North American Climate Extremes Monitoring

  • Introduction
  • Indices
  • Data Source
  • Download Data
  • Methodology
  • References

Introduction

Extreme weather and climate events—such as drought, heavy rain, and heat waves—are a natural part of the Earth's climate system. Nonetheless, extreme weather and climate events can have significant impacts on our lives and on the environment. In a non-changing climate, society and the environment are more likely to be resilient to weather and climate extremes as they acclimate to the historical range of extremes. However, as the climate changes these extremes may occur outside the historical range, resulting in societal and environmental vulnerabilities.

Certain weather and climate extremes are expected to become more frequent during the 21st century. According to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (IPCC, 2007), "confidence has increased that some weather events and extremes will become more frequent, more widespread or more intense during the 21st century". Thus monitoring and analyzing climate extremes is an important component of assessing the climate system and has received a great deal of attention, especially because the impacts of climate-related changes can vary among regions.

Changes in extremes across North America have already been observed in recent decades. According to the U.S. Climate Change Science Program (CCSP, 2008), "most of North America has been experiencing more unusually hot days and nights, fewer unusually cold days and nights and fewer frost days. Heavy downpours have become more frequent and intense. Droughts are becoming more severe in some regions". The North American Climate Extremes Monitoring (NACEM) product was developed to provide an accessible analysis tool that will help improve the understanding of observed changes in extreme climate conditions by providing users the ability to examine trends and occurrences of certain types of extreme or threshold events at the station-by-station level.

The NACEM currently provides data and analysis for eight indices that have been defined by the World Meteorological Organization (WMO) Commission for Climatology/CLIVAR Expert Team on Climate Change Detection Monitoring and Indices (ETCCDMI). Additional indices will be added to the NACEM at a later time. The NACEM computes each available index at the station-level and provides corresponding anomalies, data permitting, with respect to the 1961–90 long-term average. An interactive map allows users to select a month, season, or specific year (from 1955 to present) to view a snapshot of values for a specific index across North America. There is also an option to view time series graphics for a station of interest by simply selecting the station.

Indices

The current NACEM indices are based on daily minimum and maximum temperature values. Some indices are computed using a fixed reference temperatures—these reference temperatures are the same for all stations across North America. Other indices are computed using reference temperatures that vary from station to station—in these cases, reference temperatures are typically defined as a percentile of the relevant station data series. Using percentiles allows for the selection of a reference temperature for each station used in the analysis, resulting in a unique reference temperature for each station.

Index Definition
Number of Frost Days Count of days with daily minimum temperature less than the reference temperature of 0°C.
Number of Summer Days Count of days with daily maximum temperature greater than the reference temperature of 25°C.
Number of Icing Days Count of days with daily maximum temperature less than the reference temperature of 0°C.
Number of Tropical Nights Count of days with daily minimum temperature greater than the reference temperature of 20°C.
Much Below Average Minimums Percentage of days when daily minimum temperature was less than the 10th percentile.
Much Below Average Maximums Percentage of days when daily maximum temperature was less than the 10th percentile.
Much Above Average Minimums Percentage of days when daily minimum temperature was greater than the 90th percentile.
Much Above Average Maximums Percentage of days when daily maximum temperature was greater than the 90th percentile.

Data Source

The daily temperature data used to compute the indices are obtained from the Global Historical Climatology Network-Daily (GHCN-D) data set.

The period of record varies among stations—some of which extend back to the late 19th century. Station records are updated daily where possible and are usually available one to two days after the date and time of the observation. Several parameters are provided in the GHCN-D, including:

  • Daily Maximum Temperature
  • Daily Minimum Temperature
  • Precipitation
  • Snowfall
  • Snow Depth

The current indices available focus on the analysis of maximum and minimum temperatures which are given in degrees Celsius. Additional indices that are expected to be added in the future will explore more of the complete GHCN-D observation suite.

Please visit our Global Historical Climatology Network-Daily page for more information on the data set.

Methodology

A description of the methodology employed in the calculation of the monthly, seasonal, and annual index values are presented below.


Number of Frost Days, Summer Days, Icing Days, and Tropical Nights

The indices for the number of frost days, summer days, icing days, and tropical nights are computed using a standard reference temperature, which are the same for all stations used in the analysis.

  • Number of Frost Days: days when the daily minimum temperature is less than 0°C (32°F).
  • Number of Summer Days: days when the daily maximum temperature is greater than 25°C (77°F).
  • Number of Icing Days: days when the daily maximum temperature is less than 0°C (32°F).
  • Number of Tropical Nights: days when the daily minimum temperature is greater than 20°C (68°F).

A minimum amount of data is required in order to produce a meaningful index. If 70 percent of data are available, then the specific index can be calculated. If more than 30 percent of data is missing, the index is set to missing.


Much Below Average Maximums & Minimums and Much Above Average Maximums & Minimums

There is a broad range of climates across North America and conditions can vary greatly from location to location. For this reason a reference temperature for determining extreme weather conditions should differ from location to location. The computation of percentiles allows us to select a reference temperature unique for each station used in the analysis. The reference temperature for each station is determined by performing a distribution of observed frequencies of occurrences of all temperature values for a specific time period (either monthly, seasonal, or annual) and computing the top and bottom 10 percent of all occurrences.

  1. Computation of Percentiles

    The 10th percentile (bottom 10 percent of all occurrences) and 90th percentile (top 10 percent of all occurrences) for maximum and minimum temperatures are calculated for each station. These percentiles use all available data for a specified time period (month, season, or year). Available daily data from 1955 through present is sorted in ascending order. If 70 percent of data are available, then the computation continues; otherwise, the reference temperature and index values are set to missing. The reference temperature for the 10th percentile is computed by determining which temperature marks off the lowest 10 percent of the observations from the rest, while the temperature threshold for the 90th percentile is computed by determining which temperature exceeds all but the highest 10 percent of the values.

  2. Computation of Days Above or Below Percentiles

    After computing the reference temperature for the 10th and 90th percentile, for each station, and for maximum and minimum temperatures for a particular time period, the number of days above the 90th percentile value or below the 10th percentile value is totaled for a given month, season, or year.

  3. Computation of the Percentage of Days Above or Below Percentiles

    After computing the number of days above or below percentile for each station, that number is divided by the total number of days in that particular month/season/year. Multiplying this value by 100 provides the percentage of days above or below percentiles.


Computation of the 1961–90 Average for All Indices

A 30-year average is computed for each index at all stations where adequate data is available. The 30-year average is computed if the index has at least 21 years of data available. A straight average is computed with the data available from 1961–90. If more than 30 percent of data is not available, the 30-year average will be set to missing.


Computation of Anomalies with respect to the 1961–1990 Average

An anomaly is the deviation of a single value from the average. If a 30-year average temperature is computed for a station, an anomaly is computed. If a 30-year average temperature is not computed due to lack of data, anomalies are not computed.

References

  • CCSP, 2008: Weather and Climate Extremes in a Changing Climate. Regions of Focus: North America, Hawaii, Caribbean, and U.S. Pacific Islands. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. [Thomas R. Karl, Gerald A. Meehl, Christopher D. Miller, Susan J. Hassol, Anne M. Waple, and William L. Murray (eds.)]. Department of Commerce, NOAA's National Climatic Data Center, Washington, D.C., USA, 164 pp.
  • IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.
  • Alexander, L.V., et al., 2006: Global Observed Changes in Daily Climate Extremes of Temperature and Precepitation. J.Geophys. Res., 111, D05109, doi:10.1029/2005JD006290.
  • Menne,M.J. and C.N.Williams, 2005: Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series, J. Climate, 18, 4271-4286.
  • Vincent,L.A, X.Zhang, B.R.Bonsal and W.D.Hogg, 2002: Homogenization of daily temperatures over Canada. J. Climate, 15, 1322-1344.
  • IPCC, 2001: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J.T.,Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881 pp.
  • Mekis,E. and W.Hogg, 1999: Rehabilitation and Analysis of Canadian Daily Precipitation Time Series. Atmosphere-Ocean, 37, 1, 53-85.