Achieving Brazil's deforestation target will reduce fire and deliver air quality and public health benefits

Climate, deforestation, and forest fires are closely coupled in the Amazon, but models of fire that include these interactions are lacking. We trained machine learning models on temperature, rainfall, deforestation, land-use, and fire data to show that spatial and temporal patterns of fire in the Amazon are strongly modified by deforestation. We find that fire count across the Brazilian Amazon increases by 0.44 percentage points for each percentage point increase in deforestation rate. We used the model to predict that the increased deforestation rate in the Brazilian Amazon from 2013 to 2020 caused a 42% increase in fire counts in 2020. We predict that if Brazil had achieved the deforestation target under the National Policy on Climate Change, there would have been 32% fewer fire counts across the Brazilian Amazon in 2020. Using a regional chemistry-climate model and exposure-response associations, we estimate that the improved air quality due to reduced smoke emission under this scenario would have resulted in 2300 fewer deaths due to reduced exposure to fine particulate matter. Our analysis demonstrates the air quality and public health benefits that would accrue from reducing deforestation in the Brazilian Amazon

Sensitivity of air pollution exposure and disease burden to emission changes in China using machine learning emulation

Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual−mean fine particulate matter (PM(2.5)) and ozone (O(3)) concentrations and their associated chronic health impacts from changes in five major emission sectors (residential, industrial, land transport, agriculture, and power generation) in China. The emulators predicted 99.9% of the variance in PM(2.5) and O(3) concentrations. We used these emulators to estimate how emission reductions can attain air quality targets. In 2015, we estimate that PM(2.5) exposure was 47.4 μg m(−3) and O(3) exposure was 43.8 ppb, associated with 2,189,700 (95% uncertainty interval, 95UI: 1,948,000–2,427,300) premature deaths per year, primarily from PM(2.5) exposure (98%). PM(2.5) exposure and the associated disease burden were most sensitive to industry and residential emissions. We explore the sensitivity of exposure and health to different combinations of emission reductions. The National Air Quality Target (35 μg m(−3)) for PM(2.5) concentrations can be attained nationally with emission reductions of 72% in industrial, 57% in residential, 36% in land transport, 35% in agricultural, and 33% in power generation emissions. We show that complete removal of emissions from these five sectors does not enable the attainment of the WHO Annual Guideline (5 μg m(−3)) due to remaining air pollution from other sources. Our work provides the first assessment of how air pollution exposure and disease burden in China varies as emissions change across these five sectors and highlights the value of emulators in air quality research

Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India

Exposure to ambient fine particulate matter (PM2.5) is a leading contributor to diseases in India. Previous studies analysing emission source attributions were restricted by coarse model resolution and limited PM2.5 observations. We use a regional model informed by new observations to make the first high-resolution study of the sector-specific disease burden from ambient PM2.5 exposure in India. Observed annual mean PM2.5 concentrations exceed 100 mu g m(-3) and are well simulated by the model. We calculate that the emissions from residential energy use dominate (52%) population-weighted annual mean PM2.5 concentrations, and are attributed to 511,000 (95UI: 340,000-697,000) premature mortalities annually. However, removing residential energy use emissions would avert only 256,000 (95UI: 162,000-340,000), due to the non-linear exposure-response relationship causing health effects to saturate at high PM2.5 concentrations. Consequently, large reductions in emissions will be required to reduce the health burden from ambient PM2.5 exposure in India

Late-spring and summertime tropospheric ozone and NO2 in western Siberia and the Russian Arctic : regional model evaluation and sensitivities

We use a regional chemistry transport model (Weather Research and Forecasting model coupled with chemistry, WRF-Chem) in conjunction with surface observations of tropospheric ozone and Ozone Monitoring Instrument (OMI) satellite retrievals of tropospheric column NO2 to evaluate processes controlling the regional distribution of tropospheric ozone over western Siberia for late spring and summer in 2011. This region hosts a range of anthropogenic and natural ozone precursor sources, and it serves as a gateway for near-surface transport of Eurasian pollution to the Arctic. However, there is a severe lack of in situ observations to constrain tropospheric ozone sources and sinks in the region. We show widespread negative bias in WRF-Chem tropospheric column NO2 when compared to OMI satellite observations from May-August, which is reduced when using ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) v5a emissions (fractional mean bias (FMB) = -0.82 to -0.73) compared with the EDGAR (Emissions Database for Global Atmospheric Research)-HTAP (Hemispheric Transport of Air Pollution) v2.2 emissions data (FMB = -0.80 to -0.70). Despite the large negative bias, the spatial correlations between model and observed NO2 columns suggest that the spatial pattern of NOx sources in the region is well represented. Scaling trans-port and energy emissions in the ECLIPSE v5a inventory by a factor of 2 reduces column NO2 bias (FMB = -0.66 to -0.35), but with overestimates in some urban regions and little change to a persistent underestimate in background regions. Based on the scaled ECLIPSE v5a emissions, we assess the influence of the two dominant anthropogenic emission sectors (transport and energy) and vegetation fires on surface NOx and ozone over Siberia and the Russian Arctic. Our results suggest regional ozone is more sensitive to anthropogenic emissions, particularly from the transport sector, and the contribution from fire emissions maximises in June and is largely confined to latitudes south of 60 degrees N. Ozone dry deposition fluxes from the model simulations show that the dominant ozone dry deposition sink in the region is to forest vegetation, averaging 8.0 Tg of ozone per month, peaking at 10.3 Tg of ozone deposition during June. The impact of fires on ozone dry deposition within the domain is small compared to anthropogenic emissions and is negligible north of 60 degrees N. Overall, our results suggest that surface ozone in the region is controlled by an interplay between seasonality in atmospheric transport patterns, vegetation dry deposition, and a dominance of transport and energy sector emissions.Peer reviewe

Explaining global surface aerosol number concentrations in terms of primary emissions and particle formation

We use observations of total particle number concentration at 36 worldwide sites and a global aerosol model to quantify the primary and secondary sources of particle number. We show that emissions of primary particles can reasonably reproduce the spatial pattern of observed condensation nuclei (CN) (R2=0.51) but fail to explain the observed seasonal cycle at many sites (R2=0.1). The modeled CN concentration in the free troposphere is biased low (normalised mean bias, NMB=−88%) unless a secondary source of particles is included, for example from binary homogeneous nucleation of sulfuric acid and water (NMB=−25%). Simulated CN concentrations in the continental boundary layer (BL) are also biased low (NMB=−74%) unless the number emission of anthropogenic primary particles is increased or an empirical BL particle formation mechanism based on sulfuric acid is used. We find that the seasonal CN cycle observed at continental BL sites is better simulated by including a BL particle formation mechanism (R2=0.3) than by increasing the number emission from primary anthropogenic sources (R2=0.18). Using sensitivity tests we derive optimum rate coefficients for this nucleation mechanism, which agree with values derived from detailed case studies at individual sites

Logging intensity drives variability in carbon stocks in lowland forests in Vietnam

Forest degradation in the tropics is generating large carbon (C) emissions. In tropical Asia, logging is the main driver of forest degradation. For effective implementation of REDD+ projects in logged forests in Southeast Asia, the impacts of logging on forest C stocks need to be assessed. Here, we assess C stocks in logged lowland forests in central Vietnam and explore correlations between logging intensity, soil, topography and living aboveground carbon (AGC) stocks. We present an approach to estimate historical logging intensities for the prevalent situation when complete records on logging history are unavailable. Landsat analysis and participatory mapping were used to quantify the density of historical disturbances, used as a proxy of logging intensities in the area. Carbon in AGC, dead wood, belowground carbon (BGC) and soil (SOC) was measured in twenty-four 0.25 ha plots that vary in logging intensity, and data on recent logging, soil properties, elevation and slope were also collected. Heavily logged forests stored only half the amount of AGC of stems ≥10 cm dbh as lightly logged forests, mainly due to a reduction in the number of large (≥60 cm dbh) trees. Carbon in AGC of small trees (5–10 cm dbh), dead wood and BGC comprised only small fractions of total C stocks, while SOC in the topsoil of 0–30 cm depth stored ~50% of total C stocks. Combining logging intensities with soil and topographic data showed that logging intensity was the main factor explaining the variability in AGC. Our research shows large reductions in AGC in medium and heavily logged forests. It highlights the critical importance of conserving big trees to maintain high forest C stocks and accounting for SOC in total C stock estimates

Carbon storage and sequestration of re-growing montane forests in southern Ecuador

The storage and sequestration of carbon by tropical montane forests is poorly understood. We quantified the above-ground biomass (AGB) storage in secondary tropical montane forests in southern Ecuador. The AGB in older secondary (>40 years old) forest was found to be 158±38Mgha-1 of land surface at 1000 m elevation and 104±25Mgha-1 of land surface at 2250 m elevation. This is less than the storage reported in a recent synthesis of AGB observations in mature tropical montane forests, potentially due to a legacy of selective logging within our study sites. The slope angle resulted in AGB being 1.5-10% greater when reported on a planimetric compared to land surface area basis. We also quantified AGB in areas of abandoned pasture where grazing and fire had been excluded. Pasture that had been recently abandoned (1-2years) stored 2-18Mgha-1 of AGB with the higher values due to the presence of relict trees. Re-growing secondary forests, established through natural regeneration, accumulated AGB at a rate of 10Mgha-1yr-1 at 1000 m elevation and 4Mgha-1yr-1 at 2250m elevation, for the first 5-7years after pasture abandonment. After 12-15 years, accumulation of AGB slowed to 1-2Mgha-1yr-1. Net biomass accumulation rates were similar to those observed in lowland humid tropical forests, suggesting that regenerating tropical montane forests provide an important carbon sequestration. In newly regenerating forests, small trees (DBH < 10cm) contributed up to 50% of total AGB. In the older secondary forest at high elevation coarse dead wood contributed 34% of total AGB

A perspective for advancing climate prediction services in Brazil

The Climate Science for Service Partnership Brazil (CSSP-Brazil) project provides Brazil and UK partners the opportunity to address important challenges faced by the climate modeling community, including the need to develop subseasonal and seasonal prediction and climate projection services. This paper provides an overview of the climate modeling and prediction research conducted through CSSP-Brazil within the context of a framework to advance climate prediction services in Brazil that includes a research-to-services (R2S) and a services-to-research (S2R) feedback pathway. The paper also highlights plans to advance scientific understanding and capability to produce beneficial climate knowledge and new products to improve climate prediction services to support decisions in various industries in Brazil. Policy-relevant outcomes from climate modeling and prediction exercises illustrated in this paper include supporting stakeholders with climate information provided from weeks to months ahead for (a) improving water management strategies for human consumption, navigation, and agricultural and electricity production; (b) defining crop variety and calendars for food production; and (c) diversifying energy production with alternatives to hydropower

Trees, forests and water: Cool insights for a hot world

Forest-driven water and energy cycles are poorly integrated into regional, national, continental and global decision-making on climate change adaptation, mitigation, land use and water management. This constrains humanity’s ability to protect our planet’s climate and life-sustaining functions. The substantial body of research we review reveals that forest, water and energy interactions provide the foundations for carbon storage, for cooling terrestrial surfaces and for distributing water resources. Forests and trees must be recognized as prime regulators within the water, energy and carbon cycles. If these functions are ignored, planners will be unable to assess, adapt to or mitigate the impacts of changing land cover and climate. Our call to action targets a reversal of paradigms, from a carbon-centric model to one that treats the hydrologic and climate-cooling effects of trees and forests as the first order of priority. For reasons of sustainability, carbon storage must remain a secondary, though valuable, by-product. The effects of tree cover on climate at local, regional and continental scales offer benefits that demand wider recognition. The forest- and tree-centered research insights we review and analyze provide a knowledge-base for improving plans, policies and actions. Our understanding of how trees and forests influence water, energy and carbon cycles has important implications, both for the structure of planning, management and governance institutions, as well as for how trees and forests might be used to improve sustainability, adaptation and mitigation efforts

Global modeling of tropospheric iodine aerosol

Natural aerosols play a central role in the Earth system. The conversion of dimethyl sulfide to sulfuric acid is the dominant source of oceanic secondary aerosol. Ocean emitted iodine can also produce aerosol. Using a GEOS-Chem model, we present a simulation of iodine aerosol. The simulation compares well with the limited observational data set. Iodine aerosol concentrations are highest in the tropical marine boundary layer (MBL) averaging 5.2 ng (I) m −3 with monthly maximum concentrations of 90 ng (I) m −3. These masses are small compared to sulfate (0.75% of MBL burden, up to 11% regionally) but are more significant compared to dimethyl sulfide sourced sulfate (3% of the MBL burden, up to 101% regionally). In the preindustrial, iodine aerosol makes up 0.88% of the MBL burden sulfate mass and regionally up to 21%. Iodine aerosol may be an important regional mechanism for ocean-atmosphere interaction