Comparison of radiative feedback variability over multiple time scales in climate model and reanalysis data Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/hh63sz97f

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  • In a steady state, the Earth's absorbed solar radiation (ASR) balances the outgoing longwave radiation (OLR) at the top of the atmosphere (TOA). In response to a radiative forcing, that is, an external perturbation to the top of the atmosphere energy balance, the Earth's climate system adjusts until reaching a new state of radiative equilibrium. For example, an increased amount of carbon dioxide in the atmosphere will absorb more terrestrial radiation thus decreasing the amount of outgoing longwave radiation at the top of the atmosphere. Since more energy is kept in the system, the global temperature rises, and the Earth emits more radiation until the OLR is in equilibrium with the incoming solar radiation. The magnitude of the climate response to an imposed forcing is dependent upon the strength of physical climate feedbacks within the system (i.e., water vapor, temperature, surface albedo, and clouds) which act to amplify or dampen the response. Global climate models project that the Earth's climate, represented by the globally averaged surface temperature, will warm between 2.0-4.5 Kelvin if we double the concentration of carbon dioxide in the atmosphere (Soloman et al., 2007). The differences in global climate model simulations of the climate response to an imposed forcing are largely due to differences in climate feedback strengths among individual models (Soloman et al., 2007). This thesis assesses how well short-term feedback variability relates to long-term feedbacks with the goal of using an observational dataset to ultimately constrain long-term feedback estimates. First, feedbacks and feedback variability are quantified on three time scales over two time periods in the 20th century as simulated by 13 global climate models. The three time scales are: annual, interannual, and decadal. These time scales are characterized, respectively, by the amplitude of the seasonal cycle, standard deviation of TOA flux anomalies, and least-squares linear trend of TOA flux anomalies. Second, time scales of feedback variability are compared over the two time periods. The two time periods are: 20-years (short-term) and 100-years (long-term). Third, modeled short-term feedback variability is compared with the European Center for Medium-range Weather Forecasts ERA-Interim reanalysis observational data product. The method used to quantify individual climate feedbacks in models is the radiative kernel technique (Soden et al., 2008). This technique decomposes each feedback into two components: the TOA flux change due to a standard change in the feedback variable (radiative kernel), and the change in the feedback variable due to a particular climate forcing (climate response). The radiative kernel technique can also be used effectively to analyze climate feedbacks in reanalysis datasets. Monthly departures from the mean of each feedback variable (specific humidity, atmospheric temperature, and surface albedo), at each grid point and vertical level, are multiplied by the corresponding radiative kernel (Shell et al., 2008) to obtain TOA radiative flux anomalies due to each variable. The annual cycle provides a better constraint than interannual or decadal variability on global and hemispheric long-term feedbacks. For water vapor and atmospheric temperature, this result is strong for both the northern and southern hemispheres. For surface albedo, the strongest relationship between the annual cycle and long-term feedback occurs in the southern hemisphere. However, using the annual cycle to estimate the long-term feedback still results in a large uncertainty. For atmospheric temperature and water vapor, the reanalysis observations of the annual cycle are within the range of models, but for surface albedo, the reanalysis annual cycle is smaller in magnitude than all models. Understanding the differences between modeled and observed annual, interannual, and decadal variability of climate feedbacks and corresponding TOA flux anomalies and how they relate to climate sensitivity will help reduce the uncertainty associated with future climate projections.
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