Intercomparison of interannual variability and potential predictability: an AMIP diagnostic subproject

 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. Received: 7 July 1996 / Accepted: 8 January 1998     

Signal detectability in extreme precipitation changes assessed from twentieth century climate simulations

This study assesses the detectability of external influences in changes of precipitation extremes in the twentieth century, which is explored through a perfect model analysis with an ensemble of coupled global climate model (GCM) simulations. Three indices of precipitation extremes are defined from the generalized extreme value (GEV) distribution: the 20-year return value (P ₂₀), the median (P _m), and the cumulative probability density as a probability-based index (PI). Time variations of area-averages of these three extreme indices are analyzed over different spatial domains from the globe to continental regions. Treating all forcing simulations (ALL; natural plus anthropogenic) of the twentieth century as observations and using a preindustrial control run (CTL) to estimate the internal variability, the amplitudes of response patterns to anthropogenic (ANT), natural (NAT), greenhouse-gases (GHG), and sulfate aerosols (SUL) forcings are estimated using a Bayesian decision method. Results show that there are decisively detectable ANT signals in global, hemispheric, and zonal band areas. When only land is considered, the global and hemispheric detection results are unchanged, but detectable ANT signals in the zonal bands are limited to low latitudes. The ANT signals are also detectable in the P _m and PI but not in P ₂₀ at continental scales over Asia, South America, Africa, and Australia. This indicates that indices located near the center of the GEV distribution (P _m and PI) may give better signal-to-noise ratio than indices representing the tail of the distribution (P ₂₀). GHG and NAT signals are also detectable, but less robustly for more limited extreme indices and regions. These results are largely insensitive when model data are masked to mimic the availability of the observed data. An imperfect model analysis in which fingerprints are obtained from simulations with a different GCM suggests that ANT is robustly detectable only at global and hemispheric scales, with high uncertainty in the zonal and continental results.     

An improved scheme for time-dependent boundary conditions in atmospheric general circulation models

 Atmospheric general circulation models (AGCMs) are often “coupled” with time varying observations of boundary conditions or some other aspect of the climate system. A typical example is the Atmospheric Model Intercomparison Project (AMIP) experimental protocol, which required the specification of sea surface temperature and sea-ice extent from observed monthly means. AGCMs ordinarily incorporate the prescribed conditions by evaluating an interpolating function at each time step. Typical schemes, such as that used in the second generation GCM (GCM2) of the Canadian Centre for Climate Modelling and Analysis (CCC), do not preserve monthly means and have a smoothing effect on the interpolated time series which tends to reduce the amplitude of annual cycle and interannual variability of sea surface temperature (SST). By solving a large set of linear equations, a simple linear time-interpolation scheme that preserves the observed monthly mean SST and hence its variability can be obtained. The new scheme improves upon that used previously in CCC GCM2 by eliminating the substantial loss of interannual variability (up to 20%) and the small attenuation of the annual cycle (less than 4% on average) incurred with the old scheme. The improved linear interpolation scheme is easily adapted to other quantities. Received: 4 August 1997 / Accepted: 26 November 1997     

Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2

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. Received: 18 December 1995 / Accepted: 4 April 1996     

Covariability of SST and surface heat fluxes in reanalyses and CMIP3 climate models

The generation and dissipation of SST anomalies is mediated by the covariability of SST and surface heat fluxes. The connection between the variability of heat flux (including its radiative and turbulent components) and that of SST is investigated using the NCEP-NCAR and ERA-40 reanalyses and the CMIP3 multi-model collection of climate simulations. The covariance patterns of SST and heat flux are broadly similar in the two reanalyses. The upward heat fluxes are positively correlated with the SST anomalies in the tropics, the northern Pacific mid-latitudes, and over the Gulf Stream, and negatively correlated in the northern subtropics and the SPCZ region. Common covariance features are seen in all climate models in the tropics and the subtropics, while covariances differ considerably among models at northern mid-latitudes, where weak values of the ensemble mean are seen. Lagged covariances are broadly similar in the two reanalyses and among the models, implying that heat flux feedback is also similar. The heat flux feedback parameter is determined from the lagged cross-covariances together with the auto-covariance of SST. Feedback is generally negative and is dominated by the turbulent component. The strongest feedback is found at mid-latitudes in both hemispheres, with the largest values occurring in the western and central portions of the oceans with extensions to higher latitudes. The latter are also areas with large inter-model differences. The heat flux feedback strengthens in winter and fall and weakens in spring and summer. The magnitudes of the annual and seasonal feedback parameters are slightly weaker in most models compared to the reanalysis-based estimates. The mean model feedback parameter has the best pattern correlation and the smallest mean square difference compared to the reanalysis-based values, although spatial variances are weak. Model resolution shows no relationship with the heat flux feedback parameters obtained from model results. The SST-heat flux covariance is decomposed into components associated with surface heat flux feedback and atmospheric forcing processes. Heat flux feedback dominates over the atmospheric forcing and heat flux damps SST anomalies on average at northern Pacific mid-latitudes and southern Atlantic mid-latitudes; while the reverse occurs in the SPCZ and northern Atlantic mid-latitudes.     

Probabilistic estimates of recent changes in temperature: a multi-scale attribution analysis

The role of anthropogenic forcings in temperature changes during recent decades is investigated over a range of spatial scales. Changes in the annual mean surface temperature and also in the warmest night of the year, which has implications for human health, are considered. Distributions of regional trends with and without the effect of human activity are produced, using constraints from a global optimal detection analysis. Anthropogenic forcings are estimated to have more than doubled the likelihood of mean warming in all regions considered except central North America, where results are more model dependent. The likelihood of warming of the warmest night has also increased, but the estimated change is more uncertain. Inferences on sub-continental scales are indicative rather than definitive because of the absence of locally important forcings and processes in model simulations, as well as model biases. As model inconsistencies may impact regional analyses, a multi-model approach is essential.     

Skill of seasonal hindcasts as a function of the ensemble size

 Forecast skill as a function of the ensemble size is examined in a 24-member ensemble of northern winter (DJF) hindcasts produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis. These integrations are initialized from the NCEP reanalyses at 6 h intervals prior to the forecast season. The sea surface temperatures that are applied as lower boundary conditions are predicted by persisting the monthly mean anomaly observed prior to the forecast period. The potential predictability that is attributed to lower boundary forced variability is estimated. In lagged-average forecasting, the forecast skill in the first two weeks, which originates predominately from the initial conditions, is greatest for relatively small ensemble sizes. The forecast skill increases monotonically with the ensemble size in the rest of the season. The skill of DJF 500 hPa geopotential height hindcasts in the Northern Hemisphere and in the Pacific/North America sector improves substantially when the ensemble size increases from 6 to 24. A statistical skill improvement technique based on the singular value decomposition method is also more successful for larger ensembles. Received: 22 February 2000 / Accepted: 6 December 2000