Methods for uncertainty assessment of climate models and model predictions over East Asia

Abstract

Model performance and uncertainty have been assessed using simulations of the climate in the 20th century based on the 21 models of the Intergovernmental Panel on Climate Change's Fourth Assessment Report (IPCC AR4) and NCEP/NCAR reanalysis data. To evaluate the fidelity and reliability of the simulations of East Asian climate change, the following approaches are compared to assess the uncertainty of East Asian monsoon and climate projection in conjunction with global warming: Taylor diagrams using correlation and standard deviation of model results over East Asia (100• N), signal-to-noise ratio (SNR), and principal-mode comparison identified by empirical orthogonal function (EOF) analysis. On the basis of the Taylor diagram and SNR results, good performance models with statistically high fidelity produce higher values of warming over East Asia and an enhancement of the northwest-southeast temperature gradient between the land and ocean. This enhanced temperature gradient may strengthen the East Asian summer monsoon flow, resulting in a greater increase in precipitation along the East Asian summer rain band on the continental side of East Asia. However, the good performance models as determined by the principal-mode comparison produce lower values of warming over the East Asia region during winter; these values are clearly different from the corresponding values obtained from the Taylor diagram and SNR approaches. These results suggest that the models that give priority to the signal associated with the first leading mode of EOF or the principal mode may predict less warming than other models. The models that predict an El Niño-like state in response to greenhouse warming produce less warming over East Asia, corresponding to the results of the principal-mode comparison.

Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

“…The distance from the reference point (observations) is a measure of the centered RMSE (Taylor, 2001(Taylor, , 2005. Therefore, an ideal model (being in full agreement with the observations) is marked by the reference point with the correlation coefficient equal to 1, and the same amplitude of variations compared with the observations (Heo et al, 2014). Figure 3 displays the normalized standard deviation (SD) and correlation coefficient R 2 of the ANN and regression models.…”

“…The results revealed that monthly precipitation anomalies could be forecasted about 10 months in advance using the selected indices in Table 7, although the R 2 is not high. The relatively low R 2 can be attributed to the far distance between original location of climatic indices and the study area, and to the long temporal duration as shown in Heo et al (2014) and Li et al (2012). In other words, the higher rates of observed precipitation variation compared with the modeled predictions implied that other regional conditions (such as temporal wind and local humidity, orographic conditions) also affected and complicated the precipitation system, which is crucial to be considered in future research.…”

Forecast Skill of Minimum and Maximum Temperatures on Subseasonal‐to‐Seasonal Timescales Over South Africa

“…These diagrams are widely used to evaluate multiple aspects of complex models and measure the relative skill of many different models against observations (e.g., Intergovernmental Panel on Climate Change, ; Kalognomou et al, ). The diagrams provide a statistical way of graphically summarizing how well the modeled pattern (or a set of patterns) matches observations in terms of their correlation, their centered RMSE, and the amplitude of their variations (represented by their standard deviations) (Heo et al, ). The diagram is usually visualized as a series of points on a polar plot.…”

“…Earth and Space Science diagrams provide a statistical way of graphically summarizing how well the modeled pattern (or a set of patterns) matches observations in terms of their correlation, their centered RMSE, and the amplitude of their variations (represented by their standard deviations) (Heo et al, 2013). The diagram is usually visualized as a series of points on a polar plot.…”

Future Change Using the CMIP5 MME and Best Models: I. Near and Long Term Future Change of Temperature and Precipitation over East Asia

“…에 따라서 결과가 크게 달라지는 경향이 있다 (Heo et al, 2014;Seo et al, 2014). Heo et al (2014) 로 안정될 것이라 고 가정하고 CMIP5에서 "중간단계"의 시나리오를 선 택하였다 (Taylor et al, 2012).…”