2018
DOI: 10.1029/2018gl079293
|View full text |Cite
|
Sign up to set email alerts
|

Global Search for Autumn‐Lead Sea Surface Salinity Predictors of Winter Precipitation in Southwestern United States

Abstract: Sea surface salinity (SSS) is sensitive to changes in ocean evaporation and precipitation, that is, to changes in the oceanic water cycle. Through the close connection between the oceanic and terrestrial water cycle, SSS can be used as an indicator of rainfall on land. Here we search globally for teleconnections between autumn‐lead September‐October‐November SSS signals and winter December‐January‐February precipitation over southwestern United States. The SSS‐based model (R2 = 0.61) outperforms the sea surfac… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
22
0

Year Published

2018
2018
2022
2022

Publication Types

Select...
8

Relationship

2
6

Authors

Journals

citations
Cited by 17 publications
(23 citation statements)
references
References 68 publications
1
22
0
Order By: Relevance
“…Teleconnections between SSS in certain oceanic regions and terrestrial rainfall one season later are likely due to the role of SSS as an indicator of the net moisture export from the ocean. Liu et al (2018) find that SSS is superior to SST and other traditional climate indices for autumn lead predictions of winter precipitation in the US Southwest. This is just one example of advances in sub-seasonal to seasonal forecasting through the combined use of Artificial Intelligence tools and Bayesian statistical techniques applied to an expanding suite of ocean state variables.…”
Section: Improving Weather Forecasting On Sub-seasonal To Seasonal Timentioning
confidence: 76%
“…Teleconnections between SSS in certain oceanic regions and terrestrial rainfall one season later are likely due to the role of SSS as an indicator of the net moisture export from the ocean. Liu et al (2018) find that SSS is superior to SST and other traditional climate indices for autumn lead predictions of winter precipitation in the US Southwest. This is just one example of advances in sub-seasonal to seasonal forecasting through the combined use of Artificial Intelligence tools and Bayesian statistical techniques applied to an expanding suite of ocean state variables.…”
Section: Improving Weather Forecasting On Sub-seasonal To Seasonal Timentioning
confidence: 76%
“…Anomalously large water export leads to higher SSS, guaranteeing that some part of the climate system will experience more rain. Recent studies have found that seasonal anomalies in SSS in particular areas of the ocean have remarkable skill for predicting terrestrial rain in the next season in certain regions on land (Li et al 2016a(Li et al , b, 2018Liu et al 2018;Chen et al 2019). The SCS is an important moisture source of terrestrial rain over China.…”
Section: Conclusion and Discussionmentioning
confidence: 99%
“…In recent decades, the relationship between the subtropical North Atlantic water cycle and US summer precipitation pattern has become closer, allowing SSS to be used to predict extreme rainfall events . Liu et al (2018) searched globally for teleconnections between autumn-lead SSS and SST signals and winter precipitation over the southwestern United States and were able to explain 67% of the variations in winter precipitation. These new results on the use of ocean salinity for predicting seasonal rainfall, and the similar geography of China and the US in being west of their respective subtropical gyres, motivated us to examine the predictability of rainfall over China using SSS.…”
Section: Introductionmentioning
confidence: 99%
“…However, more recent studies have challenged this viewpoint as too simplistic e.g., Chadwick et al, 2013) and there remains a large degree of uncertainty in the spatial patterns of precipitation changes among climate models in general . Given the challenges of measuring changes in global precipitation and evaporation, using observations of sea surface salinity may be the most tractable approach to monitoring long-term changes in the global hydrological cycle (e.g., Durack and Wijffels, 2010;Durack et al, 2012;Li et al, 2016a,b;Liu et al, 2018).…”
Section: Projected Changes In Ocean Heat and Freshwater Contentmentioning
confidence: 99%