ABSTRACT
Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015-2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security.
ABSTRACT
The nature of canopy radiative transfer mechanism (CRTM) describes the amount of beam penetration through a canopy and governs the nature of canopy illumination, i.e. the abundance of sunlit and shaded portions. Realistic representation of canopy illumination is critical for simulating various canopy biophysical processes associated with vegetated land surfaces. The adequate representation of CRTM can be attributed to the parameterizations of the two main canopy characteristics: the foliage projection (G-function) and the clumping effect (Ω function). Herein, using various types of G and Ω functions developed in a previous study, I tested 15 CRTM scenarios that combine different types of G and Ω functions to predict the dynamics of sunlit fraction (ε) of canopies having a wide range of plant area index (Ptotal) at various solar zenith angles (SZAs). It was observed that, for a given Ptotal, ε decreases as the SZA increases. However, ε significantly changed in accordance with the type of G and Ω functions used. Scenarios that employed random distribution of elements in space (S-4, S-9, and S-14) consistently returned larger ε values even at lower SZAs. This means that ignoring the clumping behavior of canopies could result in greater proportion of sunlit elements thereby reducing the beam penetration deeper into the canopy as opposed to those canopies where the elements are more aggregated. Beyond 70° SZA, almost all the scenarios returned similar ε values for a given Ptotal, which implied that the methods used is less sensitive at higher SZAs. The values of ε calculated by all the scenarios were significantly different from the S-6 (the ideal case). This observation highlights the importance of explicitly describing the G and Ω functions to adequately depict canopy illumination conditions.
