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C. Kendra Gotangco Castillo, Samuel Levis, and Peter Thornton

models (e.g., CLM-DGVM) enforce a leaf-off period on tropical deciduous trees. The MODIS dataset has evergreens dominating the tropics, occupying up to 90% of the land area in the Amazon. Shrubs and grasses are sparse in tundra regions in CNDV, a known bias due to soil moisture also in the standard CLM4CN ( Lawrence et al. 2011 ). Shrubs cover more of the western half of the United States, as well as South America and Central Asia compared to MODIS. Grass cover is higher across central Africa in the

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Keith Oleson

investigations by Oke and others using observations and numerical modeling (e.g., Oke 1981 , 1982 ; Oke et al. 1991 ). The average UHI for a midlatitude city is 1°–3°C but may reach up to 12°C at night under optimal conditions ( Oke 1997 ) and is generally largest during the summer season ( Karl et al. 1988 ; Klysik and Fortuniak 1999 ). In the tropics, seasonal variations of the UHI are evidently more related to urban–rural surface moisture characteristics with higher intensities during the dry season

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Samuel Levis, Gordon B. Bonan, Erik Kluzek, Peter E. Thornton, Andrew Jones, William J. Sacks, and Christopher J. Kucharik

for CROP −CTRL (labeled ΔCROP) and LateP −CTRL (labeled ΔLateP), and boldface indicates statistically significant difference in the means at the 95% confidence level. Variables are LAI: leaf area index, L : latent heat flux, P : precipitation, β : soil moisture limitation on photosynthesis (1 means no limitation), NEE: net ecosystem exchange, α : surface albedo, and R n : net radiation at the surface; seasons are DJF: December–February, MAM: March–May, and SON: September–November. We evaluate

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David M. Lawrence, Keith W. Oleson, Mark G. Flanner, Christopher G. Fletcher, Peter J. Lawrence, Samuel Levis, Sean C. Swenson, and Gordon B. Bonan

were changes and additions to the model hydrology, including changes to surface runoff and frozen soil parameterizations and the addition of a simple groundwater scheme. Other changes included revised canopy integration, canopy interception scaling, and a plant functional type (PFT) dependency on the soil moisture stress function. CLM3.5 exhibited a much better annual cycle of soil water storage, which was far too weak in CLM3, and an improved representation of evapotranspiration (ET), including a

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Richard B. Neale, Jadwiga Richter, Sungsu Park, Peter H. Lauritzen, Stephen J. Vavrus, Philip J. Rasch, and Minghua Zhang

. Larger values of ε tend to inhibit deep convection and moisten the lower troposphere too much, while higher values exhibit a response that retains the lack of moisture sensitivity seen in CAM3. The vertical profiles of T , q υ , and q T in the assumed convecting parcels are derived from a simple inversion of s and are then used for the calculation of buoyancy, total CAPE, and triggering CAPE as in CAM3. The DCAPE calculation was first used in simulations using the interim CAM3.5 release

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Markus Jochum, Alexandra Jahn, Synte Peacock, David A. Bailey, John T. Fasullo, Jennifer Kay, Samuel Levis, and Bette Otto-Bliesner

landmass. There are no areas of reduced forcing change <−10 W m −2 . Because of the larger meridional temperature ( Fig. 1a ) and moisture gradient ( Fig. 4a ), the lateral atmospheric heat flux into the Arctic is increased from 2.88 to 3.00 PW. This 0.12 PW difference translates into an Arctic average of 3.1 W m −2 ; this is a negative feedback as large as the cloud feedback, and 6 times as large as the increase in the ocean meridional heat transport at 60°N (next section). Thus, the negative feedback

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Laura Landrum, Bette L. Otto-Bliesner, Eugene R. Wahl, Andrew Conley, Peter J. Lawrence, Nan Rosenbloom, and Haiyan Teng

Greenland, Central and South America, and North America. The LM simulation indicates enhanced winter MCA moisture over most of southern and western Greenland, which is related to the reduced sea ice extent simulated in adjacent portions of the North Atlantic and the polar amplification of relative MCA warming, especially in winter. The model also simulates MCA boreal winter dryness extending from Panama to Venezuela consistent with a speleothem record from southern Panama ( Lachniet et al. 2004 ), but

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Jenny Lindvall, Gunilla Svensson, and Cecile Hannay

theory with the stability functions from Zeng et al. (1998) . They are similar to the ones in CAM4 ( Holtslag and Boville 1993 ) but differ substantially from those in CAM5 ( Bretherton and Park 2009 ). The PBL schemes in CAM4 and CAM5 have both been tested using the first and second GABLS case. The GABLS1 case is an idealized, weakly stable case without moisture and radiative cooling ( Cuxart et al. 2006 ). The University of Washington (UW) scheme, used in CAM5, performs quite well, whereas the

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A. Gettelman, J. E. Kay, and J. T. Fasullo

) . The results are spatially coherent, leading naturally to a discussion of particular physical regimes. The current analysis confirms that cloud feedbacks are a critical source of intermodel spread in climate sensitivity, and therefore we focus on parameters that are related to cloud radiative effects and explore how specific properties of clouds in the simulations are related to the climate sensitivity. Cloud radiative properties are evaluated, and then three areas are highlighted: 1) moisture in

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Kevin Raeder, Jeffrey L. Anderson, Nancy Collins, Timothy J. Hoar, Jennifer E. Kay, Peter H. Lauritzen, and Robert Pincus

al. 2012 ). b. Adaptation to CAM features 1) Supports arbitrary number of additional tracers The definition of the “state” of the CAM atmosphere is somewhat flexible; besides the usual dynamical variables and temperature, it can include an arbitrary number of tracers, such as moisture and chemical species. DART/CAM accommodates this flexibility by allowing specification of the model state without recompiling DART. An example of how this could be used is that assimilations could be run with a full

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