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Summary To what degree plant ecosystems thermoregulate their canopy temperature (Tc) is critical to assess ecosystems' metabolisms and resilience with climate change, but remains controversial, with opinions from no to moderate thermoregulation capability.With global datasets ofTc, air temperature (Ta), and other environmental and biotic variables from FLUXNET and satellites, we tested the ‘limited homeothermy’ hypothesis (indicated byTc&Taregression slope < 1 orTc < Taaround midday) across global extratropics, including temporal and spatial dimensions.Across daily to weekly and monthly timescales, over 80% of sites/ecosystems have slopes ≥1 orTc > Taaround midday, rejecting the above hypothesis. For those sites unsupporting the hypothesis, theirTc–Tadifference (ΔT) exhibits considerable seasonality that shows negative, partial correlations with leaf area index, implying a certain degree of thermoregulation capability. Spatially, site‐mean ΔTexhibits larger variations than the slope indicator, suggesting ΔTis a more sensitive indicator for detecting thermoregulatory differences across biomes. Furthermore, this large spatial‐wide ΔTvariation (0–6°C) is primarily explained by environmental variables (38%) and secondarily by biotic factors (15%).These results demonstrate diverse thermoregulation patterns across global extratropics, with most ecosystems negating the ‘limited homeothermy’ hypothesis, but their thermoregulation still occurs, implying that slope < 1 orTc < Taare not necessary conditions for plant thermoregulation.
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Mapping Daily Air Temperature Over the Hawaiian Islands From 1990 to 2021 via an Optimized Piecewise Linear Regression Technique
Abstract Gridded air temperature data are required in various fields such as ecological modeling, weather forecasting, and surface energy balance assessment. In this work, a piecewise multiple linear regression model is used to produce high‐resolution (250 m) daily maximum (Tmax), minimum (Tmin), and mean (Tmean) near‐surface air temperature maps for the State of Hawaiʻi for a 32‐year period (1990–2021). Multiple meteorological and geographical variables such as the elevation, daily rainfall, coastal distance index, leaf area index, albedo, topographic position index, and wind speed are independently tested to determine the most well‐suited predictor variables for optimal model performance. During the mapping process, input data scarcity is addressed first by gap‐filling critical stations at high elevation using a predetermined linear relationship with other strongly‐correlated stations, and second, by supplementing the training dataset with station data from neighboring islands. Despite the numerous covariates physically linked to temperature, the most parsimonious model selection uses elevation as its sole predictor, and the inclusion of the additional variables results in increased cross‐validation errors. The mean absolute error of resultant estimatedTmaxandTminmaps over the Hawaiian Islands from 1990 to 2021 is 1.7°C and 1.3°C, respectively. Corresponding bias values are 0.01°C and −0.13°C, respectively for the same variables. Overall, the results show the proposed methodology can robustly generate daily air temperature maps from point‐scale measurements over complex topography.
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- Award ID(s):
- 2117975
- PAR ID:
- 10488258
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Earth and Space Science
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2333-5084
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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