1 / 22

Long-term Trend of Global Land Precipitation: Uncertainties in Gauge-based Analyses

Mingyue Chen 1) , Pinging Xie 2) , John E. Janowiak 2) , & Phillip A. Arkin 3). Long-term Trend of Global Land Precipitation: Uncertainties in Gauge-based Analyses. 1) RS Information Systems, Inc. 2) Climate Prediction Center, NCEP/NWS/NOAA

Télécharger la présentation

Long-term Trend of Global Land Precipitation: Uncertainties in Gauge-based Analyses

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.


Presentation Transcript

  1. Mingyue Chen1) , Pinging Xie2), John E. Janowiak2), & Phillip A. Arkin3) Long-term Trend of Global Land Precipitation: Uncertainties in Gauge-based Analyses 1) RS Information Systems, Inc. 2) Climate Prediction Center, NCEP/NWS/NOAA 3) Earth Systems Science Interdisciplinary Center, UMD The 29th Annual Climate Diagnostics & Prediction Workshop, 2004

  2. Background • Long-term trends in temperature and precipitation have been examined using STATION OBSERVATIONS [e.g. Karl et al. 1993; Lamb and Peppler 1991]; • SPATIAL DISTRIBUTION of the long-term trend is needed for many applications such as model verifications; • Long-term trend in analysis field may be biased due to changes in gauge network;

  3. Objectives • To describe the spatial distribution of long-term trend of precipitation using gauge-based analyses over land, and • To explore ways to quantify uncertainties of the long-trend in the gauge based analyses due to changes of gauge networks;

  4. Data PREC/L: • The global monthly precipitation analysis over land from 1948-present; • Optimal interpolation (OI) of gauge observation; • 2.5o lat/lon; Gauge observations: • Monthly precipitation collected in GHCN v2 of NCDC/NOAA; • Monthly precipitation collected in CAMS of CPC; • Over 17,000 stations; • From 1948 to the present;

  5. Linear Trend of Annual Mean Precipitation (PREC/L, 1948-2003) • Increasing trend over the US, NW Australia, …; • Decreasing trend over the equatorial Africa, E Australia, …; • The similar patterns are observed in other published gauge • based analyses, e.g. Dai et al. (1997), and New et al. (2000);

  6. Spatial Distribution of Available gauges • The spatial distribution of gauge network changes; • Good coverage in earlier years over most regions; • The US region has good coverage through the period;

  7. Time Series of the Total Number of Available Gauges Used to Define the Gauge-Based Analysis • The total number of available gauges changes; • The maximum during 1960s; • Decreased during later period;

  8. We conducted comparative studies to examine how the magnitude of the gauge-based analyses vary with • Gauge network configuration; and • Interpolation algorithms;

  9. Detailed Examinations of the Gauge-Based Analyses over the Sahel Region

  10. Time series of reporting station number • The number of gauge stations changes; • Subset stations with relatively high reporting rates;

  11. Experiment I:Comparisons of gauge-based analyses using various gauge networks (1931-1980) • Select a period with the best gauge availability over the region [1931 – 1980]; • Construct analyses using observations at stations with 80% or higher reporting rates (the fixed network) and those available at 1921, 1931, …, 1991, 2001 (the changing networks); • Compare the trends calculated from the analyses based on different gauge networks; • Analyses are created using the OI and Shepard algorithms;

  12. Number of gauge stations on 0.5olat/lon grid • The gauge coverage is reasonably well, but • Less stations at the northern dry regions;

  13. Interpolation Algorithms • OI (Optimal Interpolation of Gandin [1965]) Interpolate the monthly anomalies; Weighting statistically; Add the interpolated anomalies to climatology; • Shepard (1965) Interpolate the monthly total; Inverse-distance weighting; Using 4-10 nearest stations;

  14. Areal mean of annual precipitation from OI/Shepard over the Sahel region(1931-1980, June-Sep.) • Similar trends in the analyses with various gauge networks; • The RMSD is much less the magnitude of long-term trend; • OI interpolation is less affected by the gauge network • than Shepard;

  15. Spatial distributions of annual mean, trend, RMSD of trend(1931-1980, June-Sep.) • Over most of the Sahel region the trend uncertainties due to • change of gauge network is very limited; • The Shepard produce more small scale feature of trend pattern; • OI is less affected by the change of gauge network;

  16. Experiment II:Comparisons of trends interpolated using using various gauge networks for data period [1948-2003] • Assume the trend calculated from the PREC/L gauge-based analysis for 1948 – 2003 is true; • Interpolate the trend using gauge networks for each year of the 56-year period; • Compare the 56 sets of interpolated trend distribution to get insight into the uncertainties

  17. Spatial distribution of trend calculated with gauge networks of different years(1948-2003) • Trend distribution is smoothed; • The overall patterns • of trend are similar even when networks are very sparse (e.g.2000);

  18. Trend calculated with gauge networks of different years over the Sahel region • Overall, trends calculated using various gauge networks do not show big difference with that based on a dense network; • Differences in the calculated trend are larger when networks are sparser;

  19. Summary of Results for the Sahel Region • The annual precipitation over the regions of Sahel have been decreasing during the periods of 1931-1980 & 1948-2003; • The uncertainties exist due to the change of network through the period; • The magnitude of the uncertainties in trend is much less than that of the trend itself; • The OI algorithm produces gauge-based analysis with less alias in magnitude than the Shepard;

  20. Examinations over the Global Land Areas for 1948 – 2003

  21. Spatial distributions of annual mean, trend, RMSE of trend (1948-2003)

  22. Summary and Future Work • The spatial distribution of major trend of annual precipitation has been described from the long-term gauge based analysis; • The uncertainties due to the change of gauge network through the period has been explored; • The uncertainties are related to interpolation algorithms, the OI interpolation is better than the Shepard; • The trend are related to gauge network but the trend alias over the major trend regions is limited; • Future work is underway to further quantify the uncertainties, such as, significance test, etc.

More Related