CliMA Publications

Submitted/in press

  • de Jong, E.K., Singer, C.E., Azimi, S., Bartman, P., Derlatka, K., Dula, I., Jaruga, A., Mackay, J.B., Ward, R.X., Arabas, S., 2022: New developments in PySDM and PySDM-examples v2: collisional breakup, immersion freezing, dry aerosol initialization, and adaptive time-stepping. Journal of Open Source Software, submitted.
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  • de Jong, E., Bischoff, T., Nadim, A., Schneider, T., 2022: Spanning the gap from bulk to bin: a novel spectral microphysics method. Journal of Advances in Modeling Earth Systems, in review.
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  • Wang, Y., Frankenberg, C., 2022: Technical note: common ambiguities in plant hydraulics. Biogeosciences, in review.
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  • Wang, Y., Braghiere, R.K., Longo, M., Norton, A.J., Köhler, P., Doughty, R., Yin, Y., Bloom, A.A., Frankenberg, C., 2022: Modeling global carbon and water fluxes and hyperspectral canopy radiative transfer simultaneously using a next generation land surface model-CliMA Land. Journal of Advances in Modeling Earth Systems, in review.
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  • Gallet, B., Miquel, B., Hadjerci, G., Burns, K., Flierl, G., Ferrari, R., 2022: Transport and emergent stratification in the equilibrated Eady model: the vortex gas scaling regimeJournal of Fluid Mechanics, submitted.
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  • Huang, D.Z., Huang J., Reich, S., Stuart, A.M., 2022: Efficient derivative-free Bayesian inference for large-scale inverse problems. Inverse Problems, submitted.
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  • de Hoop, M.V., Kovachki, N.B., Nelsen, N.H., Stuart, A.M., 2021: Convergence rates for learning linear operators from noisy data. arXiv pre-print arXiv:2108.12515, in review.
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  • de Hoop, M.V., Huang, D.Z., Qian, E., Stuart, A.M., 2022: The cost-accuracy trade-off in operator learning with neural networks. arXiv pre-prints arXiv:2203.13181, submitted.
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  • Lopez-Gomez, I., Christopoulos, C., Langeland Ervik, H.L., Dunbar, O.R.A., Cohen, Y., Schneider, T., 2022: Training physics-based machine-learning parameterizations with gradient-free ensemble Kalman methods. Journal of Advances in Modeling Earth Systems, accepted.
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  • Bieli, M., Dunbar, O.R.A., de Jong, E.K., Jaruga, A., Schneider, T., Bischoff, T., 2022: An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using “Cloudy”, a new n-moment bulk microphysics scheme. Journal of Advances in Modeling Earth Systems, in review.
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  • Dunbar, O.R.A., Howland, M.F., Schneider, T., Stuart, A.M., 2022: Ensemble-based experimental design for targeted high-resolution simulations to inform climate models. Journal of Advances in Modeling Earth Systems, submitted.
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  • 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., Baker, I., Williams, M., Shi, M., Zhang, Y., Qiu, B., 2021: Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model-data fusion framework. Biogeosciences, in review.
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  • Schneider, T., Stuart, A.M., Wu, J., 2022; Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data. arXiv pre-prints arXiv:2007.06175, accepted.
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  • Duncan, A.B., Stuart, A.M., Wolfram, M.-T., 2021; Ensemble inference methods for models with noisy and expensive likelihoods. arXiv pre-prints arXiv:2104.03384, submitted.
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  • Carrillo, A., Hoffmann, F., Stuart, A.M., Vaes, U., 2021: Consensus based sampling. arXiv pre-prints arXiv:2106.02519, submitted.
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  • Levine, M.E., Stuart, A.M., 2021: A framework for machine learning of model error in dynamical systems. arXiv pre-prints arXiv:2107.06658, submitted.
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  • Ramadhan, A., Marshall, J. , Souza, A., Wagner, G.L., Ponnapati, M., Rackauckas, C., 2021: Capturing missing physics in climate model parameterizations using neural differential equations. arXiv pre-prints arXiv:2010.12559, submitted.
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2022

  • Wang, Y., Köhler P., Braghiere, R.K., Longo, M., Doughty, R., Bloom, A.A., Frankenberg, C., 2022: GriddingMachine: a database and software for earth system modeling at global and regional scales. Scientific Data, 9, s41597-022-01346-x.
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  • Bartman, P., Bulenok, O., Górski, K., Jaruga, A., Łazarski, G., Olesik, M.A., Piasecki, B., Singer, C.E., Talar, A., Arabas, S., 2022: PySDM v1: particle-based cloud modeling package for warm-rain microphysics and aqueous chemistry. Journal of Open Source Software, 7, 3219.
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  • Huang, D.Z., Schneider, T., Stuart, A.M., 2022: Iterated Kalman methodology for inverse problems. Journal of Computational Physics, 463, 111262.
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  • Zhang, X.Schneider, T.Shen, Z.Pressel, K.G., Eisenman, I., 2022: Seasonal cycle of idealized polar clouds: Large eddy simulations driven by a GCM. Journal of Advances in Modeling Earth Systems14, e2021MS002671.
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  • Yang, Y., Bloom, A.A., Ma, S., Levine, P., Norton, A., Parazoo, N.C., Reager, J.T., Worden, J., Quetin, G.R., Smallman, T.L., Williams, M., Xu, L., Saatchi, S., 2021: CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimatesGeoscientific Model Development, 15, 1789-1802.
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  • Schneider, T., Dunbar, O.R.A., Wu, J., Böttcher, L., Burov, D., Garbuno-Iñigo, A., Wagner, G.L., Pei, S., Daraio, C., Ferrari, R., Shaman, J., 2022: Epidemic management and control through risk-dependent individual contact interventions. PLOS Computational Biology, 18, e1010171.
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  • Sridhar, A., Tissaou, Y., Marras, S., Shen, Z., Kawczynski, C., Byrne, S., Pamnany, K., Waruszewski, M., Gibson, T.H., Kozdon, J.E., Churavy, V., Wilcox, L.C., Giraldo, F.X., Schneider, T., 2022: Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs. Geoscientific Model Development, 15, 6259-6284.
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  • Howland, M.F., Dunbar, O.R.A., Schneider, T., 2022: Parameter uncertainty quantification in an idealized GCM with a seasonal cycleJournal of Advances in Modeling Earth Systems, 14, e2021MS002735.
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  • Wang, Y., Frankenberg, C., 2022: On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes. Biogeosciences, 19, 29-45.
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  • Shen, Z., Sridhar, A., Tan, Z., Jaruga, A., Schneider, T., 2022: A library of large-eddy simulations forced by global climate modelsJournal of Advances in Modeling Earth Systems,
    14, e2021MS002631.
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2021

  • Gallet, B., Ferrari, R., 2021: A quantitative scaling theory for meridional heat transport in planetary atmospheres and oceansAGU Advances, 2, e2020AV000362.
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  • Massoud, E.C., Bloom, A.A., Longo, M., Reager, J.T., Levine, P.A., Worden, J.R., 2022: Information content of soil hydrology in the Amazon as informed by GRACE. Hydrology and Earth System Sciences, 26, 1407–1423.
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  • 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., Bloom, A.A., Williams, M., Konings, A.G., 2021: Optimal model complexity for terrestrial carbon cycle prediction. Biogeosciences, 18, 2727–2754.
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  • Bonan, D.B., Schneider, T., Eisenman, I., Wills, R.C.J., 2021: Constraining the date of a seasonally ice-free Arctic using a simple model. Geophysical Research Letters, 48, e2021GL094309.
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  • Yuval, J., O’Gorman, P.A., Hill, C.N., 2021: Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision. Geophysical Research Letters, 48, e2020GL091363.
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  • Wang, Y., Köhler, P., He, L., Doughty, R., Braghiere, R., Wood, J.D., Frankenberg, C., 2021: Testing stomatal models at stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1). Geoscientific Model Development, 14, 6741-6763.
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  • Nelsen, N.H., Stuart, A.M., 2021: The random feature model for input-output maps between Banach spaces. SIAM Journal on Scientific Computing, 43, A3212–A3243.
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  • Dunbar, O.R.A., Dunlop, M.M., Elliott, C.M., Hoang, V.H., Stuart, A.M., 2021: Reconciling Bayesian and perimeter regularization for binary inversion. SIAM Journal on Scientific Computing, 42, A1984−A2013.
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  • Byrne, S., Wilcox, L.C., Churavy, V., 2021: MPI.jl: Julia bindings for the Message Passing Interface. JuliaCon Proceedings, 1, 68.
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  • Schneider, T., Jeevanjee, N., Socolow, R., 2021: Accelerating progress in climate science. Physics Today, 74, 44-51.
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  • Christopoulos, C., Schneider, T., 2021: Assessing biases and climate implications of the diurnal precipitation cycle in climate models. Geophysical Research Letters, 48, e2021GL093017.
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  • Ming, Y., Loeb, N.G., Lin, P., Shen, Z., Naik, V., Singer, C.E., et al., 2021:. Assessing the influence of COVID-19 on the shortwave radiative fluxes over the East Asian marginal seas. Geophysical Research Letters, 48, e2020GL091699.
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  • Arnscheidt, C.W., Marshall, J., Dutrieux, P., Rye, C.D., Ramadhan, A., 2021: On the settling depth of meltwater escaping from beneath Antarctic ice shelves. Journal of Physical Oceanography, 51, 2257–2270.
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  • Wagner, G.L., Chini, G.P., Ramadhan, A., Gallet, B., Ferrari, R., 2021: Near-inertial waves and turbulence driven by the growth of swell. Journal of Physical Oceanography, 51, 1337-1351.
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  • Braghiere, R.K., Wang, Y., Doughty, R., Sousa, D., Magney, T., Widlowski, J., et al., 2021: Accounting for canopy structure improves hyperspectral radiative transfer and sun-induced chlorophyll fluorescence representations in a new generation Earth System model. Remote Sensing of Environment261, 112497.
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  • Cheng, R., Novak, L., Schneider, T., 2021: Predicting the interannual variability of California’s total annual precipitation. Geophysical Research Letters, 48, e2020GL091465.
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  • Wang, Y., Anderegg, W.R.L., Venturas, M.D., Trugman, A.T., Yu, K., Frankenberg, C., 2021: Optimization theory explains nighttime stomatal responses. New Phytologist230, 1550-1561.
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  • Dunbar, O.R.A., Garbuno-Inigo, A., Schneider, T., Stuart, A.M., 2021: Calibration and uncertainty quantification of convective parameters in an idealized GCM. Journal of Advances in Modeling Earth Systems, 13, e2020MS002454.
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  • Singer, C.E., Lopez-Gomez, I., Zhang, X., Schneider, T., 2021: Top-of-atmosphere albedo bias from neglecting three-dimensional radiative transfer through cloudsJournal of Atmospheric Sciences, 78, 4052-4069.
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  • Burov, D., Giannakis, D., Manohar, K., Stuart, A.M., 2021: Kernel analog forecasting: multiscale test problems. SIAM Journal on Multiscale Modeling and Simulation, 19, 1011–1040.
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  • Schneider, T., Stuart, A.M., Wu, J., 2021: Learning stochastic closures using ensemble Kalman inversion. Transactions of Mathematics and its Applications, 5, 1-31.
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2020

  • 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., Quetin, G.R., Smallman, T.L., Exbrayat, J.-F., Yin, Y., Saatchi, S.S., Williams, M., Schimel, D.S., 2020: Lagged effects regulate the inter-annual variability of the tropical carbon balance. Biogeosciences, 17, 6393–6422.
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  • Bhamidipati, N., Souza, A.N., Flierl, G.R., 2020: Turbulent mixing of a passive scalar in the ocean mixed layer. Ocean Modelling, 149, 101615.
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  • Schneider, T., Kaul, C.M., Pressel, K.G., 2020: Solar geoengineering may not prevent strong warming from direct effects of CO2 on stratocumulus cloud cover. Proceedings of the National Academy of Sciences, 117, 30179-30185.
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  • Lopez-Gomez, I., Cohen, Y., He, J., Jaruga, A., Schneider, T., 2020: A generalized mixing length closure for eddy-diffusivity mass-flux schemes of turbulence and convectionJournal of Advances in Modeling Earth Systems, 12, e2020MS002161.
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  • Souza, A.N., Wagner, G.L., Ramadhan, A., Allen, B., Churavy, V., Schloss, J., Campin, J., Hill, C., Edelman, A., Marshall, J., Flierl G., Ferrari, R., 2020: Uncertainty quantification of ocean parameterizations: application to the K-Profile-Parameterization for penetrative convection. Journal of Advances in Modeling Earth Systems, 12, e2020MS002108.
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  • Gallet, B., Ferrari, R., 2020: The vortex gas scaling regime of baroclinic turbulence. Proceedings of the National Academy of Sciences, 117, 4491-4497.
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  • Cohen, Y., Lopez-Gomez, I., Jaruga, A., He, J., Kaul, C.M., Schneider, T., 2020: Unified entrainment and detrainment closures for extended eddy-diffusivity mass-flux schemes. Journal of Advances in Modeling Earth Systems, 12, e2020MS002162.
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  • Cleary, E., Garbuno-Inigo, A., Lan, S., Schneider, T., Stuart, A.M., 2020: Calibrate, emulate, sample. Journal of Computational Physics, 424, 109716.
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  • Shen, Z., Pressel, K.G., Tan, Z., Schneider, T., 2020: Statistically steady state large-eddy simulations forced by an idealized GCM: 1. Forcing framework and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12, e2019MS001814.
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  • Ramadhan, A., Wagner, G., Hill, C., Campin, J.M., Churavy, V., Besard, T., et al., 2020: Oceananigans.jl: fast and friendly geophysical fluid dynamics on GPUs. Journal of Open Source Software, 5, doi 10.21105/joss.02018.
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  • Garbuno-Inigo, A., Hoffmann, F. Li, W., Stuart, A.M., 2020: Interacting Langevin diffusions:
    gradient structure and ensemble Kalman sampler. SIAM Journal on Applied Dynamical Systems, 19, 412-441.
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  • Novak, L., Schneider, T., Ait-Chaalal, F., 2020: Midwinter suppression of storm tracks in an idealized zonally symmetric setting. Journal of the Atmospheric Sciences. 77, 297-313.
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2019

2018

  • Callies, J., Ferrari, R., 2018: Baroclinic instability in the presence of convection. Journal of Physical Oceanography, 48, 1543–1553.
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  • Stuart, A.M., Teckentrup, A.L., 2018: Posterior consistency for Gaussian process approximations of Bayesian posterior distributions. Mathematics of Computation, 87, 721-753.
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  • Tan, Z., Kaul, C.M., Pressel, K.G., Cohen, Y., Schneider, T., Teixeira, J., 2018: An extended eddy-diffusivity mass-flux scheme for unified representation of subgrid-scale turbulence and convection. Journal of Advances in Modeling Earth Systems, 10, 770-800.
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  • Callies, J., Ferrari, R., 2018: Note on the rate of restratification in the baroclinic spindown of fronts. Journal of Physical Oceanography, 48, 45–60.
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2017

  • Schneider, T., Lan, S., Stuart, A.M., Teixeira, J., 2017: Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations. Geophysical Research Letters, 44, 12396–12417.
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  • Schillings, C., Stuart, A.M., 2017: Analysis of the ensemble Kalman filter for inverse problems. SIAM Journal on Numerical Analysis, 55, 1264-1290.
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  • Iglesias, M.A., Lin, K., Lu, S., Stuart, A.M., 2017: Filter based methods for statistical linear inverse problems. Communications in Math. Sciences, 15, 1867-1896.
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  • Schneider, T., Teixeira, J., Bretherton, C.S., Brient, F., Pressel, K.G., Schär, C., Siebesma, A.P., 2017: Climate goals and computing the future of clouds. Nature Climate Change, 7, 3-5.
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