Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository.

The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. We present the Atlas chapter of Working Group I (WGI) as a test case. We describe the application of the FAIR principles in the Atlas, the challenges faced during its implementation, and those that remain for the future. We introduce the open source repository resulting from this process, including coding (e.g., annotated Jupyter notebooks), data provenance, and some aggregated datasets used in some figures in the Atlas chapter and its interactive companion (the Interactive Atlas), open to scrutiny by the scientific community and the general public. We describe the informal pilot review conducted on this repository to gather recommendations that led to significant improvements. Finally, a working example illustrates the re-use of the repository resources to produce customized regional information, extending the Interactive Atlas products and running the code interactively in a web browser using Jupyter notebooks.

Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain.

In response to the COVID-19 pandemic, governments declared severe restrictions throughout 2020, presenting an unprecedented scenario of reduced anthropogenic emissions of air pollutants derived mainly from traffic sources. To analyze the effect of these restrictions derived from COVID-19 pandemic on air quality levels, relative changes in NO, NO2, O3, PM10 and PM2.5 concentrations were calculated at urban traffic sites in the most populated Spanish cities over different periods with distinct restrictions in 2020. In addition to the changes calculated with respect to the observed air pollutant levels of previous years (2013-2019), relative changes were also calculated using predicted pollutant levels for the different periods over 2020 on a business-as-usual scenario using Multiple Linear Regression (MLR) models with meteorological and seasonal predictors. MLR models were selected among different data mining techniques (MLR, Random Forest (RF), K-Nearest Neighbors (KNN)), based on their higher performance and accuracy obtained from a leave-one-year-out cross-validation scheme using 2013-2019 data. A q-q mapping post-correction was also applied in all cases in order to improve the reliability of the predictions to reproduce the observed distributions and extreme events. This approach allows us to estimate the relative changes in the studied air pollutants only due to COVID-19 restrictions. The results obtained from this approach show a decreasing pattern for NOx, with the largest reduction in the lockdown period above -50%, whereas the increase observed for O3 contrasts with the NOx patterns with a maximum increase of 23.9%. The slight reduction in PM10 (-4.1%) and PM2.5 levels (-2.3%) during lockdown indicates a lower relationship with traffic sources. The developed methodology represents a simple but robust framework for exploratory analysis and intervention detection in air quality studies.

Assessing the effect of spatial-temporal droughts on dominant crop yield changes in Central Malawi.

Central Malawi has intensely been subjected to different climate-related shocks such as floods, dry spells, and droughts, resulting in decreases in crop yields. Due to their recurrence arising from the effects of climate change, drought characterization, monitoring, and prediction are crucial in guiding agriculture-water users and planners to prepare drought risk management plans and early warning systems. This research analyzed droughts, using multiple drought indices and their impacts on dominant crops over Central Malawi. Forty years of hydro-meteorological data (1977-2017) from nine rain-gauging stations and crop yield data from 1983 to 2017 from four districts were analyzed. The study discovered that drought events in the Agricultural Development Division (ADD) are highly a function of rainfall deficit and high temperatures. The results highlighted that the rainfall patterns in the area are not dependable, calling for the utilization of climate-smart irrigation systems such as drip irrigation and rainwater harvesting technologies. Furthermore, we achieved that crops such as cassava and groundnuts must be promoted to withstand the long water stress duration. These crops also have a multiplier effect; hence, they can enhance food security in the region. This study recommends that using more robust variables in drought analysis studies is necessary for effective drought monitoring and early warning systems. In corroboration with disaster management NGOs, it is recommended that the government should be proactive in developing integrated drought management policies and planning strategies for drought adaptation and mitigation.