Modeling and Predicting Global Climate Variability and Climate Change

Overview

The scale and complexity of the climate system have rendered computer models an indispensable tool in our effort to understand and predict the behavior of the climate system. The ultimate test of our understanding of the working of the climate system is how well our models simulate and predict the behavior of the climate system.

Our efforts in helping to build better and better models are currently focused on diagnosing the causes of outstanding biases in the state-of-the-art climate models--Coupled General Circulation Models (CGCMs) (or Climate System Models). We are currently working with modelers at NCAR, GFDL, and NASA, attempting to understand the causes of outstanding tropical biases in CGCMs. A problem of a particular interest to us is the excessive cold-tongue and the associated double ITCZ syndrome in the climate models. We are also particularly concerned with the fidelity of the models in simulating ENSO--its diabatic and nonlinear aspects in particular. Relatedly, we are also interested in assessing whether the coupled tropical Pacific system or the other regional systems or the climate system as a whole as represented in the climate models is in the same dynamic regime as in observations.

In addition to helping with the improvement of climate models that are primarily used for climate simulation and projection, we are also working with modelers and forecasters at NCEP in improving the interannual and decadal forecasts through the use of NCEP Climate Forecast System (CFS)

A Brief History of Climate Modeling

Modeling efforts for climate were spurred in the 1960s by the advent of computers and a renewed concern with the impact on the earth's temperature by the rising of CO2. The efforts co-occurred with those exploiting the computer power for better weather forecasting. Simple energy balance models in which the motions are parameterized were co-developed with two-D or three-D general circulation models in which only sub-scale motions are parameterized. Edwards (2011) gave a good account of this historical development.

Challenges and Problems

Although three-dimensional models have now been developed, the conceptual picture about the effect of an increase in CO2 is still largely underpinned by the results from those one-dimensional models, as is evident in the notion that a significant linear trend in temperature will be the defining feature of global warming caused by anthropogenic enhancement of the greenhouse effect. The continuing influence from these early models over the way people conceptualize the anthropogenic effect is also because the state-of-the-art three-dimensional models still do not properly simulate natural variability such as MJO and ENSO. As a result, models are not yet able to capture the anthropogenic effect that takes place in the form of climate variability. In other words, our models may be underestimating the effect from anthropogenic forcing on natural variability.

Focusing on the task of estimating the global temperature response to the rise of CO2, one may be tempted to assume that even if we don’t simulate natural variability very well, we may still get anthropogenic global warming right. Such an assertion is probably too optimistic. One of the key questions is whether the simulated climate system is in the correct dynamic regime, because a system near a critical point can respond very differently than a system that is in a very stable regime. A poor simulation of a natural mode of variability such as MJO or ENSO suggests that the involved system is not in the correct dynamic regime. Also, those feedbacks that affect global energy balance, such as cloud and water vapor feedbacks, may depend on the natural variability—MJO, ENSO, etc. However successful state-of-the-art climate models may be in simulating some key features of the climate system, the question of whether these models capture fully the complexity of the dynamics—in particular, whether or not these models are in the same dynamic regime as the observed climate— has yet to be answered.