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アプリケーションラボ(APL)

セミナーのお知らせ

第56回 APLオープンフォーラム

日時:
2019年9月6日(金) 14:30~16:00
場所:
横浜研究所 交流棟2F 小会議室1+2
発表者1:
1:J. Venkata Ratnam
タイトル:
Statistical prediction of Indian Ocean Dipole index
概要
In this study an attempt is made to predict the Indian Ocean Dipole (IOD) index using statistical techniques such as linear regression and artificial neural networks. The predictors are identified from the sea surface temperature, geopotential height, zonal wind and meridional wind fields variables for the period spanning from 1949 to 2018. The results indicate the statistical models to be capable of forecasting the IOD index in advance with high skill.
発表者2:
尾形 友道
タイトル:
Mechanisms of long-term variability and recent trend of salinity along 137°E
概要
To investigate mechanisms for observed features along 137°E such as freshening trend since the 1990s and decadal variability, spatial pattern of salinity anomaly at 25.4σ isopycnal surface and its variability were investigated based on OGCM outputs. While sea surface salinity (SSS) is restored to its climatology in the OGCM, the model captures recent salinity trend and meridionally coherent interannual to decadal variability at 137°E. The trend signal seems to be traced back over the eastern North Pacific (around 30°N, 160°W). In spite of the limited SSS variability, meridional shift of outcrop line caused by sea surface temperature variation is found to determine the decadal spiciness variability subducting on the isopycnal surface at 25.4σ, that is, warm-saltier anomalies with southward shift, and cold-fresh anomalies with northward shift as colder-fresher water distributes to the north at the sea surface. Furthermore, we conducted tracer experiments with/without mesoscale eddies to examine possible roles of mesoscale eddies in propagation of the spiciness anomalies. Tracer diffuses across mean stream line by mesoscale eddies, and spreads from 10°N to 25°N. However, in tracer experiment with steady mean flow (i.e. without mesoscale eddies), center of tracer propagates along mean stream line and does not diffuse across mean stream line. This suggests that mesoscale eddies are important for the synchronized salinity trend and variability found in 10°N to 30°N.

第55回 APLオープンフォーラム

日時:
2019年8月2日(金) 13:30~15:00
場所:
横浜研究所 交流棟2F 小会議室1+2
発表者1:
山本 絢子
タイトル:
Key role of western boundary currents in wintertime Euro-Atlantic blocking
概要
Atmospheric blocking events are well known for their crucial role in modulating the mid-latitude subseasonal atmospheric variability. The detailed physical and dynamical mechanisms responsible for the formation and maintenance of blocking events, however, are yet to be fully understood, and state-of-the-art climate models still have deficiency in accurately simulating their observed occurrence. Conventionally, atmospheric blocking was considered in the dry dynamics framework. Recent studies, nonetheless, attributed some of the modelled blocking frequency deficit to biases in the modelled ocean, implying the importance of the air-sea exchange of heat and moisture in the formation of the blocking events. Furthermore, a crucial role of condensational warming in the blocking formation and maintenance has recently been highlighted. The source of heat and moisture associated in this process, however, is yet to be identified. In this study, we aim to identify the sources of the atmospheric heat and moisture involved in wintertime Euro-Atlantic blocking events using a Lagrangian approach driven with CFSR. Using an atmospheric dispersion model, we track atmospheric particles from the blocking centres backward in time for 10 days, and estimate the associated turbulent heat fluxes whenever the particles fall within the planetary boundary layer over the ocean. Our results indicate that approximately half of the particles released from the blocking centres receive moisture and heat from the western boundary currents and their extensions, which then undergo latent warming while ascending, consistent with the previous studies. We found that the potential vorticity along these particles which undergo diabatic processes is more strongly anticyclonic compared to the adiabatic particles, implying the central role of the diabatic processes in blocking events.
発表者2:
土井 威志
タイトル:
Westerly Wind Burst/Easterly Wind Surge-like stochastic forcing and its potential roles to remedy the over-confident problem of ENSO prediction ~ Lesson from failure of 2014 El Niño prediction ~
概要
Over-confident problem of ENSO prediction: the ensemble spread is too small relative to the root mean square error of the ensemble mean prediction, is common among almost all dynamical prediction systems based on GCMs. That problem was clearly exposed in 2014; most of the dynamical prediction systems had alerted high chance of El Niño occurrence in the boreal summer, however the prediction failed. In this study, it is found that the high-frequency zonal wind variability over the western tropical Pacific, including the strong easterly wind surges in June-July 2014, is responsible for failure of 2014 El Niño prediction by the SINTEX-F2 seasonal prediction system. It is almost impossible to predict individual high-frequency wind events beyond the weather prediction limitation, because the occurrences are not determined by the sea surface temperature-state. However, by adding easterly wind surge-like stochastic forcing into the prediction system, we have successfully fallen the 2014 El Niño prediction within the expected uncertainty. It is also shown that the additional stochasticity of westerly wind burst and/or easterly wind surge could effectively remedy the over-confident of ENSO prediction on the condition that the Pacific Warm Pool extended further eastward. The presented results are useful to reduce false alarm of ENSO prediction, and thus its related possible human and economic losses.

第54回 APLオープンフォーラム

日時:
2019年6月28日(金) 14:30~16:00
場所:
横浜研究所 情報技術棟5F 会議室
発表者1:
青木 邦弘
タイトル:
Application of Machine learning for Probabilistic Forecast of Kuroshio Large Meander
概要
This study proposes a method of the probabilistic forecast, in which the solutions of an ensemble forecasts with 80 members are clustered by the mixture Gaussian distribution model (MGM), an unsupervised machine learning technique. Targeting the period before the development of the Kuroshio large meander in 2017, we conduct the ensemble forecast in a long-range initialized by the assimilation based on the Local Ensemble Kalman Filter algorithm applied for the forecasts spun up from the different initial conditions. All the members show the non-large meander path almost along the coast in the initial stage, but finally some members show the large meander path at the end date. Applying MGM for a traditional one-dimensional Kuroshio large meander index successfully identifies the bistates with correponding uncertainties and occurrence probabilities. The statistical significance of the bimodality can be evaluated by information criteria. With the help of the empirical orthogonal function (EOF) analysis, the application of this clustering method can be extended to the sea surface height field, which has the dimension as large as the number of horizontal model grids. We found that the bimodal nature appears only on the axis of the first EOF mode in the phase space even for such a large dimensional data. From both a theoretical argument and data analysis, we also show that the first EOF mode is almost equivalent to the first singular vector associated with the tangential operator of the equation for the sea surface height. This suggests that the bistates found here are sets of non-linearly stable solutions evolved through a linear growth at an initial stage.
発表者2:
小守 信正
タイトル:
Experimental seasonal climate prediction using CFES: Comparison with the SINTEX-F systems
概要
An experimental seasonal climate prediction system has been developed based on the Coupled atmosphere-ocean general circulation model for the Earth Simulator (CFES). Following the well-established system based on the SINTEX-F model, initial conditions for seasonal climate prediction are constructed by strongly nudging sea surface temperature (SST) to observed one.
At this stage, 12-member ensemble 6-month predictions from the 1st day of each month have been conducted from 1983 through 2018, after 32-years of coupled spin-up integration with SST-nudging to the observed climatology. The experimental system exhibits skill in predicting variability of seasonal-mean 2-m air temperature over the tropical Pacific, and contributes to the improvement of multi-model ensemble prediction with the SINTEX-F systems.

APL ゲストセミナー

日時:
2019年6月3日(月) 14:30~15:30
場所:
横浜研究所 交流棟2F 小会議室1+2室
発表者:
Dr. Dmitri Kondrashov (University of California at Los Angeles, USA)
タイトル:
Data-driven climate modeling and real-time prediction
概要
Climate dynamics is fundamentally nonlinear, high-dimensional and extremely complex. General circulation climate models (GCMs) based on first principles, are typically expensive to run and are subject to various biases due to imperfect physical parameterizations of unresolved physical processes. These shortcomings in turn are affecting accuracy of seasonal-to-intraseasonal prediction of key climate phenomena with global social-economic impacts, such as El-Nino–Southern Oscillation (ENSO) and summertime Arctic Sea Ice Extent (SIE).
This talk will highlight recent advances in alternative data-driven methodologies to help analyze and model high-dimensional observational datasets. Real-time predictions in multi-model ensembles demonstrate that data-driven forec

第53回 APLオープンフォーラム

日時:
2019年5月24日(金) 14:30~16:00
場所:
横浜研究所 情報技術棟5F 会議室
発表者1:
Yu-Lin (Eda) Chang
タイトル:
Tropical and subtropical tropical cyclone formation in the western Pacific and the prediction based on seasonal forecasting model
概要
ENSO is considered to be the leading factor in affecting tropical cyclone (TC) activity in the Pacific, and it has been widely used in the statistical model in predicting TC activity. The recent study noticed the importance of the local low-level wind curl, which revealed a higher correlation to TC formation in comparison to ENSO indices. This study explores the air-sea coupled response and its link to subtropical and tropical TC formation in the western Pacific. The results suggest that the formation of subtropical and tropical TC formation was affected by the local low-level wind curl, which was trigger by the eastern and central Pacific warming/cooling, respectively. The dynamic will then be applied using the seasonal forecasting model results from SINTEX-F to examine the retrospective and future prediction.
発表者2:
森岡 優志
タイトル:
Role of sea-ice initialization in climate predictability over the Weddell Sea
概要
Potential influence of sea-ice cover initialization on the interannual climate predictability over the Weddell Sea is identified using a coupled general circulation model (SINTEX-F2). Climate variability in the Weddell Sea is generally believed to have association with remote forcing such as El Niño-Southern Oscillation and the Southern Annual Mode. However, sea-ice variability in the Weddell Sea has recently been suggested to play additional roles in modulating local atmospheric variability through changes in surface air temperature and near-surface baroclinicity. When both the model’s sea-surface temperature (SST) and sea-ice concentration (SIC) are initialized with observations using nudging schemes, reforecast experiments from September 1st of 1982-2016 show improvements in predicting the observed SIC anomalies in the Weddell Sea up to four months ahead, compared to the other experiments with only SST initialization. During austral spring (Oct-Dec) of lower-than-normal sea-ice years in the Weddell Sea, reforecast experiments with the SST and SIC initializations reasonably predict high surface air temperature anomalies in the Weddell Sea and high sea-level pressure anomalies over the Atlantic sector of the Southern Ocean. These results suggest that accurate initialization of sea-ice conditions during austral winter is necessary for skillful prediction of climate variability over the Weddell Sea during austral spring.

APL ゲストセミナー

日時:
2019年5月8日(水)14:00~15:00
場所:
横浜研究所 交流棟2F 小会議室1+2室
発表者:
Dr. Andréa Sardinha Taschetto (Climate Change Research Centre, University of New South Wales, Australia)
タイトル:
The contribution of ocean versus atmospheric variability to droughts in the tropics
概要
Variations in ocean temperature, such as the El Niño – Southern Oscillation (ENSO), drive fluctuations in rainfall extremes, and are generally associated with droughts and wet periods over land. Droughts however can occur due to causes unrelated to ocean temperature, for example, as a result of intrinsic atmospheric and land variability. Disentangling the contribution of each climate component to drought frequency, intensity and persistence is a complicated task using observations. We assess the contribution of sea surface temperature variability to droughts over land within a state-of-the-art climate model. The National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) is used to perform multi-century numerical experiments where ocean variability is eliminated from simulation. Results show that interannual variations of sea surface temperature play an important role in increasing rainfall variability everywhere in the globe. However, multiyear drought events are also simulated by internal atmosphere variability. This suggests that despite making droughts worse, ocean variability does not add predictive skill for duration of long-term drought events. The results for rainfall mean and extremes with and without ocean variability will be discussed in this talk.

第52回 APLオープンフォーラム

日時:
2019年4月26日(金) 14:30~16:00
場所:
横浜研究所 交流棟2F 小会議室1+2
発表者1:
Swadhin Behera
タイトル:
Air-sea interaction in the Gulf of Alaska: ENSO and beyond
概要
The interannual variation of sea surface temperature (SST) in Gulf of Alaska are said to be linked to the ENSO variability. The ENSO teleconnection, typically seen as the PNA pattern, is such that an El Nino teleconnection strengthens westerlies in the midlatitude North Pacific causing cold SST anomaly in the central and western subpolar gyre. At this time, it is noted that a strengthened subpolar gyre advects anomalously warm water northward along the eastern boundary into the Gulf of Alaska. While this teleconnection could explain some of the warm events, it does not explain all the warm events in the Gulf. This is also reflected in the fact that Nino3 index is not so-well-correlated with the Alaska coastal SST index derived in this study. Other mechanisms, including local air-sea interactions, play important roles in the development of warm and cold anomalies in the Gulf of Alaska.
発表者2:
宮澤 泰正
タイトル:
Applying a variant of four-dimensional ensemble-based variational method (4DEnVAR) to the Kuroshio variations south of Japan
概要
Operational ocean nowcast/forecast systems require real-time sampling of oceanic data for representing realistic oceanic conditions. The satellite altimetry plays a key role in detecting mesoscale ocean current variations. A major sampling period of 10-day and a maximum horizontal gap between the altimetry tracks of 100km cause difficulty to capture shorter-term/smaller-scale ocean current variations. Our current operational systems based on a three-dimensional variational method (3DVAR) simply omit spatio-temporally varying information of the observation data within data assimilation time windows. Four-dimensional frameworks of data assimilation could be considered as a possible direction toward more effective utilization of the available observation data by extracting dynamical information especially from the satellite altimetry data. We are developing a variant of four-dimensional ensemble-based variational method (4DEnVAR) for our operational applications. Preliminary experiments targeting the Kuroshio variations south of Japan within a time window of 9-day indicate that our 4DEnVAR scheme surely improves 3DVAR results by more effectively assimilating the satellite altimetry data.

APL ゲストセミナー

日時:
2019年3月27日(水) 14:30~15:30
場所:
横浜研究所 交流棟2F 小会議室1+2室
発表者:
Dr. Manali Pal (Indian Institute of Technology, Kharagpur, India)
タイトル:
The Long-lead Prediction of El Nino Modoki using Machine Learning algorithms
概要
The focus of this study is to evaluate the efficacy of the Machine Learning algorithms in the long-lead prediction of the El Nino Modoki index after identification of the significantly contributing variables. We used three Machine Learning methods namely Random Forest (RF), Support Vector Regression (SVR) and Artificial Neural Network (ANN) to deal with the highly non-linear and complex behavior of the ocean-atmosphere coupled processes and their efficacies are assessed at four different lags i.e. 6, 12, 18 and 24 months to predict the Modoki index. The contributing variables are identified using Kendall rank correlation coefficient (also known as Kendall's tau), which is a non-parametric measure of relationships. Furthermore, the supervised principal component analysis is used to assess the contribution of each identified variable. The results of long-lead prediction show all the three models to capture the peaks of the Modoki index with lags up to 12 months. For the lags beyond 12 months, the models showed limitation in the predictability perhaps owing to over fitting issue in the short-period datasets.

APL ゲストセミナー

日時:
2019年3月4日(月) 14:00~15:00
場所:
横浜研究所 IT棟5F大会議室
発表者:
Prof. X. San Liang (Nanjing Institute of Meteorology, Nanjing, China)
タイトル:
Causality, Information Flow, and Quantitative Causal Inference with Time Series
概要
Causal inference is a fundamental problem lying at the heart of scientific research. Recently, a rigorous formalism has been established ab initio for information flow/transfer and causality within dynamical systems. The “principle of nil causality” that reads, an event is not causal to another if the evolution of the latter is independent of the former, which classical formalisms fail to verify in many situations, turns out to be a proven theorem here. Moreover, the so-obtained information flow and causality is invariant upon nonlinear coordinate transformation, indicating that it should be an intrinsic property in physical world. For linear systems, there exists a remarkably concise formula, which asserts analytically that causation implies correlation, while correlation does not imply causation, resolving the long-standing philosophical debate over causation versus correlation ever since George Berkeley (1710).
This formalism has been validated with many benchmark systems (e.g., baker transformation, Hénon map, etc.), and touchstone series purportedly generated with one-way causality that defies the classical approaches. It has also been applied to the investigation of many real world problems. For example, we have studied the cause-effect relation between the two climate modes El Niño and Indian Ocean Dipole (IOD). In general, these modes are mutually causal, but the causality is asymmetric. To El Niño, the information flowing from IOD manifests itself as a propagation of uncertainty from the Indian Ocean.
In the second example, an unambiguous one-way causality is found between CO2 and the global mean temperature anomaly. While it is confirmed that CO2 indeed drives the recent global warming, on paleoclimate scales the cause-effect relation may be completely reversed.
Also presented will be the causation and information exchange during the interaction between transient storms and large-scale atmospheric flows, the identification of PM2.5 sources, network deconvolution, financial time series analysis, to name a few.

第51回 APLオープンフォーラム

日時:
2019年2月22日(金) 15:45~17:15
場所:
横浜研究所 交流棟2F 小会議室1+2
発表者1:
美山 透
タイトル:
Role of river inflows from Kamchatka Peninsula in the Okhotsk Sea
概要
The Okhotsk Sea is a unique ocean in that sea ice forms at the lowest latitude in the world. The sea ice formation plays important roles in water mass formations not only in the Okhotsk Sea but also in the North Pacific Ocean. The river inflows from the surrounding coasts affect the sea ice formation by changing stratification. Especially, the contribution from the river inflow from the Amur River on the western side of the Okhotsk Sea has been widely discussed. The roles of the Amur River in chemical and biological cycles have been also investigated. On the hand, the role of the river inflows from the Kamchatka Peninsula on the eastern side of the Okhotsk Sea is not well known. In this study, we simulated the northern part of the Okhotsk Sea to investigate the roles of river inflows from the Kamchatka Peninsula. FVCOM (Finite-Volume, Primitive equation Community Ocean Model; Chen et al. 2003) was used. JRA55-do (Tsujino et al., 2018) was used for the atmospheric forcing. The river inflow dataset also came from JRA55-do based on Suzuki et al. (2017). The unstructured horizontal grid spacing was decreased from about 9.0 km at the southern boundary to about 1.4 km along the northern boundary. The model runs with and without the river inflows from the Kamchatka Peninsula were compared. The salinity significantly increased without the river inflow along the coast of Kamchatka (by more than 1 unit at maximum). The salinity increase also spread toward the western Okhotsk Sea. With these changes in salinity, ice formation in the Okhotsk Sea was also affected.
発表者2:
Sergey Varlamov
タイトル:
Regional JCOPE modeling with Multi Scale 3DVar data assimilation
概要
The regional JCOPE-T models are quite intensively used for evaluation and forecast of oceanic circulation and thermodynamic conditions for number of practical applications, like real-time navigation and planning of marine activities. As examples, we could mention an informational support of JAMSTEC drilling ship "Chikyu" ongoing operations. It makes quite important to keep model state close to real observed oceanic conditions by performing observational data assimilation.
In the versions of regional model prior to 2018 it was a simple nudging method to the assimilative coarse resolution "mother" model JCOPE2M used for these purposes. From this year direct Multi Scale 3DVar assimilation method replaced nudging. It allowed to utilize modern satellite high resolution SST data provided from Himawari-8 geostationary satellite and other observed data. Details of realized assimilation process and some cases of solved problems during tuning the method would be reported.

APL ゲストセミナー

日時:
2019年2月20日(水) 15:00~16:00
場所:
横浜研究所 IT棟5F大会議室
発表者:
Mr. Givo Alsepan(北海道大学)
タイトル:
Relation between interannual variability of regional-scale Indonesian precipitation and large-scale climate modes in the last half century
概要
Regional–scale precipitation responses over Indonesia to major climate modes in the tropical Indo–Pacific Oceans, i.e., canonical El Niño–Southern Oscillation (ENSO), El Niño Modoki, and Indian Ocean Dipole (IOD), are investigated using a terrestrial precipitation dataset, Asian Precipitation–Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE). The spatial resolution of the precipitation data is 0.5° x 0.5°, much higher than the precipitation datasets used in previous studies, allowing us to clarify spatial patterns of precipitation responses in greater detail. The explained variance ratios of climate modes to precipitation generally have stronger magnitudes from July to November, corresponding to the dry season and transition period from the dry to wet. Since the climate modes are not statistically independent, we employ standard and partial correlation analyses. A standard correlation for a climate mode is regarded as the correlation when the corresponding climate mode is the primary driver. On the other hand, partial correlation is the correlation that is intrinsic to the mode even after removal of other modes’ influences. In order to know seasonal development of climate modes’ influence, we calculate the standard and partial correlations between precipitation and climate modes in each calendar month after smoothing with a three–month running mean. The standard correlations show that canonical ENSO reduces precipitation in a wide area from western to eastern Indonesia from July to November and northern Indonesia slightly after it from December to April. El Niño Modoki also causes precipitation reduction with weaker correlations than those of the canonical ENSO from July to November, but conversely induces precipitation increase in western Indonesia for some months in the wet season. IOD also reduces precipitation with an overlapping spatial pattern to the canonical ENSO, and this influence is limited from July to November. Interestingly, the partial correlations produce a more limited spatial pattern of intrinsic influence of canonical ENSO and IOD to precipitation than those in standard correlations, while for El Niño Modoki the standard and partial correlations produce similar features. Intrinsic influence of canonical ENSO to precipitation reduction is confined in central and eastern Indonesia from July to November, and for IOD it is mainly pronounced in western Indonesia. Most of these precipitation reductions are strongly associated with the contraction of convergence zones from their mean state, except for positive precipitation response to El Niño Modoki.

APL ゲストセミナー

日時:
2019年2月18日(月) 14:00~15:30
場所:
横浜研究所 交流棟 2階 小会議室
発表者1:
Dr. Sébastien Masson (LOCEAN/IPSL)
タイトル:
Impact of the current / wind stress coupling on the eddy activity
概要
Ocean mesoscale eddies are major players of the ocean circulation, and transport of heat, salt, and biogeochemical tracers. These coherent structures are characterized by rotating currents that imprint the low atmosphere and feedback to the wind power input to the ocean. With a hierarchy of coupled models, this study shows that the eddy kinetic energy (EKE) of the ocean is decreased by ~30% in average by current feedback. The large impact of this feedback is often described as the “eddy killing effect”. Our simulations show that the amplitude (in %) of the eddy killing effect is mainly controlled by the large scale wind. Locally this effect is modulated by the balance between wind work at mesoscale, and ocean intrinsic instabilities that are the main source of EKE. The efficiency of current feedback appears quite insensitive to model resolution, and in particular to the atmospheric resolution. These results provide guidance for required processes, and resolution for a correct representation of air-sea energy transfers.
発表者2:
Dr. Clement Rousset (LOCEAN/IPSL)
タイトル:
What does it take to model the cold oceans?
概要
The cold oceans occupy roughly 35 million km2 which represents about 10% of the global ocean surface. They are characterized by a seasonal presence of sea-ice which acts as an insulator between the atmosphere and the ocean by reflecting most of the incoming solar radiation and damping the air-sea momentum and heat exchanges. They are also the main locations of dense water formation in the world. Their role for climate is thus crucial. In this talk I will set out the key ingredients to model these regions, with a focus on the NEMO model.
発表者3:
Dr. Eric Maisonnave (CERFACS)
タイトル:
NEMO 4.0 performance: most recent improvements
概要
A non-intrusive instrumentation of the NEMO code and the development of a simplified configuration (called BENCH) brought information about MPI communications cost and structure. It helped us to identify the most appropriate incremental developments that model needs to enhance its scalability. We prioritised the reduction of extra calculations and communications required at the North Polar folding, the grouping of boundary exchanges and the replacement of global communications by alternative algorithms. Appreciable speed up (x2 in some cases) is measured. Scalability limit is pushed below a size of 7x7 grid points per sub-domain, showing that the limitation of the North Polar folding solution can be compared with the supposed icosahedral grid one. However, disk access and per-user bandwidth availability clearly limits over-10,000 CPU resource performance. We consider that scalability is not the major well of future performance gain, neither horizontal resolution increase, whereas potentiality of extra developments accelerating cache access (horizontal domain tiling and single precision computations) is favourably evaluated.

APL ゲストセミナー

日時:
2019年1月28日(月) 11:00~12:00
場所:
横浜研究所 交流棟2F 小会議室1+2
発表者:
Dr. Ibrahim Hoteit (Professor, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia)
タイトル:
An Integrated Data-Driven Modeling System to Study and Predict the Circulation, Productivity and Climate of the Red Sea
概要
The talk will present the integrated data-driven modeling and forecasting system that we have developed to study and understand the physical and biological variability of the Red Sea. I will first describe the modeling system and summarize our key findings on the Red Sea general circulation, including the striking seasonally overturning circulation, the dominant eddy activity, and the occasional northern deep water formation events, and discuss their impact on the Red Sea ecosystem. I will then focus on our efforts to develop an efficient ensemble data assimilation and forecasting system for the Red Sea, presenting recent algorithmic developments and results, and discussing our future plans.

第50回 APLオープンフォーラム

日時:
2019年1月25日(金) 15:45~17:15
場所:
横浜研究所 交流棟2F 小会議室1+2
発表者1:
佐々木 英治
タイトル:
A Quasi-Global Eddying Hindcast Ocean Simulation of OFES2
概要
Outputs from global eddying oceanic simulations have been widely used to study various oceanic features with broad spatiotemporal scales from mesoscale to large scales and from intraseasonal to decadal scales. Long-term hindcast outputs from OFES have been bringing out a number of research achievements through its release to the public. However, there are several issues with unrealistic properties in OFES. Therefore, I have conducted a quasi-global eddying oceanic simulation using a new version of OFES, which we call OFES2. In this presentation, I show the descriptions and simulated oceanic fields in OFES2 compared to OFES. We implemented the sea ice model and tidal mixing scheme in OFES2 and OFES2 is forced by newly created atmospheric reanalysis data called JRA55-do. Although a few issues remain, we found several improvements in OFES2: small biases in global sea surface temperature, sea surface salinity, and water properties in the Indonesian Seas compared to OFES. Time series of El Niño and Indian Ocean Dipole indexes are a bit better simulated in OFES2 than OFES. The well-verified outputs from OFES2 are expected to be widely used to study various oceanic phenomena with broad spatiotemporal scales.
発表者2:
Pascal Oettli
タイトル:
Surface air temperatures in Japan: A story
概要
The mean areal surface air temperature anomalies are used to group 762 AMeDAS stations, taken throughout Japan, into homogeneous clusters. Clustering is performed seasonally, with a focus on boreal winter (DJF) and boreal summer (JJA) and associated time series of temperature anomalies are created. Subsequently, correlation between these indices and different climatic variables anomaly fields are calculated to explore the possible source(s) of the interannual variability of surface air temperature anomalies.