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DOE OSTI2023

Amazon drought resilience – emerging results point to new empirical needs

Will climate change turn tropical forests from a carbon sink to a source (Pan et al., 2011)? Tropical forests cycle more carbon, water, and energy than any other biome (Bonan, 2008). The Amazon is the largest tropical forest, spanning one-third of South America, and any perturbation in its function has repercussions on the global climate. Mild and severe droughts in the Amazon are predicted to double and triple in area, respectively, by 2100, while the area under wet extremes will increase (Duffy et al., 2015). Our understanding of the role of plants' hydrological environments in determining their drought response, however, is limited (Chitra-Tarak et al., 2018, 2021). An important review by Costa et al. (2023; pp. 714–733) published in this issue of New Phytologist highlights that shallow-water table (WT) forests constitute c. 50% of the Amazon and may act as hydrological refugia during droughts, yet they have been neglected in Amazon forest research to date. Emerging results suggest that the shallow-WT forests that dominate the Amazon basin may increase in productivity under moderate droughts due to relief from hypoxia, suggesting a potential buffer from drought relative to deeper WT forests (Fig. 1). During severe droughts, however, shallow-WT forests may be vulnerable to collapse due to drought-intolerant traits. Addressing the underrepresentation of relatively drought-resilient shallow-WT forests in the Amazon's forest inventories may help resolve the much-debated incongruence in recent studies of the Amazon's drought resilience. Data from forest inventories (biased toward deep-WT forests) indicate that the Amazon's carbon sink is declining, partially because of drought impacts (Phillips et al., 2009; Brienen et al., 2015), whereas basin-wide satellite-based measures of gross primary productivity, which include shallow-WT forests, indicate varied regional responses to droughts (Saleska et al., 2007; Brando et al., 2010). Costa et al.'s (2023) descriptive insights of the intricate balance of geology, topography, hydrology, vegetation, and drought on ecosystem function, as well as their conceptual predictive framework, are useful for developing new empirical research in these understudied ecosystems and improving Earth system models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2023

Observational Constraints Reduce Model Spread but Not Uncertainty in Global Wetland Methane Emission Estimates

The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Kuang-Yu Chang↗

DOE OSTI2022

Role of climate goals and clean-air policies on reducing future air pollution deaths in China: a modelling study

Over 3 million people still die every year from diseases caused by exposure to outdoor PM 2.5 air pollution, and more than a quarter of these premature deaths occur in China. In addition to clean air policies that target pollution emissions, climate policies aimed at reducing fossil-fuel CO 2 emissions (e.g., to avoid 1.5°C of warming) may also dramatically improve air quality and public health. Yet there has been no comprehensive accounting of public health outcomes under different energy pathways and local clean air management decisions in China. In particular, further research is needed to understand the relationships among climate and clean air polices and future health burdens in China, where an aging population will further exacerbate the impacts of air pollution. Using a China-focused integrated assessment model (GCAM-China) and a dynamic emission projection model (DPEC), we project future Chinese air quality in scenarios spanning a range of global climate targets (i.e. 1.5°0C, 2°C, NDC, unambitious, baseline, and 4.5°C) as well as national clean air actions (i.e. 2015-pollution, current-pollution, and ambitious-pollution). We then evaluate the health impacts of PM2·5 air pollution of scenario matrix using the chemical transport model WRF-CMAQ and the latest epidemiological concentration–response (C-R) functions (i.e. GBD2019). We find that, without ambitious climate mitigation (e.g., under current NDC pledges), Chinese deaths related the PM2.5 air pollution do not substantially decrease—and often grow—by mid-century, regardless of clean air policies and air quality improvements. For example, in scenarios that track China’s current NDC pledge and deploy best-available pollution control technologies, PM 2.5 -related deaths in China decrease slightly by 2030 (to 1.2 million per year) but no further by mid-century (Ambitious-pollution-NDC-goals; 1.2 million deaths in 2050) despite substantial and continuous improvements in population-weighted air quality (27.2 to 16.0 µg/m 3 from 2030 to 2050). The contrary trends of improving air quality and increasing PM 2·5 -related deaths in many of our scenarios reveals the extent to which extra efforts are needed to compensate for the established fact of increasing age of China’s population in future. Substantial decreases in China’s PM 2.5 -related deaths and age-standardized death rates (e.g., decreasing by 0.3–0.5 million deaths and 10.2–14.2 per 100,000 population age-standardized death rates per year) thus require the sort of large-scale transition in energy sources entailed by scenarios that meet international climate goals to avoid 1.5°C and 2°C of warming (Ambitious-pollution-2°C- and 1.5°C-goals).

58 GEOSCIENCES↗

DOE OSTI2024

Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Abstract Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

Results from the fully and biogeochemically coupled simulations in which CO 2 increases at a rate of 1 % yr –1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon–concentration and carbon–climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO 2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent (sixth) Coupled Model Intercomparison Project (CMIP6) and compared with eight models from the fifth CMIP (CMIP5). The strength of the carbon–concentration feedback is of comparable magnitudes over land (mean ± standard deviation = 0.97 ± 0.40 PgC ppm –1 ) and ocean (0.79 ± 0.07 PgC ppm –1 ), while the carbon–climate feedback over land (–45.1 ± 50.6 PgC °C –1 ) is about 3 times larger than over ocean (–17.2 ± 5.0 PgC °C –1 ). The strength of both feedbacks is an order of magnitude more uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77 ± 0.37 °C EgC –1 and is similar to that found in CMIP5 models (1.63 ± 0.48 °C EgC –1 ) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Global biogeography of fungal and bacterial biomass carbon in topsoil

Bacteria and fungi, representing two major soil microorganism groups, play an important role in global nutrient biogeochemistry. Biogeographic patterns of bacterial and fungal biomass are of fundamental importance for mechanistically understanding nutrient cycling. We synthesized 1323 data points of phospholipid fatty acid-derived fungal biomass C (FBC), bacterial biomass C (BBC), and fungi:bacteria (F:B) ratio in topsoil, spanning 11 major biomes. The FBC, BBC, and F:B ratio display clear biogeographic patterns along latitude and environmental gradients including mean annual temperature, mean annual precipitation, net primary productivity, root C density, soil temperature, soil moisture, and edaphic factors. At the biome level, tundra has the highest FBC and BBC densities at 3684 (95% confidence interval: 1678–8084) mg kg -1 and 428 (237–774) mg kg -1 , respectively; desert has the lowest FBC and BBC densities at 16.92 (14.4–19.89) mg kg -1 and 6.83 (6.1–7.65) mg kg -1 , respectively. The F:B ratio varies dramatically, ranging from 1.8 (1.6–2.1) in savanna to 8.6 (6.7–11.0) in tundra. An empirical model was developed for the F:B ratio and it is combined with a global dataset of soil microbial biomass C to produce global maps for FBC and BBC in 0–30 cm topsoil. Across the globe, the highest FBC is found in boreal forest and tundra while the highest BBC is in boreal forest and tropical/subtropical forest, the lowest FBC and BBC are in shrub and desert. Global stocks of living microbial biomass C were estimated to be 12.6 (6.6–16.4) Pg C for FBC and 4.3 (0.5–10.3) Pg C for BBC in topsoil. These findings advance our understanding of the global distribution of fungal and bacterial biomass, which facilitates the incorporation of fungi and bacteria into Earth system models. The global maps of bacterial and fungal biomass serve as a benchmark for validating microbial models in simulating the global C cycle under a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Future Projections of the El Niño—Southern Oscillation and Tropical Pacific Mean State in CMIP6

Abstract The El Niño—Southern Oscillation (ENSO) is an important mode of tropical Pacific atmosphere‐ocean variability that drives teleconnections with weather and climate globally. However, prior studies using state‐of‐the‐art climate models lack consensus regarding future ENSO projections and are often impacted by tropical Pacific sea‐surface temperature (SST) biases. We used 173 simulations from 29 climate models participating in the Coupled Model Intercomparison Project, version 6 (CMIP6) to analyze model biases and future ENSO projections. We analyzed two ENSO indices, namely the ENSO Longitude Index (ELI), which measures zonal shifts in tropical Pacific deep convection and accounts for changes in background SST, and the Niño 3.4 index, which measures SST anomalies in the central‐eastern equatorial Pacific. We found that the warm eastern tropical‐subtropical Pacific SST bias typical of previous generations of climate models persists into many of the CMIP6 models. Future projections of ENSO shift toward more El Niño‐like conditions based on ELI in 48% of simulations and 55% of models, in association with a future weakening of the zonal equatorial Pacific SST gradient. On the other hand, none of the models project a significant shift toward La Niña‐like conditions. The standard deviation of the Niño 3.4 index indicates a lack of consensus on whether an increase or decrease in ENSO variability is expected in the future. Finally, we found a possible relationship between historical SST and low‐level cloud cover biases in the ENSO region and future changes in ELI; however, this result may be impacted by limitations in data availability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Exposing Process‐Level Biases in a Global Cloud Permitting Model With ARM Observations

The emergence of global convective‐permitting models (GCPMs) represents a significant advancement in climate modeling, offering improved representation of deep convection and complex precipitation patterns. In this study, we evaluate the performance of the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) using its doubly periodic configuration (DP‐SCREAM) against large eddy simulations and modern observational data sets from the Atmospheric Radiation Measurement program. We introduce several new transitional cloud regime cases, such as the transition from shallow to deep convection and from stratocumulus to cumulus, as well as cold‐air outbreak scenarios. The results reveal both strengths and limitations of SCREAM, particularly in the accurate simulation of cloud transitions and midlevel convection, with varying degrees of sensitivity to horizontal and vertical resolution. Despite improvements at higher resolutions, key biases remain, including the abrupt transition from shallow to deep convection and the lack of congestus clouds. These findings underscore the need for further refinement in turbulence parameterizations and vertical grid resolution in GCPMs.

Bogenschutz, Peter A. [Lawrence Livermore National↗

DOE OSTI2020

A historical and comparative review of 50 years of root data collection in Puerto Rico

Fine roots play an important role in plant nutrition, as well as in carbon, water, and nutrient cycling. Fine roots account for a third of terrestrial net primary production (NPP), and inclusion of their structure and function in global carbon models should improve predictions of ecosystem responses to climate change. However, studies focusing on underground plant components are much less frequent than those on aboveground structure. This is more marked in the tropics, where one-third of the planet's terrestrial NPP is produced. Some tropical forests have been more represented in the literature than others, as demonstrated in the collective studies in Puerto Rico. This Caribbean island's biodiversity, frequency of natural disturbances, ease of access to forests, and long-term plots have created an ideal place for the study of tropical ecological processes. This review of the literature emphasizes 50 years of root research and patterns revealed around Puerto Rico. The data in this review were compiled from scientific publications, conference reports, symposiums, and raw data shared by some researches. Emergent patterns include the shallow distribution of fine roots, the great variation in root biomass among different forest types, little variation in root phosphorus concentrations, the slow recovery of root biomass after Hurricane Hugo, and the fact that most data on roots come from the wet tropical Luquillo Experimental Forest, causing other habitat types to be underrepresented. This review also shows the gaps in knowledge about fine roots in the island's ecosystems, which should be used to promote and guide future studies. Abstract in Spanish is available with online material.

tropics↗

NASA NTRS1987

The First ISLSCP Field Experiment (FIFE)

In 1987, the International Satellite Land Surface Climatology Project (ISLSCP) will conduct a major field experiment aimed at the development of superior methods for relating satellite remote sensing to biological and physical parameter and exchange process data obtained during simultaneous ground truth measurements. This, the First ISLSCP Field Experiment, will attempt to arrive at a better understanding of the role of the land surface in the behavior of the global climate system, as required by next-generation global circulation models and general biospheric models. The field experiment will be conducted in the Konza Prairie of Kansas.

Murphy, Robert E.↗

NASA NTRS1989

Global Climate Study

The Global Surface Radiation Budget Experiment, which determines if current climate models are accurate, is explained.

Source record↗

NASA NTRS2003

Using Satellite-derived Ice Concentration to Represent Antarctic Coastal Polynyas in Ocean Climate Models

The focus of this paper is on the representation of Antarctic coastal polynyas in global ice-ocean general circulation models (OGCMs), in particular their local, regional, and high-frequency behavior. This is verified with the aid of daily ice concentration derived from satellite passive microwave data using the NASATeam 2 (NT2) and the bootstrap (BS) algorithms. Large systematic regional and temporal discrepancies arise, some of which are related to the type of convection parameterization used in the model. An attempt is made to improve the fresh-water flux associated with melting and freezing in Antarctic coastal polynyas by ingesting (assimilating) satellite ice concentration where it comes to determining the thermodynamics of the open-water fraction of a model grid cell. Since the NT2 coastal open-water fraction (polynyas) tends to be less extensive than the simulated one in the decisive season and region, assimilating NT2 coastal ice concentration yields overall reduced net freezing rates, smaller formation rates of Antarctic Bottom Water, and a stronger southward flow of North Atlantic Deep Water across 30 S. Enhanced net freezing rates occur regionally when NT2 coastal ice concentration is assimilated, concomitant with a more realistic ice thickness distribution and accumulation of High-Salinity Shelf Water. Assimilating BS rather than NT2 coastal ice concentration, the differences to the non-assimilated simulation are generally smaller and of opposite sign. This suggests that the model reproduces coastal ice concentration in closer agreement with the BS data than with the NT2 data, while more realistic features emerge when NT2 data are assimilated.

Stoessel, Achim↗

NASA NTRS2005

Aura MLS Cloud Measurements: First-Year Results

Aura MLS provides the first vertical upper tropospheric cloud profiling from space, enabling global survey of the vertical structure of cloud systems, with seasonal and geographical variations, needed to evaluate the way clouds are parameterized in global models, thereby contributing to the understanding of cloud-climate feedbacks, and improved weather and climate predictions. The vertical structure of cloud systems is fundamentally important for understanding how clouds affect both their regional and large-scale atmospheric and radiative environments. The regional cloud profiles provide a critical tests of important parameterizations that enable the calculation of radiative flux profiles and heating rates throughout the atmospheric column, which in turn also regulates the water and energy cycles in the upper troposphere

Microwave Limb Sounder (MLS)↗

NASA NTRS2010

A Geostatistical Data Fusion Technique for Merging Remote Sensing and Ground-Based Observations of Aerosol Optical Thickness

Particles in the atmosphere reflect incoming sunlight, tending to cool the Earth below. Some particles, such as soot, also absorb sunlight, which tens to warm the ambient atmosphere. Aerosol optical depth (AOD) is a measure of the amount of particulate matter in the atmosphere, and is a key input to computer models that simulate and predict Earth's changing climate. The global AOD products from the Multi-angle Imaging SpectroRadiometer (MISR) and the MODerate resolution Imaging Spectroradiometer (MODIS), both of which fly on the NASA Earth Observing System's Terra satellite, provide complementary views of the particles in the atmosphere. Whereas MODIS offers global coverage about four times as frequent as MISR, the multi-angle data makes it possible to separate the surface and atmospheric contributions to the observed top-of-atmosphere radiances, and also to more effectively discriminate particle type. Surface-based AERONET sun photometers retrieve AOD with smaller uncertainties than the satellite instruments, but only at a few fixed locations. So there are clear reasons to combine these data sets in a way that takes advantage of their respective strengths. This paper represents an effort at combining MISR, MODIS and AERONET AOD products over the continental US, using a common spatial statistical technique called kriging. The technique uses the correlation between the satellite data and the "ground-truth" sun photometer observations to assign uncertainty to the satellite data on a region-by-region basis. The larger fraction of the sun photometer variance that is duplicated by the satellite data, the higher the confidence assigned to the satellite data in that region. In the Western and Central US, MISR AOD correlation with AERONET are significantly higher than those with MODIS, likely due to bright surfaces in these regions, which pose greater challenges for the single-view MODIS retrievals. In the east, MODIS correlations are higher, due to more frequent sampling of the varying AOD. These results demonstrate how the MISR and MODIS aerosol products are complementary. The underlying technique also provides one method for combining these products in such a way that takes advantage of the strengths of each, in the places and times when they are maximal, and in addition, yields an estimate of the associated uncertainties in space and time.

Chatterjee, Abhishek↗

NASA NTRS2010

Inferring Cirrus Size Distributions Through Satellite Remote Sensing and Microphysical Databases

Since cirrus clouds have a substantial influence on the global energy balance that depends on their microphysical properties, climate models should strive to realistically characterize the cirrus ice particle size distribution (PSD), at least in a climatological sense. To date, the airborne in situ measurements of the cirrus PSD have contained large uncertainties due to errors in measuring small ice crystals (D<60 m). This paper presents a method to remotely estimate the concentration of the small ice crystals relative to the larger ones using the 11- and 12- m channels aboard several satellites. By understanding the underlying physics producing the emissivity difference between these channels, this emissivity difference can be used to infer the relative concentration of small ice crystals. This is facilitated by enlisting temperature-dependent characterizations of the PSD (i.e., PSD schemes) based on in situ measurements. An average cirrus emissivity relationship between 12 and 11 m is developed here using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and is used to retrieve the PSD based on six different PSD schemes. The PSDs from the measurement-based PSD schemes are compared with corresponding retrieved PSDs to evaluate differences in small ice crystal concentrations. The retrieved PSDs generally had lower concentrations of small ice particles, with total number concentration independent of temperature. In addition, the temperature dependence of the PSD effective diameter De and fall speed Vf for these retrieved PSD schemes exhibited less variability relative to the unmodified PSD schemes. The reduced variability in the retrieved De and Vf was attributed to the lower concentrations of small ice crystals in the retrieved PSD.

Mitchell, David↗

NASA NTRS2014

A Decade of Volcanic Observations from Aura and the A-Train

Aura observations have made many seminal contributions to volcanology. Prior to the Aura launch, satellite observations of volcanic degassing (e.g., from TOMS) were mostly restricted to large eruptions. However, the vast majority of volcanic gases are released during quiescent 'passive' degassing between eruptions. The improved sensitivity of Aura OMI permitted the first daily, space-borne measurements of passive volcanic SO2 degassing, providing improved constraints on the source locations and magnitude of global SO2 emissions for input to atmospheric chemistry and climate models. As a result of this unique sensitivity to volcanic activity, OMI data were also the first satellite SO2 measurements to be routinely used for volcano monitoring at several volcano observatories worldwide. Furthermore, the Aura OMI SO2 data also offer unprecedented sensitivity to volcanic clouds in the UTLS, elucidating the transport, fate and lifetime of volcanic SO2 and providing critical input to aviation hazard mitigation efforts. Another major advance has been the improved vertical resolution of volcanic clouds made possible by synergy between Aura and other A-Train instruments (e.g., AIRS, CALIPSO, CloudSat), advanced UV SO2 altitude retrievals, and inverse trajectory modeling of detailed SO2 cloud maps. This altitude information is crucial for climate models and aviation hazards. We will review some of the highlights of a decade of Aura observations of volcanic activity and look ahead to the future of volcanic observations from space.

Aura↗

NASA NTRS2016

FluidCam 1 & 2 - UAV-based Fluid Lensing Instruments for High-Resolution 3D Subaqueous Imaging and Automated Remote Biosphere Assessment of Reef Ecosystems

We present NASA ESTO FluidCam 1 & 2, Visible and NIR Fluid-Lensing-enabled imaging payloads for Unmanned Aerial Vehicles (UAVs). Developed as part of a focused 2014 earth science technology grant, FluidCam 1&2 are Fluid-Lensing-based computational optical imagers designed for automated 3D mapping and remote sensing of underwater coastal targets from airborne platforms. Fluid Lensing has been used to map underwater reefs in 3D in American Samoa and Hamelin Pool, Australia from UAV platforms at sub-cm scale, which has proven a valuable tool in modern marine research for marine biosphere assessment and conservation. We share FluidCam 1&2 instrument validation and testing results as well as preliminary processed data from field campaigns. Petabyte-scale aerial survey efforts using Fluid Lensing to image at-risk reefs demonstrate broad applicability to large-scale automated species identification, morphology studies and reef ecosystem characterization for shallow marine environments and terrestrial biospheres, of crucial importance to improving bathymetry data for physical oceanographic models and understanding climate change’s impact on coastal zones, global oxygen production, carbon sequestration.

Chirayath, Ved↗

NASA NTRS2019

Towards a Satellite Formaldehyde – in situ Hybrid Estimate for Organic Aerosol Abundance

Organic aerosol (OA) is one of the main components of the global particulate burden and intimately links natural and anthropogenic emissions with air quality and climate. It is challenging to accurately represent OA in global models. Direct quantification of global OA abundance is not possible with current remote sensing technology; however, it may be possible to exploit correlations of OA with remotely observable quantities to infer OA spatiotemporal distributions. In particular, formaldehyde (HCHO) and OA share common sources via both primary emissions and secondary production from oxidation of volatile organic compounds (VOCs). Here, we examine OA–HCHO correlations using data from summertime airborne campaigns investigating biogenic (NASA SEAC4RS and DC3), biomass burning (NASA SEAC4RS), and anthropogenic conditions (NOAA CalNex and NASA KORUS-AQ). In situ OA correlates well with HCHO (r=0.59–0.97), and the slope and intercept of this relationship depend on the chemical regime. For biogenic and anthropogenic regions, the OA–HCHO slopes are higher in low NOx conditions, because HCHO yields are lower and aerosol yields are likely higher. The OA–HCHO slope of wildfires is over 9 times higher than that for biogenic and anthropogenic sources. The OA–HCHO slope is higher for highly polluted anthropogenic sources (e.g., KORUS-AQ) than less polluted (e.g., CalNex) anthropogenic sources. Near-surface OAs over the continental US are estimated by combining the observed in situ relationships with HCHO column retrievals from NASA's Ozone Monitoring Instrument (OMI). HCHO vertical profiles used in OA estimates are from climatology a priori profiles in the OMI HCHO retrieval or output of specific period from a newer version of GEOS-Chem. Our OA estimates compare well with US EPA IMPROVE data obtained over summer months (e.g., slope =0.60–0.62, r=0.56 for August 2013), with correlation performance comparable to intensively validated GEOS-Chem (e.g., slope =0.57, r=0.56) with IMPROVE OA and superior to the satellite-derived total aerosol extinction (r=0.41) with IMPROVE OA. This indicates that OA estimates are not very sensitive to these HCHO vertical profiles and that a priori profiles from OMI HCHO retrieval have a similar performance to that of the newer model version in estimating OA. Improving the detection limit of satellite HCHO and expanding in situ airborne HCHO and OA coverage in future missions will improve the quality and spatiotemporal coverage of our OA estimates, potentially enabling constraints on global OA distribution.

anthropogenic emissions↗