scholarly journals Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method

2011 ◽  
Vol 24 (20) ◽  
pp. 5275-5291 ◽  
Author(s):  
Bettina C. Lackner ◽  
Andrea K. Steiner ◽  
Gabriele C. Hegerl ◽  
Gottfried Kirchengast

Abstract The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere–lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995–2010 intermittently and 2001–10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 50°S–50°N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 276-289
Author(s):  
Milena Vuckovic ◽  
Johanna Schmidt

The importance of high-resolution meteorological time-series data for detection of transformative changes in the climate system is unparalleled. These data sequences allow for a comprehensive study of natural and forced evolution of warming and cooling tendencies, recognition of distinct structural changes, and periodic behaviors, among other things. Such inquiries call for applications of cutting-edge analytical tools with powerful computational capabilities. In this regard, we documented the application potential of visual analytics (VA) for climate change detection in meteorological time-series data. We focused our study on long- and short-term past-to-current meteorological data of three Central European cities (i.e., Vienna, Munich, and Zürich), delivered in different temporal intervals (i.e., monthly, hourly). Our aim was not only to identify the related transformative changes, but also to assert the degree of climate change signal that can be derived given the varying granularity of the underlying data. As such, coarse data granularity mostly offered insights on general trends and distributions, whereby a finer granularity provided insights on the frequency of occurrence, respective duration, and positioning of certain events in time. However, by harnessing the power of VA, one could easily overcome these limitations and go beyond the basic observations.


2015 ◽  
Vol 120 (5) ◽  
pp. 1678-1689 ◽  
Author(s):  
Chi O. Ao ◽  
Jonathan H. Jiang ◽  
Anthony J. Mannucci ◽  
Hui Su ◽  
Olga Verkhoglyadova ◽  
...  

2000 ◽  
Vol 27 (4) ◽  
pp. 569-572 ◽  
Author(s):  
Chris E. Forest ◽  
Myles R. Allen ◽  
Peter H. Stone ◽  
Andrei P. Sokolov

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