Research Interests

• Climate model development and validation
• Earth system model post-processing and evaluation tools
• Cloud microphysics and dynamics

Ph.D., Meteorology, Penn State University, 2012

B.S., Atmospheric Sciences, Nanjing Institute of Meteorology, 2007

Zhang, J, Bogenschutz, P., Tang, Q., Cameron-smith,P., Zhang, C. Leveraging Regional Mesh Refinement to Simulate Future Climate Projections for California Using the Simplified Convection Permitting E3SM Atmosphere Model Version 0.  Submitted to GMD, 2023

Fasullo, J., Golaz, J.-C., Caron, J., Rosenbloom, N., Meehl, G., Strand, W., Glanville, S., Stevenson, S., Molina, M., Shields, C., Zhang, C., Benedict, J., and Bartoletti, T.: An Overview of the E3SM version 2 Large Ensemble and Comparison to other E3SM and CESM Large Ensembles, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2310, 2023.

Zheng, X., Tao, C., Zhang, C., Xie, S., Zhang, Y., Xi, B., & Dong, X. (2023). Assessment of CMIP5 and CMIP6 AMIP simulated clouds and surface shortwave radiation using ARM observations over different climate regions. Journal of Climate, 36(24), 8475-8495.

Gong, Q., Zhang, C., Liang, X., Reshniak, V., Chen, J., Rangarajan, A., Ranka, S., Vidal, N., Wan, L., Ullrich, P. and Podhorszki, N., 2023, October. Spatiotemporally adaptive compression for scientific dataset with feature preservation–A case study on simulation data with extreme climate events analysis. In 2023 IEEE 19th International Conference on e-Science (e-Science) (pp. 1-10). IEEE.

Tang, Qi, …Zhang, C. et al. "The Fully Coupled Regionally Refined Model of E3SM Version 2: Overview of the Atmosphere, Land, and River." Geoscientific Model Development 16.13 (2023): 3953-3995.

Tao, C., Xie, S., Tang, S., Lee, J., Ma, H.Y., Zhang, C. and Lin, W., 2023. Diurnal cycle of precipitation over global monsoon systems in CMIP6 simulations. Climate Dynamics, 60(11-12), pp.3947-3968.

Zhang, C, et al. "The E3SM Diagnostics Package (E3SM Diags v2. 7): A Python-based Diagnostics Package for Earth System Models Evaluation." Geoscientific Model Development 15 (24), 9031-9056. (2022).

Golaz, Jean‐Christophe…Zhang C, et al. "The DOE E3SM Model Version 2: Overview of the physical model and initial model evaluation." Journal of Advances in Modeling Earth Systems 14.12 (2022).

Emmenegger, T., Kuo, Y. H., Xie, S., Zhang, C., Tao, C., & Neelin, J. D. (2022). Evaluating Tropical Precipitation Relations in CMIP6 Models with ARM data. Journal of Climate, 1-55.

Tang, S., Gleckler, P., Xie, S., Lee, J., Ahn, M. S., Covey, C., & Zhang, C. (2021). Evaluating the Diurnal and Semidiurnal Cycle of Precipitation in CMIP6 Models Using Satellite-and Ground-Based Observations. Journal of Climate, 34(8), 3189-3210.

Zhang, C., Xie, S., Tao, C., Tang, S., Emmenegger, T., Neelin, J. D., ... & Shaheen, Z. (2020). The ARM Data- Oriented Metrics and Diagnostics Package for Climate Models: A New Tool for Evaluating Climate Models with Field Data. Bulletin of the American Meteorological Society, 101(10), E1619-E1627.

Golaz, J. C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., ... & 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.

Phillips, T. J., Bonfils, C. J., & Zhang, C. (2019). Model consensus projections of US regional hydroclimates under greenhouse warming. Environmental Research Letters, 14(1), 014005.

Zhang, C., Xie, S., Klein, S. A., Ma, H. Y., Tang, S., Van Weverberg, K., ... & Petch, J. (2018). CAUSES: Diagnosis of the summertime warm bias in CMIP5 climate models at the ARM Southern Great Plains site. Journal of Geophysical Research: Atmospheres, 123(6), 2968-2992.

Van Weverberg, K., Morcrette, C. J., Petch, J., Klein, S. A., Ma, H. Y., Zhang, C., ... & Thieman, M. M. (2018). CAUSES: Attribution of surface radiation biases in NWP and climate models near the US Southern Great Plains. Journal of Geophysical Research: Atmospheres, 123(7), 3612-3644.

Morcrette, C. J., Van Weverberg, K., Ma, H. Y., Ahlgrimm, M., Bazile, E., Berg, L. K., .. ., Zhang, C. & Petch, J. (2018). Introduction to CAUSES: Description of weather and climate models and their near-surface temperature errors in 5 day hindcasts near the Southern Great Plains. Journal of Geophysical Research: Atmospheres, 123(5), 2655-2683.

Ma, H. Y., Klein, S. A., Xie, S., Zhang, C., Tang, S., Tang, Q., ... & Wang, Y. C. (2018). CAUSES: On the role of surface energy budget errors to the warm surface air temperature error over the Central United States. Journal of Geophysical Research: Atmospheres, 123(5), 2888-2909.

Kim, S., Ames, S., Lee, J., Zhang, C., Wilson, A. C., & Williams, D. (2017, November). Resolution reconstruction of climate data with pixel recursive model. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 313-321). IEEE.

Kim, S., Ames, S., Lee, J., Zhang, C., Wilson, A. C., & Williams, D. (2017). Massive scale deep learning for detecting extreme climate events. Climate Informatics.

Zhang, C., & Harrington, J. Y. (2015). The effects of surface kinetics on crystal growth and homogeneous freezing in parcel simulations of cirrus. Journal of the Atmospheric Sciences, 72(8), 2929-2946.

Zhang, C., Wang, M., Morrison, H., Somerville, R. C., Zhang, K., Liu, X., & Li, J. L. F. (2014). Investigating ice nucleation in cirrus clouds with an aerosol-enabled multiscale modeling framework. Journal of Advances in Modeling Earth Systems, 6(4), 998-1015.

Zhang, C., & Harrington, J. Y. (2014). Including surface kinetic effects in simple models of ice vapor diffusion. Journal of the Atmospheric Sciences, 71(1), 372-390.