The 2dF Galaxy Redshift Survey: luminosity dependence of galaxy clustering

We investigate the dependence of the strength of galaxy clustering on intrinsic luminosity using the Anglo-Australian two degree field galaxy redshift survey (2dFGRS). The 2dFGRS is over an order of magnitude larger than previous redshift surveys used to address this issue. We measure the projected two-point correlation function of galaxies in a series of volume-limited samples. The projected correlation function is free from any distortion of the clustering pattern induced by peculiar motions and is well described by a power law in pair separation over the range     . The clustering of     galaxies in real space is well-fitted by a correlation length     and power-law slope     . The clustering amplitude increases slowly with absolute magnitude for galaxies fainter than M *, but rises more strongly at higher luminosities. At low luminosities, our results agree with measurements from the Southern Sky Redshift Survey 2 by Benoist et al. However, we find a weaker dependence of clustering strength on luminosity at the highest luminosities. The correlation function amplitude increases by a factor of 4.0 between     and −22.5, and the most luminous galaxies are 3.0 times more strongly clustered than L * galaxies. The power-law slope of the correlation function shows remarkably little variation for samples spanning a factor of 20 in luminosity. Our measurements are in very good agreement with the predictions of the hierarchical galaxy formation models of Benson et al.     

A search for giant pulses in Vela-like pulsars

We have carried out a survey for 'giant pulses' in six young, Vela-like pulsars. In no cases did we find single pulses with flux densities more than 10 times the mean flux density. However, in PSR  B1706–44  we have detected giant micro-pulses very similar to those seen in the Vela pulsar. In PSR  B1706–44  these giant micro-pulses appear on the trailing edge of the profile and have an intrinsic width of ∼1 ms. The cumulative probability distribution of their intensities is best described by a power law. If the power law continues to higher intensities, then  3.7×10⁶  rotations are required to obtain a pulse with 20× the mean pulse flux. This number is similar to the giant pulse rate in PSR B1937+21 and PSR  B1821–24  but significantly higher than that for the Crab.     

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO₂ concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2–5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6–9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2–5 years and 6–9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6–9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.     

Real-time multi-model decadal climate predictions

We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change.     

Reconstruction of RHESSI Solar Flare Images with a Forward Fitting Method

We describe a forward-fitting method that has been developed to reconstruct hard X-ray images of solar flares from the Ramaty High-Energy Solar Spectroscopic Imager (RHESSI), a Fourier imager with rotation-modulated collimators that was launched on 5 February 2002. The forward-fitting method is based on geometric models that represent a spatial map by a superposition of multiple source structures, which are quantified by circular gaussians (4 parameters per source), elliptical gaussians (6 parameters), or curved ellipticals (7 parameters), designed to characterize real solar flare hard X-ray maps with a minimum number of geometric elements. We describe and demonstrate the use of the forward-fitting algorithm. We perform some 500 simulations of rotation-modulated time profiles of the 9 RHESSI detectors, based on single and multiple source structures, and perform their image reconstruction. We quantify the fidelity of the image reconstruction, as function of photon statistics, and the accuracy of retrieved source positions, widths, and fluxes. We outline applications for which the forward-fitting code is most suitable, such as measurements of the energy-dependent altitude of energy loss near the limb, or footpoint separation during flares. Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1023/A:1022469811115