ESDEarth System DynamicsESDEarth Syst. Dynam.2190-4987Copernicus PublicationsGöttingen, Germany10.5194/esd-9-679-2018Euro-Atlantic winter storminess and precipitation extremes under 1.5 ∘C vs. 2 ∘C warming scenariosEuro-Atlantic winter storminess and precipitation extremesBarcikowskaMonika J.mbarcikowska@edf.orgWeaverScott J.FeserFraukehttps://orcid.org/0000-0002-0252-468XRussoSimoneSchenkFrederikhttps://orcid.org/0000-0002-4768-9832StoneDáithí A.WehnerMichael F.https://orcid.org/0000-0001-5991-0082ZahnMatthiasEnvironmental Defense Fund, New York City, USAEnvironmental Defense Fund, Washington, D.C., USAInstitute of Coastal Research, Helmholtz Centre Geesthacht, Geesthacht, GermanyEuropean Commission, Joint Research Centre, Via Enrico Fermi, Ispra, ItalyBolin Centre for Climate Research, Department of Geological Sciences, Stockholm University, Stockholm, SwedenLawrence Berkeley National Laboratory, Berkeley, CA, USAGlobal Climate Adaptation Partnership, Oxford, UKMonika J. Barcikowska (mbarcikowska@edf.org)5June2018926796991November201721November201725April20183May2018This 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://esd.copernicus.org/articles/9/679/2018/esd-9-679-2018.htmlThe full text article is available as a PDF file from https://esd.copernicus.org/articles/9/679/2018/esd-9-679-2018.pdf
Severe winter storms in combination with precipitation extremes pose a
serious threat to Europe. Located at the southeastern exit of the North
Atlantic's storm track, European coastlines are directly exposed to impacts
by high wind speeds, storm floods and coastal erosion. In this study we
analyze potential changes in simulated winter storminess and extreme
precipitation, which may occur under 1.5 or 2 ∘C warming scenarios.
Here we focus on a first simulation suite of the atmospheric model CAM5
performed within the HAPPI project and evaluate how changes of the horizontal
model resolution impact the results regarding atmospheric pressure, storm
tracks, wind speed and precipitation extremes.
The comparison of CAM5 simulations with different resolutions indicates that
an increased horizontal resolution to 0.25∘ not only refines
regional-scale information but also improves large-scale atmospheric
circulation features over the Euro-Atlantic region. The zonal bias in monthly pressure at mean sea level
and wind fields, which is typically found in low-resolution models, is
considerably reduced. This allows us to analyze potential changes in
regional- to local-scale extreme wind speeds and precipitation in a more
realistic way.
Our analysis of the future response for the 2 ∘C warming scenario
generally confirms previous model simulations suggesting a poleward shift
and intensification of the meridional circulation in the Euro-Atlantic
region. Additional analysis suggests that this shift occurs mainly after
exceeding the 1.5 ∘C global warming level, when the midlatitude
jet stream manifests a strengthening northeastward. At the same time, this
northeastern shift of the storm tracks allows an intensification and
northeastern expansion of the Azores high, leading to a tendency of less
precipitation across the Bay of Biscay and North Sea.
Regions impacted by the strengthening of the midlatitude jet, such as the
northwestern coasts of the British Isles, Scandinavia and the Norwegian Sea,
and over the North Atlantic east of Newfoundland, experience an increase in
the mean as well as daily and sub-daily precipitation, wind extremes and
storminess, suggesting an important influence of increasing storm activity in
these regions in response to global warming.
Introduction
International climate policy discussions use annual mean globally averaged
temperature targets as the metric to anchor climate mitigation and
adaptation strategies. While useful for climate policy development and
implementation, global temperature targets do not explicitly convey the
climate impacts that may be felt by society at seasonal and regional scales
and hence make it difficult to justify any target as a safe level of warming
(Knutti et al., 2016). The recent Paris agreement (Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1, UNFCCC, 2017) hopes to
limit the rise in postindustrial globally averaged temperature to no more
than 2 ∘C, while pursuing efforts toward the more ambitious
1.5 ∘C target. Accordingly, understanding the changes in regional
climate as the result of this half-a-degree difference in these two global
temperature levels is important to clarify projected near-term climate change impacts.
In this study, we focus on projected changes in winter storminess and
precipitation extremes over the Euro-Atlantic region. The winter climate in
the North Atlantic–European sector is dominated by variations in
midlatitude westerly winds, which determine the position and intensity of
storm tracks and thus the pathways of momentum, moisture and temperature
transport. Extratropical cyclones dominate the redistribution of energy
with a net poleward heat transport. They typically form in the region of
strong baroclinic activity at the (sub)polar front of Arctic vs. (sub)tropical
air masses. Stronger pressure gradients are linked to
increased storminess and precipitation over central and northern Europe and
less storms and precipitation over southern Europe and vice versa for weak
pressure gradients (e.g., Pinto et al., 2009a). Large-scale storminess is
dominated by multi-decadal variations in response to a complex interplay of
different factors which may lead to changes in storm track position and
intensity. The location of the storm track generally changes seasonally in
response to solar insolation. Here, changes in the position of the sea-ice
front push storm tracks southward while tropical sea surface temperatures
(SSTs) build a barrier in the south (Shaw et al., 2016).
Owing to its exposure to the direct impact by cyclones from the North
Atlantic, weather extremes in this region frequently cause profound
socioeconomic costs. Heavy rainfall and intense winds are often associated
with extratropical cyclones, and may cause flooding and storm surge,
damaging infrastructure, industry, agriculture and forestry. As an example
for the North Sea region, extreme wind gusts can exceed category 3 hurricane
wind forces like during storms Christian and Allan on 28 and 29 October 2013 with
171 km h-1 at the German North Sea coast and 193 km h-1 over
Denmark (von Storch et al., 2014). Hydrological extremes like the coastal as
well as inland flooding over the southern United Kingdom during winter 2013/2014
(Schaller et al., 2016; Priestley et al., 2017) are also closely tied
to unusual series of low-pressure systems including severe storm clusters
and persistent rain. Given the large spatial variation in winter European
climate affected by Euro-Atlantic storminess, any effect of global climate
change on storminess could profoundly contribute to the associated regional impacts.
Many observational studies on the hydrological cycle in the recent century
show wettening tendencies in the Northern Hemisphere highlighted by annual
precipitation increases over large portions of the European continent
including Scandinavia and central-eastern Europe. While these tendencies
have also been detected in the winter season over most of these regions,
they are not present over the southern flanks (Maraun, 2013), leading to a
north–south dipole structure in precipitation anomalies over the European
sector. A similar dipole pattern, with positive sign tendencies for the
north and a negative sign for the south of the continent, was also found in
the records of winter extreme rainfall (Donat et al., 2013; Fischer et al.,
2014) and river flows (Stahl et al., 2010, 2012). Other studies (Casanueva
et al., 2014; Fleig et al., 2015) linked these changes directly to the
altered large-scale circulation patterns. Hov et al. (2013) have shown that
the intensification of the winter heavy rainfall in northern and
northeastern Europe is directly associated with the observed poleward shift
of the North Atlantic storm track and weakening of Mediterranean storms.
Nevertheless, spatial changes of the storm track activity in this region
feature much higher complexity, as will be discussed in the latter part.
There is however an insufficient understanding of long-term changes in
storminess and their drivers (Seneviratne et al., 2012). Records of extreme
winds suffer from large inhomogeneities, contributing to uncertainty of the
derived statistics the satellite era (Hartmann et al.,
2013; Feser et al., 2015a) in addition to spurious long-term trends in
global reanalysis data (e.g., Krueger et al., 2013; Schenk and Stendel,
2016). There is however consistency across multiple data sets and medium
confidence in a poleward shift of storm tracks since the second half of
the 20th century (Seneviratne et al., 2012). The observed increase in
northern hemispheric storminess towards northern latitudes and a decrease
southwards during the past several decades is consistent with the northward
shift of storm tracks and their intensity since at least 1970 (e.g., Ulbrich
et al., 2009; Lehmann et al., 2011; Hov et al., 2013; Feser et al., 2015a).
Wang et al. (2009) attributes these changes since 1950 at least partly to
external drivers.
Recent efforts to better understand future impacts of global warming on the
Euro-Atlantic climate and weather and their extremes such as midlatitude
storminess typically involve an assessment of changes to various properties
of atmospheric dynamics in global climate models (GCMs) (e.g., changes in wind and
sea level pressure variance) under various Representative Concentration
Pathway (RCP) greenhouse gas (GHG) forcing scenarios (Yin, 2005; Lu et al., 2007;
Wu et al., 2010; Feser et al., 2015b).
Projections of future annual precipitation indicate an increase for the
northern parts and a decrease for the southern parts of Europe. Studies based on GCMs (Sillmann et al., 2013; Giorgi et al., 2014) as well as
studies based on regional climate models (RCMs) (Rajczak et al., 2013; Jacob et al.,
2014) agree that the strongest increase in the heavy winter rainfall will
occur over Scandinavia and eastern Europe. Moreover, Sillmann et al. (2013)
have shown that heavy rainfall is projected to increase even in the regions
with a mean precipitation decrease (e.g., over the Mediterranean region).
Studies analyzing high-resolution, single-model projections (Kitoh and Endo,
2016; Barcikowska et al., 2018) corroborate these results. This bipolar
pattern, with positive tendencies over the northern flanks of central and
western Europe and a decrease over southern parts of Europe, has also been
found in a multi-model ensemble projection (Donat et al., 2012) for wind speeds.
Projections of future changes in the midlatitude storms in the Northern
Hemisphere indicate remarkable changes; however their features (e.g., spatial
patters and intensity) show a strong dependency on the analysis method as
well as the generation of the models. Projections based on the ensemble mean
of 16 CMIP3 (early 2000s generation) GCMs (Lambert and Fyfe, 2006) as well as
earlier modeling studies (Lambert, 1995, 2004) suggest a reduced frequency
of extratropical cyclones due to a decreased surface meridional temperature
gradient over the Northern Hemisphere. However, this decrease is not
spatially uniform as storm activity south of 60∘ N over the
northeastern Atlantic and western Europe opposes this tendency, showing an
increase in the CMIP3 projections (Leckebusch et al., 2006). Most of CMIP3
and earlier studies (Della-Marta and Pinto, 2009; Pinto et al., 2006, 2009b;
Bengtsson et al., 2006; Geng and Sugi, 2003; Leckebusch et al., 2006)
indicate an eastward extension of storminess associated with
an increase in frequency of strong storms over the British Isles, the North
Sea and northwestern Europe. Moreover, Zappa et al. (2013) have shown that
the winter storm track's response in CMIP5 (late 2000s generation)
projections manifests as a tripolar pattern, with an increase over the
British Isles and decreased activity over both the Norwegian and the
Mediterranean seas.
In most of the modeling applications, the horizontal resolution constrains
the ability of GCMs to simulate both the important regional features and the
large-scale circulation. So far, the quality of the simulated present
climate and thus presumably projections of future climate have improved over
time owing to progressing development of GCMs including resolution and
representation of the physical process. Nevertheless, present climate
simulations in CMIP5 models still suffer from notable biases, i.e., on a regional scale.
Zappa et al. (2012) have shown that CMIP5-based cyclones are generally too
weak and the DJF storm track pattern is too zonal. These deficiencies are
associated with the tripolar bias, manifested by negative anomalies over the
Norwegian Sea and central-eastern parts of the Mediterranean, and positive
anomalies spreading across northwestern to central Europe towards the Black
Sea. These biases are largely due to the inability of low-resolution models
to correctly capture flow–orography interactions and thus correctly
represent the tilt of the eddy-driven jet stream over the North Atlantic.
Kelley et al. (2011) showed that the increased horizontal resolution in
CMIP5 (∼ 200 km) models potentially allowed for a spatial
refinement in the simulated geographical pattern and for improvements in the
simulated amplitude of precipitation indices. However the resolutions of the
CMIP5 GCMs are not sufficiently high to correctly represent daily
precipitation extremes (and their changes) and lead to severe
underestimations (Sillmann et al., 2013).
Projections downscaled with RCMs may refine
spatial details but will mostly inherit the large-scale circulation biases
from the driving GCMs. Therefore, increasing spatial and temporal resolution
in GCMs is crucial to improve the representation of the simulated mean
climate, weather extremes and their changes. The PRIMAVERA project
(https://www.primavera-h2020.eu/about/objectives/, last access: February 2018) focuses
specifically on high-resolution modeling of the Euro-Atlantic climate.
Modeling efforts pursued within this project facilitate an analysis of
regional changes and associated impacts. For example, Schiemann et al. (2017)
have shown an improved representation of atmospheric blocking, which often
redirects storm tracks, when simulated at higher (i.e., 25 km) resolution.
Yang et al. (2015) used a high-resolution climate prediction model and
highlight the importance of credibly resolved upper tropospheric jet flow
in order to skillfully predict storm track statistics and associated
extremes. Other studies (Kitoh and Endo, 2016; Barcikowska et al., 2018)
employing relatively high-resolution models (∼ 20 to
∼ 50 km) pointed to much higher skill in capturing
large-scale circulation features, spatial features and magnitude of
precipitation extremes. First experimental simulations at even higher
resolution (1–5 km, Kendon et al., 2014; Ban et al., 2015; Lehmann et al.,
2015) were capable of projecting changes in heavy rainfall on sub-daily timescales but are usually too expensive to perform.
While it is important to understand the impacts from the worst-case
emissions scenarios in order to support policy-relevant mitigation and
adaptation strategies as expressed in the Paris agreement, it is also
necessary to assess the role of near-term global climate change in
anticipating the shifts in regional climate and weather as a function of the
1.5 and 2 ∘C climate policy goals. However, there is
a wide range of global temperature responses and considerable overlap of the
CMIP5 models to lower emission scenarios that encompass the 1.5 and
2 ∘C levels of global warming (Mitchell et al., 2017). As
such, teasing out the relative differences between these two temperature
targets is not trivial and requires an alternate modeling strategy that
obviates the transient uncertainty with respect to when a given model
crosses either the 1.5 or 2 ∘C threshold (Kalmarkar
and Bradley, 2017), mitigates the impact of potential differences in the
phasing and amplitude of internal climate variability, and provides enough
ensemble members to adequately distinguish the relevant climate change statistics.
The high-resolution CAM5 simulations as part of the Half a degree Additional
warming, Prognosis, and Projected Impacts (HAPPI) project provides such a
set of model experiments targeted specifically at differentiating the
climate response between the 1.5 and 2 ∘C global
temperature levels and their regional implications (Mitchell et al., 2017).
The high spatiotemporal resolution of the CAM5 HAPPI experiments are unique
in that they allow for a detailed analysis of large-scale changes to North
Atlantic storm track activity and differential impacts as a function of
model resolution – a necessary component for studying changes in
precipitation and atmospheric circulation on sub-daily timescales and for
the representation of extreme weather events.
The aim of this study is to assess changes in the winter climate and weather
extremes over the Euro-Atlantic region associated with the 1.5 and
2 ∘C levels of global warming. In this study we employ the
Community Atmospheric Model version 5 (CAM5), which is available at
different horizontal resolutions. This allows us to investigate the impacts
of a very high model resolution on the representation of large-scale and
regional features in comparison to a coarser resolution. Additionally this
model provides unprecedented opportunity to investigate extremes on
sub-daily timescales. Our primary focus here is on the differences between
these two temperature levels in the context of extreme precipitation, winds
and storminess. The availability of high-frequency model output (3 hourly)
allows us to investigate changes in sub-daily events and also to extract
storm tracks using a tracking algorithm (Feser et al., 2015b).
The structure of the study is as follows: Sect. 2 describes the data and
explains the methods used in the analysis. The impact of the horizontal
resolution on the representation of atmospheric large-scale circulation is
investigated in Sect. 3.1. The historical runs are validated against
observed mean atmospheric circulation and precipitation, as well as high
percentiles of daily precipitation in Sect. 3.2. Section 4 focuses on
changes in the mean climate and weather extremes. A summary and discussion
follow in Sect. 5.
Data and methodsData
To assess the importance of the horizontal model resolution, we first
analyzed historical runs of CAM5.1 (http://www.cesm.ucar.edu/models/cesm1.0/cam/, last access: February 2018), provided by the C20C+
Detection and Attribution Project (http://portal.nersc.gov/c20c/main.html/, last access: February 2018). We compare three runs, which
cover the period 1979–2005 and are performed at different resolutions. The
CAM5-1-2degree run (hereafter CAM5_2, Wolski et al., 2014),
the CAM5-1-1degree run (hereafter CAM5_1, Stone et al., 2018)
and the CAM5-1-0.25degree run (hereafter CAM5_0.25, Wehner et
al., 2015) are performed at atmospheric horizontal grid distances of
2.5∘× 1.875∘, 1.25∘× 0.937∘ and
0.3125∘× 0.234∘, respectively. The 1979–2005 runs use
historical values for all forcings (GHGs, ozone, volcanic aerosol, solar),
except land-use changes (set at year-1850), and without changes in
non-volcanic aerosols, which adopt a year-2000-era repeated annual cycle.
Projected climate change impacts on the mean climate state and on extreme
weather are investigated based on model simulations with CAM5.1.2 (hereafter
CAM5.1.2_0.25) at the highest available ∼ 0.25∘ horizontal resolution. The simulations are part of the HAPPI
experiment (Mitchell et al., 2017). The project is designed to provide model
output data describing climate and weather changes under 1.5 and
2 ∘C levels of global warming, as compared to preindustrial
conditions (1861–1880). The design of HAPPI (Mitchell et al., 2017) provides
three time slice experiments, using atmosphere-only models, to create large
ensembles of 10-year simulations for the present climate (2006–2015) and
potential future climate under 1.5 and 2 ∘C levels of
warming (2106–2115). The two future run ensembles will hereafter be referred
to +1.5 and +2 ∘C, respectively. Observed
forcing conditions include SSTs and sea ice
(Taylor et al., 2012). SSTs in future scenarios are prescribed by summation
of the observed 2006–2015 SSTs and an offset estimated between
decadal averages of the 2006–2015 period and the projected warmer global
conditions for the 2091–2100 period. The 2006–2015 runs use 2006–2015 values
for all forcings (GHGs, nonvolcanic aerosols, ozone, volcanic aerosol,
solar), except land cover (set at 1850). Representative Concentration Pathway 2.6 (RCP2.6, year 2095)
is used to provide the model boundary conditions, including
atmospheric GHG concentrations, aerosols, ozone, land use and land
cover for the 1.5 ∘C scenario. For the 2 ∘C scenario
these conditions are the same, except the CO2 concentration, which is
set to a weighted combination of the RCP2.6 and RCP4.5 scenarios
It is important to underline that the design of HAPPI future simulations use
the same aerosol forcing (RCP2.6, year 2095). This protocol differs
essentially from the protocol of historical simulations, causing a
nonnegligible decrease in the aerosol forcing in both future scenarios.
Wehner et al. (2018a, b) (accepted in Earth System Dynamics) found a remarkable
reduction in total aerosol optical thickness over the Northern Hemisphere for these scenarios,
reaching up to 50 % over the North Atlantic and European regions. Thus the
interpretation of differences between future and present climate could be
complicated by the combined effects of the reduced aerosols and increasing CO2.
The simulated features of large-scale circulation are compared with
reanalysis data of monthly pressure at mean sea level (hereafter SLP), winds
at 850 hPa level and DJF precipitation rates (hereafter PR) for the period 1979–2005.
For SLP and wind we use ERA-Interim, provided by the European
Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/en/research/climate-reanalysis/era-interim, last access: Feburary 2018), at the
spatial resolution of ∼ 0.75∘× 0.75∘. We
also use NCEP-DOE AMIP-II Reanalysis 2 (Kanamitsu et al., 2002,
https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html, last access: January 2018), at
2.5∘× 2.5∘ resolution. Precipitation is provided by the
University of Delaware (V4.01), http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html (last access: January 2018). It is a global gridded land data
set, with 0.5∘× 0.5∘ horizontal resolution. For
comparison of the large-scale features, all variables were interpolated on a
common 2.5∘× 2.5∘ horizontal grid. For our analysis of
daily precipitation data, we use E-OBS (Haylock et al., 2008;
http://www.ecad.eu, last access: January 2018), provided by the European Climate Assessment and
Dataset. The data set contains daily precipitation sums on a 0.25∘
regular latitude–longitude grid for the period 1950–2015.
Methods
Our analysis of projected climate change focuses on the North Atlantic and
European sector (27–75∘ N, 80∘ W–45∘ E). While most of the analysis focuses on the DJF season,
the analysis of storm tracks is extended to the period of October to March (ONDJFM).
We use the long historical run of CAM5.1 at ∼ 0.25∘
resolution (CAM5_0.25), which includes the 1979–2005 period,
and also a five-member ensemble for the period 2006–2015
(CAM5.1.2_0.25), when referring to present climate.
Five-member ensemble simulations for the 1.5 and 2 ∘C
levels of warming are referred to as future +1.5 and
+2 ∘C runs, respectively. Comparison of the mean DJF climate in
the present and future runs was computed by averaging differences between
the two paired samples (present vs. future runs, or future +1.5 ∘C vs. +2 ∘C runs), each consisting of 50 seasonal (DJF) values.
Statistical significance of differences in the mean DJF climate between
future and present climate is tested with the Wilcoxon signed rank test at
the 5 % significance level. It is a nonparametric test and hence it can
be used without the assumption that the population follows Gaussian distribution.
The analysis of the simulated large-scale circulation will be based on
monthly means of hydrometeorological variables for the winter (December,
January, February, hereafter DJF) season. Ambient flow over the North
Atlantic is described by the meridional SLP gradient between the SLP in the
vicinity of the Azores and SLP over Iceland. The metric is relevant to the North
Atlantic Oscillation index; hence the location and size of the regions are
chosen to match the location of the simulated maxima and minima of SLP,
i.e., 30–20∘ W, 30–40∘ N and
25–15∘ W, 60–70∘ N, in the
present climate and future projections. Spatial patterns of the mean SLP
fields were compared using centered pattern correlation. The maximum of the
zonal wind was estimated for the region 0–30∘ W, 50–65∘ N.
The extreme precipitation analysis is based on the 95th percentiles of
3 h and daily total precipitation ratio and return values (RVs) for a return
period T= 10 years. The RVs were estimated by fitting generalized extreme
value (GEV) distribution by the method of maximum log-likelihood estimation (MLE)
(Coles, 2001; Smith, 2003; Wilks, 2006; Gilleland and Katz, 2014) to a
block (seasonal) maximum in the 50-year sample of concatenated member runs.
The design of the HAPPI simulations satisfies requirements of stationarity
and independence necessary to fit with a stationary GEV model.
RV for a given return period (T) are defined as values
expected to be exceeded once per T years. RVs are estimated as the values
corresponding to [(1 -1/T)th quantile] of a sample fitted to the GEV
model. For example the 90th quantile (10 % exceedance probability)
is an RV for a T= 10-year period. The analysis here focuses on 10-year
periods of RVs because estimations for longer periods (e.g., 50-year periods
with an exceedance probability of 2 %) are more prone to sampling errors
and biases due to large uncertainties on the tails given relatively short samples.
The goodness of fit to the GEV model is estimated with the Anderson–Darling (A–D)
test. The test is a modified version of the Kolmogorov–Smirnov
goodness-of-fit test. The A–D test gives more weight to the tail and
therefore is more suitable for EV distribution analysis. Analysis shows
that it validates most of the estimations of extreme precipitation for
midlatitude and high latitudes. However, approximations for the regions in
the southern parts of Europe, where the mean precipitation is much lower,
have shown larger uncertainty. Similar results were obtained in the analysis
of the extreme precipitation, where the Kolmogorov–Smirnov test was applied
(Barcikowska et al., 2018).
Storm tracks
Changes in storminess were explored with two measures of daily values during
the DJF season. The first one uses high percentiles (95th) of daily
wind speed. The second is a transient poleward temperature flux at 700 hPa,
computed with the daily meridional wind and temperature deviations from the
wintertime average. Anomalies were filtered with a 2–10-day band-pass
(Butterworth) filter and averaged over the DJF season.
Storm tracks were extracted using a tracking algorithm according to Feser
and von Storch (2008). The automated tracking approach facilitates the
analysis of spatiotemporal variability in cyclones, their lifetime and
intensity (Ulbrich et al., 2009; Neu et al., 2013). The algorithm consists of
two parts: detection and tracking. The first part searches for the local
minimum SLP and maximum wind speed. Additionally, before tracking, a spatial
digital band-pass filter (Feser and von Storch, 2005) was applied to the
3-hourly output of SLP fields to extract mesoscale features of variability.
A storm was identified when a lifetime wind speed maximum exceeded 18 m s-1,
and a pressure minimum dropped to 950 hPa and a filtered pressure
anomaly of -1 hPa. Only tracks lasting more than 96 h were taken into
account in order to extract relatively long-lived and intense storms.
Cyclones forming at latitudes higher than 60∘ N were excluded to
align with the purpose of the study, which focuses on the European climate.
Seasonal fields of spatial density (SPD) of 3-hourly storm occurrences were
accumulated within a 4∘× 4∘ grid and weighted by the
unit area. Spatial intensity fields were computed by aggregating the number of 3 h storm occurrences within
3∘× 3∘ grid boxes
with maximum intensity exceeding certain thresholds. The threshold for the
accumulated wind fields is 10 m s-1 and 0.25 mm h-1 for precipitation.
Additionally, maximum intensity values were chosen from each
3∘× 3∘ grid falling within an area of 9∘× 9∘ from
the center of the storm. This approach facilitates the analysis of the
storm's impact not only in the regions with local maximum but also for the
exposed regions within larger distances from the center.
Simulated winter mean climate and weather extremes
To evaluate the performance of the CAM5 simulation, we compare time-average (1979–2005)
SLP fields from observations with three CAM5 historical
simulations each run at different resolution, where all data sets are
interpolated to the lowest data set resolution (2.5∘× 2.5∘ lat–long grid).
ERA-I (∼ 0.75∘) and NCEP-CFSR (∼ 0.34∘) observations are provided at
higher resolutions than NCEP/DOE 2 (∼ 2.5∘); hence
they better serve the purpose. The SLP fields in ERA-I and NCEP-CFSR are
almost identical, with small differences over Greenland (not shown). Hodges
et al. (2011) also found that these reanalyses agree, in both terms of numbers
and locations of extratropical cyclones, much better than the older ones (JRA-25)
for both hemispheres and that intensities are higher. A comparison
of ERA-I with NCEP/DOE 2 shows most differences in the vicinity of
Greenland. The latter shows slightly higher SLP values over land and lower
SLP values southeast of Greenland. Nevertheless, the SLP patterns share
very high correlation (uncentered), which is 0.98. As shown below, the
differences between ERA-I and CAM5 are of larger magnitude than the
observational differences.
Figure 1 shows that all simulations exhibit realistic patterns of the
meridional SLP gradient. However, the gradient between the Icelandic Low and
Azores High, which characterizes the typical North Atlantic Oscillation (NAO)
pattern, intensifies with increasing resolution. The magnitude and the
pattern in ERA-I correlate best with the one simulated at similar horizontal
resolution (CAM5_1) (r= 0.96). Correlations with the
remaining two are slightly smaller, i.e., 0.95 for CAM5_2 and
0.94 for CAM5_0.25. The magnitude in CAM5_0.25
is most intense, which agrees well with stronger westerlies from Greenland
towards the British Isles, indicating a stronger midlatitude jet stream.
Secondly, both CAM5_2 and CAM_1 show a strong
positive SLP bias in the subtropical part of Europe and North Africa and a
negative bias extending from Iceland towards southeastern Europe and the
Caspian Sea, causing the mean ambient flow (Fig. 1 contours of differences
between CAM5 and reanalysis) over the eastern North Atlantic and most of Europe
to be more zonally oriented when compared to the reanalysis.
The deficiencies in the SLP fields are also reflected in the anomalies of
zonal wind (Fig. 2) along the borders of the SLP circulation patterns. Both
CAM5_2 and CAM5_1 exhibit anomalously strong
westerlies extending across Europe from the British Isles towards Turkey.
This corresponds with the zonal bias in the ambient flow and pattern of
storm tracks, found in the same regions in CMIP5 models (Zappa et al., 2012).
This zonal bias in the ambient flow (Fig. 1) is strongly reduced in the
high-resolution run (CAM5_0.25).
The results presented here indicate that using high-resolution CAM5
simulations in applications to the winter climate over the Euro-Atlantic
regions adds considerably better performance than simply spatially more
detailed information. At higher resolution, the large-scale atmospheric flow
and associated midlatitude jet stream is better represented, in terms
of both the pattern and the magnitude. This improvement will presumably lead to a
more realistic representation of the midlatitude storm tracks and, associated
with them, wind and precipitation over Europe.
Figure 3a shows that mean seasonal precipitation in CAM5_0.25
indeed bears a very close resemblance to observations. However the
comparison also indicates a much higher magnitude of precipitation over
regions with complex orography (up to 1 mm day-1) such as the Alps and the
western coasts of Scandinavia and the UK. Our comparison of observed (E-OBS) and
simulated daily precipitation at the same resolution (∼ 0.25∘)
also demonstrates very high skill of CAM5_0.25 in simulating precipitation
extremes. Figure 3b compares 90th percentiles of daily precipitation extremes, indicating that CAM5 skillfully
captures the structure and sharp gradients over orographically complex
subdomains. Again, in some mountainous regions like the northwestern coast of
the Balkan Peninsula and southwestern coast of Turkey, the simulated values
are much higher than the observed ones.
Time-mean average of the DJF sea level pressure (hPa) over the
period 1979–2005, regridded to a 2.5∘× 2.5∘ horizontal
grid for ERA-Interim (ERA-I, ∼ 0.75∘ lat–long original resolution),
and CAM5 at ∼ 2∘ (CAM5_2deg), ∼ 1∘ (CAM5_1deg)
and
∼ 0.25∘ (CAM5_0.25deg) lat–long resolution. Contours show a
difference in reference to (a) NCEP-DOE 2 (2.5∘ lat–long)
and
(b–d) ERA-Interim.
Time-mean average of the DJF zonal wind (m s-1) over the
period 1979–2005, regridded to a 2.5∘× 2.5∘ horizontal
grid for ERA-Interim (ERA-I, ∼ 0.75∘ lat–long original resolution),
and CAM5 at ∼ 2∘ (CAM5_2deg), ∼ 1∘ (CAM5_1deg)
and
∼ 0.25∘ (CAM5_0.25deg) lat–long resolution. Contours show the
difference, in reference to ERA-Interim.
(a) Time-mean average of DJF monthly means of precipitation
ratio (mm day-1), averaged over the period 1979–2005, in observations (Delaware,
0.5∘ resolution), and the CAM5_0.25 model, smoothed to 0.5∘× 0.5∘
horizontal resolution; (b) DJF daily precipitation 90th percentiles
(mm day-1), averaged over the period 1980–2005 in observations (E-OBS,
0.25∘ resolution) and the CAM5_0.25 model.
At the same time, it is important to note that constructing homogenous and
high-resolution observational data sets is severely limited over these
regions. Thus the differences among these data sets may originate either
from the model bias or from observational bias (deficient quality or lack of
the observations in these regions). As pointed out for Spain, the
differences among different observational data sets may be higher than
differences between model simulations and a certain observational data set
(Gómez-Navarro et al., 2012).
Overall, the comparison strongly suggests that high-resolution runs provide
a more accurate representation of the winter climate and weather for the
Euro-Atlantic sector, where storms play an important role. A correct
representation of storm tracks, governed by the ambient flow, is crucial for
capturing the wind and precipitation extremes in the European region; thus
in the following section we will focus on the analysis of CAM5 simulations
on a 0.25∘× 0.25∘ horizontal grid.
Impacts of climate warming at the +1.5 and +2 ∘C temperature levels: large-scale atmospheric circulation and precipitation changes
In this section, we investigate climate and weather changes associated with
the two global warming temperature levels 1.5 and 2 ∘C,
specified at the Paris climate agreement, and the recently experienced
climate. Differences in the forcing between two future sets of HAPPI
experiments is confined to different CO2 forcing and also to the SST
offset, which corresponds to the difference between the decadal average of
SSTs in the present climate and in projections reaching 1.5 ∘C or
2 ∘C levels of warming. Each of the experiment also includes
internal climate SST variations (e.g., ENSO), in the same phase,
i.e., during experiment 2006–2015. Therefore it is expected that the impacts of
internal variations will be canceled out while discriminating among all
three experiments. The interpretation between the future and present climate
is more complex in HAPPI experiments, as the latter also includes impacts
resulting from reduced aerosol forcing. Unfortunately, the protocol of the
project lacks an additional experiment, which would allow isolating these
impacts. Thus at the moment we have to accept the possibility of different
factors dominating the changes derived between future vs. present climate
and changes derived between the future scenarios. In the following part we
will investigate future changes in the mean winter climate including
precipitation and atmospheric circulation over the North Atlantic and Europe.
Difference between +2 ∘C (runs 2106–2115) and present
climate ensemble means (2006–2015) in DJF (a) sea level pressure
(shaded, hPa) and wind vector at 850 hPa (m s-1). Contours show DJF sea
level pressure in present climate ensemble, with a local maximum in the vicinity
of the Azores and minimum in the vicinity of Iceland; (b) precipitation
(mm day-1) and zonal wind (contours, m s-1) in CAM5_0.25.
Difference between +2 and 1.5 ∘C ensembles in DJF
(a) sea level pressure (shaded, hPa) and wind vector at 850h Pa
(m s-1). Contours show (as in Fig. 4a) DJF sea level pressure in the present
climate, with a local maximum in the vicinity of the Azores and minimum in the
vicinity of Iceland; (b) precipitation (mm day-1) and zonal wind
(contours, m s-1) in CAM5_0.25.
Here we explore the future response of large-scale winter circulation to
the specified levels of global warming. Figure 4a and b depicts differences
between the large-scale circulation at the 2 ∘C level of warming
(CAM5.1.2_0.25) and the present climate. To aid
interpretation of these changes in the context of the mean ambient flow,
Fig. 4a also shows the present climate SLP pattern, featured with the
maximum in the vicinity of the Azores and minimum over Iceland. The average SLP
difference between these two regions is estimated as 29.6 hPa and the
interseasonal standard deviation within the ensemble is estimated as 6.5 hPa.
At the 2 ∘C warming level the meridional SLP gradient
intensifies to 31.3 hPa, which is reflected in the positive SLP anomalies in
the subtropical regions and the negative anomalies in the upper
latitudes. The depicted pattern resembles to some extent the fingerprint of
the previously found global warming response, characterized by intensified
and poleward-shifted meridional circulation cells and corresponding
intensification and shift of the westerlies among these cells (Lu et al.,
2007; Yin, 2005; Bengtsson et al., 2006; Wu et al., 2010). Anomalously intense
westerlies (Fig. 4a and b) extend eastward from north of the British Isles to the
northern coast of Scandinavia. This feature corresponds to an increase in
precipitation in these regions (Fig. 4b). The maximum change is located at
the northwestern coasts of the British Isles (up to ∼ 0.8 mm day-1)
and Norway (∼ 1 mm day-1), which are directly exposed
to the influence of extratropical cyclones and the associated large
quantities of moisture. Precipitation increases slightly over northwestern
Europe (France and Germany, up to 0.3 mm day-1). The intensification of the
subtropical high (Fig. 4a) is accompanied by easterly anomalies at the
southern (equatorward) flanks of the anomalous divergent flow, which reduces
precipitation, with a maximum near the center of the anticyclonic anomaly.
The anomalies extend eastward and cover most regions of the Iberian
Peninsula, but mostly they do not exceed a reduction stronger than 0.1 mm day-1
(Fig. 4b). At the same time, the derived pattern is not entirely clear
as it depicts positive SLP anomalies over Greenland, reductions south of
Greenland, and very weak zonal wind and precipitation anomalies in the
subtropics. In the comparison between future scenarios, the pattern seems
more robust and of larger magnitude.
The difference estimated between the two warming levels (Fig. 5a) clearly
depicts that the additional half a degree warming added to the
+1.5 ∘C level yields not only a remarkable intensification of
the SLP gradient but also a strong poleward shift of the circulation cells,
midlatitude westerlies and precipitation anomalies. The estimated SLP
differences show statistical significance at the 5 % level, with most regions
showing nonzero changes (Fig. S2). Figure 5 shows that the maximum SLP
anomaly is located over the northern part of Bay of Biscay, while reduced
precipitation expands northeastward, through the Bay of Biscay, France,
southern parts of the British Isles and the North Sea. Drying over the
northwestern coast of the Iberian Peninsula is even stronger, compared to the
difference in reference to the present climate. Therefore, the zone of
increased precipitation is more confined towards the north, covering
northern parts of the British Isles and the Norwegian coast. The changes in
the large-scale circulation are also manifested in the meridional SLP
gradient, which increases from 29.1 to 31.3 hPa. It is worth noting that
this difference is even larger than the one derived between the
+2 ∘C warming and the present climate.
At the same time, changes associated with warming at the +1.5 ∘C
level are quantitatively and qualitatively different from those shown above.
In fact, the derived changes (Fig. S1a in the Supplement) in large-scale circulation manifest
an opposite tendency, when compared with the previous results. However the
magnitude of these changes is very small. The meridional SLP gradient
decreases from 29.6 to 29.1 hPa. Consistent with it are (Fig. S1a)
pronounced positive SLP anomalies over Greenland and negative SLP anomalies
southeastward of the British Isles, which contributes to the weakening of the
meridional cells. These results also explain why the +2 ∘C minus
present pattern is much weaker in comparison to the one derived between
future scenarios. The derived discrepancy points again to the combination of
competing factors, i.e., reduced aerosols and increasing CO2, which
renders the interpretation of the comparison of future and present climate more
complex. This interpretation requires a separate analysis of an additional,
properly designed experiment to isolate the effect of aerosol reductions.
Thus in the following sections, we will focus mostly on the difference
between the +1.5 and +2 ∘C scenarios.
Changes in daily and sub-daily precipitation extremes
In this section we investigate changes in daily and 3 h precipitation
extremes associated with an increase in global warming from 1.5 to
2 ∘C (CAM5.1.2_0.25). Precipitation extremes are
defined here as 95th percentiles and 10-year RVs, derived by
fitting a GEV distribution to the HAPPI model outputs.
Difference between +2 and +1.5 ∘C ensemble experiments
for DJF (a) 95th percentile of 3-hourly precipitation,
(b) 95th percentile of daily precipitation (mm h-1) and
(c) 10-year return values in 3 h precipitation CAM5.1.2_0.25.
Percentiles and return values are derived from the samples with values larger
than 1 mm day-1. Regions in (a) and (b) are stippled
for differences significant at the 10 % level.
Figure 6a and b present the future response derived for 95th percentiles
of daily and 3 h precipitation, associated with an additional half a degree
of warming. The response shows a bipolar pattern, with an increase over the
North Atlantic over the northern part of the typical midlatitude storm track
region and a decrease southward, over the region of anticyclonic anomalies.
Significantly increased precipitation anomalies extend northeastward from
Nova Scotia through the northwestern British Isles towards the Norwegian Sea
and northern Scandinavia. The maximum change is located along the
northwestern
coasts of the British Isles and Scandinavia (up to ∼ 0.2 and
0.24 mm h-1 in 3 h precipitation, respectively), which corresponds well with
the derived changes in mean precipitation.
Figure 6 also exhibits a significant (at the 10 % significance level)
reduction over the Iberian Peninsula, northwestern Europe and southern
flanks of the British Isles. The most radical decrease in sub-daily
precipitation extremes occurs along the northwestern coast of the Iberian
Peninsula (-0.25 mm h-1) and in the vicinity of the Bay of Biscay (-0.18 mm h-1).
It is worth noting that changes in extremes of sub-daily precipitation are
larger and more significant over larger areas. For example, the local
minimum found in the extremes of sub-daily precipitation northwestward from
the Iberian Peninsula is less recognizable in daily precipitation extremes,
which may indicate a smaller impact of storminess on the daily scale, as
compared to 3 h data.
Fractional change between +2 and +1.5 ∘C ensembles (ratio
of the difference and the climatological mean in the +1.5 ∘C experiment)
for 10-year return values of 3-hourly precipitation (× 100 %) in
CAM5.1.2_0.25. Differences in precipitation were estimated for the values
larger than 1 mm day-1.
The future response in 10-year RVs for sub-daily precipitation
(Fig. 6c), derived from GEV statistical models, is consistent with the pattern
derived from 95th percentiles and indicates even larger changes. For
example, the increase over the northwestern coasts of the British Isles and
northwestern Scandinavia reaches up to 0.3 mm h-1. The decrease in the
western
part of the continent, found in the analysis of the percentiles, covers a
larger area and extends more towards the center of the continent. The
magnitude of the precipitation and the changes in the off-coastal areas is
often smaller. Nevertheless, the percent changes (in reference to the
climatological values at the 1.5 ∘C level) (Fig. 7) indicate
pronounced decreases (approaching a 15 %) in the interior of France, over
the North Sea, southern Scandinavia and southeastern Europe and up to a 25 %
increase in the interior of northeastern Scandinavia.
Climatology and changes in sub-daily wind extremes and storminess
In this section we investigate future changes in storminess associated with
an increase in global warming from 1.5 to 2 ∘C. Apart
from the chosen forcing scenario, additional uncertainty in predictions of
future climate may also be related to the general model performance, known
bias in the historical period and the ability to simulate certain features
of interest. As such, a validation of the model skill in simulating the
long-term climate is not necessarily a guarantee for skillful future
projections. However, it is a useful indicator for the model's fidelity to
reasonably simulate features of interest. Hence, before analyzing projected
changes for the future, we will start the analysis of storminess here by
focusing first on the long-term mean, simulated with CAM5_0.25 for the period 1979–2005.
Here we use three different measures of storminess: the 95th percentile
3 h wind speeds, band-pass-filtered transient poleward temperature flux (VT)
and density of storm tracks, which are explicitly extracted with a
tracking algorithm. All of these measures have certain limitations in
characterizing storminess. Measures of wind extremes and transient
temperature fluxes will not distinguish the cause of the changes,
e.g., changing frequency or intensity of storms. An application of the Lagrangian
approach facilitates extraction of storm tracks and their properties.
However potential deficiencies of models in realistically representing storm
features (e.g., underestimated intensity) often limit the feasibility of
tracking algorithms to construct a representative sample of storms. Thus the
robustness of that approach can be limited due to the sampling bias. An
interpretation using all three measures facilitates a more complete
description of the present climate and future changes in storminess.
The analysis of the historical run for the period 1979–2005 shows that
CAM5_0.25 reproduces the spatial features of storminess very
realistically compared to the observationally based data sets. For example, a
strong meridional tilt is skillfully captured in all three measures (Figs. 8b,
9a and S3). For VT (Fig. 8b), not only the spatial pattern but also
the intensity agrees remarkably well upon direct comparison with the
observational climatology (http://www.met.reading.ac.uk/~swrshaff/sstanom.html, last access: January 2018). The
VT pattern manifests the full spatial spectrum of the location of
extratropical cyclone activity. The pattern spreads across the subtropical
and midlatitude North Atlantic, featuring maximum values along the region
from Newfoundland, across the eastern Atlantic between the British Isles and
Iceland, to the Norwegian Sea. The simulated maximum intensity of VT yields
the value of approximately 25 ∘C m s-1, which is very close
to the derived values from the ECMWF reanalysis. The simulated intensity
with CAM_0.25 is much more realistic in comparison with one
of the CMIP3 models (http://www.met.reading.ac.uk/~swrshaff/sstanom.html, last access: January 2018), with
typically much lower horizontal resolution. In the latter, the strength of
the storm intensity was found to be nearly half of the observed one.
For high wind speed percentiles (Fig. S3), which have been widely used
(e.g., Krueger et al., 2013) as a simple measure of storm activity,
CAM5_0.25 reproduces the pattern of local maximum very
closely to the one found in VT. The simulated intensities also bear a close
resemblance to the wind extremes (not shown) in reanalysis data, i.e., ERA-Interim
and CFSR. CFSR, which has the finest (∼ 0.25–0.5∘) horizontal resolution, shows a much better agreement with
the model. The ERA data set shows lower values than CFSR, especially over
the vicinity of the local maximum. The apparent difference most likely stems
from the underestimation of midlatitude extreme winds in ERA-Interim and
ERA-40, which appears to be related to their relatively coarser spatial and
temporal resolution (Chawla et al., 2013; Pielke, 2002; Stopa and Cheung,
2014; Sterl and Caires, 2005; Campos and Guedes Soares, 2017). As in the
case of precipitation mentioned previously, this points again towards the
finding that differences among different observational data products may
be as large or even larger than deviations of climate simulation relative to
a certain reference data set (Gómez-Navarro et al., 2012). A coarse model
resolution is however not the only explanation for too low wind speeds for
high wind percentiles. As shown by Rockel and Woth (2007), for example, even
RCMs simulate too low wind speeds for high percentiles if
no gustiness correction is applied to the model output.
(a) Difference between +2 and +1.5 ∘C ensemble
experiments for DJF 95th daily wind percentiles, (b) DJF 700 hPa
transient poleward temperature flux in CAM5.1.2_0.25. Contours show the
climatology derived for the period 1979–2005 (CAM5_0.25)
(∘C m s-1);
values over high orography are masked.
(a) Difference between +2 minus +1.5 ∘C ensemble
experiments (contour levels: -4.5, -3, -1.5, 1.5, 3, 4.5, 6; contours
for negative values are dashed) and mean climatology (shaded) in the years 1979–2005,
derived for the number of 3 h storm occurrences accumulated within 4∘× 4∘
grid boxes (number/decade); climatology derived for the period 1979–2005 is
shaded. The ensembles for
+2 and +1.5 ∘C constitute five decadal periods each; the
1979–2005 period is missing one year (1981). (b) Difference between
+2 minus +1.5 ∘C ensemble experiments, estimated for a number of
3 h occurrences with a maximum larger than 0.25 (mm h-1) and wind larger
than 10 m s-1 (number/decade). Maximum values were chosen from 3 h
precipitation data at 0.25∘, which falls into 3∘× 3∘
grid boxes. Differences were computed for grid boxes with the number over the
threshold of at least 20 per decade in both experiments. Contours for negative
values are dashed.
The climatology of the spatial track density (Fig. 9a), derived from the
tracking algorithm, agrees reasonably well with the tracks gleaned from
observations (Zappa et al., 2012, 2013; Hodges et al., 2003).
However, the pattern in CAM5_0.25 exhibits a maximum shifted
towards the Norwegian Sea and does not manifest strong activity in the
region southeast of Greenland. This feature is well captured in the
CAM5_0.25 wind speed percentiles and is most likely
associated with the short-lived katabatic winds that descend from the
Greenland ice sheet. These features are however not of interest for our
analysis and the tracking algorithm used in this study is tailored to
extract only the long-lived and most intense cyclones. Upon visual
inspection it can also be suggested that the track density simulated in
CAM5_0.25 is improved, compared to the low-resolution
CMIP3 and CMIP5 models. The CMIP models have been shown to exhibit a very
strong zonal bias with positive anomalies in central Europe and negative
values over the Norwegian Sea (Zappa et al., 2012). We note however that the
verification of whether increasing the resolution improves the simulated
climatology of midlatitude storms demands further analysis. This would
require a unified methodology, with the same tracking algorithm applied to
all of the data sets. Overall, the first results shown here indicate that the
CAM5_0.25 reproduces features of storminess considerably more
realistically than coarse-resolution simulations, in terms of both spatial
pattern and intensity. This increases our confidence in the skill in
projections of future storminess projected with the CAM5_025
and is the focus of the remainder of this section.
Figure 8a depicts differences in the response between 1.5 and
2 ∘C levels of warming, derived for the 95th percentile of 3 h
wind speed. The derived changes show a bipolar pattern, similar to the one
found for extreme precipitation. The most radical decrease in wind speeds
manifests at the poleward fringe of the subtropics (40∘ N),
between the Iberian Peninsula and the Azores. This region overlaps well with
the location of maximum easterly anomalies at the southern flanks of the
winter anticyclonic anomaly, found in the analysis of changes in general
atmospheric circulation (see Fig. 5). Thus it is likely that the simulated
reductions in extreme winds are to a large extent caused by the poleward
shift of the large-scale circulation, the signature of which is the
weakening of the westerlies at the poleward flanks of the subtropics.
The response to the additional half a degree of warming is also expressed as a
remarkable increase in extreme winds over the northern half of the typical
storm track region, with the maximum located between Iceland and the British
Isles and along the Scandinavian coast. This feature is highly consistent
with the response pattern derived for VT (Fig. 8b). Changes in VT indicate a
pronounced intensification of storminess on the poleward flanks of their DJF
climatology, again featuring a maximum between Iceland and the British Isles
and an eastward extension along the Scandinavian coast. Small negative
anomalies occur over the Norwegian Sea, northeast of Iceland. A similar
response is found in the storm track density (Fig. 9a), showing an increase
over the eastern North Atlantic and negative anomalies northeast of
Iceland. Positive anomalies found in all measures of storminess collocate
well with the local maximum of the intensification of the mean DJF
westerlies (Fig. 5), which is consistent with the eddy-driven nature of the
midlatitude jet stream.
The analysis of the intensity accumulated along the extracted tracks
provides further insights. Figure 9b shows an increase in the number of 3 h
storm occurrences, which exceed certain thresholds of precipitation and wind
(0.25 mm h-1 and 10 m s-1, respectively). The derived pattern shows similar
features to those in the track density, except that the positive changes are
extended northeast of the Norwegian Sea. An additional analysis (not
shown), repeated for higher thresholds of wind and precipitation, confirms
the previous results in that it also exhibits an increase along the
Scandinavian coast, indicating that the pattern becomes more zonal for higher intensities.
Overall, the increase manifested in the track density fields over the
eastern North Atlantic, between the British Isles and Iceland, is consistent
with the anomalies in VT. This suggests that the change in storm activity in
this region is influenced by the increased frequency of storms. The increase
in VT and in the number of high-intensity days (as diagnosed from wind and
precipitation) also becomes clearly visible over the Norwegian Sea, despite
no tendencies in track density in this area. For the increased thresholds of
the intensity, positive anomalies also emerge at the coastal regions of
Scandinavia, which are also accompanied by insignificant or zero
tendencies in track density. Therefore the response found in storm activity
over the Norwegian Sea could be alternatively explained by an increase in
the intensity of the storms, rather than frequency. This is however a
subject for a separate and more thorough analysis. It is also important to
note that the storm tracks analyzed here exhibit a very strong year-to-year
variability. Thus the statistics derived here may suffer from large
uncertainty and should be repeated when a larger number of ensemble
simulations become available, in order to facilitate a reduction in the sampling error.
Summary and discussion
In this study we assess near-term regional winter climate and weather
changes over the North Atlantic Ocean and Europe associated with the
1.5 and 2 ∘C levels of global warming. The design of
most state-of-the-art experiments, e.g., Coupled Model Inter-comparison
Project (CMIP), is not well suited to address questions on climatic changes
associated with the specific climate policy goals. This is due to the fact
that CMIP experiments are set in the framework of responses to the
particular concentration scenarios, rather than to the particular level of
warming. Therefore, we use a set of ensemble simulations provided by
the HAPPI project. The design of that experiment reduces the impacts of
different phases of climate variations and thus facilitates differentiation
of the climate response between the two warming levels. The CAM5 simulations
provide a set of future climate experiments, describing the global climate
and weather at ∼ 0.25∘ horizontal resolution and at a
sub-daily timescale (3 h). Hence these simulations create a unique
opportunity to explore changes and physical linkages between them across
spatial and temporal scales. Additionally, a set of CAM5 historical
simulations, provided at different horizontal resolutions, facilitates an
insightful analysis of the benefits of increasing horizontal resolution in
regional climate applications.
In the first part of our paper, we focused on the assessment of the
model's ability to realistically represent key features of winter climate
and weather over the Euro-Atlantic sector. Our analysis of the runs,
performed at horizontal resolutions ranging from ∼ 2 to 0.25∘, has shown a substantial improvement in
simulated large-scale circulation, specifically the meridional SLP gradient
and midlatitude zonal winds. The zonal bias of the ambient flow over the
North Atlantic and Europe, common for low-resolution CMIP3 and CMIP5 (Zappa
et al., 2012) models, has been very clearly reduced with the highest model
resolution. To a large extent, the reduction of the zonal bias may result
from a much better skill to capture ambient flow–orographic interactions in
the model with finer horizontal resolution, suggesting an important
upscaling impact of regional scales in shaping the large-scale circulation.
In the second part of the paper, we investigated near-future changes,
associated with global warming at the temperature levels specified by the Paris
agreement. The pattern of the future response, when 2 ∘C warming
is compared to the present climate, confirms typical fingerprints of climate
response. These are characterized by a poleward shift and intensification of
the meridional circulation cells, manifested here as strengthening
meridional SLP gradient, and poleward strengthening and eastward extension
of midlatitudes (Lu et al., 2007; Yin, 2005; Bengtsson et al., 2006; Wu et
al., 2010; Feser et al., 2015a, b).
However, different to previous studies, our analysis did not identify a local
maximum of anticyclonic SLP anomalies over the central Mediterranean. This
feature was found in many CMIP3 and CMIP5 simulations (Giorgi and Lionello,
2008; Giorgi and Coppola, 2007; AR5, IPCC, 2007) and was often used as an
explanation (Giorgi and Lionello, 2008) for reduced precipitation in most
parts of this region. Instead, in our analysis, the center of the
anticyclonic anomaly is shifted northwestward, which locates it over the
North Atlantic, northwestward of the Iberian Peninsula. This feature
corresponds well with the shift in drying anomalies, which extend from the
eastern North Atlantic and cover only western parts of the Mediterranean.
The reason for this difference may be associated again with a strong
positive bias in SLP over the Mediterranean and associated zonal bias of
ambient flow, persisting in most CMIP3 and CMIP5 models. Thus, the
maximum of the SLP field over the Mediterranean might be partly an
expression of that bias. Increasing horizontal resolution to
∼ 0.25∘ reduces the SLP bias almost completely, as shown in our
analysis, which might explain the difference in the response pattern. In
contrast to this result, other simulations using a
∼ 0.5∘ horizontal model resolution (Barcikowska et al., 2018)
indicated a strong anticyclonic intensification and drying over most of the
Mediterranean, despite remarkable reduction of the bias. Therefore, the
explanation of this difference in the projected pattern may have other/or
additional causes and demands further exploration running different models
at different resolutions.
Our analysis also provides additional insights into the evolution of the
response, as a function of changing global temperature and suggests that the
poleward shift and intensification of the meridional circulation cells and
midlatitude westerlies occurs mostly during the additional half a degree of
warming beyond the 1.5 ∘C level. The difference in the response
between 2 and 1.5 ∘C levels is shifted poleward,
compared to the changes estimated between 2 ∘C and present
climate. The maximum anticyclonic SLP anomaly is located over the Bay of
Biscay, which corresponds well with strong relative drying in this region.
These drying anomalies also extend further northeast towards the North Sea,
shifting the borderline between opposite sign tendencies northwards. Maximum
precipitation anomalies occur in the northwestern parts of the British Isles,
along the
northwestern coast of Scandinavia and the Norwegian Sea.
The evolution of the future response shows a much stronger and distinct
pattern compared to the changes prior to the 1.5 ∘C level of
warming. This amplification in the change may hence be a reflection of the
asymmetry in forcing changes between present climate and for the
1.5 and 2.0 ∘C experiments. The changes associated
with warming at the 1.5 ∘C level stem from an interplay of a
number of forcings, including strong aerosol reductions, while an additional
half a degree of warming is solely a consequence of CO2 increases and ocean warming.
The response found here of winter weather over the North Atlantic and Europe
is largely consistent with the changes found for the mean climate state and
large-scale circulation. An increase in warming from the +1.5 level to
2 ∘C level suggests a poleward intensification of daily and
sub-daily extreme wind and precipitation. These tendencies show the most
pronounced impact in the regions most exposed to the inflow of moisture from
the North Atlantic, e.g., the British Isles and northwestern Scandinavia, where
the 95th percentiles of 3 h precipitation increase up to 0.2 and
0.24 mm h-1, respectively. The response pattern derived from daily
precipitation shows a very similar pattern to the one derived from 3 h data.
However, the latter exhibits larger magnitudes and encompasses larger areas
with significant changes. Changes derived with GEV approximations,
indicating even more radical shifts, show an increase in 10-year return levels
of up to 0.3 mm h-1 in the coastal regions of the British Isles and
northwestern
Scandinavia. The magnitude of changes in precipitation is smaller in the
inland areas. However, many regions like northeastern Scandinavia may still be
strongly impacted by an increase of up to 20 %, when compared to the
1.5 ∘C level. Consistent with changes in the mean precipitation
along the southern coast of Scandinavia, the east coast of the British Isles
and North Sea indicate a slight decrease. These tendencies are more intense
and expand towards western Europe, exhibiting a decrease of up to 15 % over
France and exceeding a 25 % decrease over the interior and eastern
Iberian Peninsula.
Derived changes in extreme precipitation and wind correspond well with
changes in storminess, measured here with the transient poleward temperature
flux (VT) and features of explicitly extracted storm tracks. The
projected future response, derived from sub-daily VT and from spatial
density of the extratropical storm tracks, indicates an increase in storm
activity towards the northern side of the current storm track (between
Iceland and the British Isles) but also a decrease northeast of Iceland.
The decrease in storminess at the northern flanks of the storm track,
measured as the frequency of intense storms, has been identified in the
CMIP5 projections (Zappa et al., 2013). Similar to our analysis, the future response
according to CMIP5 models suggests a polar amplification of global warming,
associated strongly with the Arctic sea-ice loss. This in turn reduces the
lower-atmosphere meridional temperature gradient and also baroclinicity,
shown here by the decrease in zonal wind northeastward from Iceland, which
is consistent with the reduced storminess in this region. At the same time,
the minimum of warming SSTs over the North Atlantic could lead to increased
surface atmospheric baroclinicity (Brayshaw et al., 2009; Woollings et al.,
2012) and thus enhance storminess over the eastern North Atlantic.
An increase in transient poleward temperature flux is also found over the
Norwegian Sea, along the Scandinavian coast, which collocates well with the
local maxima of increase in extreme precipitation and wind. The density of
storm tracks does not indicate any spatially coherent tendencies in this
region. However, the positive tendencies in this region emerge when the
extreme precipitation and wind events, associated with the extracted storm
tracks, are analyzed. In these regions we found an increase in frequency of
3-hourly storm occurrences with exceptionally high intensities. The strength of
this tendency increases with the intensity of the extreme event, which
suggests the possibility of increased frequency of more intense storms.
These results should however be confirmed by a more elaborate analysis,
specifically targeting changes in storms, and are the subject of further research.
Data is available from C20C+ Detection and Attribution Project
(Stone and Krishnan, 2018; http://portal.nersc.gov/c20c).
The supplement related to this article is available online at: https://doi.org/10.5194/esd-9-679-2018-supplement.
The authors declare that they have no conflict of interest.
This article is part of the special issue “The Earth system at a
global warming of 1.5 ∘C and 2.0 ∘C”. It is not associated with a conference.
Acknowledgements
The authors are grateful to Ángel Muñoz and Alex Petrescu for helpful
discussions. Matthias Zahn was supported through the Cluster of Excellence
“CliSAP” (EXC177), Universität Hamburg, funded through the German Research
Foundation (DFG). Dáithí A. Stone and Michael F. Wehner were supported
by the U.S. Department of Energy, Office of Science, Office of Biological and
Environmental Research, under contract number DE-AC02-05CH11231.
Edited by: Rui A. P. Perdigão
Reviewed by: Joaquim G. Pinto and one anonymous referee
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