HESSDHydrology and Earth System Sciences DiscussionsHESSDHydrol. Earth Syst. Sci. Discuss.1812-2116Copernicus GmbHGöttingen, Germany10.5194/hessd-12-13197-2015Climate change increases the probability of heavy rains like those of storm Desmond in the UK – an event attribution study in near-real timevan OldenborghG. J.oldenborgh@knmi.nlhttps://orcid.org/0000-0002-6898-9535OttoF. E. L.HausteinK.https://orcid.org/0000-0003-3126-7851CullenH.Royal Netherlands Meteorological Institute (KNMI), R&D Models, De Bilt, the NetherlandsEnvironmental Change Institute, University of Oxford, South Parks Road, Oxford OX1 3QY, UKClimate Central, Princeton, USAG. J. van Oldenborgh (oldenborgh@knmi.nl)16December20151212131971321610December201514December2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/preprints/12/13197/2015/hessd-12-13197-2015.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/preprints/12/13197/2015/hessd-12-13197-2015.pdf
On 4–6 December 2015, the storm “Desmond” caused very heavy rainfall in
northern England and Scotland, which led to widespread flooding. Here we
provide an initial assessment of the influence of anthropogenic climate
change on the likelihood of one-day precipitation events averaged over an
area encompassing northern England and southern Scotland using data and
methods available immediately after the event occurred. The analysis is based
on three independent methods of extreme event attribution: historical
observed trends, coupled climate model simulations and a large ensemble of
regional model simulations. All three methods agree that the effect of
climate change is positive, making precipitation events like this about 40 %
more likely, with a provisional 2.5–97.5 % confidence interval of 5–80 %.
Introduction
Atlantic storm “Desmond” passed over Ireland, Scotland and
northern England from early Friday, 4 December to early Sunday, 6 December,
causing very heavy rainfall and gale-force winds. Its severity is illustrated
by reports that the UK provisionally set a new record for the greatest
24 h rainfall recorded with 341.1 mm in Honister, Cumbria between 18:30, 4 December
and 18:30 (all times are in UTC, which is also local time), 5 December. The UK Met Office issued a rare red “take
action” warning
(http://www.metoffice.gov.uk/news/releases/archive/2015/storm-desmond-red-warning))
– the first since 12 February 2014 – for parts of Cumbria and the Scottish
Borders as a result of this powerful storm. The heavy rainfall indeed led to
widespread flooding in these regions. It was reported that about 5000 homes
and businesses were flooded and 60 000 people lost power (The Telegraph 8
December 2015, CNN 7 December 2015). The excessive nature of this record
rainfall event has led many to question whether climate change played a role,
especially since there have been several large floods over the last decades.
These questions are asked on the days following the event when usually no
scientific information is available, so answers are given based on opinions
or generalities rather than specific scientific evidence. Here we demonstrate
that it is possible to provide a robust estimate of the overall role
anthropogenic climate change played in the likelihood of this type of heavy
precipitation events to occur on a time scale of a week.
Although the Clausius-Clapeyron relation points to the possibility of about
6 %/K more water vapour in a warmer atmosphere, this is not the only factor
influencing heavy precipitation as changes in the atmospheric circulation may
also play an important role . Hence the answer to
the question of attribution depends on the region and season. Earlier work
found no trend in the chance of heavy precipitation causing the Thailand
floods or the Elbe and Danube floods
, but a strong increase in Southern France
.
We investigated the precipitation leading to the UK floods of early
December using three independent methods of probabilistic event attribution
: a statistical analysis of the observations
, the trend in a coupled climate model
similar to, and the difference between the actual climate
and one without anthropogenic emissions in a very large ensemble of
atmosphere-only general circulation model run by volunteers designed to
address this question . The observational analysis gives a
probability p1 of the event occurring in the current climate, typically
expressed as a return time τ1=1/p1. This can in principle also be
estimated from the models, but only after careful bias correction, which was
not available at the time of writing. All methods provide changes in the
return time, f1/f0=τ0/τ1. However, these are calculated to
answer subtly different questions: how much the probability changed due to
the observed trend for the observations, due to all forcings in the coupled
model and due to anthropogenic forcings in the large ensemble. In the limit
that the trend is completely due to anthropogenic forcings these coincide. In
the UK in winter this is as far as we know a reasonable approximation. The
influence of natural forcings is small: solar forcings have very little
influence and there were no major volcanic
eruptions the last few years. Low-frequency variations also play a minor role
here. The largest uncertainties arise from the random weather, which affects
all three methods equally.
Event definition
As shown in Fig. , most rain fell in Northwestern England, with
orographically-driven maxima in Cumbria and southern Scotland. The rain
mostly fell from 18:30 on 4 December to 09:00 on the 6th. This implies that
the 24 h period from midnight to midnight 5 December (00:00–00:00) captured
most of the precipitation, but the 09:00–09:00 rain stations only show the full
extent by adding the precipitation recorded on 5 and 6 December. The Met
Office reported two-day sums of more than 170 mm at four stations
(http://blog.metoffice.gov.uk/2015/12/06/wind-and-rain-records-for-storm-desmond/).
However, stations very close to these stations received much less rain, due
to the mountainous terrain.
We investigated the event at two spatial scales to capture both the
orography-driven localised events of very high rainfall and increase
confidence in the results by making use of model simulations that only
simulate much larger scales. A large area with heavy precipitation was
defined as the land area of 54–57∘ N, 6∘ W–2∘ E. Results were
checked against smaller areas and local station data. Over the large area,
the ECMWF analysis gives an average precipitation of 36.4 mm on 5 December 2015 (00:00–00:00).
Observational analysis
To investigate trends in daily and two-daily sums of precipitation we
analysed two relatively long area-averaged time daily series available from
the Met Office in areas with severe precipitation: Northwest England and
Southern Scotland precipitation. Since the underlying station data are
obtained from different networks operated by various agencies and have to
undergo quality control, the area averages are only publicly released the
following month. The observations for December 2015 are therefore not yet
available, so we can only study the trends over 1931–2014; the return time
of this event could not be computed at the time of writing. A preliminary
indication was obtained from the ECMWF analysis, which gives about
28 mm day-1
for Northwest England and 31 mm day-1 for South Scotland. There is only one
publicly available precipitation series in the area, Eskdalemuir in southern
Scotland, which recorded 77 mm on the morning of December 5 and 139 mm over
the two days.
The daily and two-daily maxima occurring over the period October–February
were computed for each year. As can be seen in Fig. this
encompasses the season of heavy large-scale precipitation in this area and
excludes the season of heavy thunderstorms in the (late) summer. Adding the
months of October and November increases the signal-to-noise ratio greatly.
These block maxima were fitted to a Generalised Extreme Value function (GEV)
scaled with the low-pass filtered global mean temperature, a proxy for
anthropogenic climate change. The results are shown in Fig. 2 for the two
regions. The horizontal line denotes the preliminary indication for
precipitation in these areas.
The Northwest England region shows no trend in the maximum daily
precipitation over October–February, with a 95 % uncertainty margin on the
change in return times of these extremes of a factor 0.3–2.1 (1 indicates no
change). In South Scotland there is a strong positive trend in precipitation,
giving an increase in probability of 1.8–4 times what it used to be at the
beginning of the series, 1931. This is due to large extent to a heavy
precipitation event in 2005. However, even without that year the trend is
positive. The trends in the two regions are compatible with each other, with
the difference mainly due to natural variability: the maxima are
uncorrelated. Averaging them gives an increase in probability of a factor
1.3–2.8 (95 % confidence interval).
The Eskdalemuir series shows a strong increase in daily mean precipitation,
in agreement with the South Scotland series (which likely includes this
station). Again, the trend is already positive over the period before a spate
of high-precipitation events starting in 2004. The 77 mm observed in one day
has a return time of 4 to 13 years in the current climate, the more relevant
two-day sum of 139 mm a return time of 20 to 250 years. At this location the
heavy rain associated with storm “Desmond” was a fairly rare event even in the
current climate. In the early twentieth century this fit indicates that it
would have been even more rare, with a return time of hundreds of years and a
lower bound of 75 years.
Coupled climate model
We applied the same method on general circulation model data to decrease the
statistical uncertainty at the expense of an increased systematic
uncertainty. We used 16 experiments covering 1861–2100 of the EC-Earth 2.3
model using the CMIP5 protocol . This
model is very similar to the ECMWF seasonal forecasting model. The resolution
is T159, this is about 150 km, too low to resolve the mountains that show the
highest precipitation in Fig. . We therefore only use the large
area, 54–57∘ N, 6∘ W–2∘ E. Precipitation in this area shows a
climatology comparable to ERA-interim which is made with a very
similar model;. Extreme winter precipitation is concentrated
in October–December, as in the observations.
Figure shows the return times of winter precipitation in this
area based on the EC-Earth simulations with a GEV fit under the same
assumptions as for the observations. The figure shows an increase in the
return time for an extreme event of the magnitude of 36.4 mm as calculated
from ERA-Interim (pink horizontal line) due to the external forcings of 1.2
to 2.3 (95 % CI). However, we do not trust this result or return time itself
as the ECMWF forecast on which the horizontal line is based has a much higher
resolution and hence the precipitation includes orographic effects that are
absent in EC-Earth. For a 1 in 100 year event, which is roughly the return
time of the event in observations calculated from the one station and from
ERA-interim, the likelihood of such an event to occur has increased by a
factor of 1.1–1.8 (Fig. , for 30 mm day-1).
The increase in return time (a shift to the left in Fig. ) can
be translated to a shift in intensity (upwards shift) of such an event in
intensity . For heavy precipitation in northern England
in the EC-Earth model this is about 4 %. The full CMIP5 ensemble for annual
maxima , a much softer extreme, gives a
range of 3 to 8 % (interquartile range) using the methods of
(not shown). We do not use this CMIP5 range in our
synthesis as this ensemble includes many models with a resolution that is too
low to resolve events like storm “Desmond”.
The increase in probability of these kinds of events in EC-Earth is in line with the observational one,
although we expect a difference due to the different framing of the attribution question within
the different methodologies. The observational analysis considers the change due to the observed
trend, independent of the cause of this trend, while the coupled model shows the change due to the
external anthropogenic and natural forcing prescribed in the model. The differences are mainly in
the response to the aerosol and greenhouse gas forcings of the climate model used, which may
differ somewhat from the real world. Very low frequency natural variability could also cause
the results to diverge.
Large ensemble of regional climate models
The fact that the northern part of England is a mountainous region led to
very heavy precipitation observed at some stations and almost none in
neighbouring stations. Therefore capturing the nature of the precipitation
event requires relatively high resolution climate models that include local
orography. Furthermore, using a very large ensemble it is not necessary to
fit an extreme value distribution to analyse the rare events, hence no
assumptions about the shape of the tails of the distribution is made, nor is
the change in softer extremes related to the change in larger extremes.
Using the distributed computing framework weather@home very large ensembles
of regional climate models at 50 km resolution over Europe are available for
the last decade. Corresponding to these simulations of possible weather in
Europe under current climate conditions (“all forcings”) ensembles of
counterfactual simulations of possible weather in a world as it might have
been without anthropogenic climate drivers (“natural”) are run. As
weather@home is an atmosphere-only modelling framework, observed SSTs are
necessary to drive the model. SST for the “natural” simulations is obtained
by subtracting various estimates of the difference between pre-industrial and
present-day conditions from CMIP5 .
Because current SSTs are not yet available, simulations are only available up
to October 2015. We therefore analyse the winter season DJF of the last year,
2014/2015, and the previous year, 2013/2014, for which the largest number of
simulations are available. SST patterns like El Niño have very little
influence over winter precipitation extremes in England and Scotland, so
these years can be taken as equivalent to 2015. The two winter seasons were
investigated separately at first (not shown) to confirm that the assumption
is justified that the specific SST patterns have a negligible influence on
the possible winter precipitation in the Northern UK. As simulations of
October/November 2013 were not available, only DJF is analysed with an
ensemble size of over 8800 for the all forcing simulations and 17 800 natural
ensemble members. This allows us to obtain a good signal to noise ratio for
events more frequent than 1 in 880 years.
Figure 5 shows the return time of the winter maximum precipitation averaged
over the area (54–57∘ N, 6∘ W–2∘E) in the combined ensemble simulations. The
results are remarkably similar to those from the coupled model, in spite of
slightly different definition of seasons. As in the EC-Earth results the
return time of an event of the magnitude estimated from the high-resolution
ECMWF analysis, without bias corrections, would be very high, with a return
time of about 1600 years and a 5–95 % confidence interval of 1000 to 2500
years under actual climate conditions. The confidence interval represents the
sampling uncertainty after bootstrapping.
Previous studies using the same model in a very similar region have shown
however that the model in the region is biased towards too low precipitation
. This is not unexpected given that for heavy precipitation
in mountainous, or at least hilly, terrain a resolution under 10 km would be
needed to simulate the mechanisms leading to the heaviest rainfall. As before
we therefore use the return time calculated from observations rather than the
magnitude of the observed event. This leads to a more realistic estimate of
changes in the likelihood of the occurrence of an event like the one observed
on the 5th of December, which is on the order of a 1 in 100 year event. In
the weather@home ensemble simulations the return time for a 1 in 100 year
event in the world that might have been without anthropogenic climate change
is now an approx. 1 in 83 (72 to 95) year event, increasing the likelihood of
such an event to occur by a factor of 1.05 to 1.4.
Again, the question addressed with the atmosphere-only large ensemble method
is slightly different from the other two methods. Here we ask how much the
probability has changed given the influence of prescribed anthropogenic
forcings and the observed SST patterns. We checked that the different SST
patterns in 2012/2013 and 2013/2014 indeed did not make an appreciable
difference. The natural forcings, that were included in the coupled climate
model but not here, also have a small influence, as argued in the
introduction.
Discussion
There is remarkable agreement between all three methods used here to
investigated the role of anthropogenic climate change in the type of heavy
precipitation events as associated with storm “Desmond” that passed over the
Northern Part of the UK from 4–6 December 2015. We find that climate
change clearly increased the likelihood of large precipitation events in all
three analyses. The likelihood of a 1 in a 100 year event of daily
autumn/winter precipitation averaged over the land area of 54–57∘ N and
6∘ W–2∘ E increased by a factor of between 1.3–2.8 (95 % confidence
interval) based on the past trends in the observations, 1.1–1.8 in the
coupled climate model EC-Earth and 1.05–1.8 in the large ensemble of
regional climate model simulations. The fits to the observations and the
coupled model are dominated by the more frequent events and the assumption
that the two distributions scale with the global mean temperature propagates
that information to the high tail. This assumption is not made in the large
ensemble.
All methods show a small increase in this factor as the return time
increases, showing that the scaling assumption in the GEV fitting method is
not unreasonable in this case. Given the fact that all three applied
methodologies frame the attribution question in a different way
e.g., and that the station data includes
the effects of local orography that the climate models cannot capture, the
quantification of the increase agrees surprisingly well. This corroborates
the assumption that this increase is indeed mainly due to anthropogenic
climate forcings made in the observational analysis, and that the influence
of other factors such as SST patterns is small.
This initial analysis looks at the combined effect of a thermodynamic
increase in precipitation and potential changes in the atmospheric
circulation and thus gives an estimate of the overall change of the
likelihood of occurrence of this type of event. However, it only considers
trends in precipitation and does not take into account other factors that
influenced the flooding in northern England, such as flood defenses and
increased exposure due to development in flood-prone areas
e.g.,.
Conclusions
After a an impactful climate event like the floods in the UK following heavy
rains around 5 December 2015, the question arises immediately what role climate
change has played in it. Using real-time observations and weather analyses,
historical data, reanalyses and climate model output we can now give a first
scientific assessment of the effect of climate change in a relatively short time.
For the analysis of the heavy precipitation event in North England and South Scotland
caused by the storm “Desmond” we used three independent methods: a statistical analysis
of observed trends, coupled climate model simulations and a large ensemble of regional
climate model simulations. In this case a lack of observations of the event precluded us
from establishing the return time of the event with accuracy, but statements can be made
about relative return times under various scenarios. Based on the currently available data
it appears to be very roughly a one in a hundred year event when averaged over a large region, but with an uncertainty
range from about 20 years to many hundreds of years. Locally return times may
be very different from this. As more observations become available these will be better-defined.
The increase in likelihood of the event does not depend strongly on
the return time and was found to be in remarkable agreement between the three
methods. Overall, we find a roughly 40 % increase in likelihood, with a 95 %
uncertainty range of 5 to 80 % for a return time of 100 years. The
reference for this change is different for all three methods: a century ago,
due to all forcings and due to anthropogenic forcings respectively. However,
the results coincide within the uncertainties of natural variability, showing
that for this event these different framings largely agree. Techniques for
the attribution of the resulting floods to climate change, or even the
damages, are being developed, but not yet mature enough for use on this time
scale. Further analyses should also take into account all other factors that
affect flooding apart from the small but robust contribution of climate
change.
Acknowledgements
We thank the UK Met Office and ECA&D for providing the historical
observational data and ECMWF for the (re)analyses. We would like to thank all
of the volunteers of weather@home who have donated their computing time to
generate the large ensemble simulations. This project was supported by the
World Weather Attribution initiative and the EU project EUCLEIA under Grant
Agreement 607085.
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Precipitation on 5 December 2015 in mm day-1 (ECMWF 24 h forecast from
00:00).
Climatology of rainfall in Northwest England. South Scotland is very
similar. (The seasonal cycle is repeated twice). Source: UK Met Office.
The maximum of daily precipitation amount over October–February in
(a) Northwest England and (b) South Scotland precipitation from the Met Office,
1931–2014, plotted against the smoothed global mean surface temperature
(GMST). The thick line is the position paramater μ and the thin lines are
drawn σ and 2σ above it. (c, d) Gumbel plots of the same values
and GEV fit. The red lines indicate the fit in the current climate, the blue
ones in the climate of 1931. The stars denotes the observed block maxima,
shifted up with the fitted trend to 2015 (red) or down to 1931 (blue). The
values for 2015 (purple lines) are preliminary indications based on the ECMWF
24hr forecast.
Gumbel plot of the maximum of 2-day precipitation amount over
October–February at Eskdalemuir, Scotland. The lines indicate a GEV fit
assuming the distribution scales with the smoothed observed global mean
temperature. Red values indicate the climate of 2015, blue lines the climate
of 1931. The stars denotes the observed block maxima, shifted up with the
fitted trend to 2015 (red) or down to 1931 (blue). The purple line denotes
the maximum observed so far in 2015/2016. Source: ECA&D.
Gumbel plot of the maximum daily precipitation amount in
October–February in 16 EC-Earth experiments 1889–2014. The lines indicate a
GEV fit assuming the distribution scales with the smoothed observed global
mean temperature. Red values indicate the climate of 2015, blue lines the
climate of 1931. The stars denotes the simulated block maxima, shifted up
with the fitted trend to 2015 (red) or to 1931 (blue). The value from the
ECMWF analysis is taken for 2015 (purple line).
The maximum of winter (DJF) daily precipitation averaged over the
Northern UK, 54–57∘ N, 6∘ W–2∘ E. Red indicates the probability
of daily mean precipitation under observed climate conditions, blue in the
counterfactual simulations. The black dashed line marks the 1 in 100 year
event.