GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus GmbHGöttingen, Germany10.5194/gmd-7-2983-2014The regional MiKlip decadal forecast ensemble for Europe: the added value of downscalingMieruchS.sebastian.mieruch@kit.eduFeldmannH.https://orcid.org/0000-0001-6987-7351SchädlerG.LenzC.-J.KotheS.KottmeierC.Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, GermanyDeutscher Wetterdienst, Offenbach, GermanyInstitute for Atmospheric and Environmental Sciences, Goethe-University Frankfurt, Frankfurt am Main, GermanyS. Mieruch (sebastian.mieruch@kit.edu)17December2014762983299930September201322November201318October201431October2014This 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://www.geosci-model-dev.net/7/2983/2014/gmd-7-2983-2014.htmlThe full text article is available as a PDF file from https://www.geosci-model-dev.net/7/2983/2014/gmd-7-2983-2014.pdf
The prediction of climate on time scales of years to decades is
attracting the interest of both climate researchers and
stakeholders. The German Ministry for Education and Research (BMBF)
has launched a major research programme on decadal climate
prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal
Climate Prediction) in order to investigate the prediction potential
of global and regional climate models (RCMs). In this paper we describe a
regional predictive hindcast ensemble, its validation, and the added
value of regional downscaling. Global predictions are obtained from an
ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs),
which were downscaled for Europe using the COSMO-CLM regional
model. Decadal hindcasts were produced for the 5 decades starting in
1961 until 2001. Observations were taken from the E-OBS data set. To
identify decadal variability and predictability, we removed the long-term
mean, as well as the long-term linear trend from the data. We
split the resulting anomaly time series into two parts, the first
including lead times of 1–5 years, reflecting the skill which
originates mainly from the initialisation, and the second including
lead times from 6–10 years, which are more related to the
representation of low frequency climate variability and the effects of
external forcing. We investigated temperature averages and
precipitation sums for the summer and winter half-year. Skill
assessment was based on correlation coefficient and reliability. We
found that regional downscaling preserves, but mostly does not
improve the skill and the reliability of the global predictions for
summer half-year temperature anomalies. In contrast, regionalisation
improves global decadal predictions of half-year precipitation sums in
most parts of Europe. The added value results from an increased
predictive skill on grid-point basis together with an improvement of
the ensemble spread, i.e. the reliability.
Introduction
Interest in longer-term climate predictions in the range of about 10
years is growing. Such predictions, as opposed to projections that do
not take into account the influence of initial conditions, would be
very useful for all branches of public life and for planning purposes,
e.g. in agriculture, energy management, hydrology, and health. In the
sense of seamless predictions , a decadal prediction
system would well complement existing short range systems, as well as
seasonal predictions provided by ECMWF
(http://www.ecmwf.int/products/changes/system4/) and CPC
(http://www.cpc.ncep.noaa.gov/products/predictions/90day/), for
instance. Decadal climate predictions present a major scientific
challenge. It is not known yet to what extent useful predictions are
possible in terms of lead time, geographical position, spatial
resolution, meteorological variables, and statistics such as means or
extremes. There is, however, widespread agreement about the necessary
requirements for such predictions to be successful: (i) coupled
ocean-atmosphere models are likely the most effective means for making
global climate predictions (and projections); (ii) predictability lies
mainly in the slow components of the climate system, i.e. oceans,
sea ice, soil , and
atmospheric processes such as the quasi-biennial oscillation (QBO) and the North Atlantic oscillation (NAO)
. Skilful modelling and good initialisation of
these components is essential ; (iii) predictability
must come from the large-scale processes and
interactions, such as those of the Atlantic multidecadal oscillation (AMO), El Niño–Southern Oscillation (ENSO), QBO, and NAO, which must be captured by the
global models; (iv) assuming the models capture the effects
of external forcing (especially concentration changes of greenhouse
gases), prediction means, essentially, prediction of long-term (decadal)
internal variability. Since both deterministic and stochastic
processes contribute to internal variability, ensembles of simulations
are required. The effects of initialisation have been discussed
by
, , , , , and .
For regional scale applications, the information provided by global
models is much too coarse, so that regional downscaling to resolutions
in the order of 10 km will be necessary. For climate projections
and climate assessment, this has been shown to yield
added value . Whether
such added value can also be found in regionally downscaled
predictions is presently an open question and one of the aims of this
study. Another open question is what metrics should be used to
measure the skill of predictions, and what metrics are useful for
applications. Whereas science is interested in variability and
ensemble metrics , practitioners
require either categorical (e.g. above/below climatology) or
statistical information, such as return values, frequency, and duration of
extremes with high spatial resolution .
The German Ministry for Education and Research (BMBF) has launched a
major programme called MiKlip (Mittelfristige Klimaprognosen, Decadal
Climate Prediction, http://www.fona-miklip.de/en/index.php)
with the aim to establish an operational decadal climate prediction
system for Europe, based on the MPI-ESM-LR global model
system and the regional climate models (RCMs)
COSMO-CLM and REMO
for regional downscaling. The project consists of
five modules assessing the different aspects of predictability
described above: initialisation, relevant processes, regionalisation,
validation, and synthesis. The skill of the predictions will be
assessed from an ensemble of decadal hindcasts, which are compared to
observations mainly of temperature and precipitation.
This paper describes the regional predictive hindcast ensemble and its
validation and discusses the added value of regional downscaling with
COSMO-CLM. Section 2 briefly describes the set-up of the MPI-ESM-LR
simulations and gives an overview over the set-up of the COSMO-CLM
simulations, the construction of the ensemble, and the data used for
validation. Section 3 describes detrending and debiasing, and the
validation framework, including the basic set of metrics used. In
Sect. 4 we present results and ensemble statistics for Europe. A
summary, conclusions, and a brief outlook are given in Sect. 5.
Experimental design – construction of the regional decadal ensemble
The aim of MiKlip is to develop a decadal prediction system in several
development stages. A first phase has been established with a so called “baseline ensemble”
of decadal predictions. It encompasses the global
decadal simulations performed with the MPI-ESM
according to the CMIP5 protocol . The atmospheric
resolution is T63 (1.86∘) horizontally with 47 vertical
levels up to 0.1 hPa in the vertical. The resolution of the ocean
component is 1.5∘ on average. The ocean is initialised by
applying the temperature and salinity anomalies from an ocean-only
simulation forced by NCAR/NCEP reanalysis, as described in
. A global ensemble is generated from perturbations
of the initial atmospheric states with 1-day time lag. The hindcast
periods start annually from 1961 to 2012. The ensemble size is 10 members every 5 years (starting dates 1 January 1961, 1966, 1971, and so on) and three
members with a starting date 1 January of
the in-between years. More details and first results from the global
ensemble can be found in . This global baseline
ensemble is used as a starting point for the downscaling exercise
described here. For our regional ensemble, a larger ensemble for a
given starting date was preferred to a higher number of starting dates
with a smaller ensemble, since this is a first step to analyse the
spread and reliability of the regional ensemble with respect to the
global ensemble. The number of starting dates will be increased in the
next development stage. On the other hand, a model climatology for the
whole period 1961–2010 is necessary to calculate the model
anomalies. Therefore, all 10 available realisations of the MPI-ESM-LR
10 year hindcasts for five starting dates (1 January
of the years 1961, 1971, 1981, 1991,
2001) were downscaled covering the whole 50 year period. Europe was
selected for the regional downscaling.
Two RCMs – namely COSMO-CLM (Consortium for
small-scale modelling in climate mode, CCLM hereafter,
) and REMO have been tested. The
common simulation domain is chosen according to the CORDEX-EU
specifications with a grid resolution
of 0.22∘ and a rotated pole at -162∘ longitude and
39.25∘ latitude. The model configuration uses 40 vertical
levels. This paper refers only to the simulations with CCLM; the CCLM
and REMO simulations will be combined in a later project phase. CCLM
is used in the same model version as for CORDEX
(cf. ).
All initial and boundary conditions are obtained from the driving
model except for the soil. The soil is a compartment of the earth
system with long memory and exhibits a considerable spatial
heterogeneity, which cannot be fully captured by the global climate model. Therefore,
CCLM is initialised using the following strategy. A long-term CCLM
reference simulation covering the whole hindcast period is performed
forced by reanalysis data. This simulation starts in 1959 using ERA40
as initial and boundary conditions. The first 2
years are used as spin-up. From 1979 until 2010 ERA Interim-forcing
is applied. At the transition date (1 January 1979), the atmospheric and ocean
boundary forcing is switched from ERA40 to ERA Interim, but the soil
fields are kept from the ERA40 forced simulation. Additional tests for
the first years after the transition from ERA40 to ERA Interim showed
only small discrepancies: less than 0.2∘ C deviation in the European
annual mean temperature in the first year (1979), and
<0.1∘ C after 1980. Therefore, no special treatment for the
transition phase has been applied. The initial soil conditions for
the CCLM hindcast simulations are then obtained at the respective
starting date from this long-term reference simulation. Although
there could be some residual drift if the soil moisture climatology
under MPI-ESM-LR forcing differs from that under ERA analysis forcing,
this approach at least provides a reasonable estimation of the soil
moisture initial mean state and anomalies.
To evaluate the model performance, E-OBS v8.0 climatology
for near-surface temperature and precipitation
was used. E-OBS is a gridded observational data set and available in
daily resolution from 1 January 1950
until 31 December 2012. It comprises the
variables precipitation, temperature, and sea level pressure in Europe
at 25 km over land and is based on ECA&D (European Climate
Assessment & Dataset; http://eca.knmi.nl/).
Data pre-processing and skill metricsData pre-processing
In order to assess the predictive potential of decadal hindcasts in
terms of skill and reliability
, the data pre-processing and
metrics to be used must be determined. Different approaches can be found in the literature:
analyses both anomalies including
the long-term trends and anomalies, where the long-term trend has been
removed; and use anomalies
including the long-term trend, but compare initialised forecasts with
uninitialised projections. Following , and partly
, we decided to extract decadal variability from
the time series by removing the long-term means and trends, thus
avoiding the problem of interpreting a mixture of long-term and
decadal changes.
Another question is about the best practice of removing the long-term
trend, which could be non-linear. This problem was assessed
by , who applied different trend
definitions, such as global CO2 data as the regressor, modelled and
observed global temperatures, and simple linear regression. They found
that the “…fluctuations in the forecasts and observations are so
much larger than the non-linearities in the trend …” that the
“…exact definition of the trend does not affect the
results”. Finally, they decided to use the global CO2 data to
describe the trend because of physical reasoning. Furthermore,
used the global data to detrend on grid point
basis and stated that the global trend also described a large part of
the data on the regional scale. In contrast, due to
small trends and large variability, did not
remove the trends for precipitation and rather analysed the hindcast skill of the
un-modified data. However, we are explicitly interested in regional
differences and the different seasons (summer/winter).
Based on climate projections, the Intergovernmental Panel on Climate Change (IPCC; )
found: (i) annual temperatures in Europe are likely to increase
more than the global mean, (ii) northern Europe will experience a
larger warming in winter and southern Europe in summer, and (iii) annual precipitation will be increased in northern Europe, whereas a
decrease in southern Europe will be likely observed. Due to the fact
that the response to greenhouse gas forcing depends on the region and
season, we decided to remove the long-term trend on grid point basis
using simple linear regression. This procedure additionally assures
zero-mean residuals on grid basis. We applied the regression to both
temperature and precipitation. More sophisticated approaches of de-trending exist, e.g. the
Empirical Mode Decomposition (EMD) method , and could
be a valuable alternative.
To illustrate the problems associated with decadal
variability, an E-OBS anomaly time series of summer half-year
precipitation sums at a grid point in Germany is shown in
Fig. .
Summer half-year observational (E-OBS) precipitation anomalies at a single grid point
in western Germany (circles connected with a thin line). The long term mean and
trend have been removed by a linear regression. A moving average filter of 5 years is applied
to illustrate variability on multi-year time scales (green line).
The detrended and unfiltered anomalies are shown as a thin line.
The summer to summer variability (thin line) is high, and these high
frequency fluctuations are unlikely to be predictable using decadal
model initialisations. On the other hand, the low pass filtered data
(thick line) appear more likely to be predictable by decadal
predictions.
Spatial smoothing of the data is beneficial in skill assessment due to
reduction of grid-scale noise .
advocate a 5∘ latitude × 5∘ longitude spatial smoothing for precipitation and
a 10∘ latitude × 10∘ longitude smoothing for temperature, which is not
appropriate for the regionalisation purpose. They also present
analyses on different time scales, i.e. year 1, years 2–5, years 6–9
and years 2–9, to discuss the effect of different lead times and
temporal averaging. In contrast to , whose data
sets start annually, we are limited to five starting dates in the first
regional ensemble generation, as explained above, and, therefore, have a much
smaller data sample available. As a compromise, we decided to split
the data into two parts, one with lead times 1–5 years and the other
with lead times 6–10 years. The first data set can be considered as
representing the skill which mainly originates from the
initialisation, while the second data set is more related to the
representation of slow varying climate components. Note that for each
prediction horizon, lead times are not averaged, as suggested
in , but are considered together
successively, yielding 25 data points for each part of the time
series.
Since we are interested in the regional scale, we
will analyse the data at their original 25 km resolution without spatial
smoothing. For comparison of the global model with the regional
model, we will interpolate the global MPI-ESM-LR data to the 25 km grid.
To summarise, our pre-processing consists of (i) aggregating half-year
precipitation sums and temperature averages, (ii) removing long-term
means and trends, and (iii) splitting the data into two parts: the
first with lead times 1–5 years and the second with lead times 6–10 years.
Metrics
Snapshot from our time series showing the
“added value” of downscaling, based on real data.
The following metrics will be used to characterise the
CCLM and MPI-ESM-LR ensembles vs. observations and to identify the potential added value:
Skill:To quantify the predictive skill of the CCLM
and MPI-ESM-LR ensembles against observations we will use the Pearson
correlation coefficient ρ applied to anomalies (also known as
ACC (anomaly correlation coefficient);
). If we denote the anomaly ensemble mean at
a specific location i as mt,i and the corresponding
observed anomalies as ot,i, where t=1,…,N represents the time
index with N=25 data points (semi-annual means
1961–2010, 5 lead years), the correlation coefficient is given byρi=1/N⋅∑tmt,i⋅ot,iσmi⋅σoi.
We have estimated the statistical significance of
the correlation coefficient on grid point basis with the test statistic:t=ρ(1-ρ2)/Neffon a t distribution with Neff (two-sided) degrees of freedom.
Due to serial autocorrelations the effective number of degrees of freedom is
reduced. Therefore, we account for autocorrelations according to :Neff=N⋅1-ϕ1+ϕ,where ϕ is the autocorrelation at lag 1 of [mt,i⋅ot,i]. Due to the temporal gaps in the data we used the Discrete
Autocorrelation Function developed by .
Although only lag 1 is considered explicitly, further
autocorrelations at higher lags are also considered implicitly, since
the correction approach is based on autoregressive processes of order
1 (AR[1]) , whose autocorrelation function
decreases exponentially, thus considering also higher lags than 1.
For uncorrelated data, the statistical significance on the 10%
level for 25 data points is achieved by a correlation coefficient of
|ρ|≥0.33. Only in few cases we will observe correlations
fulfilling the significance criterion, taking into account serial
autocorrelations (indicated as stippling in the respective
figures). Mostly we observe “non-significant” correlations between
the model data and the observations. Such results are difficult to
interpret in the sense of statistical hypothesis testing. For
instance, claim that “…a statistical null
hypothesis may not be a well-posed problem …” and “Even if
statistical testing were completely appropriate, the dependency of the
power of statistical tests on the sample size n remains a limitation
on interpretation”. We therefore follow who proposed
“…a simple descriptive approach for
characterising the information in an ensemble …”. This means a
hypothesis test will be performed, but we will not completely rely on the
significance, especially because we are dealing with small sample
sizes (25) on grid pixel basis and the power of the test is
questionable. Thus, if we observe weak positive correlations for
Europe between model and observations in 90 % of the grid pixels,
we believe that there is a certain relationship, even if it is
not significant on a single grid pixel basis.
Reliability:Concerning the reliability of an
ensemble forecast we follow who define
reliability as a measure of “how consistent the forecast
probabilities are with the relative frequencies of the observed
outcomes” (cf. also ) and give the following definition:RELi=RMSE(μt,i,xt,i)-σi,ens2tRMSE(μt,i,xt,i),where the index i indicates a single grid point and t is the time
index. According to this definition, and to interpret the results
correctly, explain that a normally distributed
ensemble is reliable
if, and only if, the root mean square error (RMSE) between the ensemble mean and the observations
is identical to the time-mean ensemble spread σens2. Temperatures easily
fulfil the normality assumption and even half-year precipitation sums
are quite normally distributed, due to the central limit theorem. The
ensemble is called underconfident (REL<0) if
RMSE(μ,x)<σens2; it is called overconfident (REL>0) if
RMSE(μ,x)>σens2, and calibrated (REL=0) if
RMSE(μ,x)=σens2. Loosely speaking, reliability measures if the ensemble
spread covers the model errors. Underconfidence is generally
considered less harmful than overconfidence, as long as the
forecasts/hindcasts have similar predictive skill. To test the
reliability on statistical significance we used a two-sided F-test
with the null hypothesis H0:MSE(μ,x)=σens2, that the mean square
error is similar to the ensemble spread. The test statistic is given
byF=MSE(μ,x)σens2ifMSE(μ,x)>σens2andF=σens2MSE(μ,x)ifMSE(μ,x)<σens2and evaluated on an F distribution with NeffMSE=Neffσ degrees
of freedom (based on the original (N-1)=24 data points) according to
Eq. (). The autocorrelations at lag 1 are estimated using
the Discrete Autocorrelation Function on the time series (for each
grid pixel) of the (μt,i-xt,i)2 and
σens,t2. It turned out that the null
hypothesis cannot be rejected on the 10 % level as long as
REL lies approximately in the range of ±0.2. Cases
where we have to accept the null hypothesis, i.e. the model is
“reliable”, will be indicated by stippling in the respective figures.
Left: Summer half-year precipitation sums of E-OBS (lilac)
and CCLM ensemble mean (orange) at a location in Germany for
lead years 1–5. The correlation coefficient is 0.39. The grey
shaded area depicts the CCLM ensemble spread, i.e. the standard
deviation over the ensemble. Right: Same location but for MPI-ESM-LR
ensemble mean (orange). The correlation coefficient is 0.18.
Decadal variability assessmentAn example
We illustrate the idea of added value using a typical time series from
1961 to 1965 at a grid point (8.125∘ longitude and
51.325∘ latitude) in Germany shown in the left panel of
Fig. .
The black time series in Fig. are the E-OBS summer
precipitation sums, the red data are the MPI-ESM-LR simulations and the blue
data are the CCLM hindcasts. Large-scale decadal predictability is
inherited from the global model to the regional one: if there is no
predictability in the global model, there will be no predictability in
the regional model. We expect that due to the higher resolution and
better representation of small-scale processes, the regional model
will be closer to the observations. The left panel of
Fig. shows that the regional model uses
the already skillful global simulations to move a bit
closer to the observation,
which results in a slightly better correlation between the regional
model and observations (0.66) than for the global model
(0.47). The ensemble spread of the global model is too small and is
indicated by the reliability of 0.27. Due to the downscaling, the
spread is increased (reliability is -0.2) and thus accounts better
for the uncertainties. Additionally, the concept of reliability is
schematically shown in the right panel of Fig. . The
black line depicts the E-OBS observations and the MPI-ESM-LR (red) and CCLM
(blue) data are represented by Gaussians, with the respective RMSE as
mean and the ensemble spread as standard deviation. Again, it can be
seen that due to the downscaling, the hindcast moves slightly closer
to the observation and simultaneously the spread is increased,
yielding a higher probability for the E-OBS outcome.
Thus, the “added value” which we expect from the downscaling is an increase
of skill together with an improvement of the ensemble spread.
This requirement is not trivial: ensemble recalibration techniques,
such as the CCR (Climate Conserving Recalibration,
), are able to increase the ensemble spread, but at
the cost of a loss in correlation.
Clear signals of such an “added value” of the downscaling can be observed for summer
precipitation for lead years 1–5, as shown in Fig. .
Summer half-year precipitation anomaly sums for lead years
1–5 from 1961 to 2010. The top panels show the correlation
coefficient between E-OBS observations and CCLM (left) and
MPI-ESM-LR (right). The bottom panels show the reliability of the
CCLM (left) and MPI-ESM-LR (right) ensembles with respect to the
E-OBS observations. Stippling indicates results, significant at the
10 % level.
The left panel of Fig. shows yearly E-OBS data in lilac
and the CCLM ensemble mean anomalies in orange. Additionally, the CCLM
ensemble spread, i.e. the standard deviation over the ensemble for
each time step, is shown as the grey shaded area. Due to the decadal
initialisation of CCLM in 1961, 1971, 1981, 1991, and 2001, we have
separated the decades from each other and show the lead years 1–5. The
correlation coefficient is 0.39. The right panel shows MPI-ESM-LR
data at the same location. The correlation between MPI-ESM-LR and
E-OBS is 0.18. We observe a clear improvement of the
correlation using CCLM, which arises from moving closer to the
observations and, hence, also better description of the low frequency
variability.
Concerning the reliability, it can clearly be seen that the CCLM spread (left) is much
larger than the MPI-ESM-LR spread (right) and covers the observations in most cases. This
indicates that single CCLM ensemble members show very similar variability
to the observations, whereas single MPI-ESM-LR ensemble members are
overconfident.
It appears that, due to the regionalisation, we are able to
increase the predictive skill, i.e. the correlation and simultaneously
the ensemble spread. However, it is worth to recall that when there is
no skill in MPI-ESM-LR, there is no skill in CCLM.
CCLM summer precipitation for lead years 1–5 in northern
Spain at the border to Portugal.
Another aspect relates to the long-term performance of the models.
The long-term means and trends of our regional simulations belong (by
definition) not to the quantities, which vary on decadal time
scales; hence, they are not the subject of our assessment of decadal
predictability. The long-term performance of the initialised
predictions is, in principle, similar to the long-term performance of
the projections
.
Summer precipitationSkill and reliability: years 1–5
Figure shows the correlation coefficient
and the reliability for Europe of summer precipitation sums for lead
years 1–5. The top left panel of Fig. presents the
correlation between CCLM and E-OBS and the top right panel shows the
correlations between MPI-ESM-LR and E-OBS. The bottom panels of Fig.
display the respective reliabilities. The stippling
indicates results, significant at the 10 % level.
To judge the decadal predictability, both correlation
and reliability should be evaluated together. In large parts of
Europe we observe an increase in correlation between the
regional CCLM model and E-OBS, with respect to the MPI-ESM-LR. This
comprises the British Isles, the Benelux region, the northern part of
France, Germany, Poland, the Czech Republic, and Austria, as well as
Scandinavia. There seems to be a small loss in skill over France, the
Mediterranean region and more or less skill preservation in eastern
Europe. However, there are also regions with negative skill, e.g. in
Portugal and northern Spain. A negative anomaly correlation at
the Iberian Peninsula has also been found by for
a multi-model ensemble. Figure shows a
time series in northern Spain at the border to Portugal.
Here, a correlation of -0.59 has been computed and it is clearly
seen how the time series evolve in different directions. The quite
strong correlation indicates that opposed dynamics are probably
not by chance, but rather they are systematic.
The reason for such behaviour cannot be explained within the scope of
this study.
Summer half-year precipitation anomaly sums for lead years
6–10 from 1961 to 2010. The top panels show the correlation
coefficient between E-OBS observations and CCLM (left) and
MPI-ESM-LR (right). The bottom panels show the reliability of the
CCLM (left) and MPI-ESM-LR (right) ensembles with respect to the
E-OBS observations. Stippling indicates results, significant at the
10 % level.
In summary, we can say that downscaling is, in general, beneficial
and the CCLM can add value to the global driving data.
Regarding reliability, shown in the bottom panels of
Fig. , the regional model clearly improves the results.
Based on a significance test on a grid basis, it is evident that
good values of the reliability for the CCLM and MPI-ESM-LR hindcasts
lie approximately within ±0.2, which is indicated by stippling in
the respective figures. However, a reliability of -0.2 is preferable
to a reliability of +0.2 (as long as both have the same skill), since
underconfident (negative REL) ensembles
better account for uncertainties, and overconfident
ensembles (positive REL) tend toward incorrect predictions.
The MPI-ESM-LR (bottom right panel of Fig. ) shows too
much overconfidence and cannot reproduce the uncertainties
reliably. Thus we can state an added value of the regionalisation in
the sense explained in Sect. 4.1, namely the increase of predictive
skill and simultaneously improving the ensemble spread.
Skill and reliability: years 6–10
Figure shows the correlation coefficient
and the reliability for Europe of summer precipitation sums for lead
years 6–10. The top left panel of Fig. presents the
correlation between CCLM and E-OBS and the top right panel shows the
correlations between MPI-ESM-LR and E-OBS. The bottom panels of Fig.
display the respective reliabilities. The stippling
indicates results, significant at the 10 % level.
Winter half-year precipitation anomaly sums for lead years
1–5 from 1961 to 2010. The top panels show the correlation
coefficient between E-OBS observations and CCLM (left) and
MPI-ESM-LR (right). The bottom panels show the reliability of the
CCLM (left) and MPI-ESM-LR (right) ensembles with respect to the
E-OBS observations. Stippling indicates results, significant at the
10 % level.
The decadal predictions of lead years 6-10 are not so strongly
influenced by the initialisation at the beginning of the decades. Of
higher importance is if the model was able to capture low frequency
modes such as NAO or AMO. Interesting cases of positive and negative
correlations are found, where the CCLM in general shows slightly
higher correlations, at least the preservation of skill. In eastern
Europe for example we have skill observed in lead years 1–5 and also
in years 6–10. Thus it seems that the initialisation at the beginning
of the decade is beneficial and also longer term climate signals could
be reproduced. In Central Europe, a good skill in lead years 1–5 turns
into a relative strong anti-correlation in lead years 6–10. Here we
speculate that the initialisation is advantageous, but after about 5 years the model (MPI-ESM-LR) undergoes a kind of phase shift.
Contrastingly, in South Europe (especially Iberian Peninsula) the lead
years 1–5 showed negative correlations and for lead years 6–10 we have
observed a positive skill. Thus, it seems that the model initially
moves into the wrong (phase shifted) direction and after about 5 years
it is able to swing into the correct phase of low frequency climate
signals. As mentioned above, the reason for such a behaviour cannot be
explained within this study. One possible cause could be explained by
a beat, whereas the model exhibits a slightly different frequency than
the real frequency of e.g. NAO or AMO.
Winter half-year precipitation anomaly sums for lead years
6–10 from 1961 to 2010. The top panels show the correlation
coefficient between E-OBS observations and CCLM (left) and
MPI-ESM-LR (right). The bottom panels show the reliability of the
CCLM (left) and MPI-ESM-LR (right) ensembles with respect to the
E-OBS observations. Stippling indicates results, significant at the
10 % level.
The reliability, shown in Fig. (bottom panels) is much
improved by the downscaling and together with the skill improvement
(preservation) constitutes again the added value of dynamical downscaling
global decadal predictions.
Summer half-year temperature anomaly means for lead years
1–5 from 1961 to 2010. The top panels show the correlation
coefficient between E-OBS observations and CCLM (left) and
MPI-ESM-LR (right). The bottom panels show the reliability of the
CCLM (left) and MPI-ESM-LR (right) ensembles with respect to the
E-OBS observations. Stippling indicates results, significant at the
10 % level.
Winter precipitationSkill and reliability: years 1-5
Figure shows the correlation coefficient
and the reliability for Europe of winter precipitation sums for lead
years 1–5. The top left panel of Fig. presents the
correlation between CCLM and E-OBS and the top right panel shows the
correlations between MPI-ESM-LR and E-OBS. The bottom panels of
Fig. display the respective reliabilities. The stippling
indicates results, significant at the 10 % level.
Positive predictive skill is in principle only found in southern
Europe, i.e. Iberian Peninsula, Italy and South-East Europe. Similar to the
results from the analysis of summer precipitation, downscaling
slightly increases the correlation, e.g. at the Iberian
Peninsula. This amplification process works also for negative
correlations. For instance in central Germany, the MPI-ESM-LR yields
weak negative correlations. The regional model amplifies the
correlations and achieves even the 10 % significance level. As
discussed above the reason for strong negative correlations is up to
now unclear.
The reliability of the regional model is mainly improved compared to the global
model, but slightly to underconfident in parts of southern Europe.
Skill and reliability: years 6–10
Figure shows the correlation coefficient
and the reliability for Europe of winter precipitation sums for lead
years 6–10. The top left panel of Fig. presents the
correlation between CCLM and E-OBS and the top right panel shows the
correlations between MPI-ESM-LR and E-OBS. The bottom panels of
Fig. display the respective reliabilities. The stippling
indicates results, significant at the 10 % level.
Summer temperatures at a grid point near Exeter in South England. Left: Lead years 1–5. Right: Lead years 1–2.
The results for winter precipitation for lead years 6–10 are very
similar to the finding at the beginning of the decade. However, the
correlations are slightly weaker in the second half of the decade and the
reliability is comparable. Again downscaling improves the reliability
especially in southern Europe.
Summer half-year temperature anomaly means for lead years
6–10 from 1961 to 2010. The top panels show the correlation
coefficient between E-OBS observations and CCLM (left) and
MPI-ESM-LR (right). The bottom panels show the reliability of the
CCLM (left) and MPI-ESM-LR (right) ensembles with respect to the
E-OBS observations. Stippling indicates results, significant at the
10 % level.
Summer temperaturesSkill and reliability: years 1-5
Figure shows the correlation coefficient
and the reliability for Europe of summer temperature anomaly means for lead
years 1–5. The top left panel of Fig. presents the
correlation between CCLM and E-OBS and the top right panel shows the
correlations between MPI-ESM-LR and E-OBS. The bottom panels of
Fig. display the respective reliabilities. The stippling
indicates results, significant at the 10 % level.
Summer temperature anomalies in South-East Poland. Shown are lead years 6–10.
Most regions of Europe show similar positive correlations in both
the MPI-ESM-LR and CCLM models. The correlations are mostly
non-significant at the 10 % level on grid pixel basis. However,
according to , hypothesis testing on
model ensembles has to be interpreted carefully. The standard
interpretation of non-significant correlations would be that the
observed correlations are more or less found by chance. Inspecting the
positive correlations covering almost all of Europe (top panels of
Fig. ), it is hard to believe that such patterns are a
fortunate coincidence. Thus, in spite of the weak correlations, the
small sample population on grid basis, as well as serial and spatial
autocorrelations, there seems to be some decadal predictive skill in the
model. Although evidence for this statement is not provided, a
look into a typical time series may yield more confidence. Figure shows CCLM summer temperature anomalies from a grid
point near Exeter in southern England. The left panel shows the lead
times of 1–5 years and typical weak correlations are found.
Winter half-year temperature anomaly means for lead years
1–5 from 1961 to 2010. The top panels show the correlation
coefficient between E-OBS observations and CCLM (left) and
MPI-ESM-LR (right). The bottom panels show the reliability of the
CCLM (left) and MPI-ESM-LR (right) ensembles with respect to the
E-OBS observations. Stippling indicates results, significant at the
10 % level.
Winter half-year temperature anomaly means for lead years
6–10 from 1961 to 2010. The top panels show the correlation
coefficient between E-OBS observations and CCLM (left) and
MPI-ESM-LR (right). The bottom panels show the reliability of the
CCLM (left) and MPI-ESM-LR (right) ensembles with respect to the
E-OBS observations. Stippling indicates results, significant at the
10 % level.
The right panel depicts only the lead times of years 1–2, where it can be
seen that in 4 out of 5 decades (1961, 1971, 1991, 2001), the CCLM
model evolves in the correct direction during the first 2 years. It
seems plausible that we observe higher skill at the very beginning of
the decade, shortly after the initialisation. Despite
non-significant results, according to the hypothesis test, there appears
to be predictive skill in the models. However, due to the small sample size, a definitive conclusion cannot be made.
Regarding the potential added value of the regional model, downscaling
appears not to be beneficial for summer temperature anomalies. This is likely
due to half-year temperature anomalies that cannot be attributed
to a small-scale process and, hence, regionalisation only preserves the
skill, and does not improve it. A half-year temperature
anomaly in a 200×200 km box does not differ distinctively
to a temperature anomaly in a 25×25 km box.
In addition, the reliability patterns (bottom panels of Fig. )
show similarities between MPI-ESM-LR and CCLM with values within ±0.2.
This is also indicated by the stippling denoting
no significant difference between the MSE(model, observations)
and the model spread on the 10 % level.
Skill and reliability: years 6–10
Figure shows the correlation coefficient
and the reliability for Europe of summer temperature anomalies for lead
years 6–10. The top left panel of Fig. presents the
correlation between CCLM and E-OBS and the top right panel shows the
correlations between MPI-ESM-LR and E-OBS. The bottom panels of
Fig. display the respective reliabilities. The stippling
indicates results, significant at the 10 % level.
As can be seen, there is a large similarity between the correlations
of the global and the regional model, thus the predictive skill could
be preserved but not improved. A strong significant (on grid pixel
basis) skill is observed in eastern Europe. Figure
shows time series from South-East Poland. It can be seen that
there is a left residual trend in the E-OBS anomalies (lilac). The period 1961–1970
was recorded with higher temperatures than normal, and therefore potentially
influence the linear trend estimations strongly during the
1961–2010 time period.
However, this residual trend accounts only for a minor part of the observed
predictive skill. This is demonstrated well in the right panel of
Fig. , which shows the MPI-ESM-LR results. The residual
trend is minimal, but in most decades the year-to-year
variations are reproduced with weaker amplitude due to the
ensemble mean.
The reliabilities of the MPI-ESM-LR and the CCLM are comparable in a
satisfactory region within ±0.2.
Winter temperaturesSkill and reliability: years 1–5
Figure shows the correlation coefficient
and the reliability for Europe of winter temperature anomaly means for lead
years 1–5. The top left panel of Fig. presents the
correlation between CCLM and E-OBS and the top right panel shows the
correlations between MPI-ESM-LR and E-OBS. The bottom panels of
Fig. display the respective reliabilities. The stippling
indicates results, significant at the 10 % level.
Again, the correlation pattern for winter temperature anomalies of
MPI-ESM-LR and CCLM are very similar. Nevertheless, we found regions
where the skill is slightly larger for CCLM, e.g. at the northern
coast of Norway and where it is slightly smaller, e.g. in South-East
Europe. In general, we have mostly a preservation but no improvement of
skill. The correlations are generally weak, but
stronger in the south in contrast to the summer temperatures, where
the correlations are larger in the north. The reliability is also
comparable between MPI-ESM-LR and CCLM, but it appears downscaling
worsens the reliability slightly. The reason is not a smaller
spread, but a slightly larger RMSE, which yields slightly more
overconfident results.
Skill and reliability: years 6–10
Figure shows the correlation coefficient
and the reliability for Europe of winter temperature anomalies for lead
years 6–10. The top left panel of Fig. presents the
correlation between CCLM and E-OBS and the top right panel shows the
correlations between MPI-ESM-LR and E-OBS. The bottom panels of
Fig. display the respective reliabilities. The stippling
indicates results, significant at the 10 % level.
The correlations of winter temperatures of lead years 6–10 are stronger
than for lead years 1–5, which seems to be counterintuitive. A possible
cause could be, as speculated above, the global model generates a
slightly different low frequency variability than the
observations, resulting in being slightly out of phase at the
beginning of the decade and later within phase at the end of the
decade. However, definitive conclusions are not within the scope of this
study.
With regards to reliability, both models are slightly overconfident. A
small loss in correlation of the CCLM yields to a slightly larger
RMSE for CCLM and to a small increase of the values of the
reliability.
Summary and conclusions
In this study we have analysed regional climate predictions
downscaled from global decadal hindcasts using a regional model.
We generated a 10 member ensemble of climate simulations (1961–2010)
with the regional model CCLM at 25 km resolution, driven by decadal
predictions of the global model MPI-ESM-LR. These model runs have
been initialised every 10 years from 1961 to 2001. Decadal
variability of detrended anomalies of summer and winter precipitation,
as well as of summer and winter temperatures, in Europe were compared
with observations and results of the global model in
order to identify a possible added value of regionalisation. We
define the added value of the regional model as an increase of
predictive skill and a simultaneous improvement of the reliability. To
quantify the predictability we used the correlation coefficient to
measure the skill and the RMSE and ensemble spread to characterise
reliability (cf. Sect. 3.2).
Predictive skill, covering almost all of Europe, has
been found for summer temperature anomalies at lead years
1–5. This skill has been induced by the global MPI-ESM-LR
model and could be preserved, but mostly not improved, by
regionalisation. This could be due to the well-known fact that spatial
patterns of mean temperature anomalies are quite homogeneous and,
therefore, are already well captured at the resolution of the global
model so there is little room for improvement due to
downscaling. The reliability of summer temperature anomalies is good
for both models, which is a typical feature of an ensemble with a low
signal-to-noise ratio. A possible option to increase the skill could
be an increase in the ensemble size ().
This situation is similar for lead years 6–10. Here, a good skill is
observed in eastern Europe, Italy, and the Iberian Peninsula, which
clearly originates in the MPI-ESM-LR. The reason for this skill is
likely due to low frequency processes associated with large-scale
phenomena, such as NAO and AMO, captured by the global model.
The reliability is quite satisfactory for both models and the
predictability could be preserved but not improved by the downscaling
due to reasons already mentioned above.
The predictive skill for European temperatures in winter is, in general,
smaller than in summer. However, the correlation at lead years 6–10 is
better than at lead years 1–5, which is counterintuitive. As
speculated, slightly different representations of low frequency
variability between the global model and observations could be a
possible explanation.
A clear added value could be achieved by downscaling half-year
precipitation sums with the regional model CCLM. Especially in summer,
the regionalisation yields an increase in the predictive skill, while
simultaneously improving the reliability. This is true for lead years
1–5 and lead years 6–10. The reason for the added value is the better
representation of small-scale precipitation features by the regional
model. However, the precipitation predictive skill over Europe shows
a complex pattern, where positive and negative
correlations have been found. The negative correlations originate in
the global model and have been transferred to the regional scale. We
speculated on several possible explanations, which could be the basis
for further analysis, such as the beat frequencies and a possible
problem of the zonal representation in MPI-ESM-LR.
The advantage of regionalisation for winter precipitation is
slightly smaller. The positive predictive skill seems to be constrained
to southern Europe, where an increase in skill is observed at the
Iberian Peninsula for lead years 1–5. The predictive skill for lead
years 6–10 is very weak for both models. The reliability of the
hindcasts is improved by the downscaling with CCLM for all lead
years and especially advantageous in mountainous regions.
Thus, dynamical downscaling is a valuable approach to improve decadal
predictions of precipitation on finer spatial scales. The reason for
the improvement of the predictability of precipitation is most likely
that summer precipitation is a small-scale process and the
dynamics of precipitation features, such as convection, are much better
represented by the regional model as compared to the global model.
We have presented a first analysis of the possibilities and
limitations of regional decadal predictions for Europe and shown that
added value in terms of the metrics used could be achieved by
regionalisation. However, a range of open questions remain: e.g. the negative
correlations with observations in Central Europe;
which metrics to use, e.g. terciles, contingency tables; the size
of the region under study; which statistics to consider:
practitioners might be more interested in the prediction of extremes
like heavy precipitation, droughts, or heat waves. We will consider
the prediction of extremes in a further study and expect added value
through regionalisation. We also plan to study the effect of
finer resolution (below 10km). Further development
stages within the MiKlip project include the production and
finalisation of an improved regional ensemble system based on a new
ocean initialisation of the global model (e.g. ).
Further opportunities for enhanced skill arise from combining the CCLM
and REMO results to an extended multi-RCM ensemble. Additionally, an
ensemble with resolution 0.44∘ has been produced employing
annual starting dates to analyse lead time dependencies.
Acknowledgements
We acknowledge the E-OBS data set from the EU-FP6 project ENSEMBLES
(http://ensembles-eu.metoffice.com) and the data providers in the
ECA&D project (http://www.ecad.eu).
The research programme MiKlip is
funded by the German ministry of education and research (BMBF).
We also thank Andreas Weigel for fruitful
discussion.The service charges for this open access publication have been covered by a
Research Centre of the Helmholtz Association.Edited by: W. Hazeleger
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