Transcript Occasion - Hans von Storch

Towards downscaling oceanic hydrodynamics Suitability of a high-resolution OGCM for describing
regional ocean variability in the South China Sea
针对海洋水动力的降尺度 ― 高分辨率海洋环流模式对
南海海洋变率模拟的适用性研究
Zhang Meng (张萌) and Hans von Storch
Abstract (摘要)
Towards downscaling oceanic
hydrodynamics -Suitability of a highresolution OGCM for describing regional ocean
variability in the South China Sea
•
For building empirical downscaling models,
we have examined the simulation STORM
with the 0.1 grid resolution, produced by the
ocean GCM MPI-OM forced with NCEP
atmospheric re-analyses. By comparing the
variability of sea surface height anomaly, sea
surface currents and sea surface temperature
from the simulation with satellite data or an
ocean reanalysis, we found a good similarity
between the different data sets. We conclude
that STORM is suitable for developing
empirical downscaling models.
针对海洋水动力的降尺度 ― 高分辨
率海洋环流模式对南海海洋变率模拟的
适用性研究
•
为建立经验降尺度模型，我们检验了
一个分辨率接近0.1度的全球海洋模
式模拟结果（STORM）。该模拟采用
MPI-OM模式，并由NCEP大气再分析
资料驱动。与卫星观测资料和海洋再
分析资料对比发现，模拟的海面高度
异常，表层流以及海表温度 均与对比
资料表现出良好的一致性。证明
STORM模拟结果适合用于发展经验降
尺度模型。
2
Motivations & Objectives
Motivations:
 For building empirical downscaling models, reliable, consistent,
and homogeneous data sets of the both, large-scale and small-scale
dynamics are needed.
 Such observational data sets of sufficient lengths are not available.
 OGCM simulations, with OGCMs exposed to atmospheric forcing,
may be valid candidates for building downscaling models.
Objective:
 Assessing the realism of one such simulation (STORM) and its
performance in the SCS.
3
Comparisons with AVISO observations and ocean
re-analysis data set C-GLORS
 The global STORM/NCEP simulation: about 0.1° resolution;
covering 1950-2010; forced by the NCEP1 (“observed”
atmosphere).
 The AVISO sea surface height anomaly (SSHA) altimeter
observations: 0.25° resolution; covering 1993-2014; merge
TOPEX-POSEIDON, ERS, JASON and ENVISAT products.
 The C-GLORS re-analysis dataset: 0.25° resolution; covering 19822013; forced by ERA_Interim; assimilated AVISO satellite data,
moorings, ARGO floats.
4
SSHA in AVISO, C-GLORS and STORM
The seasonal mean SSHA of STORM and C-GLORS
(Fig. 1) shows good agreement with AVISO:
1. In winter (DJF), basin-wide cyclonic currents control
most part of the SCS.
2. In summer (JJA), anti-cyclonic currents dominate the
SCS region.
3. The value in the center of the gyres is similar.
1993-2010 Seasonal mean of detrended sea surface height
anomalies (SSHA). Units: m
5
SSHA in AVISO, C-GLORS and STORM
AVISO
DJF
AVISO
MAM
C-GLORS
DJF
C-GLORS
MAM
STORM
DJF
STORM
MAM
 The patterns of SSHA standard deviation (STD)
distributions (Fig. 2) for four seasons of three
datasets are similar. The variability near Luzon Strait
and off Vietnam is stronger than adjacent sea.
 STORM simulates stronger variability in Luzon Strait
in summer (JJA) and autumn (SON), yet weaker
variability in the Vietnam coast in spring, compared
with AVISO.
AVISO
JJA
C-GLORS
JJA
STORM
JJA
AVISO
SON
C-GLORS
SON
STORM
SON
1993-2010 Standard deviation (STD) of seasonal
detrended SSHA. Units: m
6
SSHA in AVISO, C-GLORS and STORM
AVISO
EOF1
27.0%
AVISO
EOF2
9.5%
C-GLORS
EOF1
36.6%
C-GLORS
EOF2
11.6%
STORM
EOF1
25.1%
STORM
EOF2
17.4%
 The main feature of EOF1 in the three
dataset are similar, which show SSHA of
the whole basin increase (or decline)
simultaneously.
 The explained variance of the dominate
mode in STORM is closer to AVISO.
 The EOF2 patterns all show a strong
anti-cyclonic gyre located off the Vietnam
coast and extending northeastward to
reach Philippine Islands, covering most
part of the north SCS.
The first two EOFs (Units: m) of 1993-2010 monthly
detrended SSHA (removing mean annual cycle)
7
SSHA in AVISO, C-GLORS and STORM
AVISO
EOF1
C-GLORS
EOF1
AVISO
EOF2
C-GLORS
EOF2
Table 1: The correlation coefficients of the
EOF time series between AVISO and CGLORS, STORM
1st
2nd
C-GLORS
0.936
0.795
STORM
0.911
0.773
 The coefficient time series of STORM and
C-GLORS are highly correlated with the
“true” AVISO.
STORM
EOF1
STORM
EOF2
 C-GLORS correlates better with AVISO than
STORM.
The corresponding coefficients of the first two EOFs
8
SSHA in AVISO, C-GLORS and STORM
Statistical analysis demonstrates that C-GLORS and STORM
have the ability to capture the main variability features of the
SCS dynamic in terms of SSHA.
C-GLORS shows greater similarity, which is not surprising as
it has assimilated AVISO satellite data.
The significant conclusion is that we may use other variables
provided by C-GLORS as a reference to assess the skill of
STORM to describe past variability.
9
Surface current fields in C-GLORS and STORM
CGLORS
DJF
STORM
JJA
STORM
DJF
STORM
SON
C-GLORS
JJA
STORM
JJA
 The seasonal mean surface current fields of
STORM and C-GLORS show similar
variability: the strong current along the
western boundary and the gyre in the south
SCS with opposite directions in winter and
summer.
 The speeds in STORM are generally higher
than in C-GLORS, which may be a result that
STORM with much higher resolution has
presented more small-scale phenomena.
1982-20 10 DJF and JJA means of sea surface currents
(at 6m depth). Units: m/s
10
Surface current fields in C-GLORS and STORM
 The first EOFs of sea surface current
anomalies (after subtracting the temporal
mean) from the two data sets show
similar variability, describing the annual
cycle..
 The main features of the EOF1 pattern
are a large strong cyclone located in the
southern
SCS
and
alongshore
southward currents from southeast of
China cross the Equator.
The first vector EOF (Units: m/s) and the
corresponding coefficient of 1982-2010
monthly sea surface currents (at 6m depth)
11
SST in C-GLORS and STORM datasets
C-GLORS
DJF
STORM
DJF
C-GLORS
C-GLORS
MAM
JJA
STORM
STORM
MAM
JJA
 STORM agrees well with C-GLORS
in the seasonal SST (sea surface
temperature) pattern..
 In summer, STORM reveals stronger
upwelling with colder water and
larger temperature gradient than CGLORS off the Southeast Vietnam
coast, which may caused by the
coarser resolution of C-GLORS.
 STORM shows more details along
the coast area, due to its higher
resolution.
1982-2010 Seasonal mean of sea surface temperature (SST). Units: ℃
12
Conclusions
STORM realistically captures regional-scale dynamical features
in the South China Sea.
The STORM simulation is suitable for building empirical
downscaling models in the SCS.
13
Concept of Statistical Downscaling
The concept of „statistical) downscaling“ was introduced into climate sciences in
the late 1980s/early 1990s when the need for regional and local information about
climate change emerged. A number of different methods have evolved and
matured since then.
"Downscaling" is based on the view that regional climate is conditioned by climate
on larger, for instance continental or even planetary, scales. Information is cascaded
"down" from larger to smaller scales. The regional climate is the result of interplay
of the overall atmospheric, or oceanic circulation and of regional specifics, such as
topography, land-sea distribution and land-use.
As such, empirical/statistical downscaling seeks to derive the local scale information
from the larger scale through inference from the cross-scale relationships, using a
function F such that:
local climate response = F (external, large scale forcing)
Concept of Statistical Downscaling
Case 1
• Predictand: Monthly mean sea level along the Coast of Japan; local
observations
• Predictor: North Pacific monthly mean sea level air pressure; historical
analyses
• Link built with Canonical Correlation Analysis (CCA)
• Reduction of degrees of freedom by prior EOF expansion
• A relevant pair of patterns describes a link between an SLP pattern and a
pattern of sea level anomalies, with a correlation of 0.44; 60% of the sea
level variance is described
• 崔茂常 (Cui M.), H. von Storch, and E. Zorita, 1995: Coastal sea level and
the large-scale climate state: A downscaling exercise for the Japanese
Islands. - Tellus 47 A, 132-144
Case 1
Case 2
Percentiles as Predictands
R = percentiles of high tide water levels at a number of tide gauges
along the North Sea coast. Each 3-months winter has about 180 high
tides; from the distribution of these 180 values, percentiles are
derived and related to L = large-scale winter mean SLP.
Langenberg, H. , A. Pfizenmayer, H. von Storch and J. Sündermann, 1999: Storm related sea level variations along the
North Sea coast: natural variability and anthropogenic change.- Cont. Shelf Res. 19:821-842
Case 2
Thermal expansion not taken into
account!
Case 2
North Sea
Scenario “1% CO2
increase” at the end
of the 21st century
Summary
• Empirical downscaling methods have matured in the past
decades.
• They are useful tools for estimating the state and statistics of
a wide range of meteorological variables
• So far, applications to oceanographic and ecological variables
are rare (or?)
• Empirical downscaling is a method which may be useful for
deriving past and possible future changes of small scale
oceanic and coastal states and statistics of states from better
on observable large-scale climatic states