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- Author or Editor: Andrew D. Richardson x
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Abstract
Data from 24 airport weather stations along the north–south axis (35°–45°N) of the Appalachian Mountains are used to show a significant rising trend in cloud-ceiling height over the past three decades. The mean change in cloud-ceiling height was 4.14 ± 1.03 m yr−1 [mean ± 1 SE (standard error), p ≤ 0.001] across all stations. The trend was negative (−2.22 ± 0.67 m yr−1) for the six stations south of 37.5°N, but positive (6.26 ± 0.89 m yr−1) for the 18 stations north of this latitude. Mean ceiling height for broken cloud cover was higher and rising faster than mean ceiling height for overcast cloud cover. There were strong seasonal patterns that varied between the northernmost and southernmost stations; differences were most pronounced during the spring and summer months. Some of the potential ecological effects on high-elevation forests, where the transition from deciduous to coniferous forest is thought to be controlled by the height of the cloud base, are discussed.
Abstract
Data from 24 airport weather stations along the north–south axis (35°–45°N) of the Appalachian Mountains are used to show a significant rising trend in cloud-ceiling height over the past three decades. The mean change in cloud-ceiling height was 4.14 ± 1.03 m yr−1 [mean ± 1 SE (standard error), p ≤ 0.001] across all stations. The trend was negative (−2.22 ± 0.67 m yr−1) for the six stations south of 37.5°N, but positive (6.26 ± 0.89 m yr−1) for the 18 stations north of this latitude. Mean ceiling height for broken cloud cover was higher and rising faster than mean ceiling height for overcast cloud cover. There were strong seasonal patterns that varied between the northernmost and southernmost stations; differences were most pronounced during the spring and summer months. Some of the potential ecological effects on high-elevation forests, where the transition from deciduous to coniferous forest is thought to be controlled by the height of the cloud base, are discussed.
Abstract
Large-scale changes in the state of the land surface affect the circulation of the atmosphere and the structure and function of ecosystems alike. As global temperatures increase and regional climates change, the timing of key plant phenophase changes are likely to shift as well. Here we evaluate a suite of phenometrics designed to facilitate an “apples to apples” comparison between remote sensing products and climate model output. Specifically, we derive day-of-year (DOY) thresholds of leaf area index (LAI) from both remote sensing and the Community Land Model (CLM) over the Northern Hemisphere. This systematic approach to comparing phenologically relevant variables reveals appreciable differences in both LAI seasonal cycle and spring onset timing between model simulated phenology and satellite records. For example, phenological spring onset in the model occurs on average 30 days later than observed, especially for evergreen plant functional types. The disagreement in phenology can result in a mean bias of approximately 5% of the total estimated Northern Hemisphere NPP. Further, while the more recent version of CLM (v5.0) exhibits seasonal mean LAI values that are in closer agreement with satellite data than its predecessor (CLM4.5), LAI seasonal cycles in CLM5.0 exhibit poorer agreement. Therefore, despite broad improvements for a range of states and fluxes from CLM4.5 to CLM5.0, degradation of plant phenology occurs in CLM5.0. Therefore, any coupling between the land surface and the atmosphere that depends on vegetation state might not be fully captured by the existing generation of the model. We also discuss several avenues for improving the fidelity between observations and model simulations.
Abstract
Large-scale changes in the state of the land surface affect the circulation of the atmosphere and the structure and function of ecosystems alike. As global temperatures increase and regional climates change, the timing of key plant phenophase changes are likely to shift as well. Here we evaluate a suite of phenometrics designed to facilitate an “apples to apples” comparison between remote sensing products and climate model output. Specifically, we derive day-of-year (DOY) thresholds of leaf area index (LAI) from both remote sensing and the Community Land Model (CLM) over the Northern Hemisphere. This systematic approach to comparing phenologically relevant variables reveals appreciable differences in both LAI seasonal cycle and spring onset timing between model simulated phenology and satellite records. For example, phenological spring onset in the model occurs on average 30 days later than observed, especially for evergreen plant functional types. The disagreement in phenology can result in a mean bias of approximately 5% of the total estimated Northern Hemisphere NPP. Further, while the more recent version of CLM (v5.0) exhibits seasonal mean LAI values that are in closer agreement with satellite data than its predecessor (CLM4.5), LAI seasonal cycles in CLM5.0 exhibit poorer agreement. Therefore, despite broad improvements for a range of states and fluxes from CLM4.5 to CLM5.0, degradation of plant phenology occurs in CLM5.0. Therefore, any coupling between the land surface and the atmosphere that depends on vegetation state might not be fully captured by the existing generation of the model. We also discuss several avenues for improving the fidelity between observations and model simulations.
