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:

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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.