Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

1. Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.
   
2. Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.
    
3. Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, T_max, and T_min are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily T_max and T_min, coldest daily T_max and T_min , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily T_max, T_min, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

• See included README file for more information.
   
• Additional technical details and analyses can be found in:  Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320.  https://doi.org/10.1175/JCLI-D-18-0630.1
   
• Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538
  
• ACIS database for historical observations: http://scacis.rcc-acis.org/  
   
• GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/
   
• Station information for each city can be accessed at: http://threadex.rcc-acis.org/
  

- 2024 August updated - 

1. Annual calculations for 2022 and 2023 were added.
    
2. Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.
    
3. Note that future updates may be infrequent. 

- 2022 January updated -

1. Annual calculations for 2021 were added.
    
2. Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

- 2021 January updated -

1. Annual calculations for 2020 were added.
    
2. Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

- 2020 January updated -

1. Annual calculations for 2019 were added.
    
2. Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.
    
3. Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

- 2019 June updated -

1. Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.
    
2. README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).