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A. Anav, P. Friedlingstein, M. Kidston, L. Bopp, P. Ciais, P. Cox, C. Jones, M. Jung, R. Myneni, and Z. Zhu

models at the global scale or over large latitudinal bands (see below). For all other model variables, the evaluation is performed at the grid level, conserving the spatial information. However, when presenting the results, all model performances are averaged over the following domains for land variables: global (90°S–90°N), Southern Hemisphere (20°–90°S), Northern Hemisphere (20°–90°N), and the tropics (20°S–20°N). Considering the ocean carbon, according to Gruber et al. (2009) , we aggregate

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Ting Liu, Jianping Li, Juan Feng, Xiaofan Wang, and Yang Li

prediction. Comprehensive coupled general circulation models are powerful tools for examining large-scale climatic behavior and dynamics. For instance, the performances of the CMIP models in representing the surface winds ( Bracegirdle et al. 2013 ) and the SST responses to the SAM over the Southern Ocean ( Screen et al. 2010 ) have been evaluated. In addition, the influence of the SAM on SH precipitation ( Cai and Cowan 2006 ; Karpechko et al. 2009 ; Purich et al. 2013 ) and Antarctic surface air

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Ioannis Sofokleous, Adriana Bruggeman, Silas Michaelides, Panos Hadjinicolaou, George Zittis, and Corrado Camera

resolution ( Buizza et al.1999 ; Lo et al. 2008 ). Various studies have evaluated the performance of different physics parameterization schemes for the dynamical downscaling of reanalysis data, in order to reproduce precipitation with the WRF Model over different regions. At horizontal resolutions well above the convection-resolving scales, several studies [ Ji et al. (2014) , 10 km; Hu et al. (2018) , 20 km; Zittis et al. (2014) , 50 km; and Katragkou et al. (2015) , 50 km] found that planetary

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Andreas Wagner, Dominikus Heinzeller, Sven Wagner, Thomas Rummler, and Harald Kunstmann

topographies is analyzed. Our simulations make use of the urban canopy model to account for the impact of the urban geometry on surface energy budgets and wind shear calculations. We investigate grid spacings of 1 and 5 km without convection parameterization and also apply the scale-aware Grell–Freitas convection parameterization for 5-km grid spacing. This allows us to evaluate the strength and weakness of each dataset regarding its performance of simulating the temporal, spatial, and intensity

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Lei Cai, Vladimir A. Alexeev, John E. Walsh, and Uma S. Bhatt

the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). As the population of GCMs increases, the need to evaluate GCMs for their performance in reproducing climate variability also becomes greater. As the previous generation of CMIP5, the CMIP3 models have been evaluated in terms of major climatic variabilities and have been found to be more successful in retrieving the spatial patterns than the temporal indices ( Xin et al. 2008 ; Stoner et al. 2009

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Amandeep Vashisht, Benjamin Zaitchik, and Anand Gnanadesikan

and change ( Bhattacharjee and Zaitchik 2015 ; Randall et al. 2007 ). GCMs show little consensus in seasonality, interannual variability, and teleconnections of precipitation in the region when compared to observational records, and this uncertainty undermines projections of future climate change for many models ( Bhattacharjee and Zaitchik 2015 ). This means that projections based on a flat multimodel mean approach have limited value in climate adaptation, and it is imperative to evaluate these

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Kwesi A. Quagraine, Bruce Hewitson, Christopher Jack, Piotr Wolski, Izidine Pinto, and Christopher Lennard

climate models (GCMs). GCMs are our primary tool to understand past and future changes in our climate ( Frame and Stone 2013 ; Knutti and Sedláček 2013 ). The ability of GCMs to reasonably simulate already identified co-behavior modes in observed datasets may permit climate model weighting and the evaluation of climate model simulations by making available a consistent set of GCMs pertaining to any region of interest. Over time, we have significantly improved how GCMs are able to simulate the

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Detelina P. Ivanova, Peter J. Gleckler, Karl E. Taylor, Paul J. Durack, and Kate D. Marvel

consider the potential importance of internal variability, which has been found previously to be a major factor of uncertainty when evaluating model performance ( Notz 2014 , 2015 ; Zunz et al. 2013 ; Santer et al. 2011 ). We note, however, that for our hemispheric climatological tests we have found single model interrealization differences to be small compared to intermodel differences. Additional information about the individual models and their sea ice components is summarized in Table 2 . Table

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Maik Renner, Axel Kleidon, Martyn Clark, Bart Nijssen, Marvin Heidkamp, Martin Best, and Gab Abramowitz

, which include many different process parameterizations, may not represent diurnal land–atmosphere interaction well. This is important because land–atmosphere feedbacks propagate to larger scales and may ultimately affect model sensitivity to global change ( Miralles et al. 2019 ). Here, we employ a diagnostic model evaluation based on diurnal signatures of surface heat fluxes to quantify the performance of LSMs specifically for diurnal heat redistribution processes and point toward parameterizations

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Andrew J. Newman, Naoki Mizukami, Martyn P. Clark, Andrew W. Wood, Bart Nijssen, and Grey Nearing

1. Introduction Hydrologic and land models typically evolve through an iterative process of model refinement, evaluation, and diagnosis. This process necessarily requires testing these models against data. Traditionally, we compare the performance of a set of models at reproducing the observations; for example, via likelihood ratios or Bayesian model selection methods, or less rigorously by calculating error statistics across several competing models (e.g., Henderson-Sellers et al. 1993

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