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

2 Towards downscaling oceanic hydrodynamics - Suitability of a high- resolution 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。 Abstract ( 摘要 )

3 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.

4 Comparisons with AVISO observations and ocean re-analysis data set C-GLORS  The global STORM/NCEP simulation: about 0.1° resolution; covering ; forced by the NCEP1 (“observed” atmosphere).  The AVISO sea surface height anomaly (SSHA) altimeter observations: 0.25° resolution; covering ; merge TOPEX-POSEIDON, ERS, JASON and ENVISAT products.  The C-GLORS re-analysis dataset: 0.25° resolution; covering ; forced by ERA_Interim; assimilated AVISO satellite data, moorings, ARGO floats.

SSHA in AVISO, C-GLORS and STORM Seasonal mean of detrended sea surface height anomalies (SSHA). Units: m 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.

6 SSHA in AVISO, C-GLORS and STORM Standard deviation (STD) of seasonal detrended SSHA. Units: m 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 DJF C-GLORS DJF STORM DJF AVISO MAM C-GLORS MAM STORM MAM AVISO JJA C-GLORS JJA STORM JJA AVISO SON C-GLORS SON STORM SON

7 AVISO EOF1 27.0% C-GLORS EOF1 36.6% STORM EOF1 25.1% AVISO EOF2 9.5% C-GLORS EOF2 11.6% STORM EOF2 17.4% The first two EOFs (Units: m) of monthly detrended SSHA (removing mean annual cycle) SSHA in AVISO, C-GLORS and STORM 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.

8 1st2nd C-GLORS STORM The corresponding coefficients of the first two EOFs Table 1: The correlation coefficients of the EOF time series between AVISO and C- GLORS, STORM SSHA in AVISO, C-GLORS and STORM The coefficient time series of STORM and C-GLORS are highly correlated with the “true” AVISO. C-GLORS correlates better with AVISO than STORM. AVISO EOF1 C-GLORS EOF1 STORM EOF1 AVISO EOF2 C-GLORS EOF2 STORM EOF2

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 SSHA in AVISO, C-GLORS and STORM

Surface current fields in C-GLORS and STORM DJF and JJA means of sea surface currents (at 6m depth). Units: m/s 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. STORM JJA STORM SON C- GLORS DJF STORM DJF C-GLORS JJA STORM JJA

11 Surface current fields in C-GLORS and STORM The first vector EOF (Units: m/s) and the corresponding coefficient of monthly sea surface currents (at 6m depth) 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.

SST in C-GLORS and STORM datasets Seasonal mean of sea surface temperature (SST). Units: ℃ 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 C- GLORS 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. C-GLORS DJF STORM DJF C-GLORS MAM STORM MAM C-GLORS JJA STORM JJA

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

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,

Case 1

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. Case 2 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:

Case 2

Scenario “1% CO 2 increase” at the end of the 21 st century Thermal expansion not taken into account! North Sea Case 2

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