湿度和模型参数的不确定性对飞行迹线能量强迫预测的影响

IF 2.5 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES Environmental Research Communications Pub Date : 2024-09-16 DOI:10.1088/2515-7620/ad6ee5
John C Platt, Marc L Shapiro, Zebediah Engberg, Kevin McCloskey, Scott Geraedts, Tharun Sankar, Marc E J Stettler, Roger Teoh, Ulrich Schumann, Susanne Rohs, Erica Brand and Christopher Van Arsdale
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引用次数: 0

摘要

以往的研究表明,虽然飞机凝结的尾迹对气候的净影响是变暖,但每米尾迹能量强迫的确切大小仍不确定。在本文中,我们探讨了拉格朗日烟云模型(CoCiP)在识别高烟云能量强迫的飞行段方面的技能。我们发现,即使考虑到天气场和模型参数的不确定性,该技能也比单纯的气候学预测要强。我们使用欧洲中期天气预报中心(ECMWF)的 ERA5 天气再分析集合作为 CoCiP 的蒙特卡罗输入,估算了湿度带来的不确定性。我们通过强制与巡航高度的实地湿度测量分布相匹配,对ERA5湿度数据进行去偏和修正。我们将使用集合成员之一计算的 CoCiP 能量强迫估算值作为地面实况的替代值,并报告了 CoCiP 在识别具有大量正替代能量强迫的区段方面的技能。通过蒙特卡罗模拟,我们进一步估算了CoCiP模型参数的不确定性,CoCiP模型参数取自与文献一致的不确定性分布。当CoCiP输出被平均到各季以形成气候学预测时,预测代用值的技能为44%,而每飞行CoCiP输出的技能为84%。如果这些结果也适用于真实的(未知的)飞行禁忌 EF,则表明每次飞行的能量强迫预测可以将潜在的飞行禁忌路线调整次数减少 2 倍,从而降低飞行禁忌的成本和对燃料的影响。
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The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing
Previous work has shown that while the net effect of aircraft condensation trails (contrails) on the climate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain. In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flight segments with high contrail energy forcing. We find that skill is greater than climatological predictions alone, even accounting for uncertainty in weather fields and model parameters. We estimate the uncertainty due to humidity by using the ensemble ERA5 weather reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correct under-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humidity measurements taken at cruising altitude. We take CoCiP energy forcing estimates calculated using one of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifying segments with large positive proxy energy forcing. We further estimate the uncertainty due to model parameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn from uncertainty distributions consistent with the literature. When CoCiP outputs are averaged over seasons to form climatological predictions, the skill in predicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carry over to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions can reduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the cost and fuel impact of contrail avoidance.
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来源期刊
Environmental Research Communications
Environmental Research Communications ENVIRONMENTAL SCIENCES-
CiteScore
3.50
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0.00%
发文量
136
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