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The article describes the U.S. National Oceanic and Atmospheric Administration's (NOAA) Merged Land-Ocean Surface Temperature analysis or the MLOST version 3.5. It explains that this version features improved adjustments for instrumentation, station location and siting conditions. It describes the differences between MLOST 3.5 and the previous version and discusses possible improvements with MLOST through the use of improved spatial sampling.

In support of climate monitoring and assessments, the National Oceanic and Atmospheric Administration's (NOAA's) National Climatic Data Center has developed an improved version ofthe U.S. Historical Climatology Network temperature dataset (HCN version 2). In this paper, the HCN version 2 temperature data are described in detail, with a focus on the quality-assured data sources and the systematic bias adjustments. The bias adjustments are discussed in the context of their effect on U.S. temperature trends from the period 1895-2007 and in terms of the differences between version 2 and its widely used predecessor (now referred to as HCN version 1). Evidence suggests that the collective effect of changes in observation practice at U.S. HCN stations is systematic and ofthe same order of magnitude as the background climate signal. For this reason, bias adjustments are essential to reducing the uncertainty in U.S. climate trends. The largest biases in the HCN are shown to be associated with changes to the time of observation and with the widespread changeover from liquid-in-glass thermometers to the maximum minimum temperature system (MMTS). With respect to HCN version 1, HCN version 2 trends in maximum temperatures are similar, while minimum temperature trends are somewhat smaller because of 1) an apparent overcorrection in HCN version 1 for the MMTS instrument change and 2) the systematic effect of undocumented station changes, which were not addressed in HCN version 1. [ABSTRACT FROM AUTHOR]

An automated homogenization algorithm based on the pairwise comparison of monthly temperature series is described. The algorithm works by forming pairwise difference series between serial monthly temperature values from a network of observing stations. Each difference series is then evaluated for undocumented shifts, and the station series responsible for such breaks is identified automatically. The algorithm also makes use of station history information, when available, to improve the identification of artificial shifts in temperature data. In addition, an evaluation is carried out to distinguish trend inhomogeneities from abrupt shifts. When the magnitude of an apparent shift attributed to a particular station can be reliably estimated, an adjustment is made for the target series. The pairwise algorithm is shown to be robust and efficient at detecting undocumented step changes under a variety of simulated scenarios with step- and trend-type inhomogeneities. Moreover, the approach is shown to yield a lower false-alarm rate for undocumented changepoint detection relative to the more common use of a reference series. Results from the algorithm are used to assess evidence for trend inhomogeneities in U.S. monthly temperature data. [ABSTRACT FROM AUTHOR]

An evaluation of three hypothesis test statistics that are commonly used in the detection of undocumented changepoints is described. The goal of the evaluation was to determine whether the use of multiple tests could improve undocumented, artificial changepoint detection skill in climate series. The use of successive hypothesis testing is compared to optimal approaches, both of which are designed for situations in which multiple undocumented changepoints may be present. In addition, the importance of the form of the composite climate reference series is evaluated, particularly with regard to the impact of undocumented changepoints in the various component series that are used to calculate the composite. In a comparison of single test changepoint detection skill, the composite reference series formulation is shown to be less important than the choice of the hypothesis test statistic, provided that the composite is calculated from the serially complete and homogeneous component series. However, each of the evaluated composite series is not equally susceptible to the presence of changepoints in its components, which may be erroneously attributed to the target series. Moreover, a reference formulation that is based on the averaging of the first-difference component series is susceptible to random walks when the composition of the component series changes through time (e.g., values are missing), and its use is, therefore, not recommended. When more than one test is required to reject the null hypothesis of no changepoint, the number of detected changepoints is reduced proportionately less than the number of false alarms in a wide variety of Monte Carlo simulations. Consequently, a consensus of hypothesis tests appears to improve undocumented changepoint detection skill, especially when reference series homogeneity is violated. A consensus of successive hypothesis tests using a semihierarchic splitting algorithm also compares favorably to optimal solutions, even when changepoints are not hierarchic. [ABSTRACT FROM AUTHOR]

Temperature time series for stations in western North Carolina are used to evaluate the potential for an urban signal in the local temperature trend, and to compare a homogeneous temperature record from a mountain-top station to two versions of the lower-tropospheric, satellite-derived temperatures from the Microwave Sounding Unit (MSU). Results regarding the urban signal are in agreement with the conclusion from previous investigations that after a location is urbanized, the local temperature trend is consistent with trends derived from surrounding, more rural stations. With respect to the mountain top and lower-tropospheric temperature comparison, the magnitudes of the two MSU-derived trends for the western North Carolina area are closer to the average annual minimum temperature trend than to the annual average maximum temperature trend. [ABSTRACT FROM AUTHOR]