# Presentation: 462033

## PDFS of Upper Tropospheric Humidity

- Darryn Waugh,  Ju-Mee Ryoo, Tak Igusa
- Johns Hopkins University

## Introduction

- Climate is sensitive to upper tropospheric humidity, and it is important to know
-  the distributions of water vapor in this region, and
-  the processes that determine these distributions.
- We examine the probability distribution functions (PDFs) of upper tropospheric relative humidity (RH) for measurements from
- Aura MLS
- Aqua AIRS
- UARS MLS
- Consider spatial variations of PDFs. Focus here on DJF, ~215hPa
- Also compare with theoretical distributions (generalization of Sherwood et al (2006) model).

## Climatological UT Relative Humidity

- DJF 200-250hPa
- Relative Humidity (AIRS)

**Subtropics is drier than the Tropics**
**But also significant zonal variations**

## PDFs: AIRS

***PDFs: AIRS***

- Subtropics
- (15-25N)

- Tropics
- (5S-5N)

- 40E-60E

- 260E-280E

- Large variation in PDFs - spread, skewness, ...

- similar

- Different shape

- 120E-140E

- 200-250hPa

## PDFS: AIRS - Aura MLS Comparison

***PDFS: AIRS - Aura MLS Comparison***

- Subtropics
- (15-25N)

- Tropics
- (5S-5N)

- 40E-60E

- 120E-140E

- 260E-280E

- Good agreement between AIRS and Aura MLS, with some exceptions.

## Theoretical Model: Sherwood et al (2006)

- Sherwood et al (J. Clim, 2006) showed that PDFs of Relative Humidity (R) in simple “advection-condensation” model are of the form:

- Larger r implies more rapid remoistening

- where
- 	*r *dry moist ,
- dry  is drying time due to subsidence
- 			[R~exp(-t/dry)],
- 	moist  is time scale of random remoistening events 	       	  	[P(t) = exp(-t/moist) / moist ], .

## Theoretical Model: Generalized Version

-  Larger r implies more rapid remoistening
-  Larger k implies less random remoistening processes.

- Generalized version of Sherwood et al model:

- k is measure of randomness of remoistening events.
- k=1 is original Sherwood et al. model.

- where time since last saturation is now modeled as

## PDFs: Data and Model

- How well do the theoretical models fit the observed PDFs?

- Subtropics
- (15-25N)

- Tropics
- (5S-5N)

- 40E-60E

- 120E-140E

- 260E-280E

- Model can fit the observed PDFs, with r and k varying with location.

- k=1
- (Sherwood)

- k>1

## Spatial Variations in r

- r = dry / moist

- Good agreement between different data sets.
- All show
-     r>1in tropical convective regions, and
-     r<1 in dry regions.
- Expected as larger r implies more rapid remoistening

- Subtropics
- (15-25N)

- Tropics
- (5S-5N)

## Maps of “r” and “k”

***Maps of “r” and “k”***

- r

- k

- Convective Regions:
-   r>1 and low k
-   Rapid, random remoistening
- Non-convective Regions:
-   r<1 and high k
-   Slower, more regular remoistening (horizontal transport)

- mean RH

## Aura MLS - AIRS bias

- There are some differences between MLS and AIRS PDFs.
- Differences are not simply a function of RH.
- Is there a simple mapping between MLS and AIRS?

- AIRS   MLS

## Aura MLS - AIRS bias

- Transform MLS Data

- RMLS/RAIRS = f(RMLS,OLR)

- RMLS/RAIRS

- NOAA OLR

- 200

- RMLS

- 300

- 150

- 0

- 100

- 1

- 1.5

- .8

- AIRS   MLS

- 2.0

- .4

- RMLS> RAIRS

## Conclusions

- Several robust features are found in the observed PDFs from all three data-sets (Aura and UAR MLS, AIRS):
-  Well fit by a generalized version of the Sherwood et al. (2006) theoretical model.
-  Consistent spatial variations in “r” (ratio of drying and moistening times) and “k” (randomness of moistening process).
-  Variations in r and k can be related to variations in the physical processes controlling the RH distributions.
- Differences between MLS and AIRS do exist.  There is a rather simple mapping, which depends on OLR and RH, to account for bias between MLS and AIRS.