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Miao Yu, Jorge González, Shiguang Miao, and Prathap Ramamurthy

. Kondo , and Y. Shimoda , 2009 : Effects of anthropogenic heat release upon the urban climate in a Japanese megacity . Environ. Res. , 109 , 421 – 431 , https://doi.org/10.1016/j.envres.2009.02.013 . 10.1016/j.envres.2009.02.013 Ohashi , Y. , Y. Genchi , H. Kondo , Y. Kikegawa , H. Yoshikado , and Y. Hirano , 2007 : Influence of air-conditioning waste heat on air temperature in Tokyo during summer: Numerical experiments using an urban canopy model coupled with a building

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Hong Yin and Ying Sun

inconsistent with CMIP5 historical runs driven by the ALL forcing; the reason for this discrepancy is unknown, but it may be due to the effects of anthropogenic aerosols, atmospheric circulation, internal climate variability, and changes in land use, among other factors. This study shows that the warming hole also cannot be simulated by the changes in the warm fixed threshold indices. In summary, all of these results confirm and support the findings from Fig. 1 . In addition, we compare the 5-yr anomaly

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Jin-Ming Feng, Yong-Li Wang, Zhu-Guo Ma, and Yong-He Liu

reports point to the importance of anthropogenic heat emissions in the urban environment. At present, the numerical simulations of the effects of AHR and USCU on temperature or other meteorological factors mainly focus on a single city or smaller spatial scales, and their time scales are mostly restricted to seasonal changes or even diurnal changes. There are few simulation studies that address the effect of AHR on climate on a regional scale. Therefore, in this paper, the WRF model coupled with UCM

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Thomas R. Knutson, Fanrong Zeng, and Andrew T. Wittenberg

. Inspection of Fig. 7a further indicates that this detection and attribution result is sufficiently strong that the uncertainty associated with the combined effects of internal climate variability, uncertainty in the model responses to natural forcing, and the uncertainty in the observed ensemble could be a factor of 2 larger than shown here and the same conclusion would still hold for start dates from the late 1800s to about the mid-twentieth century. Our attribution conclusion for anthropogenic

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J. Boé and L. Terray

. Detection may thus be facilitated for some variables. The main limitation of the forced framework is that the possible effects of changes in SST resulting from anthropogenic forcing on the atmosphere are not detectable as being anthropogenic. The optimal detection algorithm used in this study is described in Allen and Tett (1999) and Tett et al. (2002) and is briefly summarized in the appendix . We focus on anthropogenic forcing only and do not try to separate GHG and SUL influences. Intraensemble

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Vijayakumar S. Nair, S. Suresh Babu, K. Krishna Moorthy, and S. S. Prijith

could lead to altering the regional climate through the dynamical feedbacks ( Chung et al. 2002 ; Menon et al. 2002 ; Lau et al. 2006 ). Recently, Bollasina et al. (2011) have shown that spatially heterogeneous anthropogenic aerosol forcing dynamically induce circulation changes and that could explain the decrease in monsoon precipitation over central-northern India. Slowdown of tropical circulation in association with anthropogenic forcing was reported earlier by Vecchi et al. (2006

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Nikolaos Christidis, Peter A. Stott, Simon Brown, David J. Karoly, and John Caesar

phases of plant development like leafing (unfolding, coloring, and fall) and flowering (first bloom), in order to determine the beginning of spring and/or the end of autumn. This approach is of course spatially limited to regions of common vegetation. It should be noted that seasonal plant phases are not dependent only on temperature, but also on other parameters like rainfall, light, biotic factors, and CO 2 fertilization, which can also be altered by anthropogenic climate change, and so it could

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Yong-Sang Choi, Chang-Hoi Ho, Jinwon Kim, Dao-Yi Gong, and Rokjin J. Park

function of aerosol concentrations. The samples are categorized by successive rainless and rainy days as well as aerosol measurements over China during summer. Here R, C , and C * denote rainy, rainless, and aerosol-measured rainless days, respectively; RC*R, RCC*R , and RCCC*R indicate the cases of one, two, and three consecutive rainless days between rain days, respectively. The error bar corresponds to ±1 std error. Fig . 3. Possible long-term influence of anthropogenic effects such as the

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Terry C. K. Lee, Francis W. Zwiers, Xuebin Zhang, and Min Tsao

anthropogenic and natural external forcing, and generally include indirect aerosol effects. Three long control simulations from CGCM2, HadCM2, and HadCM3 are also used in this study. All simulations, which are available in a variety of grid sizes ( Table 1 ), were interpolated onto the 5° × 5° grid of the observations and subsequently averaged into regional decadal means (details to be described below). An analysis is conducted for each individual model and for the ensemble mean of the simulations from the

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Kai Wang, Hong Ye, Feng Chen, Yongzhu Xiong, and Cuiping Wang

remarkable difference in the DTR can be found in the urban and rural areas during this time, revealing a significant urbanization effect. Fig . 5. DTR changes of the large city, small city, and rural stations. Reports stated that the anthropogenic aerosols were responsible for the solar dimming and brightening ( Streets et al. 2006 ; Ohmura 2009 ; Wild 2009 ). The dimming may result from the radiation scattering and absorption effects by aerosols, which also makes daytime temperatures cooler. For T

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