Estimations of climate sensitivity based on top-of-atmosphere radiation imbalance

Abstract

Abstract. Large climate feedback uncertainties limit the accuracy in predicting the response of the Earth's climate to the increase of CO 2 concentration within the atmosphere. This study explores a potential to reduce uncertainties in climate sensitivity estimations using energy balance analysis, especially top-of-atmosphere (TOA) radiation imbalance. The time-scales studied generally cover from decade to century, that is, middle-range climate sensitivity is considered, which is directly related to the climate issue caused by atmospheric CO 2 change. The significant difference between current analysis and previous energy balance models is that the current study targets at the boundary condition problem instead of solving the initial condition problem. Additionally, climate system memory and deep ocean heat transport are considered. The climate feedbacks are obtained based on the constraints of the TOA radiation imbalance and surface temperature measurements of the present climate. In this study, the TOA imbalance value of 0.85 W/m 2 is used. Note that this imbalance value has large uncertainties. Based on this value, a positive climate feedback with a feedback coefficient ranging from −1.3 to −1.0 W/m 2 /K is found. The range of feedback coefficient is determined by climate system memory. The longer the memory, the stronger the positive feedback. The estimated time constant of the climate is large (70∼120 years) mainly owing to the deep ocean heat transport, implying that the system may be not in an equilibrium state under the external forcing during the industrial era. For the doubled-CO 2 climate (or 3.7 W/m 2 forcing), the estimated global warming would be 3.1 K if the current estimate Correspondence to: B. Lin (bing.lin@nasa.gov) of 0.85 W/m 2 TOA net radiative heating could be confirmed. With accurate long-term measurements of TOA radiation, the analysis method suggested by this study provides a great potential in the estimations of middle-range climate sensitivity.

Bayesian estimation of climate sensitivity using observationally constrained simple climate models

“…Since MM2009, a number of papers based on SCMs and the Earth's energy balance have been produced, but few were observationally based calibration studies employing Bayesian methods. A further four were identified for this review: Aldrin et al, Bodman et al, Skeie et al and Johansson et al (hereinafter respectively Aldrin2012, Bodman2013, Skeie2014, and Johansson2015).…”

Can climate sensitivity be estimated from short-term relationships of top-of-atmosphere net radiation and surface temperature?

“…In these plots, the memory length t m of the climate system is set to be one year. From GISS global surface temperature measurements, Lin et al [12] estimated that the climate system has at least about eight years of memories. The reason of one-year memory used in this study is to show that climate feedbacks do not fully exhibit in the shortterm relationship (or the linear striations) between surface temperature and TOA net radiation even for a climate system with a minimal length of memory.…”

“…Thus, the À 6.0 W/m 2 /K climate radiative feedback parameter estimated from CERES short-term measurements might not be considered as the total feedback parameter, and its predicted corresponding 0.6 K potential warming for the 2 Â CO 2 atmosphere (3.7 W/m 2 forcing) may not be reliable. More importantly, as discussed in [12], climate feedbacks and sensitivities strongly depend on time scales. Different time scales have different inherent climate physical, chemical and biological processes and thus, could have different memories, feedbacks and sensitivities.…”

“…As mentioned in the introduction, the actual climate system also has certain memories for climate states. These memories may be a result of different climate physical processes such as those associated with soil moisture, frozen soil/snow/ice in cold regions, and oceanic circulations [12]. Thus, a feedback term for climate system memory f m is added to Eq.…”

“…The major difference is that a more realistic climate system with system memory is considered. As pointed out by Lin et al [12], the actual climate system has certain memories for climate states. For example, the global mean surface temperature anomalies of the Goddard Institute for Space Studies (GISS; [13]; updated at http://data.giss.nasa.gov/ gistemp/) exhibit significant memories up to about eight years in their autocorrelation function (95% or higher confidence level).…”