Cloud distribution at Azores is similar to the mean global distribution and can therefore be used to evaluate cloud simulations in global models.
The Azores is located on the subtropics-midlatitude boundary and observes a wide range of cloud conditions. A recent satellite analysis revealed that the frequency of different cloud regimes observed at the Azores are similar to the frequency of global cloud regimes, indicating that the Azores is a unique site for evaluating global climate models.
The previous Atmospheric Radiation Measurement (ARM) field campaign and the current ARM fixed site in the Azores provide detailed process-level data for understanding deficiencies in climate model simulations of cloud processes in this important region.
Marine stratocumulus clouds are considered significant contributors to cloud-climate feedbacks and are a large source of uncertainty in climate model simulations. Many past studies of marine stratocumulus have focused on ‘‘ideal’’ stratocumulus regions of the southeast Pacific Ocean or California coast, while ignoring regions where stratiform low clouds form behind midlatitude baroclinic weather systems. From its location on the subtropics-midlatitude boundary, the Azores is influenced by both the Azores High, a semi-permanent region of high pressure, and midlatitude baroclinic storm systems. Therefore, the Azores experiences a wide range of cloud structures, from fair-weather scenes to stratocumulus sheets and deep convective systems. In this study, researchers combined three types of datasets to study cloud variability in the Azores: a satellite analysis of cloud regimes, a reanalysis characterization of storminess, and data from a 19-month Department of Energy (DOE) ARM field campaign that occurred on Graciosa Island. Combined analysis of the three datasets provides a detailed picture of cloud variability and the respective dynamic influences, with emphasis on low clouds that constitute a major uncertainty source in climate model simulations. The cloud regime analysis shows that the Azores cloud distribution is similar to the mean global distribution and can therefore be used to evaluate cloud simulation in global models. Regime analysis of low clouds shows that stratocumulus decks occur under the influence of the Azores High, while shallow cumulus clouds are sustained by cold-air outbreaks, as revealed by their preference for postfrontal environments and northwesterly flows. An evaluation of climate model output over the Azores shows that all models severely underpredict shallow cumulus clouds, while most models also underpredict the occurrence of stratocumulus cloud decks in this region. This study also demonstrates that regime-based methods applied to in situ and satellite observations can be used to study cloud processes and evaluate models ranging from process-resolving to global climate models. The presence of a permanent ARM site in the Azores will provide a wealth of data to study a wide range of cloud fields and their environment. The present study demonstrates that all the tools are now in place to perform process-resolving model simulations of individual cases observed during the ARM field campaign and to generalize the case study results and attempt to explain whether major general circulation model cloud deficiencies relate to the poor representation of atmospheric dynamics mechanisms or to issues related to the parameterization of cloud microphysical processes.
Contacts (BER and non-BER)
BER - Sally McFarlane, SC-23.1, 301-903-0943; and Shaima Nasiri, SC-23.1, 301-903-0207
School of Marine and Atmospheric Sciences
Stony Brook University
100 Nicolls Rd.
Stony Brook, NY 11794-5000
This study uses ARM data and was supported by the DOE Office of Science, Office of Biological and Environmental Research, Atmospheric System Research program under grant DE-SC0006712.
Rémillard, J., and G. Tselioudis. 2015. “Cloud Regime Variability over the Azores and Its Application to Climate Model Evaluation,” Journal of Climate 28, 9707-20. DOI: 10.1175/JCLI-D-15-0066.1. (Reference link)
SC-23.1 Climate and Environmental Sciences Division, BER
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