Abstract

Results of calculations of temperature trends in the free atmosphere (troposphere and lower stratosphere) using the quantile regression apparatus are considered and analyzed. In traditional techniques used in climatology, trends are estimated by use of regression based on the least squares method. Quantile regression, in contrast to these techniques, makes it possible to estimate regression parameters for each quantile of predictand values in the quantile range from zero to one. Using quantile regression to estimate climate changes results in a detailed picture of the dependence of the climate trend on the variation range of meteorological parameters in the quantile range of these parameters from zero to one. In particular, climate trends can be estimated for meteorological parameter values close to extreme. This paper uses the global radiosonde data array from which the stations are selected if the completeness of their data meets the requirements stated. Using the radiosonde data from the selected stations, the dependences of climatic trends of temperature on isobaric surfaces on values of quantiles (so-called process diagrams), as well as vertical quantile cross sections of climate trend values, are calculated, plotted, and analyzed. For thirteen high-latitude stations in the Northern Hemisphere among the selected ones, temperature trends are estimated both using radiosonde data and based on the ERA 5/ERA 5.1 reanalyses. An analysis of the results allows one to note the nonuniform character of tropospheric warming trends in the range of quantile variation, which is more apparent in the winter season. The nonuniform (for the range of quantile variation) character of tropospheric temperature trends is due to the fact that the tropospheric warming rate in the “cold” part of the quantile range is higher than that in its “warm” part. This agrees with the results obtained previously by analysis of surface temperature trends using the quantile regression method (QRM). The nonuniform character of cooling trends in the lower stratosphere is noted for the range of quantile variations. In winter and, to a lesser extent, in spring, the rate of stratospheric cooling decreases in absolute magnitude with an increase in quantile values at some stations in northern latitudes. Moreover, for the quantiles close to 1.0, negative trends can change sign. This can be both due to incomplete data on lower stratospheric temperature, which is particularly inherent in the high-latitude regions of the Northern Hemisphere, and due to the influence of more frequently occurring sudden stratospheric warmings (SSWs) on the temperature trend structure that is detailed within the range of quantile values. In is noted that the detailed structures of climate temperature trends that are obtained on the basis of radiosonde data proved to be very similar to those obtained based on arrays of ERA 5/ERA 5.1 reanalysis.

中文翻译：

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自由大气中的温度趋势：使用分位数回归方法进行计算

摘要

考虑并分析了使用分位数回归装置计算自由大气（对流层和平流层低层）温度趋势的结果。在气候学中使用的传统技术中，趋势是通过基于最小二乘法的回归来估计的。与这些技术相比，分位数回归可以估计从 0 到 1 的分位数范围内的预测值的每个分位数的回归参数。使用分位数回归来估计气候变化，可以详细了解气候趋势对气象参数从零到一的分位数范围内的变化范围的依赖性。特别是，可以估计接近极端的气象参数值的气候趋势。本文使用全球无线电探空仪数据阵列，如果数据完整性满足要求，则从中选择台站。使用来自所选站点的无线电探空仪数据，计算、绘制和分析等压表面温度气候趋势对分位数值（所谓的过程图）以及气候趋势值的垂直分位数横截面的依赖性。对于所选站点中的北半球 13 个高纬度站点，使用无线电探空仪数据和 ERA 5/ERA 5.1 重新分析来估计温度趋势。通过对结果的分析，人们可以注意到对流层变暖趋势在分位数变化范围内的不均匀特征，这在冬季更为明显。对流层温度趋势的不均匀（分位数变化范围）特征是由于分位数范围“冷”部分的对流层变暖速率高于“暖”部分的对流层变暖速率。这与之前使用分位数回归方法 (QRM) 分析表面温度趋势所获得的结果一致。平流层下部冷却趋势的不均匀特征通过分位数变化的范围被注意到。在冬季，以及较小程度上的春季，平流层变冷速率的绝对量值随着北纬一些站点分位数值的增加而降低。此外，对于接近 1.0 的分位数，负趋势可能会改变符号。这可能是由于关于低平流层温度的不完整数据（尤其是北半球高纬度地区所固有的），也是由于更频繁发生的平流层突然变暖（SSW）对温度趋势结构的影响，即分位数值范围内的详细信息。值得注意的是，根据无线电探空仪数据获得的气候温度趋势的详细结构被证明与根据 ERA 5/ERA 5.1 再分析阵列获得的气候温度趋势的详细结构非常相似。