Leela M. Frankcombe

The 6dF Galaxy Survey: final redshift release (DR3) and southern large-scale structures

We report the final redshift release of the 6dF Galaxy Survey (6dFGS), a combined redshift and peculiar velocity survey over the southern sky (|b| > 10°). Its 136 304 spectra have yielded 110 256 new extragalactic redshifts and a new catalogue of 125 071 galaxies making near-complete samples with (K, H, J, r_F, b_J) ≤ (12.65, 12.95, 13.75, 15.60, 16.75). The median redshift of the survey is 0.053. Survey data, including images, spectra, photometry and redshifts, are available through an online data base. We describe changes to the information in the data base since earlier interim data releases. Future releases will include velocity dispersions, distances and peculiar velocities for the brightest early-type galaxies, comprising about 10 per cent of the sample. Here we provide redshift maps of the southern local Universe with z ≤ 0.1, showing nearby large-scale structures in hitherto unseen detail. A number of regions known previously to have a paucity of galaxies are confirmed as significantly underdense regions. The URL of the 6dFGS data base is http://www-wfau.roe.ac.uk/6dFGS.

Separating Internal Variability from the Externally Forced Climate Response

Separating low-frequency internal variability of the climate system from the forced signal is essential to better understand anthropogenic climate change as well as internal climate variability. Here both synthetic time series and the historical simulations from phase 5 of CMIP (CMIP5) are used to examine several methods of performing this separation. Linear detrending, as is commonly used in studies of low-frequency climate variability, is found to introduce large biases in both amplitude and phase of the estimated internal variability. Using estimates of the forced signal obtained from ensembles of climate simulations can reduce these biases, particularly when the forced signal is scaled to match the historical time series of each ensemble member. These so-called scaling methods also provide estimates of model sensitivities to different types of external forcing. Applying the methods to observations of the Atlantic multidecadal oscillation leads to different estimates of the phase of this mode of variability in recent decades.

Tropical Pacific SST Drivers of Recent Antarctic Sea Ice Trends

AbstractA strengthening of the Amundsen Sea low from 1979 to 2013 has been shown to largely explain the observed increase in Antarctic sea ice concentration in the eastern Ross Sea and decrease in the Bellingshausen Sea. Here it is shown that while these changes are not generally seen in freely running coupled climate model simulations, they are reproduced in simulations of two independent coupled climate models: one constrained by observed sea surface temperature anomalies in the tropical Pacific and the other by observed surface wind stress in the tropics. This analysis confirms previous results and strengthens the conclusion that the phase change in the interdecadal Pacific oscillation from positive to negative over 1979–2013 contributed to the observed strengthening of the Amundsen Sea low and the associated pattern of Antarctic sea ice change during this period. New support for this conclusion is provided by simulated trends in spatial patterns of sea ice concentrations that are similar to those obse...

Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations

AbstractLow-frequency internal climate variability (ICV) plays an important role in modulating global surface temperature, regional climate, and climate extremes. However, it has not been completely characterized in the instrumental record and in the Coupled Model Intercomparison Project phase 5 (CMIP5) model ensemble. In this study, the surface temperature ICV of the North Pacific (NP), North Atlantic (NA), and Northern Hemisphere (NH) in the instrumental record and historical CMIP5 all-forcing simulations is isolated using a semiempirical method wherein the CMIP5 ensemble mean is applied as the external forcing signal and removed from each time series. Comparison of ICV signals derived from this semiempirical method as well as from analysis of ICV in CMIP5 preindustrial control runs reveals disagreement in the spatial pattern and amplitude between models and instrumental data on multidecadal time scales (>20 yr). Analysis of the amplitude of total variability and the ICV in the models and instrumental d...

Predictability of the recent slowdown and subsequent recovery of large‐scale surface warming using statistical methods

All raw data, (c) Matlab code, and results from our analysis are available at the supplementary website: http://www.meteo.psu.edu/~mann/supplements/GRL2016. We acknowledge the World Climate Research Programmes Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. B.A.S. acknowledges support by the U.S. National Science Foundation (EAR-1447048). M.H.E. and L.M.F. acknowledge support from the Australian Research Council (FL100100214). A.H.C. acknowledges support from the U.S. National Science Foundation (AGS-1263225). Kaplan SST V2 data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. HadISST data were provided by theMet Office Hadley Centre: www.metoffice.gov.uk/hadobs. ERSST data were provided by NOAA:www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v3b.

Response to Comment on "Atlantic and Pacific multidecadal oscillations and Northern Hemisphere temperatures"

Kravtsov et al. claim that we incorrectly assess the statistical independence of simulated samples of internal climate variability and that we underestimate uncertainty in our calculations of observed internal variability. Their analysis is fundamentally flawed, owing to the use of model ensembles with too few realizations and the fact that no one model can adequately represent the forced signal.

Tidal modulation of two-layer hydraulic exchange flows

Abstract. Time-dependent, two layer hydraulic exchange flow is studied using an idealised shallow water model. It is found that barotropic time-dependent perturbations, representing tidal forcing, increase the baroclinic exchange flux above the steady hydraulic limit, with flux increasing monotonically with tidal amplitude (measured either by height or flux amplitude over a tidal period). Exchange flux also depends on the non-dimensional tidal period, γ, which was introduced by by Helfrich (1995). When tidal amplitude is characterised by the barotropic flux amplitude, exchange flux is a monotonic function of γ as predicted by Helfrich (1995). However, the relationship between the (imposed) free surface amplitude and flux amplitude is complicated by reflections within the channel and by the baroclinic response of the two layer system, leading to a non-monotonic relationship between the height amplitude and γ.

On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

AbstractIn this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. W...

Reply to “Comment on ‘Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations’”

AbstractIn a comment on a 2017 paper by Cheung et al., Kravtsov states that the results of Cheung et al. are invalidated by errors in the method used to estimate internal variability in historical surface temperatures, which involves using the ensemble mean of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to estimate the forced signal. Kravtsov claims that differences between the forced signals in the individual models and as defined by the multimodel ensemble mean lead to errors in the assessment of internal variability in both model simulations and the instrumental record. Kravtsov proposes a different method, which instead uses CMIP5 models with at least four realizations to define the forced component. Here, it is shown that the conclusions of Cheung et al. are valid regardless of whether the method of Cheung et al. or that of Kravtsov is applied. Furthermore, many of the points raised by Kravtsov are discussed in Cheung et al., and the disagreements of Kravtsov appear ...