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Wenqing Zhang, Lian Xie, Bin Liu, and Changlong Guan

1. Introduction The track, intensity, and size of tropical cyclones (TCs) have been used as evaluation parameters in assessing TC forecasts or the performance of TC numerical forecast models since the first attempts were made at forecasting TCs in the Atlantic region in the 1870s ( Sheets 1990 ). For instance, Neumann and Pelissier (1981) analyzed Atlantic tropical cyclone forecast errors in track and intensity, separately. Liu and Xie (2012) used errors in track, intensity, and size to

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Steven A. Lack, George L. Limpert, and Neil I. Fox

. However, these scores alone can be misleading, especially in high-resolution models. A finescale convective product may show skill as part of a decision process that is not captured by these standard statistics; these common metrics may even show zero skill when calculated. Additional metrics are then needed to provide insights into the evaluation process. Object-oriented methods, also referred to as feature-based approaches, can be used in a supplementary nature to common metrics in an evaluation

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Chunxi Zhang, Yuqing Wang, and Ming Xue

Model, and compared its performance with three other commonly used PBL parameterization schemes, the YSU, UW, and MYNN schemes. The simulations with the four PBL schemes are conducted over the SEP where stratocumulus and shallow cumulus are dominant and over the SGP where strong diurnal variation of the PBL is common. The simulations are evaluated/compared against various observations during two field campaigns: the VOCALS 2008 over the SEP and the LAFE 2017 over the SGP. The TKE fields simulated

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Liang Chen and Oliver W. Frauenfeld

models driven by historical natural and anthropogenic forcings in CMIP3, Zhou and Yu (2006) found that the robustness of temperature estimates averaged over China is lower than that of the global and hemispheric average, and discrepancies exist between the observed and simulated spatial patterns of temperature trends. By comparing output from 24 models with observational data in China, Miao et al. (2012) evaluated the performance of the CMIP3 GCMs in simulating temperature, and found that 18

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James E. Overland, Muyin Wang, Nicholas A. Bond, John E. Walsh, Vladimir M. Kattsov, and William L. Chapman

and the worst results for SLP, whereas the second model has relatively low errors for both variables. If one is only interested in SAT, is it better to use the “best” model? Or is overall model performance a more important evaluation factor? Value judgments about different selection criteria quickly arise. This paper is organized as follows. We first summarize the sources of uncertainty in AOGCM projections and continue with a discussion of selection criteria. Examples from Arctic and subarctic

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Grey S. Nearing, Benjamin L. Ruddell, Martyn P. Clark, Bart Nijssen, and Christa Peters-Lidard

used to understand model consistency, accuracy, and precision. They defined model evaluation as a process in which model outputs are compared against observations, model intercomparison as a process in which multiple models are applied to specific test cases, and model benchmarking as a process in which model outputs are compared against a priori expectations of model performance. Our first purpose here is to formalize the concept of model benchmarking. A benchmark consists of three distinct

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Zili Shen, Anmin Duan, Dongliang Li, and Jinxiao Li

, and poleward atmospheric heat transport, which could lead to a nonlinear sea ice decrease, might also be causes of the mismatch between modeled and observed results ( Koldunov et al. 2010 ; Melsom et al. 2009 ; Notz et al. 2013 ). The variety of possible biases does not allow us to rank the performances of models based on one single parameter. This is why, in this study, we select several metrics to give a synthesis of the sea ice cover performance of each model. In this study, we evaluate the

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Brian A. Colle, Zhenhai Zhang, Kelly A. Lombardo, Edmund Chang, Ping Liu, and Minghua Zhang

, but there are likely major differences between the models attributed to spatial resolution and physics. We evaluate the models separately, rank them, and use the selected members based on model performance during the historical period to determine if this approach has an impact on the future cyclone results. Finally, some physical reasons for the model difference in cyclone frequency and intensity have been explored in the western Atlantic storm-track region. In summary, this paper will address

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Fengge Su, Xiaolan Duan, Deliang Chen, Zhenchun Hao, and Lan Cuo

important part og evaluating the GCM projections ( Mote and Salathé 2010 ; Phillips and Gleckler 2006 ; Randall et al. 2007 ; Walsh et al. 2008 ). Randall et al. (2007) and Bader et al. (2008) evaluated the performance of the models archived at the Program for Climate Model Diagnosis and Intercomparison (PCMDI) in simulating various aspects of global climate in the twentieth century. Their results suggest that the models can capture the large-scale features of climate, but more uncertainties

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Cezar Kongoli, William P. Kustas, Martha C. Anderson, John M. Norman, Joseph G. Alfieri, Gerald N. Flerchinger, and Danny Marks

(see review by Kalma et al. 2008 ), to the authors’ knowledge such methods are virtually nonexistent for applications over snow. In this study, a diagnostic snow–vegetation energy balance model based on remote measurements of land surface temperature is developed and evaluated in comparison with flux measurements made at two snow-covered sites. This approach is very different from, yet complementary to, existing prognostic snow modeling systems based on mass balance. An advantage of the diagnostic

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