreme climate events such as drought.Natural Environment Research Council (NERC)Newton FundAustralian Research Council (ARC)Spanish Ministry of Economy and Competitiveness (MINECO)Engineering and Physical Sciences Research Council (EPSRC)JPL-Caltech President's and Director's Research & Development FundMet Office Hadley Centre Climate ProgrammeEuropean Union Horizon 202

Dynamic modelling shows substantial contribution of ecosystem restoration to climate change mitigation

This is the final version. Available from the Global Systems Institute, University of Exeter via the link in this recordGSI scientific working paper series number 2021/02Limiting global warming to a 1.5°C temperature rise requires drastic emissions reductions and removal of carbon dioxide from the atmosphere. Most modelled pathways for 1.5°C assume substantial removals in the form of biomass energy with carbon capture and storage, which brings with it increasing risks to biodiversity and food security via extensive land-use change. Recently, multiple efforts to describe and quantify potential removals via ecosystem-based approaches have gained traction in the climate policy discourse. However, these options have yet to be evaluated in a systematic and scientifically robust way. We provide spatially explicit estimates of ecosystem restoration potential quantified with a Dynamic Global Vegetation Model. Simulations covering forest restoration, reforestation, reduced harvest, agroforestry and silvopasture were combined and found to sequester an additional 93 Gt C by 2100, reducing mean global temperature increase by ~0.12°C (5-95% range 0.06-0.21°C) relative to a baseline mitigation pathway. Ultimately, pathways to achieving the 1.5°C goal garner broader public support when they include land management options that can bring about multiple benefits, including ecosystem restoration, biodiversity protection, and resilient agricultural practices

Regional carbon fluxes from land use and land cover change in Asia, 1980–2009

This is the final version of the article. Available from IOP Publishing via the DOI in this record.We present a synthesis of the land-atmosphere carbon flux from land use and land cover change (LULCC) in Asia using multiple data sources and paying particular attention to deforestation and forest regrowth fluxes. The data sources are quasi-independent and include the U.N. Food and Agriculture Organization-Forest Resource Assessment (FAO-FRA 2015; country-level inventory estimates), the Emission Database for Global Atmospheric Research (EDGARv4.3), the 'Houghton' bookkeeping model that incorporates FAO-FRA data, an ensemble of 8 state-of-the-art Dynamic Global Vegetation Models (DGVM), and 2 recently published independent studies using primarily remote sensing techniques. The estimates are aggregated spatially to Southeast, East, and South Asia and temporally for three decades, 1980–1989, 1990–1999 and 2000–2009. Since 1980, net carbon emissions from LULCC in Asia were responsible for 20%–40% of global LULCC emissions, with emissions from Southeast Asia alone accounting for 15%–25% of global LULCC emissions during the same period. In the 2000s and for all Asia, three estimates (FAO-FRA, DGVM, Houghton) were in agreement of a net source of carbon to the atmosphere, with mean estimates ranging between 0.24 to 0.41 Pg C yr−1, whereas EDGARv4.3 suggested a net carbon sink of −0.17 Pg C yr−1. Three of 4 estimates suggest that LULCC carbon emissions declined by at least 34% in the preceding decade (1990–2000). Spread in the estimates is due to the inclusion of different flux components and their treatments, showing the importance to include emissions from carbon rich peatlands and land management, such as shifting cultivation and wood harvesting, which appear to be consistently underreported.This work was supported by the Asia Pacific Network for Global Change Research (ARCP2013-01CMY-Patra/Canadell). LC was supported by the National Science Foundation East Asia Pacific Summer Institute (EAPSI) Fellowship. KI and PP were supported by the Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan. JGC thanks the support from the Australian Climate Change Science Program. AI and EK were supported by ERTDF (S-10) by the Ministry of the Environment, Japan. CK is supported by DOE-BER through BGC-Feedbacks SFA and NGEE-Tropics. AW was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101) and EU FP7 Funding through project LUC4C (603542)

JULES-GL7: The Global Land configuration of the Joint UK Land Environment Simulator version 7.0 and 7.2

This is the final version. Available on open access from the European Geosciences Union via the DOI in this recordData availability. The model configuration and associated forcing data are available via the indicated methods in the manuscript (see Appendix A). JULES and associated configurations are freely available for non-commercial research use as set out in the JULES user terms and conditions (http://jules-lsm.github.io/access_req/JULES_Licence.pdf, last access: 31 January 2020).Code availability. This work is based on JULES version 5.3 with specific configurations included in the form of suites. For full information regarding accessing the code and configurations, please refer to Appendix A.We present the latest global land configuration of the Joint UK Land Environment Simulator (JULES) model as used in the latest international Coupled Model Intercomparison Project (CMIP6). The configuration is defined by the combination of switches, parameter values and ancillary data, which we provide alongside a set of historical forcing data that defines the experimental setup. The configurations provided are JULES-GL7.0, the base setup used in CMIP6 and JULES-GL7.2, a subversion that includes improvements to the representation of canopy radiation and interception. These configurations are recommended for all JULES applications focused on the exchange and state of heat, water and momentum at the land surface. In addition, we provide a standardised modelling system that runs on the Natural Environment Research Council (NERC) JASMIN cluster, accessible to all JULES users. This is provided so that users can test and evaluate their own science against the standard configuration to promote community engagement in the development of land surface modelling capability through JULES. It is intended that JULES configurations should be independent of the underlying code base, and thus they will be available in the latest release of the JULES code. This means that different code releases will produce scientifically comparable results for a given configuration version. Versioning is therefore determined by the configuration as opposed to the underlying code base.BEIS and DEFRA Met Office Hadley Centre Climate ProgrammeEuropean Union Horizon 202