Global warming, the phenomenon of increasing global average temperature in the recent decades, is receiving wide attention due to its very significant adverse effects on climate. Whether global warming will continue even in the future, is a question that is most important to investigate. In this regard, the so-called general circulation models (GCMs) have attempted to project the future climate, and nearly all of them exhibit alarming rates of global temperature rise in the future. Although global warming in the current time frame is undeniable, it is important to assess the validity of the future predictions of the GCMs. In this article, we attempt such a study using our recently-developed Bayesian multiple testing paradigm for model selection in inverse regression problems. The model we assume for the global temperature time series is based on Gaussian process emulation of the black box scenario, realistically treating the dynamic evolution of the time series as unknown. We apply our ideas to datasets available from the Intergovernmental Panel on Climate Change (IPCC) website. The best GCM models selected by our method under different assumptions on future climate change scenarios do not convincingly support the present global warming pattern when only the future predictions are considered known. Using our Gaussian process idea, we also forecast the future temperature time series given the current one. Interestingly, our results do not support drastic future global warming predicted by almost all the GCM models.

翻译：全球变暖，即近几十年来全球平均气温持续上升的现象，因其对气候产生的极其显著的不利影响而受到广泛关注。全球变暖在未来是否会持续，是一个亟待探究的关键问题。在这方面，所谓的大气环流模型（GCMs）已尝试对未来气候进行预测，几乎所有模型都显示出未来全球气温将出现令人担忧的上升速率。尽管当前时间范围内的全球变暖无可否认，但评估GCMs对未来预测的有效性至关重要。在本文中，我们尝试使用我们最近开发的、用于逆回归问题模型选择的贝叶斯多重检验范式进行此类研究。我们为全球气温时间序列假设的模型基于黑箱情景的高斯过程仿真，现实地将时间序列的动态演化视为未知。我们将我们的方法应用于政府间气候变化专门委员会（IPCC）网站提供的数据集。在不同未来气候变化情景假设下，由我们方法筛选出的最佳GCM模型，在仅考虑未来预测已知的情况下，并不能令人信服地支持当前的全球变暖模式。利用我们的高斯过程思想，我们还基于当前序列预测了未来的气温时间序列。有趣的是，我们的结果并不支持几乎所有GCM模型所预测的未来剧烈的全球变暖。