HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-22-3933-2018Mean and extreme precipitation over European river basins better
simulated in a 25 km AGCMEuropean precipitation in a 25 km AGCMSchiemannReinhardr.k.schiemann@reading.ac.ukhttps://orcid.org/0000-0003-3095-9856VidalePier Luigihttps://orcid.org/0000-0002-1800-8460ShaffreyLen C.https://orcid.org/0000-0003-2696-752XJohnsonStephanie J.RobertsMalcolm J.DemoryMarie-Estellehttps://orcid.org/0000-0002-5764-3248MizielinskiMatthew S.https://orcid.org/0000-0002-3457-4702StrachanJaneNational Centre for Atmospheric Science, Department of Meteorology,
University of Reading, Reading, UKMet Office Hadley Centre, Exeter, UKEuropean Centre for Medium-Range Weather Forecasts, Reading, UKCenter for Space and Habitability, University of Bern, Bern, SwitzerlandReinhard Schiemann (r.k.schiemann@reading.ac.uk)20July20182273933395014December201716January20184June201812July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://hess.copernicus.org/articles/22/3933/2018/hess-22-3933-2018.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/22/3933/2018/hess-22-3933-2018.pdf
Limited spatial resolution is one of the factors that may hamper applications
of global climate models (GCMs), in particular over Europe with its complex
coastline and orography. In this study, the representation of European mean
and extreme precipitation is evaluated in simulations with an atmospheric
GCM (AGCM) at different resolutions between about 135 and 25 km grid spacing in the
mid-latitudes. The continent-wide root-mean-square error in mean precipitation
in the 25 km model is about 25 % smaller than in the 135 km model in
winter. Clear improvements are also seen in autumn and spring, whereas the
model's sensitivity to resolution is very small in summer. Extreme
precipitation is evaluated by estimating generalised extreme value
distributions (GEVs) of daily precipitation aggregated over river basins whose
surface area is greater than 50 000 km2. GEV location and scale parameters
are measures of the typical magnitude and of the interannual variability of
extremes, respectively. Median model biases in both these parameters are
around 10 % in summer and around 20 % in the other seasons. For some river
basins, however, these biases can be much larger and take values between 50 %
and 100 %. Extreme precipitation is better simulated in the 25 km model,
especially during autumn when the median GEV parameter biases are more than
halved, and in the North European Plains, from the Loire in the west to the
Vistula in the east. A sensitivity experiment is conducted showing that these
resolution sensitivities in both mean and extreme precipitation are in many
areas primarily due to the increase in resolution of the model orography. The
findings of this study illustrate the improved capability of a global
high-resolution model in simulating European mean and extreme precipitation.
Introduction
There is an obvious requirement for climate models to simulate precipitation in
a realistic way if the models are to be applied, for example, in process and
impact studies of the hydrological cycle
e.g., prediction at
different lead times e.g., and
extreme event attribution e.g.. Due to the
wide range of applications, the required realism concerns all aspects of the
precipitation distribution in space and time including the probability
distribution function of the precipitation time series and its extremes.
Limited grid resolution is one of the factors that may hamper model
application. At the turn of the millennium, state-of-the-art global climate
models (GCMs) had grid spacings of several hundred kilometres
. This resolution is too low for many applications,
particularly so over Europe with its complex coastline and orography, and
higher resolution regional climate models (RCMs), driven at their boundaries by
GCM or reanalysis data, were used to overcome this limitation. At the time,
RCMs' grid spacings were on the order of 50 km .
Model development and the availability of more and more powerful computing and
data analysis facilities have changed this situation radically. Multidecadal GCM
simulations can now be carried out at grid spacings of about 20 km
. This offers the
possibility of using a single global physically consistent model in applications
that until a few years ago were in the realm of RCM simulations
. GCMs also remain important for providing initial and
boundary conditions for RCMs e.g., which now yield
kilometre-scale climate simulations , allowing for
convection-permitting simulations crucial for representing, in particular,
sub-daily precipitation extremes and the soil
moisture–precipitation feedback .
Additionally, a systematic analysis of the role of resolution in RCMs has been
conducted as part of the EURO-CORDEX project. The EURO-CORDEX simulations are
driven by ERA-Interim reanalysis data and are available at a low (0.44∘)
and high (0.11∘) resolution. Evaluating these simulations with respect to
their representation of precipitation in Europe shows that for seasonal mean
quantities averaged over large European subdomains no clear benefit of an
increased spatial resolution can be identified . At the
same time, the 0.11∘ simulations better reproduce spatial precipitation
patterns and this improvement is seen both for mean and
moderately heavy (95 and 97.5 daily percentiles) precipitation
. These benefits due to high resolution are also
seen when the evaluation is carried out on the coarser 0.44∘ grid and are
largely attributed to the better representation of orography in the
higher resolution RCMs .
The aims of this study are threefold. Firstly, rapid model development requires
continued model evaluation, and we evaluate here the representation of European
precipitation in the UPSCALE (UK on PRACE: weather-resolving Simulations of
Climate for globAL Environmental risk) atmospheric GCM (AGCM) simulations
, which are still rather exceptional in their
combination of model resolution, simulation length, and ensemble size. We
evaluate both seasonal mean and extreme precipitation. These evaluation results
are to serve as a benchmark for future generations of GCMs with grid spacings on
the order of 10 km, as well as for kilometre-scale RCM simulations. The second aim of
this study is to determine to what extent the resolution sensitivity in
precipitation is due to the sensitivity to resolution of the simulated North
Atlantic storm track as shown in previous studies ,
and to contrast that with the role of local forcing from the orography at
different resolutions. The third aim concerns the methodology used for
evaluating extreme precipitation. We combine two approaches used previously: we
characterise daily precipitation extremes by fitting extreme value distributions
as done, for example, by , , and
. Furthermore, we conduct model evaluation over large
(>50000 km2) river basins in Europe. At these scales, evaluation has typically been
carried out for RCMs in the past, often coupled to hydrological impact
models. Due to
the recent increase in global model resolution, we can meaningfully evaluate a
GCM at such scales in this study. For some kinds of impacts these scales are
relevant, as demonstrated for example by flood events affecting much or all of a
river basin such as during the Elbe and Danube flood in summer 2013
e.g.. For applications at smaller scales RCM output may be
preferable, but the quality of the RCM output may still largely depend on the
performance of the driving GCM . Coupling to impact models is
not part of our study, yet we choose to conduct the evaluation for river basins
in the hope that our results will prove informative for such future
applications.
The manuscript is structured as follows: Sect. describes the
climate model and simulations and the observational reference data. The mean
precipitation distribution is evaluated in Sect. , and the
representation of precipitation extremes over European river basins is evaluated
in Sect . The roles of the North Atlantic storm track and of
the orography are discussed in Sect. . We
conclude the paper in Sect. .
Methods and dataModel ensemble
The AGCM used here, HadGEM3-GA3.0, is the Global Atmosphere 3.0 configuration of
the HadGEM3 family of the Met Office Unified Model . We use
an ensemble of simulations at three different horizontal resolutions: N96 with a
grid spacing of 135 km at 50∘ N, N216 (60 km), and N512 (25 km). There are
85 vertical levels, the same at all three resolutions. These simulations were
conducted within the UPSCALE project and their generation and analysis
required exceptionally large computing resources and big data infrastructure
. These resources allowed five simulations to be produced at
N512 resolution, each lasting 26 years from 1986 to 2011 and using OSTIA sea
surface temperature forcing . Lower resolution simulations
with the same forcing were conducted at N216 resolution (3×26 years)
and at N96 resolution (5×26 years). These experiments were designed to
test the sensitivity of the simulated climate to horizontal resolution only and
therefore parameter changes between the different resolutions have been kept to
the minimum necessary to ensure numerical stability
seefor more details on this point. Convection
is parameterised at all three resolutions.
We have also conducted a sensitivity experiment to test the role played by the
orography boundary conditions as the resolution is increased. This experiment was
conducted with the HadGEM3-GA6.0 configuration of the Met Office Unified Model
and is described in
Sect. . HadGEM3-GA6.0 was the closest available model
configuration to HadGEM3-GA3.0 at the time the sensitivity experiment was
conducted. Differences between GA6.0 and GA3.0 include small adaptations to the
semi-implicit semi-Lagrangian dynamical core from “New Dynamics”
to ENDGame Even Newer Dynamics for General atmospheric
modelling of the environment; and the new “5A”
subgrid orographic drag parameterisation replacing
the previous “4A” scheme .
Observed precipitation
We evaluate the simulated precipitation against gridded observed precipitation
from the E-OBS dataset , version 9. As part of the ENSEMBLES
project , this dataset was designed for the evaluation of
daily precipitation across Europe in RCMs at similar resolutions to the AGCM
simulations used here. This makes E-OBS the dataset of choice for our purposes
and we refer to differences of model precipitation from E-OBS as model
biases.
Nonetheless, gridded precipitation data are subject to
uncertainties from both the measurement error of point observations and the
limited spatial representativity of gauges see, e.g.for an
overview. A general dry bias, which can be exacerbated in
mountainous terrain and for localised extremes, has been reported for E-OBS
.
Extreme value analysis
We evaluate daily extreme precipitation by fitting a generalised extreme value
(GEV) distribution to daily precipitation averaged over a number of European
river basins (Table S1 in the Supplement). We choose basins with surface
areas larger than 50 000 km2 so that even at N96 resolution the basins will be
represented by several model grid boxes. The GEV distribution is defined as in
and is characterised by three parameters referred to as
location μ, scale σ, and shape ξ. Illustrations of how these
three parameters influence the GEV distribution will be provided in
Sect. and Fig. S1 in the Supplement. We estimate the GEV parameters
using the block maxima approach, in which each block consists of daily mean
precipitation in a river basin throughout one season. The estimation is carried
out in a two-step process. First, we estimate μ, σ, and ξ for
each basin and each season using maximum likelihood estimation
e.g.. We find that this yields plausible spatial variations
of μ and σ with typically similar values in neighbouring basins,
while there is considerable scatter in ξ with no systematic dependence on
the basin location or area. This indicates that the shape parameter cannot be
robustly fitted for each basin separately with the available data. We therefore
conduct a second estimation step, in which we fix the value of ξ to the average
value of all basins for each season, and then, also using maximum likelihood
estimation, determine the values of μ and σ for each
basin. Uncertainty in the fitted GEV parameters is estimated by parametric
resampling.
Mean precipitation
European mean precipitation in the different seasons is shown in
Table . HadGEM3-GA3.0 precipitation is 10–50 % larger than in E-OBS
at all three resolutions. This difference likely reflects a wet bias of
the model, but the magnitude of this bias is hard to assess because of the known
problems associated with estimating area-average precipitation from a network of
gauges (see also Sect. ). Despite the fact that no retuning has
been performed at the different resolutions, the resolution sensitivity in the
Europe-wide mean precipitation is small (<0.1 mm day-1) and not systematic
across the seasons.
Seasonal mean precipitation in Europe in mm day-1 (land area in
-12–50∘ E, 35–72∘ N including all model grid boxes with a land fraction
greater than 0.5).
Based on the ensemble spread, approximate 95 % confidence intervals
are within ±0.06 mm day-1 for N96 and N512 and within ±0.1 mm day-1 for N216.
The observed climatological mean precipitation distribution for winter and
summer is shown in Fig. a and d. During winter, there is a general
continental-scale gradient from higher precipitation in western Europe to lower
precipitation in eastern Europe, with pronounced mesoscale
variations. Particularly wet regions are west-facing or north-west-facing coasts and/or
mountains such as the north-west of the Iberian Peninsula, Ireland, Scotland, the
Norwegian coast, and an area between the Alps and the North Sea. In summer,
the continental-scale gradient is from the dry Mediterranean in the south to
much wetter conditions in central and northern Europe, especially so over the
Alps, British Isles, southern Scandinavia, and an area extending from the
Carpathians to the White Sea.
Climatological mean precipitation (mm day-1, 1986–2011) according to
(a, d) E-OBS, (b, e) N96 and (c, f) N512 for (a–c) December–February and
(d–f) June–August. Contour levels are not equidistant to better capture mesoscale
variations.
During winter (Fig. b, c), the HadGEM3-GA3.0 wet bias can be seen
throughout the continent and is particularly apparent in the north and west of
the European mainland. Despite this general wet bias, the N512 model can capture
some mesoscale variations that are absent or poorly represented in the N96
model. An example is the comparatively dry swath ranging from the south-east of
England to the Mediterranean Sea, arguably in the rain shadow of the British
Isles and western France. Moreover, areas of high coastal precipitation, for example in north-west Spain and the west of
the British Isles and Norway, are better resolved at N512 resolution. In summer (Fig. e, f), wet biases over regions
of high topography such as the Pyrenees, Scandinavian Mountains, Alps,
Carpathians, and the Caucasus, can be seen in the N512 model. There is also a
wet bias in both the N96 and N512 models in the north and north-east of
Europe. This is consistent with results in (their Figs. 7 and 15) showing for this model that there is a mean negative geopotential
height bias and an underestimation of summer blocking in the Baltic area.
We proceed with two quantitative evaluations of seasonal mean model
precipitation. The root-mean-square error (RMSE) between the model simulations
and E-OBS is plotted in Fig. against the spatial correlation between
the same two fields, so that the better the agreement of the model simulation is
with E-OBS, the closer will the corresponding entry be to the lower right corner
of the diagram. There is an improvement in the simulated precipitation with
resolution in autumn, winter, and spring, and this improvement is significant in
the sense that it can be seen in all ensemble members. The improvement in winter
is larger than in the transition seasons. In summer, the sensitivity to
resolution is very small, the RMSE is slightly larger in the N512 model than in
N96 and N216, arguably due to the higher precipitation over mountainous regions
in this model (Fig. ).
Root-mean-square error (RMSE) and spatial correlation between
HadGEM3-GA3 precipitation and E-OBS observations in Europe
(-14–50∘ E, 38–70∘ N). The domain-mean bias is discarded
before calculating the RMSE.
We also conduct scale-dependent evaluation and calculate the fractions skill
score (FSS) for different horizontal scales following . The
FSS is obtained by comparing binary fields, defined in terms of exceedance of a
threshold, between model and observation for different sizes of an averaging
neighbourhood, i.e. for different horizontal scales. The FSS takes values
between 0 and 1, and typically increases with horizontal scale as shown in
Fig. 3 of . Here, we use different quantiles of each of the
precipitation fields as the exceedance threshold in the FSS calculation so that
the FSS approaches unity for large scales and the domain-mean bias
(Table ) is disregarded in this evaluation. In Fig. ,
instead of showing the FSS directly, we show the relative
improvement/deterioration of N512 versus N96 calculated in terms of the distance
from the FSS=1 asymptote as
|FSSN512-FSSN96|/(1-min(FSSN96,FSSN512)).
For winter (Fig. a), we find an improvement for all quantile thresholds,
which is consistent with Figs. a, b, c and . This improvement
with resolution is seen across all horizontal scales. For summer
(Fig. b), there is no systematic improvement of the mean precipitation
field with resolution, also in agreement with the previous analyses.
Relative improvement in the fractions skill score (FSS) of
HadGEM3-GA3.0 N512 over N96 using E-OBS seasonal mean precipitation as a reference, for (a) December–February and (b) June–August, and for different
quantile thresholds of the spatial precipitation distribution in Europe
(-14–50∘ E, 38–70∘ N).
Extreme precipitationExamples
In this section, we present results of extreme value analysis and evaluate the
amount, frequency, and annual cycle of extreme precipitation in terms of GEV
distributions. GEV distributions are conveniently shown in terms of return value
plots, also called Gumbel diagrams. The effect of the GEV parameters on the
distribution is illustrated for fictitious data in Fig. S1. The larger the
values of these parameters, the larger the precipitation extremes. An
increase in the location parameter μ corresponds to a constant increase in
return value for all return times (Fig. S1a). The scale parameter σ is
associated with the interannual variability of extreme precipitation. The larger
the σ, the larger the increase in return value for a given increase in return
time (Fig. S1b). The shape parameter ξ determines the behaviour of the tail
of the GEV distribution and determines if the distribution is bounded (ξ<0) or unbounded (ξ>0) for large
return times (Fig. S1c). The shape parameter is held constant for all basins as explained in
Sect. (ξ=-0.13, -0.05, -0.02, and -0.05 for DJF, MAM,
JJA, and SON, respectively).
Examples of fitted GEV distributions for three river basins are shown in
Fig. . Each panel shows GEV distributions for the four seasons
alongside the observed precipitation maxima. The top row (Fig. a–c)
shows results for the Loire river basin. The annual cycle of extreme
precipitation is not very pronounced for this basin, and estimated 50-year
return values are between about 22 (spring) and 26 mm day-1
(autumn), although the differences between the seasons are not statistically
significant (Fig. a). The precipitation extremes simulated by the N96
model are very different from those in the observations. The estimates are
larger than for E-OBS (50-year return values between about 30 for
summer and 40 mm day-1 for winter) and there is a clear annual cycle with
larger extremes during the cold season (Fig. b). At N512 resolution,
the model-simulated extremes are in closer agreement with E-OBS than at N96
resolution (Fig. c): the annual cycle is very small and 50-year return
values are between 23 and 29 mm day-1. Quantitatively, the reduction of the
extreme precipitation biases can be corroborated by comparing the estimated
values of the GEV location parameter μ and scale parameter σ between
E-OBS, N96, and N512. For all seasons, E-OBS and N512 agree more
closely with one another than with N96 (Fig. a–c). For the Loire basin,
the biases in modelled extreme precipitation and their reduction at N512
resolution are consistent with the results seen for mean precipitation, i.e. a
winter wet bias at N96 resolution that is alleviated at N512 resolution
(Fig. ).
Fitted GEV distributions for (a–c) Loire, (d–f) Elbe,
and (g–i) Po river basins, and for (a, d, g) E-OBS observed
precipitation, (b, e, h) the N96 model, and (c, f, i) the N512 model. 95 %
resampling confidence intervals are shown as shaded areas for winter
(DJF) and summer (JJA). Block maxima (circles) are shown at return periods
m+1m+1-i, where m is the number of these maxima, i.e. here the number of years
× ensemble members, and
i=1,…,m is their rank. Results for N216 are generally between those for
N96 and N512 (not shown). The three basins are indicated in Fig. a and d.
Fitted GEV distributions for the Elbe basin are shown in Fig. d–f. For
this basin, there is a pronounced annual cycle of extremes. The largest
extremes occur during summer with a 50-year return value of about 30 mm day-1, while the same return value
for winter is only about 17 mm day-1 (Fig. d). The amplitude of the annual cycle is underestimated
in the N96 model. The quantitative agreement with E-OBS is close in summer
(50-year return values of about 30 mm day-1 in both datasets), but the
simulated winter extremes are larger than the observed ones and the N96 50-year
return value is about 25 mm day-1 (Fig. e). At N512 resolution,
the model better captures the annual cycle of extreme precipitation
but also somewhat overestimates the 50-year return values, which are about 34 mm day-1 in summer and
19 mm day-1 in winter. For a shorter return period
of 2 years, however, all three datasets are in close agreement on a return value
of about 15 mm day-1 during summer. This example illustrates how the
quantification of a single return value or return period only insufficiently
characterises extreme precipitation, which is why two GEV parameters are used for
quantitative model evaluation in this study.
The third basin we discuss is that of the Po river (Fig. g–i). In this
basin, daily precipitation extremes are considerably larger in autumn than in
the other seasons. This behaviour is also captured by HadGEM3-GA3.0 at both
resolutions, yet the magnitude of the simulated extremes is much larger than in
E-OBS, especially at N512 resolution. The 50-year return value for autumn is
about 53 (55, 73) mm day-1 in E-OBS (N96, N512). Moreover, and especially
during winter, both the N96 and N512 models overestimate the interannual
variability of precipitation extremes as shown by the overestimation of the
scale parameter σ: for return periods of less than 2 years, a number of
winter seasons never exceed a precipitation amount of 10 mm day-1, which is
not seen in E-OBS. On the other hand, winter precipitation extremes are
overestimated by our model for return periods greater than 2 years. These
results clearly show that the resolution increase from 135 to 25 km is not a
panacea for improving the representation of extreme precipitation, and we will
summarise results for all of the European basins in the remainder of
Sect. to assess the overall effect of increased resolution
on model performance.
Before proceeding with this assessment, we briefly discuss the goodness of the
GEV distribution fits, which can be assessed by comparing the observed maxima
(open circles) with the parametric GEV fits (solid lines) for the different
examples shown in Fig. . For most cases, the statistical model fits the
observed maxima well, but there are a few discrepancies for larger return
periods of more than about 20 years. For example, for the Po and in particular
the Elbe basin, a small number of very heavy precipitation events, which exceed
the GEV fit by more than the sampling uncertainty, can be seen during summer for
the observations and the N512 model (Fig. d, f, g, i). Such discrepancies
are partly due to the fact that we choose a constant shape parameter ξ for
all basins (see Sect. ). These results illustrate that our
parametric approach is valid in general and serves our purpose of characterising
the variation of daily extreme precipitation across European river basins, and of
evaluating GCM simulated extreme precipitation. Especially for return periods of
more than 20 years, however, our estimates of return values in individual
basins/seasons should not be used as the only source to inform impact studies or
adaptation and mitigation measures. While our results can provide initial
guidance for such applications, they will need to be considered in the context
of local expertise, process-based case studies of individual heavy precipitation
events, and additional local observations if available.
Winter
The estimates of the location and scale GEV parameters for winter based on E-OBS
are shown in Fig. a and d. There is a large-scale gradient from high values
of μ and σ in the west and south-west of Europe to smaller values in
the east and north-east. This geographical variation is similar to that in mean
precipitation (Fig. a), but there are some interesting differences. For
example, comparatively high values of both GEV parameters are seen for some
southern European basins (e.g. Po, Guadalquivir) even though the mean
precipitation for these basins is not larger than for basins further north. This
may indicate fewer but stronger precipitation events in these basins.
Estimated GEV parameters for December–February daily basin-average
precipitation: (a–c) location parameter μ and (d–f) scale parameter
σ, both in mm day-1, and for (a, d) observations (E-OBS), (b, e) N96
bias, and (c, f) N512 bias with respect to E-OBS. Stippling (hatching) shows
statistically significant differences between the models and E-OBS (between
N512 and N96). Letters L, E, and P show the Loire, Elbe, and Po basins.
The N96 bias in the GEV parameters is shown in Fig. b and e. As can been
seen from the predominance of green colours, both the location and scale
parameters tend to be overestimated, so the wet bias seen for mean precipitation
(Sect. ) is also found in the extremes. The magnitude of the
bias varies strongly between basins and can be as high as about +60 % for μ
(Elbe) and about +80 % for σ (Loire), and the median of the absolute
relative bias, i.e. of
maxθN96θE-OBS,θE-OBSθN96-1,
across all basins is 22 % for θ=μ and 17 % for θ=σ
(Table ). The wet bias is particularly pronounced for the basins in the
North European Plain, from the Loire in the west to the Vistula in the east,
where both μ and σ are overestimated. For some southern European
basins (Guadalquivir, Ebro, Po), there are significant negative biases in μ
and significant positive biases in σ, indicating a difference in the
character of the simulated and observed extreme value distribution.
Summer
During summer, the GEV location parameter μ takes larger values in the
centre, north-west, and north-east of Europe than in the south-west (Iberia),
south-east, and east (Fig. a), in rough agreement with the geographical
variation of mean precipitation (Fig. d). Particularly high values of
μ are seen for basins draining the Alps (e.g. Rhône, Po, Upper Danube)
and for the Kuban draining the Caucasus Mountains. The scale parameter σ
generally follows the geographical distribution of μ, but we find
comparatively larger values of σ for drier climates, as can be seen, for
example, when comparing the Iberian to the French river basins.
As Fig. but for summer (June–August).
More so than in winter, there is a dependence of both μ and σ not
only on geographical location and local climate, but also on the size of the
river basin considered. Larger extremes, i.e. greater values of μ and
σ, are seen for smaller basins. A case in point are the Dniester and
Southern Bug basins compared to the neighbouring larger basins of the Danube and
Dnieper. This appears to be due to the nature of summer (convective) precipitation,
which is smaller in scale and shorter in duration than frontal winter
precipitation. Convective summer rain, though locally intense, may therefore not
take large values when averaged over a large river basin over a day.
The biases of μ and σ for the N96 model are shown in
Fig. b and e. These biases are generally smaller than in winter, and for many
basins they are not statistically significant. There is a general tendency
for dry biases in the south and wet biases in the north of Europe, especially in
μ. The median relative bias across all basin is 9 % for μ and 10 % for
σ (Table ).
The sensitivity to the resolution increase (Fig. c, f) is smaller than
in winter, consistent with the results for mean precipitation, and has mixed
effects for the biases with respect to E-OBS. At N512, stronger heavy
precipitation is seen over the Alps, rather than to the north-west of the Alps
over France at N96, leading to weaker extremes and smaller biases for some
basins (especially Loire, Seine) and to stronger extremes and a larger bias in
particular for the Po basin.
Summary statistics
Two metrics are used to summarise model performance. The first metric is the
median absolute relative bias as already introduced and discussed in Sects.
and . The values of
this metric are shown in Table . The second metric is obtained by
counting for each resolution for how many basins the minimum bias is attained
at this resolution, so that a higher count corresponds to a better
model performance. The values of this metric are shown in Table .
Median across all basins of absolute relative bias (%; see Sect. ) in μ and
σ. Numbers in bold indicate the resolution with the smallest bias.
Number of basins with smallest bias in μ and σ. For example,
the number 4 for μ in DJF at N96 resolution means that for 4 out of all 33 basins
the bias in μ of the N96 model is smaller than that of both the N216 and
N512 models. Numbers in bold indicate the resolution with the largest count.
Both metrics agree on the following qualitative results: extreme precipitation
is overall better represented as resolution is increased from N96 to N512. The
clearest and strongest improvement is seen in autumn for both the location
parameter μ and the scale parameter σ (see also Fig. S3). There is
also an improvement in μ in winter and spring, but, for Europe as a whole,
biases in σ do not decrease with higher resolution in these seasons. The
resolution sensitivity in both GEV parameters is small in summer, with possibly
a slightly better performance for the N96 model. Arguably, this result is due to
the fact that extreme orographic precipitation in the higher resolution models
is larger than that in E-OBS, as also seen for mean precipitation
(Fig. ). The performance of the N216 model is generally in between that
of the N96 and N512 models, consistent with the result obtained for mean
precipitation (Fig. ), and also seen in maps of GEV parameter biases
analogous to the ones shown in Figs. 5 and 6 (not shown).
Discussion
In the previous two sections ( and ), we have
described the sensitivity to resolution in both mean and extreme precipitation.
While a comprehensive analysis of how and why mean and extreme precipitation
are sensitive to resolution in HadGEM3-GA3.0 is beyond the scope of this
evaluation paper, we briefly discuss two relevant issues, namely the role of
the large-scale circulation, specifically that of the North Atlantic storm
track, and that of orography, in this section.
North Atlantic storm track
European precipitation is strongly determined by the character of the North
Atlantic and Mediterranean storm tracks. According to ,
roughly 70 % of European winter precipitation is associated with extratropical
cyclones. It is therefore logical to ask to what extent the resolution
sensitivity seen in the precipitation is due to resolution sensitivity in the
simulation of the storm track. By analysing the historical simulations of the models
participating in phase five of the Coupled Model Intercomparison Project
(CMIP5), identified four models with a comparatively good
representation of the North Atlantic storm track. These four models have
horizontal grid spacings of about 100 km, which is at the high end of CMIP5
model resolutions. The resolution dependence of European precipitation in the
EC-EARTH AGCM version 2.3 was analysed by by comparing
simulations at ≈112 and ≈25 km grid spacing, and the authors find
that at 25 km EC-EARTH has a better representation of the winter North Atlantic
storm track and therefore precipitation over Europe.
Similar to and , we evaluate the
representation of the North Atlantic storm track in HadGEM3-GA3.0 by calculating
the standard deviation of the 2–8-day band-pass-filtered 500 hPa geopotential
height. The results are shown in Fig. for winter. At all resolutions,
the HadGEM3-GA3.0 storm-track location and strength is similar to that in
ERA-Interim (Fig. a, b, d, g). The model biases with respect to
ERA-Interim (Fig. c, f, j) show an overestimation of the Mediterranean
storm-track strength by about 10 %, and an underestimation of about 10 % over
north-east Europe, though these differences are not significant given the
interannual variability. The resolution sensitivity in the storm-track strength
over Europe attains values of about 5 % of the mean
(Fig. e, h, i). Interestingly, the resolution sensitivity is of opposite
sign for the two resolution increases: there is a reduction in storm-track
strength when going from N96 to N216 over central and western Europe and the
Mediterranean (Fig. e), but then an increase of similar magnitude over
much of Europe when going from N216 to N512 (Fig. h).
Standard deviation of 2–8-day band-pass-filtered 500 hPa geopotential
height (m). (a) ERA-Interim reanalysis, (b, d, g) model (HadGEM3-GA3) at
resolutions N96, N216, and N512, (c, f, j) model biases with respect to
ERA-Interim, and (e, h, i) differences between model resolutions. Stippling
shows statistically significant differences.
We proceed by revisiting the resolution sensitivity in mean precipitation, shown
in Fig. and in greater detail in Fig. , and by
comparing it to the sensitivity seen in the storm track. In winter, precipitation
decreases with resolution over the North European Plain and increases over
north-western and western European coasts, Iberia, and to the south of the Alps
and Italy (Fig. i). In contrast to the sensitivity in the storm track, this
pattern can be seen for both steps of resolution increase (Fig. e, h), showing
that the storm-track sensitivity is not the main factor explaining the
sensitivity seen in mean precipitation. At the same time, the precipitation
sensitivity is generally smaller for the N216 to N512 resolution increase than
for the N96 to N216 increase, especially so over the North European Plain,
where the drying with higher resolution is comparatively small (Fig. h). This
is consistent with increased storm-track activity in this region when going to
N512 resolution (Fig. h), suggesting that storm-track changes with
resolution may somewhat modulate the total precipitation sensitivity in
HadGEM3-GA3.0.
Winter (December–February) mean precipitation. (a) Observations
(E-OBS), (b, d, g) model (HadGEM3-GA3.0) at resolutions N96, N216, and N512,
(c, f, j) model biases with respect to E-OBS, and (e, h, i) differences between
model resolutions. Stippling shows statistically significant differences.
Similar conclusions can be drawn for summer: the storm-track strength decreases as
resolution is increased from N96 to N216 and it increases as resolution is
increased from N216 to N512 (Fig. S6e, h). Over the north-west of Europe and
the North Sea there is a slight increase of precipitation when going to N512
resolution (Fig. S7h), consistent with the corresponding increase in storm-track
strength (Fig. S6h). In other parts of Europe, however, the precipitation
response to resolution is qualitatively similar for the two steps of resolution
increase (compare Fig. S7e with Fig. S7h).
In the transition seasons, too, there is an decrease of storm-track strength for
the N96–N216 resolution increase (Figs. S4e and S8e), but an increase of
storm-track strength for the N216-N512 resolution increase (Figs. S4e and S8e). With the exception of Iberia in spring, no such fundamental difference is
seen for the resolution sensitivity of mean precipitation (Figs. S5e, h and S9e, h).
We have shown here, using a simple metric based on synoptic-scale
geopotential-height variance, that mean storm-track changes with resolution do
not primarily explain mean precipitation changes with resolution in our
model. More detailed analyses based on individually tracked extratropical cyclones and the precipitation associated with them
are required for a comprehensive assessment of the role of
storms in the resolution sensitivity of extreme precipitation in particular. We
are currently conducting such analyses in a separate study.
Orography
It has been shown in previous sections that in and around mountainous areas
there is particularly high sensitivity to resolution in both mean
(Figs. and ) and extreme (Figs. –)
precipitation. We therefore investigate the role of orography explicitly in
this section. To this end, we conduct a sensitivity experiment in which we use
HadGEM3-GA6.0 (see Sect. ) at high resolution (N480,
i.e. very similar to N512) but apply orographic boundary conditions at coarse
(N96) resolution. This sensitivity experiment is similar to the orography
experiment in , except that in this study we bilinearly
interpolate the N96 orography onto the N480 grid to avoid “blocks” of grid
boxes of constant orographic height on the N480 grid. The interpolation is
applied both to the (resolved) grid-box mean orographic height and to the
boundary conditions used by the parameterisations that represent different
effects of subgrid orography. The sensitivity of European mean precipitation to
resolution seen in HadGEM3-GA6.0, i.e. for N96–N216–N480 resolution
increases, is very similar to that seen in HadGEM3-GA3.0 (not shown).
The results of the orography sensitivity experiment are shown in Fig.
for winter. The panels on the diagonal show mean precipitation for the
observations (E-OBS), the N480 experiment with N96 orography
(N480N96), and the N480 control experiment
(N480N480), and the off-diagonal panels show the differences
between these three fields. These results can be compared to the corresponding
results of the full resolution sensitivity experiment, especially the ones
corresponding to the N96–N512 resolution increase (Fig. ). First,
comparing N96 with N480N96 (Figs. a–c and a–c), it can be seen that the precipitation distribution and its
bias with respect to E-OBS are broadly similar in these two
experiments. Likewise, the total resolution sensitivity (Fig. i) and
the effect of introducing high-resolution orographic boundary conditions
(Fig. e) are similar, both in terms of the geographical distribution and
the magnitude of the response. This similarity is particularly clear near
complex orography such as the Alps and along the Scottish and Norwegian
coastlines, but there is also agreement in a large-scale drying with higher
resolution (and with more highly resolved orography) over a wide area of the
North European Plain and some precipitation increase over the very
north-east of Europe. An exception to this overall similarity is the Iberian
Peninsula, where the full response to the resolution increase is a precipitation
increase, but N480N480 is drier than N480N96
over much of the peninsula. In summer, the total precipitation sensitivity is
also very similar to the response to increasing the resolution of orography only
(compare Figs. S5i and S11e). In summary, these results show that better
resolved orography at the higher resolution is a major factor determining the
total model sensitivity to resolution in mean precipitation over Europe.
Winter (December–February) precipitation in mm day-1 in
(a) observations (E-OBS), (b) the N480 model with N96 orography, and (d) the N480
control simulation. (c, f) Model bias with respect to E-OBS and (e) difference
between the two model simulations. Stippling shows statistically significant
differences.
We proceed by comparing the effect of orography with the total sensitivity to
resolution for extreme precipitation. The ratios of GEV parameters of the N512
and N96 model for winter are shown in the upper panels of Fig. and
should be compared with the same ratios for the N480N480 and
N480N96 models in the lower panels of Fig. . Analogous
figures for the other seasons are shown in the Supplement (Figs. S12–S14). As
seen earlier, the resolution increase leads to an overall reduction of both the
location and scale parameters at the N512 resolution (Fig. a, c). This
reduction is particularly pronounced over the North European Plain in
north-west and central Europe. At the same time, the GEV parameters increase with
resolution for some of the Alpine basins (Rhône, Po, Upper Danube). In this
north-west/central part of Europe, a fairly similar pattern can be seen when
considering the resolution increase in the orographic boundary conditions in
isolation (Fig. b, d). In other parts of Europe (the Iberian Peninsula,
eastern Europe), there is no clear correspondence between the sensitivity to
more highly resolved orography and the total resolution sensitivity. In summer,
too, the total and orography-only responses are broadly similar and
agree on smaller extremes at higher resolution for most basins (Fig. S14). In
summary, orographic effects dominate the sensitivity to resolution in simulated
extreme precipitation around the Alps and over the North European Plain, but
not necessarily in other parts of Europe.
Winter (December–February) ratios of fitted GEV parameters for (a, b) the location parameter μ and for (c, d) the scale parameter σ,
between (a, c) the N512 and N96 simulations and between (b, d) the N480 control
simulation and the N480 simulation with N96 orography. Hatching shows
statistically significant differences.
Conclusions
In this study, we have evaluated the representation of mean and extreme
precipitation over Europe in an ensemble of simulations with the HadGEM3-GA3.0
GCM at resolutions between N96 (about 135 km in the mid-latitudes) and N512
(about 25 km). This model ensemble has been designed to test the immediate
effects of the resolution increase, and parameter changes between the different
resolutions have been kept to the minimum required to ensure numerical
stability. Convection is parameterised at all three resolutions. We have
evaluated HadGEM3-GA3.0 against gridded observations from the E-OBS dataset. For
the representation of mean precipitation, we find the following.
The continent-wide mean precipitation in HadGEM3-GA3.0 is greater than
that in E-OBS by, depending on the season, 20–50 %.
After correcting for the continent-wide mean bias, the root-mean-squared error in
the spatial precipitation field is between 0.5 and 1 mm day-1, with
larger values during winter than during summer.
As the resolution is increased from N96 to N512, the model biases in the mean
precipitation field decrease in winter, spring, and autumn. The largest improvement
with resolution is seen in winter, when the RMSE is reduced by about 25 %. The
resolution sensitivity is very small in summer.
During winter, the spatial bias pattern shows too little precipitation
over the very north and west of Europe (Scottish and Scandinavian Mountains),
over the Iberian Peninsula, and south of the Alps, and too much precipitation
over the rest of Europe, in particular in the North European Plain to the
north and west of the Alps. These biases are significantly reduced as
resolution is increased to N512.
During summer, the main precipitation bias is a wet bias in the north and
north-east of Europe. This bias improves a little with resolution, but at the
same time there is a wet bias over areas of high topography (Alps,
Scandinavian Mountains) that increases with resolution.
We have evaluated extreme daily precipitation by estimating the extreme value
(GEV) distribution location and scale parameters over large (>50000 km2) European river basins in the model simulations at different resolutions
and the E-OBS observations. We find the following.
Typical (median) biases in the location and scale parameters are around
20 %, but these biases can take very large values, between 50 and 100 %, over
individual river basins. Most of these very large biases constitute
overestimations of the extremes, but underestimations are also seen for a
small number of cases in semiarid regions during the warm season.
Biases in extreme precipitation at these scales are smaller in summer than
during the other seasons, around 10 % for the median biases in GEV location
and scale parameters.
Extreme precipitation is better simulated as the model resolution is
increased. This improvement is seen particularly clearly in autumn, when the
median GEV parameter biases for the 25 km simulations are less than half of
those in the 135 km simulations. Improvements are also seen for winter and
spring, but not for summer.
We have tested two complementary hypotheses explaining the sensitivity of the
simulated mean and extreme precipitation to resolution in HadGEM3-GA3.0. The
first hypothesis is that the sensitivity to resolution in precipitation is due
to the sensitivity in the large-scale circulation, specifically the North
Atlantic storm track. The second hypothesis concerns the fact that as the model
resolution is increased, the orographic boundary conditions are prescribed at a
higher resolution, which impacts the simulated precipitation.
We find that for many areas the improvements in mean winter precipitation
seen with increased resolution in HadGEM3-GA3.0 are primarily related to the
better resolved orography, while resolution sensitivity in the simulated
storm track plays a lesser role.
This important role of orography concerns particularly an improvement seen
in the North European Plain, from the Loire basin in the west to the
Vistula basin in the east. In this area, this result also extends to extreme
precipitation, whereas in other regions, such as Iberia, the better resolved
orography is not the main factor explaining resolution sensitivity in extreme
precipitation.
The dominant role of orography is also seen for summer mean precipitation
response to the resolution increase, and broadly also for the resolution
sensitivity of extreme precipitation during summer.
In this study, we have quantified biases in mean and extreme precipitation over
Europe in a state-of-the-art AGCM, and we have shown that these biases are
generally reduced as model resolution is increased. Such biases are important to
consider when assessing if a GCM is suitable for a certain application, and the
assessment has to be specific for the particular application at hand. Formal
event attribution studies, for example, require very large ensembles of
simulations with a model that is able to simulate the extreme value distribution
for the type of event under consideration. We have shown that at the scales
considered here (daily precipitation and river basins >50000 km2), our
model exhibits large biases for some of the basins considered. Increasing model
resolution to about 25 km may reduce these model biases, but at this resolution
the generation of large ensembles remains computationally prohibitive. For such
a challenging application, our results therefore raise questions about whether
current global modelling capability is fit for purpose. Numerical downscaling,
i.e. nesting a high-resolution RCM in a low-resolution GCM, is a potential
alternative, yet at the comparatively coarse resolutions that are currently
feasible for the driving GCM, e.g. N96 resolution with 19 vertical levels in
the weather@home system , concerns remain over the ability of
the modelling system to represent critically important processes such as
mid-latitude circulation regimes or tropical cyclones
undergoing extratropical transition . For other applications
the assessment will be more positive. For example, small ensembles of
simulations with a high-resolution model may yield more credible results for the
climate change response in extreme precipitation than simulations with a low-resolution model, in situations in which the low-resolution model exhibits biases
in the simulation of extreme precipitation that are reduced at the high
resolution, and where the reasons for the improvement with resolution are
sufficiently well understood.
Compared to a study where resolution was increased in a different AGCM
EC-EARTH version 2.3,, we have shown for HadGEM3-GA3.0 that
the sensitivity to resolution seen in the simulated precipitation depends more
strongly on the better resolved orography at the higher resolution and that the
sensitivity to resolution of the North Atlantic storm track is comparatively less
important. Our study shows that the role of resolution in different GCMs is not
necessarily the same and it is therefore interesting and important to explore
the role of resolution systematically in multi-model studies. The simulations
currently carried out within CMIP6-HighResMIP , e.g. in the
PRIMAVERA
https://www.primavera-h2020.eu, last access: 18 July 2018
project, will allow for
such studies based on a well-designed ensemble of high-resolution coupled GCMs.
The MetUM is available for use under licence. A number of research organisations
and national meteorological services use the MetUM in collaboration with the Met
Office to undertake basic atmospheric process research, produce forecasts,
develop the MetUM code, and build and evaluate Earth system models. For further
information on how to apply for a licence see
http://www.metoffice.gov.uk/research/collaboration/um-partnership (Fock et al., 2018).
Versions
8.0 (HadGEM3-GA3) and 8.5 (HadGEM3-GA6.0) of the source code are used in this
paper. JULES is available under licence free of charge. For further information
on how to gain permission to use JULES for research purposes see
https://jules-lsm.github.io/access_req/JULES_access.html (last access: 18 July 2018).
Extreme value
analysis is based on the R package gevXgpd developed by Christoph
Frei at MeteoSwiss/ETH Zürich. For access to and documentation of the datasets used in this study see
https://www.ecad.eu/download/ensembles/download.php (Haylock et al., 2018) for the E-OBS
precipitation and
https://hrcm.ceda.ac.uk (last access: 18 July 2018) for the HadGEM3 simulations.
The supplement related to this article is available online at: https://doi.org/10.5194/hess-22-3933-2018-supplement.
Contributions by the different authors include conceiving
the study, all data analysis and visualisation, and writing of the manuscript
(RS), conducting the coarse-orography sensitivity experiment (SJJ, PLV, RS),
all aspects of creating the UPSCALE ensemble of simulations (PLV (Principle
Investigator, PI), MJR, MSM, RS, MED, JS), and comments on the analysis as it
progressed and on the manuscript (LCS, PLV, MJR, SJJ, MED).
The authors declare that they have no conflict of
interest.
Acknowledgements
Reinhard Schiemann acknowledges NERC-Met Office JWCRP HRCM funding. Pier Luigi Vidale, Marie-Estelle Demory, and Jane Strachan
acknowledge NCAS Climate Contract R8/H12/83/001 for the High Resolution
Climate Modelling program. Pier Luigi Vidale (UPSCALE PI) acknowledges the Willis Chair in
Climate System Science and Climate Hazards that supports his research. Malcolm J. Roberts
and Matthew S. Mizielinski were supported by the Joint UK DECC/DEFRA Met Office Hadley Centre
Climate Programme (GA01101). Reinhard Schiemann, Pier Luigi Vidale, Malcolm J. Roberts, and Marie-Estelle Demory also were supported by the
PRIMAVERA project under grant agreement no. 641727 in the European Commission's
Horizon 2020 research programme. Len C. Shaffrey received funding from the European Union's
Horizon 2020 research and innovation programme under the IMPREX grant
agreement no. 641811. We thank the team of model developers and
infrastructure experts required to conduct the large UPSCALE simulation
campaign and acknowledge use of the MONSooN system, a collaborative facility
supplied under the JWCRP, the PRACE infrastructure, the HLRS
High-Performance Computing Center Stuttgart, and the STFC CEDA service for data storage and analysis
using the JASMIN platform.
Edited by: Carlo De Michele
Reviewed by: two anonymous referees
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