| Title |
Dynamically downscaling predictions for deciduous tree leaf emergence in California under current and future climate
|
|---|---|
| Published in |
International Journal of Biometeorology, October 2015
|
| DOI | 10.1007/s00484-015-1086-7 |
| Pubmed ID | |
| Authors |
David Medvigy, Seung Hee Kim, Jinwon Kim, Menas C. Kafatos |
| Abstract |
Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical downscaling of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically downscaled to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of downscaling from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of downscaling are unlikely to be stationary. |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 6% |
| Unknown | 15 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 25% |
| Student > Ph. D. Student | 3 | 19% |
| Student > Postgraduate | 2 | 13% |
| Student > Master | 1 | 6% |
| Student > Doctoral Student | 1 | 6% |
| Other | 2 | 13% |
| Unknown | 3 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Environmental Science | 5 | 31% |
| Agricultural and Biological Sciences | 3 | 19% |
| Earth and Planetary Sciences | 2 | 13% |
| Economics, Econometrics and Finance | 1 | 6% |
| Physics and Astronomy | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 3 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 July 2016.
All research outputs
#5,908,139
of 22,893,031 outputs
Outputs from International Journal of Biometeorology
#593
of 1,296 outputs
Outputs of similar age
#72,902
of 283,310 outputs
Outputs of similar age from International Journal of Biometeorology
#9
of 21 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,296 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 283,310 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.