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Cheng et al. (2019b) . Representative concentration pathway scenario with radiative forcing of 4.5 W m −2 at 2100 (RCP4.5) projections were used to extend CMIP5 historical simulations beyond 2005. However, a multimodel mean averages out the internal natural variability, although external variability, such as from volcanic eruptions and greenhouse gases, becomes emphasized. Although CMIP6 models are recently available they underestimate the ocean trend for the upper 2000 m according to the
Cheng et al. (2019b) . Representative concentration pathway scenario with radiative forcing of 4.5 W m −2 at 2100 (RCP4.5) projections were used to extend CMIP5 historical simulations beyond 2005. However, a multimodel mean averages out the internal natural variability, although external variability, such as from volcanic eruptions and greenhouse gases, becomes emphasized. Although CMIP6 models are recently available they underestimate the ocean trend for the upper 2000 m according to the
. 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
. 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
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
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
redistributed by the ocean is essential for predicting future changes in sea level. Numerical ocean models forced with historical atmospheric conditions have proved to be useful tools in quantifying how variability in atmospheric forcing can set variability in OHC ( Drijfhout et al. 2014 ) and sea level ( Penduff et al. 2011 ) at interannual to decadal time scales. However, such models can be unrealistic for simulating multidecadal climate change because of model drift and inaccuracies in long-term changes
redistributed by the ocean is essential for predicting future changes in sea level. Numerical ocean models forced with historical atmospheric conditions have proved to be useful tools in quantifying how variability in atmospheric forcing can set variability in OHC ( Drijfhout et al. 2014 ) and sea level ( Penduff et al. 2011 ) at interannual to decadal time scales. However, such models can be unrealistic for simulating multidecadal climate change because of model drift and inaccuracies in long-term changes
the ocean, mean sea level, and climate—are required along with Coupled Model Intercomparison Project (CMIP) simulations ( Eyring et al. 2016 ) to attribute the detected changes to natural and anthropogenic radiative forcing ( Bilbao et al. 2019 ; Gleckler et al. 2012 ; Marcos et al. 2017 ; Slangen et al. 2014 ; Tokarska et al. 2019 ) and to constrain uncertainties in CMIP projections used in policy-making and risk management ( Carson et al. 2019 ; Lyu et al. 2021 ; IPCC 2019 ; van de Wal et
the ocean, mean sea level, and climate—are required along with Coupled Model Intercomparison Project (CMIP) simulations ( Eyring et al. 2016 ) to attribute the detected changes to natural and anthropogenic radiative forcing ( Bilbao et al. 2019 ; Gleckler et al. 2012 ; Marcos et al. 2017 ; Slangen et al. 2014 ; Tokarska et al. 2019 ) and to constrain uncertainties in CMIP projections used in policy-making and risk management ( Carson et al. 2019 ; Lyu et al. 2021 ; IPCC 2019 ; van de Wal et
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
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
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
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
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
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
state-of-the-art coupled climate models are computationally expensive, which makes a “spinup” period of many thousands of years impractical. Instead, models are generally spun up for a few hundred years. Experiments will therefore exhibit changes/trends associated with incomplete model spinup, as well as changes related to external forcing or internal climate variability. The overall reduction in drift from CMIP2+ to CMIP5 has been primarily attributed to longer spinup times and more careful
state-of-the-art coupled climate models are computationally expensive, which makes a “spinup” period of many thousands of years impractical. Instead, models are generally spun up for a few hundred years. Experiments will therefore exhibit changes/trends associated with incomplete model spinup, as well as changes related to external forcing or internal climate variability. The overall reduction in drift from CMIP2+ to CMIP5 has been primarily attributed to longer spinup times and more careful
-thirds of the precipitation falling over the continents, terrestrial evaporation is the second largest hydrological flux over land ( Gimeno et al. 2010 ; Miralles et al. 2011 ). Its fast response to radiative forcing makes evaporation an early diagnostic of changes in climate, while its pivotal influence on land–atmosphere interactions leads to either amplification or dampening of weather extremes such as droughts or heatwaves ( Miralles et al. 2019 ; Seneviratne et al. 2010 ). Today, terrestrial
-thirds of the precipitation falling over the continents, terrestrial evaporation is the second largest hydrological flux over land ( Gimeno et al. 2010 ; Miralles et al. 2011 ). Its fast response to radiative forcing makes evaporation an early diagnostic of changes in climate, while its pivotal influence on land–atmosphere interactions leads to either amplification or dampening of weather extremes such as droughts or heatwaves ( Miralles et al. 2019 ; Seneviratne et al. 2010 ). Today, terrestrial
