DOI: 10.5061/dryad.w0vt4b8wk
title: Data and code for: A century of reforestation reduced anthropogenic warming in the eastern United States

description: Restoring and preserving the world’s forests are promising natural
 pathways to mitigate some aspects of climate change. In addition to
 regulating atmospheric carbon dioxide concentrations, forests modify
 surface and near-surface air temperatures through biophysical processes.
 In the eastern United States (EUS), widespread reforestation during the
 20th century coincided with an anomalous lack of warming, raising
 questions about reforestation’s contribution to local cooling and climate
 mitigation. Using new cross-scale approaches and multiple independent
 sources of data, we uncovered links between reforestation and the response
 of both surface and air temperature in the EUS. Ground- and
 satellite-based observations showed that EUS forests cool the land surface
 by 1–2 °C annually compared to nearby grasslands and croplands, with the
 strongest cooling effect during midday in the growing season, when cooling
 is 2 to 5 °C. Young forests (20–40 years) have the strongest cooling
 effect on surface temperature. Surface cooling extends to the near-surface
 air, with forests reducing midday air temperature by up to 1 °C compared
 to nearby non-forests. Analyses of historical land cover and air
 temperature trends showed that the cooling benefits of reforestation
 extend across the landscape. Locations surrounded by reforestation were up
 to 1 °C cooler than neighboring locations that did not undergo land cover
 change, and areas dominated by regrowing forests were associated with
 cooling temperature trends in much of the EUS. Our work indicates
 reforestation contributed to the historically slow pace of warming in the
 EUS, underscoring reforestation’s potential as a local climate adaptation
 strategy in temperate regions.
description: The data utilized in this study is openly available and can be
 accessed via the links provided below. The data has been processed in
 accordance with the methodology outlined in the methods section of the
 paper. Data Purpose in Paper Specific Figure in Paper
 URL 
 Delaware Air
 Temperature & Precipitation Dataset Gridded 0.5 ° time series of monthly
 Ta Fig 1c,Fig
 5d https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html North American Carbon Program Forest Age Maps Gridded 1 km forest age estimates Fig 1a, Fig 5c https://daac.ornl.gov/NACP/guides/NA_Tree_Age.html FOREcasting SCEnarios of Land-use Change  (FORE-SCE) Backcasting Grids Historical Land Use Change - 250 m resolution Fig 1b, Fig 5b, Fig 5d https://www.sciencebase.gov/catalog/item/59d3c73de4b05fe04cc3d1d1 MODIS Land Surface Temperature Product (MYD11A1v6.1) Remotely Sensed Surface Temperature Fig 3a, 3b https://lpdaac.usgs.gov/products/myd11a1v061/ Ameriflux Tower Data Tower Data for Paired and Flux Tower Syntheses Fig 3c, 3d, 4 a-d. https://ameriflux.lbl.gov/ NEON Tower Data Tower Data for Flux Tower Synthesis Fig 4 a-d https://data.neonscience.org/data-products/DP4.00200.001 National Land Cover Database 30 m Land cover for transect analyses Fig 4e https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover&f%5B1%5D=region%3Aconus Landsat Provisional Surface Temperature Remotely Sensed Surface Temperature for Transect analyses Fig 4e https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-level-2-provisional-surface United States Historical Climate Network  (USHCN) Meteorological Station Historical Air Temperature Data Fig 5a, 5b https://www.ncei.noaa.gov/pub/data/ushcn/v2.5/ Daymet Gridded Mesoscale Air Temperature Fig 3a, Fig 3b, Fig 4a-d, Fig 4e  
description: All the data can be processed using open-source programs such as
 R or Python. 
description: # Data and code for: A century of reforestation reduced anthropogenic
 warming in the eastern United States The dataset used in the study
 consists of various sources of data that were utilized to investigate the
 links between reforestation and the response of surface and air
 temperature in the Eastern United States. The data includes gridded time
 series of monthly air temperature, forest age estimates, historical land
 use change data, remotely sensed surface temperature data, tower data for
 paired and flux tower syntheses, historical air temperature data, and
 gridded mesoscale air temperature data. The data was processed in
 accordance with the methodology outlined in the methods section of the
 paper, and was used to generate various figures throughout the paper to
 support the experimental procedures and results. ## Description of the
 Data and file structure The data and file structure for this project
 include an R Markdown file named Dryad_Figs.Rmd which produces the main
 text figures, and a setup .R code file named 00_setup_project.R that
 contains all the user-defined functions required for data processing. All
 intermediate data products required for reproducing the figures are also
 included. The folder where all the data resides is named data_dryad and
 the R Markdown file points to this folder. The files contained within the
 folder are as follows: * 00_setup_project.R *
 12_03_cleaned_transectpoints.csv * annual_late_SeasonTrend_9_16_2022.tif *
 Aqua_biggerbox_4Lats_2022.csv * Change_LC_FORESCE_7cats_2021_processed.tif
 * Change_LC_FORESCE.tif * Dryad_Figs.Rmd (R markdown) *
 Forest_Age_Conus.tif * Forest_Age_CropHerb_Zero.tif *
 highres_historical_photo.png * landcover_proj.tif * README.md (this file)
 * Ta_ann_abs.csv * Ta_AquaTs_All_Bigger.tif * Tavg_ann_sc.csv *
 Temp_Change_Map.tif * Tmax_gs_abs.csv * Tmax_gs_sc.csv *
 ushcn-v2-stations.csv ### Descriptions of Data Files
 **00_setup_project.R** This script initializes the project's analysis
 environment, setting up libraries, user-defined functions, and
 environmental variables. It ensures all subsequent scripts and R Markdown
 files run within a consistent framework, crucial for the project's
 data analysis. **12_03_cleaned_transectpoints.csv** This dataset contains
 processed transect analysis data, detailing the transition between forest
 and herbaceous cover. Variables include distance measurements, landcover
 classification, environmental variables like Land Surface Temperature
 (LST), and difference values (diff), providing a basis for spatial pattern
 analysis. **annual_late_SeasonTrend_9_16_2022.tif** An intermediate raster
 data product that displays the spatial pattern of annual late-season
 temperature trends, indicating regional temperature changes throughout the
 study area. **Aqua_biggerbox_4Lats_2022.csv** Monthly statistical
 summaries of temperature data at different elevations and land covers
 (herbaceous; 'Her', forested; 'For', and cropland,
 'Crp') are provided in this file. It includes average
 temperatures and standard deviations by month for each land cover type and
 elevation level. **Change_LC_FORESCE_7cats_2021_processed.tif** This TIFF
 file presents land cover change data processed from the FORE-SCE model,
 segmented into seven distinct categories to represent different land cover
 states throughout the years 1938, 1965, and 1992. The categorization aids
 in the analysis of land cover change impacts on regional temperature
 trends. The categories are defined as follows: 1='Agriculture',
 2='Early Deforestation', 3='Late Deforestation',
 4='Early Reforestation', 5='Late Reforestation',
 6='Persistent Forest', and 7='High Turnover', where
 'High Turnover' indicates areas with frequent land cover
 changes. **Change_LC_FORESCE.tif** This TIFF file contains the original
 land cover change data from the FORE-SCE model without processing into
 specific categories. It provides the baseline from which the processed
 data was derived. **Dryad_Figs.Rmd (R Markdown)** Scripts within this
 document generate the manuscript's main figures. It includes
 comprehensive annotations and explanations, ensuring transparency in data
 visualization processes. **Forest_Age_Conus.tif** Forest age data across
 the contiguous United States, obtained from the North American Carbon
 Program. This 1 km resolution data informs the analysis of forest
 maturation effects on regional temperatures.
 **Forest_Age_CropHerb_Zero.tif** This file modifies the forest age dataset
 by assigning a zero age value to non-forest areas, such as croplands and
 herbaceous covers, to focus analyses on forested regions.
 **highres_historical_photo.png** This high-resolution PNG image is a
 historical photograph used qualitatively illustrate land cover and tree
 planting. **landcover_proj.tif** This file contains land cover data from
 the National Land Cover Database (NLCD), which has been geographically
 projected for accurate spatial analysis and cropped to the study area. It
 is utilized to determine the land cover types surrounding weather station
 locations and for conducting transect analyses within the study.
 **README.md (this file)** Provides a detailed overview of the dataset,
 file descriptions, data structure, and replication instructions for the
 analysis. **Ta_ann_abs.csv** Annual absolute temperature records from 398
 United States Historical Climatology Network (USHCN) weather stations.
 This CSV file is organized with rows representing the years 1900 to 2010
 and columns named after the USHCN weather station identifiers. It provides
 a comprehensive dataset for analyzing long-term temperature trends
 **Ta_AquaTs_All_Bigger.tif** Expanded gridded temperature data. It's
 calculated as Ts-Ta, where Ts represents the Aqua MODIS Surface
 Temperature, and Ta is the 2m air temperature obtained from Daymet data.
 **Tavg_ann_sc.csv** Annual scaled temperature records from 398 weather
 stations. For each column, a z-score is calculated, resulting in
 temperatures ranging from -2 to 2 for a given site. This CSV file is
 organized with rows representing the years 1900 to 2010 and columns named
 after the UHSCN weather station identifiers. The scaling facilitates
 comparative temperature trend analysis across different climatic zones and
 conditions. **Temp_Change_Map.tif** Visualizes the spatial temperature
 change pattern across the study area, marking zones of temperature
 increase and decrease. **Tmax_gs_abs.csv** Growing season maximum
 temperature records from 398 weather stations. This CSV file is organized
 with rows representing the years 1900 to 2010 and columns named after the
 USHCN weather station identifiers. It provides a comprehensive dataset for
 analyzing long-term temperature trends **Tmax_gs_sc.csv** A scaled version
 of the growing season maximum temperature data for all 398 weather station
 sites. For each column, a z-score is calculated, resulting in temperatures
 ranging from -2 to 2 for a given site. This CSV file is organized with
 rows representing the years 1900 to 2010 and columns named after the USHCN
 weather station identifiers. The scaling facilitates comparative
 temperature trend analysis across different climatic zones and conditions.
 **ushcn-v2-stations.csv** This CSV file serves as a reference key for the
 USHCN weather station metadata used in various analyses throughout the
 project. It lists station identifiers, geographical coordinates (latitude
 and longitude), elevation, state, and station name. The file's
 structure includes columns for each metadata attribute, essential for
 linking temperature records and other data points to specific weather
 station locations. This data and file structure is necessary for
 reproducing the main text figures and conducting further analysis. Much of
 the necessary information is in the Dryad_Figs.Rmd, which is the code to
 create the components of all main text figures in the manuscript. This
 code also contains chunks printing out some of the numbers from the
 results section. ## Data analysis and figure generation The
 `Dryad_Figs.Rmd` file is a comprehensive R Markdown document that contains
 all the code necessary for reproducing the figures presented in our
 manuscript. This file includes: * Analytical code for each figure,
 organized by figure number. * Descriptive annotations explaining the
 purpose and methodology of each code chunk. For each figure in the
 manuscript, `Dryad_Figs.Rmd` provides detailed code, ensuring transparency
 and reproducibility of our results. Users can refer to this file to
 understand the exact methods used for data analysis and figure generation.
 ### Summary of Processing Steps in `Dryad_Figs.Rmd` **Setup:** - Various
 libraries and options are set up for the code execution, including
 settings for figures, fonts, and data loading. **Data Loading:** - Several
 raster files and CSV data files are loaded into the script. These files
 contain data related to forest age, land cover, temperature, and more.
 **Figure 1:** - Figure 1 is created, displaying forest age estimates in
 the Southeastern United States (US) calculated from forest age data from
 the North American Carbon Program. Land conversion between agricultural
 land and forests from 1938 to 1992 is also visualized, along with
 temperature change trends from 1900 to 2010. **Figure 3:** - Figure 3
 displays the difference between daily surface temperature (Ts) and daily
 maximum air temperature (Ta) for forests and combined grasslands and
 croplands. It highlights the surface cooling effect. **Figure 4:** -
 Figure 4E is created, visualizing temperature differences in near-surface
 air using transect data. Problematic transects are filtered out, and data
 manipulation is performed for plotting. **Figure 5:** - Figure 5 consists
 of several subplots: - (A) Shows the average trend in pooled annual
 temperature anomalies for USHCN sites in the Eastern US (EUS). - (B)
 Displays the difference in temperature between reforested and
 non-reforested USCHN sites within a 50 km radius. - (C) Depicts the July
 temperature difference (Ts - Ta, Daymet) versus forest age using data from
 30,000 randomly selected MODIS pixels across the study area. - (D) Shows
 the spatial moving window correlation between forest age and recent
 long-term temperature trends (1970-present) with a 5 x 5 window. ###
 Figure Structure in `Dryad_Figs.Rmd` * **Figure 1**: Analysis of forest
 age estimates, land conversion data, temperature trends, and historical
 reforestation efforts. * **Figure 3**: Examination of surface cooling
 effects using satellite and flux tower data. * **Figure 4**: Exploration
 of the extension of surface cooling to near-surface air. * **Figure 5**:
 Impact assessment of land cover and forest age on long-term temperature
 trends. * **Additional Analyses**: Additional sensitivity analyses
 supporting the robustness of the presented results Each section in the
 `Dryad_Figs.Rmd` corresponds to a figure in the manuscript and is clearly
 labeled for easy navigation. The file also includes additional analyses
 and sensitivity checks pertinent to our findings. ## Sharing/access
 Information Links to other publicly accessible locations of the raw data:
 Was data derived from another source? If yes, list source(s):Sources: ---
 | Data Source | Purpose in Paper | Specific Figure in Paper | URL |
 Licensing/Usage Notes | | ------------------------------------------------
 | --------------------------------------------------------- |
 -------------------------------- |
 ------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------- | | Delaware Air Temperature & Precipitation Dataset | Gridded 0.5° time series of monthly Ta | Fig 1c, Fig 5d | [Link](https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html) | Citation requested for data use. | | North American Carbon Program Forest Age Maps | Gridded 1 km forest age estimates | Fig 1a, Fig 5c | [Link](https://daac.ornl.gov/NACP/guides/NA_Tree_Age.html) | Data openly shared, in accordance with NASA's Earth Science program Data and Information Policy. | | FORE-SCE Backcasting Grids | Historical Land Use Change - 250 m resolution | Fig 1b, Fig 5b, Fig 5d | [Link](https://www.sciencebase.gov/catalog/item/59d3c73de4b05fe04cc3d1d1) | Data from USGS, publicly available with no explicit restrictions noted. | | MODIS Land Surface Temperature Product | Remotely Sensed Surface Temperature | Fig 3a, Fig 3b | [Link](https://lpdaac.usgs.gov/products/myd11a1v061/) | Available freely under NASA open data standards. | | AmeriFlux Tower Data | Tower Data for Paired and Flux Tower Syntheses | Fig 3c, Fig 3d, Fig 4a-d | [Link](https://ameriflux.lbl.gov/) | Shared under the AmeriFlux CC-BY-4.0 License from August 2021 onwards. Prior data under AmeriFlux Legacy Data Policy. | | NEON Tower Data | Tower Data for Flux Tower Synthesis | Fig 4a-d | [Link](https://data.neonscience.org/data-products/DP4.00200.001) | Data shared openly, specific licensing terms not stated. | | National Land Cover Database (NLCD) | 30 m Land cover for transect analyses | Fig 4e | [Link](https://www.mrlc.gov/data) | Public domain information, available at no charge. | | Landsat Provisional Surface Temperature | Remotely Sensed Surface Temperature for Transect analyses | Fig 4e | [Link](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-level-2-provisional-surface) | Available freely under NASA open data standards. | | USHCN Meteorological Station Data | Historical Air Temperature Data | Fig 5a, Fig 5b | [Link](https://www.ncei.noaa.gov/pub/data/ushcn/v2.5/) | Data shared openly, usage requests citation. | | Daymet Data | Gridded Mesoscale Air Temperature | Fig 3a, Fig 3b, Fig 4a-d, Fig 4e | [Link](https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=32) | Data openly shared, in accordance with NASA's Earth Science program Data and Information Policy. | --- Note: Licensing and Compatibility We have made every effort to review and provide information about the licensing terms and compatibility of the data sources used in our study. However, we would like to clarify that while we have strived for compliance with licensing requirements, there are some datasets for which explicit CC0 compatibility could not be confirmed. Please see the Licensing/Usage Notes for each dataset for more information.