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Abstract
The new Hadley Centre system for attribution of weather and climate extremes provides assessments of how human influence on the climate may lead to a change in the frequency of such events. Two different types of ensembles of simulations are generated with an atmospheric model to represent the actual climate and what the climate would have been in the absence of human influence. Estimates of the event frequency with and without the anthropogenic effect are then obtained. Three experiments conducted so far with the new system are analyzed in this study to examine how anthropogenic forcings change the odds of warm years, summers, or winters in a number of regions where the model reliably reproduces the frequency of warm events. In all cases warm events become more likely because of human influence, but estimates of the likelihood may vary considerably from year to year depending on the ocean temperature. While simulations of the actual climate use prescribed observational data of sea surface temperature and sea ice, simulations of the nonanthropogenic world also rely on coupled atmosphere–ocean models to provide boundary conditions, and this is found to introduce a major uncertainty in attribution assessments. Improved boundary conditions constructed with observational data are introduced in order to minimize this uncertainty. In more than half of the 10 cases considered here anthropogenic influence results in warm events being 3 times more likely and extreme events 5 times more likely during September 2011–August 2012, as an experiment with the new boundary conditions indicates.
Abstract
The new Hadley Centre system for attribution of weather and climate extremes provides assessments of how human influence on the climate may lead to a change in the frequency of such events. Two different types of ensembles of simulations are generated with an atmospheric model to represent the actual climate and what the climate would have been in the absence of human influence. Estimates of the event frequency with and without the anthropogenic effect are then obtained. Three experiments conducted so far with the new system are analyzed in this study to examine how anthropogenic forcings change the odds of warm years, summers, or winters in a number of regions where the model reliably reproduces the frequency of warm events. In all cases warm events become more likely because of human influence, but estimates of the likelihood may vary considerably from year to year depending on the ocean temperature. While simulations of the actual climate use prescribed observational data of sea surface temperature and sea ice, simulations of the nonanthropogenic world also rely on coupled atmosphere–ocean models to provide boundary conditions, and this is found to introduce a major uncertainty in attribution assessments. Improved boundary conditions constructed with observational data are introduced in order to minimize this uncertainty. In more than half of the 10 cases considered here anthropogenic influence results in warm events being 3 times more likely and extreme events 5 times more likely during September 2011–August 2012, as an experiment with the new boundary conditions indicates.
Abstract
The response of precipitation to global warming is manifest in the strengthening of the hydrological cycle but can be complex on regional scales. Fingerprinting analyses have so far detected the effect of human influence on regional changes of precipitation extremes. Here we examine changes in seasonal precipitation in Europe since the beginning of the twentieth century and use an ensemble of new climate models to assess the role of different climatic forcings, both natural and anthropogenic. We find that human influence gives rise to a characteristic pattern of contrasting trends, with drier seasons in the Mediterranean basin and wetter over the rest of the continent. The trends are stronger in winter and weaker in summer, when drying is more spatially widespread. The anthropogenic signal is dominated by the response to greenhouse gas emissions, but is also weakened, to some extent, by the opposite effect of anthropogenic aerosols. Using a formal fingerprinting attribution methodology, we show here for the first time that the effects of the total anthropogenic forcing, and also of its greenhouse gas component, can be detected in observed changes of winter precipitation. Greenhouse gas emissions are also found to drive an increase in precipitation variability in all seasons. Moreover, the models suggest that human influence alters characteristics of seasonal extremes, with the frequency of high precipitation extremes increasing everywhere except the Mediterranean basin, where low precipitation extremes become more common. Regional attribution information contributes to the scientific basis that can help European citizens build their climate resilience.
Abstract
The response of precipitation to global warming is manifest in the strengthening of the hydrological cycle but can be complex on regional scales. Fingerprinting analyses have so far detected the effect of human influence on regional changes of precipitation extremes. Here we examine changes in seasonal precipitation in Europe since the beginning of the twentieth century and use an ensemble of new climate models to assess the role of different climatic forcings, both natural and anthropogenic. We find that human influence gives rise to a characteristic pattern of contrasting trends, with drier seasons in the Mediterranean basin and wetter over the rest of the continent. The trends are stronger in winter and weaker in summer, when drying is more spatially widespread. The anthropogenic signal is dominated by the response to greenhouse gas emissions, but is also weakened, to some extent, by the opposite effect of anthropogenic aerosols. Using a formal fingerprinting attribution methodology, we show here for the first time that the effects of the total anthropogenic forcing, and also of its greenhouse gas component, can be detected in observed changes of winter precipitation. Greenhouse gas emissions are also found to drive an increase in precipitation variability in all seasons. Moreover, the models suggest that human influence alters characteristics of seasonal extremes, with the frequency of high precipitation extremes increasing everywhere except the Mediterranean basin, where low precipitation extremes become more common. Regional attribution information contributes to the scientific basis that can help European citizens build their climate resilience.
Human influence and persistent low pressure are estimated to make extreme May rainfall in the United Kingdom, as in year 2021, about 1.5 and 3.5 times more likely, respectively.
Human influence and persistent low pressure are estimated to make extreme May rainfall in the United Kingdom, as in year 2021, about 1.5 and 3.5 times more likely, respectively.
Abstract
Spatially and temporally dependent fingerprint patterns of near-surface temperature change are derived from transient climate simulations of the second Hadley Centre coupled ocean–atmosphere GCM (HADCM2). Trends in near-surface temperature are calculated from simulations in which HADCM2 is forced with historical increases in greenhouse gases only and with both greenhouse gases and anthropogenic sulfur emissions. For each response an ensemble of four simulations is carried out. An estimate of the natural internal variability of the ocean–atmosphere system is taken from a long multicentury control run of HADCM2.
The aim of the study is to investigate the spatial and temporal scales on which it is possible to detect a significant change in climate. Temporal scales are determined by taking temperature trends over 10, 30, and 50 yr using annual mean data, and spatial scales are defined by projecting these trends onto spherical harmonics.
Each fingerprint pattern is projected onto the recent observed pattern to give a scalar detection variable. This is compared with the distribution expected from natural variability, estimated by projecting the fingerprint pattern onto a distribution of patterns taken from the control run. Detection is claimed if the detection variable is greater than the 95th percentile of the distribution expected from natural variability. The results show that climate change can be detected on the global mean scale for 30- and 50-yr trends but not for 10-yr trends, assuming that the model’s estimate of variability is correct. At subglobal scales, climate change can be detected only for 50-yr trends and only for large spatial scales (greater than 5000 km).
Patterns of near-surface temperature trends for the 50 yr up to 1995 from the simulation that includes only greenhouse gas forcing are inconsistent with the observed patterns at small spatial scales (less than 2000 km). In contrast, patterns of temperature trends for the simulation that includes both greenhouse gas and sulfate forcing are consistent with the observed patterns at all spatial scales.
The possible limits to future detectability are investigated by taking one member of each ensemble to represent the observations and other members of the ensemble to represent model realizations of future temperature trends. The results show that for trends to 1995 the probability of detection is greatest at spatial scales greater than 5000 km. As the future signal of climate change becomes larger relative to the noise of natural variability, detection becomes very likely at all spatial scales by the middle of the next century.
The model underestimates climate variability as seen in the observations at spatial scales less than 2000 km. Therefore, some caution must be exercised when interpreting model-based detection results that include a contribution of small spatial scales to the climate change fingerprint.
Abstract
Spatially and temporally dependent fingerprint patterns of near-surface temperature change are derived from transient climate simulations of the second Hadley Centre coupled ocean–atmosphere GCM (HADCM2). Trends in near-surface temperature are calculated from simulations in which HADCM2 is forced with historical increases in greenhouse gases only and with both greenhouse gases and anthropogenic sulfur emissions. For each response an ensemble of four simulations is carried out. An estimate of the natural internal variability of the ocean–atmosphere system is taken from a long multicentury control run of HADCM2.
The aim of the study is to investigate the spatial and temporal scales on which it is possible to detect a significant change in climate. Temporal scales are determined by taking temperature trends over 10, 30, and 50 yr using annual mean data, and spatial scales are defined by projecting these trends onto spherical harmonics.
Each fingerprint pattern is projected onto the recent observed pattern to give a scalar detection variable. This is compared with the distribution expected from natural variability, estimated by projecting the fingerprint pattern onto a distribution of patterns taken from the control run. Detection is claimed if the detection variable is greater than the 95th percentile of the distribution expected from natural variability. The results show that climate change can be detected on the global mean scale for 30- and 50-yr trends but not for 10-yr trends, assuming that the model’s estimate of variability is correct. At subglobal scales, climate change can be detected only for 50-yr trends and only for large spatial scales (greater than 5000 km).
Patterns of near-surface temperature trends for the 50 yr up to 1995 from the simulation that includes only greenhouse gas forcing are inconsistent with the observed patterns at small spatial scales (less than 2000 km). In contrast, patterns of temperature trends for the simulation that includes both greenhouse gas and sulfate forcing are consistent with the observed patterns at all spatial scales.
The possible limits to future detectability are investigated by taking one member of each ensemble to represent the observations and other members of the ensemble to represent model realizations of future temperature trends. The results show that for trends to 1995 the probability of detection is greatest at spatial scales greater than 5000 km. As the future signal of climate change becomes larger relative to the noise of natural variability, detection becomes very likely at all spatial scales by the middle of the next century.
The model underestimates climate variability as seen in the observations at spatial scales less than 2000 km. Therefore, some caution must be exercised when interpreting model-based detection results that include a contribution of small spatial scales to the climate change fingerprint.
Abstract
Attribution analyses of extreme events estimate changes in the likelihood of their occurrence due to human climatic influences by comparing simulations with and without anthropogenic forcings. Classes of events are commonly considered that only share one or more key characteristics with the observed event. Here we test the sensitivity of attribution assessments to such event definition differences, using the warm and wet winter of 2015/16 in the United Kingdom as a case study. A large number of simulations from coupled models and an atmospheric model are employed. In the most basic case, warm and wet events are defined relative to climatological temperature and rainfall thresholds. Several other classes of events are investigated that, in addition to threshold exceedance, also account for the effect of observed sea surface temperature (SST) anomalies, the circulation flow, or modes of variability present during the reference event. Human influence is estimated to increase the likelihood of warm winters in the United Kingdom by a factor of 3 or more for events occurring under any atmospheric and oceanic conditions, but also for events with a similar circulation or oceanic state to 2015/16. The likelihood of wet winters is found to increase by at least a factor of 1.5 in the general case, but results from the atmospheric model, conditioned on observed SST anomalies, are more uncertain, indicating that decreases in the likelihood are also possible. The robustness of attribution assessments based on atmospheric models is highly dependent on the representation of SSTs without the effect of human influence.
Abstract
Attribution analyses of extreme events estimate changes in the likelihood of their occurrence due to human climatic influences by comparing simulations with and without anthropogenic forcings. Classes of events are commonly considered that only share one or more key characteristics with the observed event. Here we test the sensitivity of attribution assessments to such event definition differences, using the warm and wet winter of 2015/16 in the United Kingdom as a case study. A large number of simulations from coupled models and an atmospheric model are employed. In the most basic case, warm and wet events are defined relative to climatological temperature and rainfall thresholds. Several other classes of events are investigated that, in addition to threshold exceedance, also account for the effect of observed sea surface temperature (SST) anomalies, the circulation flow, or modes of variability present during the reference event. Human influence is estimated to increase the likelihood of warm winters in the United Kingdom by a factor of 3 or more for events occurring under any atmospheric and oceanic conditions, but also for events with a similar circulation or oceanic state to 2015/16. The likelihood of wet winters is found to increase by at least a factor of 1.5 in the general case, but results from the atmospheric model, conditioned on observed SST anomalies, are more uncertain, indicating that decreases in the likelihood are also possible. The robustness of attribution assessments based on atmospheric models is highly dependent on the representation of SSTs without the effect of human influence.
Abstract
Although it is critical to assess the accuracy of attribution studies, the fraction of attributable risk (FAR) cannot be directly assessed from observations since it involves the probability of an event in a world that did not happen, the “natural” world where there was no human influence on climate. Instead, reliability diagrams (usually used to compare probabilistic forecasts to the observed frequencies of events) have been used to assess climate simulations employed for attribution and by inference to evaluate the attribution study itself. The Brier score summarizes this assessment of a model by the reliability diagram. By constructing a modeling framework where the true FAR is already known, this paper shows that Brier scores are correlated to the accuracy of a climate model ensemble’s calculation of FAR, although only weakly. This weakness exists because the diagram does not account for accuracy of simulations of the natural world. This is better represented by two reliability diagrams from early and late in the period of study, which would have, respectively, less and greater anthropogenic climate forcing. Two new methods are therefore proposed for assessing the accuracy of FAR, based on using the earlier observational period as a proxy for observations of the natural world. It is found that errors from model-based estimates of these observable quantities are strongly correlated with errors in the FAR estimated in the model framework. These methods thereby provide new observational estimates of the accuracy in FAR.
Abstract
Although it is critical to assess the accuracy of attribution studies, the fraction of attributable risk (FAR) cannot be directly assessed from observations since it involves the probability of an event in a world that did not happen, the “natural” world where there was no human influence on climate. Instead, reliability diagrams (usually used to compare probabilistic forecasts to the observed frequencies of events) have been used to assess climate simulations employed for attribution and by inference to evaluate the attribution study itself. The Brier score summarizes this assessment of a model by the reliability diagram. By constructing a modeling framework where the true FAR is already known, this paper shows that Brier scores are correlated to the accuracy of a climate model ensemble’s calculation of FAR, although only weakly. This weakness exists because the diagram does not account for accuracy of simulations of the natural world. This is better represented by two reliability diagrams from early and late in the period of study, which would have, respectively, less and greater anthropogenic climate forcing. Two new methods are therefore proposed for assessing the accuracy of FAR, based on using the earlier observational period as a proxy for observations of the natural world. It is found that errors from model-based estimates of these observable quantities are strongly correlated with errors in the FAR estimated in the model framework. These methods thereby provide new observational estimates of the accuracy in FAR.
