Uncertainty in Signals of Large-Scale Climate Variations in Radiosonde and Satellite Upper-Air Temperature Datasets

There is no single reference dataset of long-term global upper-air temperature observations, although several groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of 1976–77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in any individual dataset. The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upperair temperature trends gives a more complete characterization of their uncertainty than reliance on a single dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary. However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle, augmenting the 10 principles that have now been generally accepted (although not generally implemented) by the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent observing systems for measuring the variable, and multiple, independent groups analyzing the data

Uncertainty in Signals of Large-Scale Climate Variations in Radiosonde and Satellite Upper-Air Temperature Datasets

There is no single reference dataset of long-term global upper-air temperature observations, although several groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of 1976–77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in any individual dataset. The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upperair temperature trends gives a more complete characterization of their uncertainty than reliance on a single dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary. However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle, augmenting the 10 principles that have now been generally accepted (although not generally implemented) by the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent observing systems for measuring the variable, and multiple, independent groups analyzing the data

Assessing bias and uncertainty in the HadAT-adjusted radiosonde climate record

Uncertainties in observed records of atmospheric temperature aloft remain poorly quantified. This has resulted in considerable controversy regarding signals of climate change over recent decades from tem-perature records of radiosondes and satellites. This work revisits the problems associated with the removal of inhomogeneities from the historical radiosonde temperature records, and provides a method for quan-tifying uncertainty in an adjusted radiosonde climate record due to the subjective choices made during the data homogenization. This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. A number of validation experiments have been conducted to test the system performance and impact on linear trends. Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. This situation arises from the combi-nation of systematic (unknown magnitude) and random uncertainties (of order 0.05 K decade1) in the radiosonde data. Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homog-enization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from radio-sondes. 1

Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts

The relative merits of three forecast systems addressing the impact of model uncertainty on seasonal/annual forecasts are described. One system consists of a multi-model, whereas two other systems sample uncertainties by perturbing the parametrization of reference models through perturbed parameter and stochastic physics techniques. Ensemble re-forecasts over 1991 to 2001 were performed with coupled climate models started from realistic initial conditions. Forecast quality varies due to the different strategies for sampling uncertainties, but also to differences in initialisation methods and in the reference forecast system. Both the stochastic-physics and perturbed-parameter ensembles improve the reliability with respect to their reference forecast systems, but not the discrimination ability. Although the multi-model experiment has an ensemble size larger than the other two experiments, most of the assessment was done using equally-sized ensembles. The three ensembles show similar levels of skill: significant differences in performance typically range between 5 and 20%. However, a nine-member multi-model shows better results for seasonal predictions with lead times shorter than five months, followed by the stochastic-physics and perturbed-parameter ensembles. Conversely, for seasonal predictions with lead times longer than four months, the perturbed-parameter ensemble gives more often better results. All systems suggest that spread cannot be considered a useful predictor of skill. Annual-mean predictions showed lower forecast quality than seasonal predictions. Only small differences between the systems were found. The full multi-model ensemble has improved quality with respect to all other systems, mainly from the larger ensemble size for lead times longer than four months and annual predictions

Absolute Proper Motions to B~22.5: IV. Faint, Low Velocity White Dwarfs and the White Dwarf Population Density Law

The reduced proper motion diagram (RPMD) for a complete sample of faint stars with high accuracy proper motions in the North Galactic Pole field SA57 is investigated. Eight stars with very large reduced proper motions are identified as faint white dwarf candidates. We discriminate these white dwarf candidates from the several times more numerous QSOs based on proper motion and variability. We discuss the implausibility that these stars could be any kind of survey contaminant. If {\it bona fide} white dwarfs, the eight candidates found here represent a portion of the white dwarf population hitherto uninvestigated by previous surveys by virtue of the faint magnitudes and low proper motions. The newly discovered stars suggest a disk white dwarf scaleheight larger than the values of 250-350 pc typically assumed in assessments of the local white dwarf density. Both a <V/V_{max}> and a more complex maximum likelihood analysis of the spatial distribution of our likely thin disk white dwarfs yield scaleheights of 400-600 pc while at the same time give a reasonable match to the local white dwarf volume density found in other surveys. Our results could have interesting implications for white dwarfs as potential MACHO objects. We can place some direct constraints (albeit weak ones) on the contribution of halo white dwarfs to the dark matter of the Galaxy. Moreover, the elevated scale height that we measure for the thin disk could alter the interpretation of microlensing results to the extent of making white dwarfs untenable as the dominant MACHO contributor. (Abridged)Comment: 38 pages, 5 figures, to appear in April Ap

Analysis of the annual cycle of the precipitable water vapour over Spain from 10-year homogenized series of GPS data

This study reports a characterization of the precipitable water vapor (PWV) at ten sites over Spain from 10 years of hourly data from ground-based GPS receivers. The GPS-PWV data series turned out to be inhomogeneous due to the change in the calibration procedure of the variations of the antenna phase center in November 2006. Radiosonde data were used to homogenize the GPS data series and to assess the quality of the GPS measurements. The annual average value of PWV ranges from 14.5 to 20.0 mm, with an average of 18.3 ± 1.9 for the entire Spain. The highest values are registered at the sites on the coast, especially on the Mediterranean coast, and the lowest ones at inland sites. The PWV presents a clear annual cycle, with a minimum in winter and maximum at the end of the summer. However, the southwestern sites present a relative minimum in July. This minimum seems to be related with the presence of drier air masses in the atmospheric layers between 1 and 4 km altitude. The amplitude of the cycle ranged from 8.9 to 18.7 mm. The largest amplitudes are found at the Mediterranean coastal sites (approx. 15-19 mm) and the lowest ones at inland sites (approx. 9-10 mm). A harmonic analysis of the annual cycle showed that the 12-month period harmonic explains, on average, over 96 % of the variance. The average annual regime of PWV followed the cycle of the temperature, except for the relative minimum of PWV in July at the southwestern sites

Critically Reassessing Tropospheric Temperature Trends from Radiosondes Using Realistic Validation Experiments

Biases and uncertainties in large-scale radiosonde temperature trends in the troposphere are critically reassessed. Realistic validation experiments are performed on an automatic radiosonde homogenization system by applying it to climate model data with four distinct sets of simulated breakpoint profiles. Knowledge of the “truth” permits a critical assessment of the ability of the system to recover the large-scale trends and a reinterpretation of the results when applied to the real observations. The homogenization system consistently reduces the bias in the daytime tropical, global, and Northern Hemisphere (NH) extratropical trends but underestimates the full magnitude of the bias. Southern Hemisphere (SH) extratropical and all nighttime trends were less well adjusted owing to the sparsity of stations. The ability to recover the trends is dependent on the underlying error structure, and the true trend does not necessarily lie within the range of estimates. The implications are that tropical tropospheric trends in the unadjusted daytime radiosonde observations, and in many current upper-air datasets, are biased cold, but the degree of this bias cannot be robustly quantified. Therefore, remaining biases in the radiosonde temperature record may account for the apparent tropical lapse rate discrepancy between radiosonde data and climate models. Furthermore, the authors find that the unadjusted global and NH extratropical tropospheric trends are biased cold in the daytime radiosonde observations. Finally, observing system experiments show that, if the Global Climate Observing System (GCOS) Upper Air Network (GUAN) were to make climate quality observations adhering to the GCOS monitoring principles, then one would be able to constrain the uncertainties in trends at a more comprehensive set of stations. This reaffirms the importance of running GUAN under the GCOS monitoring principles

Wind speed variability over the Canary Islands, 1948-2014: focusing on trend differences at the land-ocean interface and below-above the trade-wind inversion layer

This study simultaneously examines wind speed trends at the land?ocean interface, and below?above the trade-wind inversion layer in the Canary Islands and the surrounding Eastern North Atlantic Ocean: a key region for quantifying the variability of trade-winds and its response to large-scale atmospheric circulation changes. Two homogenized data sources are used: (1) observed wind speed from nine land-based stations (1981?2014), including one mountain weather station (Izaña) located above the trade-wind inversion layer; and (2) simulated wind speed from two atmospheric hindcasts over ocean (i.e., SeaWind I at 30 km for 1948?2014; and SeaWind II at 15 km for 1989?2014). The results revealed a widespread significant negative trend of trade-winds over ocean for 1948?2014, whereas no significant trends were detected for 1989?2014. For this recent period wind speed over land and ocean displayed the same multi-decadal variability and a distinct seasonal trend pattern with a strengthening (late spring and summer; significant in May and August) and weakening (winter?spring?autumn; significant in April and September) of trade-winds. Above the inversion layer at Izaña, we found a predominance of significant positive trends, indicating a decoupled variability and opposite wind speed trends when compared to those reported in boundary layer. The analysis of the Trade Wind Index (TWI), the North Atlantic Oscillation Index (NAOI) and the Eastern Atlantic Index (EAI) demonstrated significant correlations with the wind speed variability, revealing that the correlation patterns of the three indices showed a spatio-temporal complementarity in shaping wind speed trends across the Eastern North Atlantic.C. A. -M. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 703733 (STILLING project). This research was also supported by the Research Projects: Swedish BECC, MERGE, VR (2014–5320), PCIN-2015-220, CGL2014-52135-C03-01 and Red de variabilidad y cambio climático RECLIM (CGL2014-517221-REDT). M.M is indebted to the Spanish Government for funding through the “Ramón y Cajal” program and supported by Grant PORTIO (BIA2015-70644-R