Six climate simulations of the Met Office Unified Model Global
Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against
observations and reanalysis data for their ability to simulate the mean state
and year-to-year variability of precipitation over China. To analyse the
sensitivity to air–sea coupling and horizontal resolution, atmosphere-only
and coupled integrations at atmospheric horizontal resolutions of N96, N216
and N512 (corresponding to ∼ 200, 90 and 40 km in the zonal direction
at the equator, respectively) are analysed. The mean and interannual variance
of seasonal precipitation are too high in all simulations over China but
improve with finer resolution and coupling. Empirical orthogonal
teleconnection (EOT) analysis is applied to simulated and observed
precipitation to identify spatial patterns of temporally coherent interannual
variability in seasonal precipitation. To connect these patterns to
large-scale atmospheric and coupled air–sea processes, atmospheric and
oceanic fields are regressed onto the corresponding seasonal mean time series.
All simulations reproduce the observed leading pattern of interannual
rainfall variability in winter, spring and autumn; the leading pattern in
summer is present in all but one simulation. However, only in two simulations
are the four leading patterns associated with the observed physical
mechanisms. Coupled simulations capture more observed patterns of variability
and associate more of them with the correct physical mechanism, compared to
atmosphere-only simulations at the same resolution. However, finer resolution
does not improve the fidelity of these patterns or their associated
mechanisms. This shows that evaluating climate models by only geographical
distribution of mean precipitation and its interannual variance is
insufficient. The EOT analysis adds knowledge about coherent variability and
associated mechanisms.
Introduction
Rainfall over China is mainly influenced by the East Asian Summer Monsoon
(EASM), characterised by southerly winds, and the East Asian Winter Monsoon
(EAWM), featuring northerly winds. Annual mean precipitation decreases from
southeast to northwest China. Precipitation exhibits a strong seasonal cycle,
as well as substantial intraseasonal, interannual and decadal variability.
Droughts, floods and cold surges severely impact the lives of millions of
people by affecting water resource management, infrastructure and the
ecological environment
.
Therefore, regional interannual variability (IAV) of rainfall is likely to
cause more damage to human life, agriculture and infrastructure than a slowly
changing mean state arising from climate change.
A complex mixture of physical mechanisms and a highly spatially variable
climate and orography make it difficult for general circulation models (GCMs)
to correctly reproduce the geographical distribution of precipitation and its
variability over China. Precipitation is influenced by local air–sea
interactions over the South China Sea (SCS), by tropical and subtropical
circulation systems , and by teleconnections to
modes of variability in the Indian and Pacific oceans.
The modulation by the El Niño–Southern Oscillation (ENSO) has been studied
for many years. Developing El Niños favour droughts in northern China
. A weaker EAWM has been associated with El Niño and
a stronger monsoon with La Niña . Summer
flooding in the Yangtze River valley can occur during the decaying stage of
El Niño . In southern China, following
El Niño, spring rainfall increases , while autumn rainfall
is reduced . Extratropical teleconnection patterns
also affect interannual rainfall variability over east Asia .
During recent decades GCMs have become more complex by coupling to dynamical
ocean models and including more, and more complex, sub-grid-scale parameterisations. GCM assessment efforts, such as the third and fifth
Coupled Model Intercomparison Projects (CMIP3 and CMIP5; ;
), show that large biases remain in the simulation of the
East Asian monsoon . Over China, most current GCMs produce
cold near-surface temperature biases and excessive precipitation. In
addition, GCMs underestimate the southeast–northwest precipitation gradient
and overestimate the magnitude and spatial variability of IAV, with little
change from CMIP3 to CMIP5 .
Increases in computational power allow GCMs to run at increasingly finer
horizontal resolution. Previous studies have investigated the effect of
resolution on the fidelity of the simulation of rainfall and IAV in China.
grouped coupled GCMs participating in CMIP3 and CMIP5 into
groups of < 2∘ (222 km), 2–3∘ (222–334 km)
and > 3∘ (334 km) resolution and found that finer resolution
improved GCM fidelity for winter, spring and autumn precipitation over
China. found no resolution sensitivity for precipitation in
summer. This is consistent with , who examined the simulation
of the EASM in CMIP3 and CMIP5 atmosphere-only GCMs and found no evidence for
a relationship between model fidelity and resolution (ranging from
∼ 22–500 km). In their analysis of CMIP5 coupled models,
also concluded that horizontal resolution did not influence
climatological precipitation and IAV over China. On the other hand,
performed a series of experiments with the Community Atmosphere
Model (CAM5) at resolutions of T42 (310 km at the equator), T106 (125 km) and T266 (50 km). At finer resolution, spatial patterns of rainfall along
the southern edge of the Himalayas became more realistic, and a large
positive precipitation bias to the east of the Tibetan Plateau reduced
significantly, due to more realistic orography. Based on experiments with a
regional climate model at resolutions of 45–240 km and keeping all other
settings identical, also found that horizontal resolution of
60 km or finer was important to accurately simulate monthly mean
precipitation over China. By comparing to a set of simulations with variable
resolution along with degraded-resolution topography, they concluded that the
improvement was due to better-resolved physical processes rather than a
better-resolved topography.
In contrast, few studies have directly assessed the effects of air–sea
coupling on the fidelity of east Asian precipitation. In experiments with
coupled and atmosphere-only GCMs, found that including
coupling is important for correctly simulating the long auto-decorrelation
time of daily rainfall in summer monsoon regions. However, coupled models
struggle to correctly represent the seasonal cycle of sea surface temperatures (SSTs) and the spatial
pattern and magnitude of ENSO SST anomalies .
Errors in position or amplitude of equatorial ENSO SSTs have substantial
impacts on teleconnections to rainfall in CMIP1/2 and
CMIP3 models. found that the spatial
structure and amplitude of ENSO-related SST anomalies are key factors for the
ENSO-EAWM relationship in CMIP5 models. Even in atmosphere-only models and in
the absence of SST biases, observed rainfall patterns associated with ENSO
are poorly captured, with little or no improvement from CMIP3 to CMIP5
.
Improvements in GCMs rely on continuous evaluation at the regional scale, as
well as understanding the physical processes that must be captured to
reliably simulate regional climate and its variability. The latter is
particularly important for a densely populated country such as China.
Previous studies have mainly assessed the ability of GCMs to simulate the
distribution of mean precipitation and its interannual standard deviation
over China. This study adds a comprehensive assessment of the leading
patterns of coherent precipitation variability in all seasons, using
empirical orthogonal teleconnection (EOT) analysis. EOT analysis objectively
identifies regions in China that show strong coherent IAV in seasonal
precipitation. showed that EOT analysis is useful for
assessing regional climate variability in GCMs through the analysis of the
leading patterns of Australian annual rainfall. In a previous study,
performed EOT analysis on 1951–2007 high-resolution
gridded precipitation data over China in all seasons. Their results serve as
a benchmark for our model assessment.
Grid-point mean orographic height for (a) N96,
(b) N216 and (c) N512 horizontal resolution.
Changes in the physics schemes between different models participating in CMIP experiments may obscure the direct effects of changes in resolution or the
addition of air–sea coupling. Therefore, we analyse simulations with Met
Office Unified Model (MetUM) Global Atmosphere 6.0 and Global Coupled 2.0
configurations at resolutions of N96, N216 and N512 (corresponding to
∼ 200, 90 and 40 km in the zonal direction at the equator); physical
parameterisations remain unchanged. This allows us to focus on the effects of
coupling and resolution. We aim to answer three questions: (i) do simulations
produce observed patterns of regional precipitation variability? (ii) are
those patterns associated with the observed physical mechanisms? (iii) is the
fidelity of these patterns and their associated mechanisms sensitive to
horizontal resolution and/or air–sea coupling?
Section describes the model simulations, observational data
sets and analysis methods. Section presents model biases in
seasonal mean precipitation and IAV; Sect. reports biases in the
teleconnection to ENSO. Results from the EOT analysis are shown in
Sect. . Section is a discussion;
Sect. summarises the main results.
Data and methodsMetUM simulations
We analyse two Atmospheric Model Intercomparison Project (AMIP)-style simulations from 1982–2008 at resolutions of N96
(1.875∘×1.25∘, 208 km × 139 km in longitude
and latitude at the equator) and N216 (0.83∘×0.55∘,
93 km × 62 km). We refer to these as A96 and A216, respectively. They
use the Global Atmosphere configuration 6.0 of the MetUM (GA6;
), driven by monthly mean SSTs from the Reynolds product and varying solar,
greenhouse gas and aerosol forcings as observed in 1982–2008.
The coupled simulations use the Global Coupled configuration 2.0 of the MetUM
(GC2; ). We analyse two GC2 simulations at N96 and N216
resolutions in the atmosphere, referred to as C96 and C216, that are
initialised with present-day ocean data (EN3; ) and spun-up
sea-ice and land surface conditions. We further analyse two coupled
simulations (C512a and C512b) at N512 resolutions
(0.35∘×0.23∘, 39 km × 26 km). Both are
initialised with ocean conditions from a previous N512 simulation but with
an offset of 55 years to sample different phases of decadal variability. The
coupling frequency is 3 h and the ocean resolution is fixed at
0.25∘ on the ORCA025 tri-polar grid .
The four GC2 simulations are present-day control simulations using emissions
and solar forcing with constant 1990 values and are integrated over 100 years.
Observed 1961–2012 precipitation trends over China do not exceed
2 mm year-1 in any season in any region . The
interannual variability of the EOT time series are typically 2 orders of
magnitude larger. Therefore, differences in the applied forcing between the
GA6 and GC2 simulations can be assumed to have negligible effects on the
results. Figure shows the grid-point mean orographic height at
each resolution. As described in , subgrid orographic
boundary layer drag, flow blocking and gravity wave drag are parameterised in
the MetUM. With finer resolution, more orography is resolved explicitly and
less is parameterised. Please refer to for further details
on physical parameterisations and their dependence on resolution.
Empirical orthogonal teleconnections
In a previous study, performed EOT analysis on 1951–2007
gridded Asian Precipitation –
Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) precipitation data (see Sect. ). Unlike empirical
orthogonal functions (EOFs), which are orthogonal in space and time, EOTs are
orthogonal either in space or time . To investigate coherent
temporal rainfall variability, we first compute the China-averaged time series
of rainfall anomalies and search for the grid point that best explains its
variance . All MetUM precipitation data are interpolated to
the APHRODITE grid to allow for a comparison with observations. We chose the
APHRODITE grid so that the observed EOT patterns do not change from the ones
reported in . To quantify the spatial coherence of
rainfall, we correlate rainfall anomalies at each grid point with this
so-called base point. To compute EOTs of second and lower orders, we remove
all higher-order EOT time series from all points in the domain by linear
regression and then repeat the above steps. We compute up to three EOTs
because lower-order patterns explain only small amounts of variance. The EOT
method identifies independent regional patterns of coherent rainfall
variability. The associated time series can be used to connect the patterns to
regional and large-scale atmospheric and coupled atmosphere–ocean mechanisms,
as demonstrated for China and as was
previously shown for other regions
.
It is important to note that EOT analysis does not maximise the percentage of
explained space–time variance, resulting in several benefits: (i) the
associated time series are connected to specific grid points; (ii) the lack of
spatial orthogonality means it is possible to find more than one EOT that
explains variance in the same area; (iii) because EOTs are orthogonal only in
time, the method is less sensitive to the choice of the spatial domain.
Connections between rainfall patterns and drivers are established by linearly
regressing atmospheric and oceanic fields onto time series corresponding to
each EOT pattern. We show regressions associated with a 1 standard
deviation increase in an EOT time series, but all discussions hold for
negative anomalies in the EOT time series, with the opposite sign. We use
Spearman's rank correlations because the rainfall data are not normally
distributed.
To test whether MetUM simulations reproduce observed patterns, we interpolate
simulated precipitation to the APHRODITE grid and regress China-wide
seasonal precipitation against simulated and observed EOT time series. If the
linear Pearson pattern correlation coefficients between simulated and
observed regression maps are greater than a subjectively chosen threshold of
0.38, we consider them a match. This value was chosen because patterns with a
smaller correlation were located far away from the respective observed
pattern.
Observational data and indices
Seasonal total precipitation for winter (DJF), spring (MAM), summer (JJA) and
autumn (SON) over China is obtained from the APHRODITE data set . It is a long-term (1951–2007),
continental-scale daily product with a resolution of
0.5∘× 0.5∘, produced from rain-gauge data. Before the
data are gridded, an objective quality control procedure is applied
. Previous studies confirmed the high quality of the data
. The rain-gauge network in China
is dense except in climatologically dry regions, i.e. northwest China and
the Tibetan Plateau (see , for a map of rain-gauge
locations). However, EOT patterns tend to peak in climatologically wet
regions, and all EOTs in this study are in areas of high-density
observations.
We show regressions onto the EOT time series of surface pressure
(PSFC), 200 hPa geopotential height (Z200)
and 850 hPa wind from the European Centre for Medium-Range Weather
Forecasting Interim global reanalysis (ERA-Interim, from now on also referred
to as observations), available at 0.7∘×0.7∘ resolution
from 1979–2007 . We compute area averages of
PSFC in the northwest Pacific (NWP; 10–30∘ N,
120–170∘ E) and 500 hPa geopotential height (Z500) in
25–45∘ N, 120–140∘ E to connect patterns of rainfall
variability with circulation anomalies in the NWP and around Japan,
respectively.
SSTs for 1870–2010 are obtained from the 1∘× 1∘ Hadley
Centre sea Ice and SST data set (HadISST; ). We average
linearly detrended SSTs in the South China Sea (SCS, 5–25∘ N,
100–120∘ E) and in the eastern tropical Pacific (Niño3.4; 5∘ S–5∘ N, 190–240∘ E) to associate rainfall
anomalies with ocean variability.
As a proxy for convective activity, we show regressions onto EOT time series of
seasonal mean 2.5∘×2.5∘ interpolated
satellite-retrieved outgoing longwave radiation data (OLR;
), available for 1974–2013.
Climatological seasonal total precipitation (grey contours) and
interannual standard deviation (shading) for 1951–2007 observations (top)
and the full length of each simulation. The season is indicated above the
panels.
Annual mean SST bias in the coupled simulations relative to
observations (1870–2010 HadISST).
Climatology
MetUM GA6 and GC2 show substantial biases in seasonal mean precipitation and
IAV (Fig. ). Observed DJF mean precipitation does not exceed
300 mm anywhere in China, but all simulations show substantially larger
amounts. A96 produces wet biases of ∼ 100 mm and ∼ 50–100 %
larger IAV in southwest and southeast China. Increasing resolution from N96
to N216 in coupled (C216 vs. C96) and atmosphere-only (A216 vs. A96)
simulations improves biases near orography in southwest China (compare
Fig. ). Positive biases in seasonal mean precipitation and IAV in
the N512 simulations are confined to southeast China. Observed IAV is largest
along the southeast coast, but in A96 and A216 it peaks further north along
the eastern Yangtze basin. In this area, higher resolution increases the
positive bias in IAV by ∼ 50 %, as is also seen between C96 and
C216. This may be related to orographically forced precipitation
. However, adding coupling and resolution tends to shift areas
of high IAV further south, closer to where high variability is observed.
C512a and C512b still overestimate observed variability, but regions of high
variability agree better with observations, i.e. they are located in the
southeast of China and south of the Yangtze valley.
The positive precipitation bias in MetUM GA6/GC2 is consistent with a weaker
than observed EAWM circulation (not shown). Because the EAWM circulation is
strongest in the lower troposphere over coastal east Asia ,
we compute the spatial correlation coefficient (r) and the normalised
root-mean square error (e) of simulated relative to observed meridional
winds at 10 m in 25–40∘ N,
120–140∘ E plus 10–25∘ N,
110–130∘ E, as in . Increasing
resolution from N96 to N216 improves the spatial correlation and reduces the
error (r= 0.75, e= 0.84 in A96 versus r= 0.93, e= 0.59 in A216;
r= 0.64, e= 1.02 in C96 versus r= 0.82, e= 0.82 in C216). No
improvement is seen from C216 to C512.
In MAM the spatial patterns of seasonal mean precipitation and IAV are
similar in observations and simulations. All simulations show positive biases
in mean precipitation and IAV, particularly in southeast China south of the
Yangtze River, where the bias in A96 is > 400 mm, double the observed
precipitation; variability is also doubled. Increasing resolution to N216
reduces wet biases in southwest China in the GA6 and GC2. Coupling at N96 and
N216 reduces wet biases in southeast China; however, there is little change
between C216 and C512.
In JJA climatological observed rainfall decreases from southeast to northwest
China; variability is highest in the eastern Yangtze valley. In A96 total
rainfall is too high and the isolines are too zonal, creating large wet
biases in southwest China and the Tibetan Plateau, consistent with the poor
simulation of the southeast–northwest precipitation gradient in CMIP3 and
CMIP5 . IAV is doubled compared to observations in south
China and the Yangtze valley. In most areas biases become small with finer
resolution and coupling; C216 and C512 compare well with observations except
for a wet bias in south China. To measure the strength of the EASM
circulation, we again follow and compare 850 hPa meridional
winds in 20–40∘ N, 105–120∘ E. The EASM is too weak in
all simulations, which results in a weak northward transport of moisture and
positive precipitation biases in south China (not shown).
In SON observed total rainfall in southeast China is ∼ 200–300 mm,
and rainfall variability is relatively spatially homogenous compared to the
other seasons. In A96 total precipitation and its variability are realistic
in the eastern part of southeast China, but wet biases become large toward
the Tibetan Plateau. Biases reduce with resolution and coupling.
Comparing GA6 and observations using their common record period does not
change the above findings. In summary, mean precipitation and IAV tend to be
too high in the MetUM. Finer resolution reduces biases near steep topography,
particularly in southwest China. Adding coupling reduces wet biases along the
eastern Yangtze valley in DJF and JJA and in southeast China in MAM.
Regression of DJF precipitation against observed or simulated DJF
Niño3.4 (a); lagged regression of MAM precipitation against preceding
DJF Niño3.4 (b); lagged regression of JJA precipitation against
preceding DJF Niño3.4 (c); regression of SON precipitation
against SON Niño3.4 (d). Stippling indicates confidence levels
exceeding 95 %.
Regression of observed or simulated JJA 850 hPa wind against
observed or simulated normalised DJF Niño3.4. Observations use 1982–2008
to match the years of the atmosphere-only simulations. Arrows indicate the
wind direction and are drawn where wind speeds exceed 0.2 m s-1.
Teleconnection to ENSO
Since ENSO has an important influence on IAV in China, particularly in DJF,
MAM and JJA (see Sect. ), we now examine how well the MetUM
captures climatological SSTs and the teleconnection to ENSO. While SST
variability in other ocean basins has also been connected to IAV in China
(e.g. ; ), did not
find an influence of other basins on their EOT patterns. Therefore, we here
focus on the teleconnection to ENSO. The annual mean SST bias in the coupled
simulations (Fig. ) is characterised by cold biases in the
Northern Hemisphere and a warm bias in the Southern Ocean. These biases are
associated with an Inter-Tropical Convergence Zone (ITCZ) that is displaced
to the south over the Atlantic and Indian Ocean . In C512
the Indian Ocean has a slight warm bias, when in C96 and C216 it has a slight
cold bias. Compared to C96, C216 and observations, SSTs in the tropical
Pacific and tropical Atlantic are also warmest in C512 (Fig. ).
GC2 compares favourably with other CMIP5 models ,
simulating an approximately correct spatial pattern of ENSO SSTs, albeit with
slightly weaker variability in the central Pacific, a power spectrum with
frequencies within the observed range (3–7 years), and good seasonality with
maximum (minimum) variability in boreal winter (spring) .
For our four GC2 simulations specifically, the ratios of simulated to
observed standard deviations of DJF SSTs in the Niño3.4 region are 0.61
(C96), 0.71 (C216, C512a) and 0.83 (C512b).
Performance statistics of simulated EOTs compared to observations
for DJF. Column (1) observed (Obs) and simulated (labelled by simulation name)
EOT patterns; numbers indicate the order of the EOT pattern. (2) Linear
pattern correlation coefficient of simulated and observed precipitation
anomalies; (3) explained space–time variance of the EOT pattern; (4) standard
deviation of the EOT time series; (5)–(7) Spearman's rank correlation of the
EOT time series with the (5) Niño3.4 SST index; (6) SCS SST index and
(7) NWP PSFC index (defined in Sect. ).
Correlation coefficients in (5)–(7) are shown only when they are significant
at the 90 % confidence level and are marked with an asterisk for confidence
levels < 95 %.
In observations, ENSO is positively correlated with DJF precipitation in
southeast China (Fig. ). A96 produces enhanced rainfall in
southeast China in DJF in response to ENSO, but the maximum is shifted to the
eastern Yangtze valley and southwest China is too dry. A216 produces only a
slightly better agreement with observations. C96 has no significant
teleconnection to ENSO; C216 and C512 on the other hand compare well to
observations.
The observed rainfall pattern in MAM consists of anomalously dry areas in
southeast China and anomalously wet areas in eastern China in response to
ENSO. All simulations reproduce a dipole pattern, but statistically
significantly increased precipitation to the north of the Yangtze valley is
only seen in C216 and C512.
DJF Niño3.4 is positively correlated with precipitation in the following
JJA along the central Yangtze valley. A96 shows increased precipitation along
the Yangtze River but also in southeast China. In A216 precipitation is also
increased in southeast China and reduced in southwest China and between the
Yangtze and Huai He rivers. All coupled simulations show a very weak
teleconnection to ENSO. Only in C96 are there positive anomalies in the
Yangtze valley. Observed JJA circulation anomalies following DJF ENSO are
characterised by a strong anticyclonic circulation in the western North
Pacific (Fig. ). Dynamical mechanisms proposed for this
anticyclonic circulation include suppressed convection over the western
equatorial Pacific due to a weakened Walker circulation during El Niño
, Rossby waves triggered by SST anomalies over the western
tropical Pacific , and the non-linear
atmospheric interaction between the annual cycle and ENSO variability
. Explanations for the long
persistence include local wind–evaporation–SST feedback and
the delayed Indian Ocean warming (e.g. ;
). A96 and A216 produce northeasterlies over southeast China
instead of southwesterlies (Fig. ), which may be associated
with a weak western North Pacific subtropical high that does not extend far
enough westward (not shown). The pattern of the circulation response in GC2
is similar at all resolutions and scales with the strength of ENSO
variability (Fig. ). Over southeast China there are weak
southwesterlies in C96 and C216. In C512 westerlies are present over the
eastern Yangtze–Huai He basin. Pacific wind anomalies in GC2 are more zonal
compared to GA6, and compare better to observations. Hence, the JJA response
to ENSO is sensitive to coupling, but no MetUM configuration produces the
observed response.
Shading shows regressions of DJF precipitation against observed or
simulated EOT time series. Also shown are positive (solid lines) and negative
(dashed lines) correlations of the full (leading-order) or residual (higher-order) precipitation-anomaly time series with the EOT base point exceeding 0.8
(magenta), 0.6 (orange) and 0.4 (green). The EOT base point is marked by the
orange inverted triangle. Panel (a) shows patterns that have a linear
correlation coefficient exceeding 0.38 with the first observed pattern and
(b) with the second observed pattern. The number next to the
simulation name above each panel indicates the order of the simulated EOT
pattern.
Observed rainfall anomalies in SON show a dipole of wet conditions in
southern China and dry conditions in central China. The simulations show no
consistent response to ENSO. However, the observed SON EOTs in this study are
not related to ENSO.
Coupling and higher resolution improve the teleconnection to ENSO in DJF and
in MAM, but the teleconnection is poor in JJA in all simulations.
EOT results
Based on APHRODITE data, identified the dominant regions
of observed coherent IAV in seasonal precipitation in China and linked that
variability to large-scale and regional mechanisms. They analysed only EOTs
that explained at least 5 % of the total space–time variance, which
resulted in two patterns in DJF, JJA and SON and three in MAM. They found
connections to ENSO for DJF precipitation variability in large parts of
eastern China (DJF Obs-1), MAM precipitation variability in southeast China
(MAM Obs-1), and JJA precipitation variability in the southern Yangtze River
valley (JJA Obs-1). Relationships to extratropical wave propagation were
found for DJF along the southeast coast of China (DJF Obs-2), MAM in the
Yangtze region (MAM Obs-2) and the northern parts of eastern China (MAM
Obs-3), and for large areas of eastern China in SON (SON Obs-1). JJA IAV in
the northern areas of the Yangtze region (JJA Obs-2) and SON IAV in the
coastal area of southern China (SON Obs-2) were associated with local
pressure anomalies.
Regressions of PSFC (shading) and
Z200 (contours at intervals of 0.02 m; pink – positive; green – negative) against normalised DJF EOT time series. Panel (a) is for EOTs
matching the first observed pattern and (b) for the second observed
pattern. All shown values are significant at the 90 % confidence level.
Here, we compare simulated and observed EOTs for each season in turn. Results
based on APHRODITE observations will be denoted “Obs-1”, “Obs-2” and “Obs-3”, and
results based on model simulations will be denoted by the simulation
identifier and the order of the EOT (e.g. “A96-2” stands for the
second-order EOT in A96).
WinterPattern 1
Obs-1 describes rainfall variability in large areas of eastern China
(Fig. ) and explains 34 % of the space–time variance. It is
associated with ENSO, with a correlation coefficient of 0.44 with DJF
Niño3.4 (Table ). The Walker circulation is weakened, with
negative PSFC anomalies in the eastern tropical Pacific
and positive anomalies in the western tropical Pacific (r= 0.42 with the
NWP PSFC index; Table ). The EAWM is
weakened, and anomalous southwesterlies along the coast of southeast China
transport moisture from the Bay of Bengal and the SCS (not shown), where SSTs
are anomalously warm (r= 0.40 with SCS SST index; Table ).
All simulations reproduce the leading spatial pattern (Fig. ),
with pattern correlation coefficients between 0.85 (A96) and 0.94 (C96,
C512a) and similar percentages of explained variance (Table ).
Shading shows regressions of MAM precipitation against observed or
simulated EOT time series that have a linear correlation coefficient exceeding
0.38 with Obs-1 (a), Obs-2 (b) or Obs-3 (c). Also
shown are positive (solid lines) and negative (dashed lines) correlations of
the full (leading-order) or residual (higher-order) precipitation-anomaly
time series with the EOT base point exceeding 0.8 (magenta), 0.6 (orange) and 0.4 (green). The EOT base point is marked by the orange inverted triangle. The
number next to the simulation name above each panel indicates the order of
the simulated EOT pattern.
Performance statistics of simulated EOTs compared to observations
for MAM. Column (1) observed (Obs) and simulated (labelled by simulation name)
EOT patterns; numbers indicate the order of the EOT pattern. (2) Linear
pattern correlation coefficient of simulated and observed precipitation
anomalies; (3) explained space–time variance of the EOT pattern; (4) standard
deviation of the EOT time series; (5)–(7) Spearman's rank correlation of the
EOT time series with the (5) DJF Niño3.4 SST index; (6) MAM SCS SST index
and (7) MAM Z500 index over Japan (defined in Sect. ).
Correlation coefficients in (5)–(7) are shown only when they are significant
at the 95 % confidence level.
Pattern corr.Expl. var. (%)SD (mm)DJF Niño3.4MAM SCS SSTMAM JPN Z200Obs-120116–0.31–A96-10.64251920.560.440.38A216-10.83242110.66––C96-10.53161740.38–0.50C216-10.73181910.320.29–C512a-10.72201990.220.220.22C512b-10.43262230.390.370.42Obs-28113–––A216-20.501383––0.68C96-2–0.4713163–––C216-20.719131––0.34C512a-20.878223––0.20C512b-2–0.3812156–––Obs-37340.36–0.52C216-30.59649––0.43C512b-30.65455––0.34
The C216 and C512 EOT time series are statistically significantly correlated
with Niño3.4, SCS SST and NWP PSFC, with correlation
coefficients similar to observations (Table ). Global
regressions of SST and PSFC on DJF EOT 1 confirm ENSO
signals (not shown). C96, A96 and A216 miss the connection to ENSO. This
result is expected because only C216 and C512 have a realistic
teleconnection to ENSO in DJF (Sect. ). Note that for observations,
the correlation to Niño3.4 is only 0.44, so ENSO explains only ∼ 20 %
of the variance in the EOT time series. There are positive
Z200 anomalies over Europe, northern Africa and northeast Asia,
and negative anomalies over the Middle East and southern China in
ERA-Interim. Similar extratropical Z200 perturbations exist in
all simulations. Such perturbations can arise from a variety of pathways and
are not always connected to ENSO.
Pattern 2
Obs-2 peaks along the southeast coast with another area of coherent rainfall
variability further north along the coast. It explains 12 % of the
space–time variance and is associated with low Z200 over China,
positive PSFC over and to the east of Japan (first row of Fig. 7b), negative OLR anomalies over the western equatorial Indian
Ocean and positive OLR anomalies over the eastern equatorial Atlantic Ocean
(not shown). showed that the pressure distribution is
consistent with a wavenumber-3 Rossby wave originating over central Africa or
the Arabian Sea and propagating across China and Japan into the Pacific.
A pattern with a peak along the southeast coast is found in A216, C96, C216
and C512b. Only A96 produces a coastal pattern with separate southern and
northern areas of co-variability. The A96 pattern has a correlation of 0.53
with Obs-2, less than the other simulations (between r= 0.70 (C96) and
r= 0.83 (C216)). Only in A96 is the base point situated inside the
northern area. The pattern describes between 8 % (A216) and 14 %
(C512b) of the space–time variance in the simulations.
In A216, C216 and C512b the pattern is weakly correlated with ENSO
(Table ). In C96 northward moisture transport is associated
with low pressure over south Asia (Fig. ). In A96 onshore flow is
created by high pressure over the Korean peninsula (Fig. ). We
conclude that the simulations that capture Obs-2 do not associate
precipitation with the correct large-scale driving mechanism. The pressure
anomalies associated with Obs-2 are not present in any of the simulations.
Regressions of PSFC (shading) and 850 hPa wind
(arrows) against normalised MAM EOT time series corresponding to Obs-1.
Stippling indicates confidence levels exceeding 90 %. Wind arrows are
drawn when at least one component is statistically significant and the wind
speed exceeds 0.1 m s-1.
SpringPattern 1
Obs-1 explains 20 % of the total space–time variance with a peak in
southeast China (Fig. ). Northeastward flow along the western side
of an anomalous anticyclone in the tropical western Pacific transports
moisture into southern China (Fig. ). showed
that the 850 hPa circulation in Fig. strongly resembles the
antisymmetric “C-mode” response to ENSO, even though the EOT time series
itself is not significantly correlated with Niño3.4 but only with SSTs in
the SCS (r= 0.31; Table ). The C-mode circulation is
characterised by a strong anticyclone in the NWP, resulting from the
non-linear interaction between the annual cycle of wind and SST and ENSO
variability .
All simulations show a leading EOT that resembles the observed one
(Fig. ) but with westward shifts of the EOT base points.
Simulations have weaker precipitation variability in southern China than
observations, which could have important consequences for river discharge on
the southern slopes of the mountainous terrain of southeast China
(Fig. ). Pattern correlations range from 0.43 (C512b) to 0.83
(A216). The pattern explains a similar fraction of variance in simulations
and observations: between 16 % (C96) and 26 % (C512b).
All simulated EOTs lag behind ENSO (Table ), and their circulation
anomalies in the NWP show strong similarities to the observations in terms of
the wind direction along the southeast coast (Fig. ).
Correlations with DJF Niño3.4 are largest in A96 (0.56) and A216 (0.66).
All simulations produce the leading pattern of MAM precipitation variability
for the correct physical reason.
Pattern 2
Obs-2 is centred along the Yangtze valley, with variability of opposite
phase in southeast China (Fig. ). It explains 8 % of the total
space–time variance and is associated with an upper-tropospheric wave train
in the high and midlatitudes. It creates significant upper-level divergence
over the Yangtze River and convergence over the coastline of south China.
showed that Z200 anomalies are consistent
with a Rossby wave pattern of Atlantic origin.
Performance statistics of simulated EOTs compared to observations
for JJA. Column (1) observed (Obs) and simulated (labelled by simulation name)
EOT patterns; numbers indicate the order of the EOT pattern. (2) Linear
pattern correlation coefficient of simulated and observed precipitation
anomalies; (3) explained space–time variance of the EOT pattern; (4) standard
deviation of the EOT time series; (5)–(7) Spearman's rank correlation of the
EOT time series with the (5) DJF Niño3.4 SST index; (6) JJA SCS SST index
and (7) JJA NWP PSFC index (defined in Sect. ).
Correlation coefficients in (5)–(7) are shown only when they are significant
at the 95 % confidence level.
Pattern corr.Expl. var. (%)SD (mm)DJF Niño3.4JJA SCS SSTJJA NWP PSFCObs-1121930.380.420.40A96-10.7523296–––A216-10.7110207–––C96-10.75152110.200.370.29C216-10.7211210–0.21–C512a-10.669182–0.250.24Obs-25137–—–C512b-30.654155–––
Meridional dipoles are found in A216 and all coupled simulations. C216 and
C512a best match the observed pattern with pattern correlations of 0.71
(C216) and 0.87 (C512a) and similar explained variances of 9 % (C216)
and 8 % (C512a). In C96 and C512b, the phase of the dipole is reversed
and pattern correlations are therefore negative (-0.47 for C96 and -0.38 for
C512b). We consider those to be matches as well, as the signs of the EOT
pattern and time series are arbitrary.
Simulations do not match Obs-2 Z200 anomalies (not shown). C96-2
is associated with low PSFC and a cyclonic circulation
anomaly over the coast. C512b-2 is associated with anomalous easterly flow
along the Yangtze River driven by high PSFC over central
China. A216-2, C216-2 and C512a-2 are associated with a Z500
ridge over Japan (JPN Z500 index in Table ) and an
anomalous anticyclonic circulation.
Pattern 3
Obs-3 rainfall anomalies are found in large areas of eastern China
(Fig. ). This pattern explains 7 % of the total space–time
variance. It has a correlation of 0.36 with the preceding DJF Niño3.4
index, consistent with Fig. . Southeastward-propagating waves from
high latitudes and northeastward-propagating waves from south China terminate
over Korea and Japan in a region of high pressure, indicating blocking.
argue that these waves are likely triggered by anomalous
equatorial heating associated with a decaying ENSO state.
Only two simulations, C216 and C512b, partly produce this pattern, with
pattern correlations of 0.59 and 0.65. In these simulations anomalously dry
conditions exist in southern China but not in Obs-3. In the simulations, the
pattern is not associated with ENSO, but there exist significant OLR
anomalies and Rossby wave sources over Africa (not shown), significant
extratropical upper-tropospheric geopotential height anomalies (not shown),
and anomalously high pressure over Japan (JPN Z500 index in
Table ), indicating that these simulations produce Obs-3 for
the correct reason.
SummerPattern 1
Obs-1 explains 12 % of the total space–time variance and describes
coherent precipitation variability along the southern Yangtze valley
(Fig. ). It lags behind ENSO with a significant correlation of 0.38 with
DJF Niño3.4 and with JJA SCS SSTs (0.42) and JJA NWP
PSFC (0.4) (Table ). The
positive NWP PSFC anomaly and the associated anticyclonic
circulation strengthen the summer monsoon circulation, as discussed in
Sect. .
A96, A216, C96, C216 and C512a produce similar leading patterns with pattern
correlations between 0.66 (C512a) and 0.75 (A96, C96) (Fig. and
Table ). The A96-1 area is larger than in observations, and the
total explained variance (23 %) is almost double the observed value. The
other simulations have explained variances closer to observations between 9 %
(C512a) and 15 % (C96). Only C512b does not capture this pattern.
Shading shows regressions of JJA precipitation against observed or
simulated EOT time series. Also shown are positive (solid lines) and negative
(dashed lines) correlations of the full (leading-order) or residual (higher-order) precipitation-anomaly time series with the EOT base point exceeding 0.8
(magenta), 0.6 (orange) and 0.4 (green). The EOT base point is marked by the
orange inverted triangle. Panel (a) shows patterns that have a linear
correlation coefficient exceeding 0.38 with the first observed pattern and
(b) with the second observed pattern. The number next to the
simulation name above each panel indicates the order of the simulated EOT
pattern.
Only C96 produces a significant correlation with DJF Niño3.4, albeit a weak
one (r= 0.2). This is expected because C96 is the only simulation with a
significant projection of Yangtze valley rainfall onto DJF Niño3.4
(Fig. ). In all simulations, as in observations, the pattern is
accompanied by an anomalous anticyclonic circulation in the NWP (not shown).
C96 and C512a also show areas of significantly increased
PSFC that extend far eastward into the North Pacific
(Table ). In A96, A216 and C216 this circulation is more
locally confined. All GC2 simulations associate the pattern with warm SSTs in
the SCS, as in observations (Table ). This is consistent with
the more accurate JJA circulation response to ENSO in GC2 than in GA6
(Fig. ).
Pattern 2
Obs-2 peaks along the northern reaches of the Yangtze valley
(Fig. ). Precipitation variability in this region is associated
with increased PSFC over a small area of south China that
creates a regional lower-tropospheric circulation anomaly with increased
westerlies along the Yangtze River.
The only simulation with a pattern of coherent IAV in the northern Yangtze
valley is C512b, with a pattern correlation of 0.65 with Obs-2. The pattern
is C512b-3, but it explains a similar fraction of variance as Obs-2 (4
and 5 %, respectively). Note that C512b is the only simulation that
did not capture JJA Obs-1. Pressure and circulation anomalies in C512b-3
spread further into the North Pacific than in observations (not shown).
AutumnPattern 1
Obs-1 describes rainfall anomalies in the eastern Yangtze region and an area
to the southwest. The observed pattern explains 13 % of the total
space–time variance (Fig. ). It is associated with a high-pressure
anomaly southeast of China and southerly flow into southeast China.
Significantly increased pressure is present both at the surface and in the
upper troposphere (Fig ).
The observed pattern exists in all simulations, but the southwestward
extension of the area of coherent rainfall is only present in A96, A216 and
C512b. This explains the higher pattern correlations in these simulations
(0.75–0.82), compared to those where variability is confined to the Yangtze
(0.60–0.67) (Table ). The simulations with the southwest extension explain more
variance (15–30 %) than the others (7–13 %).
Shading shows regressions of SON precipitation against observed or
simulated EOT time series. Also shown are positive (solid lines) and negative
(dashed lines) correlations of the full (leading-order) or residual (higher-order) precipitation-anomaly time series with the EOT base point exceeding 0.8
(magenta), 0.6 (orange) and 0.4 (green). The EOT base point is marked by the
orange inverted triangle. Panel (a) shows patterns that have a linear
correlation coefficient exceeding 0.38 with the first observed pattern and
(b) with the second observed pattern. The number next to the
simulation name above each panel indicates the order of the simulated EOT
pattern.
Regressions of PSFC (shading) and
Z200 (contours at intervals of 0.02 m; pink – positive; green – negative) against normalised SON EOT time series corresponding to Obs-1. All
shown values are significant at the 90 % confidence level.
Figure shows that in observations the high-pressure anomaly over
eastern China and the western North Pacific is part of a wave pattern with
another positive anomaly over Europe and a negative anomaly over northwestern
China and Mongolia. Partial agreement with these anomalies is seen in all
simulations except in A96. In A96 the pattern corresponding to Obs-1 is
associated with ENSO; A96 has a correlation of 0.4 with the SON Niño3.4;
PSFC and Z200 anomalies over the eastern
tropical Pacific are typical of El Niño (Fig. ). C216-2 and
C512b-1 are also weakly correlated with SON Niño3.4 (Table ). The connections to
ENSO in A96, C216 and C512b are consistent with Fig. .
Pattern 2
Obs-2 explains 9 % of the total rainfall variability in SON, with a base
point located near the coast of southern China (Fig. ). It was
associated with anomalously high OLR in the western tropical Pacific,
indicating suppressed convection.
A96 and all GC2 simulations also produce patterns centred in south China.
They explain between 7 % (C512b) and 14 % (A96) of the space–time
variance. The smallest pattern correlation of 0.68 is found in C512a, where
the base point is shifted northward. In A96, EOT 2 describes a dipole between
the Yangtze and the Huai He rivers. A216 misses the pattern.
As in the observations, the patterns in C96, C216 and C512b are associated
with high OLR in the western tropical Pacific (not shown). The lack of this
signature in A96 and C512a points to a different physical mechanism. In C512a
the subtropical westerly jet stream over east Asia is shifted northward and
upper-level divergence over the Yangtze basin and southern China drives a
lower-tropospheric anticyclonic circulation over southeastern China. In A96
the precipitation anomaly cannot be associated with any statistically
significant circulation feature.
Summary of EOT results
To summarise model performance for EOTs, Fig. shows the patterns
that each simulation was able to produce (circles) and the ones that it
missed (crosses). For the former it shows the pattern correlations with the
observed pattern and the difference in the percentages of explained
space–time variance. Large circles indicate that a simulated pattern is
associated with a similar physical mechanism as in observations, according to
the arguments presented above.
Observed patterns a simulation was able to produce (circles) and the
ones that it missed (crosses). For patterns that could be reproduced, the
y axis gives the pattern correlation coefficient and the x axis shows the
difference in the percentages of explained space–time variance. Large circles
indicate that a simulated pattern is associated with a similar physical
mechanism as in observations.
Performance statistics of simulated EOTs compared to observations
for SON. Column (1) observed (Obs) and simulated (labelled by simulation name)
EOT patterns; numbers indicate the order of the EOT pattern. (2) Linear
pattern correlation coefficient of simulated and observed precipitation
anomalies; (3) explained space–time variance of the EOT pattern; (4) standard
deviation of the EOT time series; (5) Spearman's rank correlation of the EOT
time series with the SON Niño3.4 that are significant at the 95 %
confidence level.
All simulations produce the leading patterns of variability in DJF, MAM and
SON. The leading pattern in JJA is also present in all simulations except for
C512b. C96 and C512a capture the observed mechanisms for all leading
patterns. C96 and C512a also produce the secondary patterns in MAM and SON;
in C96 this includes associated mechanisms. In addition, C96 produces the
secondary pattern in DJF.
A96, A216 and C512a produce six of the nine observed patterns: two, three and
four of them, respectively, with the observed mechanism. C96 (C216 and C512b)
produces seven (eight) patterns and five with the observed mechanism.
Thus, by these measures C216 is the best-performing simulation because all
patterns in C216 have pattern correlation coefficients of 0.59 or higher. One
may further conclude that A96 has the poorest performance.
Comparing A96 to C96 and A216 to C216 shows that coupling increases the
number of simulated patterns and the number of patterns associated with the
observed physical mechanisms. In contrast, there is no evidence that
simulated IAV benefits from a higher horizontal resolution.
Discussion
In addition to examining the horizontal distributions of mean precipitation
and its interannual standard deviation in Met Office GCM simulations, we
performed a comprehensive assessment of the leading patterns of coherent
precipitation variability in all seasons using EOT analysis. The EOT method
is robust to large biases in mean precipitation. For example, A216 has the
greatest bias in IAV in DJF (Fig. ). In A216 IAV is greatest in the
eastern Yangtze valley and not at ∼ 111∘ E, the location of
the base point of the leading pattern in A216. Consistently, in
Fig. regressed rainfall is greatest in the eastern Yangtze River
basin, not at the base point. We search for the point that best explains the
area-averaged time series; while areas of large variability tend to contribute
most to this time series, the base point in A216 remains close to the observed
point. A similar case is in JJA, when IAV peaks in south China in all
simulations, not along the Yangtze valley as is observed. Nevertheless, five
of six simulations produce a leading pattern that peaks in the southern
Yangtze River valley.
We measured the similarity between simulated and observed patterns by pattern
correlation coefficients and differences in explained space–time variance.
These are important and objective metrics, but they are relatively
insensitive to small spatial shifts in the patterns of anomalous rainfall.
For some potential uses of simulated precipitation data, such shifts may be
important (e.g. hydropower); a more detailed analysis is warranted.
Furthermore, we used correlations with indices of atmospheric and oceanic
variability and regressions of atmospheric fields onto EOT time series to
argue whether or not two patterns are associated with similar physical
mechanisms. To support our classifications, we analysed lead–lag regressions
of additional fields for observations and all simulations (including SST,
OLR, surface wind, surface temperature, wind at 850, 500, 200 hPa,
Z500, Z200, column-integrated moisture flux,
divergence of horizontal wind at 200 hPa, Rossby wave sources, wave activity
flux). This additional information supported the classifications we
presented.
A related problem is that precipitation is inherently a local phenomenon and
affected by more than one process. One example is Obs-1 in DJF, which is
correlated with Niño3.4 but also shows substantial extratropical
circulation anomalies that may be unrelated to ENSO. In some simulations this
pattern was correlated with Niño3.4, but in others only the extratropical
circulation anomalies were statistically significant. It is possible for a
simulation to fail to reproduce a significant physical mechanism in
observations but to reproduce an alternative mechanism that is not
significant in observations. There may be value in simulations that produce
observed patterns but not the leading mechanism.
Regression against the PDO index of December–May mean detrended and
low-pass-filtered (> 10 years) SST anomalies for observations (1871–2010
HadISST) and the coupled simulations. All shown values exceed the 90 %
confidence level. The PDO index is the principal component of the leading EOF
of December–May mean detrended SST anomalies north of 20∘ N in the
North Pacific; the explained variance is shown above the panel. See
Sect. for more details.
We analysed one GC2 and one GA6 simulation each at resolutions of N96 and
N216 but two GC2 simulations at N512. These GC2 simulations differ only in
their initial conditions; both span 100 years. Their climatological mean
rainfall and IAV are almost identical (Fig. ). The teleconnection
to ENSO is also similar except in SON, when Niño3.4 in C512a is negatively
correlated with rainfall anomalies in central China, while C512b Niño3.4 is
positively correlated with anomalies in southeast China (Fig. ).
Nevertheless, it is clear from Fig. that patterns of rainfall
variability and their causes are as different between C512a and C512b as they
are between any other pair of simulations. This has several implications.
First, integrations over 100 years may not be long enough to eliminate
effects of internal variability. Mean state biases alone cannot explain the
discrepancies: the two simulations have similar biases in SST
(Fig. ), monsoon circulation and the seasonal cycle of
precipitation (not shown). The importance of internal variability was
previously highlighted by , who estimated that half of the
inter-model spread in projected climate trends for air temperature,
precipitation and sea level pressure during 2005–2060 in the CMIP3
multi-model ensemble is due to internal variability. Alternatively, the
observed record used in this study is not long enough to robustly estimate
precipitation variability.
A mode of internal multi-decadal variability relevant to this study is the
Pacific Decadal Oscillation (PDO). ENSO affects precipitation variability in
all seasons in observations and all simulations. However, the PDO modulates
the effect of ENSO on east Asian precipitation during JJA, DJF and MAM
. For instance,
showed when ENSO and PDO are in phase, MAM rainfall anomalies in south China
are strongly correlated with ENSO. In contrast, when ENSO and PDO are out of
phase, the ENSO–precipitation relationship over China weakens or becomes
insignificant. All simulations showed a significant correlation between their
leading pattern of MAM precipitation variability and the preceding DJF
Niño3.4, but observations did not. The absence of a significant correlation
with Niño3.4 in the observations may be attributed to the PDO: the average
value of the observed leading MAM EOT time series in units of standard
deviations is 0.45 for El Niño-PDO + (9 years), -0.43 for El
Niño-PDO - (10 years), 0.00 for La Niña-PDO + (8 years) and -0.38 for La
Niña-PDO - (7 years). This example shows that modes of internal multi-decadal
variability can play an important role.
To diagnose the PDO in the simulations, we computed anomalies of December–May
mean detrended SSTs north of 20∘ N in the North Pacific. We
computed the first EOF of these anomalies, after weighting them by the cosine
of latitude and interpolating to a 1∘× 1∘
latitude–longitude grid. Figure shows regressions of detrended and
10-year low-pass filtered SST anomalies against the principal component
time series for observations (1871–2010 HadISST) and the coupled simulations.
The observations show typical PDO SST anomalies with opposite-signed
variability in the North Pacific and along the central and eastern equatorial
Pacific. In the simulations, equatorial SST anomalies are very weak. In C512,
the SST pattern in the North Pacific is not correct and explains only half of
the variance as in observations. This shows that the PDO in the coupled
simulations differs from the observed PDO. It is plausible that this creates
biases in the ENSO–precipitation relationship over China.
Summary
This is the first study to assess GCM simulations over China in terms of
their ability to reproduce not only the geographical distributions of mean
rainfall and its interannual variability (IAV) but also the leading patterns
of coherent precipitation variability by applying empirical orthogonal
teleconnection (EOT) analysis. We used EOT analysis to identify strong
coherent regional rainfall variability and regressions of atmospheric fields
and SSTs onto the EOT time series to identify associated physical processes.
Accurately simulating such variability is crucial if GCMs are to be used to
understand the risk of disastrous climate impacts such as droughts and
floods. We examined two climate simulations of MetUM GA6 at resolutions of
N96 (A96, 200 km) and N216 (A216, 90 km) and four of GC2 at horizontal
resolutions of N96 (C96, 200 km; C216, 90 km) and N512 (C512a and C512b,
40 km). For all seasons, we tested how well simulations produce observed
patterns of regional precipitation variability and whether these patterns
are associated with the same physical mechanisms described in
for observations.
Positive biases in simulated seasonal mean precipitation and IAV of
∼ 100 % are found in all seasons (Fig. ), particularly in
southern China. In most seasons and areas, increasing resolution and adding
air–sea coupling improve biases, particularly near orography in southwest
China. These findings are consistent with and ,
indicating that better-resolved orography and more resolved physical
processes may both play a role depending on season and location. The fact
that the sensitivity of model performance to resolution and coupling varies
seasonally and depends on the area of interest may explain the lack of
consensus among previous studies on the importance of resolution.
ENSO is the main driver of IAV in China in DJF, MAM and JJA. The presence and
strength of the teleconnection to ENSO is sensitive to coupling and
resolution (Fig. ). In MetUM, coupling and resolution improve the
teleconnection to ENSO in DJF and in MAM, but it remains poor in JJA in all
simulations.
All simulations accurately capture the leading pattern of observed DJF
precipitation variability over southeast China (Fig. ), but only
three (C216, C512a, C512b) have a significant relationship with ENSO
(Table ). Nevertheless, all simulations are similar to
observations in their associated extratropical circulations, indicating that
simulated DJF precipitation variability is linked to similar physical
processes as in observations (Fig. ). In contrast, simulated DJF
precipitation variability along the southeast coast (Fig. ) is not
driven by the observed mechanisms (Fig. ).
All simulations correctly produce the leading pattern of MAM rainfall
variability in southeast China in response to ENSO (Fig. ). The
second pattern, a meridional dipole, is found in all simulations except A96,
but no simulation associates it with observed anomalies (Fig. ).
The third leading pattern in MAM, located in northern China, is captured by
C216 and C512b and associated with observed anomalies (Table ).
Five simulations produce the leading pattern of JJA precipitation
variability, located in the southern Yangtze valley (Fig. ), but
only C96 is weakly correlated with DJF Niño3.4. Associated observed SST
anomalies in the SCS and atmospheric pressure and circulation anomalies are
present in C96 and C512a (Table ).
All simulations except A96 capture the leading pattern of SON precipitation
variability along the Yangtze River and associate it with extratropical wave
disturbances similar to observations (Fig. ). The second pattern
in SON peaks in southeast China and is found in all simulations except in
A216, but only C96, C216 and C512b associate it with observed processes.
Overall, coupled simulations capture more observed patterns of variability
and associate more of them with the correct physical mechanism, compared to
atmosphere-only simulations at the same resolution (Fig. ). In our
six simulations, changes in resolution within the range N96-N512
(200–40 km) do not change the fidelity of these patterns or their
associated mechanisms. Evaluating climate models in terms of the geographical
distribution of climatological mean precipitation and its interannual
standard deviation is insufficient; attention should also be paid to
associated mechanisms. This conclusion even holds for the two C512
simulations that differ only in their initial ocean state. Their mean
rainfall and its IAV are almost identical (Fig. ), but patterns of
rainfall variability and their causes are as different between C512a and
C512b as they are between any two other simulations. Further research is
needed into what causes such different model behaviour, particularly when
there are no apparent differences in mean state biases between these two
simulations. C216 is the best-performing simulation in terms of reproducing
the most patterns with the highest pattern correlation coefficients of 0.59
or higher and in terms of reproducing the most observed mechanisms
(Fig. and Sect. ). Similarly, one can conclude that
A96 has the poorest performance.
Code and data availability
Data and code will be made available upon request
through JASMIN (http://www.jasmin.ac.uk/). MetUM simulation data are
available via the National Centre for Atmospheric Science and the UK Met Office, High Resolution Climate Modelling group; access is restricted, and to request data, please use the following contact form: https://hrcm.ceda.ac.uk/contact/.
APHRODITE data are available from
http://www.chikyu.ac.jp/precip/. OLR data are provided by the NOAA/OAR/ESRL
PSD, Boulder, Colorado, USA, at http://www.esrl.noaa.gov/psd/. The Rossby
wave source function was computed using code from the Python package
windspharm v1.5.0 available at http://ajdawson.github.io/windspharm.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work and some of its contributors (Claudia Christine Stephan, Pier Luigi Vidale, Andrew G. Turner, Marie-Estelle Demory
and Liang Guo) were supported by the UK-China Research & Innovation Partnership
Fund through the Met Office Climate Science for Service Partnership (CSSP)
China as part of the Newton Fund. Nicholas P. Klingaman was supported by an
Independent Research Fellowship from the Natural Environment Research Council
(NE/L010976/1). The high-resolution model C512 was developed by the
JWCRP-HRCM group. The C512 simulations were supported by the NERC HPC grants
FEBBRAIO and FEBBRAIO-2 (NE/R/H9/37), and they were performed on the UK
National Supercomputing Service ARCHER by Pier Luigi Vidale and Karthee Sivalingam.
Edited by: Richard Neale
Reviewed by: two anonymous referees
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