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

values of certain vegetation parameters to their crops. A few years later, climate modelers began introducing adaptations of complex crop models into land surface models to better depict managed ecosystems ( Tsvetsinskaya et al. 2001a ), as they had done earlier with ecosystem models of unmanaged vegetation ( Foley et al. 1996 ). Originally intended to facilitate agricultural management, such crop models usually combine mechanistic and empirical algorithms to simulate complex crop behavior to project

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

available DA algorithms. That effort has been greatly reduced by the advent of ensemble DA, so that climate model development and research can now benefit greatly and directly from the variety of tools available from DA. Several generations of the Community Atmosphere Model [CAM, the atmospheric component of the Community Earth System Model (CESM)] can now be used with ensemble DA using the Data Assimilation Research Testbed (DART). The DART algorithm and software are described briefly here ( section 2

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

–stem area index (LAI, SAI, respectively), and canopy height. In CNDV, the gap mortality calculation includes heat stress and growth efficiency considerations from the corresponding DGVM algorithm (section 2.8 in Levis et al. 2004 ). All other ecosystem processes (allocation, phenology, fire, etc.) are handled by CN (more details in Oleson et al. 2010 ). Including the nitrogen cycle in the CLM has been shown to dampen the response of land–atmosphere carbon exchange to climate variability as the

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Alexandra Jahn, Kara Sterling, Marika M. Holland, Jennifer E. Kay, James A. Maslanik, Cecilia M. Bitz, David A. Bailey, Julienne Stroeve, Elizabeth C. Hunke, William H. Lipscomb, and Daniel A. Pollak

) (AMSR-E) bootstrap algorithm applied to the brightness temperature data from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite and from three Special Sensor Microwave Imager (SSM/I) sensors on the Defense Meteorological Satellite Program’s (DMSP’s) F8 , F11 , and F13 satellites. We find that the simulated spatial sea ice concentration pattern as well as the location of the sea ice edge (defined as 15% ice concentration contour) is in close agreement with the SSMR

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Alicia R. Karspeck, Steve Yeager, Gokhan Danabasoglu, Tim Hoar, Nancy Collins, Kevin Raeder, Jeffrey Anderson, and Joseph Tribbia

development is part of a broader initiative at the National Center for Atmospheric Research (NCAR) to build assimilation capabilities for the atmosphere, land, sea ice, and ocean components of the community model. There is currently an array of global ocean assimilation products available to the climate-science community that employ various ocean general circulation models and assimilation algorithms. The assimilation methods used to construct these products are all least squares methods that attempt to

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Semyon A. Grodsky, James A. Carton, Sumant Nigam, and Yuko M. Okumura

simulated by CCSM4 and CAM4/AMIP. Observed LHTFL is calculated from the buoy data using version 3 of the Coupled Ocean–Atmosphere Response Experiment (COARE 3.0) algorithm of Fairall et al. (2003) . Fig . 15. Annual mean SSS (psu, shading) and precipitation (mm day −1 , contours): (a) Simple Ocean Data Assimilation (SODA) salinity and CMAP precipitation; (b),(c) CCSM4, CCSM3 SSS, and precipitation; and (d) data from two independent uncoupled runs: POP/NYF SSS and CAM4/AMIP precipitation. e

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J. E. Kay, B. R. Hillman, S. A. Klein, Y. Zhang, B. Medeiros, R. Pincus, A. Gettelman, B. Eaton, J. Boyle, R. Marchand, and T. P. Ackerman

smaller than the LWCF bias in CAM5 (−4 W m −2 ). The larger LWCF bias in CAM5 has been partially traced to cold and moist biases in the middle and upper troposphere; however, observational uncertainty has complicated assessment of model LWCF bias magnitudes. Changes in the CERES–EBAF data processing algorithm from version 1.0 to version 2.6 resulted in a 4 W m −2 decrease in the global annual mean LWCF from 30 to 26 W m −2 , primarily due to a 3 W m −2 decrease in clear-sky longwave fluxes from 269

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Stephen Yeager, Alicia Karspeck, Gokhan Danabasoglu, Joe Tribbia, and Haiyan Teng

meaningfully different results, so the longer 20C time series was chosen for robustness. The linear trend is determined independently from 1850 to 1960 and from 1961 to 2005 by simple least squares minimization. We fix the pivot point in 1960 a priori because of a change in trend around that time. Once the trend has been removed, we estimate the remaining parameters α and σ 2 . Details of the least squares fitting algorithm for the AR(1) parameters are documented in Schneider and Neumaier (2001) . The

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

since we are comparing changes in numerics, algorithms, nominal resolution, and coupling to the physical parameterizations. Figure 2 shows the zonally averaged zonal surface stress biases comparing different dynamical core and development versions from CAM3 to CAM4. Different resolutions using the spectral dynamical core largely show more similarities with each other than with a 2° FV resolution. Excessive tropical seasonal monsoonal westerly flow biases are also present (not shown), but in the

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Gijs de Boer, William Chapman, Jennifer E. Kay, Brian Medeiros, Matthew D. Shupe, Steve Vavrus, and John Walsh

sensors employed in different studies and the locations included. SATEST differences are largely due to instrument specifications, sampling, and thresholds used in cloud-detection algorithms. The idea is that combined presentation of various estimates from different locations and with different thresholds employed begins to capture the true variability in Arctic cloud cover and that despite dataset differences, patterns emerge. When compared to all other estimates, CCSM4 provides the lowest cloud

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