Land surface drying and its feedback to surface air temperature

Currently, I am working on understanding the spatiotemporal distribution of land surface drying and its temperature feedback. When does the land surface get drier and impact surface air temperature? Can we find consistency across different regions?

More later.

Duan, S. Q., Findell, K. L., Fueglistaler, S. A. The spatiotemporal distribution of land surface drying and its temperature feedback, GRL, submitted.

Temperature distribution change

 

 

 

 

 

 

 

 

Duan, S. Q., Findell, K. L., Wright, J. S. Three regimes of temperature distribution change over dry land, moist land, and oceanic surfaces, GRL, 47, e2020GL090997. https://doi.org/10.1029/2020GL090997

Previous studies report that extreme temperature events will occur more often and become more extreme in the future, yet there is no consensus on how much this increased likelihood of extreme heat events is due to a shift of temperature distribution mean or a changed temperature distribution shape. We found that summertime local temperature distribution changes can be summarized into three regimes according to surface properties: dry land exhibits a shift of the entire distribution with pronounced warming in the mean; moist land shows a smaller change in the mean compared with dry land but features an elongated upper tail relative to the mean; oceanic surface shows a smaller shift in the mean relative to land surface, with no significant elongation of the upper tail. The elongated upper tail over moist land indicates an amplified warming of extreme hot days. This amplified extreme warming over moist land is compounded on top of the land-ocean contrast in mean warming, and is related to suppressed evaporation and associated land surface feedbacks.

NewFig1_summer_annotband1_random1.png

Mean warming:

 

Land warms more than the ocean;

Dry land warms more than moist land.

Extreme-relative-to-mean warming:

 

Moist land in tropics/subtropics features amplified warming in the extreme relative to the mean, indicating an elongated upper tail.

On using stable water isotope (HDO) to constrain convection

 

 

 

 

 

 

 

 

 

Here in our study, we simulate HDO in a bulk plume model of cumulus convection, and test the sensitivity of HDO to three convective parameters: entrainment/detrainment rate, raindrop re-evaporation fraction, and the distance of the raindrop fall/get-lofted before its re-evaporation. 

 
 

However, we find that at a given relative humidity, the possible range of HDO is small: its range is comparable to both the measurement uncertainty of tropical mean profile and the structural uncertainty of a single-column model. Therefore, we conclude that the mean tropical HDO profile is unlikely to add information about free tropospheric convective processes in a bulk-plume framework that cannot already be learned from relative humidity alone. Our message is, if we want to explore physics using water isotope, free tropospheric convection is not a good place to apply--apply it for tracing water sources for the topical tropopause layer and/or the boundary layer instead.

 

 

Duan, S. Q., Wright, J. S., & Romps, D. M. (2018). On the utility (or futility) of using stable water isotopes to constrain the bulk properties of tropical convection. Journal of Advances in Modeling Earth Systems, 10. https://doi.org/10.1002/2017MS001074

Stable water isotope has a property that heavy isotopes preferably stay in the condensed phase during phase change. This phenomenon is called fractionation. Due to the property of fractionation, water isotope can track water history, and can potentially serve as proxies for physics that we cannot directly measure. 

In the recent decade, people are proposing that water isotopes are promising to add constraints on convective physics which are hard to measure and simulate. Satellite measurements of water isotopes and isotope-enabled numerical models are also more developed to lend people better tools to make further explorations. 

IsotopeTrack.001.jpeg
Figure1.jpg

Sketch of our bulk-plume model of the tropical atmosphere. q and q' denote the mass fractions of H2O and HDO.

Figure3_Percentile.jpg

𝞭D (HDO) profiles at a given RH value from our model solutions with three free parameters (entrainmet rate, reevaporation fraction and evaporation distance) ranging in their plausible ranges. The colors show the cumulative distribution function of 𝞭D for the subset solutions with that RH value. 

Past projects:

Rapid intensification of tropical cyclones and potentially related environmental factors

 
Using the IBTrACS dataset, we explored the statistics of tropical cyclones (TCs) in six basins recorded by different TC observation centres. We examine the time series of rapid (6-hr) and sustained-rapid (24-hr) intensification (an increase of max-sustained wind above a threshold) of TCs in different basins. 
The variance of RI trend among different data centres for a basin is large. Trends among different basins are not consistent neither. We have also interpolated several environmental factors including wind shear, mid-tropospheric humidity, thermodynamic efficiency and so on using JRA-55 reanalysis data to the TC records. We planed to refine the results by using daily reanalysis data for interpolation and calculating environment variables for enclosed TC area with a few different TC radii, but it was then interrupted by other projects.
 
 
 
 
 
 
 
Duan, S. Q. and Wright, J. S.: Variations of rapid and sustained-rapid intensification of tropical cyclones in a changing climate, end up as a conference presentation.
 
Using complex network method to construct the global monsoon system. 
Y. W. Wang, J. S. Wright and S. Q. Duan: A network analysis of the global monsoon system in observations, reanalyses and CMIP5 simulations, end up as a conference presentation.
 
RIFrac_6basins.001.jpeg