We synthesized N2O emissions over North America using 17 bottom-up (BU) estimates from 1980-2016 and five top-down (TD) estimates from 1998-2016. The BU-based total emission shows a slight increase owing to U.S. agriculture, while no consistent trend is shown in TD estimates. During 2007-2016, North American N2O emissions are estimated at 1.7 (1.0-3.0) Tg N yr-1 (BU) and 1.3 (0.9-1.5) Tg N yr-1 (TD). Anthropogenic emissions were twice larger than natural fluxes from soil and water. Direct agricultural and industrial activities accounted for 68% of total anthropogenic emissions, 71% of which was contributed by the U.S. Our estimates of U.S. agricultural emissions are comparable to the EPA greenhouse gas (GHG) inventory, which includes estimates from IPCC tier 1 (emission factor) and tier 3 (process-based modeling) approaches. Conversely, our estimated agricultural emissions for Canada and Mexico are twice as large as the respective national GHG inventories based on tier 1 approaches.

Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many traditional dust emission models (TEMs) assume that the Earth’s land surface is devoid of vegetation, adjust dust emission using a vegetation cover complement, and calibrate the magnitude of modelled emissions to atmospheric dust. We compare this approach with a novel albedo-based dust emission model (AEM) which calibrates Earth’s land surface normalised shadow (1-albedo) to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. Existing datasets of satellite observed dust emission from point sources (DPS) and dust optical depth (DOD) show little spatial relation and DOD frequency exceeds DPS frequency by up to two orders of magnitude. Relative to DPS frequency, both dust emission models showed strong relations, but over-estimate dust emission frequency, suitable for calibration to observed dust emission. Our results show that TEMs over-estimate large dust emission over vast vegetated areas and produce considerable false change in dust emission, relative to the AEM. It is difficult to avoid the conclusion, raised by other literature, that calibrating dust cycle models to atmospheric dust has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without masks or vegetation cover. Considerable potential exists for Earth System Models driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.

It has been claimed that COVID-19 public stimulus packages could be sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 °C target of the Paris Agreement. Here we provide complementary perspectives to reiterate that substantial, broad, and sustained engagements beyond recovery packages will be needed for achieving the Paris Agreement long-term targets. Low-carbon investments will need to scale up and persist over the next several decades following short-term stimulus packages. The required total energy investments in the real world can be larger than the currently available estimates from Integrated Assessment Models (IAMs). Existing databases from IAMs are not sufficient for analyzing the effect of public spending on emission reduction. To inform what role COVID-19 stimulus packages and public investments may play for reaching the Paris Agreement targets, explicit modelling of such policies is required.

Simulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta-model based on 8703 site-level process-model simulations of yield with contrasting future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to relate the percentage change in future yield relative to the baseline period (2000-2010) to explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6–21%. Adaptation alleviates this average loss by 1–13%. Maize was most responsive to adaptive practices with a mean yield loss of -21 % [range across locations: -63%, +3.7%] without adaptation and -7.5% [range: -46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types most able to prevent large yield losses, respectively. When adaptation practices are applied, some areas may experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields.

Part of the economic recovery plans implemented by governments following COVID-19 is directed towards the energy transition. To understand the potential effects of these post-COVID green recovery packages on reductions of greenhouse gases emissions, we investigated three different approaches. Firstly, we analysed simulation results of Integrated Assessment Models (IAMs) to infer the change in CO2 intensity of GDP that could result from post-COVID low-carbon investment plans. Secondly, we investigated the scenarios provided by the International Energy Agency (IEA) based on a bottom-up energy system model. By combining the two approaches, we found that green recovery packages implemented and planned globally can lead to an emission reduction of merely 1%-6% from 2030 baseline levels at most. Thirdly, we looked into the results of the Adaptative Regional Input-Output model, which simulates the dynamic effects of economic crisis and fiscal stimuli through supply chains following labour shortage. The third approach shows that the increase of activity driven by fiscal stimuli leads to a rebound of CO2 emissions even if they do not target carbon-intensive sectors. We conclude that green recovery packages targeting low-carbon technologies have a limited impact on near-term CO2 emissions and that demand-side incentives, as well as other policy efforts to disincentivise the use of fossil fuels, are also important for scaling up climate mitigation.

The livestock sector is the largest source of anthropogenic methane emissions, and is projected to increase in the future with increased demand for livestock products. Here, we compare livestock methane emissions and emission intensities, defined by the amount of methane emitted per unit of animal proteins, estimated by different methodologies, and identify mitigation potentials in different regions of the world based on possible future projections. We show that emission intensity decreased for most livestock categories globally during 2000-2018, due to an increasing protein-production efficiency, and the IPCC Tier 2 method should be used for capturing the temporal changes in the emission intensities. We further show that efforts on the demand-side to promote balanced, healthy and environmentally-sustainable diets in most countries will not be sufficient to mitigate livestock methane emissions without parallel efforts to improve production efficiency. The latter efforts have much greater mitigating effects than demand-side efforts, and hence should be prioritized in a few developing countries that contribute most of the mitigation potential.