North American historical monthly spatial climate dataset, 1901–2016

MacDonald, Heather; McKenney, Daniel W.; Papadopol, Pia; Lawrence, Kevin; Pedlar, John H.; Hutchinson, Michael F.

doi:10.1038/s41597-020-00737-2

Cited by 14 publications

(15 citation statements)

References 31 publications

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“…We found qualitatively similar results when we repeated the analysis with climate time‐lags of 10 and 20 years. These climate data were obtained from the government of Canada (MacDonald et al, 2020) with raster pixels of 10 km × 10 km (McKenney et al, 2006, 2011; Pedlar et al, 2015). We generated climate changes values for the 50, 100 and 200 km grid cells by averaging climate values across the forest plots within each cell.…”

Section: Methodsmentioning

confidence: 99%

Tree biodiversity in northern forests shows temporal stability over 35 years at different scales, levels and dimensions

2022

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1. At global scales, species richness is declining. However, at local scales, understanding exactly how, where and why biodiversity is changing becomes challenging since researchers have assessed biodiversity trends using different indicators, data sources and methods (e.g. repeated measurements at the same site over time vs. space-for-time substitutions).2. In this study, we present a multifaceted analysis of biodiversity change by assessing how tree diversity in Québec, Canada changed between two sampling periods (1970-1977 and 2005-2016), in regards to different: levels of diversity (alpha diversity, temporal turnover and spatial beta diversity), dimensions of diversity (taxonomic, functional and phylogenetic), metrics of diversity (presenceabsence and abundance based), and spatial scales of analysis (plot, 50, 100 and 200 km). We then assess how well potential drivers of biodiversity change (climate change and land cover change) explain the observed changes in alpha diversity. Since the data came from plots that remained forested over the course of the study, we used historical land cover change data and scenario analyses to test whether results from forest plots were likely to be representative of the broader landscape.3. Across all levels, dimensions, metrics and spatial scales of analysis, we found either increases or no net change in diversity over time, with wide distributions of values around the mean. Presence-absence metrics often indicated increases in diversity over time, while abundance-based metrics were more likely to show no net change. Potential drivers such as climate change and land cover change explained only a small fraction of the variation in alpha diversity change (i.e. why particular sites experienced positive vs. negative change) at the plot scale (adjusted R 2 ≈ 0.03), but a greater fraction at coarser spatial scales (adjusted R 2 of ~0.10 to ~0.50). Results from these forest plots are likely representative of the diversity change within the study region, since estimates of alpha diversity change only became negative under scenarios with the most extreme disturbance impacts.4. Synthesis. None of our indicators showed evidence of declines in alpha or beta diversity of trees in temperate and boreal forests in Quebec (except for simulations with extremely high forest loss), but we did find temporal turnover in

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Section: Methodsmentioning

confidence: 99%

Tree biodiversity in northern forests shows temporal stability over 35 years at different scales, levels and dimensions

2022

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“…The spatial resolution of existing global T a datasets with daily frequencies and long-term coverage is generally low (e.g., 0.25 • ) (Hersbach et al, 2018;Kalnay et al, 1996). T a datasets with improved spatial resolutions (e.g., 1 km) are usually only available at the continental or national scales (Chen et al, 2021;Fang et al, 2021;MacDonald et al, 2020;Oyler et al, 2015;Thornton et al, 2021).…”

Section: Comparison With Existing T a Datasetsmentioning

confidence: 99%

“…Many global or regional T a datasets have been previously published (Chen et al, 2021;Crespi et al, 2021;Fang et al, 2021;Hersbach et al, 2018;Hooker et al, 2018;Kalnay et al, 1996;MacDonald et al, 2020;Meyer et al, 2019;Nashwan et al, 2019;Oyler et al, 2015;Thornton et al, 2021;Werner et al, 2019); however, these have either coarse spatiotemporal resolutions or only cover specific regions (Table S3 in the Supplement). For example, some global T a datasets have daily frequencies but at coarse spatial resolutions (e.g., 0.05 • or even coarser) (Hersbach et al, 2018;Hooker et al, 2018;Kalnay et al, 1996); other T a datasets with medium spatial resolutions (∼ 1 km) are only available for specific regions such as North America and mainland China (MacDonald et al, 2020;Oyler et al, 2015;Thornton et al, 2021;Chen et al, 2021). There are also several T a datasets at even finer spatial resolutions but generated only for much smaller spatial regions (Crespi et al, 2021;Meyer et al, 2019;Nashwan et al, 2019;Werner et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)

Zuyi

Zhou²,

Zhao³

et al. 2022

Earth Syst. Sci. Data

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Abstract. Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax⁡ and Tmin⁡) is particularly valuable and critically needed in the scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily Tmax⁡ and Tmin⁡ at 1 km resolution over land across 50∘ S–79∘ N from 2003 to 2020 through the combined use of ground-station-based Ta measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates ranged from 1.20 to 2.44 ∘C for Tmax⁡ and 1.69 to 2.39 ∘C for Tmin⁡. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship between Ta and elevation and a positive relationship between Ta and land surface temperature; it captured spatial and temporal patterns of Ta realistically. This global 1 km gridded daily Tmax⁡ and Tmin⁡ dataset is the first of its kind, and we expect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data have been published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).

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“…For example, Ta from ERA5 and NCEP/NCAR reanalysis datasets have a spatial resolution of 0.25° and 2.5°, respectively, although The gridded Ta in this study can effectively capture the spatial variation of Ta under clear physical meanings (i.e., negative and positive relationship with elevation and LST, respectively), which is not always true in other gridded Ta datasets. The existing Ta datasets were created using regression methods such as PRISM (Crespi et al, 2021), thin plate smoothing spline models (MacDonald et al, 2020;Werner et al, 2019), and GWR (Hooker et al, 2018), and machine learning methods such as random forest (Chen et al, 2021;Meyer et al, 2019), which had no explicit constraints on the relationship between Ta with elevation and/or LST. The normal temperature lapse rates were considered using a parameter named vertical temperature gradient for estimating Ta in Daymet, but the temperature lapse rates were limited to at most a 12 °C decrease and 1 °C increase in temperature per 1000 m elevation increase (Thornton et al, 2021), which is not a fully negative relationship between Ta and elevation.…”

Section: Comparison With Existing Ta Datasetsmentioning

confidence: 99%

“…Besides,Hooker et al(2018) generated global Ta with 0.05° spatial resolution on a monthly scale from 2003 to 2016. Ta datasets that have improved spatial resolutions are usually available on a continental/national scale(Chen et al, 2021;Fang et al, 2021;MacDonald et al, 2020;Oyler et al, 2015;Thornton et al, 2021) and can reach 1-km spatial resolution and daily frequency. Crespi et al(2021) created the Ta dataset with 250 m spatial resolution daily frequency from 1980 to 2018, but it is only available in North-eastern Italy.…”

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confidence: 99%

A global dataset of daily near-surface air temperature at 1-km resolution (2003–2020)

Zuyi

Zhou

Zhao

et al. 2022

Preprint

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Abstract. Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable and critically needed in the scientific and policy communities, but is still not available. In this paper, we developed a global dataset of daily Tmax and Tmin dataset at 1-km resolution from 2003 to 2020 through the combined use of station-based ground Ta measurements and satellite observations (i.e., digital elevation model, and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors of our estimates ranged from 1.20 to 2.44 for Tmax and 1.69 to 2.39 ºC for Tmin. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents the negative and positive relationships between Ta with elevation or land surface temperature; it captured spatial and temporal patterns of Ta realistically. This global 1-km gridded daily Tmax and Tmin dataset is the first of its kind and we expect it to be of great value to global studies such as urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data are available at Iowa State University’s DataShare at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).

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North American historical monthly spatial climate dataset, 1901–2016

Cited by 14 publications

References 31 publications

Tree biodiversity in northern forests shows temporal stability over 35 years at different scales, levels and dimensions

Tree biodiversity in northern forests shows temporal stability over 35 years at different scales, levels and dimensions

A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)

A global dataset of daily near-surface air temperature at 1-km resolution (2003–2020)

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