Comparison of decadal global water vapor changes derived from independent satellite time series

Mieruch, S.; Schröder, Marc; Noël, Stefan; Schulz, Jörg B.

doi:10.1002/2014jd021588

Cited by 36 publications

(45 citation statements)

References 42 publications

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“…Significant negative (drying) trends are observed in south-tropical eastern Pacific region, west of the United States and generally south of 60°S. The dipole structure in the south-eastern tropical Pacific area is consistent with the findings of Mieruch et al (2014) and 25 is due to the different ENSO phases for this time period, as reported by Trenberth et al (2005). Over land, significant positive trends are observed in equatorial region along the ITCZ, especially in northern South America, Central Africa, and Indonesia, and in the northern hemisphere, especially over northern North America, Greenland, most of Europe and Siberia.…”

Section: Trends In Gps and Era-interim Iwv (1995-2010) 20supporting

confidence: 89%

Global IWV trends and variability in atmospheric reanalyses and GPS observations

Parracho¹,

Bock²

2018

Preprint

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Abstract. Water vapour plays a key role in the climate system. However, its short residence time in the atmosphere and its high variability in space and time make it challenging when it comes to study trends and variability. There are several sources of water vapour data. In this work we use Integrated Water Vapour (IWV) estimated from GPS observations and atmospheric 10 reanalyses. Monthly and seasonal means, interannual variability, and linear trends are analysed and compared for the period between 1995 and 2010. A general good agreement is found but this study highlights issues in both GPS and reanalysis data sets. In GPS, gaps and inhomogeneities in the time series are evidenced, which affect mainly variability and trend estimation.In ERA-Interim, too strong trends in certain regions (e.g. drying over northern Africa and Australia, and moistening over northern South America) were found. Representativeness differences in coastal areas and regions of complex topography 15 (mountain ranges, islands) are also evidenced as limitations to the intercomparison of the point observations and reanalysis data. A general good agreement is found for the means and variabilities, with the exception of a few stations where representativeness issues are suspected. Monthly IWV trends are also found to be in good sign agreement, with the exception of a handful of stations where, in addition to representativeness errors, there might be inhomogeneities in the GPS time series.Seasonal trends are found to be different and more intense than monthly trends, which emphasizes the influence of atmospheric 20 circulation on IWV trends. In order to assess strong trends over regions lacking GPS stations, a second reanalysis, MERRA-2, is introduced. The period of analysis is extended to 1980-2016 (the longest period the reanalyses have in common) and differences with the shorter period are found. This exemplifies how much IWV trends are dependent on the time period at study and must be interpreted carefully. Temperature trends are also computed for both reanalyses. The Clausius-Clapeyron scaling ratio is found to not be a good humidity proxy at seasonal and regional scales. Regions over northern Africa and 25 Australia, where ERA-Interim and MERRA-2 disagree, are investigated further. Dynamics at these regions is assessed by analyzing the wind fields at 925 hPa and is shown to be tightly linked with the trends and variability in IWV.

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Section: Trends In Gps and Era-interim Iwv (1995-2010) 20supporting

confidence: 89%

Global IWV trends and variability in atmospheric reanalyses and GPS observations

Parracho¹,

Bock²

2018

Preprint

View full text Add to dashboard Cite

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“…Although only lag 1 is considered explicitly, further autocorrelations at higher lags are also considered implicitly, since the correction approach is based on autoregressive processes of order 1 (AR[1]) (Thiebaux and Zwiers, 1984;Mieruch et al, 2014), whose autocorrelation function decreases exponentially, thus considering also higher lags than 1. For uncorrelated data, the statistical significance on the 10 % level for 25 data points is achieved by a correlation coefficient of |ρ| ≥ 0.33.…”

Section: Metricsmentioning

confidence: 99%

The regional MiKlip decadal forecast ensemble for Europe: the added value of downscaling

et al. 2014

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Abstract. The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1-5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6-10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability.

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“…Thus, TCWV products from satellite observations in the red spectral range are a valuable complement to MW, IR, and RO water vapor products, which are sensitive only to specific surfaces or altitude ranges. TCWV products derived from GOME, SCIAMACHY, and GOME-2 have already been used to investigate the water vapor evolution over time on a global scale, e.g., the effects of El Niño (Wagner et al, 2005;Loyola et al, 2006) or trends Mieruch et al, 2008Mieruch et al, , 2011Mieruch et al, , 2014.…”

Section: Introductionmentioning

confidence: 99%

The ESA GOME-Evolution “Climate” water vapor product: a homogenized time series of H<sub>2</sub>O columns from GOME, SCIAMACHY, and GOME-2

Beirle¹,

Lampel

Wang³

et al. 2018

Earth Syst. Sci. Data

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Abstract. We present time series of the global distribution of water vapor columns over more than 2 decades based on measurements from the satellite instruments GOME, SCIAMACHY, and GOME-2 in the red spectral range. A particular focus is the consistency amongst the different sensors to avoid jumps from one instrument to another. This is reached by applying robust and simple retrieval settings consistently. Potentially systematic effects due to differences in ground pixel size are avoided by merging SCIAMACHY and GOME-2 observations to GOME spatial resolution, which also allows for a consistent treatment of cloud effects. In addition, the GOME-2 swath is reduced to that of GOME and SCIAMACHY to have consistent viewing geometries. Remaining systematic differences between the different sensors are investigated during overlap periods and are corrected for in the homogenized time series. The resulting "Climate" product v2.2 (https://doi.org/10.1594/ WDCC/GOME-EVL_water_vapor_clim_v2.2) allows the study of the temporal evolution of water vapor over the last 20 years on a global scale.

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Comparison of decadal global water vapor changes derived from independent satellite time series

Cited by 36 publications

References 42 publications

Global IWV trends and variability in atmospheric reanalyses and GPS observations

Global IWV trends and variability in atmospheric reanalyses and GPS observations

The regional MiKlip decadal forecast ensemble for Europe: the added value of downscaling

The ESA GOME-Evolution “Climate” water vapor product: a homogenized time series of H<sub>2</sub>O columns from GOME, SCIAMACHY, and GOME-2

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Comparison of decadal global water vapor changes derived from independent satellite time series

Cited by 36 publications

References 42 publications

Global IWV trends and variability in atmospheric reanalyses and GPS observations

Global IWV trends and variability in atmospheric reanalyses and GPS observations

The regional MiKlip decadal forecast ensemble for Europe: the added value of downscaling

The ESA GOME-Evolution “Climate” water vapor product: a homogenized time series of H&lt;sub&gt;2&lt;/sub&gt;O columns from GOME, SCIAMACHY, and GOME-2

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Resources

About

The ESA GOME-Evolution “Climate” water vapor product: a homogenized time series of H<sub>2</sub>O columns from GOME, SCIAMACHY, and GOME-2