by Judith Curry
The CMIP5 decadal simulations are now available for seven climate models. The first intercomparison results have just been published.
Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts
Hyemi Kim, Peter Webster, Judith Curry
Abstract. 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 initial- ized 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 west- ern 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 ensem- ble has in general better-forecast quality than the single-model systems for global mean surface temperature, AMO and PDO.
Citation: 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.
Links: full paper [Kim et al. 2012_GRL] and auxiliary material [Kim et al. 2012_GRL_Auxiliary]
The CMIP5 simulations were described in a previous post.
Background information from the Introduction:
The prediction of decadal climate variability against a background of global warming is one of the most important and challenging tasks in climate science. Not only does natural variability have a large-amplitude influence over broad regions of the globe, it is an integral component of climate variability that modulates low-frequency climate phenomena as well as extreme climate events such as trop- ical cyclone activity. On decadal timescales, some aspects of internal climate variability may be predictable . However, the actual prediction skill of natural climate variability on decadal timescales using various current climate models has received little attention.
The Coupled Model Intercomparison Project Phase 5 (CMIP5) has devised an innovative experimental design to assess the predictability and prediction skill on decadal time scales of state-of-the-art climate models, in support of the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report [Taylor et al., 2012]. The decadal pre- dictability and prediction skill of individual models have been analyzed separately for multi-year prediction horizons over different time periods and regions [Pohlmann et al., 2009; Fyfe et al., 2011; Chikamoto et al., 2012; Mochizuki et al., 2012]. However, the CMIP5 decadal predictions from different models have not been evaluated and com- pared using the same evaluation matrix. The choice of one model over the other, or the use of sets of models in a multi- model ensemble (MME), requires information that compares the predictions of individual models. Here, we compare the ability of currently available CMIP5 decadal hindcasts to simulate the mean climate and decadal climate variability from individual coupled models and a multi-model ensem- ble. We focus on the surface temperature and two dominant internal climate modes: the Atlantic Multidecadal Oscilla- tion (AMO) and Pacific Decadal Oscillation (PDO). This study addresses how well the CMIP5 multi-model decadal hindcasts simulate the spatio-temporal climate variability.
JC comment: this post is cut short by my hopping on a plane to head back to the US. This is a technical thread, comments will be moderated for relevance. I will be back online tomorrow and will provide further comments and participate in the discussion.