This code repository is for the University of Edinburgh / NCEO (UK) CARbon DAta MOdel FraMework (CARDAMOM). CARDAMOM is a Bayesian framework that retrieves ensembles of parameters for models of the terrestrial carbon (C) cycle that are consistent with observational constrains and their associated uncertainties. From these parameter ensembles a probabilistic assessment of terrestrial ecosystem processes can be made. Additional information on different terrestrial C cycle models is given within the code.
For access and further information on this code please contact contact Luke Smallman (t . l . smallman @ ed . ac . uk) or Mathew Williams (Mat . Williams @ ed . ac . uk). For the JPL, Stanford & UCSB CARDAMOM code (github.com/CARDAMOM-framework/CARDAMOM_2.1.6c) contact Anthony Bloom (JPL, abloom @ jpl . nasa . gov), Caroline Famiglietti (Stanford University, cfamigli @ stanford . edu) or Gregory Quetin (UC Santa Barbara, gquetin @ ucsb . edu).
For general information on the scientific applications of both CARDAMOM frameworks, we refer users to the below papers.
Bloom, A.A., & Williams, M. (2015). Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological "common sense" in a model–data fusion framework. Biogeosciences, 12(5), 1299-1315. https://doi.org/10.5194/bg-12-1299-2015.
Bloom, A.A., Exbrayat, J-F., Van der Velde, I., Feng, L., & 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), 1285-1290. https://doi.org/10.1073/pnas.1515160113
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., Smallman, T. L., Bloom, A. A., Hutley, L. B., & Williams, M. (2018). Inverse determination of the influence of fire on vegetation carbon turnover in the pantropics. Global Biogeochemical Cycles. https://doi.org/10.1029/2018GB005925
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.
Revill, A., Bloom, A.A., Williams, M., 2016. Impacts of reduced model complexity and driver resolution on cropland ecosystem photosynthesis estimates. Field Crops Research, 187. https://doi.org/10.1016/j.fcr.2015.12.006.
Revill, A., Myrgiotis, V., Florence, A., Hoad, S., Rees, R., MacArthur, A., Williams, M., 2021. Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield. Agronomy, 11(2):314. https://doi.org/10.3390/agronomy11020314.
Smallman, L., Exbrayat, J-F., Mencuccini, M., Bloom, A. A., & Williams, M. (2017). Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests. Journal of Geophysical Research: Biogeosciences. https://doi.org/10.1002/2016JG003520.
Smallman, T. L., & Williams, M. (2019). Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapotranspiration: ACM-GPP-ETv1. Geoscientific Model Development, 12(6). https://doi.org/10.5194/gmd-12-2227-2019.
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.