Russell S. Vose

An Overview of the Global Historical Climatology Network Temperature Database

Abstract The Global Historical Climatology Network version 2 temperature database was released in May 1997. This century-scale dataset consists of monthly surface observations from ∼7000 stations from around the world. This archive breaks considerable new ground in the field of global climate databases. The enhancements include 1) data for additional stations to improve regional-scale analyses, particularly in previously data-sparse areas; 2) the addition of maximum–minimum temperature data to provide climate information not available in mean temperature data alone; 3) detailed assessments of data quality to increase the confidence in research results; 4) rigorous and objective homogeneity adjustments to decrease the effect of nonclimatic factors on the time series; 5) detailed metadata (e.g., population, vegetation, topography) that allow more detailed analyses to be conducted; and 6) an infrastructure for updating the archive at regular intervals so that current climatic conditions can constantly be put...

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...

Overview of the Integrated Global Radiosonde Archive

This paper provides a general description of the Integrated Global Radiosonde Archive (IGRA), a new radiosonde dataset from the National Climatic Data Center (NCDC). IGRA consists of radiosonde and pilot balloon observations at more than 1500 globally distributed stations with varying periods of record, many of which extend from the 1960s to present. Observations include pressure, temperature, geopotential height, dewpoint depression, wind direction, and wind speed at standard, surface, tropopause, and significant levels. IGRA contains quality-assured data from 11 different sources. Rigorous procedures are employed to ensure proper station identification, eliminate duplicate levels within soundings, and select one sounding for every station, date, and time. The quality assurance algorithms check for format problems, physically implausible values, internal inconsistencies among variables, runs of values across soundings and levels, climatological outliers, and temporal and vertical inconsistencies in temperature. The performance of the various checks was evaluated by careful inspection of selected soundings and time series. In its final form, IGRA is the largest and most comprehensive dataset of quality-assured radiosonde observations freely available. Its temporal and spatial coverage is most complete over the United States, western Europe, Russia, and Australia. The vertical resolution and extent of soundings improve significantly over time, with nearly three-quarters of all soundings reaching up to at least 100 hPa by 2003. IGRA data are updated on a daily basis and are available online from NCDC as both individual soundings and monthly means.

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.

Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set

Received 27 May 2005; revised 16 September 2005; accepted 4 November 2005; published 1 March 2006. [1] A gridded land-only data set representing near-surface observations of daily maximum and minimum temperatures (HadGHCND) has been created to allow analysis of recent changes in climate extremes and for the evaluation of climate model simulations. Using a global data set of quality-controlled station observations compiled by the U.S. National Climatic Data Center (NCDC), daily anomalies were created relative to the 1961–1990 reference period for each contributing station. An angular distance weighting technique was used to interpolate these observed anomalies onto a 2.5� latitude by 3.75� longitude grid over the period from January 1946 to December 2000. We have used the data set to examine regional trends in time-varying percentiles. Data over consecutive 5 year periods were used to calculate percentiles which allow us to see how the distributions of daily maximum and minimum temperature have changed over time. Changes during the winter and spring periods are larger than in the other seasons, particularly with respect to increasing temperatures at the lower end of the maximum and minimum temperature distributions. Regional differences suggest that it is not possible to infer distributional changes from changes in the mean alone.

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...

Monitoring and Understanding Changes in Heat Waves, Cold Waves, Floods, and Droughts in the United States: State of Knowledge

Weather and climate extremes have been varying and changing on many different time scales. In recent decades, heat waves have generally become more frequent across the United States, while cold waves have been decreasing. While this is in keeping with expectations in a warming climate, it turns out that decadal variations in the number of U.S. heat and cold waves do not correlate well with the observed U.S. warming during the last century. Annual peak flow data reveal that river flooding trends on the century scale do not show uniform changes across the country. While flood magnitudes in the Southwest have been decreasing, flood magnitudes in the Northeast and north-central United States have been increasing. Confounding the analysis of trends in river flooding is multiyear and even multidecadal variability likely caused by both large-scale atmospheric circulation changes and basin-scale “memory” in the form of soil moisture. Droughts also have long-term trends as well as multiyear and decadal variability...

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.

Global Land-Based Datasets for Monitoring Climatic Extremes

AMERICAN METEOROlOGICAl SOCIETy | July 2013| 997 PB AFFILIATIONS: Donat, alexanDer, anD Yang—Climate Change Research Centre, and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, Australia; Durre anD Vose—NOAA’s National Climatic Data Center, Asheville, North Carolina; Caesar—Met Office Hadley Centre, Exeter, United Kingdom CORRESPONDING AUTHOR: Markus Donat, Climate Change Research Centre, University of New South Wales, Sydney, Australia E-mail: m.donat@unsw.edu.au

The Definition of the Standard WMO Climate Normal: The Key to Deriving Alternative Climate Normals

©2011 American Meteorological Society T he World Meteorological Organization (WMO) and its predecessor, the International Meteorological Organization (IMO), have been coordinating the publication of global climate normals at the monthly scale for about 75 years. Member nations of the IMO/WMO were first mandated to compute climate normals for their respective countries for the 1901–30 period, and are required to update these climate normals every 30 years, resulting in the 1931–60 normals and the 1961–90 normals. Since 1956, the WMO has recommended that each member country recompute their 30-year climate normals every 10 years. Although some member countries do not update their climate normals every decade, for ease of comprehension we hereafter refer to the recommended decadally updated 30-year average as the standard WMO climate normal. Given substantial evidence (e.g., Solomon et al. 2007; Milly et al. 2008) indicating that the stationarity of climate statistics can no longer be (and never should have been) taken for granted, the justification for using a 30-yr normal for describing current and future climate conditions has increasingly been called into question (e.g., the 2007 Journal of Applied Meteorology and Climatology article by Livezey et al., hereafter referred to as L07). The key problem is that climate normals are calculated retrospectively, but are often utilized prospectively. Specifically, climate normals are calculated using data from a recent 30-yr The Definition of the Standard WMO Climate Normal The Key to Deriving Alternative Climate normals