After that, the five GCMs were the averaged FRAISIMIP over all regions is 33 %. The 8-year low-pass filter was achieved by smoothing the data with a 5-point binomial filter (weights 1-3-4-3-1) for each season separately (Trenberth et al. Soc., 78, 2539–2558, The wet bias mainly resides near the British Columbia coast in winter and over on the eastern side of the Canadian Rockies in summer. The relationship between the These limits are not caused by model errors; they correspond to limits on the range of useful forecasts that would exist even if nature behaved exactly as the model behaves. The annular modes account for similar percentages of the total variance in both models, with more variance explained by the SAM compared to the NAM especially in DJF (60–65% compared to 36–37%). We now turn to analyzing the CAM3 integrations with single forcings, that is, the atmospheric model integrations with SSTs and atmospheric constituents altered independently. The leading EOF of extra-tropical SLP trends for each season (DJF and JJA) and hemisphere are shown in Fig. possible uncertainty range of future projections. the minimum in Southeast Asia and the Arctic and nine at the maximum in Africa. impact assessments. The lagged-regression analysis for the ocean temperature variation relative to the wind stress variation indicates that interdecadal We explored the ability of the subsets of the CMIP5 multimodel ensemble used in Although the use of a single ocean initial condition may potentially underestimate the true internal variability of the simulated climate system, a recent predictability study using the same 40-member ensemble shows that the effect of ocean initial conditions is lost within 6–7 years for upper ocean (0–300 m) heat content, and even more rapidly for surface temperature (Branstator and Teng 2010). An underlying assumption of studies based on the CMIP3 archive is that the multi-model mean response to external forcing yields a more robust estimate of the forced climate signal than the response of any single model due to the reduction in uncertainty associated with model and internal variability (e.g., Tebaldi and Knutti 2007). 2016). This pattern is associated with the Northern Hemisphere annular mode (NAM) that accounts for about 1/3 of total variance of the spring sea level pressure (SLP) trends. By means of several different analysis techniques, the time variability of the leading EOF of the global SST field is separated into two components: one identified with the ''ENSO cycle-related'' variability on the inter- annual timescale, and the other a linearly independent ''residual'' comprising all the interdecadal variability in the record. Is there a relationship between the patterns of the forced response and the leading patterns of internal variability?

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