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- Author or Editor: Nikolaos Christidis x
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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
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.
Abstract
This study determines whether observed recent changes in the frequency of hot and cold extremes over land can be explained by climate variability or whether they show a detectable response to external influences. The authors analyze changes in the frequency of moderate-to-extreme daily temperatures—namely, the number of days exceeding the 90th percentile and the number of days not reaching the 10th percentile of daily minimum (tn90 and tn10, respectively) and maximum (tx90 and tx10, respectively) temperature—for both cold and warm seasons. The analysis is performed on a range of spatial scales and separately for boreal cold- and warm-season data. The fingerprint for external forcing is derived from an ensemble of simulations produced with the Hadley Centre Global Environmental Model, version 1 (HadGEM1), with both anthropogenic and natural forcings. The observations show an increase in warm extremes and a decrease in cold extremes in both seasons and in almost all regions that are generally well captured by the model. Some regional differences between model and observations may be due to local forcings or changes in climate dynamics. A detection analysis, using both optimized and nonoptimized fingerprints, shows that the influence of external forcing is detectable in observations for both cold and warm extremes, and cold and warm seasons, over the period 1951–2003 at the 5% level. It is also detectable separately for the Northern and Southern Hemispheres, and over most regions analyzed. The model shows a tendency to significantly overestimate changes in warm daytime extremes, particularly in summer.
Abstract
This study determines whether observed recent changes in the frequency of hot and cold extremes over land can be explained by climate variability or whether they show a detectable response to external influences. The authors analyze changes in the frequency of moderate-to-extreme daily temperatures—namely, the number of days exceeding the 90th percentile and the number of days not reaching the 10th percentile of daily minimum (tn90 and tn10, respectively) and maximum (tx90 and tx10, respectively) temperature—for both cold and warm seasons. The analysis is performed on a range of spatial scales and separately for boreal cold- and warm-season data. The fingerprint for external forcing is derived from an ensemble of simulations produced with the Hadley Centre Global Environmental Model, version 1 (HadGEM1), with both anthropogenic and natural forcings. The observations show an increase in warm extremes and a decrease in cold extremes in both seasons and in almost all regions that are generally well captured by the model. Some regional differences between model and observations may be due to local forcings or changes in climate dynamics. A detection analysis, using both optimized and nonoptimized fingerprints, shows that the influence of external forcing is detectable in observations for both cold and warm extremes, and cold and warm seasons, over the period 1951–2003 at the 5% level. It is also detectable separately for the Northern and Southern Hemispheres, and over most regions analyzed. The model shows a tendency to significantly overestimate changes in warm daytime extremes, particularly in summer.
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
Meteorological drought in the Hadley Centre global climate model is assessed using the Palmer Drought Severity Index (PDSI), a commonly used drought index. At interannual time scales, for the majority of the land surface, the model captures the observed relationship between the El Niño–Southern Oscillation and regions of relative wetness and dryness represented by high and low values of the PDSI respectively. At decadal time scales, on a global basis, the model reproduces the observed drying trend (decreasing PDSI) since 1952. An optimal detection analysis shows that there is a significant influence of anthropogenic emissions of greenhouse gasses and sulphate aerosols in the production of this drying trend. On a regional basis, the specific regions of wetting and drying are not always accurately simulated. In this paper, present-day drought events are defined as continuous time periods where the PDSI is less than the 20th percentile of the PDSI distribution between 1952 and 1998 (i.e., on average 20% of the land surface is in drought at any one time). Overall, the model predicts slightly less frequent but longer events than are observed. Future projections of drought in the twenty-first century made using the Special Report on Emissions Scenarios (SRES) A2 emission scenario show regions of strong wetting and drying with a net overall global drying trend. For example, the proportion of the land surface in extreme drought is predicted to increase from 1% for the present day to 30% by the end of the twenty-first century.
Abstract
Meteorological drought in the Hadley Centre global climate model is assessed using the Palmer Drought Severity Index (PDSI), a commonly used drought index. At interannual time scales, for the majority of the land surface, the model captures the observed relationship between the El Niño–Southern Oscillation and regions of relative wetness and dryness represented by high and low values of the PDSI respectively. At decadal time scales, on a global basis, the model reproduces the observed drying trend (decreasing PDSI) since 1952. An optimal detection analysis shows that there is a significant influence of anthropogenic emissions of greenhouse gasses and sulphate aerosols in the production of this drying trend. On a regional basis, the specific regions of wetting and drying are not always accurately simulated. In this paper, present-day drought events are defined as continuous time periods where the PDSI is less than the 20th percentile of the PDSI distribution between 1952 and 1998 (i.e., on average 20% of the land surface is in drought at any one time). Overall, the model predicts slightly less frequent but longer events than are observed. Future projections of drought in the twenty-first century made using the Special Report on Emissions Scenarios (SRES) A2 emission scenario show regions of strong wetting and drying with a net overall global drying trend. For example, the proportion of the land surface in extreme drought is predicted to increase from 1% for the present day to 30% by the end of the twenty-first century.
