Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance
Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance
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ERA5综合水汽趋势用于欧洲大陆气候变化分析的可行性：考虑统计显着性的GPS评估（1994年至2019年）
DOI：
10.1016/j.rse.2021.112416
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发表时间：
2021
影响因子：
13.5
通讯作者：
Kutterer
中科院分区：
文献类型：
--
作者：
Hunegnaw;Alshawaf;Awange;Teferle;Kutterer
虽然综合水汽（IWV）趋势的统计意义是正确解释气候变化信号的关键，但在现实不确定性的情况下获得准确的IWV趋势估计仍然是一个挑战。这项研究评估的可行性IWV的趋势来自新发布的第五代欧洲中期天气预报中心（ECMWF）大气再分析（ERA 5）在欧洲大陆的气候变化分析。这是通过比较1994年至2019年109个台站的地基全球定位系统（GPS）每日IWV系列得出的趋势来实现的。通过对IWV趋势噪声特性的时间序列分析和适当的噪声模型，评估了IWV趋势的实际不确定性和统计意义。结果表明，自回归滑动平均阿尔马（1，1）噪声模型，而不是通常假设的白色噪声（WN）或一阶自回归AR（1）噪声约68%的ERA 5和GPS IWV序列。一个不适当的噪声模型会错误地评估IWV时间序列的趋势不确定性，与其特定的首选噪声模型相比。例如，阿尔马（1，1）可能会将其趋势估计值（0.1-0.3 kg m-2decade-1）的标准差误估10%。尽管如此，建议将阿尔马（1，1）作为ERA 5和GPS IWV系列的默认噪声模型。然而，每个ERA 5减去GPS（E-G）IWV系列的首选噪声模型应该具体确定，因为AR（1）相关模型可能导致其趋势不确定性被低估90%。相比之下，幂律（PL）模型可能会导致高达9倍的高估。E-G IWV趋势在−0.2-0.4 kg m−2decade−1之间，表明ERA 5是欧洲大陆气候变化分析IWV趋势的潜在数据来源。ERA 5和GPS IWV趋势在其量级和地理模式上是一致的，在西北欧较低（0-0.4 kg m-2decade-1），但在地中海周围较高（0.6-1.4 kg m-2decade-1）。
Although the statistical significances for the trends of integrated water vapor (IWV) are essential for a correct interpretation of climate change signals, obtaining accurate IWV trend estimates with realistic uncertainties remains a challenge. This study evaluates the feasibility of the IWV trends derived from the newly released fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) for climate change analysis in continental Europe. This is achieved by comparing the trends derived from in-situ ground-based Global Positioning System (GPS)’s daily IWV series from 1994 to 2019 at 109 stations. The realistic uncertainties and statistical significances of the IWV trends are evaluated with the time series analysis on their noise characteristics and proper noise models. Results show that autoregressive moving average ARMA(1,1) noise model is preferred rather than the commonly assumed white noise (WN) or first-order autoregressive AR(1) noise for about 68% of the ERA5 and GPS IWV series. An improper noise model would misevaluate the trend uncertainty of an IWV time series, compared with its specific preferred noise model. For example, ARMA(1,1) may misevaluate the standard deviations of their trend estimates (0.1–0.3 kg m−2decade−1) by 10%. Nevertheless, ARMA(1,1) is recommended as the default noise model for the ERA5 and GPS IWV series. However, the preferred noise model for each ERA5 minus GPS (E-G) IWV series should be specifically determined, because the AR(1)-related models can result in an underestimation on its trend uncertainty by 90%. In contrast, power-law (PL) model can lead to an overestimation by up to nine times. TheE-G IWV trends are within −0.2–0.4 kg m−2decade−1, indicating that the ERA5 is a potential data source of IWV trends for climate change analysis in continental Europe. The ERA5 and GPS IWV trends are consistent in their magnitudes and geographical patterns, lower in Northwest Europe (0–0.4 kg m−2decade−1) but higher around the Mediterranean Sea (0.6–1.4 kg m−2decade−1).
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