Suqin Duan
Ongoing
Currently, I am splitting 50% - 50% on two projects. For one part, I run large ensembles of CESM2 experiments to examine the role of atmospheric circulation versus soil moisture on heatwave events. For the other, I study the stochastic precipitation probability distribution in different weather systems.
* Welcome to chat more if you run models. I hope to learn about other people's experiences :)
Moist Heatwaves
Moist heatwaves, featuring both high temperatures and humidity, impose substantial stress on human physiology. Being warm, moist, and densely populated, the tropics and subtropics have the largest potential exposure to moist heatwaves. Previous studies have endeavored to understand the mechanisms governing these moist heatwaves. One key idea is that warm, moist near-surface air generates deep convection, which in turn cools the surface with rain and limits the moist heatwaves. An elegant, idealized convection-dynamics framework (idealized QE-WTG) based on this idea has been proposed by other researchers to study behaviors of tropical/subtropical moist heat and temperature extremes. Here in this study, we emphasize the role of dry air in the lower free troposphere (roughly 1--3 km above the surface) on top of this idealized constraint. This dry air entrainment effect can curtail deep convection, allowing much higher near-surface moist heat to develop. This effect also explains why, in addition to typically warm and moist places with active convection, regions with a dry lower-free troposphere---such as coastal areas adjacent to hot and arid land---are particularly susceptible to moist heatwaves. Concerningly, this effect is expected to intensify as the climate continues to warm.
Joint frequency distribution of the boundary layer instability measure and the lower-free tropospheric dryness measure (measuring the dry air entrainment effect) for raining (green shadings) and non-raining (brown shadings) days over land plus coast. The left panel shows results from the CAM5 experiment without entrainment (𝜖 = 0), and the right panel shows the experiment with default entrainment (𝜖 = 1). The orange lines indicate the near-surface wet-bulb temperatures (WBTs). Values of WBTs averaged over the top 1% days are annotated in the upper right. The entrainment effect can enhance the extreme WBTs by about 2°C.
Duan, S. Q., Ahmed, F., and Neelin, J. D. (2024) Moist heatwaves intensified by entrainment of dry air that limits deep convection.
Nature Geoscience, https://doi.org/10.1038/s41561-024-01498-y [pdf]
Research Briefing: https://www.nature.com/articles/s41561-024-01507-0 [pdf]
Hydroclimatic changes over land
We have a generally good understanding of the first principles of how the global mean hydrological cycle changes with warming. However, the picture gets complicated and predictions become uncertain if we want to know short-time events over land. Unlike the ocean, land has limited soil water and therefore it dries. Drying of the land surface exerts a nonlinear impact on the hydrological cycle and surface air temperature. Furthermore, land surface properties vary vastly from region to region. Besides and partly owing to these complexities, model representations of the land properties and the land-atmosphere coupling are highly uncertain. All these factors confound our understanding of whether, when, and where we will have a drier or a more moist homeland. How does drying/moistening of the land surface feedback to surface air temperature? Can we find some consistent rules for these heterogeneous responses across different regions and models?
In this study, we reveal to you consistent patterns of changes in multiple key hydroclimatic variables over land (hard to get!), and show you the coherent mechanistic links between these key variables.
Duan, S. Q., Findell, K. L., and Fueglistaler, S. A. (2023) Coherent mechanistic patterns of land hydroclimatic changes, GRL, 50, e2022GL102285. https://doi.org/10.1029/2022GL102285 Editor's Highlight NOAA Climate Highlight
Also, welcome to check out a detailed paper on connecting the land surface-based and the atmospheric dynamics-based perspectives on amplified warming over tropical land.
Duan, S. Q., Mckinnon, K. A., and Simpson, I. R. (2024) Two perspectives on amplified warming over tropical land examined in CMIP6 models, JCLI, https://doi.org/10.1175/JCLI-D-22-0955.1
Temperature distribution change
Duan, S. Q., Findell, K. L., and Wright, J. S. (2020) 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 the 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.
Mean warming:
Land warms more than ocean;
Dry land warms more than moist land.
Extreme-relative-to-mean warming:
Moist land in the 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., and 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.
Sketch of our bulk-plume model of the tropical atmosphere. q and q' denote the mass fractions of H2O and HDO.
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.
