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Seiji Kato, Norman G. Loeb, John T. Fasullo, Kevin E. Trenberth, Peter H. Lauritzen, Fred G. Rose, David A. Rutan, and Masaki Satoh

1. Introduction Ocean in situ temperature observations show increasing ocean temperatures (e.g., Lyman and Johnson 2014 ; Cheng et al. 2017 ), which is the primary manifestation of Earth’s energy imbalance (EEI) derived from top-of-atmosphere (TOA) radiation ( von Schuckmann et al. 2016 ). Ocean temperature changes are driven by increasing anthropogenic forcing, associated with changes in anthropogenic aerosols and greenhouse gases, which in turn drive changes in the flows of energy through

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Elodie Charles, Benoit Meyssignac, and Aurélien Ribes

1. Introduction The ocean stores most of the Earth energy uptake associated with historical forcings ( Levitus et al. 2012 ; Rhein et al. 2013 ; von Schuckmann et al. 2016 ). This energy is primarily stored in the form of heat and warms the various layers of the ocean. Because the warming is mixed and transported into the ocean depths, it delays surface warming. As such, the ocean heat uptake plays an important role in the global climate system’s temperature response to radiative forcing

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Yang Liu, Jisk Attema, and Wilco Hazeleger

. Karspeck et al. (2017) report that with the same surface forcing (CORE-II forcing), ocean models can behave very differently without further constraints from observations. They find large differences in volume transports between six reanalysis products (also including ORAS4 and SODA) in their tests. The OMET time series considered here do not differ much from each other statistically. The mean OMET at 60°N in ORAS4 is 0.47 ± 0.06 PW, while in GLORYS2V3 it is 0.44 ± 0.07 PW, and in SODA3 it is 0.46 ± 0

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Tristan S. L’Ecuyer, Yun Hang, Alexander V. Matus, and Zhien Wang

1. Introduction Earth’s climate is strongly regulated by the spatial and temporal variability of clouds. Variations in cloud phase, height, thickness, and vertical structure all modulate the way clouds influence the propagation of solar and thermal radiation through the atmosphere. Accurately modeling the sensitivity of climate to external forcing, therefore, requires a precise accounting of the radiative feedbacks owing to cloud changes. Yet it is not sufficient to merely tune models to

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Kevin E. Trenberth and Yongxin Zhang

Zealand downstream. Hence the change in local winds also force some modifications in surface fluxes and wind stress. Any link between ENSO-related variations in the ITF and the Tasman Sea heat waves has been generally assigned to the atmospheric bridge connections. The studies thus far have overlooked the likelihood that there is also a direct ocean connection through the changes in mass and heat transport with the ITF that indeed relate to opposite changes in the East Australian Current region

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Jake J. Gristey, J. Christine Chiu, Robert J. Gurney, Keith P. Shine, Stephan Havemann, Jean-Claude Thelen, and Peter G. Hill

al. 2005 ). These observations have had many uses, including quantifying fundamental climate parameters such as the planetary brightness ( Vonder Haar and Suomi 1971 ), understanding climate forcing and feedbacks ( Futyan et al. 2005 ; Loeb et al. 2007 ; Brindley and Russell 2009 ; Dessler 2013 ; Ansell et al. 2014 ), and evaluating and improving climate models ( Forster and Gregory 2006 ; Tett et al. 2013a , b ; Hartmann and Ceppi 2014 ). Although the RSR has most commonly been observed

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Kevin E. Trenberth, Yongxin Zhang, John T. Fasullo, and Lijing Cheng

comprehensively evaluated for conservation properties by Berrisford et al. (2011) and for air temperatures and humidity ( Simmons et al. 2010 , 2014 ) and the water and energy cycles ( Trenberth et al. 2011 ; Trenberth and Fasullo 2013 ). ERA-I did not include comprehensive TOA forcings and volcanic aerosols, such as those from the eruption of Mount Pinatubo in 1991, and the TOA radiation is biased ( Trenberth and Fasullo 2013 ). Accordingly, we have here confined diagnostics to after 2000. The budget

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Christopher M. Thomas, Bo Dong, and Keith Haines

Ocean Reanalysis 4 (UR025.4; Haines et al. 2012 ), and Global Ocean Reanalysis 2 (GLORYS2v4; Ferry et al. 2012 ). Each reanalysis was produced using the ocean model NEMO v3 coupled to the Louvain-la-Neuve sea ice model (LIM) v2 with a model resolution of 0.25°. All products assimilated sea surface temperature (in situ observations, satellite data, or both), sea level from satellite altimetry, subsurface temperature and salinity profiles, and sea ice concentration, together with atmospheric forcing

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