ALBERT

Notes: Abstract. This study considers an ensemble of six 10-year climate simulations conducted with the Canadian Climate Centre 2nd generation General Circulation Model (CCC GCM2). Each simulation was forced according to the Atmospheric Model Intercomparison Project (AMIP) experimental protocol using monthly mean sea surface temperatures and sea-ice extents based on observations for January, 1979 to December 1988. One simulation, conducted on a CRAY computer, was initiated from analysed 1 January 1979 conditions while the remaining 5 simulations, conducted on a NEC computer, were initiated from previously simulated model states obtained from a long control integration. The interannual variability and potential predictability of simulated and observed 500 hPa geopotential, 850 hPa temperature and 300 hPa stream function are examined and inter-compared using statistical analysis of variance techniques to partition variance into a number of components. The boundary conditions specified by AMIP are found to induce statistically significant amounts of predictable variance on the interannual time scale in the tropics and, to a lesser extent, at extratropical latitudes. In addition, local interactions between the atmosphere and the land surface apparently induce significant amounts of potentially predictable interannual variance in the tropical lower atmosphere and also at some locations in the temperate lower atmosphere. No evidence was found that the atmosphere's internal dynamics on their own generate potentially predictable variations on the interannual time scale. The sensitivity of the statistical methods used is demonstrated by the fact that we are able to detect differences between the climates simulated on the two computers used. The causes of these physically insignificant changes are traced. The statistical procedures are checked by confirming that the choice of initial conditions does not lead to significant inter-simulation variation. The simulations are also interpreted as an ensemble of climate forecasts that rely only on the specified boundary conditions for their predictive skill. The forecasts are verified against observations and against themselves. In agreement with other studies it was found that the forecasts have very high skill in the tropics and moderate skill in the extratropics.

Notes: Abstract.  The inter-annual variability and potential predictability of 850 hPa temperature (T 850), 500 hPa geopotential (φ500) and 300 hPa stream function (ψ300) simulated by the models participating in the Atmospheric Model Intercomparison Project (AMIP) are examined. The total inter-annual variability is partitioned into a potentially predictable component which arises from the forcing implied by the prescribed SST and sea-ice evolution, or from sources internal to the simulated climate, and an unpredictable low frequency component induced by “weather noise”. There is wide variation in the ability to simulate observed inter-annual variability, both total and weather-noise induced. A majority of models under simulate seasonal mean φ500 variability in DJF and JJA and over simulate ψ300 variability in JJA. All but one model simulates less T 850 total inter-annual variability than in the analysed data. There is little apparent connection between gross model characteristics and the corresponding ability to simulate observed variability, with the possible exceptions of resolution.

Notes: Abstract  Understanding natural atmospheric decadal variability is an important element of climate research, and here we investigate the geographic and seasonal diversity in the balance between its competing sources. Data are provided by an ensemble of multi-decadal atmospheric general circulation model experiments, forced by observed sea surface temperatures (SSTs), and verified against observations. First, the nature of internal atmospheric variability is studied. By assessing its spectral character, we refute the idea that internal modes may persist or oscillate on multi-annual time-scales, either through mechanisms purely internal to the atmosphere, or via coupling to the land surface; instead, they behave as a white noise process. Second, and more importantly, the role of oceanic forcing, relative to internal variability, is investigated by extending the ‘analysis of variance’ technique to the frequency domain. Significance testing and confidence intervals are also discussed. In the tropics, atmospheric decadal variability is usually dominated by oceanic forcing, although for some regions less so than at interannual time-scales. A moderate oceanic impact is also found for some extratropical regions in some seasons. Verification against observed mean sea-level pressure (MSLP) data suggests that many of these influences are realistic, although some model errors are also revealed. In other mid- and high-latitude regions, local simulated decadal variability is dominated by random processes, i.e. the integrated effects of chaotic weather systems. Third, we focus on the mechanisms of decadal variability in two specific regions (where the model is well behaved). Over the tropical Pacific, the relative impact of SSTs on decadal MSLP is strongly seasonal such that it peaks in September to November (SON). This is explained by noting that the model atmosphere is responsive to SSTs a little farther west in SON than it is in other seasons, and here it picks up relatively more decadal power from the ocean (the western Pacific being less dominated by ENSO time-scales), causing atmospheric ‘signal-to-noise ratios’ to be enhanced at decadal timescales in SON. Over southern North America, a strong SST impact is found in summer and autumn, resulting in an upward trend of MSLP over recent decades. We suggest this is caused by decadal SST variability in the Caribbean (and to some extent the tropical northeast Pacific in summer), which induces anomalous convective heating over these regions and hence the wider MSLP response.

Notes: Abstract.  This study describes a method of calculating the mean squared error (MSE) incurred when estimating the spherical harmonic coefficients of a climatological field that is sampled at a small network of points. The method can also be applied to the coefficients of any other set of orthonormal basis functions that are defined on the sphere. It, therefore, provides a formalism that can be applied in a variety of contexts, such as in climate change detection, where inferences are attempted about fingerprint coefficients that are imperfectly estimated from observational data. By incorporating the fingerprint as part of a set of basis functions, the methodology can be used to estimate the sampling error in the fingerprint coefficient. The MSE is expressed in terms of the spherical harmonics (or other orthonormal expansion) of the empirical orthogonal functions (EOFs), the locations of the points in the network and a set of weights that are applied at these points. The weights are optimised by minimising the expected MSE. The method is applied to a number of network configurations using monthly-mean screen temperature and 500 mb height simulated by the Canadian Climate Centre 2nd generation general circulation model in an ensemble of six 10-year simulations. In comparison with uniform weighting, optimal weighting can reduce the MSE by an order of magnitude or more for some spherical harmonic coefficients and some network configurations. Also, the MSEs vary seasonally for each network. In particular, the relative MSE of low order spherical harmonic coefficients is found to be larger in DJF than in JJA. We demonstrate how MSEs improve with increasing network density and identify graphically, the coefficients that can be estimated reliably with each network configuration.