Show simple item record

Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model

dc.contributor.authorKyker-Snowman, Emily
dc.contributor.authorLombardozzi, Danica L.
dc.contributor.authorBonan, Gordon B.
dc.contributor.authorCheng, Susan J.
dc.contributor.authorDukes, Jeffrey S.
dc.contributor.authorFrey, Serita D.
dc.contributor.authorJacobs, Elin M.
dc.contributor.authorMcNellis, Risa
dc.contributor.authorRady, Joshua M.
dc.contributor.authorSmith, Nicholas G.
dc.contributor.authorThomas, R. Quinn
dc.contributor.authorWieder, William R.
dc.contributor.authorGrandy, A. Stuart
dc.date.accessioned2022-01-06T15:50:38Z
dc.date.available2023-02-06 10:50:37en
dc.date.available2022-01-06T15:50:38Z
dc.date.issued2022-01
dc.identifier.citationKyker-Snowman, Emily ; Lombardozzi, Danica L.; Bonan, Gordon B.; Cheng, Susan J.; Dukes, Jeffrey S.; Frey, Serita D.; Jacobs, Elin M.; McNellis, Risa; Rady, Joshua M.; Smith, Nicholas G.; Thomas, R. Quinn; Wieder, William R.; Grandy, A. Stuart (2022). "Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model." Global Change Biology (2): 665-684.
dc.identifier.issn1354-1013
dc.identifier.issn1365-2486
dc.identifier.urihttps://hdl.handle.net/2027.42/171207
dc.description.abstractTerrestrial ecosystems regulate Earth’s climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change. Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist’s role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data- model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change, and broadening the impact of ecological research.Terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. The prevalent existing paradigm in ecology- ESM integration separates tasks along disciplinary lines. We recommend a new set of steps for ecology- ESM integration that shifts away from this historical paradigm toward a more collaborative one in which empiricists and modelers are involved in coproducing knowledge at every stage of data collection, theory development, and model integration.
dc.publisherCambridge University Press
dc.publisherWiley Periodicals, Inc.
dc.subject.othercollaborative bridging
dc.subject.otherdata- model integration
dc.subject.otherEarth system models
dc.subject.otherglobal ecology
dc.subject.otherhistory of models
dc.subject.otherinterdisciplinary workflow
dc.subject.othermodeling across scales
dc.titleIncreasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelEcology and Evolutionary Biology
dc.subject.hlbsecondlevelGeology and Earth Sciences
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/171207/1/gcb15894_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/171207/2/gcb15894.pdf
dc.identifier.doi10.1111/gcb.15894
dc.identifier.sourceGlobal Change Biology
dc.identifier.citedreferenceRogers, A., Medlyn, B. E., Dukes, J. S., Bonan, G., von Caemmerer, S., Dietze, M. C., Kattge, J., Leakey, A. D. B., Mercado, L. M., Niinemets, Ã ., Prentice, I. C., Serbin, S. P., Sitch, S., Way, D. A., & Zaehle, S. ( 2017 ). A roadmap for improving the representation of photosynthesis in Earth system models. New Phytologist, 213 ( 1 ), 22 - 42. https://doi.org/10.1111/nph.14283
dc.identifier.citedreferenceScott, H. G., & Smith, N. G. ( 2021 ). A model of C 4 photosynthetic acclimation based on least- cost optimality theory suitable for Earth System Model incorporation. Earth and Space Science Open Archive ESSOAr. https://doi.org/10.1002/essoar.10505842.1
dc.identifier.citedreferenceSellers, P. J., Mintz, Y., Sud, Y. C., & Dalcher, A. ( 1986 ). A Simple Biosphere Model (SIB) for use within General Circulation Models. Journal of the Atmospheric Sciences, 43 ( 6 ), 505 - 531. https://doi.org/10.1175/1520- 0469(1986)043<0505:ASBMFU>2.0.CO;2
dc.identifier.citedreferenceSellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B., Dazlich, D. A., Zhang, C., Collelo, G. D., & Bounoua, L. ( 1996 ). A revised land surface parameterization (SiB 2 ) for atmospheric GCMS. Part I: Model formulation. Journal of Climate, 9 ( 4 ), 676 - 705. https://doi.org/10.1175/1520- 0442(1996)009<0676:ARLSPF>2.0.CO;2
dc.identifier.citedreferenceShi, Z., Crowell, S., Luo, Y., & Moore, B. 3rd ( 2018 ). Model structures amplify uncertainty in predicted soil carbon responses to climate change. Nature Communications, 9 ( 1 ), 2171. https://doi.org/10.1038/s41467- 018- 04526- 9
dc.identifier.citedreferenceShukla, J., & Mintz, Y. ( 1982 ). Influence of land- surface evapotranspiration on the Earth’s climate. Science, 215 ( 4539 ), 1498 - 1501. https://doi.org/10.1126/science.215.4539.1498
dc.identifier.citedreferenceSitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., & Venevsky, S. ( 2003 ). Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9 ( 2 ), 161 - 185. https://doi.org/10.1046/j.1365- 2486.2003.00569.x
dc.identifier.citedreferenceSmith, N. G., & Dukes, J. S. ( 2013 ). Plant respiration and photosynthesis in global- scale models: Incorporating acclimation to temperature and CO 2. Global Change Biology, 19 ( 1 ), 45 - 63. https://doi.org/10.1111/j.1365- 2486.2012.02797.x
dc.identifier.citedreferenceSmith, N. G., & Keenan, T. F. ( 2020 ). Mechanisms underlying leaf photosynthetic acclimation to warming and elevated CO 2 as inferred from least- cost optimality theory. Global Change Biology, 26 ( 9 ), 5202 - 5216. https://doi.org/10.1111/gcb.15212
dc.identifier.citedreferenceSmith, N. G., Keenan, T. F., Colin Prentice, I., Wang, H., Wright, I. J., Niinemets, Ã ., Crous, K. Y., Domingues, T. F., Guerrieri, R., Yoko Ishida, F., Kattge, J., Kruger, E. L., Maire, V., Rogers, A., Serbin, S. P., Tarvainen, L., Togashi, H. F., Townsend, P. A., Wang, M., - ¦ Zhou, S.- X. ( 2019 ). Global photosynthetic capacity is optimized to the environment. Ecology Letters, 22 ( 3 ), 506 - 517. https://doi.org/10.1111/ele.13210
dc.identifier.citedreferenceSmith, N. G., Lombardozzi, D. L., Tawfik, A., Bonan, G. B., & Dukes, J. S. ( 2017 ). Biophysical consequences of photosynthetic temperature acclimation for climate. Journal of Advances in Modeling Earth Systems, 9 ( 1 ), 536 - 547. https://doi.org/10.1002/2016MS000732
dc.identifier.citedreferenceSmith, N. G., Malyshev, S. L., Shevliakova, E., Kattge, J., & Dukes, J. S. ( 2015 ). Foliar temperature acclimation reduces simulated carbon sensitivity to climate. Nature Climate Change, 6 ( 4 ), 407 - 411. https://doi.org/10.1038/nclimate2878
dc.identifier.citedreferenceSulman, B. N., Moore, J. A. M., Abramoff, R. Z., Averill, C., Kivlin, S., Georgiou, K., Sridhar, B., Hartman, M., Wang, G., Wieder, W. R., Bradford, M. A., Luo, Y., Mayes, M. A., Morrison, E., Riley, W. J., Salazar, A., Schimel, J. P., Tang, J., & Classen, A. T. ( 2018 ). Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Ecology Letters, 14, 109 - 123. https://doi.org/10.1007/s10533- 018- 0509- z
dc.identifier.citedreferenceThomas, R. Q., Zaehle, S., Templer, P. H., & Goodale, C. L. ( 2013 ). Global patterns of nitrogen limitation: Confronting two global biogeochemical models with observations. Global Change Biology, 19 ( 10 ), 2986 - 2998. https://doi.org/10.1111/gcb.12281
dc.identifier.citedreferenceThornton, P. E., Doney, S. C., Lindsay, K., Moore, J. K., Mahowald, N., Randerson, J. T., Fung, I., Lamarque, J.- F., Feddema, J. J., & Lee, Y.- H. ( 2009 ). Carbon- nitrogen interactions regulate climate- carbon cycle feedbacks: Results from an atmosphere- ocean general circulation model. Biogeosciences Discussions, 6, 2099 - 2120. https://doi.org/10.5194/bg- 6- 2099- 2009
dc.identifier.citedreferenceThornton, P. E., Lamarque, J.- F., Rosenbloom, N. A., & Mahowald, N. M. ( 2007 ). Influence of carbon- nitrogen cycle coupling on land model response to CO 2 fertilization and climate variability. Global Biogeochemical Cycles, 21 ( 4 ). https://doi.org/10.1029/2006gb002868
dc.identifier.citedreferenceTorn, M. S., Trumbore, S. E., Chadwick, O. A., Vitousek, P. M., & Hendricks, D. M. ( 1997 ). Mineral control of soil organic carbon storage and turnover. Nature, 389 ( 6647 ), 170 - 173. https://doi.org/10.1038/38260
dc.identifier.citedreferenceWang, H., Atkin, O. K., Keenan, T. F., Smith, N. G., Wright, I. J., Bloomfield, K. J., Kattge, J., Reich, P. B., & Prentice, I. C. ( 2020 ). Acclimation of leaf respiration consistent with optimal photosynthetic capacity. Global Change Biology, 26 ( 4 ), 2573 - 2583. https://doi.org/10.1111/gcb.14980
dc.identifier.citedreferenceWang, H., Prentice, I. C., Davis, T. W., Keenan, T. F., Wright, I. J., & Peng, C. ( 2017 ). Photosynthetic responses to altitude: An explanation based on optimality principles. New Phytologist, 213 ( 3 ), 976 - 982. https://doi.org/10.1111/nph.14332
dc.identifier.citedreferenceWang, H., Prentice, I. C., Keenan, T. F., Davis, T. W., Wright, I. J., Cornwell, W. K., Evans, B. J., & Peng, C. ( 2017 ). Towards a universal model for carbon dioxide uptake by plants. Nature Plants, 3 ( 9 ), 734 - 741. https://doi.org/10.1038/s41477- 017- 0006- 8
dc.identifier.citedreferenceWang, K., Peng, C., Zhu, Q., Zhou, X., Wang, M., Zhang, K., & Wang, G. ( 2017 ). Modeling global soil carbon and soil microbial carbon by integrating microbial processes into the ecosystem process model TRIPLEX- GHG. Journal of Advances in Modeling Earth Systems, 9 ( 6 ), 2368 - 2384. https://doi.org/10.1002/2017MS000920
dc.identifier.citedreferenceWang, Y. P., Law, R. M., & Pak, B. ( 2010 ). A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences, 7 ( 7 ), 2261 - 2282. https://doi.org/10.5194/bg- 7- 2261- 2010
dc.identifier.citedreferenceWang, Y., Zhang, H., Ciais, P., Goll, D., Huang, Y., Wood, J. D., Ollinger, S. V., Tang, X., & Prescher, A. ( 2021 ). Microbial activity and root carbon inputs are more important than soil carbon diffusion in simulating soil carbon profiles. Journal of Geophysical Research: Biogeosciences, 126 ( 4 ). https://doi.org/10.1029/2020JG006205
dc.identifier.citedreferenceWashington, W. M., Buja, L., & Craig, A. ( 2009 ). The computational future for climate and Earth system models: On the path to petaflop and beyond. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 367 ( 1890 ), 833 - 846. https://doi.org/10.1098/rsta.2008.0219
dc.identifier.citedreferenceWeng, E. S., Malyshev, S., Lichstein, J. W., Farrior, C. E., Dybzinski, R., Zhang, T., Shevliakova, E., & Pacala, S. W. ( 2015 ). Scaling from individual trees to forests in an Earth system modeling framework using a mathematically tractable model of height- structured competition. Biogeosciences, 12 ( 9 ), 2655 - 2694. https://doi.org/10.5194/bg- 12- 2655- 2015
dc.identifier.citedreferenceWieder, W. R., Allison, S. D., Davidson, E. A., Georgiou, K., Hararuk, O., He, Y., Hopkins, F., Luo, Y., Smith, M. J., Sulman, B. N., Todd- Brown, K., Wang, Y.- P., Xia, J., & Xu, X. ( 2015 ). Explicitly representing soil microbial processes in Earth system models. Global Biogeochemical Cycles, 29, 1782 - 1800. https://doi.org/10.1002/2015GB005188
dc.identifier.citedreferenceWieder, W. R., Cleveland, C. C., Lawrence, D. M., & Bonan, G. B. ( 2015 ). Effects of model structural uncertainty on carbon cycle projections: Biological nitrogen fixation as a case study. Environmental Research Letters, 10 ( 4 ), 044016. https://doi.org/10.1088/1748- 9326/10/4/044016
dc.identifier.citedreferenceWieder, W. R., Grandy, A. S., Kallenbach, C. M., & Bonan, G. B. ( 2014 ). Integrating microbial physiology and physio- chemical principles in soils with the MIcrobial- MIneral Carbon Stabilization (MIMICS) model. Biogeosciences, 11 ( 14 ), 3899 - 3917. https://doi.org/10.5194/bg- 11- 3899- 2014
dc.identifier.citedreferenceWieder, W. R., Grandy, A. S., Kallenbach, C. M., Taylor, P. G., & Bonan, G. B. ( 2015 ). Representing life in the Earth system with soil microbial functional traits in the MIMICS model. Geoscientific Model Development, 8 ( 6 ), 1789 - 1808. https://doi.org/10.5194/gmd- 8- 1789- 2015
dc.identifier.citedreferenceWieder, W. R., Hartman, M. D., Sulman, B. N., Wang, Y. P., Koven, C. D., & Bonan, G. B. ( 2018 ). Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models. Global Change Biology, 24 ( 4 ), 1563 - 1579. https://doi.org/10.1111/gcb.13979
dc.identifier.citedreferenceWieder, W. R., Pierson, D., Earl, S., Lajtha, K., Baer, S., Ballantyne, F., Berhe, A. A., Billings, S., Brigham, L. M., Chacon, S. S., Fraterrigo, J., Frey, S. D., Georgiou, K., Marie- Anne de Graaff, A., Grandy, S., Hartman, M. D., Hobbie, S. E., Johnson, C., & Kaye, J., - ¦ Weintraub, S. ( 2020 ). SoDaH: The SOils DAta Harmonization database, an open- source synthesis of soil data from research networks, version 1.0. Earth System Science Data Discussions, 13, 1843 - 1854. https://doi.org/10.5194/essd- 13- 1843- 2021
dc.identifier.citedreferenceWieder, W. R., Sulman, B. N., Hartman, M. D., Koven, C. D., & Bradford, M. A. ( 2019 ). Arctic soil governs whether climate change drives global losses or gains in soil carbon. Geophysical Research Letters, 46 ( 24 ), 14486 - 14495. https://doi.org/10.1029/2019GL085543
dc.identifier.citedreferenceYang, X., Thornton, P. E., Ricciuto, D. M., & Post, W. M. ( 2014 ). The role of phosphorus dynamics in tropical forests - A modeling study using CLM- CNP. Biogeosciences, 11 ( 6 ), 1667 - 1681. https://doi.org/10.5194/bg- 11- 1667- 2014
dc.identifier.citedreferenceZaehle, S., & Friend, A. D. ( 2010 ). Carbon and nitrogen cycle dynamics in the O- CN land surface model: 1. Model description, site- scale evaluation, and sensitivity to parameter estimates. Global Biogeochemical Cycles, 24 ( 1 ), 1 - 13. https://doi.org/10.1029/2009GB003521
dc.identifier.citedreferenceZaehle, S., Medlyn, B. E., De Kauwe, M. G., Walker, A. P., Dietze, M. C., Hickler, T., Luo, Y., Wang, Y.- P., El- Masri, B., Thornton, P., Jain, A., Wang, S., Warlind, D., Weng, E., Parton, W., Iversen, C. M., Gallet- Budynek, A., McCarthy, H., Finzi, A., - ¦ Norby, R. J. ( 2014 ). Evaluation of 11 terrestrial carbon- nitrogen cycle models against observations from two temperate Free- Air CO 2 Enrichment studies. New Phytologist, 202 ( 3 ), 803 - 822. https://doi.org/10.1111/nph.12697
dc.identifier.citedreferenceZhang, H., Goll, D. S., Wang, Y.- P., Ciais, P., Wieder, W. R., Abramoff, R., Huang, Y., Guenet, B., Prescher, A.- K., Viscarra Rossel, R. A., Barré, P., Chenu, C., Zhou, G., & Tang, X. ( 2020 ). Microbial dynamics and soil physicochemical properties explain large- scale variations in soil organic carbon. Global Change Biology, 26 ( 4 ), 2668 - 2685. https://doi.org/10.1111/gcb.14994
dc.identifier.citedreferenceZiehn, T., Kattge, J., Knorr, W., & Scholze, M. ( 2011 ). Improving the predictability of global CO 2 assimilation rates under climate change. Geophysical Research Letters, 38 ( 10 ). https://doi.org/10.1029/2011gl047182
dc.identifier.citedreferenceAdams, G. S., Converse, B. A., Hales, A. H., & Klotz, L. E. ( 2021 ). People systematically overlook subtractive changes. Nature, 592 ( 7853 ), 258 - 261. https://doi.org/10.1038/s41586- 021- 03380- y
dc.identifier.citedreferenceAinsworth, E. A., & Long, S. P. ( 2005 ). What have we learned from 15 years of free- air CO 2 enrichment (FACE)? A meta- analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO 2. New Phytologist, 165 ( 2 ), 351 - 371. https://doi.org/10.1111/j.1469- 8137.2004.01224.x
dc.identifier.citedreferenceAnadu, J., Ali, H., & Jackson, C. ( 2020 ). Ten steps to protect BIPOC scholars in the field. Eos, 101, https://doi.org/10.1029/2020eo150525
dc.identifier.citedreferenceAnderson- Cook, C. M., Lu, L., & Parker, P. A. ( 2019 ). Effective interdisciplinary collaboration between statisticians and other subject matter experts. Quality Engineering, 31 ( 1 ), 164 - 176. https://doi.org/10.1080/08982112.2018.1530357
dc.identifier.citedreferenceBasile, S. J., Lin, X., Wieder, W. R., Hartman, M. D., & Keppel- Aleks, G. ( 2020 ). Leveraging the signature of heterotrophic respiration on atmospheric CO 2 for model benchmarking. Biogeosciences, 17 ( 5 ), 1293 - 1308. https://doi.org/10.5194/bg- 17- 1293- 2020
dc.identifier.citedreferenceBaskaran, P., Hyvönen, R., Berglund, S. L., Clemmensen, K. E., à gren, G. I., Lindahl, B. D., & Manzoni, S. ( 2017 ). Modelling the influence of ectomycorrhizal decomposition on plant nutrition and soil carbon sequestration in boreal forest ecosystems. New Phytologist, 213 ( 3 ), 1452 - 1465. https://doi.org/10.1111/nph.14213
dc.identifier.citedreferenceBernard, R. E., & Cooperdock, E. H. G. ( 2018 ). No progress on diversity in 40 years. Nature Geoscience, 11 ( 5 ), 292 - 295. https://doi.org/10.1038/s41561- 018- 0116- 6
dc.identifier.citedreferenceBonan, G. B. ( 1995 ). Land- atmosphere CO 2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. Journal of Geophysical Research, 100 ( D2 ), 2817. https://doi.org/10.1029/94JD02961
dc.identifier.citedreferenceBonan, G. B. ( 2008 ). Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320 ( 5882 ), 1444 - 1449. https://doi.org/10.1126/science.1155121
dc.identifier.citedreferenceBonan, G. B. ( 2016 ). Forests, climate, and public policy: A 500- year interdisciplinary odyssey. Annual Review of Ecology, Evolution, and Systematics, 47 ( 1 ), 97 - 121. https://doi.org/10.1146/annurev- ecolsys- 121415- 032359
dc.identifier.citedreferenceBonan, G. B. ( 2019 ). Climate change and terrestrial ecosystem modeling. Cambridge University Press.
dc.identifier.citedreferenceBonan, G. B., & Doney, S. C. ( 2018 ). Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models. Science, 359 ( 6375 ), eaam8328. https://doi.org/10.1126/science.aam8328
dc.identifier.citedreferenceBonan, G. B., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M., Lawrence, D. M., & Swenson, S. C. ( 2011 ). Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data. Journal of Geophysical Research, 116 ( G2 ). https://doi.org/10.1029/2010jg001593
dc.identifier.citedreferenceBonan, G. B., & Levis, S. ( 2010 ). Quantifying carbon- nitrogen feedbacks in the Community Land Model (CLM4). Geophysical Research Letters, 37 ( 7 ). https://doi.org/10.1029/2010gl042430
dc.identifier.citedreferenceBonan, G. B., Levis, S., Sitch, S., Vertenstein, M., & Oleson, K. W. ( 2003 ). A dynamic global vegetation model for use with climate models: Concepts and description of simulated vegetation dynamics. Global Change Biology, 9 ( 11 ), 1543 - 1566. https://doi.org/10.1046/j.1365- 2486.2003.00681.x
dc.identifier.citedreferenceBracco, A., Long, M. C., Levine, N. M., Thomas, R. Q., Deutsch, C., & McKinley, G. A. ( 2015 ). NCAR’s summer colloquium: Capacity building in cross- disciplinary research of earth system carbon- climate connections. Bulletin of the American Meteorological Society, 96 ( 8 ), 1381 - 1384. https://doi.org/10.1175/BAMS- D- 13- 00246.1
dc.identifier.citedreferenceCarey, C. C., Farrell, K. J., Hounshell, A. G., & O’Connell, K. ( 2020 ). Macrosystems EDDIE teaching modules significantly increase ecology students’ proficiency and confidence working with ecosystem models and use of systems thinking. Ecology and Evolution, 10 ( 22 ), 12515 - 12527. https://doi.org/10.1002/ece3.6757
dc.identifier.citedreferenceCharney, J., Stone, P. H., & Quirk, W. J. ( 1975 ). Drought in the Sahara: A biogeophysical feedback mechanism. Science, 187 ( 4175 ), 434 - 435. https://doi.org/10.1126/science.187.4175.434
dc.identifier.citedreferenceChaudhary, V. B., & Berhe, A. A. ( 2020 ). Ten simple rules for building an antiracist lab. PLoS Computational Biology, 16 ( 10 ), e1008210. https://doi.org/10.1371/journal.pcbi.1008210
dc.identifier.citedreferenceCheng, S. J., Smith, N. G., & Marklein, A. R. ( 2018, March 16). Modeling global change ecology in a high- carbon dioxide world. https://eos.org/meeting- reports/modeling- global- change- ecology- in- a- high- carbon- dioxide- world
dc.identifier.citedreferenceCheng, S. J., Hess, P. G., Wieder, W. R., Thomas, R. Q., Nadelhoffer, K. J., Vira, J., Lombardozzi, D. L., Gundersen, P., Fernandez, I. J., Schleppi, P., Gruselle, M.- C., Moldan, F., & Goodale, C. L. ( 2019 ). Decadal fates and impacts of nitrogen additions on temperate forest carbon storage: A data- model comparison. Biogeosciences, 16 ( 13 ), 2771 - 2793. https://doi.org/10.5194/bg- 16- 2771- 2019
dc.identifier.citedreferenceClark, M. P., Kavetski, D., & Fenicia, F. ( 2011 ). Pursuing the method of multiple working hypotheses for hydrological modeling: Hypothesis testing in hydrology. Water Resources Research, 47 ( 9 ). https://doi.org/10.1029/2010wr009827
dc.identifier.citedreferenceClark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., & Rasmussen, R. M. ( 2015 ). A unified approach for process- based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51 ( 4 ), 2498 - 2514. https://doi.org/10.1002/2015WR017198
dc.identifier.citedreferenceClark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., & Hay, L. E. ( 2008 ). Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models: Differences between hydrological models. Water Resources Research, 44 ( 12 ). https://doi.org/10.1029/2007wr006735
dc.identifier.citedreferenceCollier, N., Hoffman, F. M., Lawrence, D. M., Keppel- Aleks, G., Koven, C. D., Riley, W. J., Mu, M., & Randerson, J. T. ( 2018 ). The international land model benchmarking (ILAMB) system: Design, theory, and implementation. Journal of Advances in Modeling Earth Systems, 10 ( 11 ), 2731 - 2754. https://doi.org/10.1029/2018MS001354
dc.identifier.citedreferenceCooley, E. ( 1994 ). Training an interdisciplinary team in communication and decision- making skills. Small Group Research, 25 ( 1 ), 5 - 25. https://doi.org/10.1177/1046496494251002
dc.identifier.citedreferenceCox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., & Totterdell, I. J. ( 2000 ). Acceleration of global warming due to carbon- cycle feedbacks in a coupled climate model. Nature, 408 ( 6809 ), 184 - 187. https://doi.org/10.1038/35047138
dc.identifier.citedreferenceDagon, K., Sanderson, B. M., Fisher, R. A., & Lawrence, D. M. ( 2020 ). A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5. Advances in Statistical Climatology Meteorology and Oceanography, 6 ( 2 ), 223 - 244. https://doi.org/10.5194/ascmo- 6- 223- 2020
dc.identifier.citedreferenceDanabasoglu, G., Lamarque, J.- F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., - ¦ Strand, W. G. ( 2020 ). The community earth system model version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12 ( 2 ). https://doi.org/10.1029/2019ms001916
dc.identifier.citedreferenceDe Kauwe, M. G., Medlyn, B. E., Zaehle, S., Walker, A. P., Dietze, M. C., Hickler, T., Jain, A. K., Luo, Y., Parton, W. J., Prentice, I. C., Smith, B., Thornton, P. E., Wang, S., Wang, Y.- P., WÃ¥rlind, D., Weng, E., Crous, K. Y., Ellsworth, D. S., Hanson, P. J., - ¦ Norby, R. J. ( 2013 ). Forest water use and water use efficiency at elevated CO 2: A model- data intercomparison at two contrasting temperate forest FACE sites. Global Change Biology, 19 ( 6 ), 1759 - 1779. https://doi.org/10.1111/gcb.12164
dc.identifier.citedreferenceDe Kauwe, M. G., Medlyn, B. E., Zaehle, S., Walker, A. P., Dietze, M. C., Wang, Y.- P., Luo, Y., Jain, A. K., El- Masri, B., Hickler, T., WÃ¥rlind, D., Weng, E., Parton, W. J., Thornton, P. E., Wang, S., Prentice, I. C., Asao, S., Smith, B., McCarthy, H. R., - ¦ Norby, R. J. ( 2014 ). Where does the carbon go? A model- data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free- air CO 2 enrichment sites. New Phytologist, 203 ( 3 ), 883 - 899.
dc.identifier.citedreferenceDenning, A. S., Randall, D. A., Collatz, G. J., & Sellers, P. J. ( 1996 ). Simulations of terrestrial carbon metabolism and atmospheric CO 2 in a general circulation model. Part 2: Simulated CO 2 concentrations. Tellus. Series B, Chemical and Physical Meteorology, 48 ( 4 ), 543 - 567. https://doi.org/10.3402/tellusb.v48i4.15931
dc.identifier.citedreferenceDeutsch, C. A., Tewksbury, J. J., Tigchelaar, M., Battisti, D. S., Merrill, S. C., Huey, R. B., & Naylor, R. L. ( 2018 ). Increase in crop losses to insect pests in a warming climate. Science, 361 ( 6405 ), 916 - 919. https://doi.org/10.1126/science.aat3466
dc.identifier.citedreferenceDickinson, R. E. ( 1984 ). Modeling evapotranspiration for three- dimensional global climate models. Climate Processes and Climate Sensitivity, 29, 58 - 72. https://doi.org/10.1029/GM029p0058
dc.identifier.citedreferenceDickinson, R. E. ( 1986 ). Biosphere/Atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model. Technical report. NCAR. https://ci.nii.ac.jp/naid/10009851528/
dc.identifier.citedreferenceDickinson, R. E., & Henderson- Sellers, A. ( 1988 ). Modelling tropical deforestation: A study of GCM land- surface parametrizations. Quarterly Journal of the Royal Meteorological Society, 114 ( 480 ), 439 - 462. https://doi.org/10.1002/qj.49711448009
dc.identifier.citedreferenceDickinson, R. E., Jaeger, J., Washington, W. M., & Wolski, R. ( 1981 ). Boundary subroutine for the NCAR global climate model. National Center for Atmospheric Research.
dc.identifier.citedreferenceDietze, M. C. ( 2017 ). Prediction in ecology: A first- principles framework. Ecological Applications, 27 ( 7 ), 2048 - 2060. https://doi.org/10.1002/eap.1589
dc.identifier.citedreferenceDuffy, M. A., García- Robledo, C., Gordon, S. P., Grant, N. A., Green, D. A., Kamath, A., Penczykowski, R. M., Rebolleda- Gómez, M., Wale, N., & Zaman, L. ( 2021 ). Model systems in ecology, evolution, and behavior: A call for diversity in our model systems and discipline. The American Naturalist, 198 ( 1 ), 53 - 68. https://doi.org/10.1086/714574
dc.identifier.citedreferenceDufresne, J.- L., Foujols, M.- A., Denvil, S., Caubel, A., Marti, O., Aumont, O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., - ¦ Vuichard, N. ( 2013 ). Climate change projections using the IPSL- CM5 Earth System Model: From CMIP3 to CMIP5. Climate Dynamics, 40 ( 9 ), 2123 - 2165. https://doi.org/10.1007/s00382- 012- 1636- 1
dc.identifier.citedreferenceDunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E., Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C., Durachta, J., Dussin, R., - ¦ Zhao, M. ( 2020 ). The GFDL earth system model version 4.1 (GFDL- ESM 4.1): Overall coupled model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12 ( 11 ). https://doi.org/10.1029/2019ms002015
dc.identifier.citedreferenceDuursma, R. A. ( 2015 ). Plantecophys - An R package for analysing and modelling leaf gas exchange data. PLoS One, 10 ( 11 ), e0143346. https://doi.org/10.1371/journal.pone.0143346
dc.identifier.citedreferenceDybzinski, R., Farrior, C. E., & Pacala, S. W. ( 2015 ). Increased forest carbon storage with increased atmospheric CO 2 despite nitrogen limitation: A game- theoretic allocation model for trees in competition for nitrogen and light. Global Change Biology, 21 ( 3 ), 1182 - 1196. https://doi.org/10.1111/gcb.12783
dc.identifier.citedreferenceEdburg, S. L., Hicke, J. A., Lawrence, D. M., & Thornton, P. E. ( 2011 ). Simulating coupled carbon and nitrogen dynamics following mountain pine beetle outbreaks in the western United States. Journal of Geophysical Research, 116 ( G4 ). https://doi.org/10.1029/2011jg001786
dc.identifier.citedreferenceEdwards, P. N. ( 2011 ). History of climate modeling. Wiley Interdisciplinary Reviews. Climate Change, 2 ( 1 ), 128 - 139. https://doi.org/10.1002/wcc.95
dc.identifier.citedreferenceEmery, N. C., Bledsoe, E. K., Hasley, A. O., & Eaton, C. D. ( 2021 ). Cultivating inclusive instructional and research environments in ecology and evolutionary science. Ecology and Evolution, 11 ( 4 ), 1480 - 1491. https://doi.org/10.1002/ece3.7062
dc.identifier.citedreferenceFarrior, C. E., Dybzinski, R., Levin, S. A., & Pacala, S. W. ( 2013 ). Competition for water and light in closed- canopy forests: A tractable model of carbon allocation with implications for carbon sinks. The American Naturalist, 181 ( 3 ), 314 - 330. https://doi.org/10.1086/669153
dc.identifier.citedreferenceFer, I., Gardella, A. K., Shiklomanov, A. N., Campbell, E. E., Cowdery, E. M., De Kauwe, M. G., Desai, A., Duveneck, M. J., Fisher, J. B., Haynes, K. D., Hoffman, F. M., Johnston, M. R., Kooper, R., LeBauer, D. S., Mantooth, J., Parton, W. J., Poulter, B., Quaife, T., Raiho, A., - ¦ Dietze, M. C. ( 2021 ). Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data- model integration. Global Change Biology, 27 ( 1 ), 13 - 26. https://doi.org/10.1111/gcb.15409
dc.identifier.citedreferenceField, A. P., & Gillett, R. ( 2010 ). How to do a meta- analysis. The British Journal of Mathematical and Statistical Psychology, 63 ( Pt 3 ), 665 - 694. https://doi.org/10.1348/000711010X502733
dc.identifier.citedreferenceFierer, N., Wood, S. A., & Bueno de Mesquita, C. P. ( 2021 ). How microbes can, and cannot, be used to assess soil health. Soil Biology & Biochemistry, 153, 108111. https://doi.org/10.1016/j.soilbio.2020.108111
dc.identifier.citedreferenceFisher, R. A., & Koven, C. D. ( 2020 ). Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems. Journal of Advances in Modeling Earth Systems, 12 ( 4 ). https://doi.org/10.1029/2018ms001453
dc.identifier.citedreferenceKoven, C. D., Hugelius, G., Lawrence, D. M., & Wieder, W. R. ( 2017 ). Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nature Climate Change, 7 ( 11 ), 817 - 822. https://doi.org/10.1038/nclimate3421
dc.identifier.citedreferenceFoley, J. A., Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., & Haxeltine, A. ( 1996 ). An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochemical Cycles, 10 ( 4 ), 603 - 628. https://doi.org/10.1029/96GB02692
dc.identifier.citedreferenceFord, H. L., Brick, C., Azmitia, M., Blaufuss, K., & Dekens, P. ( 2019 ). Women from some under- represented minorities are given too few talks at world’s largest Earth- science conference. Nature, 576 ( 7785 ), 32 - 35. https://doi.org/10.1038/d41586- 019- 03688- w
dc.identifier.citedreferenceFranklin, O., Harrison, S. P., Dewar, R., Farrior, C. E., Brännström, à ., Dieckmann, U., Pietsch, S., Falster, D., Cramer, W., Loreau, M., Wang, H., Mäkelä, A., Rebel, K. T., Meron, E., Schymanski, S. J., Rovenskaya, E., Stocker, B. D., Zaehle, S., Manzoni, S., - ¦ Prentice, I. C. ( 2020 ). Organizing principles for vegetation dynamics. Nature Plants, 6 ( 5 ), 444 - 453. https://doi.org/10.1038/s41477- 020- 0655- x
dc.identifier.citedreferenceFranks, P. J., Bonan, G. B., Berry, J. A., Lombardozzi, D. L., Holbrook, N. M., Herold, N., & Oleson, K. W. ( 2018 ). Comparing optimal and empirical stomatal conductance models for application in Earth system models. Global Change Biology, 24 ( 12 ), 5708 - 5723. https://doi.org/10.1111/gcb.14445
dc.identifier.citedreferenceFriedlingstein, P., Jones, M. W., O’Sullivan, M., Andrew, R. M., Hauck, J., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Bakker, D. C. E., Canadell, J. G., Ciais, P., Jackson, R. B., Anthoni, P., Barbero, L., Bastos, A., Bastrikov, V., Becker, M., - ¦ Zaehle, S. ( 2019 ). Global carbon budget 2019. Earth System Science Data, 11 ( 4 ), 1783 - 1838. https://doi.org/10.5194/essd- 11- 1783- 2019
dc.identifier.citedreferenceFriend, A. D. ( 2010 ). Terrestrial plant production and climate change. Journal of Experimental Botany, 61 ( 5 ), 1293 - 1309. https://doi.org/10.1093/jxb/erq019
dc.identifier.citedreferenceFung, I. Y., Doney, S. C., Lindsay, K., & John, J. ( 2005 ). Evolution of carbon sinks in a changing climate. Proceedings of the National Academy of Sciences of the United States of America, 102 ( 32 ), 11201 - 11206. https://doi.org/10.1073/pnas.0504949102
dc.identifier.citedreferenceGiles, S., Jackson, C., & Stephen, N. ( 2020 ). Barriers to fieldwork in undergraduate geoscience degrees. Nature Reviews Earth & Environment, 1 ( 2 ), 77 - 78. https://doi.org/10.1038/s43017- 020- 0022- 5
dc.identifier.citedreferenceGolaz, J.- C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q. I., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay- Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron- Smith, P. J., Donahue, A. S., - ¦ Zhu, Q. ( 2019 ). The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution. Journal of Advances in Modeling Earth Systems, 11 ( 7 ), 2089 - 2129. https://doi.org/10.1029/2018MS001603
dc.identifier.citedreferenceHampton, S. E., Jones, M. B., Wasser, L. A., Schildhauer, M. P., Supp, S. R., Brun, J., Hernandez, R. R., Boettiger, C., Collins, S. L., Gross, L. J., Fernández, D. S., Budden, A., White, E. P., Teal, T. K., Labou, S. G., & Aukema, J. E. ( 2017 ). Skills and knowledge for data- intensive environmental research. BioScience, 67 ( 6 ), 546 - 557. https://doi.org/10.1093/biosci/bix025
dc.identifier.citedreferenceHuang, Y., Guenet, B., Wang, Y. L., & Ciais, P. ( 2021 ). Global simulation and evaluation of soil organic matter and microbial carbon and nitrogen stocks using the microbial decomposition model ORCHIMIC v2.0. Global Biogeochemical Cycles, 35 ( 5 ). https://doi.org/10.1029/2020GB006836
dc.identifier.citedreferenceHunt, S. K., Galatowitsch, M. L., & McIntosh, A. R. ( 2017 ). Interactive effects of land use, temperature, and predators determine native and invasive mosquito distributions. Freshwater Biology, 62 ( 9 ), 1564 - 1577. https://doi.org/10.1111/fwb.12967
dc.identifier.citedreferenceJiang, C., Ryu, Y., Wang, H., & Keenan, T. F. ( 2020 ). An optimality- based model explains seasonal variation in C 3 plant photosynthetic capacity. Global Change Biology, 26 ( 11 ), 6493 - 6510. https://doi.org/10.1111/gcb.15276
dc.identifier.citedreferenceJung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps- Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., - ¦ Reichstein, M. ( 2020 ). Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17 ( 5 ), 1343 - 1365. https://doi.org/10.5194/bg- 17- 1343- 2020
dc.identifier.citedreferenceKattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., Van BODEGOM, P. M., Reichstein, M., Enquist, B. J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., - ¦ Wirth, C. ( 2011 ). TRY - A global database of plant traits. Global Change Biology, 17 ( 9 ), 2905 - 2935. https://doi.org/10.1111/j.1365- 2486.2011.02451.x
dc.identifier.citedreferenceKattge, J., & Knorr, W. ( 2007 ). Temperature acclimation in a biochemical model of photosynthesis: A reanalysis of data from 36 species. Plant, Cell & Environment, 30 ( 9 ), 1176 - 1190. https://doi.org/10.1111/j.1365- 3040.2007.01690.x
dc.identifier.citedreferenceKattge, J., Knorr, W., Raddatz, T., & Wirth, C. ( 2009 ). Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global- scale terrestrial biosphere models. Global Change Biology, 15 ( 4 ), 976 - 991. https://doi.org/10.1111/j.1365- 2486.2008.01744.x
dc.identifier.citedreferenceKennedy, D., Swenson, S., Oleson, K. W., Lawrence, D. M., Fisher, R., Lola da Costa, A. C., & Gentine, P. ( 2019 ). Implementing plant hydraulics in the community land model, version 5. Journal of Advances in Modeling Earth Systems, 11 ( 2 ), 485 - 513. https://doi.org/10.1029/2018MS001500
dc.identifier.citedreferenceKuziemsky, C. E., Borycki, E. M., Purkis, M. E., Black, F., Boyle, M., Cloutier- Fisher, D., Fox, L. A., MacKenzie, P., Syme, A., Tschanz, C., Wainwright, W., Wong, H., & Interprofessional Practices Team. ( 2009 ). An interdisciplinary team communication framework and its application to healthcare - e- teams- systems design. BMC Medical Informatics and Decision Making, 9 ( 1 ), 43. https://doi.org/10.1186/1472- 6947- 9- 43
dc.identifier.citedreferenceKyker- Snowman, E., Wieder, W. R., Frey, S. D., & Grandy, A. S. ( 2020 ). Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial- MIneral Carbon Stabilization model version 1.0 (MIMICS- CN v1. 0). Geoscientific Model Development, 13 ( 9 ), 4413 - 4434. https://doi.org/10.5194/gmd- 13- 4413- 2020
dc.identifier.citedreferenceLade, S. J., Steffen, W., de Vries, W., Carpenter, S. R., Donges, J. F., Gerten, D., Hoff, H., Newbold, T., Richardson, K., & Rockström, J. ( 2019 ). Human impacts on planetary boundaries amplified by Earth system interactions. Nature Sustainability, 3 ( 2 ), 119 - 128. https://doi.org/10.1038/s41893- 019- 0454- 4
dc.identifier.citedreferenceLaw, B. E., Hudiburg, T. W., Berner, L. T., Kent, J. J., Buotte, P. C., & Harmon, M. E. ( 2018 ). Land use strategies to mitigate climate change in carbon dense temperate forests. Proceedings of the National Academy of Sciences of the United States of America, 115 ( 14 ), 3663 - 3668. https://doi.org/10.1073/pnas.1720064115
dc.identifier.citedreferenceLeuzinger, S., & Thomas, R. Q. ( 2011 ). How do we improve Earth system models? Integrating Earth system models, ecosystem models, experiments and long- term data. New Phytologist, 191 ( 1 ), 15 - 18. https://doi.org/10.1111/j.1469- 8137.2011.03778.x
dc.identifier.citedreferenceLi, X., & Xiao, J. ( 2019 ). A global, 0.05- degree product of solar- induced chlorophyll fluorescence derived from OCO- 2, MODIS, and reanalysis data. Remote Sensing, 11 ( 5 ), 517. https://doi.org/10.3390/rs11050517
dc.identifier.citedreferenceLombardozzi, D. L., Bonan, G. B., Smith, N. G., Dukes, J. S., & Fisher, R. A. ( 2015 ). Temperature acclimation of photosynthesis and respiration: A key uncertainty in the carbon cycle- climate feedback. Geophysical Research Letters, 42 ( 20 ), 8624 - 8631. https://doi.org/10.1002/2015GL065934
dc.identifier.citedreferenceLombardozzi, D. L., Levis, S., Bonan, G. B., Hess, P. G., & Sparks, J. P. ( 2015 ). The influence of chronic ozone exposure on global carbon and water cycles. Journal of Climate, 28 ( 1 ), 292 - 305. https://doi.org/10.1175/JCLI- D- 14- 00223.1
dc.identifier.citedreferenceLombardozzi, D. L., Levis, S., Bonan, G. B., & Sparks, J. P. ( 2012 ). Predicting photosynthesis and transpiration responses to ozone: Decoupling modeled photosynthesis and stomatal conductance. Biogeosciences, 9 ( 8 ), 3113 - 3130. https://doi.org/10.5194/bg- 9- 3113- 2012
dc.identifier.citedreferenceLombardozzi, D. L., Sparks, J. P., & Bonan, G. B. ( 2013 ). Integrating O 3 influences on terrestrial processes: Photosynthetic and stomatal response data available for regional and global modeling. Biogeosciences, 10 ( 11 ), 6815 - 6831. https://doi.org/10.5194/bg- 10- 6815- 2013
dc.identifier.citedreferenceMarín- Spiotta, E., Barnes, R. T., Berhe, A. A., Hastings, M. G., Mattheis, A., Schneider, B., & Williams, B. M. ( 2020 ). Hostile climates are barriers to diversifying the geosciences. Advances in Geosciences, 53, 117 - 127. https://doi.org/10.5194/adgeo- 53- 117- 2020
dc.identifier.citedreferenceMartin, L. J., Blossey, B., & Ellis, E. ( 2012 ). Mapping where ecologists work: Biases in the global distribution of terrestrial ecological observations. Frontiers in Ecology and the Environment, 10 ( 4 ), 195 - 201. https://doi.org/10.1890/110154
dc.identifier.citedreferenceMattheis, A., Murphy, M., & Marin- Spiotta, E. ( 2019 ). Examining intersectionality and inclusivity in geosciences education research: A synthesis of the literature 2008- 2018. Journal of Geoscience Education, 67 ( 4 ), 505 - 517. https://doi.org/10.1080/10899995.2019.1656522
dc.identifier.citedreferenceMedlyn, B. E., Zaehle, S., De Kauwe, M. G., Walker, A. P., Dietze, M. C., Hanson, P. J., Hickler, T., Jain, A. K., Luo, Y., Parton, W., Prentice, I. C., Thornton, P. E., Wang, S., Wang, Y.- P., Weng, E., Iversen, C. M., McCarthy, H. R., Warren, J. M., Oren, R., & Norby, R. J. ( 2015 ). Using ecosystem experiments to improve vegetation models. Nature Climate Change, 5 ( 6 ), 528 - 534. https://doi.org/10.1038/nclimate2621
dc.identifier.citedreferenceMercado, L. M., Medlyn, B. E., Huntingford, C., Oliver, R. J., Clark, D. B., Sitch, S., Zelazowski, P., Kattge, J., Harper, A. B., & Cox, P. M. ( 2018 ). Large sensitivity in land carbon storage due to geographical and temporal variation in the thermal response of photosynthetic capacity. New Phytologist, 218 ( 4 ), 1462 - 1477. https://doi.org/10.1111/nph.15100
dc.identifier.citedreferenceMetcalfe, D. B., Hermans, T. D. G., Ahlstrand, J., Becker, M., Berggren, M., Björk, R. G., Björkman, M. P., Blok, D., Chaudhary, N., Chisholm, C., Classen, A. T., Hasselquist, N. J., Jonsson, M., Kristensen, J. A., Kumordzi, B. B., Lee, H., Mayor, J. R., Prevéy, J., Pantazatou, K., - ¦ Abdi, A. M. ( 2018 ). Patchy field sampling biases understanding of climate change impacts across the Arctic. Nature Ecology & Evolution, 2 ( 9 ), 1443 - 1448. https://doi.org/10.1038/s41559- 018- 0612- 5
dc.identifier.citedreferenceMorales, N., Bisbee O’Connell, K., McNulty, S., Berkowitz, A., Bowser, G., Giamellaro, M., & Miriti, M. N. ( 2020 ). Promoting inclusion in ecological field experiences: Examining and overcoming barriers to a professional rite of passage. Bulletin of the Ecological Society of America, 101 ( 4 ). https://doi.org/10.1002/bes2.1742
dc.identifier.citedreferenceMorford, S. L., Houlton, B. Z., & Dahlgren, R. A. ( 2011 ). Increased forest ecosystem carbon and nitrogen storage from nitrogen rich bedrock. Nature, 477 ( 7362 ), 78 - 81. https://doi.org/10.1038/nature10415
dc.identifier.citedreferenceNancarrow, S. A., Booth, A., Ariss, S., Smith, T., Enderby, P., & Roots, A. ( 2013 ). Ten principles of good interdisciplinary team work. Human Resources for Health, 11, 19. https://doi.org/10.1186/1478- 4491- 11- 19
dc.identifier.citedreferenceO’Rourke, M., Crowley, S., Eigenbrode, S. D., & Wulfhorst, J. D. ( 2013 ). Enhancing communication & collaboration in interdisciplinary research. SAGE Publications.
dc.identifier.citedreferencePrentice, I. C., Dong, N., Gleason, S. M., Maire, V., & Wright, I. J. ( 2014 ). Balancing the costs of carbon gain and water transport: Testing a new theoretical framework for plant functional ecology. Ecology Letters, 17 ( 1 ), 82 - 91. https://doi.org/10.1111/ele.12211
dc.identifier.citedreferencePrentice, I. C., Liang, X., Medlyn, B. E., & Wang, Y.- P. ( 2015 ). Reliable, robust and realistic: The three R’s of next- generation land- surface modelling. Atmospheric Chemistry and Physics, 15, 5987 - 6005. https://doi.org/10.5194/acp- 15- 5987- 2015
dc.identifier.citedreferenceReed, S. C., Yang, X., & Thornton, P. E. ( 2015 ). Incorporating phosphorus cycling into global modeling efforts: A worthwhile, tractable endeavor. New Phytologist, 208 ( 2 ), 324 - 329. https://doi.org/10.1111/nph.13521
dc.identifier.citedreferenceRichardson, L. F. ( 1922 ). Weather prediction by numerical process. Cambridge University Press.
dc.identifier.citedreferenceRogers, A., Medlyn, B. E., & Dukes, J. S. ( 2014 ). Improving representation of photosynthesis in Earth System Models. New Phytologist, 204 ( 1 ), 12 - 14. https://doi.org/10.1111/nph.12972
dc.identifier.citedreferenceSagan, C., Toon, O. B., & Pollack, J. B. ( 1979 ). Anthropogenic albedo changes and the Earth’s climate. Science, 206 ( 4425 ), 1363 - 1368. https://doi.org/10.1126/science.206.4425.1363
dc.identifier.citedreferenceSato, N., Sellers, P. J., Randall, D. A., Schneider, E. K., Shukla, J., Kinter, J. L., Hou, Y.- T., & Albertazzi, E. ( 1989 ). Effects of implementing the Simple Biosphere Model in a General Circulation Model. Journal of the Atmospheric Sciences, 46 ( 18 ), 2757 - 2782. https://doi.org/10.1175/1520- 0469(1989)046<2757:EOITSB>2.0.CO;2
dc.identifier.citedreferenceSchneider, S. H., & Dickinson, R. E. ( 1974 ). Climate modeling. Reviews of Geophysics, 12 ( 3 ), 447. https://doi.org/10.1029/rg012i003p00447
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.