Dr. Dietmar Dommenget - Honours

Simple Climate Model Projects

Supervisor(s): Dietmar Dommenget
Field of study: Climate Dynamics / Atmospheric Science
Projects available: Yes

I have developed the simple climate model: Globally Resolved Energy Balance (GREB) model, that can simulate the global climate response to external forcing. It can compute 100,000yrs of simulation per day on a standard PC computer. Thus it is a nice and simple tool that allows for wide range of studies. Projects examples could be on: Simulating ice age cycles over last 3 mill. years, include stochastic weather variability, build strange worlds or exo-planets or developing new feedbacks like ocean-carbon, glacier ice sheets or atmospheric circulation response. Detailed projects with the GREB model will be formulated together with the student, as there are simply to many different things that could be done with this model to list them all here.

Webpages:

* GREB model

* Monash Simple Climate Model

For further information, contact Dietmar Dommenget.

CMIP model errors in climate sensitivity

Supervisor(s): Dietmar Dommenget
Field of study:Climate Dynamics / Atmospheric Science / Oceanography
Projects available: Yes

The future climate change projections, such as published in the IPCC report, are based on state of the art coupled general circulation models. These simulations are made available for researchers in the Coupled Model Intercomparision Project (CMIP). The CMIP model simulations have large uncertainties in the projections of future climate change (the climate sensitivity) and also have large uncertainties in the simulation of the current mean climate. The project should focus on analysing the relationships between the biases in the mean climate and the differences in the climate sensitivity. The project could either focus on errors in the surface temperatures or on errors in the rainfall patterns.

For further information, contact Dietmar Dommenget .

El Nino dynamics in CMIP model simulations

Supervisor(s): Dietmar Dommenget
Field of study: Climate Dynamics / Atmospheric Science / Oceanography
Projects available: Yes
Collaborating organisation(s): Potentially CSIRO or Bureau of Meteorology

The El Nino Southern Oscillation (ENSO) mode is the most important mode of natural climate variability. State-of-the-art climate models (CMIP models) do simulate ENSO, but with a wide variety of characterists and significant biases towards observations. ENSO is also likely to change its characteristics during the future climate change, but how is currently not well understood. Analysis of CMIP models simulations and own model simulation with the Australian ACCESS model will be the focus of this project. The focus of this project can vary and will depend on the interest of the student. Examples, could be a focus on why ESNO dynamics are different in different modes or why models do make different future climate change projection for ENSO.

For further information, contact Dietmar Dommenget

El Nino dynamics in seasonal forecast simulations

Supervisor(s): Dietmar Dommenget
Field of study: Climate Dynamics / Atmospheric Science / Oceanography
Projects available: Yes
Support Offered: potentially Bureau of Meteorology 
Collaborating organisation(s): Potentially CSIRO or Bureau of Meteorology

The El Nino Southern Oscillation (ENSO) mode is the most important mode of natural climate variability. Seasonal weather forecasts are depending primarily on predicting the evolution of ENSO correctly. However, it has been shown that State-of-the-art climate models (CMIP models) do simulate ENSO with a wide variety of characteristics and significant biases towards observations. Such biases are also potentially affect seasonal forecast. In a recent study, it was shown that ENSO dynamics, and biases therein, can be very well diagnosed in the frame work of the ENSO recharge oscillator model. The focus of this project will be on applying the ENSO recharge oscillator model diagnostics to the Bureau of Meteorology new seasonal forecasting system to evaluate the ENSO dynamics, biases therein, and potential avenues of improving the forecasting system in respect to ENSO forecasts.

For further information, contact Dietmar Dommenget.