Online Eulerian approach for tracing moisture sources for precipitation in regions with the Fully Coupled WRF model (WRF-ARW)
Regional climate studies use the fully coupled WRF model, which was developed by the National Center for Atmospheric Research. The Advanced Research version of the WRF model (WRF-ARW, v.4.3.3) was used in this study24. The water vapour tracer tool25 was coupled into the WRF model (WRF-WVT) to track moisture sources for precipitation by replicating the prognostic equation for total moisture and creating new variables for tracers of different moisture species. The method is classified as an online Eulerian approach, as the predictive equations for the tracer moisture are solved simultaneously with the governing equations of the model16,25.
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Source: Impact of Amazonian deforestation on precipitation reverses between seasons
Subgrid vegetation cover representation in the community land model: comparison of moving window and climate modelling methods for calculating the global forest cover impacts of Amazon deforestation
Moreover, to enable the subgrid vegetation cover fraction in WRF, we coupled the Community Land Model (CLM)53 with the activated subgrid vegetation cover representation approach (CLM-tiling)25 into the WRF model to accurately represent the land and atmosphere interactions. The Community Earth System Model32,53 is a state of the art land surface model. The WRF model51 and the subgrid vegetation cover representation function can be used to deactivated the representation function. The WRF-CLM has a deactivation of the subgrid vegetation cover approach, which takes into account the surface fluxes over four vegetation cover types in the subgrid scale. The sub grid vegetation cover representation approach is important to research the climate feedback of deforestation, as it provides more accurate land properties for each model grid51
In this study, climate modelling and the moving window approach were used to quantify the precipitation impacts of Amazon deforestation. These methods have differing assumptions and strengths, according to the supplementary fig. 8). For climate modelling, we can obtain the comprehensive impacts of deforestation and further explore the local and nonlocal effects on buffers at different scales. The impact of deforestation can be quantified using satellite-observed datasets. However, this quantification is limited to deforested pixels, and the effects on neighbouring and remote pixels cannot be quantified. When deforested pixels are compared with neighbours, there are no changes to the intrinsic effects. In this section, we provided a comprehensive comparison of these two methods using model simulations and satellite observations.
We used high-resolution (30 m) forest cover data for 2000 and 2020 from the Global Land Analysis and Discovery (GLAD) forest extent and height change dataset22,23 to resample forest cover change information at a 20-km resolution (Extended Data Fig. 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. When calculating forest cover changes, we looked at the years 2000 and 2020, noting the decrease in forest cover during the course of the decade.
A two-sided Student’s t-test was used to statistically assess whether the distributions of mean precipitation effects (for example, ΔP, ΔPt and ΔPnt) are statistically different from zero. We conducted a field significance test using the Benjamini–Hochberg method to control the false discovery rate. Only model grids (ΔP) with locally significant P values (P < 0.05) that remain significant after FDR correction are highlighted in the results (Fig. 1c,d). The forest loss fraction and P over deforested grids are variables used to fit a linear regression. The P value is used to test a null hypothesis that the slope is zero.
The frontier of today’s research lies in analytical frameworks that can be used with dynamic modelling of vegetation. It’s important that the risk of tipping is assessed when the effects of climate change are linked to vegetation health. Considering seasonality — as Qin et al. have done — as well as drought and heat extremes will be essential steps in this process.