Identifying source of predictability for vapor pressure deficit variability in the southwestern United States
Abstract Atmospheric vapor pressure deficit (VPD) measures the difference between saturation vapor pressure and actual vapor pressure, and its variability is closely related to fire activity in the western United States (US). Here, we assess the forecast skill of monthly VPD variability using a stat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-01028-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850201491858522112 |
|---|---|
| author | Jiale Lou Youngji Joh Thomas L. Delworth Liwei Jia |
| author_facet | Jiale Lou Youngji Joh Thomas L. Delworth Liwei Jia |
| author_sort | Jiale Lou |
| collection | DOAJ |
| description | Abstract Atmospheric vapor pressure deficit (VPD) measures the difference between saturation vapor pressure and actual vapor pressure, and its variability is closely related to fire activity in the western United States (US). Here, we assess the forecast skill of monthly VPD variability using a state-of-the-art dynamical forecast system and statistical predictions, such as the persistence forecast and model-analog forecasts. In the model-analog framework, we select analog states resembling the observed initial conditions from the model space, and the subsequent evolution of those initial model-analogs yields forecast ensembles. Dynamical forecasts demonstrate skillful predictions of VPD variability in the western US, exceeding the persistence forecast skill, which indicates additional sources of VPD predictability within the climate system. To quantify the contribution of different climate variables to VPD prediction, we develop a weighted model-analog forecast and evaluate its skill in comparison to VPD-only and unweighted forecasts. Our findings suggest that sea surface temperature is a critical source of VPD predictability over the western US. The optimally weighted model-analog exhibits forecast skill for VPD variability comparable to that of the dynamical forecast system. |
| format | Article |
| id | doaj-art-4a59d77bc1c94d1c96fe3add424de9d0 |
| institution | OA Journals |
| issn | 2397-3722 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate and Atmospheric Science |
| spelling | doaj-art-4a59d77bc1c94d1c96fe3add424de9d02025-08-20T02:12:01ZengNature Portfolionpj Climate and Atmospheric Science2397-37222025-04-018111410.1038/s41612-025-01028-6Identifying source of predictability for vapor pressure deficit variability in the southwestern United StatesJiale Lou0Youngji Joh1Thomas L. Delworth2Liwei Jia3Atmospheric and Oceanic Sciences Program, Princeton UniversityGeophysical Fluid Dynamics Laboratory/NOAAGeophysical Fluid Dynamics Laboratory/NOAAGeophysical Fluid Dynamics Laboratory/NOAAAbstract Atmospheric vapor pressure deficit (VPD) measures the difference between saturation vapor pressure and actual vapor pressure, and its variability is closely related to fire activity in the western United States (US). Here, we assess the forecast skill of monthly VPD variability using a state-of-the-art dynamical forecast system and statistical predictions, such as the persistence forecast and model-analog forecasts. In the model-analog framework, we select analog states resembling the observed initial conditions from the model space, and the subsequent evolution of those initial model-analogs yields forecast ensembles. Dynamical forecasts demonstrate skillful predictions of VPD variability in the western US, exceeding the persistence forecast skill, which indicates additional sources of VPD predictability within the climate system. To quantify the contribution of different climate variables to VPD prediction, we develop a weighted model-analog forecast and evaluate its skill in comparison to VPD-only and unweighted forecasts. Our findings suggest that sea surface temperature is a critical source of VPD predictability over the western US. The optimally weighted model-analog exhibits forecast skill for VPD variability comparable to that of the dynamical forecast system.https://doi.org/10.1038/s41612-025-01028-6 |
| spellingShingle | Jiale Lou Youngji Joh Thomas L. Delworth Liwei Jia Identifying source of predictability for vapor pressure deficit variability in the southwestern United States npj Climate and Atmospheric Science |
| title | Identifying source of predictability for vapor pressure deficit variability in the southwestern United States |
| title_full | Identifying source of predictability for vapor pressure deficit variability in the southwestern United States |
| title_fullStr | Identifying source of predictability for vapor pressure deficit variability in the southwestern United States |
| title_full_unstemmed | Identifying source of predictability for vapor pressure deficit variability in the southwestern United States |
| title_short | Identifying source of predictability for vapor pressure deficit variability in the southwestern United States |
| title_sort | identifying source of predictability for vapor pressure deficit variability in the southwestern united states |
| url | https://doi.org/10.1038/s41612-025-01028-6 |
| work_keys_str_mv | AT jialelou identifyingsourceofpredictabilityforvaporpressuredeficitvariabilityinthesouthwesternunitedstates AT youngjijoh identifyingsourceofpredictabilityforvaporpressuredeficitvariabilityinthesouthwesternunitedstates AT thomasldelworth identifyingsourceofpredictabilityforvaporpressuredeficitvariabilityinthesouthwesternunitedstates AT liweijia identifyingsourceofpredictabilityforvaporpressuredeficitvariabilityinthesouthwesternunitedstates |