Gabriel J. Kooperman
My research investigates critical uncertainties in our understanding of Earth's climate system using controlled numerical experiments enabled by new techniques in global climate simulation. I focus on the roles of clouds and convective processes, which provide a critical link between the hydrologic and energy cycles through sensible and latent heat transport, precipitation, solar reflectance (albedo), and infrared absorption (greenhouse effect). Important cloud processes occur on scales as small as micrometer droplet nucleation, which can influence cloud reflectivity, planetary albedo, and thus the global energy balance. Small changes in cloud properties due to anthropogenic climate change can both damp and enhance climate trends, and influence the frequency and intensity of extreme events (e.g. flooding, droughts, and heat waves).
Representing the multi-scale nature of these processes in global climate models is a frontier challenge of climate research. As a doctoral student, I was supported by the NSF Science and Technology Center for Multiscale Modeling of Atmospheric Processes (CMMAP), which aims to improve the representation of cloud processes in climate models by simultaneously resolving cloud-scale and large-scale motion, in an approach called super-parameterization. In this approach, two-dimensional cloud-resolving models are embedded in each column of the Community Atmosphere Model (CAM) to explicitly resolve cloud processes on their native scales, replacing conventional statistical parameterizations.
Applying this new technology, my dissertation addressed some of the largest uncertainties limiting our ability to project future climate change related to cloud processes: aerosol indirect effects, organized convection, and regional and extreme rainfall changes. Now, as an NSF postdoctoral fellow my focus has shifted to interdisciplinary frontiers in climate science: understanding land-atmosphere interactions and their impact on atmospheric convection and rainfall. Together convective precipitation processes and land-atmosphere energetics control many aspects of the climate system that are critical to society including the availability of freshwater, droughts, floods, and temperature extremes.
- Ph.D. (2014), University of California, San Diego Climate Science
- B.S. (2004), Tufts University Applied Physics
Kooperman, G. J., M. S. Pritchard, M. A. Burt, M. D. Branson, and D. A. Randall (2016), 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., in press, doi:10.1002/2016MS000715.
Kooperman, G. J., M. S. Pritchard, M. A. Burt, M. D. Branson, and D. A. Randall (2016), Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model, J. Adv. Model. Earth Syst., 8, 140-165, doi:10.1002/2015MS000574.
Kooperman, G. J., M. S. Pritchard, and R. C. J. Somerville (2014), The response of US summer rainfall to quadrupled CO2 climate change in conventional and super-parameterized versions of the NCAR Community Atmosphere Model, J. Adv. Model. Earth Syst., 6, 859-882, doi:10.1002/2014MS000306.
Kooperman, G. J., M. S. Pritchard, and R. C. J. Somerville (2013), Robustness and sensitivities of Central US summer convection in super-parameterized CAM: Multi-model intercomparison with a new regional EOF index, Geophys. Res. Lett., 40, 3287-3291, doi:10.1002/grl.50597.
Kooperman, G. J., M. S. Pritchard, S. J. Ghan, M. Wang, R. C. J. Somerville, and L. M. Russell (2012), 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. Atmos., 117, D23204, doi:10.1029/2012JD018588.
Elliott, E. J, S. Yu, G. J. Kooperman, H. Morrison, M. Wang, and M. S. Pritchard (2016), Sensitivity of summer ensembles of fledgling superparameterized U.S. mesoscale convective systems to cloud resolving model microphysics and grid configuration, J. Adv. Model. Earth Syst., 8, doi:10.1002/2015MS000567.
Zhao, Z., G. J. Kooperman, M. S. Pritchard, L. M. Russell, and R. C. J. Somerville (2014), 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. Atmos., 119, doi:10.1002/2013JD020973.
Zhang, K., H. Wan, X. Liu, S. J. Ghan, G. J. Kooperman, P.-L. Ma, P. J. Rasch, D. Neubauer, and U. Lohmann (2014), Technical Note: On the use of nudging for aerosol-climate model intercomparison studies, Atmos. Chem. Phys., 14, 8631-8645, doi:10.5194/acp-14-8631-2014.
Shen, S. S. P., M. Velado, R. C. J. Somerville, and G. J. Kooperman (2013), Probabilistic assessment of cloud fraction using Bayesian blending of independent datasets: Feasibility study of a new method, J. Geophys. Res. Atmos., 118, 4644-4656, doi:10.1002/jgrd.50408.