本篇代寫-氣候變化研究讲了氣候模型之一CNRM-CM5.1爲例，建立了該模型的平衡必須得到改善，才能使其成功地利用千年時間尺度的積分。海溫模型漂移也必須考慮在內(Dai, 2006;Bellouin等，2011)。此外，區域海洋質量的研究也被認爲存在偏差，主要是由於模型的性能。本篇代寫文章由美國第一論文 Assignment First輔導網整理，供大家參考閱讀。
The basis of CMIP5 rests with the coupled models. This research work undertakes part of the objective of the CMIP collaboration which is to understand model differences. As stated earlier, model differences lead to discrepancies in data collected for even similar climate studies and only when there is adequate understanding of differences does it become possible to acknowledged and interpret data accurately. In this context, the coupled model comparison is of significant help. Coupled Atmosphere–Ocean General Circulation Models (AOGCMs) are significant because research understanding of different tools and mechanisms improve in their usage. Some of the climate system mechanisms such as the patio-temporal variabilities, predictability levels etc. are better understood in the use of the AOGCMs. Significant limitations existed in the past despite the regular use of the models because the models suffered from biases. Projection uncertainties are only one end of the problem, the bias occurs because of the models themselves and how they are configured for use. In such situations, the coupled model inter-comparison CMIP offers for a standardization of protocol (Taylor et al., 2012).
CMIP brings together diverse community of scientists and their research works. GCMs can be studied more systematically and the climate change and fluctuations are understood in a much interlinked way. This is an area that has generated maximum research report for understanding climatic predictions and the models. Model development, climate change studies and also past climate fluctuations are all understood more systematically using the CMIP. The Australian Bureau of Meteorology (Bureau) forecast and warning services use marine wind estimates from numerical weather prediction (NWP) systems as an important input (Durrant & Greenslade, 2012). Marine wind is also utilized as the forcing for downstream systems such as wave and ocean models (Durrant & Greenslade, 2012). The accuracy of wave model is significantly influenced by the accuracy of the surface winds, as a ten per cent error in the estimation of surface wind speed can result in 20 to 50 per cent error in wave energy and 10 to 20 per cent error in significant wave height (Durrant & Greenslade, 2012). Given this background context, this research approaches an understanding of climate models based on their configurations, background, their similarities and differences and percentage errors associated with the models based on their configurations and inherent limitations.
The paper will provide detailed description of eight climate models. For each of the climate model, the background for the climate model is presented along with the basic configuration of the climate model, the observational estimates used, the way they have evolved over times and how research scientists agree in consensus or argue about some aspects of the climate models. Decadal simulations done with these models are also presented. The research work is drawn from scientific research works and academic scholarly works. The model intercom parison study hence makes use of secondary data only. The qualities and shortcomings of the model with respect to the current day climatology and their pros relative to other models have been discussed as well.
The work also focuses on understanding the biases in term of configuration and use. For instance, in the case of the CNRM-CM5.1 which is one of the climate models that are analysed in this work, it is established that the equilibrium of the model have to be improved in order to make it successful in its use of millennium time scale integrations. SST model drift has to be accounted for as well (Dai, 2006; Bellouin et al., 2011). In addition, the study of the regional ocean mass is also seen to have biases that occur mainly because of the model performance. Such end differences in data as compared against another model such as ACCESS 1.0 should be understood to be occurring mainly because of the model issues. Intrinsic characteristics and initial configuration creates these biases and the biases must be understood as model performance only (Meehl et al., 2009). However, for this to happen, more comparative research on models has to happen, especially the ones already assessed in collaboration in CMIP. The research hence presents a way to understand the differences.
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