John R. Lanzante

RESISTANT, ROBUST AND NON‐PARAMETRIC TECHNIQUES FOR THE ANALYSIS OF CLIMATE DATA: THEORY AND EXAMPLES, INCLUDING APPLICATIONS TO HISTORICAL RADIOSONDE STATION DATA

Basic traditional parametric statistical techniques are used widely in climatic studies for characterizing the level (central tendency) and variability of variables, assessing linear relationships (including trends), detection of climate change, quality control and assessment, identification of extreme events, etc. These techniques may involve estimation of parameters such as the mean (a measure of location), variance (a measure of scale) and correlatiodregression coefficients (measures of linear association); in addition, it is often desirable to estimate the statistical significance of the difference between estimates of the mean from two different samples as well as the significance of estimated measures of association. The validity of these estimates is based on underlying assumptions that sometimes are not met by real climate data. Two of these assumptions are addressed here: normality and homogeneity (and as a special case statistical stationarity); in particular, contamination from a relatively few ‘outlying values’ may greatly distort the estimates. Sometimes these common techniques are used in order to identify outliers; ironically they may fail because of the presence of the outliers! Alternative techniques drawn from the fields of resistant, robust and non-parametric statistics are usually much less affected by the presence of ‘outliers’ and other forms of non-normality. Some of the theoretical basis for the alternative techniques is presented as motivation for their use and to provide quantitative measures for their performance as compared with the traditional techniques that they may replace. Although this work is by no means exhaustive, typically a couple of suitable alternatives are presented for each of the common statistical quantitiedtests mentioned above. All of the technical details needed to apply these techniques are presented in an extensive appendix. With regard to the issue of homogeneity of the climate record, a powerfd non-parametric technique is introduced for the objective identification of ‘change-points’ (discontinuities) in the mean. These may arise either naturally (abrupt climate change) or as the result of errors or changes in instruments, recording practices, data transmission, processing, etc. The change-point test is able to identify multiple discontinuities and requires no ‘metadata’ or comparison with neighbouring stations; these are important considerations because instrumental changes are not always documented and, particularly with regard to radiosonde observations, suitable neighbouring stations for ‘buddy checks’ may not exist. However, when such auxiliary information is available it may be used as independent confirmation of the artificial nature of the discontinuities. The application and practical advantages of these alternative techniques are demonstrated using primarily actual radiosonde station data and in a few cases using some simulated (artificial) data as well. The ease with which suitable examples were obtained from the radiosonde archive begs for serious consideration of these techniques in the analysis of climate data.

An Assessment of Satellite and Radiosonde Climatologies of Upper-Tropospheric Water Vapor

Abstract This study compares radiosonde and satellite climatologies of upper-tropospheric water vapor for the period 1979–1991. Comparison of the two climatologies reveals significant differences in the regional distribution of upper-tropospheric relative humidity. These discrepancies exhibit a distinct geopolitical dependence that is demonstrated to result from international differences in radiosonde instrumentation. Specifically, radiosondes equipped with goldbeaters skin humidity sensors (found primarily in the former Soviet Union, China, and eastern Europe) report a systematically moister upper troposphere relative to the satellite observations, whereas radiosondes equipped with capacitive or carbon hygristor sensors (found at most other locations) report a systematically drier upper troposphere. The bias between humidity sensors is roughly 15%–20% in terms of the relative humidity, being slightly greater during summer than during winter and greater in the upper troposphere than in the midtroposphere...

Sensitivity of Tropospheric and Stratospheric Temperature Trends to Radiosonde Data Quality

Abstract Radiosonde data have been used, and will likely continue to be used, for the detection of temporal trends in tropospheric and lower-stratospheric temperature. However, the data are primarily operational observations, and it is not clear that they are of sufficient quality for precise monitoring of climate change. This paper explores the sensitivity of upper-air temperature trend estimates to several data quality issues. Many radiosonde stations do not have even moderately complete records of monthly mean data for the period 1959–95. In a network of 180 stations (the combined Global Climate Observing System Baseline Upper-Air Network and the network developed by J. K. Angell), only 74 stations meet the data availability requirement of at least 85% of nonmissing months of data for tropospheric levels (850–100 hPa). Extending into the lower stratosphere (up to 30 hPa), only 22 stations have data records meeting this requirement for the same period, and the 30-hPa monthly data are generally based on ...

Temporal Homogenization of Monthly Radiosonde Temperature Data. Part I: Methodology

Abstract Historical changes in instrumentation and recording practices have severely compromised the temporal homogeneity of radiosonde data, a crucial issue for the determination of long-term trends. Methods developed to deal with these homogeneity problems have been applied to a near–globally distributed network of 87 stations using monthly temperature data at mandatory pressure levels, covering the period 1948–97. The homogenization process begins with the identification of artificial discontinuities through visual examination of graphical and textual materials, including temperature time series, transformations of the temperature data, and independent indicators of climate variability, as well as ancillary information such as station history metadata. To ameliorate each problem encountered, a modification was applied in the form of data adjustment or data deletion. A companion paper (Part II) reports on various analyses, particularly trend related, based on the modified data resulting from the method ...

Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) : A new data set of large-area anomaly time series

[1] A new data set containing large-scale regional mean upper air temperatures based on adjusted global radiosonde data is now available up to the present. Starting with data from 85 of the 87 stations adjusted for homogeneity by Lanzante, Klein and Seidel, we extend the data beyond 1997 where available, using a first differencing method combined with guidance from station metadata. The data set consists of temperature anomaly time series for the globe, the hemispheres, tropics (30N–30S) and extratropics. Data provided include annual time series for 13 pressure levels from the surface to 30 mbar and seasonal time series for three broader layers (850–300, 300–100 and 100–50 mbar). The additional years of data increase trends to more than 0.1 K/decade for the global and tropical midtroposphere for 1979–2004. Trends in the stratosphere are approximately 0.5 to 0.9 K/decade and are more negative in the tropics than for the globe. Differences between trends at the surface and in the troposphere are generally reduced in the new time series as compared to raw data and are near zero in the global mean for 1979–2004. We estimate the uncertainty in global mean trends from 1979 to 2004 introduced by the use of first difference processing after 1995 at less than 0.02–0.04 K/decade in the troposphere and up to 0.15 K/decade in the stratosphere at individual pressure levels. Our reliance on metadata, which is often incomplete or unclear, adds further, unquantified uncertainty that could be comparable to the uncertainty from the FD processing. Because the first differencing method cannot be used for individual stations, we also provide updated station time series that are unadjusted after 1997. The Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) data set will be archived and updated at NOAA’s National Climatic Data Center as part of its climate monitoring program.

Separating signal and noise in atmospheric temperature changes: The importance of timescale

We compare global-scale changes in satellite estimates of the temperature of the lower troposphere (TLT) with model simulations of forced and unforced TLT changes. While previous work has focused on a single period of record, we select analysis timescales ranging from 10 to 32 years, and then compare all possible observed TLT trends on each timescale with corresponding multi-model distributions of forced and unforced trends. We use observed estimates of the signal component of TLT changes and model estimates of climate noise to calculate timescale-dependent signal-to-noise ratios (S/N). These ratios are small (less than 1) on the 10-year timescale, increasing to more than 3.9 for 32-year trends. This large change in S/N is primarily due to a decrease in the amplitude of internally generated variability with increasing trend length. Because of the pronounced effect of interannual noise on decadal trends, a multi-model ensemble of anthropogenically-forced simulations displays many 10-year periods with little warming. A single decade of observational TLT data is therefore inadequate for identifying a slowly evolving anthropogenic warming signal. Our results show that temperature records of at least 17 years in length are required for identifying human effects on global-mean tropospheric temperature. Copyright 2011 by the American Geophysical Union.

Temporal Homogenization of Monthly Radiosonde Temperature Data. Part II: Trends, Sensitivities, and MSU Comparison

Trends in radiosonde-based temperatures and lower-tropospheric lapse rates are presented for the time periods 1959‐97 and 1979‐97, including their vertical, horizontal, and seasonal variations. A novel aspect is that estimates are made globally of the effects of artificial (instrumental or procedural) changes on the derived trends using data homogenization procedures introduced in a companion paper (Part I). Credibility of the data homogenization scheme is established by comparison with independent satellite temperature measurements derived from the microwave sounding unit (MSU) instruments for 1979‐97. The various analyses are performed using monthly mean temperatures from a near‐globally distributed network of 87 radiosonde stations. The severity of instrument-related problems, which varies markedly by geographic region, was found, in general, to increase from the lower troposphere to the lower stratosphere, although surface data were found to be as problematic as data from the stratosphere. Except for the surface, there is a tendency for changes in instruments to artificially lower temperature readings with time, so that adjusting the data to account for this results in increased tropospheric warming and decreased stratospheric cooling. Furthermore, the adjustments tend to enhance warming in the upper troposphere more than in the lower troposphere; such sensitivity may have implications for ‘‘fingerprint’’ assessments of climate change. However, the most sensitive part of the vertical profile with regard to its shape was near the surface, particularly at regional scales. In particular, the lower-tropospheric lapse rate was found to be especially sensitive to adjustment as well as spatial sampling. In the lower stratosphere, instrument-related biases were found to artificially inflate latitudinal differences, leading to statistically significantly more cooling in the Tropics than elsewhere. After adjustment there were no significant differences between the latitude zones.

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.

Identifying human influences on atmospheric temperature.

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

Lag Relationships Involving Tropical Sea Surface Temperatures

Abstract A long historical record (∼100 years) of monthly sea surface temperature anomalies from the Comprehensive Ocean–Atmosphere Data Set was used to examined the lag relationships between different locations in the global Tropics. Application of complex principal component (CPC) analysis revealed that the leading mode captures ENSO-related quasi-cyclical warming and cooling in the tropical Pacific Ocean. The dominant features of this mode indicate that SST anomalies in the eastern Pacific lead those of the central Pacific. However, a somewhat weaker aspect of this mode also indicates that SST anomalies in the tropical Indian and western tropical North Atlantic Oceans vary roughly in concert with each other but lag behind those in the central and eastern Pacific. The stability of these lag relationships is indicated by the fact that the leading mode is quite similar in three different 30-year time periods. In order to further examine these relationships some simple indexes were formed as the average ov...