Hirofumi Sakuma

A CGCM Study on the Interaction between IOD and ENSO

Abstract An atmosphere–ocean coupled general circulation model known as the Scale Interaction Experiment Frontier version 1 (SINTEX-F1) model is used to understand the intrinsic variability of the Indian Ocean dipole (IOD). In addition to a globally coupled control experiment, a Pacific decoupled noENSO experiment has been conducted. In the latter, the El Nino–Southern Oscillation (ENSO) variability is suppressed by decoupling the tropical Pacific Ocean from the atmosphere. The ocean–atmosphere conditions related to the IOD are realistically simulated by both experiments including the characteristic east–west dipole in SST anomalies. This demonstrates that the dipole mode in the Indian Ocean is mainly determined by intrinsic processes within the basin. In the EOF analysis of SST anomalies from the noENSO experiment, the IOD takes the dominant seat instead of the basinwide monopole mode. Even the coupled feedback among anomalies of upper-ocean heat content, SST, wind, and Walker circulation over the Indian...

An Eddy-Resolving Hindcast Simulation of the Quasiglobal Ocean from 1950 to 2003 on the Earth Simulator

An eddy-resolving hindcast experiment forced by daily mean atmospheric reanalysis data covering the second half of the twentieth century was completed successfully on the Earth Simulator. The domain covers quasiglobal from 75°S to 75°N excluding arctic regions, with horizontal resolution of 0.1° and 54° vertical levels. Encouraged by high performance of the preceding spin-up integration in capturing the time-mean and transient eddy fields of the world oceans, the hindcast run is executed to see how well the observed variations in the low- and midlatitude regions spanning from intraseasonal to decadal timescales are reproduced in the simulation. Our report presented here covers, among others, the El Nino and the Indian Ocean Dipole events, the Pacific and the Pan-Atlantic decadal oscillations, and the intraseasonal variations in the equatorial Pacific and Indian Oceans, which are represented well in the hindcast simulation, comparing with the observations. The simulated variations in not only the surface but also subsurface layers are compared with observations, for example, the decadal subsurface temperature change with narrow structures in the Kuroshio Extension region. Furthermore, we focus on the improved aspects of the hindcast simulation over the spin-up run, possibly brought about by realistic high-frequency daily mean forcing.

Impacts of El Niño Southern Oscillation on the global yields of major crops

The monitoring and prediction of climate-induced variations in crop yields, production and export prices in major food-producing regions have become important to enable national governments in import-dependent countries to ensure supplies of affordable food for consumers. Although the El Niño/Southern Oscillation (ENSO) often affects seasonal temperature and precipitation, and thus crop yields in many regions, the overall impacts of ENSO on global yields are uncertain. Here we present a global map of the impacts of ENSO on the yields of major crops and quantify its impacts on their global-mean yield anomalies. Results show that El Niño likely improves the global-mean soybean yield by 2.1-5.4% but appears to change the yields of maize, rice and wheat by -4.3 to +0.8%. The global-mean yields of all four crops during La Niña years tend to be below normal (-4.5 to 0.0%). Our findings highlight the importance of ENSO to global crop production.

Parallel Architecture and its Performance of Oceanic Global Circulation Model Based on MOM3 to be Run on the Earth Simulator

Abstract. In this study, we will present latest results from evaluation of our computational optimized code OFES based on MOM3 to run on the Earth Simulator. O ( 10 ) years integration with 0.1 degree for horizontal will be one of the first attempts to solve the largest scale scientific simulations. In order to keep the flexibility of MOM3 from points ofscientific view, we consider two types of parallel architectures due to the difference from resolution to represent physical performance in oceanic phenomena. One is, for the relative lower resolved phenomena with longer integration time, characterized by using shared memory system for improvement parallel performance within a single node composed of 8PEs. To achieve the most efficiency parallel computation inside of a node, we modified MPI library into assembly coded library. Another parallel computational improvement, for case of ultra high resolution of 0.1 degree for horizontal, employed by only communication with MPI library, which is not distinct from inside or outside of node. In this case, we took into account a mount of computation in halo region to attain to huge parallelized performance. As the results, the computational efficiency has been achieved high computational speed with more about 500 times performance comparing CPU time on a single node. The load imbalance was not recognized. In this paper, we will indicate optimization strategy for both two cases to attain target performance and results from measurement on the Earth Simulator. Experiments for ultra high resolution case carried out by using 188 nodes, which is composed of 1500 PEs.

Characterizing the reliability of global crop prediction based on seasonal climate forecasts

Reliable crop prediction based on seasonal climate forecasts can be achieved when a strong climate- crop relationship exists and there are reliable forecasts of the climatic constraints on crops. Here, we present global assessments of the climatic constraints on crops (maize, soybeans, rice, and wheat), the degree of the climate-crop relationship, and the reliability of seasonal forecasts of dominant climatic constraints based on statistical crop models and ensemble seasonal climate forecasts. We then classify the reliability of within-season crop prediction into four categories based on the degree of the climate-crop relationship and the reliability of the climate forecasting: (I) reliable; (II) less reliable due to the low reliability of climate forecasting; (III) not reliable due to the low reliability of climate forecasting and a weak climate-crop relationship; and (IV) less reliable due to a weak climate-crop relationship. The results showed that a strong climate-crop relationship exists in the area that produces 24-38% of the global crop production. On a global scale, 51-59% of the maize and soybean production is sensitive to soil moisture level during the reproductive growth period, whereas 47-53% of the rice and wheat production is sensitive to temperature. Due to the greater reliability of temperature forecasts, crop prediction is reliable in those areas in which the crop yield is temperature-sensitive and temperature forecasts are reliable. The categorized reliability of crop prediction indicated that improvements of soil moisture forecasts in 30-50°N during July- October and in 30-40 ? S during February-April are needed for better maize and soybean prediction, whereas improved temperature forecasts in 20-60 ° N during March-August are keys to rice and wheat prediction. This study established a novel way of assessing the reliability of crop prediction, which will enable decision-making and allow researchers to prioritize the direction of new research to improve crop prediction in a given area for global food prediction.

Coupling Strategy of Atmospheric-Oceanic General Circulation Model with Ultra High Resolution and its Performance on the Earth Simulator

Coupled global climate models (CGCM) provide the most powerful tool to reproduce main features of the observed climate. In stand-alone atmospheric and oceanic models, the computational efficiency has been progressed by tuning each of model codes respectively. Ordinarily supercomputers do not provide reasonable turnaround of CGCM run for century time scales at ultra high resolution. The great expense of running CGCM has been hesitated of development by limiting the number of calculations and by prohibiting the use of the reasonable resolution for satisfying physical requirement. The resource of the Earth Simulator might become to be able to carry out the huge scale simulation. Our objective here is to develop coupled global climate models for the Earth Simulator (CFES) with ultra high resolution to carry out century time integration within reasonable time without decrease of computational efficiency of the component models. It is composed of oceanic general circulation model for the Earth Simulator (OFES) and atmospheric general circulation model for the Earth Simulator (AFES). We provide a new coupling structure to transfer physical data from one component model to the other component through a coupler and back again. In the structure of coupling scheme, each component can run independently to avoid bias due to modeling the feedback timing. It allows us not to, worry about the sequential order of execution of component models. CFES was performed with fully paralleized implementation including I/O interaction throughout coupling scheme. Due to the parallelization, CFES was able to control concurrent performance by changing the number of nodes which employed each component of atmospheric and oceanic models.