1. SLIMM versus GCMs Leila Sloman, Shaun Lovejoy, Lenin del Rio Amador, Weylan Thompson, and David Huard I’m not sure we need him as author?? Separating Natural Variability from External Trends Two types of external trends were present in the original data: the annual cycle and anthropogenic warming. The annual cycle was removed by smoothing the spikes in the power spectrum resulting from the annual cycle. The anthropogenic trend was removed using a high pass filter. Below, the power spectrum of the data at each step of the detrending process. Abstract The prediction of atmospheric variables on monthly and yearly time scales is of interest not only to the scientific community, but also the general public. The conventional approach to making such forecasts is through the use of general circulation models, or GCMs, which simulate a future for the earth's atmosphere using known initial conditions. The potential of a simpler alternative to GCMs for three-month (seasonal) forecasts is assessed. Introduction Recent work has shown that on time scales between 10 days and several dozen years, the natural variability of atmospheric fields like temperature and precipitation rate can be modelled as: with H between -1/2 and 0. This process is a fractional Gaussian noise, and makes forecasts of the temporal evolution as where γ is the natural variability of φ, 0 is the present time, and t 0 is the length in time of the available data set. This is the basis of the Scaling Macroweather Model (SLIMM). A discrete implementation of this model, the Wiener predictor, is applied to various data sets and assessed in comparison to GCMs in terms of the skill, Sk, a nondimensional measure of the error in the prediction. Two monthly temeprature datasets were used: the 20 th Century Reanalysis released by the National Oceanic & Atmospheric Administration, and the Canadian Season to Inter-annual Prediction System (CanSIPS) reanalysis and hindcasts produced by the Canadian Centre for Climate Modelling and Analysis (CCCma). Hindcasts versus Theory this headline only covers part of this box The Scaling Macroweather Model (SLIMM) and Regional, Seasonal Temperature Forecasting Original power spectrum.Power spectrum after removing annual cycle.Power spectrum of final product. Map of skills for one-season seasonal SLIMM hindcasts on data from the 20th Century Reanalysis. Mean: (20±10)%. Map of theoretical skills for one-season seasonal hindcasts on data from the 20th Century Reanalysis. Mean: (20±20)%. Map of difference between the actual and theoretical skill on 20 th Century Reanalysis data. Mean: (0±10)%. Map of skills for one one-season seasonal SLIMM hindcasts on data from the CCCma. Mean: (10±20)%. Map of theoretical skills for one-season seasonal SLIMM hindcasts on data from the CCCma. Mean: (20±20)%. Map of difference between actual and theoretical skill on CCCma data. Mean: (0±10)%. Map of fractional reduction in error by changing hindcast from CanSIPS to SLIMM for 3-month (seasonal) forecasts. Mean: (30±20)%. Globally averaged hindcast errors compared to theoretical 95% confidence interval. Globally averaged hindcast errors compared to standard deviation of natural variability for hindcasts on CCCma data.. Hindcast errors over Montreal compared to standard deviation of natural variability for hindcasts on CCCma data. Legend for the above plots. Acknowledgements Thanks to David Huard and everyone else at Ouranos for supporting my work this summer, and to everyone in the Lovejoy group at McGill for their help in supervising my work. Theoretical skill vs. H value. Map of skills for one-season seasonal CanSIPS hindcasts on data from the CCCma. Mean: (0±100)%. Use - infinity for limit what you wrote dwon is only an approximation and will confuse people Leila: in its latest incarnation, SLIMM is a space-time process with fGn in time quit epossibly multifrctal in space. Don’t say SLIMM=fGn According to Lenin, the Ccma is has worse skill at about 90% of the pixels where is that map?? You need the map of the Cansips skill!!!