Climate Statistics based on Monthly Fields

Percentiles were computed at each grid point for the month using the climatology period of 1981 through 2010.

Percentiles:
  • T2M PCNTL The percentile at which the monthly mean 2 m temperature falls in MERRA-2
  • Precip PCNTL The percentile at which the monthly mean precipitation falls using the model generated precipitation in MERRA-2


Climate Statistics based on Daily Temperature and Precipitation

Percentiles were computed for each day of the year using a window of ± 7 days going into the computation for the climatology period of 1981 through 2010.

Indices:
  • Dry Days The number of days within the month with less than 1 mm of precipitation using the model generated precipitation in MERRA-2
  • Consecutive Dry Days (CDD) Maximum number of consecutive days within a month with below 1 mm of precipitation using the model generated precipitation in MERRA-2
  • Wet Days The number of days within the month with at least 1 mm of precipitation using the model generated precipitation in MERRA-2
  • Consecutive Wet Days (CWD) Maximum number of consecutive days within a month with at least 1 mm of precipitation using the model generated precipitation in MERRA-2
  • Min T2M < 10th Percentile (TN10p) The percent of days within the month that had a minimum temperature below the 10th percentile in MERRA-2 (Cold Nights)
  • Max T2M < 10th Percentile (TX10p) The percent of days within the month that had a maximum temperature below the 10th percentile in MERRA-2 (Cold Days)
  • Min T2M > 90th Percentile (TN90p) The percent of days within the month that had a minimum temperature above the 90th percentile in MERRA-2 (Warm nights)
  • Max T2M > 90th Percentile (TX90p) The percent of days within the month that had a maximum temperature above the 90th percentile in MERRA-2 (Warm Days)
  • Diurnal T2M Range (DTR) The monthly mean difference between the daily maximum and minimum 2 m temperature in MERRA-2 (Diurnal Temperature Range)
  • Heat Wave Frequency Count of days satisfying heat wave conditions, where heat waves are defined as MERRA-2 daily mean 2 m temperature exceeding the calendar-day 90th percentile for at least 3 days.
  • Heat Wave Mean Intensity Average daily mean 2 m temperature anomaly over all heat wave days (see Heat Wave Frequency).
  • Days w/ Precip > 90th Percentile (R90d) The number of days within the month that exceeded the 90th percentile using the model generated precipitation in MERRA-2
  • Days w/ Precip > 95th Percentile (R95d) The number of days within the month that exceeded the 95th percentile using the model generated precipitation in MERRA-2
  • Days w/ Precip > 99th Percentile (R99d) The number of days within the month that exceeded the 99th percentile using the model generated precipitation in MERRA-2
  • Precip > 90th Percentile (R90p) Mean precipitation on days within the month that exceeded the 90th percentile using the model generated precipitation in MERRA-2
  • Precip > 95th Percentile (R95p) Mean precipitation on days within the month that exceeded the 95th percentile using the model generated precipitation in MERRA-2
  • Precip > 99th Percentile (R99p) Mean precipitation on days within the month that exceeded the 99th percentile using the model generated precipitation in MERRA-2
  • Number of 5 Day Heavy Rainfall The number of five periods within the month in which the precipitation exceeds 50 mm per 5 days using the observation corrected precipitation in MERRA-2
  • Max 5 Day Precip (RX5Day) Highest precipitation accumulation within a five day period within the month using the observation corrected precipitation in MERRA-2


References

  • MERRA-2
    • Gelaro, R., W. McCarty, M.J. Suárez, R. Todling, A. Molod, L. Takacs, C.A. Randles, A. Darmenov, M.G. Bosilovich, R. Reichle, K. Wargan, L. Coy, R. Cullather, C. Draper, S. Akella, V. Buchard, A. Conaty, A.M. da Silva, W. Gu, G. Kim, R. Koster, R. Lucchesi, D. Merkova, J.E. Nielsen, G. Partyka, S. Pawson, W. Putman, M. Rienecker, S.D. Schubert, M. Sienkiewicz, and B. Zhao, 2017: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Climate, 30, 5419–5454, doi: 10.1175/JCLI-D-16-0758.1.
    • Global Modeling and Assimilation Office (GMAO), 2015a: MERRA-2 statD_2d_slv_Nx: 2d, Daily, Aggregated Statistics, Single-Level, Assimilation, Single-Level Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), doi: 10.5067/9SC1VNTWGWV3.
    • Global Modeling and Assimilation Office (GMAO), 2015b: MERRA-2 tavg1_2d_flx_Nx: 2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), doi: 10.5067/7MCPBJ41Y0K6.
    • Global Modeling and Assimilation Office (GMAO), 2015c: MERRA-2 tavgM_2d_slv_Nx: 2d, Monthly mean, Time-Averaged, Single-Level, Assimilation, Single-Level Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), doi: 10.5067/AP1B0BA5PD2K.
    • Global Modeling and Assimilation Office (GMAO), 2015d: MERRA-2 tavgM_2d_flx_Nx: 2d, Monthly mean, Time-Averaged, Single-Level, Assimilation, Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), doi: 10.5067/0JRLVL8YV2Y4.
  • Indices based on daily temperature and precipitation
    • Alexander L. V., 2015: Global observed long-term changes in temperature and precipitation extremes: A review of progress and limitations in IPCC assessments and beyond. Weather and Climate Extremes, 11, 4-6, doi:10.1016/j.wace.2015.10.007.
    • Collow, A. B. M., S. P. Mahanama, M. G. Bosilovich, R. D. Koster, and Siegfried D. Schubert, 2017: An Evaluation of Teleconnections Over the United States in an Ensemble of AMIP Simulations with the MERRA-2 Configuration of the GEOS Atmospheric Model. NASA/TM-2017-104606, Vol. 47, 68 pp, https://gmao.gsfc.nasa.gov/pubs/docs/Collow963.pdf.
  • Heatwaves
    • Perkins, S.E. and L.V. Alexander, 2013: On the Measurement of Heat Waves. J. Climate, 26, 4500–4517, doi: 10.1175/JCLI-D-12-00383.1.