skip to content

Cambridge NERC Doctoral Training Partnerships

Graduate Research Opportunities
 

Lead Supervisor: Michael Herzog, Geography

Co-Supervisor: Eimear Dunne, Geography and Alison Stirling, UK Met Office

Brief summary: 
The project will investigate the interaction between convective clouds and their impact on the cloud spectrum with the aim to improve the representation of convection in climate models.
Importance of the area of research concerned: 
The amount of convective precipitation, for example in thunderstorms, dominates atmospheric heating in the tropics and is a main driver for large scale dynamics. The key issue in representing convection in global models is that the resolutions of these models are too coarse to represent individual convective clouds. Instead, models rely on physically based parameterization of convection. Improvements in the performance of climate and numerical weather prediction models will crucially depend on the development of better parametrizations for convection. The project will contribute to the joint NERC and Met Office programme ParaCon (Parametrization of Convection). The aim of ParaCon is to significantly improve the representation of convection across model scales from 1-100km.
Project summary : 
The project will be based on the Convective Cloud Field Model (CCFM, described in Wagner and Graf, 2010). CCFM is novel type of parameterization for convection that explicitly represents and predicts the spectrum of individual clouds that are possible for given atmospheric conditions. Individual clouds are calculated based on a one-dimensional entraining parcel model; the cloud spectrum calculation is based on a predator-prey approach whereby cloud compete for the convective instability available in the environment. As part of ParaCon CCFM is currently been implemented as one option within the Met Office Unified Model (UM). The aim of this project is to improve the representation of individual clouds and their spectrum in CCFM and to test the current and improved CCFM within the UM. The ultimate goal is to reduce biases related to the representation of convection in the UM.
What will the student do?: 
The student will analyse existing output from high-resolution simulations of convection for different atmospheric conditions. These simulations have been performed as part of the ParaCon consortium. The student will interact with members of the ParaCon consortium. The simulations have been performed with a spatial resolution of several tens of metres to hundred metres so that the formation of individual clouds is explicitly resolved. Characteristics of individual clouds and of groups of clouds will be determined and compared against one-dimensional entraining parcel model simulations. Two frameworks will be considered, one based on the assumption of a steady plume and the other based on more transient thermals. The impact of the environment including deviations from the mean will be investigated. Improvements of the entraining parcel model are envisioned. These include the influence of wind shear and the predicted precipitation rates. Any improvement to the entraining parcel model and any changes to the representation of the cloud spectrum will be tested within different version of the UM from single column to idealised limited area and global atmospheric simulations.
References - references should provide further reading about the project: 
Wagner, T.M. and Graf, H.F. (2010). An Ensemble Cumulus Convection Parameterization with Explicit Cloud Treatment. J. Atmos. Sci., 67: 3854-3869, https://doi-org.ezp.lib.cam.ac.uk/10.1175/2010JAS3485.1.
Kipling, Z., Stier, P., Labbouz, L., and Wagner, T. (2017): Dynamic subgrid heterogeneity of convective cloud in a global model: description and evaluation of the Convective Cloud Field Model (CCFM) in ECHAM6–HAM2, Atmos. Chem. Phys., 17, 327–342, https://doi.org/10.5194/acp-17-327-2017.
Del Genio, A.D (2012). Representing the Sensitivity of Convective Cloud Systems to Tropospheric Humidity in General Circulation Models. Surv Geophys 33, 637–656, https://doi-org.ezp.lib.cam.ac.uk/10.1007/s10712-011-9148-9.
Applying
You can find out about applying for this project on the Department of Geography page.
Dr Michael Herzog
Department of Geography Graduate Administrator