Andrew Ciavarella

Comparing regional precipitation and temperature extremes in climate model and reanalysis products

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.

Detectable Anthropogenic Shift toward Heavy Precipitation over Eastern China

AbstractChanges in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from 1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipita...

On the nonlinearity of spatial scales in extreme weather attribution statements

In the context of ongoing climate change, extreme weather events are drawing increasing attention from the public and news media. A question often asked is how the likelihood of extremes might have changed by anthropogenic greenhouse-gas emissions. Answers to the question are strongly influenced by the model used, duration, spatial extent, and geographic location of the event—some of these factors often overlooked. Using output from four global climate models, we provide attribution statements characterised by a change in probability of occurrence due to anthropogenic greenhouse-gas emissions, for rainfall and temperature extremes occurring at seven discretised spatial scales and three temporal scales. An understanding of the sensitivity of attribution statements to a range of spatial and temporal scales of extremes allows for the scaling of attribution statements, rendering them relevant to other extremes having similar but non-identical characteristics. This is a procedure simple enough to approximate timely estimates of the anthropogenic contribution to the event probability. Furthermore, since real extremes do not have well-defined physical borders, scaling can help quantify uncertainty around attribution results due to uncertainty around the event definition. Results suggest that the sensitivity of attribution statements to spatial scale is similar across models and that the sensitivity of attribution statements to the model used is often greater than the sensitivity to a doubling or halving of the spatial scale of the event. The use of a range of spatial scales allows us to identify a nonlinear relationship between the spatial scale of the event studied and the attribution statement.

Attribution of Extreme Rainfall in Southeast China During May 2015

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Human influence on the record-breaking cold event in January of 2016 in Eastern China

Anthropogenic influences are estimated to have reduced the likelihood of an extreme cold event in midwinter with the intensity equal to or stronger than the record of 2016 in eastern China by about two‐thirds.

Different ways of framing event attribution questions: the example of warm and wet winters in the UK similar to 2015/16

AbstractAttribution 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 UK 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, which, in addition to threshold exceedence, 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 t...

Was the cold European winter 2009-2010 modified by anthropogenic climate change? An attribution study.

AbstractAn attribution study has been performed to investigate the degree to which the unusually cold European winter of 2009/10 was modified by anthropogenic climate change. Two different methods have been included for the attribution: one based on large HadGEM3-A ensembles and one based on a statistical surrogate method. Both methods are evaluated by comparing simulated winter temperature means, trends, standard deviations, skewness, return periods, and 5% quantiles with observations. While the surrogate method performs well, HadGEM3-A in general underestimates the trend in winter by a factor of ⅔. It has a mean cold bias dominated by the mountainous regions and also underestimates the cold 5% quantile in many regions of Europe. Both methods show that the probability of experiencing a winter as cold as 2009/10 has been reduced by approximately a factor of 2 because of anthropogenic changes. The method based on HadGEM3-A ensembles gives somewhat larger changes than the surrogate method because of differe...

Human Contribution to the Record Sunshine of Winter 2014/15 in the United Kingdom

Observational data of sunshine duration since 1930 from the Met Office National Climate Information Centre (NCIC; Perry and Hollis 2005) reveal that winter 2014/15 was the sunniest in the United Kingdom (Fig. 10.1a). The common perception of drab British winters is seemingly challenged by the increasing trend of 2.4 ± 0.7 sunshine hrs decade−1 (mean ± standard deviation) during 1930–2015 (Fig. 10.1a). With winters in the region projected to become warmer and wetter in a changing climate (van Oldenborgh et al. 2013), increasing sunshine would suggest longer sunny spells between heavier rainfall events. Brighter winters may also enhance solar energy production. Annual sunshine over western Europe was found to follow periods of dimming in the 1960–80s and brightening thereafter, while large positive seasonal trends are particularly evident in winter (Sanchez-Lorenzo et al. 2008). Contrary to the changes in Europe, a sunshine decline in recent decades has been observed in parts of the world where aerosol concentrations have been increasing, such as China and the Indian subcontinent (Wang et al. 2012; Liao et al. 2015; Niroula et al. 2015). We attempt to formally establish the role of the overall anthropogenic forcing on the climate based on ensembles of simulations with and without anthropogenic effects produced with an atmospheric model. This well-established methodology (Pall et al. 2011; Christidis et al. 2013) provides distributions of climatic variables in the actual (ALL forcings) and natural (NAT) climate, constructed with the two ensembles. Probabilities P1 and P0 of a threshold exceedance computed with the ALL and NAT simulations help assess the anthropogenic effect in terms of the fraction of attributable risk (FAR; Allen 2003), defined as 1 − (P0 / P1). FAR values close to unity indicate prominent human influence on the event. Changes in the return time of extreme events (estimated from inverse probabilities) can also be examined. As models do not provide a sunshine duration diagnostic, we employ the downward solar (SW) flux at the surface as a proxy (Fig. 10.1a). Observed winter sunshine hours and solar radiation averaged over the United Kingdom have a correlation of 0.9 over the common observational period, though individual years may differ in sign of anomaly (e.g., 2010). Cloud cover (correlation coefficient 0.3 for inverse variable estimated from observations) would be less suitable in our analysis, as it also incorporates a nighttime component. SW winter flux in 2014/15 is a joint record together with 2007/08, though flux observations cover a considerably shorter period than sunshine. We employe d t he Had le y C ent re e vent attribution system (Christidis et al. 2013), built on the HadGEM3–A model, to generate the ALL and NAT simulations. A major upgrade of the model was recently undertaken within the EUCLEIA project (http://eucleia.eu/). As a result, our system now features the highest resolution model used in attribution studies, with 85 vertical levels and about 60-km horizontal resolution. Ensembles of 15 simulations were produced for both the ALL and NAT experiments, which cover the period 1960–2013. Observed sea surface temperatures (SSTs) and sea ice data (Rayner et al. 2003) were used as boundary conditions in the ALL simulations. An estimate of the anthropogenic warming in the SSTs obtained from atmosphere–ocean coupled models (Stone 2013) was subtracted from the SST observations in the NAT simulations and the sea ice was adjusted accordingly (Christidis et al. 2013). Figure 10.1b depicts the modeled time series of the SW winter flux anomaly relative to 1961–90 corresponding to the ensemble Extreme winter sunshine in the United Kingdom, as observed in the record high 2014/15 season, has become more than 1.5 times more likely to occur under the influence of anthropogenic forcings.