Tracking climate models

Abstract

Climate models are complex mathematical models designed by meteorologists, geophysicists, and climate scientists, and run as computer simulations, to predict climate. There is currently high variance among the predictions of 20 global climate models, from various laboratories around the world, that inform the Intergovernmental Panel on Climate Change (IPCC). Given temperature predictions from 20 IPCC global climate models, and over 100 years of historical temperature data, we track the changing sequence of which model predicts best at any given time. We use an algorithm due to Monteleoni and Jaakkola that models the sequence of observations using a hierarchical learner, based on a set of generalized Hidden Markov Models, where the identity of the current best climate model is the hidden variable. The transition probabilities between climate models are learned online, simultaneous to tracking the temperature predictions.On historical global mean temperature data, our online learning algorithm's average prediction loss nearly matches that of the best performing climate model in hindsight. Moreover, its performance surpasses that of the average model prediction, which is the default practice in climate science, the median prediction, and least squares linear regression. We also experimented on climate model predictions through the year 2098. Simulating labels with the predictions of any one climate model, we found significantly improved performance using our online learning algorithm with respect to the other climate models and techniques. To complement our global results, we also ran experiments on IPCC global climate model temperature predictions for the specific geographic regions of Africa, Europe, and North America. On historical data, at both annual and monthly time-scales, and in future simulations, our algorithm typically outperformed both the best climate model per region and linear regression. Notably, our algorithm consistently outperformed the average prediction over models, the current benchmark.  2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 372-392, 2011

Improvement of climate predictions and reduction of their uncertainties using learning algorithms

“…Recently, sequential learning algorithms (SLAs) (CesaBianchi and Lugosi, 2006) were applied to ensembles of climate models in order to improve the predictions (Mallet et al, 2009;Mallet, 2010;Monteleoni et al, 2010Monteleoni et al, , 2011. Mallet et al (2009) andMallet (2010) combined data assimilation and SLAs in order to improve seasonal to annual ozone concentration forecasts.…”

“…Mallet et al (2009) andMallet (2010) combined data assimilation and SLAs in order to improve seasonal to annual ozone concentration forecasts. Monteleoni et al (2010Monteleoni et al ( , 2011 applied an improved version (Monteleoni and Jaakkola, 2003) of a method for learning non-stationary sequences (Herbster and Warmuth, 1998) to long-term climate predictions.…”

“…Monteleoni and Jaakkola (2003) improved this algorithm by learning the optimal switching rate between experts. This algorithm was already tested for long-term climate projections using the CMIP3 long-term experiments (Monteleoni et al, 2010(Monteleoni et al, , 2011, and here we also test its performance in decadal climate predictions for comparison with the EWA and EGA methods.…”

Climate Multi-model Regression Using Spatial Smoothing

“…These models can be broadly grouped in to three categories: (1) Weighted averaging models [22,23,47,18,26] (2) Bayesian models [42,43,18,35] and (3) Online learning models [31,27,21,8].…”

“…The posterior is obtained by combining the prior and likelihood of the model. In the online learning models [31], each GCM is modeled as an expert and for every time instance experts give predictions. The online learning approach takes one data point at a time and updates its confidence (weights) for each expert based on the accuracy of the recent prediction.…”