We study how the planetary water cycle and climate work, and how it may change in the future, focusing on cloud physics and moist convection processes. Our tools are next-gen global atmospheric simulations, ocean-coupled climate dynamics, high-performance computing, and machine learning for turbulent process emulation and neural-network assisted inquiry.
*denotes led by the Computational Clouds and Climate Lab; underlined are: PI-advised (P)ostdoctoral scholar or project scientist, (G)raduate student, and/or (U)ndergraduate student. Following atmospheric sciences convention, PI tends to occupy second author slot for closely advised work.
* Yu, S. (P), M. Pritchard, P.-L. Ma, B. Singh, & S. Silva: Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset, submitted [preprint].
* Peng, L. (P), M. Pritchard, P. Blossey, W. Hannah, C. Bretherton, C. Teri, & A. Jenney (P): Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics, submitted [preprint].
* Liu, N., M. Pritchard, A. Jenney, W. Hannah. Understanding Precipitation Bias Sensitivities in E3SM-Multi-scale Modeling Framework from a Dilution Framework, submitted [preprint].
* Mooers, G. (G), T. Beucler, M. Pritchard, & M. Stephan. An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes [link].
Connolley C., E. A. Barnes, P. Hassanzadeh and M. Pritchard, Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability (submitted Dec. 2022).
* Jenney, A. M. (P), S. L. Ferretti (G), M. Pritchard. Vertical resolution impacts explicit simulation of deep convection, in review [preprint].
* Mooers, G. (G), M. Pritchard, T. Beucler (P), P. Srivastava, H. Mangipudi, L. Peng (P), P. Gentine & S. Mandt. Comparing storm resolving models and climates via unsupervised machine learning, in review [preprint].
* Beucler, T. (P), M. Pritchard, J. Yuval, A. Gupta, L. Peng (P), S. Rasp, F. Ahmed, P. O’Gorman, J. Neelin, N. Lutsko, P. Gentine: Climate-invariant machine learning, in revision [preprint].
Graubner, A., K. Azizzadenesheli, J. Pathak, M. Mardani, M. Pritchard, K. Kashinath, & A. Anandkumar, Calibration of Large Neural Weather Models, NeurIPS workshop, 2022. [link].
57. Behrens, G., T. Beucler, P. Gentine, F. Iglesias-Suarez, M. Pritchard & V. Eyring. Non-linear dimensionality reduction with a variational encoder decoder to understand convective processes in climate models, Journal of Advances in Modeling Earth Systems, 14, 2022 [link].
* 56. Peng, L. (P), M. Pritchard, W. Hannah, P. Blossey, P. Worley and C. Bretherton: Load balancing intense physics calculations to embed regionalized high-resolution cloud resolving models in the E3SM and CESM climate models, Journal of Advances in Modeling Earth Systems, 14, 2022 [link].
55. Ma, P., and co-authors: Better calibration of cloud parameterizations and subgrid effects increases the fidelity of E3SM Atmosphere Model version 1, Geoscientific Model Development, 15, 2881-2916, 2022 [link].
54. Harrop, B., M. Pritchard., H. Parishani (P), A. Gettelman, S. Hagos, P. Lauritzen, R. Leung, J. Lu, K. Pressel, K. Sakaguchi: Conservation of dry air, water, and energy in CAM and its potential impact on tropical rainfall, Journal of Climate, 35(9), 2895-2917, 2022 [link].
* H. Mangipudi, G. Mooers, M. Pritchard, T. Beucler, & S. Mandt, Analyzing high-resolution clouds and convection using multi-channel VAEs (2021), Proceedings of the 35th Conference on Neural Information Processing (NeurIPS), 2021 [link].
* 53. Mooers, G. (G), M. Pritchard, T. Beucler (P), J. Ott (G), G. Yacalis (G), P. Baldi and P. Gentine: Assessing the potential of deep learning for emulating cloud superparameterization in climate models with real-geography boundary conditions, Journal of Advances in Modeling Earth Systems, 2021 [link].
* 52. Hendrickson, J. (G), C. Terai, M. Pritchard and P. Caldwell: Lower Tropospheric Processes: A Control on the Global Mean Precipitation Rate, Geophysical Research Letters, 48, 2021 [link].
* 51. Beucler, T. (P), M. Pritchard, S. Rasp, P. Gentine, J. Ott and P. Baldi: Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems, Physical Review Letters, 126, 2021 [link]
* Mooers, G. (G), J. Tuyls, S. Mandt, M. Pritchard and T. Beucler (P): Generative Modeling for Atmospheric Convection, Proceedings of the 10th International Conference on Climate Informatics (CI2020), 2020 [link].
50. Mamalakis, A., J. T. Randerson, J.-Y. Yu, M. Pritchard, G. Magnusdottir, P. Smyth, P. Levine, S. Yu and E. Foufoula-Georgiou: Zonally opposing shifts of the intertropical convergence zone in response to climate change, Nature Climate Change, 11, 2020 [link].
* 49. Terai, C. (P), M. Pritchard, P. Blossey and C. Bretherton: The impact of resolving subkilometer processes on aerosol-cloud interactions in global model simulations, J. Adv. Model. Earth Sys., 12, 2020. [link]
48. Brenowitz, N., T. Beucler (P), M. Pritchard, and C. S. Bretherton: Interpreting and stabilizing machine-learning parameterizations of convection, J. Atmos. Sci., 2020. [link]
47. Ott, J., M. Pritchard, N. Best, E. Linstead, M. Curcic, and P. Baldi: A Fortran-Keras Deep Learning Bridge for Scientific Computing. Scientific Programming, 2020. [link]
*46. Fowler, M. (G) and M. Pritchard, Regional MJO modulation of West Pacific tropical cyclones driven by multiple transient controls, Geophysical Research Letters, 47 (11), 2020. [link]
45. Gutowski et al., The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information, Bulletin of the American Meteorological Society, 2020. [link]
44. Hannah, W., C. Jones, B. Hillman, M. Norman, D. Bader, M. Taylor, R. Leung, M. Pritchard, M. Branson, G. Lin, K. Pressel, J. Lee. Initial Results from the Super-Parameterized E3SM, J. Adv. Model. Earth Sys., 12, 2020 [link].
43. * Fowler, M. (G), G. Kooperman, J. T. Randerson and M. Pritchard. The effect of plant-physiological responses to rising CO2 on global streamflow, Nature Climate Change, 9, 2019. [link]
42. Beucler, T. (P), T. Abbott, T. Cronin and M. Pritchard. Linking Convective Self-Aggregation in Idealized Models to Observed Moist Static Energy Variability near the Equator, Geophysical Research Letters, 46, 2019. [link]
41. * Beucler, T. (P), S. Rasp, M. Pritchard and P. Gentine (2019). Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling, Climate Change and Artificial Intelligence workshop of the 2019 International Conference on Machine Learning, arXiv preprint arXiv:1906.06622, 2019. [link]
40. * Langenbrunner, B. (P), M. Pritchard, G. Kooperman and J. Randerson. Why does Amazon precipitation decrease when tropical forests respond to increasing CO2?, Earth’s Future, , 7, 450– 468, 2019. [link]
39. * Yu, S. (G) and M. S. Pritchard. A strong role for the AMOC in partitioning global energy transport and shifting ITCZ position in response to latitudinally discrete solar forcing in the CESM1.2, J. Climate, 32, 2019. [link]
38. Levine. P., M. Xu, F. M. Hoffman, Y. Chen, M. S. Pritchard and J. T. Randerson. Soil moisture variability intensifies and prolongs Amazon temperature and carbon cycle response to El Niño-Southern Oscillation, J. Climate, 32, 2018. [link]
37. * Parishani, H. (P), M. S. Pritchard, C. S. Bretherton, C. R. Terai (P), M. C. Wyant, M. Khairoutdinov and B. Singh. Insensitivity of the cloud response to surface warming under radical changes to boundary layer turbulence and cloud microphysics: Results from the UltraParameterized CAM, J. Adv. Model. Earth Sys., 2018 [link].
36. Kooperman, G., M. Fowler (G), F. Hoffman, C. Koven, K. Lindsay, M. Pritchard, A. Swann and J. Randerson. Plant-physiological responses to rising CO2 modify daily runoff intensity with implications for global-scale flood risk assessment, Geophys. Res. Lett., 2018 [link]
35. * Sun, J. (P) and M. S. Pritchard. Effects of explicit convection on land surface air temperature and land-atmosphere coupling in the thermal feedback pathway, J. Adv. Model. Earth Sys., 2018 [link]
34. Zeng, X., D. Klocke, B. J. Shipway, M. S. Singh, I. Sandu, W. Hannah, P. Bogenschutz, Y. Zhang, H. Morrison, M. S. Pritchard, and C. Rio, 2018. Community Efforts in Understanding and Modeling Atmospheric Processes. Community efforts in understanding and modeling atmospheric processes, Bull. Amer. Met. Soc., 2018. [link]
33.* Rasp, S. (Visiting G), M. S. Pritchard, and P. Gentine. Deep learning to represent sub-grid processes in climate models, PNAS, 2018. [link]
32. Gentine, P., M. S. Pritchard, S. Rasp(Visiting G), G. Reinaudi and G. Yacalis (G). Could machine learning break the convection parameterization deadlock?, Geophys. Res. Lett., 45, 2018. [link]
31. Kooperman, G. K, Y. Chen, F. M. Hoffman, C. D. Koven, K. Lindsay, M. S. Pritchard, A. L. S. Swann, and J. T. Randerson, 2018. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land, Nature Climate Change, 8, 434–440, 2018. [link]
30.* Kooperman, G. J.(P), M. S. Pritchard, M. S., T. A. O’Brien and B. W. Timmermans. Rainfall from resolved rather than parameterized processes better represents the present‐day and climate change response of moderate rates in the community atmosphere model. J. Adv. Model. Earth Syst., 10, 2018. [link]
29.* Qin, H. (G), M. S. Pritchard,G. J. Kooperman (P)and H. Parishani (P), 2018. Global Effects of SuperParameterization on Hydro-Thermal Land–Atmosphere Coupling on Multiple Timescales, J. Adv. Model. Earth Syst., 10, 2018. [link]
28.* Fowler, M. (G), M. S. Pritchardand G. J. Kooperman (P). Assessing the impact of Californian and Indian irrigation on precipitation in the irrigation-enabled Community Earth System Model, J. Hydromet.,19(2), 427-443, 2018. [link]
27. Woelfle, M., S. Yu, (G)., C. S. Bretherton and M. S. Pritchard. Sensitivity of coupled tropical Pacific model biases to convective parameterization in CESM1, J. Adv. Model. Earth Syst., 10, 2018. [link]
26.* Parishani, H. (P).,M. S. Pritchard, C. S. Bretherton, M. C. Wyant and M. Khairoutdinov. Towards low cloud-permitting cloud superparameterization with explicit boundary layer turbulence, J. Adv. Model Earth Syst., 9, 1542–1571, 2017. [link]
25.* Kooperman, G. J (P).,M. S. Pritchard, M. A. Burt, M. D. Branson, and D. A. Randall. Impacts of cloud superparameterization on projected daily rainfall intensity climate changes in multiple versions of the Community Earth System Model, J. Adv. Model. Earth Syst., 8, 2016. [link]
24.* Sun, S. (P)and M. S. Pritchard. Effects of explicit convection on global land-atmosphere coupling in the superparameterized CAM, J. Adv. Model. Earth Syst., 8, 1248–1269, 2016. [link]
23.* Elliott, E. J. (U), S. Yu (G), G. Kooperman (P), H. Morrison , M. Wang and M. S. Pritchard. Sensitivity of summer ensembles of superparameterized US mesoscale convective systems to cloud resolving model microphysics and grid configuration, J. Adv. Model. Earth Syst.,2016.[link]
22.* Pritchard, M. S. and D. Yang. Response of the superparameterized Madden-Julian Oscillation to extreme climate and basic state variation challenges a moisture mode view. J. Climate, 29, 4995-5008, 2016. [link]
21.* Kooperman, G. J. (P), M. S. Pritchard, M. A. Burt, M. D. Branson, and D. A. Randall. Robust effects of cloud super-parameterization on simulated daily rainfall intensity statistics across multiple versions of CESM. J. Adv. Model. Earth Syst., 8, 2016. [link]
20. Benedict, J. J., M. S. Pritchard, and W. D. Collins. Sensitivity of MJO propagation to a robust positive Indian Ocean dipole event in the superparameterized CAM, J. Adv. Model. Earth Syst., 7, 1901–1917, 2015. [link]
19. * Yu, S. (G) and M. S. Pritchard. The effect of large-scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM, J. Adv. Model. Earth Syst., 7, 1977–1996, 2015. [link]
18. Jones, C., C. S. Bretherton and M. S. Pritchard. Mean-state acceleration of cloud-resolving model simulations. J. Adv. Model. Earth Syst.,07, 2015. [link]
17. Klingaman, N. P., S. J. Woolnough, X. Jiang, D. Waliser, P. K. Xavier, J. Petch, M. Caian, C. Hannay, D. Kim, H.-Y. Ma, W. J. Merryfield, T. Miyakawa, M. S. Pritchard, J. A. Ridout, R. Roehrig, E. Shindo, F. Vitart, H. Wang, N. Cavanaugh, B. E. Mapes, A. Shelly, G. J. Zhang. Vertical structure and physical processes of the Madden-Julian Oscillation: Linking hindcast fidelity to simulated diabatic heating and moistening, J. Geophys. Res. Atm.120, 10, 4690-4717, 2015. [link]
16. Xavier, P.K., J. C. Petch, N. P. Klingaman, S. J. Woolnough, X. Jiang, D. E. Waliser, M. Caian, J. Cole, S. Hagos, C. Hannay, D. Kim, T. Miyakawa, M. S. Pritchard, R. Roehrig, E. Shindo, F. Vitart, H. Wang. Vertical structure and diabatic processes of the Madden-Julian Oscillation: Biases and uncertainties at short range, J. Geophys. Res. Atm.120, 10, 4749 -4763, 2015. [link]
15. Hannah, W, M. S. Pritchardand E. Maloney. Consequences of systematic model drift in DYNAMO MJO hindcasts with SP-CAM and CAM5, J. Adv. Model. Earth Syst., 07, 2015. [link]
14. Pritchard M. S., C. DeMott and C. S. Bretherton. Restricting 32–128 km horizontal scales hardly affects the MJO in the Superparameterized Community Atmosphere Model v.3.0 but the number of cloud-resolving grid columns constrains vertical mixing, J. Adv. Model. Earth Syst., 06, 2014. [link]
13. Kooperman, G., M. S. Pritchardand R. C. J. Somerville.The response of US summer rainfall to quadrupled CO2 climate change in conventional and superparameterized versions of the NCAR Community Atmosphere Model, J. Adv. Model. Earth Syst., 06, 2014. [link]
12. Zhao, Z., G. Kooperman, M. S. Pritchard, L. M. Russell and R. C. J. Somerville. Investigating impacts of forest fires in Alaska and western Canada on regional weather over the northeastern United States using CAM5 global simulations to constrain transport to a WRF-Chem regional domain, J. Geophys. Res. Atm., 199(12): 7515-7536, 2014. [link]
11. Pritchard, M. S.and C. S. Bretherton. Causal evidence that rotational moisture advection is critical to the superparameterized Madden-Julian Oscillation, J. Atmos. Sci., 71(2) 800-815, 2014. [link]
10. Kooperman, G., M. S. Pritchard and R. C. J. Somerville. Robustness and sensitivities of central U.S. summer convection in the superparameterized CAM: Multi-model intercomparison with a new regional EOF index, 2013. Geophys. Res. Lett., 40 (12), 3287-3291, 2013. [link]
9. Kooperman, G., M. S. Pritchard, S. Ghan, R. C. J. Somerville and L. M. Russell. Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5. J. Geophys. Res. Atm.., 117, D23204, 2012. [link]
8. Schmidt, J. M., P. J. Flatau. P. R. Harasti, R. D. Yates, R. Littleton, M. S. Pritchard, and co-authors. Radar observations of individual rain drops in the free atmosphere, Proceedings of the National Academy of Sciences, 2012. [link]
7. Zhao, Z, M. S. Pritchard, and L. M. Russell. Effects on precipitation, clouds, and temperature from long-range transport of idealized aerosol plumes in WRF-Chem simulations, J. Geophys. Res. Atm., 117 (D5), 2012. [link]
6. Pritchard, M. S., M. W. Moncrieff and R. C. J. Somerville. Orogenic propagating precipitation systems over the US in a global climate model with embedded explicit convection, J. Atmos. Sci. 68 (8), 1821-1840, 2011. [link]
5. Pritchard, M. S.and R. C. J. Somerville. Assessing the Diurnal Cycle of Precipitation in a Multi-Scale Climate Model, J. Adv. Model. Earth Syst., 1, 2009. [link]
4. Pritchard, M. S.and R. C. J. Somerville. Empirical orthogonal function analysis of the diurnal cycle of precipitation in a multi-scale climate model, Geophys. Res. Lett36 (5), 2009. [link]
3. Pritchard, M. S., A. B. G. Bush and S. J. Marshall. Interannual atmospheric variability affects continental ice sheet simulations on millennial time scales. J. Climate21 (22), 5976-5992, 2008. [link]
2. Pendlebury, D., T. G. Shepherd, M. S. Pritchard, and C. McLandress. Normal mode Rossby waves and their effects on chemical composition in the late summer stratosphere. Atmos. Chem. Phys., 8 (7), 1925-1935, 2008. [link]
1. Pritchard, M. S., A. B. G. Bush and S. J. Marshall. Neglecting ice-atmosphere interactions underestimates ice sheet melt in millennial-scale deglaciation simulations. Geophys. Res. Lett. 35 (1), L01503, 2008. [link]
My research is on advancing understanding of how the planetary water cycle works, and how it may change in the future, focusing especially on cloud physics and moist convection processes. My tools are a blend of next-generation global atmospheric simulation algorithms, theoretical climate dynamics, and high-performance computing. Projects are guided by the problems of interest, and are intentionally explorative of new potentially breakthrough physical algorithms that attempt to avoid traditional approximations of cloud physics in global climate simulations. Lately this has meant significant exploration of emerging tools in the data sciences such as deep machine learning for physical process emulation and neural-network assisted dynamical inquiry. I now hold a partial industry appointment: In July 2022 I began a 80% leave of absence from UCI to lead a new research group at NVIDIA as their Director of Climate Simulation Research, helping lead their Earth-2 climate simulation initiative.
March 2020 – Present
October 2021 – Present
September 2018 – Present
September 2020 – Present
September 2021 – Present
September 2022 – Present
164 Rowland Hall
University of California, Irvine
Irvine, CA 92697-4675