Stability requirements of satellites to detect long-term stratospheric ozone trends based upon Monte Carlo simulations

环境科学 期限(时间) 臭氧层 理论(学习稳定性) 气象学 卫星 臭氧 对流层臭氧 气候学 蒙特卡罗方法 大气科学 平流层 计算机科学 数学 统计 地理 物理 地质学 天文 机器学习 量子力学
作者
Mark Weber
出处
期刊:Atmospheric Measurement Techniques [Copernicus Publications]
卷期号:17 (11): 3597-3604 被引量:1
标识
DOI:10.5194/amt-17-3597-2024
摘要

Abstract. For new satellite instruments, specifications of the stability required for climate variables are provided in order to be useful for certain applications – for instance, deriving long-term trends. The stability is usually stated in units of percent per decade (% per decade) and is often associated with or termed instrument drift. A stability requirement of 3 % per decade or better has been recently stated for tropospheric and stratospheric ozone. However, the way this number is derived is not clear. In this study, we use Monte Carlo simulations to investigate how a stability requirement translates into uncertainties in long-term trends depending on the lifetime of individual observing systems, which are merged into time series, and the period of available observations. Depending on the need to observe a certain trend over a given period, e.g., typically +1 % per decade for total ozone and +2 % per decade for stratospheric ozone over 30 years, stability for observation systems can be properly specified and justified in order to achieve statistical significance in the observed long-term trend. Assuming a typical mean lifetime of 7 years for an individual observing system and a stability of 3 % per decade results in a 2 % per decade trend uncertainty over a period of 30 years, which is barely sufficient for stratospheric ozone but too high for total ozone. Having two or three observing systems simultaneously reduces the uncertainty by 30 % and 42 %, respectively. Such redundancies may be more efficient than developing satellite instruments with higher long-term stability to reduce long-term trend uncertainties. The method presented here is applicable to any variable of interest for which long-term changes are to be detected.

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