Transformative research is needed to realize qualitative improvements in the modeling of climate. The investigators are carrying out an interdisciplinary program of direct statistical approaches to climate modeling that has the potential to dramatically improve our understanding of how different climate processes interact over short and long time scales and small and large spatial scales. The program lays the foundations for a new class of climate models that may complement or even eventually replace existing climate models. It has two parallel tracks. First, the investigators are developing methods for the direct computation of climate statistics that may eventually replace conventional numerical simulations, leading to computationally more efficient climate models. Second, they are developing improved representations of low clouds and an improved understanding of cloud interactions with large-scale circulations, leading to more accurate climate models and reducing the currently large uncertainties in the representation of clouds in them.
The methods for the direct computation of climate statistics are based upon cumulant expansions in which fast modes are integrated out. This refocuses attention on the evaluation and understanding of low-frequency modes, which dominate climate variability. Improved representations of low clouds are developed based on probabilistic closures of cloud and subgrid-scale dynamics, which can be systematically improved in accuracy. These are integrated into a general circulation model (GCM) that couples the clouds to the large-scale flows, allowing a systematic study of their interactions. The work extends past work on the direct computation of statistics in relatively simple models of geophysical flows to GCMs of increasing complexity and realism, while improving the accuracy of the statistical methods and of the GCMs themselves.
This grant supported an innovative interdisciplinary program that aimed to develop statistical approaches to climate modeling. We wanted to improve our understanding of how important climate processes interact across short and long time scales, and across small and large spatial scales (e.g., clouds and large-scale circulations). The overall goal was to (i) develop methods for the direct statistical simulation of large-scale flows, and (ii) to develop probabilistic cloud and subgrid-scale dynamics closures for climate models, to improve the representation of clouds and smaller-scale turbulence and their interaction with large-scale flows. We have made substantial progress on both fronts. Regarding the first goal, we examined the feasibility of representing large-scale atmospheric dynamics in a truncated fashion, in which the strongly nonlinear interactions among atmospheric eddies are neglected. This led us to examine one of the most conspicuous features of Earth's atmospheric circulation that still calls for an explanation: the concentration of eddy angular momentum fluxes in the upper troposphere (which is also seen in other planetary atmospheres, such as that of Jupiter and Saturn). We showed that many aspects of the atmospheric circulation, such as the pole-to-equator surface temperature contrast, can relatively successfully be accounted for if nonlinear eddy-eddy interactions are neglected. But nonlinear eddy-eddy interactions in critical layers in the upper troposphere are crucial for the concentration of eddy angular momentum fluxes in the upper troposphere. Thus, methods for the direct statistical simulation of large-scale flows must include a representation of nonlinear critical-layer dynamics. We proposed relatively simple ways in which this can be taken into account. Regarding the second goal, we developed a new unified parameterization of all cloud and subgrid-scale turbulence processes for climate models. This parameterization treats the turbulence in the planetary boundary layer and the turbulence and convection in shallow (e.g., cumulus and stratocumulus) and deep (cumulonimbus) convection through one unified set of equations, alleviating the problems that arise when, as is commonly done, these processes are treated as disjointed rather than as forming part of a continuum. We developed new high-resolution simulation tools to test the parameterizations, implemented the parameterization in an idealized general circulation model, and used the model to study the interaction between clouds and large-scale circulations. This led to several significant results, among them, a new theory, supported by simulations and observations, of how low clouds change as the climate warms---which is currently one of the central unsolved problems in climate dynamics.
