Matthew J. Menne

An Overview of the Global Historical Climatology Network-Daily Database

AbstractA database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, including climate analysis and monitoring studies that require data at a daily time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). The dataset contains records from over 80 000 stations in 180 countries and territories, and its processing system produces the official archive for U.S. daily data. Variables commonly include maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Quality assurance checks are routinely applied to the full dataset, but the data are not homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic...

Possible artifacts of data biases in the recent global surface warming hiatus

Walking back talk of the end of warming Previous analyses of global temperature trends during the first decade of the 21st century seemed to indicate that warming had stalled. This allowed critics of the idea of global warming to claim that concern about climate change was misplaced. Karl et al. now show that temperatures did not plateau as thought and that the supposed warming “hiatus” is just an artifact of earlier analyses. Warming has continued at a pace similar to that of the last half of the 20th century, and the slowdown was just an illusion. Science, this issue p. 1469 Updated global surface temperature data do not support the notion of a global warming “hiatus.” Much study has been devoted to the possible causes of an apparent decrease in the upward trend of global surface temperatures since 1998, a phenomenon that has been dubbed the global warming “hiatus.” Here, we present an updated global surface temperature analysis that reveals that global trends are higher than those reported by the Intergovernmental Panel on Climate Change, especially in recent decades, and that the central estimate for the rate of warming during the first 15 years of the 21st century is at least as great as the last half of the 20th century. These results do not support the notion of a “slowdown” in the increase of global surface temperature.

The U.S. Historical Climatology Network Monthly Temperature Data, Version 2

In support of climate monitoring and assessments, the National Oceanic and Atmospheric Administrations (NOAAs) National Climatic Data Center has developed an improved version of the 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 of the 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...

Homogenization of Temperature Series via Pairwise Comparisons

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.

Evaporation changes over the contiguous United States and the former USSR: A reassessment

Observed decreases in pan evaporation over most of the United States and the former USSR during the post-WWII period, if interpreted as a decrease in actual evaporation, are at odds with increases in temperature and precipitation over many regions of these two countries. Using parallel observations of actual and pan evaporation at six Russian, one Latvian, and one U.S. experimental sites, we recalibrate trends in pan evaporation to make them more representative of actual evaporation changes. After applying this transformation, pan evaporation time series over southern Russia and most of the United States reveal an increasing trend in actual evaporation during the past forty years.

Comprehensive Automated Quality Assurance of Daily Surface Observations

This paper describes a comprehensive set of fully automated quality assurance (QA) procedures for observations of daily surface temperature, precipitation, snowfall, and snow depth. The QA procedures are being applied operationally to the Global Historical Climatology Network (GHCN)-Daily dataset. Since these data are used for analyzing and monitoring variations in extremes, the QA system is designed to detect as many errors as possible while maintaining a low probability of falsely identifying true meteorological events as erroneous. The system consists of 19 carefully evaluated tests that detect duplicate data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of the values flagged as errors is used to set the threshold for each procedure such that its falsepositive rate, or fraction of valid values identified as errors, is minimized. In addition, the tests are arranged in a deliberate sequence in which the performance of the later checks is enhanced by the error detection capabilities of the earlier tests. Based on an assessment of each individual check and a final evaluation for each element, the system identifies 3.6 million (0.24%) of the more than 1.5 billion maximum/minimum temperature, precipitation, snowfall, and snow depth values in GHCN-Daily as errors, has a false-positive rate of 1%22%, and is effective at detecting both the grossest errors as well as more subtle inconsistencies among elements.

Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series

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.

Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions

AbstractThis paper describes an improved edition of the climate division dataset for the conterminous United States (i.e., version 2). The first improvement is to the input data, which now include additional station networks, quality assurance reviews, and temperature bias adjustments. The second improvement is to the suite of climatic elements, which now includes both maximum and minimum temperatures. The third improvement is to the computational approach, which now employs climatologically aided interpolation to address topographic and network variability. Version 2 exhibits substantial differences from version 1 over the period 1895–2012. For example, divisional averages in version 2 tend to be cooler and wetter, particularly in mountainous areas of the western United States. Division-level trends in temperature and precipitation display greater spatial consistency in version 2. National-scale temperature trends in version 2 are comparable to those in the U.S. Historical Climatology Network whereas ver...

NOAA's Merged Land–Ocean Surface Temperature Analysis

This paper describes the new release of the Merged Land–Ocean Surface Temperature analysis (MLOST version 3.5), which is used in operational monitoring and climate assessment activities by the NOAA National Climatic Data Center. The primary motivation for the latest version is the inclusion of a new land dataset that has several major improvements, including a more elaborate approach for addressing changes in station location, instrumentation, and siting conditions. The new version is broadly consistent with previous global analyses, exhibiting a trend of 0.076°C decade−1 since 1901, 0.162°C decade−1 since 1979, and widespread warming in both time periods. In general, the new release exhibits only modest differences with its predecessor, the most obvious being very slightly more warming at the global scale (0.004°C decade−1 since 1901) and slightly different trend patterns over the terrestrial surface.

Further Exploring and Quantifying Uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4)

AbstractThe uncertainty in Extended Reconstructed SST (ERSST) version 4 (v4) is reassessed based upon 1) reconstruction uncertainties and 2) an extended exploration of parametric uncertainties. The reconstruction uncertainty (Ur) results from using a truncated (130) set of empirical orthogonal teleconnection functions (EOTs), which yields an inevitable loss of information content, primarily at a local level. The Ur is assessed based upon 32 ensemble ERSST.v4 analyses with the spatially complete monthly Optimum Interpolation SST product. The parametric uncertainty (Up) results from using different parameter values in quality control, bias adjustments, and EOT definition etc. The Up is assessed using a 1000-member ensemble ERSST.v4 analysis with different combinations of plausible settings of 24 identified internal parameter values. At the scale of an individual grid box, the SST uncertainty varies between 0.3° and 0.7°C and arises from both Ur and Up. On the global scale, the SST uncertainty is substantial...