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C. M. Patricola and K. H. Cook


The African Humid Period (AHP), about 14 800 yr ago [14.8–5.5 ka (ka ≡ 1000 yr ago)], was a time of increased humidity over Africa. Paleoclimate evidence suggests that the West African summer monsoon was stronger and more extensive 6 ka than today, and that the Saharan Desert was green. Here, a regional climate model that produces an excellent simulation of today’s climate over northern Africa is used to study the dynamics of the monsoon 6 ka. Changes in insolation, atmospheric CO2, and vegetation are used to impose 6-ka conditions, and the role of each forcing is isolated. Vegetation is not interactive, and the large-scale circulation and SSTs are fixed at present-day values for the 6-ka simulations.

The regional model produces precipitation increases across the Sahel and Sahara that are in good agreement with the paleodata. However, unobserved drying is simulated over the Guinean coast region where paleodata are sparse. Precipitation increases in the Sahel are related to a northward shift of the monsoon, the elimination of the African easterly jet, and an intensification and deepening of the low-level westerly jet on the west coast. The thermal low–Saharan high system of the present-day climate is replaced by a deep thermal low. When this system becomes fully developed in midsummer, cyclonic circulations transport moisture north into the Sahara, and rainfall increases there. Surface temperatures decrease despite the increased solar forcing 6 ka because of an increase in cloudiness. A moist static energy budget analysis shows that increased low-level moisture dominates the cooling to destabilize the vertical column and enhance convection. Even though solar forcing is the ultimate cause of the AHP, the model responds more strongly to the vegetation forcing, especially early in the summer season, emphasizing the importance of vegetation in maintaining the intensified monsoon system.

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W. J. Gutowski Jr., P. A. Ullrich, A. Hall, L. R. Leung, T. A. O’Brien, C. M. Patricola, R. W. Arritt, M. S. Bukovsky, K. V. Calvin, Z. Feng, A. D. Jones, G. J. Kooperman, E. Monier, M. S. Pritchard, S. C. Pryor, Y. Qian, A. M. Rhoades, A. F. Roberts, K. Sakaguchi, N. Urban, and C. Zarzycki
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W. J. Gutowski Jr, P. A. Ullrich, A. Hall, L. R. Leung, T. A. O’Brien, C. M. Patricola, R. W. Arritt, M. S. Bukovsky, K. V. Calvin, Z. Feng, A. D. Jones, G. J. Kooperman, E. Monier, M. S. Pritchard, S. C. Pryor, Y. Qian, A. M. Rhoades, A. F. Roberts, K. Sakaguchi, N. Urban, and C. Zarzycki


Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.

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Anne S. Daloz, S. J. Camargo, J. P. Kossin, K. Emanuel, M. Horn, J. A. Jonas, D. Kim, T. LaRow, Y.-K. Lim, C. M. Patricola, M. Roberts, E. Scoccimarro, D. Shaevitz, P. L. Vidale, H. Wang, M. Wehner, and M. Zhao


A realistic representation of the North Atlantic tropical cyclone tracks is crucial as it allows, for example, explaining potential changes in U.S. landfalling systems. Here, the authors present a tentative study that examines the ability of recent climate models to represent North Atlantic tropical cyclone tracks. Tracks from two types of climate models are evaluated: explicit tracks are obtained from tropical cyclones simulated in regional or global climate models with moderate to high horizontal resolution (1°–0.25°), and downscaled tracks are obtained using a downscaling technique with large-scale environmental fields from a subset of these models. For both configurations, tracks are objectively separated into four groups using a cluster technique, leading to a zonal and a meridional separation of the tracks. The meridional separation largely captures the separation between deep tropical and subtropical, hybrid or baroclinic cyclones, while the zonal separation segregates Gulf of Mexico and Cape Verde storms. The properties of the tracks’ seasonality, intensity, and power dissipation index in each cluster are documented for both configurations. The authors’ results show that, except for the seasonality, the downscaled tracks better capture the observed characteristics of the clusters. The authors also use three different idealized scenarios to examine the possible future changes of tropical cyclone tracks under 1) warming sea surface temperature, 2) increasing carbon dioxide, and 3) a combination of the two. The response to each scenario is highly variable depending on the simulation considered. Finally, the authors examine the role of each cluster in these future changes and find no preponderant contribution of any single cluster over the others.

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