In a paper recently published in this journal, van Marle et al. (van Marle et al., 2022) introduce an interesting new data set for land use and land cover change CO2 emissions (LULCC) that they use to study whether a trend is present in the airborne fraction (AF), defined as the fraction of CO2 emissions remaining in the atmosphere. Testing the hypothesis of a trend in the AF has attracted much attention, with the overall consensus that no statistical evidence is found for a trend in the data (Knorr, 2009; Gloor et al., 2010; Raupach et al., 2014; Bennedsen et al., 2019). In their paper, van Marle et al. analyze the AF as implied by three different LULCC emissions time series (GCP, H&N, and their new data series). In a Monte Carlo simulation study based on their new LULCC emissions data, van Marle et al. find evidence of a declining trend in the AF. In this note, we argue that the statistical analysis presented in van Marle et al. can be improved in several respects. Specifically, the Monte Carlo study presented in van Marle et al. is not conducive to determine whether there is a trend in the AF. Further, we re-examine the evidence for a trend in the AF by using a variety of different statistical tests. The statistical evidence for an uninterrupted (positive or negative) trend in the airborne fraction remains mixed at best. When allowing for a break in the trend, there is some evidence for upward trends in both subsamples.

翻译：在本期刊最近发表的一篇论文中,van Marle等人(van Marle等人,2022年)介绍了一套关于土地利用和土地覆盖物变化CO2排放量的有趣新数据集(LULCC),该数据集用于研究空气中部分(AF)是否存在趋势,该部分被定义为大气中二氧化碳排放量的一小部分。测试AF趋势的假设引起了很大的注意,总体共识是,在数据趋势方面没有找到任何统计证据(Knorr,2009年;Gloor等人,2010年;Raupach等人,2014年;Bennedsen等人,2019年)。在其论文中,Van Marle等人分析了AFAF,这是三个不同的LULCC排放时间序列(GCP,H&N,及其新的数据序列)所暗示的。在蒙特卡洛的模拟研究中,根据新的LULCC排放数据,van Marle等人, 找到了数据下降趋势的最佳证据。在本说明中,van Marle等人的统计分析可以从若干方面加以改进;在Bennedsensensen等人的统计分析中,利用Melleal的混合证据进一步确定AF的多样化趋势是否有利于AF。