1. How does land surface modulation affect water and energy exchange?
Land surface modulation plays a crucial role in the exchange of water and energy between the land and atmosphere. According to Seneviratne et al. (2010), it can influence the physical state of the atmosphere, affecting seasonal to inter-seasonal predictability and climate projections (Koster et al., 2004). Biophysical land-atmosphere interactions are determined by land surface properties such as albedo, emissivity, surface roughness, and evaporation (Anderson-Teixeira et al., 2012). These properties impact the exchange of water and energy, ultimately affecting climate patterns and weather phenomena.
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2. What is the main data used in the study?
The main data used in the study is the reanalysis data, specifically the 0.25deg grid of ERA5. This data is available from the Copernicus Data Store (CDS) of the C3S service. The study focuses on ERA5-Land due to its consistency in longer time records and finer spatial resolution of 0.1 degree. However, some variables are only available in ERA5, so the study aggregates all variables back to the 0.25deg grid. The time period considered for ERA5L ranges from 2003 until 2018. The data is referred to as ERA5L in the study, while ERA5 and ERA5-Land refer to the original data sources.
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3. What is the GEOV2/AVHRR LAI product based on?
The GEOV2/AVHRR LAI product is based on applying a neural network retrieval algorithm on the AVHRR Long Term Data Record (LTDR, version 4). It also benefits from spectral harmonization and gap-filling procedures. The product aims to have high consistency with GEOV2-CGLS products derived from VEGETATION and PROBA-V sensors, distributed by the Copernicus Global Land Service (CGLS). The original product is provided at 0.05 spatial resolution with a 10-daily timestep, and it is aggregated to monthly intervals. This product was designed to improve the Land Surface Temperature (LST) bias, as found in previous studies (Nogueira et al., 2020; Nogueira et al., 2021). The use of this product in research can provide valuable insights into vegetation dynamics and land cover changes, contributing to a better understanding of ecosystems and their responses to environmental factors.
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4. What is the observational reference for Land Surface Temperature (LST)?
The observational reference for Land Surface Temperature (LST) is obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board of the Aqua satellite platform. MODIS-Aqua was selected as its overpass time is approximately 13:30 local time, which would be close to the time of the daily maximum temperature. The precise MODIS data product is labeled as MYD11A1 collection 6 (Wan et al., 2015), based on a split-window algorithm, and provides data at 1km spatial resolution at a daily frequency. This data is used to compare LST with the 'skin temperature' variable in ERA5-Land, which is defined as the theoretical temperature required to satisfy the surface energy balance. To match the reanalysis variable with remote sensing observations, special care is needed to address the clear-sky bias. The type of thermal satellite data used can only provide information on the temperature's surface in the absence of clouds, which typically leads to sampling the warmer days benefiting from unobstructed solar radiation. To ensure comparability and have information at a monthly scale, only the 5 warmest days of each month are selected from both the MYD11A1 and the ERA5-Land datasets. This procedure is directly implemented in the Google Earth Engine (GEE) platform (Gorelick et al., 2017), which hosts a copy of the MYD11A1 catalogue. The aggregation to the 0.25degree grid is done in a second step. As a consequence of this matching procedure, the LST bias is always referring to a bias in the five warmest days of the month. This assumption should generally hold as we are using LST at around 14:00, which is a variable that is highly sensitive to radiation.
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