This is one of 16 Rapid Response (RAPID) projects funded as the result of a Dear Colleague Letter (NSF 11-006) encouraging diagnostic analyses of climate model simulations prepared for the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Research conducted in these projects is expected to lead to more detailed model intercomparisons, better understanding of robust model behaviors, and better understanding and quantification of uncertainty in future climate simulations.
Research conducted in this project examines decade-to-decade natural variability in the ensemble of simulations of 20th century climate prepared for the AR5. Key questions addressed are 1) What portion of the observed global warming can be explained by natural decadal variability? In particular, to what extent was the mid-20th century warming anthropogenically induced or the result of natural variability? 2) How are decadal oscillations interlinked and how do they modulate extreme climate events such as heat waves and drought, floods and tropical cyclones? 3) How well do the models simulate the decadal variability of late 19th-20th century? One reason these questions are important is that the 20th century record of global-mean temperature contains prominent decadal fluctuations, and the extent to which these occurred as the result of natural variability rather than anthropogenic activity (changes in aerosol forcing, for instance) is not known. Natural decadal fluctuations will continue to be superimposed on anthropogenic warming, and the extent to which these fluctuations are predictable is an important question for understanding and predicting both changes in mean climate and changes in the risk of extreme climate events. Decadal climate variability patterns will be derived from climate model output and related to extreme climate risk using statistical methods, and the potential predictability of the fluctuations will be assessed through calculation of signal-to-noise ratios, composite and regression analyses, and other statistical methods.
The broader impact of the project lies in its support of the IPCC AR5, which is intended to provide information on climate change and its consequences to decision makers worldwide. Decadal climate variability affects people all over the world, and a better understanding of decadal variability would thus be of great societal benefit.
This is one of 16 Rapid Response (RAPID) projects funded as the result of a Dear Colleague Letter (NSF 11-006) encouraging diagnostic analyses of climate model simulations prepared for the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Award title is "RAPID: Prediction and Predictability for the Multi-decadal Variability in a Changing Climate" This study assesses the CMIP5 decadal hindcast/forecast simulations of seven state-of-the-art ocean-atmosphere coupled models. Each decadal prediction consists of simulations over a 10 year period each of which are initialized every five years from climate states of 1960/1961 to 2005/2006. Most of the models overestimate trends, whereby the models predict less warming or even cooling in the earlier decades compared to observations and too much warming in recent decades. All models show high prediction skill for surface temperature over the Indian, North Atlantic and western Pacific Oceans where the externally forced component and low-frequency climate variability is dominant. However, low prediction skill is found over the equatorial and North Pacific Ocean. The Atlantic Multidecadal Oscillation (AMO) index is predicted in most of the models with significant skill, while the Pacific Decadal Oscillation (PDO) index shows relatively low predictive skill. The multi-model ensemble has in general better-forecast quality than the single-model systems for global mean surface temperature, AMO and PDO. The merit of the research is in providing the scientific community with an enhanced understanding of (a) the climate system on decadal timescales and (b) the prediction skill of decadal variability by climate model simulations through intercomparison and comparison with observation. The proposed research advanced our understating of multi-decadal variability and provide guidance for improving the representation and prediction of decadal oscillations. Kim, H. M., P. J. Webster and J. A. Curry, 2012: Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts, Geophys. Res. Lett., 39, L10701, doi:10.1029/2012GL051644