Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance
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...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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ELSEVIER SCIENCE INC
2021
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| Online Access: | http://hdl.handle.net/20.500.11937/86607 |
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| author | Yuan, P. Hunegnaw, A. Alshawaf, F. Awange, Joseph Klos, A. Teferle, F.N. Kutterer, H. |
| author_facet | Yuan, P. Hunegnaw, A. Alshawaf, F. Awange, Joseph Klos, A. Teferle, F.N. Kutterer, H. |
| author_sort | Yuan, P. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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−2 decade−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. The E-G IWV trends are within −0.2–0.4 kg m−2 decade−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−2 decade−1) but higher around the Mediterranean Sea (0.6–1.4 kg m−2 decade−1). |
| first_indexed | 2025-11-14T11:25:47Z |
| format | Journal Article |
| id | curtin-20.500.11937-86607 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:25:47Z |
| publishDate | 2021 |
| publisher | ELSEVIER SCIENCE INC |
| recordtype | eprints |
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| spelling | curtin-20.500.11937-866072021-12-06T06:40:42Z Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance Yuan, P. Hunegnaw, A. Alshawaf, F. Awange, Joseph Klos, A. Teferle, F.N. Kutterer, H. Science & Technology Life Sciences & Biomedicine Technology Environmental Sciences Remote Sensing Imaging Science & Photographic Technology Environmental Sciences & Ecology Climate change Water vapor IWV Trend Uncertainty Time series Noise ERA5 GPS TIME-SERIES NOISE AUTOCORRELATION METEOROLOGY REANALYSES MODELS REGION DELAY 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−2 decade−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. The E-G IWV trends are within −0.2–0.4 kg m−2 decade−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−2 decade−1) but higher around the Mediterranean Sea (0.6–1.4 kg m−2 decade−1). 2021 Journal Article http://hdl.handle.net/20.500.11937/86607 10.1016/j.rse.2021.112416 English ELSEVIER SCIENCE INC restricted |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Technology Environmental Sciences Remote Sensing Imaging Science & Photographic Technology Environmental Sciences & Ecology Climate change Water vapor IWV Trend Uncertainty Time series Noise ERA5 GPS TIME-SERIES NOISE AUTOCORRELATION METEOROLOGY REANALYSES MODELS REGION DELAY Yuan, P. Hunegnaw, A. Alshawaf, F. Awange, Joseph Klos, A. Teferle, F.N. Kutterer, H. Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance |
| title | Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance |
| title_full | Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance |
| title_fullStr | Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance |
| title_full_unstemmed | Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance |
| title_short | Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance |
| title_sort | feasibility of era5 integrated water vapor trends for climate change analysis in continental europe: an evaluation with gps (1994–2019) by considering statistical significance |
| topic | Science & Technology Life Sciences & Biomedicine Technology Environmental Sciences Remote Sensing Imaging Science & Photographic Technology Environmental Sciences & Ecology Climate change Water vapor IWV Trend Uncertainty Time series Noise ERA5 GPS TIME-SERIES NOISE AUTOCORRELATION METEOROLOGY REANALYSES MODELS REGION DELAY |
| url | http://hdl.handle.net/20.500.11937/86607 |