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Carter Ward
Carter Ward

INTERACTIVE THERMODYNAMICS 3.0 20



Our interactive user interface enables the computation of both optimal and suboptimal structures. For a user defined sequence as well as recursion and traceback parameters, the dynamic programming table is provided along with a list of (sub)optimal structures. On selection, the according traceback is highlighted within the matrix. This is complemented with a graphical representation of the structure using Forna [44].




INTERACTIVE THERMODYNAMICS 3.0 20


Download File: https://www.google.com/url?q=https%3A%2F%2Furlcod.com%2F2u6wLl&sa=D&sntz=1&usg=AOvVaw3l92y-Xs09KWKec69kyMW0



In this study, we investigate the causes of the time dependence in the global mean feedback in two Earth system models of intermediate complexity (EMICs). These models were selected based on our earlier multimodel energy balance analysis (Pfister and Stocker 2018). Out of the 14 EMICs considered in that study, 8 lack interactive clouds (Eby et al. 2013). In only two out of these eight models, λ becomes substantially less negative with time (Pfister and Stocker 2018; Fig. S2 therein): in the Bern3D-LPX model (Ritz et al. 2011) and the LOVECLIM model (Goosse et al. 2010). As these models do not feature cloud feedbacks, and only one of them has a lapse-rate feedback (LOVECLIM), the dominant processes causing their global mean feedback time dependence must differ from those found in GCMs. It is the purpose of this study to shed light on such additional processes in the framework of these two EMICs.


Using a local rather than a global diagnostic EBM (section 4), two kinds of processes could explain the global mean feedback strengthening: 1) changing warming patterns acting on constant local feedbacks (Armour et al. 2013) and 2) changing local feedbacks (e.g., Rose et al. 2014; Rose and Rayborn 2016; Rugenstein et al. 2016a; Ceppi and Gregory 2017). Both processes can operate also in the absence of interactive clouds, and it is one of our aims to investigate their relative importance in the two analyzed EMICs.


A common feature of both atmospheric components is their lack of interactive clouds. Cloud cover is prescribed seasonally in both models, and layer-wise in LOVECLIM. Therefore, these two models do not simulate a cloud feedback, which is their main difference from GCMs with regard to feedback patterns.


Abstract. Using the Weather Research and Forecasting (WRF) model (version 3.5.1), dynamical downscaling of the Community Climate System Model, version 4 (CCSM4), simulations of the last glacial maximum (LGM) and 20th century (ensemble member #6) run were conducted to simulate ten years of climate over the western North Pacific during the LGM and modern climates, respectively. This paper describes the downscaling procedures for the Weather Research and Forecasting (WRF) model experiments and the quantitative and qualitative model validations comparing with the CCSM4 LGM and 20th century simulations results. Results of the dynamical downscaling of the CCSM4 LGM paleoclimate and twentieth century using the WRF model show not only that the WRF model is capable of long-term simulations in the paleoclimate state of LGM, but also that the WRF model can correct biases in the general circulation model (GCM), producing more realistic spatial distributions of the pressure-level variables. The downscaling of a GCM model using the WRF model (36 km) for the regional climate simulation is considered computationally cost-effective and reliable from the perspectives of model thermodynamics in general, although there are some model errors still existing with dynamic variables.


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