Evaluation of land surface models in reproducing satellite derived leaf area index over the high-latitude northern hemisphere. Part II: Earth system models

PublishedJournal ArticleLeaf Area Index (LAI) is a key parameter in the Earth System Models (ESMs) since it strongly affects land-surface boundary conditions and the exchange of matter and energy with the atmosphere. Observations and data products derived from satellite remote sensing are important for the validation and evaluation of ESMs from regional to global scales. Several decades' worth of satellite data products are now available at global scale which represents a unique opportunity to contrast observations against model results. The objective of this study is to assess whether ESMs correctly reproduce the spatial variability of LAI when compared with satellite data and to compare the length of the growing season in the different models with the satellite data. To achieve this goal we analyse outputs from 11 coupled carbon-climate models that are based on the set of new global model simulations planned in support of the IPCC Fifth Assessment Report. We focus on the average LAI and the length of the growing season on Northern Hemisphere over the period 1986-2005. Additionally we compare the results with previous analyses (Part I) of uncoupled land surface models (LSMs) to assess the relative contribution of vegetation and climatic drivers on the correct representation of LAI. Our results show that models tend to overestimate the average values of LAI and have a longer growing season due to the later dormancy. The similarities with the uncoupled models suggest that representing the correct vegetation fraction with the associated parameterizations; is more important in controlling the distribution and value of LAI than the climatic variables. © 2013 by the authors.This work was funded by the European Commission’s 7th Framework Programme under Grant Agreements number 238366 (GREENCYCLESII project) and 282672 (EMBRACE project)

Capacity for Carbon Sequestration and Climate Change Mitigation in Different Ecologically-Distinct Zones of Sri Lanka

Vegetation has the capacity to mitigate greenhouse gas (GHG) induced climate change byabsorbing and sequestering carbon dioxide, the principal GHG, in plant biomass. Sri Lanka,an island located in the humid tropical South Asia, has a considerable range of ecologicallydistinctzones (EDZs) as a result of the spatial and temporal variation of its climate. TheseEDZs are characterised by different dominant vegetation types and ecosystems with varyingground cover. Hence, the carbon sequestration capacity which determines the strength of the„land carbon sink‟ is likely to vary in the different EDZs. Analysis of long-term climatic datahas shown that trends of climate change (i.e., increasing atmospheric temperature andpotential evapotranspiration and decreasing precipitation and soil water availability) of thedifferent climatic zones of Sri Lanka reflect the established global trends. These trends inclimate change are likely to modify the carbon sequestration capacity of different EDZs overtime. Therefore, the objective of this work is to estimate the carbon sequestration and climatechange mitigation capacity of different EDZs of Sri Lanka and its historical variation todetermine the possible impacts of climate change.Simulations from nine dynamic global vegetation models (DGVMs) were used to estimatecarbon balance parameters such as net primary productivity (NPP), heterotrophic respiration(Rh) and net biome productivity (NBP) for eight 1o(latitude)×1o(longitude) grid cellscovering Sri Lanka. Models were run over the period from 1900 to 2009 using the climateforcing data from CRU-NCEP, which were validated using data from the MeteorologyDepartment of Sri Lanka. Carbon balance parameters were calculated for six ecologicallydistinctzones of Sri Lanka (i.e., south-west, central highlands, eastern coastal plain, northwest,north-east and north) that were defined based on 1o×1o grid cells. A validation check ofthe model outputs was done by comparing simulated NPP with actual NPP for selectedvegetation types. An initial analysis of all nine DGVMs, which included models running atdifferent resolutions (3.75o×2.5o, 2.5°×2.5o and 0.5o×0.5o) showed substantial within-zonevariation and did not clearly distinguish carbon sequestration capacities of different EDZs.This was probably because of spatial averaging of outputs from coarse resolution modelsacross different EDZs. A second analysis with the four finer resolution DGVMs showedsubstantially improved results. Subsequent simulations running the fine resolution modelJULES on a finer grid of 0.5o×0.5o allowed estimation of carbon balance parameters in thirtyfive0.5o×0.5o cells, which substantially-improved the spatial resolution of estimated carbonsequestration capacities of different EDZs of Sri Lanka. Temporal and spatial trends of theestimated carbon balance parameters will be presented along with analyses of theirunderlying causes and climatic drivers.Keywords: Terrestrial carbon balance, Net primary productivity, Climate change, Sri Lank

The carbon cycle in Mexico: past, present and future of C stocks and fluxes

PublishedThe Supplement related to this article is available online at doi:10.5194/bg-13-223-2016-supplement.We modeled the carbon (C) cycle in Mexico with a process-based approach. We used different available products (satellite data, field measurements, models and flux towers) to estimate C stocks and fluxes in the country at three different time frames: present (defined as the period 2000–2005), the past century (1901–2000) and the remainder of this century (2010–2100). Our estimate of the gross primary productivity (GPP) for the country was 2137 ± 1023 TgC yr−1 and a total C stock of 34 506 ± 7483 TgC, with 20 347 ± 4622 TgC in vegetation and 14 159 ± 3861 in the soil. Contrary to other current estimates for recent decades, our results showed that Mexico was a C sink over the period 1990–2009 (+31 TgC yr−1) and that C accumulation over the last century amounted to 1210 ± 1040 TgC. We attributed this sink to the CO2 fertilization effect on GPP, which led to an increase of 3408 ± 1060 TgC, while both climate and land use reduced the country C stocks by −458 ± 1001 and −1740 ± 878 TgC, respectively. Under different future scenarios, the C sink will likely continue over the 21st century, with decreasing C uptake as the climate forcing becomes more extreme. Our work provides valuable insights on relevant driving processes of the C cycle such as the role of drought in drylands (e.g., grasslands and shrublands) and the impact of climate change on the mean residence time of soil C in tropical ecosystems.The lead author (G. Murray-Tortarolo) thanks CONACYT-CECTI, the University of Exeter and Secretaría de Educación Pública (SEP) for their funding of this project. The authors extend their thanks to Carlos Ortiz Solorio and to the Colegio de Posgraduados for the field soil data and to the Alianza Redd+ Mexico for the field biomass data. This project would not have been possible without the valuable data from the CMIP5 models. A. Arneth, G. Murray-Tortarolo, A. Wiltshire and S. Sitch acknowledge the support of the European Commission-funded project LULCC4C (grant no. 603542). A. Wiltshire was partsupported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101)

Evaluation of land surface models in reproducing satellite-derived LAI over the high-latitude northern hemisphere. Part I: Uncoupled DGVMs

PublishedJournal ArticleLeaf 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. © 2013 by the authors.The corresponding author also thanks the CONACYT-CECTI and the University of Exeter for their funding during the PhD studies. The National Center for Atmospheric Research is sponsored by the National Science Foundation

Spatiotemporal patterns of terrestrial gross primary production: A review

This is the final version of the article. Available from American Geophysical Union via the DOI in this record.There is another record for this publication in ORE at http://hdl.handle.net/10871/30934Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990-2009) from two observation-based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon-climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation-based data that show little IAV and trend, while the process-based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation-based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.European Commission's Seventh Framework Programme. Grant Numbers: 238366, 28267

Spatiotemporal patterns of terrestrial gross primary production: A review

This is the final version of the article. Available from American Geophysical Union via the DOI in this record.There is another record for this publication in ORE at http://hdl.handle.net/10871/21007Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990-2009) from two observation-based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon-climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation-based data that show little IAV and trend, while the process-based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation-based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.European Commission's Seventh Framework Programme. Grant Numbers: 238366, 28267

Modelling carbon stock and carbon sequestration ecosystem services for policy design: a comprehensive approach using a dynamic vegetation model.

Ecosystem service (ES) models can only inform policy design adequately if they incorporate ecological processes. We used the Lund-Potsdam-Jena managed Land (LPJmL) model, to address following questions for Mexico, Bolivia and Brazilian Amazon: (i) How different are C stocks and C sequestration quantifications under standard (when soil and litter C and heterotrophic respiration are not considered) and comprehensive (including all C stock and heterotrophic respiration) approach? and (ii) How does the valuation of C stock and C sequestration differ in national payments for ES and global C funds or markets when comparing both approach? We found that up to 65% of C stocks have not been taken into account by neglecting to include C stored in soil and litter, resulting in gross underpayments (up to 500 times lower). Since emissions from heterotrophic respiration of organic material offset a large proportion of C gained through growth of living matter, we found that markets and decision-makers are inadvertently overestimating up to 100 times C sequestrated. New approaches for modelling C services relevant ecological process-based can help accounting for C in soil, litter and heterotrophic respiration and become important for the operationalization of agreements on climate change mitigation following the COP21 in 2015

RECCAP2 Future Component: Consistency and Potential for Regional Assessment to Constrain Global Projections

This is the final version. Available from Wiley via the DOI in this record. Data Availability Statement: All CMIP6 model output datasets analyzed during this study are available online at https://esgf-node.llnl.gov/search/cmip6/ and code required to reproduce figures is available at https://github.com/ChrisJones-MOHC/RECCAP2Future_2023 (ChrisJones-MOHC, 2023) and Zenodo at https://doi.org/10.5281/zenodo.8420250.Projections of future carbon sinks and stocks are important because they show how the world's ecosystems will respond to elevated CO2 and changes in climate. Moreover, they are crucial to inform policy decisions around emissions reductions to stay within the global warming levels identified by the Paris Agreement. However, Earth System Models from the 6th Coupled Model Intercomparison Project (CMIP6) show substantial spread in future projections—especially of the terrestrial carbon cycle, leading to a large uncertainty in our knowledge of any remaining carbon budget (RCB). Here we evaluate the global terrestrial carbon cycle projections on a region‐by‐region basis and compare the global models with regional assessments made by the REgional Carbon Cycle Assessment and Processes, Phase 2 activity. Results show that for each region, the CMIP6 multi‐model mean is generally consistent with the regional assessment, but substantial cross‐model spread exists. Nonetheless, all models perform well in some regions and no region is without some well performing models. This gives confidence that the CMIP6 models can be used to look at future changes in carbon stocks on a regional basis with appropriate model assessment and benchmarking. We find that most regions of the world remain cumulative net sources of CO2 between now and 2100 when considering the balance of fossil‐fuels and natural sinks, even under aggressive mitigation scenarios. This paper identifies strengths and weaknesses for each model in terms of its performance over a particular region including how process representation might impact those results and sets the agenda for applying stricter constraints at regional scales to reduce the uncertainty in global projections.European Union’s Horizon 2020European Union’s Horizon 2020European Union’s Horizon 2020Joint UK BEIS/Defra Met Office Hadley Centre Climate ProgrammeCarbonWatch-NZ Endeavour Research ProgrammeSão Paulo Research FoundationSão Paulo Research FoundationSão Paulo Research FoundationNational Science FoundationAndrew Carnegie Fellow ProgramCNPqKorea Ministry of EnvironmentNatural Environment Research Council (NERC)Natural Environment Research Council (NERC)National Environmental Science Progra

Modelling carbon stock and carbon sequestration ecosystem services for policy design: a comprehensive approach using a dynamic vegetation model

Ecosystem service (ES) models can only inform policy design adequately if they incorporate ecological processes. We used the Lund-Potsdam-Jena managed Land (LPJmL) model, to address following questions for Mexico, Bolivia and Brazilian Amazon: (i) How different are C stocks and C sequestration quantifications under standard (when soil and litter C and heterotrophic respiration are not considered) and comprehensive (including all C stock and heterotrophic respiration) approach? and (ii) How does the valuation of C stock and C sequestration differ in national payments for ES and global C funds or markets when comparing both approach? We found that up to 65% of C stocks have not been taken into account by neglecting to include C stored in soil and litter, resulting in gross underpayments (up to 500 times lower). Since emissions from heterotrophic respiration of organic material offset a large proportion of C gained through growth of living matter, we found that markets and decision-makers are inadvertently overestimating up to 100 times C sequestrated. New approaches for modelling C services relevant ecological process-based can help accounting for C in soil, litter and heterotrophic respiration and become important for the operationalization of agreements on climate change mitigation following the COP21 in 2015