This paper supports research in determining the most up-to-date CMIP6 model performance of precipitation regime for 1981–2014 for the Brazilian Amazon. (5) When all evaluation metrics are considered, the models that perform best are CESM2, MIROC6, MRIESM20, SAM0UNICON, and the ensemble mean. (4) EOF analysis shows that models capture the dominant mode of variability, which was the annual cycle or South American Monsoon System.
(3) Models struggle to capture extreme values of precipitation except when precipitation values are close to 0. Models tend to produce too much rainfall at the start of the wet season and tend to either over- or under-estimate the dry season, although ensemble means typically display the overall pattern more precisely. The northern Amazon dry season tends to occur in August, September, and October and the wet season occurs in March, April, and May, and models are not able to capture the climatology as well. (2) The southern Amazon has a more defined dry season (June, July, and August) and wet season (December, January, and February) and models simulate this well. Models tend to underestimate northern values or overestimate the central to northwest averages. Precipitation analysis shows: (1) This region displays higher rainfall in the north-northwest and drier conditions in the south. This is done by discussing results from spatial pattern mapping, Taylor diagram analysis and Taylor skill score, annual climatology comparison, cumulative distribution analysis, and empirical orthogonal function (EOF) analysis. This paper evaluates the performance of 13 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Brazilian Amazon provides important hydrological cycle functions, including precipitation regimes that bring water to the people and environment and are critical to moisture recycling and transport, and represents an important variable for climate models to simulate accurately.
Brazil’s current trend to facilitating the legalization of illegal claims in undesignated public lands, such as the large and medium landholdings we studied, implies vast areas of future deforestation and should be reversed. By analyzing the clearing pattern in the landholdings, deforestation monitoring can focus on specific sizes of landholdings that contribute most to the advance of the deforestation frontier. Thus, greater attention should be given to these larger landholdings to control the spread of deforestation. By 2018, 80% of the remaining forest was in larger landholdings (> 400 ha), increasing the susceptibility of this forest to being cleared in the coming years. Landholdings up to 400 ha were the majority in numbers (52%) and larger landholdings (> 400 ha) were located farther into the forest, contributing to expanding the deforestation frontier. We assessed the advance of deforestation and the spatial distribution of landholdings in relation to the main road and to land categories (e.g., protected areas and undesignated public land). We use a case of Matupi District, a hotspot of deforestation along the Transamazon Highway in the southern portion of Brazil’s state of Amazonas, where spontaneous squatters and land grabbers are the main actors occupying landholdings. Little is known about the spread of deforestation in landholdings in undesignated public lands located on cattle-ranching frontiers. The impact of deforestation in Brazilian Amazonia is a global concern, and land occupation in public lands contributes to increased deforestation rates.
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