Online Water Vapor Tracer and Precipitation Tracking Model based on Advanced Research Model of the Amazonian Deforestation
The National Center for Atmospheric Research 24 has developed a model which is widely used for regional climate studies. The study used the Advanced Research version of the WRF model. The WRF model was used to create new variables for the water vapour tracer 25 in order to track the amount and type of precipitation. The method is classified as an online version of a Eulerian approach, as the governing equations of the model are solved simultaneously with the predictive equations.
It’s related to the word ‘rain’, as it means ‘rmtrm’.
Source: Impact of Amazonian deforestation on precipitation reverses between seasons
Modeling Land Cover and Land Use Fractions Derived from 2000 and 2020: Application to Surface Summary of the Day 54 in South America
A control scenario representing land cover and land use fractions derived from forest cover in 2000 was created to be followed by a depredations scenario that took account of GLAD forest cover changes in 2000. The percentages of non-forest categories increased for grid cells that experienced net forest losses and the percentage of forest categories decreased for grid cells that did not. Conversely, in grid cells with net forest gain, the percentages of forest categories increased proportionally, whereas the percentages of non-forest categories decreased. In 2000, the S 2020 scenario varied in total forest cover percentage but the same proportion was maintained for the five MODIS forest cover types. After we derived the deforestation scenario (S2020) over the whole domain, we substituted the land cover and land use fraction of the Amazon region with MODIS land cover in 2000 and obtained the S2000 scenario. Finally, the biophysical climate effects of Amazon deforestation were quantified as the results of S2020 minus S2000, as shown in the main text. The influence of model boundary was assessed during a simulation for wet and dry seasons under a larger domain (200 480 grids, 20- km resolution).
The S2020 experiment is used for model validation. The first 16 days of simulation are used to create the spin-up time, and the last three days of simulation are used for validation analyses. The results of the test were compared with data from the Global Surface Summary of the Day 54 (GSOD), in situ weather stations in South America. The National Centers for Environmental Information has created a daily weather summary that can be found in the GSOD dataset. The GSOD database provides observations on temperature, precipitation, wind and other meteorological variables from more than 9000 global stations. In South America, 196 GSOD stations recorded available surface air temperature during the wet season in 2020, whereas 195 stations did so during the dry season in 2020. Moreover, a total of 62 and 119 stations recorded available daily precipitation in the wet and dry seasons, respectively. We used a satellite-based precipitation dataset to look for spatial patterns of the precipitation.
We used high-resolution 30 m forest cover data for 2000 and 2020 from the Global Land Analysis and Discovery to get a 20- km resolution of forest cover change information. 1c. GLAD forest cover data from 2000 and 2020 were mapped by attributing pixels with forest height ≥5 m as the ‘forest’ land cover class, which agrees with the definition of ‘forest’ by the Food and Agriculture Organization of the United Nations. forest cover changed by comparing the years 2000 and 2020 to see if there were any changes at a 20- kilometer resolution.
A two-sided Student’s t- test was used to assess the distribution of mean precipitation effects. Moreover, we conducted a more rigorous field significance test using the Benjamini–Hochberg method to control the false discovery rate (FDR) at α = 0.05. The result shows model grids with significant P values that remain significant after FDR correction. Linear regression can be used to find a relationship between forest loss fraction and P over deforested grids. The P value is obtained from the Wald test, testing the null hypothesis that the slope is zero (Fig. 1a,b).
The frontier of current research, therefore, lies in advancing analytical frameworks, such as the one presented by Qin et al., by coupling them with dynamic modelling of vegetation. Only when the interacting effects of climate change, deforestation and vegetation health are linked can the risk of tipping be truly assessed. Considering seasonality — as Qin et al. have done — as well as drought and heat extremes will be essential steps in this process.
