The Earth is warming on average, and most of the global warming of the past half-century can very likely be attributed to human influence. But the climate in particular locations is much more variable, raising the question of where and when local changes could become perceptible enough to be obvious to people in the form of local warming that exceeds interannual variability; indeed only a few studies have addressed the significance of local signals relative to variability. It is well known that the largest total warming is expected to occur in high latitudes, but high latitudes are also subject to the largest variability, delaying the emergence of significant changes there. Here we show that due to the small temperature variability from one year to another, the earliest emergence of significant warming occurs in the summer season in low latitude countries (≈25 • S-25 • N). We also show that a local warming signal that exceeds past variability is emerging at present, or will likely emerge in the next two decades, in many tropical countries. Further, for most countries worldwide, a mean global warming of 1 • C is sufficient for a significant temperature change, which is less than the total warming projected for any economically plausible emission scenario. The most strongly affected countries emit small amounts of CO 2 per capita and have therefore contributed little to the changes in climate that they are beginning to experience.

[1] The decline of Arctic sea ice is one of the most visible signs of climate change over the past several decades. Arctic sea ice area shows large interannual variability due to the numerous factors, but on longer time scales the total sea ice area is approximately linearly related to Arctic surface air temperature in models and observations. Overall, models however strongly underestimate the recent sea ice decline. Here we show that this can be explained with two interlinked biases. Most climate models simulate a smaller sea ice area reduction per degree local surface warming. Arctic polar amplification, the ratio between Arctic and global temperature, is also underestimated but a number of models are within the uncertainty estimated from natural variability. A recalibration of an ensemble of global climate models using observations over 28 years provides a scenario independent relationship and yields about 2 C change in annual mean global surface temperature above present as the most likely global temperature threshold for September sea ice to disappear, but with substantial associated uncertainty. Natural variability in the Arctic is large and needs to be considered both for such recalibrations as well as for model evaluation, in particular when observed trends are relatively short.Citation: Mahlstein, I., and R. Knutti (2012), September Arctic sea ice predicted to disappear near 2 C global warming above present,

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[1] In contrast to Arctic sea ice, average Antarctic sea ice area is not retreating but has slowly increased since satellite measurements began in 1979. While most climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive simulate a decrease in Antarctic sea ice area over the recent past, whether these models can be dismissed as being wrong depends on more than just the sign of change compared to observations. We show that internal sea ice variability is large in the Antarctic region, and both the observed and modeled trends may represent natural variations along with external forcing. While several models show a negative trend, only a few of them actually show a trend that is significant compared to their internal variability on the time scales of available observational data. Furthermore, the ability of the models to simulate the mean state of sea ice is also important. The representations of Antarctic sea ice in CMIP5 models have not improved compared to CMIP3 and show an unrealistic spread in the mean state that may influence future sea ice behavior. Finally, Antarctic climate and sea ice area will be affected not only by ocean and air temperature changes but also by changes in the winds. The majority of the CMIP5 models simulate a shift that is too weak compared to observations. Thus, this study identifies several foci for consideration in evaluating and improving the modeling of climate and climate change in the Antarctic region.

The Arctic climate is governed by complex interactions and feedback mechanisms between the atmosphere, ocean, and solar radiation. One of its characteristic features, the Arctic sea ice, is very vulnerable to anthropogenically caused warming. Production and melting of sea ice is influenced by several physical processes. The authors show that the northward ocean heat transport is an important factor in the simulation of the sea ice extent in the current general circulation models. Those models that transport more energy to the Arctic show a stronger future warming, in the Arctic as well as globally. Larger heat transport to the Arctic, in particular in the Barents Sea, reduces the sea ice cover in this area. More radiation is then absorbed during summer months and is radiated back to the atmosphere in winter months. This process leads to an increase in the surface temperature and therefore to a stronger polar amplification. The models that show a larger global warming agree better with the observed sea ice extent in the Arctic. In general, these models also have a higher spatial resolution.These results suggest that higher resolution and greater complexity are beneficial in simulating the processes relevant in the Arctic and that future warming in the high northern latitudes is likely to be near the upper range of model projections, consistent with recent evidence that many climate models underestimate Arctic sea ice decline.

[1] The global average temperature of the Earth has increased, but year-to-year variability in local climates impedes the identification of clear changes in observations and human experience. For a signal to become obvious in data records or in a human lifetime it needs to be greater than the noise of variability and thereby lead to a significant shift in the distribution of temperature. We show that locations with the largest amount of warming may not display a clear shift in temperature distributions if the local variability is also large. Based on observational data only we demonstrate that large parts of the Earth have experienced a significant local shift towards warmer temperatures in the summer season, particularly at lower latitudes. We also show that these regions are similar to those that are found to be significant in standard detection methods, thus providing an approach to link locally significant changes more closely to impacts.

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

[1] Complexity and resolution of global climate models are steadily increasing, yet the uncertainty of their projections remains large, particularly for precipitation. Given the impacts precipitation changes have on ecosystems, there is a need to reduce projection uncertainty by assessing the performance of climate models. A common way of evaluating models is to consider global maps of errors against observations for a range of variables. However, depending on the purpose, feature-based metrics defined on a regional scale and for one variable may be more suitable to identify the most accurate models. We compare three different ways of ranking the CMIP3 climate models: errors in a broad range of climate variables, errors in global field of precipitation, and regional features of modeled precipitation in areas where pronounced future changes are expected. The same analysis is performed for temperature to identify potential differences between variables. The multimodel mean is found to outperform all single models in the global field-based rankings but performs only averagely for the feature-based ranking. Selecting the best models for each metric reduces the absolute spread in projections. If anomalies are considered, the model spread is reduced in a few regions, while the uncertainty can be increased in others. We also demonstrate that the common attribution of a lack of model agreement in precipitation projections to different model physics may be misleading. Agreement is similarly poor within different ensemble members of the same model, indicating that the lack of robust trends can be attributed partly to a low signal-to-noise ratio.