Amigos de MexFlux: Annu Panwar

Annu Panwar is a postdoctoral researcher at the Max Planck Institute for Biogeochemistry, studying vegetation phenology using eddy covariance tower data.

Which are the lines of research of your working group?

I belong to two groups in my institute. Both groups are involved in various research topics, from extreme events, the carbon cycle to machine learning. I am working mainly with eddy flow tower data to study vegetation phenology.

Which project are you currently working on and participating in?

I am working with the PhenoFeedback project funded by DFG, a German research foundation. In my project, we are mainly interested in how phenological changes feed back into the climate system. For this, we are interested in variables such as albedo, evapotranspiration, and, in general, the energy balance of the biosphere-atmosphere system. I show how subjective methodological biases influence the analysis of phenology. For example, to understand the changes in the spring and fall seasons, one must first smooth the data and decide on methods to determine transition dates between phenological stages. These decisions are affected by subjective biases. My research further explores how optimal solutions can be examined and formulated.

Tell us briefly how or when you started working on the study of ecosystem fluxes.

I studied the vegetation-atmosphere interaction in my doctoral program, focusing on the surface energy fraction. I developed perspectives to model these interactions but did not work with carbon sources. Through this postdoctoral project, I added the carbon component to understand the biosphere-atmosphere interaction. And I’m glad to be working on vegetation phenology now.

What study or project lead you to think about fluxes?

Great researchers surround me at my institute, which definitely influenced me to continue this topic. In my Ph.D., I attended the AmeriFlux meeting because I worked with flow data. During these meetings, my interactions with scientists were fruitful and led me to work on atmospheric boundary layer data. After finishing my Ph.D., I knew that I wanted to approach the carbon aspect from climatology, especially considering the phenology lag. Mirco Migliavacca and his colleagues had an exciting project, and they interviewed me for it. And while doing a literature review, I realized that turbulent flow covariate data is new and has the potential to capture mechanistic information about phenological changes. Still, because it is noisy and high-frequency data, it is also prone to several methodological challenges. In remote sensing, smoothing and methods to define the phenological phase are well discussed, but more research is needed to contribute in this direction with flow data.

Which is the most interesting, surprising or unexpected discovery of your study/studies or project(s) (share links to papers)?

In my Ph.D. thesis, we wanted to show that although surface and air temperatures are measured from just 2m apart, they respond differently to changes in vegetation and evaporative conditions. Following this, we explain that the growth of the atmospheric boundary layer has a role in defining the weaker air temperature changes in response to the dry surface. The links to my article are here.

Panwar, A., Kleidon, A., & Renner, M. (2019). Do surface and air temperatures contain similar imprints of evaporative conditions? Geophysical Research Letters, 46(7), 3802–3809. Link
Panwar, A., Renner, M., & Kleidon, A. (2020). Imprints of evaporative conditions and vegetation type in diurnal temperature variations. Hydrology and Earth System Sciences, 24(10), 4923–4942. Link
Panwar, A., & Kleidon, A. (2022). Evaluating the response of diurnal variations in surface and air temperature to evaporative conditions across vegetation types in FLUXNET and ERA5. Journal of Climate, 35(19), 2701–2728. Link

What do you do for fun or a hobby beyond the academy?

I like doing yoga, going to the gym, and walking the hills around our place. I enjoy watching reality shows on Netflix. I do some significant reading, for example, psychology and mythological books. I love cooking, singing, and dancing. If I don’t have to work, I can keep busy and happy with my hobbies.