Improvement of Weather and Climate Prediction
Strategic Goal 2 was to achieve more reliable weather and climate prediction, over a greater time span than previously. Specifically, 1) to identify the mechanisms behind Arctic-derived extreme events in and outside the Arctic, and present practical indicators as well as future perspectives on the progression of global warming； 2) to contribute to the sophistication of numerical climate models by improving the representation of Arctic climate processes, and to improve weather and climate predictions in both the Arctic and globally, on time scales from day-to-day to multi-year, as well as extending the forecast period； and 3) to elucidate the progression of the Arctic warming as well as the mechanisms behind warming amplification, and deepen our understanding of climate change over a time scale of longer than several decades.
Notable achievements include: for 1), development of a new index of cutoff lows and its implementation in Japan Meteorological Agency (JMA) forecast analysis, clarification of the mechanism of the Japan sea Polar air mass Convergence Zone (JPCZ), and clarification of the factors behind the increased frequency and intensity of heat waves in the mid- and high-latitudes of the Northern Hemisphere; for 2), improvement of the reproducibility of Arctic sea ice in global climate models, improvement of the reproducibility of clouds’ impact on solar radiation in climate models, development of snow and ice models that incorporate detailed processes such as darkening of snow and ice, improvement of snow accumulation processes in climate models, clarification of the actual wave-sea ice interaction and improvement of prediction models, and establishment of methods for estimating sea ice thickness; for 3), clarification of the Arctic warming and moistening process due to heat transport effects, quantitative explanation of mid-latitude Arctic cooling due to Arctic sea ice decline, clarification of the factors of mid-latitude precipitation increase due to Arctic warming, and clarification of the interaction between cloud cover and sea ice in the Arctic Ocean.
Climate variability is caused by two different factors: forced variabilities (e.g., warming due to an increase in greenhouse gases, which has a clear cause and effect) and internal variabilities (i.e., autonomously produced by the interaction of factors in the climate, such as the Arctic oscillation), each of which has different importance in predicting. To improve the accuracy of climate prediction or to extend the prediction period for the period when internal variabilities dominate (less than about 10 years), research and development are especially required for the acquisition of observation data that will serve as initial values for prediction and for assimilation methods to properly incorporate such data into prediction models. In this project, we have already made progress in terms of sea ice thickness data sets and sea ice data assimilation methods. In the future, we will need to further improve the accuracy and sophistication of these data sets, as well as develop data sets and data assimilation methods for snow depth on land and sea ice.