The role of clouds in climate model bias and sensitivity

The role of clouds in climate model bias and sensitivity

Lacagnina, C.

Siebesma, A.P. (promotor)
Selten, F.M. (promotor)

Civil Engineering and Geosciences

Geoscience and Remote Sensing

2014-10-06

Clouds are prominent in the climate system, since they play a major role in the way energy and water are cycled through the atmosphere. One of the most relevant impacts of the clouds on the earth's climate is their interaction with the radiative fluxes. Changes in this interaction in response to an external perturbation are known as cloud radiative feedbacks, which form an important contribution to the climate sensitivity of the Earth. An outstanding unanswered question of climate science is how clouds will change as climate warms. General circulation models (GCMs) are invaluable tools for addressing this issue, but they generally disagree in simulating clouds for present-day and future climate. The main reason is that many cloud-related processes take place on spatial and temporal scales typically smaller than the model grid spacing employed, requiring their treatment by means of parameterizations. Despite parameterizations being constantly improved, they remain an approximate representation of the true atmospheric behavior and introduce substantial uncertainties. Cloud radiative effects depend critically on both the type of cloud and its frequency of occurrence, which define different cloud regimes. This thesis provides insights into the role of the various meteorological conditions in determining the different cloud regimes and transitions among these. It is shown that in the tropics these cloud regimes can be disentangled in a mid-tropospheric pressure vertical velocity (w500) and sea surface temperature (SST) phase space. Such a bivariate approach is applied using satellite observations to analyse the cloud changes during El Nino. The transitions between different cloud regimes give rise to opposing cloud feedbacks. The sign of the feedback is controlled by the cloud optical thickness. Furthermore, a novel diagnostic technique is developed to quantify the relative contribution of different meteorological factors controlling the cloud interannual and seasonal variability. Changes in the humidity near the surface and SST in the eastern equatorial Pacific and sea level pressure (SLP) in the western part of the basin describe most of the interannual variability, in terms of cloud cover and radiative effects. In addition, it is found that the well accepted relationship between lower-tropospheric stability (LTS) and marine stratocumulus cloud amount has strong seasonal dependence, especially when spatial variations are taken into account. The understanding of the underlying mechanisms regulating the interplay between clouds, radiation and meteorological conditions, along with the novel diagnostics developed, have been employed in modeling evaluation. In order to avoid much of the ambiguity when it comes to evaluating cloud simulations with satellite retrievals, satellite simulators are embedded in the model code. This approach is demonstrated to be imperative. Specific physical processes are identified as largely responsible for biases in precipitation, cloud amount and radiative fluxes in the EC-Earth GCM. These include the parameterization of the cloud droplet size, the temperature-dependent parameterization that distinguishes between ice and liquid water phases, the overestimated mass flux and the erroneous detrainment parameterization in the convection scheme. Based on these identified biases, a number of sensitivity experiments have been carried out and are described in the last part of this thesis. These serve to investigate the impact of cloud-related uncertainties on model biases and radiative feedbacks. This approach helps to understand why GCMs simulate the cloud feedbacks, and by implication the climate system, in the way they do. It is found that the details of the representation of cloud microphysical and convective processes do not appear crucial for the total feedback in the EC-Earth GCM, due to compensating effects, but are relevant for the cloud feedback itself, especially its shortwave component. Finally, connections between model bias and the projection of the tropical cloud response to global warming are demonstrated and discussed.

cloud feedbacks
climate sensitivity
cloud bias
global climate models

https://doi.org/10.4233/uuid:c5e9ab24-d12a-4bb0-938e-c87b6970df0b

Institutional Repository

doctoral thesis

(c) 2014 Lacagnina, C.