Observed trends in the intensity of hot and cold extremes as well as in dry spell length and heavy precipitation intensity are often not significant at local scales. However, using a spatially ...aggregated perspective, we demonstrate that the probability distribution of observed local trends across the globe for the period 1960–2010 is clearly different to what would be expected from internal variability. We detect a distinct intensification of heavy precipitation events and hot extremes. We show that CMIP5 models generally capture the observed shift in the trend distribution but tend to underestimate the intensification of heavy precipitation and cold extremes and overestimate the intensification in hot extremes. Using an initial condition experiment sampling internal variability, we demonstrate that much of the local to regional differences in trends of extremes can be explained by internal variability, which can regionally mask or amplify the forced long‐term trends for many decades.
Key Points
A simple and intuitive approach for detection changes in extremes is proposed
A distinct intensification of heavy precipitation and hot extremes is detected
GCMs underestimate the observed trends in cold and heavy precipitation extremes
Climate model genealogy Masson, D.; Knutti, R.
Geophysical research letters,
28 April 2011, Letnik:
38, Številka:
8
Journal Article
Recenzirano
Climate change projections are often given as equally weighted averages across ensembles of climate models, despite the fact that the sampling of the underlying ensembles is unclear. We show that a ...hierarchical clustering of a metric of spatial and temporal variations of either surface temperature or precipitation in control simulations can capture many model relationships across different ensembles. Strong similarities are seen between models developed at the same institution, between models sharing versions of the same atmospheric component, and between successive versions of the same model. A perturbed parameter ensemble of a model appears separate from other structurally different models. The results provide insight into intermodel relationships, into how models evolve through successive generations, and suggest that assuming model independence in such ensembles of opportunity is not justified.
Key Points
Models by the same institution behave similarly
Structural model uncertainty is important
Model development resembles an evolutionary process
Model projections of heavy precipitation and temperature extremes include large uncertainties. We demonstrate that the disagreement between individual simulations primarily arises from internal ...variability, whereas models agree remarkably well on the forced signal, the change in the absence of internal variability. Agreement is high on the spatial pattern of the forced heavy precipitation response showing an intensification over most land regions, in particular Eurasia and North America. The forced response of heavy precipitation is even more robust than that of annual mean precipitation. Likewise, models agree on the forced response pattern of hot extremes showing the greatest intensification over midlatitudinal land regions. Thus, confidence in the forced changes of temperature and precipitation extremes in response to a certain warming is high. Although in reality internal variability will be superimposed on that pattern, it is the forced response that determines the changes in temperature and precipitation extremes in a risk perspective.
Key Points
Model agreement on changes in extremes is high in the absence of natural variabilityPattern of forced response is robust for temperature and precipitation extremesForced response is more robust for heavy precipitation than mean precipitation
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 CO2 per capita and have therefore contributed little to the changes in climate that they are beginning to experience.
Changes in intensity and frequency of daily heavy precipitation and hot temperature extremes are analyzed in Swiss observations for the years 1901–2014/2015. A spatial pooling of temperature and ...precipitation stations is applied to analyze the emergence of trends. Over 90% of the series show increases in heavy precipitation intensity, expressed as annual maximum daily precipitation (mean change: +10.4% 100 years−1; 31% significant, p < 0.05) and in heavy precipitation frequency, expressed as the number of events greater than the 99th percentile of daily precipitation (mean change: +26.5% 100 years−1; 35% significant, p < 0.05). The intensity of heavy precipitation increases on average by 7.7% K−1 smoothed Swiss annual mean temperature, a value close to the Clausius‐Clapeyron scaling. The hottest day and week of the year have warmed by 1.6 K to 2.3 K depending on the region, while the Swiss annual mean temperature increased by 1.9 K. The frequency of very hot days exceeding the 99th percentile of daily maximum temperature has more than tripled. Despite considerable local internal variability, increasing trends in heavy precipitation and hot temperature extremes are now found at most Swiss stations. The identified trends are unlikely to be random and are consistent with climate model projections, with theoretical understanding of a human‐induced change in the energy budget and water cycle and with detection and attribution studies of extremes on larger scales.
Key Points
Increases in 1901‐2014 daily heavy precipitation intensity and frequency for >90% of Swiss stations
Hottest day/week warmed by 1.6–2.3 K, the frequency of very hot days more than tripled since 1901
Observed trends consistent with model projections and physical understanding
Large climate model ensembles are widely used to quantify changes in climate extremes. Here we demonstrate that model‐based estimates of changes in the probability of temperature extremes at 1.5 °C ...global warming regionally differ if quantified using prescribed sea surface temperatures (SSTs) instead of using a fully coupled climate model. Based on the identical climate model used in two experimental setups, we demonstrate that particularly over the tropics and Australia estimates of the changes in the odds of annual temperature extremes can be up to more than a factor of 5 to 10 larger using prescribed SSTs rather than a fully coupled model configuration. The two experimental designs imply a different perspective on framing projections. If experiments conditional on prescribed observed SSTs are interpreted as unconditional real‐world projections, they project changes in extremes that are systematically biased high and overconfident. Our results illustrate the importance of carefully considering experimental design when interpreting projections of extremes.
Plain Language Summary
There is great interest in understanding the likelihoods and associated risks of potential future climate extremes, especially at the Paris Agreement global warming targets of 1.5 and 2 °C warming above preindustrial conditions. In this study, we assess the implications of the model setup for the quantification of changes in the odds of temperature extremes between different global warming levels. Our analysis illustrates the strong sensitivity in the outcomes of such analyses related to the use of different model experiments. We demonstrate that despite using the exact same global climate model the projected changes in the probability of extreme annual temperature anomalies for a climate consistent with a 1.5 °C warming target are in some cases much larger if sea surface temperatures are prescribed over a decade rather than if the model is run in a fully coupled configuration. If prescribed sea surface temperature experiments are interpreted as a projection for the real world at the end of the 21st century independent of ocean variability, they regionally lead to estimates of changes in extremes that are systematically biased high and overconfident. Our results illustrate the importance of carefully considering experimental design when interpreting projected changes in extremes.
Key Points
Model‐based estimates of changes in probability of temperature extremes at 1.5 degrees Celsius global warming are sensitive to the experimental setup
Changes in odds of annual warm extremes in tropics more than 5 times larger in prescribed SST than fully coupled setup of same GCM
Experimental design needs to be taken into account when interpreting projected changes in probability of extremes
We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S . This evidence includes feedback process ...understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density (PDF) for S given all the evidence, including tests of robustness to difficult‐to‐quantify uncertainties and different priors. The 66% range is 2.6‐3.9 K for our Baseline calculation, and remains within 2.3‐4.5 K under the robustness tests; corresponding 5‐95% ranges are 2.3‐4.7 K, bounded by 2.0‐5.7 K (although such high‐confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent, and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S , in particular using comprehensive models and process understanding to address limitations in the traditional forcing‐feedback paradigm for interpreting past changes.
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