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https://github.com/CARDAMOM-framework/CARDAMOM


https://github.com/CARDAMOM-framework/CARDAMOM

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README 

        

## CARDAMOM framework (version 3.0)

#### *JPL, Stanford, UCSB and Caltech CARDAMOM framework*



The technical documentation, including citation instructions, can be found here https://cardamom-framework.github.io/CARDAMOM



## General description 



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 manual can be found here (https://cardamom-framework.github.io/CARDAMOM). 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 code provided here (https://github.com/CARDAMOM-framework/CARDAMOM) was used in its original version in Bloom et al. (2020), Quetin et al., (2020), Yin et al. (2020), Famiglietti et al., (2021), and a number of more recent efforts, 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 and KCARDAMOM code:

Anthony Bloom (JPL, abloom @ jpl . nasa . gov)

Alex Konings (Stanford University, konings @ stanford.edu)

Gregory Quetin (UC Santa Barbara, gquetin @ ucsb . edu)

Renato Braghiere (California Institute of Technology, renatob @ caltech.edu)



Updates to CARDAMOM  are publicly available at https://github.com/CARDAMOM-framework/CARDAMOM 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. 



## References



Au, J., Bloom, A.A., Parazoo, N.C., Deans, R.M., Wong, C.Y.S., Houlton, B.Z. and Magney, T.S., 2023. Forest productivity recovery or collapse? Model‐data integration insights on drought‐induced tipping points. Global Change Biology, 29(19), pp.5652-5665.



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.



Levine, P.A., Bloom, A.A., Bowman, K.W., Reager, J.T., Worden, J.R., Liu, J., Parazoo, N.C., Meyer, V., Konings, A.G. and Longo, M., 2023. Water Stress Dominates 21st‐Century Tropical Land Carbon Uptake. Global Biogeochemical Cycles, 37(12), p.e2023GB007702.



Ma, S., Bloom, A.A., Watts, J.D., Quetin, G.R., Donatella, Z., Euskirchen, E.S., Norton, A.J., Yin, Y., Levine, P.A., Braghiere, R.K. and Parazoo, N.C., 2023. Resolving the carbon‐climate feedback potential of wetland CO2 and CH4 fluxes in Alaska. Global Biogeochemical Cycles, 37(9), p.e2022GB007524.



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.



Norton, A.J., Bloom, A.A., Parazoo, N.C., Levine, P.A., Ma, S., Braghiere, R.K. and Smallman, T.L., 2023. Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance. Biogeosciences, 20(12), pp.2455-2484.



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. 



Stettz, S.G., Parazoo, N.C., Bloom, A.A., Blanken, P.D., Bowling, D.R., Burns, S.P., Bacour, C., Maignan, F., Raczka, B., Norton, A.J. and Baker, I., 2022. Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework. Biogeosciences, 19(2), pp.541-558.



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.



## CARDAMOM copyright statement

Copyright  (c) 2024 California  Institute  of Technology (“Caltech”) and University of Washington. U.S. Government  sponsorship acknowledged.



THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED  TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.