Detection of linear trends in multi-sensor time series in the presence of autocorrelated noise: Application to the chlorophyll-a SeaWiFS and MERIS datasets and extrapolation to the incoming Sentinel 3-OLCI mission
MetadataShow full item record
Copyright © 2013 American Geophysical Union. Reproduced with permission.
The detection of long-term trends in geophysical time series is a key issue in climate change studies. This detection is affected by many factors: the size of the trend to be detected, the length of the available data sets, and the noise properties. Although the noise autocorrelation observed in geophysical time series does not bias the trend estimate, it affects the estimation of its uncertainty and consequently the ability to detect, or not, a significant trend. Ignoring the noise autocorrelation level typically leads to an overdetection of significant trends. Due to satellite lifetime, usually between 5 and 10 years, sea surface time series do not cover the same period and are acquired by different sensors with different characteristics. These differences lead to unknown level shifts (biases) between the datasets, which affect the trend detection. In this work, we develop a generic framework to detect and evaluate linear trends and level shifts in multisensor time series of satellite chlorophyll-a concentrations, as provided by the Medium Resolution Imaging Spectrometer instrument (MERIS) and sea-viewing wide field-of-view sensor (SeaWiFS) ocean-color missions. We also discuss the optimization of the observation networks, in terms of needed time overlap between successive time series to reduce the uncertainty on the detection of long-term trends. For the incoming Sentinel 3-Ocean and Land Color Instrument (3-OLCI)mission that should be launched at the end of 2014, we propose a global map of the number of months of observations to enhance the trend detection performed with the joint SeaWiFS-MERIS analysis.
Showing items related by title, author, creator and subject.
Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significanceYuan, P.; Hunegnaw, A.; Alshawaf, F.; Awange, Joseph ; Klos, A.; Teferle, F.N.; Kutterer, H. (2021)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 ...
Khodabandeh, Amir; Amiri-Simkooei, A.; Sharifi, M. (2012)The efficacy of robust M-estimators is a well-known issue when dealing with observational blunders. When the number of observations is considerably large-long time series for instance-one can take advantage of the asymptotic ...
Parsons, Miles; McCauley, Robert; Mackie, M; Siwabessy, Paulus; Duncan, Alexander (2012)Mulloway (Argyrosomus japonicus) in Mosman Bay, Western Australia produce three call categories associated with spawning behavior. The determination of call source levels and their contribution to overall recorded sound ...