Tail-anchored (TA) proteins contain a single transmembrane domain (TMD) at the C-terminus that anchors them to the membranes of organelles where they mediate critical cellular processes. Accordingly, mutations in genes encoding TA proteins have been identified in a number of severe inherited disorders. Despite the importance of correctly targeting a TA protein to its appropriate membrane, the mechanisms and signals involved are not fully understood. In this study, we identify additional peroxisomal TA proteins, discover more proteins that are present on multiple organelles, and reveal that a combination of TMD hydrophobicity and tail charge determines targeting to distinct organelle locations in mammals. Specifically, an increase in tail charge can override a hydrophobic TMD signal and re-direct a protein from the ER to peroxisomes or mitochondria and vice versa. We show that subtle changes in those parameters can shift TA proteins between organelles, explaining why peroxisomes and mitochondria have many of the same TA proteins. This enabled us to associate characteristic physicochemical parameters in TA proteins with particular organelle groups. Using this classification allowed successful prediction of the location of uncharacterized TA proteins for the first time.

We analyse a large number of multi-century pre-industrial control simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) to investigate relationships between: net top-of-atmosphere radiation (TOA), globally averaged surface temperature (GST), and globally integrated ocean heat content (OHC) on decadal timescales. Consistent with previous studies, we find that large trends (∼0.3 K dec −1 ) in GST can arise from internal climate variability and that these trends are generally an unreliable indicator of TOA over the same period. In contrast, trends in total OHC explain 95% or more of the variance in TOA for two-thirds of the models analysed; emphasizing the oceans' role as Earth's primary energy store. Correlation of trends in total system energy (TE ≡ time integrated TOA) against trends in OHC suggests that for most models the ocean becomes the dominant term in the planetary energy budget on a timescale of about 12 months. In the context of the recent pause in global surface temperature rise, we investigate the potential importance of internal climate variability in both TOA and ocean heat rearrangement. The model simulations suggest that both factors can account for O (0.1 W m −2 ) on decadal timescales and may play an important role in the recently observed trends in GST and 0-700 m (and 0-1800 m) ocean heat uptake.

Since the end of the 20th century, global mean surface temperature (GMST) has not risen as rapidly as predicted by global climate models (GCMs) 1-3 . This discrepancy has become known as the global warming 'hiatus' and a variety of mechanisms 1, 4-17 have been proposed to explain the observed slowdown in warming. Focussing on internally generated variability, we use an observationally-constrained ensemble of GCMs and a statistical approach to evaluate the expected frequency and characteristics of global warming hiatus periods and their likelihood of future continuation. Given an expected contemporary surface warming rate of about 0.2 K/decade from GCMs, our estimated probability for a 10-year warming hiatus due to internal variability is ∼10 %, but less than 1 % for a 20-year hiatus. However, although the absolute probability of a 20-year hiatus is small, the probability that an existing 15-year hiatus will continue another five years is up to 25 %. Therefore, we should not be surprised if the present hiatus continued until the end of the current decade. Finally, following the termination of a hiatus, we show that there is an increased likelihood of accelerated global warming associated with release of heat from the sub-surface ocean and a reversal of the phase of decadal variability in the Pacific Ocean.

[1] We use control run data from three Met Office Hadley Centre climate models to investigate the relationship between: net top-of-atmosphere radiation balance (TOA), globally averaged sea surface temperature (SST); and globally averaged ocean heat content (OHC) on decadal timescales. All three models show substantial decadal variability in SST, which could easily mask the long-term warming associated with anthropogenic climate change over a decade. Regression analyses are used to estimate the uncertainty of TOA, given the trend in SST or OHC over the same period. We show that decadal trends in SST are only weakly indicative of changes in TOA. Trends in total OHC strongly constrain TOA, since the ocean is the primary heat store in the Earth System. Integrating OHC over increasing model levels, provides an increasingly good indication of TOA changes. To achieve a given accuracy in TOA estimated from OHC we find that there is a trade-off between measuring for longer or deeper. Our model results suggest that there is potential for substantial improvement in our ability to monitor Earth's radiation balance by more comprehensive observation of the global ocean.

The Atlantic meridional overturning circulation (AMOC) at 26.5• N weakened by −0.53 sverdrup (Sv)/yr between April 2004 and October 2012. To assess whether this trend is consistent with the expected "noise" in the climate system, we compare the observed trend with estimates of internal variability derived from 14 control simulations from the Climate Model Intercomparison Project 5 (CMIP5). Eight year trends of −0.53 Sv/yr are relatively common in two models but are extremely unusual (or out of range) in the other 12. However, all 14 models underestimate AMOC variability on interannual time scales. To account for this bias, we estimate plausible upper limits of internal AMOC variability by combining the temporal correlation characteristics of the AMOC from CMIP5 models with an observational estimate of interannual variability. We conclude that the observed AMOC trend is not significantly different (p > 0.01) from plausible estimates of internal variability. Detecting the influence of external climate forcings on the AMOC will require more than one decade of continuous observations.

The question of when the signal of climate change will emerge from the background noise of climate variability -the 'time of emergence' -is potentially important for adaptation planning. Mora et al. 1 (M13) presented precise projections of the time of emergence of unprecedented regional climates. However, their methodology produces artificially early dates at which specific regions will permanently experience unprecedented climates and artificially low uncertainty in those dates everywhere. This overconfidence could impair the effectiveness of climate risk management decisions 2 .Any human-induced changes in climate will be modulated by natural fluctuations of the oceans and atmosphere (e.g. El Niño events). These fluctuations occur randomly and independently, in both reality and individual modelbased projections, and act to obscure the climate change signal 3,4,5 . M13 discuss projections of when changes in climate emerge permanently above the levels of such fluctuations (a metric first considered by ref. 6). However, by ignoring the irreducible limits imposed by these same random fluctuations, M13 express their emergence dates with too much certainty.Several methodological oversights contribute to the erroneous uncertainty quantification. Firstly, M13 ignore the possibility that emergence dates before the end of the simulations are not permanent deviations from the historical range 6 (termed 'pseudo-emergence'). In many regions where emergence has not occurred by the year 2100, M13 even artificially set the emergence date to equal 2100. This oversight produces several effects, including: (i) early and overconfident estimates of regional temperature emergence, and (ii) implausible emergence dates for precipitation of exactly 2100 with zero uncertainty almost everywhere.Secondly, M13 estimate precision of regional emergence timing using the standard error of the ensemble mean (σ/√N), where N(=39) is the number of simulations and σ is their standard deviation. While the estimate of the ensemble-mean becomes more precise with larger ensemble size, natural fluctuations of the climate (such as El Niño) dictate that the future evolution of climate will not behave like the mean, but as a single realization from a range of outcomes 5,7 . The use of σ/√N greatly underestimates 8 this irreducible uncertainty, as well as the climate-response uncertainty given by the inter-model spread, and is therefore inappropriate for use in

Thanks to R. Campbell for the suggestion in using this cutting edge graphical technique. Unfortunately, the aggregated data used in this study effectively covers only a single time point, and so is impossible to adapt to the "viral spiral" plot. We look forward to using the suggested graphics technique in the future, should we extend the current study to use time series data. Interactive comment on Earth Syst. Dynam. Discuss.,

Abstract. Drought is a cumulative event, often difficult to define and involving wide-reaching consequences for agriculture, ecosystems, water availability, and society. Understanding how the occurrence of drought may change in the future and which sources of uncertainty are dominant can inform appropriate decisions to guide drought impacts assessments. Our study considers both climate model uncertainty associated with future climate projections, and future emissions of greenhouse gases (future scenario uncertainty). Four drought indices (the Standardised Precipitation Index (SPI), Soil Moisture Anomaly (SMA), the Palmer Drought Severity Index (PDSI) and the Standardised Runoff Index (SRI)) are calculated for the A1B and RCP2.6 future emissions scenarios using monthly model output from a 57-member perturbed parameter ensemble of climate simulations of the HadCM3C Earth System model, for the baseline period , and the period 2070-2099 ("the 2080s"). We consider where there are statistically significant increases or decreases in the proportion of time spent in drought in the 2080s compared to the baseline. Despite the large range of uncertainty in drought projections for many regions, projections for some regions have a clear signal, with uncertainty associated with the magnitude of change rather than direction. For instance, a significant increase in time spent in drought is generally projected for the Amazon, Central America and South Africa whilst projections for northern India consistently show significant decreases in time spent in drought. Whilst the patterns of changes in future drought were similar between scenarios, climate mitigation, represented by the RCP2.6 scenario, tended to reduce future changes in drought. In general, climate mitigation reduced the area over which there was a significant increase in drought but had little impact on the area over which there was a significant decrease in time spent in drought.