TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces. It uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser spatial resolution, but time-varying data from CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55). Conceptually, the procedure applies interpolated time-varying anomalies from CRU Ts4.0/JRA55 to the high-spatial resolution climatology of WorldClim to create a high-spatial resolution dataset that covers a broader temporal record. Temporal information is inherited from CRU Ts4.0 for most global land surfaces for temperature, precipitation, and vapor pressure. However, JRA55 data is used for regions where CRU data had zero climate stations contributing (including all of Antarctica, and parts of Africa, South America, and scattered islands). For primary climate variables of temperature, vapor pressure, and precipitation, the University of Idaho provides additional data on the number of stations (between 0 and 8) that contributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used exclusively for solar radiation and wind speeds. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. A modified Thornthwaite-Mather climatic water-balance model and extractable soil water storage capacity data was used at a 0.5° grid from Wang-Erlandsson et al. (2016). Data Limitations: Long-term trends in data are inherited from parent datasets. TerraClimate should not be used directly for independent assessments of trends. TerraClimate will not capture temporal variability at finer scales than parent datasets and thus is not able to capture variability in orographic precipitation ratios and inversions. The water balance model is very simple and does not account for heterogeneity in vegetation types or their physiological response to changing environmental conditions. Limited validation in data-sparse regions (e.g., Antarctica).

TerraClimate è un set di dati sul clima e sul bilancio idrico mensile per le superfici terrestri globali. Utilizza l'interpolazione climatica, combinando le normali climatiche ad alta risoluzione spaziale del set di dati WorldClim con una risoluzione spaziale più approssimativa, ma con dati invariabili nel tempo di CRU Ts4.0 e del Japanese 55-year Reanalysis (JRA55). A livello concettuale, la procedura applica anomalie con variazioni nel tempo interpolate da CRU Ts4.0/JRA55 alla climatologia ad alta risoluzione spaziale di WorldClim per creare un set di dati ad alta risoluzione spaziale che copra un record temporale più ampio. Le informazioni temporali sono ereditate da CRU Ts4.0 per la maggior parte delle superfici terrestri globali per temperatura, precipitazioni e pressione del vapore. Tuttavia, i dati JRA55 vengono utilizzati per le regioni in cui i dati CRU non hanno contribuito con stazioni climatiche (inclusa tutta l'Antartide e parti dell'Africa, del Sud America e di isole sparse). Per le variabili climatiche principali di temperatura, pressione del vapore e precipitazioni, l'Università dell'Idaho fornisce dati aggiuntivi sul numero di stazioni (tra 0 e 8) che hanno contribuito ai dati CRU Ts4.0 utilizzati da TerraClimate. JRA55 è stato utilizzato esclusivamente per la radiazione solare e le velocità del vento. TerraClimate produce inoltre set di dati mensili del bilancio delle acque superficiali utilizzando un modello di bilancio idrico che incorpora l'evapotraspirazione di riferimento, le precipitazioni, la temperatura e la capacità di acqua estraibile dal suolo interpolata. È stato utilizzato un modello di bilancio idrico climatico di Thornthwaite-Mather modificato e dati sulla capacità di stoccaggio dell'acqua estraibile del suolo su una griglia di 0,5° di Wang-Erlandsson et al. (2016). Limitazioni relative ai dati: Le tendenze a lungo termine dei dati vengono ereditate dai set di dati principali. TerraClimate non deve essere utilizzato direttamente per valutazioni indipendenti delle tendenze. TerraClimate non acquisisce la variabilità temporale a scale più fini rispetto ai set di dati principali e, pertanto, non è in grado di rilevare la variabilità nei rapporti e nelle inversioni delle precipitazioni orografiche. Il modello di bilancio idrico è molto semplice e non tiene conto dell'eterogeneità dei tipi di vegetazione o della loro risposta fisiologica alle condizioni ambientali in evoluzione. Convalida limitata nelle regioni con pochi dati (ad es. Antartide).

TerraClimate هي مجموعة بيانات عن المناخ الشهري وموازنة المياه المناخية للمسطحات الأرضية العالمية. ويستخدم هذا النموذج الاستقراء بمساعدة المناخ، ما يجمع بين القيم العادية للمناخ ذات الدقة المكانية العالية من مجموعة بيانات WorldClim، والبيانات ذات الدقة المكانية الأقل دقة ولكن المتغيرة بمرور الوقت من CRU Ts4.0 وبرنامج إعادة التحليل الياباني الذي يمتد على 55 عامًا (JRA55). من الناحية النظرية، تطبِّق العملية …

TerraClimate 是全球陆地表面月度气候和气候水量平衡数据集。该模型使用气候辅助插值法，将 WorldClim 数据集中的高空间分辨率气候常规数据与CRU Ts4.0 和 日本 55 年重分析(JRA55) 中的空间分辨率较粗但具有时间变化的数据相结合。从概念上讲，该过程会将CRU Ts4.0/JRA55 中插值的时间变化异常应用于WorldClim 的高空间分辨率气候学数据，以创建涵盖更广泛时间记录的高空间分辨率数据集。 温度、降水量和蒸汽压的时间信息是从CRU Ts4.0 继承的，适用于全球大多数陆地表面。不过，对于 CRU 数据没有任何气象站贡献的数据区域（包括整个南极洲以及非洲、南美洲的部分地区和分散的岛屿），则使用JRA55 数据。对于温度、蒸汽压和降水等主要气候变量，爱达荷州大学提供了有关为TerraClimate 使用的CRU Ts4.0 数据做出贡献的气象站数量（介于0 到 8 之间）的额外数据。JRA55 仅用于太阳辐射和风速。 此外，TerraClimate 还使用水量平衡模型生成每月表层水量平衡数据集，该模型包含参考蒸发蒸腾量、降水量、温度和插值的植物可提取土壤水分容量。我们使用了经过修改的Thornthwaite-Mather 气候水量平衡模型和可提取的土壤水储存容量数据（网格为0.5°），数据来自Wang-Erlandsson 等人(2016)。 数据限制： 数据的长期趋势会从父级数据集继承。 不应直接使用TerraClimate 独立评估趋势。 TerraClimate 不会比父级数据集更精确地捕获时间变化，因此无法捕获地形降水比率和逆温变化。 水量平衡模型非常简单，未考虑植被类型的异质性或植被对环境条件变化的生理响应。 在数据稀疏的区域（例如南极洲）。

TerraClimate est un ensemble de données sur le climat et l'équilibre hydrique climatique mensuels pour les surfaces terrestres mondiales. Il utilise une interpolation assistée par le climat, combinant des normales climatologiques à haute résolution spatiale de l'ensemble de données WorldClim, avec des données à résolution spatiale plus grossière, mais à évolution temporelle, de CRU Ts4.0 et de la réanalyse japonaise sur 55 ans (JRA55). Conceptuellement, la procédure applique des valeurs interpolées …

TerraClimate ist ein Datensatz mit monatlichen Klima- und klimatischen Wasserbilanzen für die globalen terrestrischen Oberflächen. Dabei wird eine klimatisch unterstützte Interpolation verwendet, bei der klimatologische Normalwerte mit hoher räumlicher Auflösung aus dem WorldClim-Dataset mit gröberer räumlicher Auflösung, aber zeitabhängigen Daten aus CRU Ts4.0 und der japanischen 55-jährigen Reanalyse (JRA55) kombiniert werden. Bei diesem Verfahren werden interpolierte …

TerraClimate, dünya üzerindeki karasal yüzeyler için aylık iklim ve iklimsel su dengesi veri kümesidir. Bu model, WorldClim veri kümesinden elde edilen yüksek mekansal çözünürlüklü iklim normallerini, CRU Ts4.0 ve Japon 55 yıllık yeniden analizinden elde edilen daha düşük mekansal çözünürlüklü ancak zaman içinde değişen verilerle birleştiren iklime dayalı yardımlı enterpolasyon kullanır. Kavramsal olarak, prosedür, daha geniş bir zamansal kaydı kapsayan yüksek mekansal çözünürlüklü bir veri kümesi oluşturmak için CRU Ts4.0/JRA55'ten interpolasyonlu zamana bağlı anomalileri WorldClim'in yüksek mekansal çözünürlüklü iklimolojisine uygular. Sıcaklık, yağış ve buhar basıncı için zamansal bilgiler, dünya genelindeki çoğu kara yüzeyinde CRU Ts4.0'tan devralınır. Ancak CRU verilerinin katkıda bulunduğu sıfır iklim istasyonunun bulunduğu bölgelerde (Antarktika'nın tamamı, Afrika'nın bazı bölgeleri, Güney Amerika ve dağınık adalar dahil) JRA55 verileri kullanılır. Idaho Üniversitesi, sıcaklık, buhar basıncı ve yağış gibi birincil iklim değişkenleri için TerraClimate tarafından kullanılan CRU Ts4.0 verilerine katkıda bulunan istasyonların sayısı (0 ile 8 arasında) hakkında ek veriler sağlar. JRA55 yalnızca güneş radyasyonu ve rüzgar hızları için kullanıldı. TerraClimate ayrıca, referans buharlaşma, yağış, sıcaklık ve bitki tarafından alınabilen toprak su kapasitesinin interpolasyonu gibi unsurları içeren bir su dengesi modeli kullanarak aylık yüzey suyu dengesi veri kümeleri oluşturur. Wang-Erlandsson ve ark. (2016) tarafından oluşturulan 0,5° ızgaradaki değiştirilmiş Thornthwaite-Mather iklimsel su dengesi modeli ve çıkarılabilir toprak su depolama kapasitesi verileri kullanıldı. Veri Sınırlamaları: Verilerdeki uzun vadeli trendler, üst veri kümelerinden devralınır. TerraClimate, trendlerin bağımsız değerlendirmeleri için doğrudan kullanılmamalıdır. TerraClimate, zamansal değişkenliği ana veri kümelerinden daha ince ölçeklerde yakalamaz ve bu nedenle orografik yağış oranları ve ters çevirmelerde değişkenliği yakalayamaz. Su dengesi modeli çok basittir ve bitki türlerindeki heterojenliği veya değişen çevre koşullarına karşı fizyolojik tepkilerini hesaba katmaz. Verilerin az olduğu bölgelerde sınırlı doğrulama (ör. Antarktika).

TerraClimate 是全球陸地地表每月氣候和氣候水平衡的資料集。這項服務會使用氣候輔助插補法，結合來自WorldClim 資料集的高空間解析度氣候標準值，以及來自CRU Ts4.0 和 日本 55 年重分析(JRA55) 的粗略空間解析度、但時間變化資料。從概念上來說，這個程序會將CRU Ts4.0/JRA55 的時間變化異常值內插至WorldClim 的高空間解析度氣候資料，以便建立涵蓋更廣泛時間記錄的高空間解析度資料集。 針對全球大部分陸地表面的溫度、降雨量和蒸氣壓資訊，我們繼承自CRU Ts4.0 的時間資訊。不過，如果 CRU 資料沒有任何氣象站提供資料的地區(包括南極洲、非洲、南美洲和零星島嶼的部分地區)，就會使用JRA55 資料。針對溫度、蒸氣壓和降雨量等主要氣候變數，愛達荷大學提供額外資料，說明 TerraClimate 使用的CRU Ts4.0 資料所參考的站點數量(介於0 和 8 之間)。JRA55 僅用於太陽輻射和風速。 TerraClimate 也會使用水分平衡模型產生每月表層水平衡資料集，該模型整合了參考蒸散、降雨量、溫度和插補的植物可提取土壤水容量。我們使用了經過修改的Thornthwaite-Mather 氣候水文模型，以及可提取的土壤水儲存容量資料，並以0.5° 格線的形式從Wang-Erlandsson 等人(2016 年) 的資料中擷取。 資料限制： 資料的長期趨勢會繼承自父資料集。請勿直接使用TerraClimate 進行趨勢的獨立評估。 TerraClimate 不會擷取比父資料集更精細的時間變化，因此無法擷取地形降雨比率和逆轉的變化。 水分平衡模型非常簡單，無法考量植被類型的異質性，或植被對變化環境條件的生理反應。 資料稀少區域的驗證功能受限(例如南極洲)。

Baseflow adjusted snowmelt runoff and rainfall runoff from TerraClimate

https://doi.org/10.5061/dryad.vx0k6dk2h

Description of the data and file structure

Input forcing of temperature, snow water equivalent, soil moisture, and precipitation comes from TerraClimate at a 1/24th degree resolution globally.

We use the runoff partitioning scheme from Qin et al., (2020) to separate runoff from snowmelt from rainfall runoff.

The original TerraClimate runoff data (q) assumed that all water (rain plus snowmelt, P) in excess of the additional soil water holding capacity (maximum soil water storage minus soil moisture or SR) and monthly evapotranspiration (ET) – or water surplus – immediately runs off that month.

TW =

S = max(0, 0.95*P - ET-SR)

q = 0.05*P + S

While this water balance approach may be valid at local scales, the approach does ...

In the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA†, published in the Global Change Biology journal, we evaluated the effects of climate conditions on vegetation composition and distribution in the conterminous United States (CONUS). To disentangle the direct effects of climate change from different non-climate factors, we applied "Liebig's law of the minimum" in a geospatial context, and determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change. We then compared these potential rates against observed change rates for the period 1986 to 2018 to identify areas of the CONUS where vegetation change is likely being limited by climatic conditions. This dataset contains the input and the resulting rasters for the study which include a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d)..., Input data  We use the available data from the “Vegetative Lifeform Cover from Landsat SR for CONUS†product (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the changes in vegetation fractional cover.  The information for the climate factors was derived from the TerraClimate data catalog (https://www.climatologylab.org/terraclimate.html). We downloaded data from this catalog for the period 1971 to 2018 for the following variables: minimum temperature (TMIN), precipitation (PPT), actual evapotranspiration (AET), potential evapotranspiration (PET), and climatic water deficit (DEF).  Preprocessing of vegetation fractional cover data  We resampled and aligned the maps of fractional cover using pixel averaging to the extent and resolution of the TerraClimate dataset (~ 4 km). Then, we calculated rates of lifeform cover change per pixel using the Theil-Sen slope analysis (Sen, 1968; Theil, 1992).  Preprocessing of climate variables data  To process the climate data, ..., , This README file was generated on 2024-03-04 by Adriana Parra.

GENERAL INFORMATION

1. Title of Dataset: Climate-limited vegetation change in the conterminous United States of America

2. Author Information

A. First Author Contact Information

Name: Adriana Parra

Institution: University of Nevada, Reno

Address: Reno, NV USA

Email: adrianaparra@unr.edu

B. Co-author Contact Information

Name: Jonathan Greenberg

 Institution: University of Nevada, Reno

 Address: Reno, NV USA

 Email: jgreenberg@unr.edu

3. Coverage period of the dataset: 1986-2018

4. Geographic location of dataset: Conterminous United States

5. Description:

This dataset contains the input and the resulting rasters for the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA†, published in the Global Change Biology journal. The dataset includes a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the dif...

We update the total runoff and runoff partitioned from snowmelt from TerraClimate using a modified water balance model that better accounts for baseflow lags in hydrology for use in basin level water supply availability at monthly time scales. These data complement existing TerraClimate 1-d water balance similations, but show much has improved montly correlative skill (median r=0.88) to observed streamflow across watersheds globally watersheds compared with the original method from TerraClimate (median r=0.75). These data can be used to quantify gaps in monthly water demand for downstream water uses including agriculture., The original TerraClimate (Abatzoglou et al., 2018) runoff data (q) assumed that all water (rain plus snowmelt, P) in excess of the additional soil water holding capacity (maximum soil water storage minus soil moisture or SR) and monthly evapotranspiration (ET) – or water surplus – immediately runs off that month. S = max(0, 0.95*P - ET-SR) q = 0.05*P + S While this water balance approach may be valid at local scales, the approach does not account for a variety of factors including baseflow contributions or stream routing and transit time. Some studies show that the transit time for runoff to reach a basin terminus is up to a month (Allen et al., 2018). We updated the approach for simulating runoff to account for the contributions of baseflow and transit time following Wolock and McCabe (1999). Specifically, half of the surplus generated each month becomes runoff while the remaining half is carried over as surplus to the following month. Supdate(month)=Supdate(month-1)+0.5*max(0, 0.95..., , # Baseflow adjusted snowmelt runoff and rainfall runoff from TerraClimate

https://doi.org/10.5061/dryad.vx0k6dk2h

Description of the data and file structure

Input forcing of temperature, snow water equivalent, soil moisture, and precipitation comes from TerraClimate at a 1/24th degree resolution globally.

We use the runoff partitioning scheme from Qin et al., (2020) to separate runoff from snowmelt from rainfall runoff.

The original TerraClimate runoff data (q) assumed that all water (rain plus snowmelt, P) in excess of the additional soil water holding capacity (maximum soil water storage minus soil moisture or SR) and monthly evapotranspiration (ET) – or water surplus – immediately runs off that month.Â

TW =Â

S = max(0, 0.95*P - ET-SR)

q = 0.05*P + S

While this water balance approach may be valid at local scales, the approach does ...

Data was obtained from TerraClimate climate principal components analysis, which are the product used to assess climate deviation under +2C and +4C warming scenarios. We elected to use the principal component analysis with the top 5, unique components, with reclassified values. Original values were on a 0- 1 scale, but were rescaled to 0- 100 to conform to other model variables.

This dataset contains 14 parquet-format files with monthly data.

File	Indicator	Unit
aet.parquet	Actual Evapotranspiration	mm
def.parquet	Climate Water Deficit	mm
pdsi.parquet	Palmer Drought Severity Index (PDSI)	unitless
pet.parquet	Precipitation	mm
ppt.parquet	Potential evapotranspiration	mm
q.parquet	Runoff	mm
soil.parquet	Soil Moisture	mm
srad.parquet	Downward surface shortwave radiation	W/m2
swe.parquet	Snow water equivalent	mm
tmax.parquet	Maximun Temperature	°C
tmin.parquet	Minimum Temperature	°C
vap.parquet	Vapor pressure	kPa
vpd.parquet	Vapor Pressure Deficit	kpq
ws.parquet	Wind speed	m/s

These data characterize active fires from MODIS, forest loss from the Global Forest Change dataset, and meteorological variables from TerraClimate. Data are summarized by 0.05 degree grid cell and by year. These data support the analyses in the following paper, which is under review in Geophysical Research Letters:Wimberly, M. C., Wanyama, D., Doughty, R., Piero, H., and Crowell, S. In Review. Increasing Fire Activity in African Tropical Forests is Associated with Land Use and Climate Change. Geophysical Research Letters.This archive contains the following files:tropical_forest_west_central_africa.shp: Ecoregion boundariestropical_forest_west_central_africa.shx: Ecoregion boundariestropical_forest_west_central_africa.dbf: Ecoregion boundariestropical_forest_west_central_africa.prj: Ecoregion boundariesAnnual_africa_modis_fd.tif: Annual count of MODIS active firesAfricaHGFCAnnualLoss2001to2021.tif: Annual count of Global Forest Change forest loss pixelsForestMask2000Prop.tif: Mean Global Forest Change forest cover in 2000ppt_mean: Annual sum of TerraClimate precipitationvpd_mean: Annual mean of TerraClimate vapor pressure deficittmax_mean: Annual mean of TerraClimate maximum temperature

TerraClimate は、世界の陸地表面の月ごとの気候と気候水収支のデータセットです。気候補正補間を使用して、WorldClim データセットの高空間解像度気候学的標準値と、空間解像度は粗いが時間変動データである CRU Ts4.0 と 日本の 55 年再解析（JRA55）を組み合わせています。概念的には、この手順では、CRU Ts4.0/JRA55 から補間された時間変動異常を WorldClim の高空間解像度気候学に適用し、より広範な時間記録をカバーする高空間解像度データセットを作成します。 時間情報は、温度、降水量、蒸気圧について、ほとんどのグローバル陸地表面で CRU Ts4.0 から継承されます。ただし、CRU データに貢献する気象観測所がゼロの地域（南極全体、アフリカ、南アメリカの一部、散在する島々など）には、JRA55 データが使用されます。気温、蒸気圧、降水量の主要な気候変数について、アイダホ大学は、TerraClimate で使用される CRU Ts4.0 データに貢献した観測所の数（0 ～ 8）に関する追加データを提供しています。JRA55 は、日射量と風速にのみ使用されていました。 TerraClimate は、基準蒸発散量、降水量、気温、植物が抽出可能な土壌水容量を補間した水収支モデルを使用して、月次地表水収支データセットも生成します。Wang-Erlandsson ら（2016）の 0.5° グリッドで、改良版の Thornthwaite-Mather 気候水収支モデルと抽出可能な土壌水貯留容量データが使用されました。 データの制限事項: データの長期的な傾向は、親データセットから継承されます。TerraClimate は、傾向の独立した評価に直接使用しないでください。 TerraClimate は、親データセットよりも細かいスケールで時間変動をキャプチャしないため、地形降水比率と逆転の変動をキャプチャできません。 水収支モデルは非常にシンプルで、植生タイプの多様性や、変化する環境条件に対する生理学的反応を考慮していません。 データが少ない地域での限定的な検証（南極）。

In Davis et al (2021) we simulated the potential productivity of Agave americana under current and future climates, with and without water management using either irrigation or rock mulch.

This repository contains data generated for the analyses in Davis et al (2021). The code and code documentation can be more easily browsed on GitHub at https://github.com/cct-datascience/agave-prediction, but are also archived here.

The key dataset of interest is allbiomass.nc, which provides predicted annual biomass increments for Agave americana under present and future climates and under rainfed, irrigated, and rock mulch + irrigation scenarios. It contains the model predictions of annual biomass production with units of Mg/ha/y for six scenarios on the same latitude and longitude dimensions as the TERRACLIMATE dataset:

• biomass: rainfed, historical climate
• biomass_irrig: irrigated, historical climate
• biomass_rockmulch: rainfed with rock mulch, historical climate
• biomass4C: rainfed, +4C climate
• biomass_irrig: irrigated, +4C climate
• biomass_rockmulch: rainfed with rock mulch, +4C climate

Other files likely of interest generated as intermediate steps in the analysis:

absmin19812010.ncand absmin4C.ncThese files contain the variable absmin, the absolute minimum temperature. These files also contain tmin, adjustC, and cadj used to compute absmin as described in the text and Calculating Absolute Minimum Annual Temperature section of the README.

• tmin: Average minimum 2-m air temperature, from TERRACLIMATE
• absmin: Absolute minimum 2-m temperature, calculated for this study to determine viable range of Agave americana

coefs19812010.ncand coefs4C.ncThese files provide calculated values of environmental productivity index model parameters alpha, beta, and gamma. These coefficients have values in the range [0,1], with 0 indicating that the environment does not allow growth and 1 indicating that the environmental factor does not limit growth. These coefficients are calculated monthly for each grid cell under 1981-2010 and +4C climate normals, and beta is calculated for the three water availability scenarios. These coefficients have values in the range [0,1], with 0 indicating that the environment does not allow growth and 1 indicating that the environmental factor does not limit growth.

• alpha: light limitation coefficient
• beta: water limitation coefficient
• beta2: water limitation coefficient to simulate irrigation
• beta3: water limitation coefficient to simulate rainfed with rock mulch
• gamma: minimum temperature coefficient

Files provided for reference, not intended for reuse:

• README.pdf contains the README that explains the analysis steps, data sources, and outputs in more detail.
• The file not_suitable.gpkg contains polygons representing the union of land that is either protected (UNEP-WCMC and IUCN 2021) or urbanized (Kelso and Patterson 2010). As UNEP-WCMC is updated monthly, researchers are advised to download the current version.
• figures.zip contains all map figures generated using Panoply, as well as Panoply settings files with the extension .pcl.
• agave-prediction-master_20220511.zip contains a snapshot of code in the GitHub repository.
• The original files containing adjustC and cadj are archived in the file absmin_adjustments.zip because these are the inputs to the analysis. The same data layers are also provided alongside calculated absmin in the files absmin19802010.nc and absmin4C.nc.

All NetCDF files (those ending in .nc) have additional metadata. The paper and software repository provide additional details.

To cite the research:

• Davis, S.C.; Abatzoglou, J.T.; LeBauer, D.S. Expanded Potential Growing Region and Yield Increase for Agave americana with Future Climate. Agronomy 2021, 11, 2109. https://doi.org/10.3390/agronomy11112109

To cite these datasets (this archive):

• LeBauer, David Shaner; Davis, Sarah; Abatzoglou, John (2022): Data from: Expanded potential growing region and yield increase for Agave americana with future climate. University of Arizona Research Data Repository. Dataset. https://doi.org/10.25422/azu.data.16828279

To cite the code:

• LeBauer, D., Davis, S., & Abatzoglou, J. (2021). Data and Code from: Expanded potential growing region and yield increase for Agave americana with future climate (Version davis_etal_2021) [Computer software]. https://github.com/cct-datascience/agave-prediction

For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

To minimize ontogenetic and methodological variation, we only included trait data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; and (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources.

Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2) if the original data were not available. The following variables were extracted from WorldClim: mean annual wind speed, mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, precipitation of driest month, and minimum temperature of coldest month. The VPD data was extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data). The moisture index (MI) is the ratio of precipitation to PET.

Simple linear regression was used to examine the relationships between two variables, utilizing the 'lm' function in R software. Partial regression analysis was conducted using the R package VISREG to investigate the relationships between wind speed and plant hydraulics while controlling for other variables. This analysis helped to illustrate the independent effect of wind on plant hydraulics. The Random Forest machine-learning algorithm (implemented using the R package randomForest) was utilized to assess the relative importance of environmental variables for each plant hydraulic trait. The Mean Decrease in Gini was calculated as the average of a variable's total decrease in node impurity, taking into account the proportion of samples that reach that node in each individual decision tree in the random forest. This provides a measure of a variable's importance in estimating the value of the target variable across all of the trees in the forest. A higher Mean Decrease in Gini value indicates greater importance of the variable. Multiple regression analyses were performed to develop predictive equations for plant hydraulic traits using environmental variables. To test for hydraulic traits-wind speed slope directions and differences among species groups in different climatic regions, we used standardized major axis (SMA) analyses. The R package SMATR was employed for these analyses. We considered p < 0.05 as the threshold for statistical significance in all models.

TerraClimate는 전 세계 지표면의 월별 기후 및 기후수분 균형 데이터 세트입니다. WorldClim 데이터 세트의 높은 공간 해상도의 기후학적 표준을 더 낮은 공간 해상도이지만 시간에 따라 변하는 CRU Ts4.0 및 일본 55년 재분석 (JRA55) 데이터와 결합하는 기후 보조 보간을 사용합니다. 개념적으로 이 절차는 보간된 …

This dataset contains 2001-2020 burned areas and climate variables for three regions with Mediterranean climates: South America from 31-46 degrees South, including Chile and the forested Andean region of Argentina; the western United States from 33-49 degrees North from the coast extending to the eastern extent of forest, and the Iberian Peninsula, including all of Spain and Portugal.

Burned areas are polygon shapefiles for all regions except Chile, for which the burn area is represented in a point shapefile. The data sources for the fire shapefiles are: Chile: unpublished, originally from Corporación Nacional Forestal (CONAF) and compiled by Miranda Argentina: unpublished, compiled by Diego Mohr-Bell and others at Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP) North America: NIFC 2023 Iberian Peninsula: EFFIS 2022

All of the fire shapefiles are contained within the zip folder fire_areas, and the individual regions are ch_fire (Chile), ar_fire (Argentina), na_fire (North America), ib_fire (Iberian Peninsula). The attributes of the shapefiles are the year and the fire area in square kilometers. For Chile, the fire start dates were documented. If the fire started in June-December, the year assigned is advanced by 1 from the original year. This is because the summer fire season straddles the calendar year boundary, and the fire year is assigned based on the year with most of the summer season. For Argentina, the end dates of the fire were available, so these end dates were used to assign the fire year.

Annual summaries of fire area and climate variables are provided in the fire_ann_all.csv file. The columns in this file are: year wetdryzone: dry if mean annual aridity index <1; wet if mean annual aridity index >1 cont: location, either Iberian Peninsula, North America, or South America area_km2: total burned area in square km AIann: annual aridity index calculated as total precipitation over total potential evapotranspiration AIjas: summer aridity index for July-September in northern hemisphere; January-March, southern hemisphere vpd: mean annual vapor pressure deficit (kPa) vpd_jas: mean summer vapor pressure deficit (kPa) def: mean annual climatic water deficit (mm) def_jas: summer climatic water deficit (mm)

Climate data were obtained from TerraClimate (Abatzoglou et al. 2018).

Mean annual summaries of fire and climate data by aridity index zone are provided in the file AI_bins_meanannual.csv. AI zones/bins are in increments of 0.2. Columns are: cont: location, either Iberian Peninsula, North America, or South America AI_max: maximum AI for the AI zone. For example, if the value is 0.2, the zone is from AI=0 to AI=0.2 zone_area: total area of the climate zone in square kilometers forest_area: total area forested in 2000 in square kilometers (Potapov et al. 2022) fire_area: total area burned from 2001-2020 in square kilometers (same sources as fire shapefiles) frac_forest: fraction of climate zone area that is forested frac_fire: fraction of the climate zone area that was burned mean_patch_area: mean size of forest patches identified within each AI zone in square kilometers fwi: mean fire weather index from the European Center for Medium-range Weather Forecasts (Vitolo et al. 2020)

Original data sources: Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., Hegewisch, K.C. (2018), Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 170191.

European Forest Fire Information System EFFIS (2022). Burnt area mapped using Sentinel-2/MODIS images. Accessed September, 2022.

NIFC 2023. Interagency Fire Perimeter History https://data-nifc.opendata.arcgis.com/search?tags=Category%2Chistoric_wildlandfire_opendata, downloaded 3/25/23.

Potapov P., Hansen M.C., Pickens A., Hernandez-Serna A., Tyukavina A., Turubanova S., Zalles V., Li X., Khan A., Stolle F., Harris N., Song X.-P., Baggett A., Kommareddy I., Kommareddy A. (2022) The global 2000-2020 land cover and land use change dataset derived from the Landsat archive: first results. Frontiers in Remote Sensing, 3.

Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libertá, G., & Krzeminski, B. (2020). ERA5-based global meteorological wildfire danger maps. Scientific data, 7(1), 1-11.

Species across the planet are shifting their ranges to track suitable climate conditions in response to climate change. Given that protected areas have higher quality habitat and often harbor higher levels of biodiversity compared to unprotected lands, it is often assumed that protected areas can serve as steppingstones for species undergoing climate-induced range shifts. However, there are several factors that may impede successful range shifts among protected areas, including the distance that must be travelled, unfavorable human land uses and climate conditions along potential movement routes, and lack of analogous climates. Through a species-agnostic lens, we evaluate these factors across the global terrestrial protected area network as measures of climate connectivity, which is defined as the ability of a landscape to facilitate or impede climate-induced movement. We found that over half of protected land areas and two-thirds of the number of protected units across the globe are at risk of climate connectivity failure, casting doubt on whether many species can successfully undergo climate-induced range shifts among protected areas. Consequently, protected areas are unlikely to serve as steppingstones for a large number of species under a warming climate. As species disappear from protected areas without commensurate immigration of species suited to the emerging climate (due to climate connectivity failure), many protected areas may be left with a depauperate suite of species under climate change. Our findings are highly relevant given recent pledges to conserve 30% of the planet by 2030 (30x30), underscore the need for innovative land management strategies that allow for species range shifts, and suggest that assisted colonization may be necessary to promote species that are adapted to the emerging climate. Methods Identifying climate analogs We followed the methods of Abatzoglou et al. (2020) and Parks et al. (2022) to characterize climate and identify backward and forward climate analogs. The specific climate variables we used were average minimum temperature of the coldest month (Tmin), average maximum temperature of the warmest month (Tmax), annual actual evapotranspiration (AET), and annual climate water deficit (CWD). AET and CWD concurrently account for evaporative demand and availability of water (N. L. Stephenson, 1990). These four variables provide complementary information pertinent to ecological systems and collectively capture the major climatic constraints on species distributions and ecological processes across a range of taxa (Dobrowski et al., 2021; Lutz et al., 2010; Parker & Abatzoglou, 2016; N. Stephenson, 1998; C. M. Williams et al., 2015). Monthly data acquired from TerraClimate (Abatzoglou et al., 2018) were used to produce these annual summaries from 1961-1990 (resolution = ~4km), which were then averaged over the same time period to represent reference period climate normals. The reference time period (1961–1990) is meant to represent climate conditions and climate niches prior to the bulk of recent warming. Future climate conditions were also computed from TerraClimate (available from www.climatologylab.org/terraclimate.html) and correspond to a 2°C increase above pre-industrial levels that are likely to manifest by mid-21st century without immediate and massive changes in global climate policies (Friedlingstein et al., 2014). As with the reference period climate, we summarized the four +2°C climate metrics annually and over a 30-year time period to represent future climate normals. All analyses in this study were conducted in the R statistical platform (R Core Team, 2020). We identified backwards and forwards analogs by estimating the climatic dissimilarity between each protected focal pixel (resolution = ~4km to match gridded climate data) and all protected pixels within a 500-km radius using a standardized Mahalanobis distance (Mahony et al., 2017). We chose the 500-km search radius as it encompasses an upper range of dispersal for some terrestrial animals and plants (Chen et al., 2011) when assuming 2°C warming by the mid-21st century; this search radius has also been used in previous studies (Bellard et al., 2014; Parks et al., 2022; J. W. Williams et al., 2007). The Mahalanobis distance metric synthesized the four climate variables (i.e. Tmin, Tmax, AET, and CWD; fig. 2a) by measuring distance in multivariate space away from a centroid using principal components analysis of standardized anomalies. Mahalanobis distance scales multivariate mean climate conditions between a pixel and those within the search radius by the focal pixel’s covariance and magnitude of interannual climate variability (ICV) across the four metrics. For backwards analogs, we characterized +2°C ICV and reference period climate normals to calculate climatic dissimilarity; for forward analogs, we used reference period ICV and +2°C climatic normals to calculate climatic dissimilarity. We standardized Mahalanobis distance to account for data dimensionality by calculating a multivariate z-score (σd) based on a Chi distribution (Mahony et al., 2017). σd represents the climate similarity between each focal pixel and its candidate backward and forward analogs (i.e. all other protected terrestrial pixels within 500 km), and we considered any protected pixels with σd ≤ 0.5 as climate analogs (fig. 2b) (following Parks et al., 2022). We were unable to calculate Mahalanobis distance when there was no ICV for any one of the four variables, and as a consequence, these areas are omitted from all analyses; this affects, for example, a relatively small tropical area in South America (CWD=0 each year) and areas perennially covered by snow (CWD=0 each year; e.g. most of Greenland). We focused our analyses on protected areas as defined by the World Database on Protected Areas (WDPA) (IUCN & UNEP-WCMC, 2019) and included protected areas classified as IUCN (International Union of Conservation for Nature) Management Categories I-VI, except those identified as ‘proposed’, ‘marine’, or otherwise aquatic (e.g. wetland, riverine, endorheic). A large number of protected areas, however, were not assigned an IUCN category in the WDPA (identified as ‘Not Reported’, ‘Not Assigned’, or ‘Not Applicable’) but are likely to have reasonably high levels of protection (e.g. Kruger National Park in South Africa). We included these additional protected areas if the level of human modification was similar or less than that observed within IUCN category I-VI protected areas. To do so, we measured mean land-use intensity within each IUCN category I-VI protected area using the Human Modification Gradient (HMG) raster dataset (Kennedy et al., 2019) and calculated the 80th percentile of the resulting distribution. Any unassigned protected areas with a mean HMG less than or equal to this identified threshold were included in our study (following Dobrowski et al., 2021). We then converted this vector-based polygon dataset to raster format (resolution = ~4km to match gridded climate data; n=1,063,748 pixels). It is well-recognized that the WDPA contains a large number of duplicate and overlapping polygons (Palfrey et al., 2022; Vimal et al., 2021). Although this does not affect summaries across the globe or for individual countries (described below), it provides a challenge when trying to summarize by individual protected areas (due to double-counting). Consequently, we ‘cleaned’ the WDPA prior to summarizing the climate connectivity metrics for individual protected areas by removing polygons that exhibited ≥ 90% overlap with another; this resulted in 29,752 individual protected areas (available in the Electronic Supplemental Material). Least-cost path modelling Following Dobrowski and Parks (2016) and Carroll et al. (2018), we used least-cost path modelling (Adriaensen et al. 2003) to build potential climate-induced movement routes between each protected focal pixel and its backward and forward analogs. The least-cost models were parameterized with resistance surfaces based on climate dissimilarity and the human modification gradient (HMG) (Kennedy et al., 2019). For backward analog modelling, we characterized climatic dissimilarity (i.e. climatic resistance) using two intermediate surfaces, the first being the Mahalanobis distance between each focal pixel (using +2°C ICV) and all other pixels using reference period climate normals (fig. 2c) and the second being the Mahalanobis distance (using +2°C ICV) and all other pixels using +2°C climate normals (fig. 2d). These two surfaces provide a proxy for climate similarity designed to capture transient changes between the reference period and +2°C climate; these were then averaged to characterize the overall climatic resistance across time and space (fig. 2d). For forward analog modelling, the process is similar except we used reference period ICV when characterizing climatic resistance (fig. 2a-2d). We then multiplied the climatic resistance (fig. 2d) by HMG (fig. 2e) to create the final resistance surface for least-cost path modeling (cf. Parks et al., 2020). Prior to this step, we rescaled HMG from its native range (0–1) to 1–25 to correspond with the range of Mahalanobis distance values and thereby grant comparable weights to climatic resistance and HMG resistance (~95% of all Mahalanobis distance values are below 25 within a 500km radius). Open water was given a resistance=25 so that paths would avoid water when possible. Least-cost path modelling was achieved using the gdistance package (van Etten, 2017); paths represent the least accumulated cost across the final resistance surface (fig. 2f) between each focal pixel and analog (fig. 2g). Because paths were rarely straight lines, some were longer than the 500km that we established as a search radius. We removed these longer paths to abide by the biologically informed upper dispersal