Weather and Climate Prediction and Its Technological Improvement
Background and Objectives of the Research Program
There is no doubt that the Arctic environment is undergoing particularly rapid changes as a result of climate warming, and that the effects of these changes are reaching beyond the Arctic in various ways. The need for accurate weather and climate prediction is increasing, whether for adaptation to environmental changes for people living in the Arctic, for preparing for disasters caused by extreme weather events outside the Arctic that originate in the Arctic, or for considering the economic effects of the use and development of the Arctic. In response to this need, this Research Program was conducted with the following objectives:
- Global climate models used for climate warming prediction and other applications continue to have major problems in reproducing the polar regions, and it is essential to solve these problems in order to improve the prediction accuracy of weather and climate not only for the Arctic but also for the entire globe. We focus on several climate processes unique to the Arctic, and work to refine climate models by comparing them with the latest observational knowledge and by utilizing Large Eddy Simulation (LES) and high-resolution regional models.
- To improve the accuracy of weather and climate prediction in the relatively short term (less than a few years), it is necessary to provide accurate initial conditions to models. We work to refine climate prediction by constructing new data sets regarding climate variables in the Arctic and developing data assimilation methods to enable these data sets to be utilized in the initialization of climate models.
- The Arctic environment, especially the snow/ice region, has been changing rapidly, and there are concerns about further discontinuous or irreversible drastic changes in the long-term (several decades or more) climate warming in the future. We investigate the possibility of drastic long-term changes in the cryosphere and, by extension, the Arctic environment by utilizing regional climate modelling and other means to express snow and ice-related processes in greater detail.
- In light of the increase in Arctic sea routes cargo traffic due to the receding Arctic sea ice and the Russian government's plans to increase LNG production, the safe and efficient use of the Arctic sea routes is an urgent issue. The risk in Arctic sea route is collisions between vessels and the increasing waves and ice sheets. On the other hand, coastal areas directly exposed to waves due to the loss of sea ice are eroding at a rate of several meters per year, threatening the habitats of coastal indigenous people. To address these issues, we work to improve the prediction precision of waves and sea ice distribution in marginal ice zones and coastal regions.
Structure of the Research Program
In this Program, of the objectives 1-4 above, 1. was conducted in Sub Program 1 "Arctic Climate Process Modeling," 2. and 3. were conducted in Sub Program 2 "Improvement of Climate Prediction," and 4. was conducted in Sub Program 3 "Towards a Coupled Wave-Ice Prediction."
Summary of the Research Results
In Sub Program 1, we conducted research on Arctic-specific climate processes, particularly cloud-radiation interaction processes, Atmosphere-sea ice-ocean fluxes, and terrestrial hydrologic processes. For cloud-radiation interaction processes, we analyzed future changes in cloud radiation processes in climate model simulations, reproduced cloud microphysical processes using the LES model and parameterized them for climate models, improved snowfall processes in climate models, evaluated the climate model Arctic radiation budget based on the latest satellite observations, and identified and improved issues related to cloud radiation processes in existing climate models by taking a multifaceted approach. With regard to the Atmosphere-sea ice-ocean fluxes, we improved the reproducibility of the stratification of the ocean, which has been a particular problem in existing climate models. Specifically, through climate model experiments with artificial constraints on sea surface fluxes, we extracted the factors necessary to maintain stratification and improved the flux calculations in climate models. In addition, we evaluated the impact of the improved representation of ocean processes and associated sea surface fluxes around the Barents Sea on the reproducibility of the Arctic in climate models by using a high-resolution ocean model that can represent the detailed inflow process of Atlantic-origin water into the Arctic Ocean. For terrestrial hydrological processes, we improved the accuracy of the representation of Arctic terrestrial hydrological processes in climate models by optimizing the regional distribution of many parameters in terrestrial hydrological models through machine learning and other methods.
In Sub Program 2, we developed data sets and assimilation methods, and evaluated the predictability of sea ice thickness, sea ice flow, and snow cover, which are essential for extending the prediction period and improving the prediction accuracy of the Arctic climate. In particular, for sea ice thickness, for which no practical data sets existed, we developed a new method for estimating sea ice thickness through sea ice age based on sea ice flow, and put it to practical use. In addition, with regard to long-term future changes in the Arctic cryosphere, we conducted a detailed study of long-term trends in sea ice change using an ocean sea ice downscaling model and a simulation of climate model outputs to a 3D ice sheet model to present future scenarios of Greenland ice sheet melting and evaluate their impacts.
In Sub Program3, we clarified the actual processes such as the effect of waves on the growth and breakdown process of sea ice and the suppression effect of sea ice on waves by sea ice, by making full use of various methods such as field observations, large-scale tank experiments, numerical modeling, and theoretical calculations. We also modeled these processes to improve our wave and sea ice prediction model, and used the prediction model to provide real-time wave and sea ice prediction information when the Research Vessel Mirai was in operation.