This tool allows a user to investigate the historical variability of precipitation and temperature at various time scales (interannual, decadal, and long-term linear trend) over the 20th century near a user-selected location.
Please see the accompanying Guidance Document and instructions page for this tool for more information on interpreting these graphs.
Click on the map to show graphs of the seasonal average , its interannual variability, decadal variability, and trend for the nearest available station with monthly data that are at least 85% complete for all Jan to Mar months over the years 1901-2000.
Peterson, T. C., and R. S. Vose, 1997: An overview of the Global Historical Climatology Network temperature database. Bull. Amer. Met. Soc., 78, 2837-2849.
Peterson, T. C., R. Vose, R. Schmoyer, and V. Razuvaev, 1998: Global Historical Climatology Network (GHCN) quality control of monthly temperature data. Int. J. Climatol., 18, 1169-1179.
Hulme, M. (1992) A 1951-80 global land precipitation climatology for the evaluation of General Circulation Models. Climate Dynamics, 7, 57-72.
Hulme, M. (1994) Validation of large-scale precipitation fields in General Circulation Models. pp. 387-406, in Global Precipitations and Climate Change, (eds.) Desbois, M. and Desalmand, F., NATO ASI Series, Springer-Verlag, Berlin, 466pp.
Hulme, M., T. J. Osborn, and T. C. Johns (1998) Precipitation sensitivity to global warming: Comparison of observations with HadCM2 simulations. Geophys. Res. Letts., 25, 3379-3382.
Brohan, P., J. J. Kennedy, I. Harris, S. F. B. Tett and P. D. Jones, 2006: Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. J. Geophysical Research, 111, D12106, doi:10.1029/2005JD006548.
Jones, P. D., New, M., Parker, D. E., Martin, S. and Rigor, I. G., 1999: Surface air temperature and its variations over the last 150 years. Reviews of Geophysics, 37, 173-199.
Amante, C. and B. W. Eakins, ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources, and Analysis., NOAA Technical Memorandum NESDIS NGDC-24, National Geophysical Data Center, NESDIS, NOAA, U. S. Department of Commerce, Boulder, CO, March 2009.
This interface allows a user to display on a map the locations of GHCN (Global Historical Climatology Network) monthly precipitation or temperature stations that meet a user-defined threshold of data completeness for a defined season and range of years and view time series of seasonal-average precipitation or temperature and its variability on various time scales over the 20th century from one of these user-selected stations.
The menu at the top of the page presents options for selecting:
Users may generate a time series analysis of seasonal-average precipitation or temperature for the station that meets the selected data completeness criteria and is closest to the location clicked on the map. Click on the map at the point of interest. Once the map is clicked several items will be displayed.
This time series is constructed by first calculating the seasonal-average temperature or precipitation values, and then calculating the anomaly from the long-term mean of the same (using all non-missing seasons available during the selected year range). Next, the linear best fit to the anomalies (as shown in plot d.) is subtracted, and, finally, the 11-year running average of the detrended anomalies (as shown in plot c.) is subtracted. Since a simple 11-year running average is used in the calculation, five years of data will be lost from each end of the time series.
The decadal component time series is constructed by first calculating the seasonal-average temperature or precipitation values, then calculating the anomaly from the long-term mean of the same (using all non-missing seasons available during the range of selected years). Next, the linear best fit to the anomalies (as shown in plot d.) is subtracted, and an un-weighted 11-year running average is applied to the result. For any given year the 11-year running average is calculated and displayed only if at least 90% of the 11 values in the moving window are non-missing, otherwise the 11-year running average is considered missing and not shown for that year. Since a simple 11-year running average is used, five years of data will be lost from each end of the time series.
This time series is constructed by first calculating the seasonal-average temperature or precipitation values, and then calculating the anomaly from the long-term mean of the same (using all non-missing seasons available during the selected year range). Next, the linear best fit to the anomalies (as shown in plot h.) is subtracted, and, finally, the 11-year running average of the detrended anomalies (as shown in plot g.) is subtracted. Since a simple 11-year running average is used in the calculation, five years of data will be lost from each end of the time series.
The decadal component time series is constructed by first calculating the seasonal-average temperature or precipitation values, then calculating the anomaly from the long-term mean of the same (using all non-missing seasons available during the range of selected years). Next, the linear best fit to the anomalies (as shown in plot h.) is subtracted, and an un-weighted 11-year running average is applied to the result. For any given year the 11-year running average is calculated and displayed only if at least 90% of the 11 values in the moving window are non-missing, otherwise the 11-year running average is considered missing and not shown for that year. Since a simple 11-year running average is used, five years of data will be lost from each end of the time series.
Contact help@iri.columbia.edu with any technical questions or problems with this Map Room, for example, the forecasts not displaying or updating properly.