The first part of the session expands the knowledge on scaling characterization of geophysical time series from diverse scientific domains to develop a knowledge base of complementary nature. The geophysical processes become even more complex because of the internal structural properties like intermittency and such an in-depth understanding will improve the accuracy of modeling of complex systems. This session aims to nurture the scientific development of scaling, fractals and related methodologies applicable to the time series observations from wide range of geophysical fields like hydrology, climatology, meteorology, atmospheric science, oceanography and statistical physics for their improved modeling and predictability :
- Scaling, fractal, multifractal characterization and modeling of complex geophysical data and extreme events
- In-depth understanding of the internal dynamics of geophysical data
- Understanding the fractal /scaling correlations between governing variables
- Linking network theoretical approach and scaling to find its applications across different geophysical fields
The second part of the session focuses on characterizing multi-decadal and longer Earth system dynamics, which has significant and direct impact on our society. This requires a combination of paleoclimate data and modeling given the insufficiency of short-term observational data. Our aim is to advance the understanding of climate variability across spatial and temporal scales through research focusing on:
-Characterizing multi-decadal to millennial climate dynamics through the use of proxy data and (conceptual or realistic) model simulations
-Evaluating the impact of Earth’s subsystem - such as the ocean, atmosphere, cryosphere and land-surface - in shaping long-term climate variability, and relevant feedback mechanisms
-Proxy system modeling, calibration and propagation of uncertainty with a focus on multi-decadal and longer timescales to aid reconstructions and model-data comparisons
-The attribution of climate variability to internal and/or forced dynamics
The cooling efficacy of green roofs in mitigating the urban heat island (UHI) effect within dense cities is largely attributed to evapotranspiration (ET) processes. Hence, accurate understanding and quantification of ET are pivotal for optimizing this cooling effect. ET estimation can be achieved either directly (weighing lysimeters) or indirectly (e.g., Penman-Monteith equation). Micro-meteorological approaches have been developed in recent years. Among which scintillometer can evaluate ET by its measurement parameter 
which corresponds to the fluctuations of air refractive index 
) in combination with surface energy balance and Monin-Obukhov similarity theory. Hence, improvement in data as well as understanding of its variability across wide range of space-time scale would result in better ET estimation and ultimately optimization. Yet it is often overlooked, and little research has focused on it and notably its variability.
This study explores the ET estimation on a wavy and vegetated green roof covering an area of 1 ha, known as the Blue Green Wave, which is located in Ecole des Ponts Paristech campus. Data from a large aperture scintillometer with 10-minute timestep during December 2019 and January 2020 is adopted. data variability across scales was analysed with the help of structure function and Universal Multifractal model (UM). The UM framework, widely employed for characterizing and simulating geophysical fields extremely variable across wide range of space-time scales, relies on two parameters with physical interpretation: the mean intermittency codimension 
and multifractality index 
(
, indicates monofractal; 
, indicates log-normal model.) An additional one, which is needed for non-conservative fields such as ET is the non-conservativeness parameter H.
Both structure function and UM approaches reveal good scaling behaviour on scales ranging from 10 min to 2h, confirming the relevance of the framework and demonstrating the potential for upscaling and downscaling. UM analysis conducted through Trace Moment and Double Trace Moment methods, provided similar values for UM parameters around 
H is approximately 0.44 in our case, which deviates from traditional scaling laws due to the intricate composition of the fluxes and requires further investigations. Indeed is influenced by temperature, humidity, air pressure and wind speed. To interpret properly structure function analysis from UM analysis, it is necessary to introduce a parameter denoted a. It corresponds to the power to which the assumed conservative underlying field should be raised before fractional integration to account for non-conservativeness to retrieve the studied field. Here, we observed that a is around 0.76 to ensure the highest consistency of the outcome from both the structure function and UM analyses. A better understanding of the underlying complexity and variability of Cn2 is achieved by our analysis. This, in turn, improves our understanding of the underlying physical processes generating variability and temporal-spatial dynamics in ET, which paves the way for future applications.
How to cite: Zhu, S., Gires, A., Maksimovic, C., Tchiguirinskaia, I., and Schertzer, D.: Multifractal analysis of Cn2 scintillometer data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13694, https://doi.org/10.5194/egusphere-egu24-13694, 2024.