2016
DOI: 10.1002/2015gl067449
|View full text |Cite
|
Sign up to set email alerts
|

A revised picture of the atmospheric moisture residence time

Abstract: Refined Lagrangian moisture source diagnostics are applied on an air mass transport climatology covering the period 1979–2013 to provide an estimate of the atmospheric moisture residence time. Our diagnostics yield an estimate of about 4–5 days for the global mean residence time, which is about half compared to depletion times that are commonly interpreted as proxies for the residence time. The discrepancies to depletion times are mainly explained by the fact that these are based on simplified representations … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

6
111
1

Year Published

2016
2016
2022
2022

Publication Types

Select...
5
1

Relationship

1
5

Authors

Journals

citations
Cited by 113 publications
(129 citation statements)
references
References 29 publications
6
111
1
Order By: Relevance
“…The capital, Kathmandu, and the city Pokhara stand out from the otherwise sparsely covered country as more stations are located close by ( Figure 2). We used two additional data sets for our analysis: 6-hourly Era-Interim reanalysis (Dee et al, 2011) at 0.75 ∘ horizontal resolution and a global particle trajectory data set consisting of 5 million particles of equal mass representing the entire atmosphere (from 1979 to 2013) (Läderach & Sodemann, 2016). This data set was computed with the Lagrangian dispersion model FLEXPART (Stohl et al, 2005) based on Era-Interim reanalysis fields.…”
Section: Datamentioning
confidence: 99%
See 1 more Smart Citation
“…The capital, Kathmandu, and the city Pokhara stand out from the otherwise sparsely covered country as more stations are located close by ( Figure 2). We used two additional data sets for our analysis: 6-hourly Era-Interim reanalysis (Dee et al, 2011) at 0.75 ∘ horizontal resolution and a global particle trajectory data set consisting of 5 million particles of equal mass representing the entire atmosphere (from 1979 to 2013) (Läderach & Sodemann, 2016). This data set was computed with the Lagrangian dispersion model FLEXPART (Stohl et al, 2005) based on Era-Interim reanalysis fields.…”
Section: Datamentioning
confidence: 99%
“…We assess moisture changes along each trajectory from Läderach and Sodemann (2016) with the moisture source diagnostic from Sodemann et al (2008). This diagnostic method allows the attribution of moisture sources by relating each moisture gain or loss along an air parcel trajectory to the current-specific humidity of the same air parcel.…”
Section: Identification Of Moisture Source Regionsmentioning
confidence: 99%
“…The global atmospheric lifetime of recycled moisture was studied by van der Ent et al [] using an Eulerian offline model. Läderach and Sodemann [] provided a revised global picture of the residence time of atmospheric moisture by using Lagrangian moisture source diagnostics. These and similar other modeling studies either have been conducted on large scales with a coarse resolution or have used relatively simple schemes for atmospheric dynamical and physical processes.…”
Section: Introductionmentioning
confidence: 99%
“…The response of the hydrological cycle to the climate regime can be studied by investigating, for example, the relationship between evaporation (E) and precipitation (P), particularly the atmospheric water residence time, here defined as time between the original evaporation and the returning of its respective water masses to the land surface as precipitation. This concept is used in various studies [Trenberth, 1998;Numaguti, 1999;James, 2003;van der Ent and Savenije, 2011;Tuinenburg et al, 2012;Wang-Erlandsson et al, 2014;van der Ent et al, 2014;Läderach and Sodemann, 2016]. It provides additional information on the timescales of evaporation and precipitation and reflects the complexity of the atmospheric water pathways and the phase changes including the formation of precipitation.…”
Section: Introductionmentioning
confidence: 99%
“…The factors influencing its spatio-temporal variability are various: convection, precipitations, temperature (Kennett and Toumi, 2005), transport and dynamical processes from eddies to synoptic scale events. The residence time of the water vapor in the atmosphere is several days to weeks (Trenberth, 1998;Läderach and Sodemann, 2016). Nevertheless, its temporal variability can be high at a scale 5 of dozens of minutes, and its spatial variability can be less than one kilometer (Vogelmann et al, 2011(Vogelmann et al, , 2015.…”
mentioning
confidence: 99%