Abstract. Spatially distributed anthropogenic and open burning emissions are fundamental data needed by Earth system models. We describe the methods used for generating gridded datasets produced for use by the modeling community, particularly for the Coupled Model Intercomparison Project Phase 6. The development of three sets of gridded data for historical open burning, historical anthropogenic, and future scenarios was coordinated to produce consistent data over 1750–2100. Historical data up to 2014 were provided with annual resolution and future scenario data in 10-year intervals. Emissions are provided on a sectoral basis, along with additional files for speciated non-methane volatile organic compounds (NMVOCs). An automated framework was developed to produce these datasets to ensure that they are reproducible and facilitate future improvements. We discuss the methodologies used to produce these data along with limitations and potential for future work.

Demeter is an open source Python package that was built to disaggregate projections of future land allocations generated by an integrated assessment model (IAM). Projected land allocation from IAMs is traditionally transferred to Earth System Models (ESMs) in a variety of gridded formats and spatial resolutions as inputs for simulating biophysical and biogeochemical fluxes. Existing tools for performing this translation generally require a number of manual steps which introduces error and is inefficient. Demeter makes this process seamless and repeatable by providing gridded and land cover change (LULCC) products derived directly from an IAM-in this case, the Global Change Assessment Model (GCAM)-in a variety of formats and resolutions commonly used by ESMs. Demeter is publicly available via GitHub and has an extensible output module allowing for future ESM needs to be easily accommodated.

In the 2015 Paris Agreement, nations worldwide pledged emissions reductions (Nationally Determined Contributions—NDCs) to avert the threat of climate change, and agreed to periodically review these pledges to strengthen their level of ambition. Previous studies have analyzed NDCs largely in terms of their implied contribution to limit global warming, their implications on the energy sector or on mitigation costs. Nevertheless, a gap in the literature exists regarding the understanding of implications of the NDCs on countries’ Energy-Water-Land nexus resource systems. The present paper explores this angle within the regional context of Latin America by employing the Global Change Assessment Model, a state-of-the-art integrated assessment model capable of representing key system-wide interactions among nexus sectors and mitigation policies. By focusing on Brazil, Mexico, Argentina and Colombia, we stress potential implications on national-level water demands depending on countries’ strategies to enforce energy-related emissions reductions and their interplays with the land sector. Despite the differential implications of the Paris pledges on each country, increased water demands for crop and biomass irrigation and for electricity generation stand out as potential trade-offs that may emerge under the NDC policy. Hence, this study underscores the need of considering a nexus resource planning framework (known as “Nexus Approach”) in the forthcoming NDCs updating cycles as a mean to contribute toward sustainable development.

With the ability to simulate historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degree, the Python package Xanthos version 1 provided a solid foundation for continuing advancements in global water dynamics science. The goal of Xanthos version 2 was to build upon previous investments by creating a Python framework where core components of the model (potential evapotranspiration (PET), runoff generation, and river routing) could be interchanged or extended without having to start from scratch. Xanthos 2 utilizes a component-style architecture which enables researchers to quickly incorporate and test cutting-edge research in a stable modeling environment prebuilt with diagnostics. Major advancements for Xanthos 2 were also achieved by the creation of a robust default configuration with a calibration module, hydropower modules, and new PET modules, which are now available to the scientific community.

Exposure to air pollution is a leading risk factor for premature death globally; however, the complexity of its formation and the diversity of its sources can make it difficult to address. The Group of Twenty (G20) countries are a collection of the world's largest and most influential economies and are uniquely poised to take action to reduce the global health burden associated with air pollution. We present a framework capable of simultaneously identifying regional and sectoral sources of the health impacts associated with two air pollutants, fine particulate matter (PM 2.5 ) and ozone (O 3 ) in G20 countries; this framework is also used to assess the health impacts associated with emission reductions. This approach combines GEOS‐Chem adjoint sensitivities, satellite‐derived data, and a new framework designed to better characterize the non‐linear relationship between O 3 exposures and nitrogen oxides emissions. From this approach, we estimate that a 50% reduction of land transportation emissions by 2040 would result in 251 thousand premature deaths avoided in G20 countries. These premature deaths would be attributable equally to reductions in PM 2.5 and O 3 exposure which make up 51% and 49% of the potential benefits, respectively. In our second application, we estimate that the energy generation related co‐benefits associated with G20 countries staying on pace with their net‐zero carbon dioxide targets would be 290 thousand premature deaths avoided in 2040; action by India (47%) would result in the most benefits of any country and a majority of these avoided deaths would be attributable to reductions in PM 2.5 exposure (68%).

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Abstract. Spatially distributed anthropogenic and open burning emissions are fundamental data needed by Earth system models. We describe the methods used for generating gridded data sets produced for use by the modelling community, particularly for the Coupled Model Inter-comparison Project Phase 6. The development of three sets of gridded data for historical open burning, historical anthropogenic, and future scenarios were coordinated to produce consistent data over 1750–2100. Historical data up to 2014 were provided with annual resolution and future scenario data in 10-year intervals. Emissions are provided on a sectoral basis, along with additional files for speciated non-Methane Volatile Organic Compounds (NMVOCs). An automated framework was developed to produce these datasets to ensure that they are reproducible and facilitate future improvements. We discuss the methodologies used to produce these data along with limitations and potential for future work.

No abstract