This version of the code was used specifically for Norton et al., (2023, https://doi.org/10.5194/egusphere-2022-1265). For updates on the CARDAMOM model framework, user manuals and general recommendations on which model to use, reach out to Alexander Norton (alexander.norton@climate-energy-college.org), Paul Levine (paul.a.levine@jpl.nasa.gov) or Anthony Bloom (alexis.a.bloom@jpl.nasa.gov), and read the information below for additional details.
The Carbon data model framework (CARDAMOM) is a Bayesian inference approach for using terrestrial ecosystem observations to optimize terrestrial carbon cycle model states and processes parameters. The CARDAMOM code presented here is the culmination of a grassroots model development effort undertaken across multiple institutions, including the Jet Propulsion Laboratory (California Institute for Technology), University of Edinburgh, Stanford University and University of California Santa Barbara. The CARDAMOM framework version 2.2 code provided here (https://github.com/CARDAMOM-framework/) was used in Bloom et al. (2020), Quetin et al., (2020), Yin et al. (2020), Famiglietti et al., (2021), and remains backward compatible with Bloom et al., (2016).
The Data Assimilation Linked Ecosystem Carbon (DALEC) model used in CARDAMOM is described in Williams et al. (2005). Additional information and references for individual DALEC versions and module components are provided throughout the code.
Points of contact for the JPL, Stanford & UCSB CARDAMOM code: Anthony Bloom (JPL, abloom @ jpl . nasa . gov) Caroline Famiglietti (Stanford University, cfamigli @ stanford . edu) Gregory Quetin (UC Santa Barbara, gquetin @ ucsb . edu)
Updates to CARDAMOM version 2.2 (and subsequent versions) will be made publicly available at https://github.com/CARDAMOM-framework/ as "read-only" github repositories. If you wish to collaborate with the CARDAMOM development team or contribute to the CARDAMOM code release, we encourage you to communicate with the points of contact (above).
For the University of Edinburgh/NCEO (UK) CARDAMOM code (used in Exbrayat et al., 2018, Smallman et al., 2021, Famiglietti et al., 2021, and references therein), the code is available at https://github.com/GCEL/CARDAMOM; contact Luke Smallman (t . l . smallman @ ed . ac . uk) and Mathew Williams (Mat . Williams @ ed . ac . uk) for access.
For general information on the scientific applications of both CARDAMOM frameworks, we refer users to aforementioned papers.
Bloom, A.A., Exbrayat, J.F., Van Der Velde, I.R., Feng, L. and Williams, M., 2016. The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times. Proceedings of the National Academy of Sciences, 113(5), pp.1285-1290.
Bloom, A.A., Bowman, K.W., Liu, J., Konings, A.G., Worden, J.R., Parazoo, N.C., Meyer, V., Reager, J.T., Worden, H.M., Jiang, Z. and Quetin, G.R., 2020. Lagged effects regulate the inter-annual variability of the tropical carbon balance. Biogeosciences, 17(24), pp.6393-6422.
Exbrayat, J.F., Smallman, T.L., Bloom, A.A., Hutley, L.B. and Williams, M., 2018. Inverse determination of the influence of fire on vegetation carbon turnover in the pantropics. Global Biogeochemical Cycles, 32(12), pp.1776-1789.
Famiglietti, C.A., Smallman, T.L., Levine, P.A., Flack-Prain, S., Quetin, G.R., Meyer, V., Parazoo, N.C., Stettz, S.G., Yang, Y., Bonal, D. and Bloom, A.A., 2021. Optimal model complexity for terrestrial carbon cycle prediction. Biogeosciences, 18(8), pp.2727-2754.
Myrgiotis, V., Blei, E., Clement, R., Jones, S.K., Keane, B., Lee, M.A., Levy, P.E., Rees, R.M., Skiba, U.M., Smallman, T.L. and Toet, S., 2020. A model-data fusion approach to analyse carbon dynamics in managed grasslands. Agricultural Systems, 184, p.102907.
Quetin, G.R., Bloom, A.A., Bowman, K.W. and Konings, A.G., 2020. Carbon flux variability from a relatively simple ecosystem model with assimilated data is consistent with terrestrial biosphere model estimates. Journal of Advances in Modeling Earth Systems, 12(3), p.e2019MS001889.
Smallman, T.L., Exbrayat, J.F., Mencuccini, M., Bloom, A.A. and Williams, M., 2017. Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests. Journal of Geophysical Research: Biogeosciences, 122(3), pp.528-545.
Smallman, T. L., Milodowski, D. T., Neto, E. S., Koren, G., Ometto, J., and Williams, M.: Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-17, in review, 2021.
Williams, M., Schwarz, P.A., Law, B.E., Irvine, J. and Kurpius, M.R., 2005. An improved analysis of forest carbon dynamics using data assimilation. Global change biology, 11(1), pp.89-105.
Yin, Y., Bloom, A.A., Worden, J., Saatchi, S., Yang, Y., Williams, M., Liu, J., Jiang, Z., Worden, H., Bowman, K. and Frankenberg, C., 2020. Fire decline in dry tropical ecosystems enhances decadal land carbon sink. Nature communications, 11(1), pp.1-7.
Copyright (c) 2020 California Institute of Technology (“Caltech”) and University of Washington. U.S. Government sponsorship acknowledged.
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