UK faces risk of ‘unprecedented’ winter rainfall: Met Office
The risk of record-breaking monthly winter rainfall in England and Wales, similar to that responsible for the flooding in 2014, has risen to one in three, according to the Met Office. Computer modelling reflecting natural variability combined with global warming was used to estimate the risk. The analysis also revealed a 7% risk of a monthly record extreme event in Southeast England over the next few years, and a 34% chance of an extreme event in at least one English or Welsh region each year. The research showed that such events might also break current records by up to 30%.

January rainfall record

The observed and modelled January rainfall totals for south east England are shown in Fig. 1. Prior to January 2014, observations do not provide a precedent for the totals seen in 2014. The observed rainfall and two examples of model simulations are shown in Fig. 1a. Both model simulations show variability that appears similar to the observations. One set of model simulations shows no rainfall totals outside of the spread of observations. However, the other shows a month with rainfall far greater than had yet been observed—and even greater than was observed in the current record month of January 2014.

Fig. 1 Unprecedented monthly rainfall. a South east England January monthly rainfall totals from 1979 to 2014, in mm (black) and two examples from model simulations (red). The black dashed line indicates the unprecedented January 2014 observed rainfall total. b Distribution of south east England January monthly rainfall totals from observations (grey) and the model (red). The box represents the interquartile range and the range of the whiskers represents the minimum and maximum monthly rainfall totals. The grey dot indicates the record observed monthly rainfall of January 2014 and ticks on the upper end of observations show the values in the upper quartile of previous events. Ticks on the model line indicate months above the observed record prior to 2014, and red dots indicate even higher totals Full size image

The distribution from all 4200 modelled January monthly rainfall totals is shown in Fig. 1b alongside the observed January rainfall. There are many model months with greater rainfall than had been observed prior to 2014, and several even exceed the January 2014 total and are therefore unprecedented. This shows that the level of rainfall seen in January 2014 could have been expected and these simulations could be used to calculate chance of exceeding current record monthly rainfall totals, assuming the climate is stationary over the observational period used.

Model fidelity

In order to accurately calculate the chance of unprecedented monthly rainfall, the model must be able to provide a realistic representation of the range of states available to the real world19. While this high-resolution climate model is able to represent weather patterns that lower resolution models struggle to simulate, e.g. Atlantic blocking20, it is still not perfect and is unable to fully represent fine scale orographic enhancement that often gives rise to flooding for example21. However, over many regions, including south east England, model error due to orography is small. We apply tests to ensure that the model represents observations accurately enough to look for unprecedented rainfall events.

To assess the model fidelity we use the Met Office NCIC data set7 covering 1981–2015, the same period as the model simulations. Later we discuss the use of longer observational data sets. The modelled and observed distribution of the monthly rainfall totals for the winter months, October–March, for south east England are statistically indistinguishable (Fig. 2a). We assess the mean, standard deviation, skewness and kurtosis (‘Methods’). The skewness and kurtosis measure the shape of the distribution—the symmetry and weighting of the tails. If any of the four measures were unrealistically high in the model then too many unprecedented extremes would be identified, leading to an overestimation of the likelihood; if they are too low then the likelihood would be underestimated. As we are looking for events in the extremes of the distribution it is important to ensure the behaviour in the model tails is indistinguishable from the observations. Proxy time series are generated by sampling the climate model data for sets of equal length to the observed record. The observed value is then compared to the distribution of values from the proxy time series. In south east England the observed values all lie within 95% of the model distribution (Fig. 2b–e) and hence the model is deemed to be statistically indistinguishable from the observations.

Fig. 2 Model and observed rainfall totals are indistinguishable. a The distribution of October to March monthly rainfall totals (in mm) of observations from the Met Office NCIC data set, 1981 to 2015, and the model simulations for south east England. b–e The distributions of proxy south east England model time series mean, standard deviation, skewness and kurtosis compared to the observed values indicated by the black vertical line Full size image

Risk of a monthly rainfall record

In January 2014 the observed monthly rainfall record was exceeded by 30%7. The model simulations (Figs. 1b, 3) show that there was a 7% chance of an unprecedented rainfall total in a given winter, suggesting that the January 2014 rainfall could have been anticipated under the current climate, with the exact timing and occurrence determined simply by natural variability. The number of unprecedented monthly rainfall totals shown by the model varies between months, with December showing the largest number of unprecedented rainfall events (Fig. 3). This suggests that the observed record for December has been fortuitously low, and that the south east of the UK has been fortunate in not experiencing a wetter December in the past 35 years. Considering the whole winter, the chance of a record rainfall in any month in south east England is 7%; therefore, in the current climate, a new record is likely to occur there in the next decade. January 2014 is the wettest month in the observational record but even this event is exceeded by the model, suggesting that even greater rainfall is possible.

Fig. 3 Unprecedented monthly rainfall in all winter months. South east England monthly rainfall totals from observations (grey) and the model (red) for October to March. The box represents the interquartile range and the range of the whiskers represents the minimum and maximum monthly rainfall totals. Red dots indicate model months with greater total rainfall than has yet been observed and ticks on the upper observations line indicate values in the upper quartile of events. For January the ticks on the model line indicate months above the observed record prior to 2014 and the grey dot above the observations indicates the record observed monthly rainfall of January 2014 Full size image

A statistical model could be used to calculate the probability of unprecedented rainfall from observational data alone using extreme value theory22,23,24. However there are many advantages of using a dynamical model. The observations are poorly sampled at the tails due to the limited length of the record; simply extrapolating from the available observations using extreme value theory would give large errors, as explored later. A statistical model can also lead to physically unrealistic results whereas a dynamical model provides physically plausible limits, but a statistical model may reduce sampling uncertainties by providing a parametric description. In this study both a statistical approach and a dynamical model are used. As the dynamical model provides global fields of many climatic indicators it allows calculation of the likelihood of concurrent climatic events in different locations, for example, the associated storm depths or extreme wind speeds. A dynamical model also allows investigation of the mechanisms of extreme events and remote precursors, which can aid prediction. This is discussed further in the next subsection.

We now quantify the probability of unprecedented rainfall in south east England as an example of our analysis (Fig. 4). Using the dynamical model rainfall data we use both a ranking method and extreme value theory to estimate the chance of record rainfall (‘Methods’). Both methods are in good agreement, indicating a 7% risk of a record monthly rainfall total in any winter month of a given year, as we previously noted. The horizontal line at 1% indicates an event with a 1% annual probability would be 20–25% wetter that the current observed monthly record. Note, for south east England longer observational records are available; this is not true for all regions globally. We have also assessed the risk using the longer time series from 1880 to 2015, containing 100 times fewer station records25, finding a likelihood for an unprecedented rainfall total in at least one month of a given winter of 6%—similar to the values using observations from the model period alone.

Fig. 4 Improved risk estimates of the chance of unprecedented rainfall. The chance of an event exceeding the observed maximum monthly rainfall total in south east England in winter (October to March) of any given year from two methods, ranking (red) and extreme value theory (blue) on the model simulations. The uncertainties indicate the 95% range Full size image

Engineers and hydrologists are likely to need the absolute magnitude of extreme events for design purposes. Figure 5 shows the chance of absolute monthly rainfall totals, calculated from both the model simulations and the observations. For the model we have calculated the uncertainty using the full model data (Fig. 5, red). By subsampling the model to the length of observations, 35 years (Fig. 5, grey), we show how uncertain results calculated using the observations alone are. This can be compared to the parametric uncertainty range calculated from observations (Fig. 5, blue), showing that the spread is similar to the subsampled model curve. The model indicates a slightly lower chance of exceeding the record than observations. From the upper limits of the subsampled model curve (Fig. 5, grey) there is a 1% chance of at least 234 mm precipitation in at least one month of a given winter, using all available model data the chance of rainfall at this level is much lower with only two events above this threshold—a 0.05% chance. The figure shows that using the large ensemble of model simulations we are able to greatly constrain the uncertainties compared to using observations alone. We can use Fig. 5 to assess the chance of different precipitation totals. The model simulations suggest a 1% annual probability of monthly rainfall of 205–210 mm. In January 2014 south east England experienced record monthly rainfall of 205 mm; Fig. 5 suggests the chance of such event is just over 1% per year.

Fig. 5 The chance of specified absolute rainfall totals. The chance, in south east England, of at least one month in a given winter (October–March) exceeding absolute rainfall totals (in mm) using extreme value theory. The 95% uncertainty cone of the model using the full model data is shown in red. Limiting the sample length to 35 years, as is available from observations, is shown in grey. The limits of the uncertainty range from the observations alone are shown in blue Full size image

Dynamics of extreme rainfall

The dynamical model provides global fields of dynamically consistent variables, allowing us to investigate the atmospheric circulation associated with extreme events. To illustrate this we investigate the sea level pressure fields over the North Atlantic and Europe for four extreme rainfall months (Fig. 6). A one-to-one mapping between the monthly circulation patterns and rainfall does not exist and a similar circulation pattern could cause a range of rainfall, especially for a region as small as south east England. Nevertheless, the model patterns show circulation patterns that appear feasible in the real world and enable us to see a range of conditions that could lead to extreme rainfall over south east England.

Fig. 6 Observed and not yet realised climate states. a, b The sea level pressure anomaly fields (in hPa, relative to the January mean sea level pressure field) of the two observed Januarys with highest rainfall totals, 2014 and 1988, using the ERA data set35. c–f The sea level pressure anomalies of four extreme simulated Januarys, one of which d presents a potential new record rainfall scenario Full size image

The observed patterns from January 2014 and from January 1988, the two wettest Januarys in observations, are shown (Fig. 6a, b). The two are similar, showing patterns that would be expected to cause high rainfall as they have low pressure over the UK. Four examples of model Januarys with extreme rainfall are shown (Fig. 6c–f). Two of the extreme rainfall months show a similar pattern to observations, with low pressure centred near the UK (Fig. 6c, d), showing the model is able to simulate realistic flow patterns. This pattern represents storms repeatedly tracking across the North Atlantic and over south east England—as was observed in January 20141. However, there is variation in the sea level pressure patterns leading to extreme monthly rainfall. For example, the circulation patterns in Fig. 6e, f project onto a negative North Atlantic Oscillation, not normally associated with wet and stormy conditions over the UK26. In these cases moist air from the subtropics moves northwards bringing rainfall to the UK. These examples serve to highlight that a variety of large-scale circulation patterns, some of them perhaps not yet realised, can drive UK regional extreme rainfall events. Future work could further investigate the dynamics of extreme and unprecedented rainfall and assess how the chance of regional rainfall extremes relates to the large-scale atmospheric circulation.