Data · dataset · 2026
Predicting the response of the Amazon rainforest to persistent drought conditions under current and future climates: A major challenge for global land surface models
Listed in National Center for Atmospheric Research
While a majority of global climate models project drier and longer dry seasons over the Amazon under higher COâ levels, large uncertainties surround the response of vegetation to persistent droughts in both present-day and future climates.
Description
We propose a detailed evaluation of the ability of the ISBACC (Interaction SoilâBiosphereâAtmosphere Carbon Cycle) land surface model to capture drought effects on both water and carbon budgets, comparing fluxes and stocks at two recent throughfall exclusion (TFE) experiments performed in the Amazon.
We also explore the model sensitivity to different water stress functions (WSFs) and to an idealized increase in COâ concentration and/or temperature. In spite of a reasonable soil moisture simulation, ISBACC struggles to correctly simulate the vegetation response to TFE whose amplitude and timing is highly sensitive to the WSF. Under higher COâ concentrations, the increased water-use efficiency (WUE) mitigates the sensitivity of ISBACC to drought.
Read the rest (1 more)
While one of the proposed WSF formulations improves the response of most ISBACC fluxes, except respiration, a parameterization of drought-induced tree mortality is missing for an accurate estimate of the vegetation response. Also, a better mechanistic understanding of the forest responses to drought under a warmer climate and higher COâ concentration is clearly needed.
Links
Get the data
- Publisher page n2t.org/ark:/85065/d7639qr0 ↗
documentation · download · from data ucar edu
Where it is published
- data.ucar.edu /dataset/predicting-the-response-of-the-amazon-rainforest-to-pe… ↗
National Center for Atmospheric Research dataset page
landing page · from data ucar edu
Catalogue records · 1
- CKAN API data.ucar.edu/api/3/action/package_show?id=06418f19-eb6b-4ad9-8132-8e4c9792d… ↗
metadata API · from data ucar edu
Topics
- From keywords
- Earth & Environmental Science · Ocean & Atmospheric Science
- Inferred from text
- Climate change science 72% · Simulation 75%
Provenance · 1 source records, 9 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| National Center for Atmospheric Research | 06418f19-eb6b-4ad9-8132-8e4c9792d6b6 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3702 | enrichment · data ucar edu | taxonomy-embedding@1.1.0 | title+keywords+description (72%) |
| concepts[field].local:field:earth-environmental | mapping · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · data ucar edu | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| description | source · data ucar edu | connector:data_ucar_edu@1.0.0 | /notes |
| publication_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| title | source · data ucar edu | connector:data_ucar_edu@1.0.0 | /title |
| updated_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 |