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\begin{document}

\title{UW GCC Abstract}



\author[1]{cihat kurt}%
\affil[1]{Texas A\&M University}%


\vspace{-1em}



  
  \date{May 07, 2024}


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We analyzed the climate feedbacks splitting into the components using
the ensemble of 28 different models from the Coupled Model
Intercomparison Project Phase 5 (CMIP5). Two different feedback schemes
were calculated and compared for two sets of model experiments (abrupt
quadruple CO2 increase and control simulations in which the forcing is
fixed to pre-industrial period values). In the first scheme, feedbacks
are calculated from a traditional energy balance equation which relates
the top of the atmosphere radiative flux anomalies to the surface
temperature anomalies. In the second framework, energy balance equation
is related~to 500hpa tropical atmospheric temperature anomalies. The
details of this new framework is~explained in a recent paper {[}1{]}.
They showed that the new framework produces a tighter~correlation with
the top of the atmosphere energy imbalance. One of the advantages of
using the second parameterization method for the energy balance equation
is to calculate the equivalent~climate sensitivity more accurately.
Furthermore, the effect of obtaining a tighter correlation when 500hpa
tropical atmospheric temperature anomalies are used as regressor~instead
of surface temperature anomalies is more pronounced in the
satellite~observations such as CERES data. Hence, the second scheme is
more suitable when studying climate sensitivity and feedbacks for the
observational data.~

~Top of the atmosphere fluxes associated with each feedback component
are calculated using radiative kernels. In general, feedbacks derived
from the control ensemble produce larger spread when the traditional
energy balance equation is used relative to the feedbacks computed using
the second scheme. This effect is also seen in the abrupt 4xCO2
simulations, however, to a lesser degree. The reason that the difference
between the two feedback schemes in terms of the spread is more
pronounced in the control simulations is the fact that control
simulations are mostly dominated by the internal variability whereas the
4xCO2 runs have strong forcing which produces a strong warming trend. In
this presentation, I will review our analyzes for each feedback
components both in terms of the surface and the tropical atmospheric
temperature including their spatial features and evolution in time.
Using the same energy balance equations, I will compare the equivalent
climate sensitivity (ECS) analyses made by using both schemes and
discuss the contribution of the individual feedback component to
uncertainties in ECS.

\par\null

~

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