Benchmarking Regridding Libraries Used in Earth System Modelling

Valcke, Sophie; Piacentini, Andrea; Jonville, G.

doi:10.3390/mca27020031

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…All other datasets used in this study are aggregated to monthly averages to predict these ignition occurrence data. Next, all datasets were rasterized (where necessary) and resampled to a 0.25 • grid using conservative remapping, described in [47], to avoid smoothing anomalies through alternative approaches, such as bilinear interpolation [21]. The values of annual datasets were repeated 12 times each year, and static datasets were replicated to match the total number of months corresponding to the ignitions target.…”

Section: Data Preparationmentioning

confidence: 99%

Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence

Mukunga

Forkel

Forrest

et al. 2023

Fire

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Fires are a pervasive feature of the terrestrial biosphere and contribute large carbon emissions within the earth system. Humans are responsible for the majority of fire ignitions. Physical and empirical models are used to estimate the future effects of fires on vegetation dynamics and the Earth’s system. However, there is no consensus on how human-caused fire ignitions should be represented in such models. This study aimed to identify which globally available predictors of human activity explain global fire ignitions as observed by satellites. We applied a random forest machine learning framework to state-of-the-art global climate, vegetation, and land cover datasets to establish a baseline against which influences of socioeconomic data (cropland fraction, gross domestic product (GDP), road density, livestock density, grazed lands) on fire ignition occurrence were evaluated. Our results showed that a baseline random forest without human predictors captured the spatial patterns of fire ignitions globally, with hotspots over Sub-Saharan Africa and South East Asia. Adding single human predictors to the baseline model revealed that human variables vary in their effects on fire ignitions and that of the variables considered GDP is the most vital driver of fire ignitions. A combined model with all human predictors showed that the human variables improve the ignition predictions in most regions of the world, with some regions exhibiting worse predictions than the baseline model. We concluded that an ensemble of human predictors can add value to physical and empirical models. There are complex relationships between the variables, as evidenced by the improvement in bias in the combined model compared to the individual models. Furthermore, the variables tested have complex relationships that random forests may struggle to disentangle. Further work is required to detangle the complex regional relationships between these variables. These variables, e.g., population density, are well documented to have substantial effects on fire at local and regional scales; we determined that these variables may provide more insight at more continental scales.

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Section: Data Preparationmentioning

confidence: 99%

Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence

Mukunga

Forkel

Forrest

et al. 2023

Fire

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ASTE: An artificial solver testing environment for partitioned coupling with preCICE

Schneider,

Yurt,

Simonis

et al. 2024

JOSS

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Milk contamination in Europe under anticipated climate change scenarios

Katsini,

Bhonsale,

Roufou

et al. 2024

Front. Sustain. Food Syst.

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Transforming the food system while addressing climate change requires proactive measures based on quantitative projections of anticipated future conditions. A key component of the food system that must be considered during this transformation is food safety, which is the focus of this paper. Milk safety has been selected as a case study. Future milk contamination levels in Europe, in terms of total bacterial counts, are evaluated under various climate change scenarios. Projections from multiple climate models are integrated into a data-driven milk contamination model, validated using data from Malta, Spain, and Belgium. The modeling framework accounts for variability among dairy farms and the inherent uncertainties in climate projections. Results are presented through geographical heatmaps, highlighting coastal and southern areas such as Portugal, Western Spain, Southern Italy, and Western France as regions expected to face the highest bacterial counts. The analysis underlines the significant roles of humidity and wind speed, alongside temperature. It also examines compliance with the regulatory threshold for raw milk, revealing an increased frequency of summer weeks exceeding the threshold of 100,000 colony-forming units. Based on this analysis, regions are classified into low-risk, high-risk, and emerging-risk categories. This classification can guide the selection of farm strategies aimed at meeting future food safety standards. By informing these decisions with the anticipated impacts of climate change, the food system can be future-proofed.

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Benchmarking Regridding Libraries Used in Earth System Modelling

Cited by 5 publications

References 10 publications

Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence

Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence

ASTE: An artificial solver testing environment for partitioned coupling with preCICE

Milk contamination in Europe under anticipated climate change scenarios

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