Ed Hawkins

The potential to narrow uncertainty in regional climate predictions

Abstract Faced by the realities of a changing climate, decision makers in a wide variety of organizations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organizations that fund climate research, is what is the potential for climate science to deliver improvements—especially reductions in uncertainty—in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty, and scenario uncertainty. Using data from a suite of climate models, we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability. O...

The 2dF Galaxy Redshift Survey: correlation functions, peculiar velocities and the matter density of the Universe

We present a detailed analysis of the two-point correlation function, xi(sigma, pi), from the 2dF Galaxy Redshift Survey (2dFGRS). The large size of the catalogue, which contains similar to220 000 redshifts, allows us to make high-precision measurements of various properties of the galaxy clustering pattern. The effective redshift at which our estimates are made is z(s) approximate to 0.15, and similarly the effective luminosity, L-s approximate to 1.4L*. We estimate the redshift-space correlation function, xi(s), from which we measure the redshift-space clustering length, s(o) = 6.82 +/- 0.28 h(-1) Mpc. We also estimate the projected correlation function, Xi(sigma), and the real-space correlation function, xi(r), which can be fit by a power law (r/r(o))(-gamma), with r(o) = 5.05 +/- 0.26 h(-1) Mpc, gamma(r) = 1.67 +/- 0.03. For r greater than or similar to 20 h(-1) Mpc, xi drops below a power law as, for instance, is expected in the popular Lambda cold dark matter model. The ratio of amplitudes of the real- and redshift-space correlation functions on scales of 8-30 h(-1) Mpc gives an estimate of the redshift-space distortion parameter beta. The quadrupole moment of xi(sigma, pi) on scales 30-40 h(-1) Mpc provides another estimate of beta. We also estimate the distribution function of pairwise peculiar velocities, f (nu), including rigorously the significant effect due to the infall velocities, and we find that the distribution is well fit by an exponential form. The accuracy of our xi(sigma, pi) measurement is sufficient to constrain a model, which simultaneously fits the shape and amplitude of xi(r) and the two redshift-space distortion effects parametrized by beta and velocity dispersion, a. We find beta = 0.49 +/- 0.09 and a = 506 +/- 52 km s(-1), although the best-fitting values are strongly correlated. We measure the variation of the peculiar velocity dispersion with projected separation, a(or), and find that the shape is consistent with models and simulations. This is the first time that beta and f (v) have been estimated from a self-consistent model of galaxy velocities. Using the constraints on bias from recent estimates, and taking account of redshift evolution, we conclude that beta(L = L*, z = 0) = 0.47 +/- 0.08, and that the present-day matter density of the Universe, Omega(m) approximate to 0.3, consistent with other 2dFGRS estimates and independent analyses.

The 2dF Galaxy Redshift Survey: the environmental dependence of galaxy star formation rates near clusters

We have measured the equivalent width of the Hα emission line for 11 006 galaxies brighter than M_b-=-−19 (Ω_Λ = 0.7, Ω_m = 0.3, H_0 = 70 km s^(−1) Mpc^(−1)) at 0.05 < z < 0.1 in the 2dF Galaxy Redshift Survey (2dFGRS), in the fields of 17 known galaxy clusters. The limited redshift range ensures that our results are insensitive to aperture bias, and to residuals from night sky emission lines. We use these measurements to trace μ*, the star formation rate normalized to L*, as a function of distance from the cluster centre, and local projected galaxy density. We find that the distribution of μ* steadily skews toward larger values with increasing distance from the cluster centre, converging to the field distribution at distances greater than ∼3 times the virial radius. A correlation between star formation rate and local projected density is also found, which is independent of cluster velocity dispersion and disappears at projected densities below ∼1 galaxy Mpc^(−2) (brighter than M_b = −19). This characteristic scale corresponds approximately to the mean density at the cluster virial radius. The same correlation holds for galaxies more than two virial radii from the cluster centre. We conclude that environmental influences on galaxy properties are not restricted to cluster cores, but are effective in all groups where the density exceeds this critical value. The present-day abundance of such systems, and the strong evolution of this abundance, makes it likely that hierarchical growth of structure plays a significant role in decreasing the global average star formation rate. Finally, the low star formation rates well beyond the virialized cluster rule out severe physical processes, such as ram pressure stripping of disc gas, as being completely responsible for the variations in galaxy properties with environment.

Galaxy ecology: groups and low-density environments in the SDSS and 2dFGRS

We analyse the observed correlation between galaxy environment and Halpha emission-line strength, using volume-limited samples and group catalogues of 24 968 galaxies at 0.05 < z < 0.095, drawn from the 2dF Galaxy Redshift Survey (M-bJ < -19.5) and the Sloan Digital Sky Survey (M-r < -20.6). We characterize the environment by: (1) Sigma(5), the surface number density of galaxies determined by the projected distance to the fifth nearest neighbour; and (2) rho(1.1) and rho(5.5), three-dimensional density estimates obtained by convolving the galaxy distribution with Gaussian kernels of dispersion 1.1 and 5.5 Mpc, respectively. We find that star-forming and quiescent galaxies form two distinct populations, as characterized by their H equivalent width, W-0(Halpha). The relative numbers of star-forming and quiescent galaxies vary strongly and continuously with local density. However, the distribution of W-0(Halpha) amongst the star-forming population is independent of environment. The fraction of star-forming galaxies shows strong sensitivity to the density on large scales, rho(5.5), which is likely independent of the trend with local density, rho(1.1). We use two differently selected group catalogues to demonstrate that the correlation with galaxy density is approximately independent of group velocity dispersion, for sigma = 200-1000 km s(-1). Even in the lowest-density environments, no more than similar to70 per cent of galaxies show significant Halpha emission. Based on these results, we conclude that the present-day correlation between star formation rate and environment is a result of short-time-scale mechanisms that take place preferentially at high redshift, such as starbursts induced by galaxy-galaxy interactions.

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

We investigate the dependence of the strength of galaxy clustering on intrinsic luminos- ity 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 galax- ies 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 0.1 < (r/h 1 Mpc) < 10. The clustering of L � (MbJ 5log10 h = 19.7) galaxies in real space is well fit by a correlation length r0 = 4.9 ± 0.3h 1 Mpc and power-law slope = 1.71 ± 0.06. 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 SSRS2 by Benoist et al. However, we find a weaker dependence of clustering strength on luminosity at the highest luminosities. The cor- relation function amplitude increases by a factor of 4.0 between MbJ 5log10 h = 18 and 22.5, and the most luminous galaxies are 3.0 times more strongly clustered than Lgalaxies. 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.

Decadal climate prediction: An update from the trenches

This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialize...

A verification framework for interannual-to-decadal predictions experiments

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.

The 2dF Galaxy Redshift Survey: galaxy clustering per spectral type

We have calculated the two-point correlation functions in redshift space, �(�,�), for galaxies of different spectral types in the 2dF Galaxy Redshift Survey. Using these correlation functions we are able to estimate values of the linear redshi ft-space distortion parameter, � ≡ 0.6 m /b, the pairwise velocity dispersion, a, and the real-space correlation function, �(r), for galaxies with both relatively low star-formation rates (for which the present rate of star formation is less than 10% of its past averaged value) and galaxies with higher current starformation activity. At small separations, the real-space c lustering of passive galaxies is very much stronger than that of the more actively star-forming galaxies; the correlation-function slopes are respectively 1.93 and 1.50, and the relative bias between the two classes is a declining function of radius. On scales larger than 10h 1 Mpc there is evidence that the relative bias tends to a constant, bpassive/bactive ≃ 1. This result is consistent with the similar degrees of redshift-space distortions seen in the correlation func tions of the two classes ‐ the contours

The 2dF Galaxy Redshift Survey: the luminosity function of cluster galaxies

We have determined the composite luminosity function (LF) for galaxies in 60 clusters from the 2dF Galaxy Redshift Survey. The LF spans the marge -22.5 < M b 1 < -15, and is well fitted by a Schechter function with M * b j = -20.07 ′ 0.07 and a = -1.28 ′ 0.03 (H 0 = 100 km s - 1 Mpc - 1 , Ω M = 0.3, Ω Λ = 0.7). It differs significantly from the field LF, having a characteristic magnitude that is approximately 0.3 mag brighter and a faint-end slope that is approximately 0.1 steeper. There is no evidence for variations in the LF across a wide range of cluster properties: the LF is similar for clusters with high and low velocity dispersions, for rich and poor clusters, for clusters with different Bautz-Morgan types, and for clusters with and without substructure. The core regions of clusters differ from the outer parts, however, in having an excess of very bright galaxies. We also construct the LFs for early (quiescent), intermediate and late (star-forming) spectral types. We find that, as in the field, the LFs of earlier-type galaxies have brighter characteristic magnitudes and shallower faint-end slopes. However, the LF of early-type galaxies in clusters is both brighter and steeper than its field counterpart, although the LF of late-type galaxies is very similar. The trend of faint-end slope with spectral type is therefore much less pronounced in clusters than in the field, explaining why variations in the mixture of types do not lead to significant differences in the cluster LFs. The differences between the field and cluster LFs for the various spectral types can be qualitatively explained by the suppression of star formation in the dense cluster environment, together with mergers to produce the brightest early-type galaxies.

Addressing uncertainty in adaptation planning for agriculture.

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop–climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.