Assessment of different metrics for physical clima

Authors: Daniel Klocke Johannes Quaas Bjorn Stevens

Publish Date: 2013/04/12

Volume: 41, Issue: 5-6, Pages: 1173-1185

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Abstract

We quantify the feedbacks from the physical climate system on the radiative forcing for idealized climate simulations using four different methods The results differ between the methods and differences are largest for the cloud feedback The spatial and temporal variability of each feedback is used to estimate the averaging scale necessary to satisfy the feedback concept of one constant global mean value We find that the yeartoyear variability combined with the methodological differences in estimates of the feedback strength from a single model is comparable to the modeltomodel spread in feedback strength of the CMIP3 ensemble The strongest spatial and temporal variability is in the shortwave component of the cloud feedback In our simulations where many sources of natural variability are neglected longterm averages are necessary to get reliable feedback estimates Considering the large natural variability and relatively small forcing present in the real world as compared to the forcing imposed by doubling CO2 concentrations in the simulations implies that using observations to constrain feedbacks is a challenging task and requires reliable longterm measurementsClimate models still give a wide range of surface temperature responses to the same idealized external forcing for example a doubling of the atmospheric CO2 concentration Solomon et al 2007 Most of these differences arise from physical processes which are usefully conceptualized as feedbacks and can be isolated through a feedback analysis Cess et al 1990 Colman 2003 Soden and Held 2006 A variety of methods have been developed to isolate specific feedback mechanisms in climate models raising the question as to how sensitive the results of such an analysis are to the methods employedThe linearization in Eq 2 is useful to disaggregate contributions of individual processes to the overall feedback and to estimate their relative importance The quantification of individual feedbacks then allows one to compare models and quantify how various processes contribute to the overall uncertainty as measured by the differences in the climate sensitivity across models eg Bony and Dufresne 2005 Bony et al 2006All processes in the climate system change in concert when the climate is changing as measured by the change in global mean surface temperature Different methods can be utilized to break down λ into the different contributions all having in common that forcing and response are separated How parts of the contributions are separated into forcing or response depends on the framework that is adopted For instance whether one adopts relative humidity or absolute humidity as a thermodynamic coordinate has a bearing on what will be identified as a feedback Held et al 2012Distinctions between feedbacks can also be arbitrary if different physical feedbacks are related to the same processes For example the water vapor feedback and the tropospheric temperature lapse rate feedback are anticorrelated In models for which the lapse rate feedback is strongly negative ie the lapse rate is reduced leading to a decrease in the greenhouse effect the water vapor feedback is strongly positive The reason is that both feedbacks are related to the same mechanism which is a change in deep convection A weaker temperature lapse rate is generated by a greater warming at high altitudes than at the surface due to heat transport by convection At the same time enhanced convection also leads to more upper tropospheric water vapor eg Cess 1975 Held and Soden 2000 For this reason these two feedbacks are often added together and considered as a single feedback λWV+LR in which they partly compensate each other By this the intermodel spread in the strength of this combined feedback is reduced Huybers 2010 reports further compensations between different feedbacks especially surface albedo and cloud feedback but argues that those relations can in fact be an artifact due to the methods used to estimate the feedbacks the representation of physical relationships in the models or how the models are conditioned on some combination of observations and expectationsThe concept of feedbacks forcing and climate sensitivity has proved to be helpful in the idealized model world but extrapolation to the real world has proven to be complicated Partial derivatives can hardly be derived from observations due to many interfering processes that are difficult to separate and to isolate from the background variability But even in a model it can be difficult to isolate processes and estimate feedbacks and as a result different methods have been developed to estimate the strength of feedbacks within models And the question arises as to what extent estimates of feedback strength depend on methodological detailsAlthough the feedback parameters are defined in Eq 2 are intensive properties of the climate system they are often estimated locally in space and time By estimating these properties by averaging over local properties the question arises as to how well such intensive properties are sampled Insufficient sampling for instance over time periods that are small compared to the timescales of internal fluctuations within the climate system may lead to biased estimates of feedback strengths A feedback estimated for a certain year may be very different in other years and may depend on the nature of the fluctuations so that the necessary averaging time may be different for different physical processes The largest problem arises for clouds which are highly variable in space and time and tend to fluctuate strongly in association with other internal fluctuations within the climate system This has implications for quantifying feedbacks from climate models and for deriving feedback factors from observations or finding observational constraintsThe aim of this study is to compare and assess different methods for quantifying the strength of specific feedbacks and to analyze the spatiotemporal variability that arise in the local contribution to the estimates of the overall feedback To do so we use climate model simulations with the atmospheric general circulation model ECHAM5 Roeckner et al 2003 coupled to a mixedlayer ocean This idealized framework neglects factors contributing to natural variability such as volcanic eruptions El Niño variability and varying modes of ocean circulations as well as less well defined contributions to the forcing such as from anthropogentic aerosols or land use change

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