The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol–cloud interactions, chemistry–climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future—for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. Its horizontal resolution is approximately 200 km, and its vertical resolution ranges approximately from 70 m near the earth’s surface to 1 to 1.5 km near the tropopause and 3 to 4 km in much of the stratosphere. Most basic circulation features in AM3 are simulated as realistically, or more so, as in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks remains problematic, as in AM2. The most intense 0.2% of precipitation rates occur less frequently in AM3 than observed. The last two decades of the twentieth century warm in CM3 by 0.32°C relative to 1881–1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of 0.56° and 0.52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol–cloud interactions, and its warming by the late twentieth century is somewhat less realistic than in CM2.1, which warmed 0.66°C but did not include aerosol–cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud–aerosol interactions to limit greenhouse gas warming.

Fires have influenced atmospheric composition and climate since the rise of vascular plants, and satellite data have shown the overall global extent of fires. Our knowledge of historic fire emissions has progressively improved over the past decades due mostly to the development of new proxies and the improvement of fire models. Currently, there is a suite of proxies including sedimentary charcoal records, measurements of fire-emitted trace gases and black carbon stored in ice and firn, and visibility observations. These proxies provide opportunities to extrapolate emission estimates back in time based on satellite data starting in 1997, but each proxy has strengths and weaknesses regarding, for example, the spatial and temporal extents over which they are representative. We developed a new historic biomass burning emissions dataset starting in 1750 that merges the satellite record with several existing proxies and uses the average of six models from the Fire Model Intercomparison Project (FireMIP) protocol to estimate emissions when the available proxies had limited coverage. According to our approach, global biomass burning emissions were relatively constant, with 10-year averages varying between 1.8 and 2.3 Pg C yr⁻¹. Carbon emissions increased only slightly over the full time period and peaked during the 1990s after which they decreased gradually. There is substantial uncertainty in these estimates, and patterns varied depending on choices regarding data representation, especially on regional scales. The observed pattern in fire carbon emissions is for a large part driven by African fires, which accounted for 58 % of global fire carbon emissions. African fire emissions declined since about 1950 due to conversion of savanna to cropland, and this decrease is partially compensated for by increasing emissions in deforestation zones of South America and Asia. These global fire emission estimates are mostly suited for global analyses and will be used in the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations.

During the SAFARI 2000 field campaign, both smoke aerosols from savanna fires and haze aerosols in the boundary layer and in the free troposphere were collected from an aircraft in southern Africa. These aerosol samples were analyzed for their water‐soluble chemical components, particularly the organic species. A novel technique, electrospray ionization‐ion trap mass spectrometry, was used concurrently with an ion chromatography system to analyze for carbohydrate species. Seven carbohydrates, seven organic acids, five metallic elements, and three inorganic anions were identified and quantified. On the average, these 22 species comprised 36% and 27% of the total aerosol mass in haze and smoke aerosols, respectively. For the smoke aerosols, levoglucosan was the most abundant carbohydrate species, while gluconic acid was tentatively identified as the most abundant organic acid. The mass abundance and possible source of each class of identified species are discussed, along with their possible formation pathways. The combustion phase of a fire had an impact on the chemical composition of the emitted aerosols. Secondary formation of sulfate, nitrate, levoglucosan, and several organic acids occurred during the initial aging of smoke aerosols. It is likely that under certain conditions, some carbohydrate species in smoke aerosols, such as levoglucosan, were converted to organic acids during upward transport.

The location, timing, spatial extent, and frequency of wildfires are changing rapidly in many parts of the world, producing substantial impacts on ecosystems, people, and potentially climate. Paleofire records based on charcoal accumulation in sediments enable modern changes in biomass burning to be considered in their long-term context. Paleofire records also provide insights into the causes and impacts of past wildfires and emissions when analyzed in conjunction with other paleoenvironmental data and with fire models. Here we present new 1000-year and 22 000-year trends and gridded biomass burning reconstructions based on the Global Charcoal Database version 3 (GCDv3), which includes 736 charcoal records (57 more than in version 2). The new gridded reconstructions reveal the spatial patterns underlying the temporal trends in the data, allowing insights into likely controls on biomass burning at regional to global scales. In the most recent few decades, biomass burning has sharply increased in both hemispheres but especially in the north, where charcoal fluxes are now higher than at any other time during the past 22 000 years. We also discuss methodological issues relevant to data–model comparisons and identify areas for future research. Spatially gridded versions of the global data set from GCDv3 are provided to facilitate comparison with and validation of global fire simulations.

We present the results of a multi-season field evaluation of a low-cost optical particle counting sensor (Purple Air PA-II) that reports mass concentration of particulate matter with diameter less than 2.5 microns (PM2.5), and is part of a relatively large and growing network of microelectronic internet-of-things sensors. We assessed 16 months of PA-II PM2.5 data collected in a near-road urban setting in the humid climate of Charlotte, North Carolina. The PA-II was collocated with a Federal Equivalent Method Beta Attenuation Monitor (BAM model 1022), and with a weather station that monitored ambient relative humidity (RH) and temperature (T). We tested and used a multiple linear regression model with BAM PM2.5, RH, and T as predictors to model the reported PA-II PM2.5. The results show a 27-57% improvement in the accuracy of the PA-II PM2.5 data relative to the reference data from the BAM, with the highest percentage improvements for moderate to high RH. The methodologies in our study are broadly applicable to other field studies of low-cost monitors, and the results are a critical improvement that suggest that PA-II may indeed be suitable for air quality, health, and urban aerosol research.

Fire emissions are a critical component of carbon and nutrient cycles and strongly affect climate and air quality. Dynamic global vegetation models (DGVMs) with interactive fire modeling provide important estimates for long-term and large-scale changes in fire emissions. Here we present the first multi-model estimates of global gridded historical fire emissions for 1700–2012, including carbon and 33 species of trace gases and aerosols. The dataset is based on simulations of nine DGVMs with different state-of-the-art global fire models that participated in the Fire Modeling Intercomparison Project (FireMIP), using the same and standardized protocols and forcing data, and the most up-to-date fire emission factor table based on field and laboratory studies in various land cover types. We evaluate the simulations of present-day fire emissions by comparing them with satellite-based products. The evaluation results show that most DGVMs simulate present-day global fire emission totals within the range of satellite-based products. They can capture the high emissions over the tropical savannas and low emissions over the arid and sparsely vegetated regions, and the main features of seasonality. However, most models fail to simulate the interannual variability, partly due to a lack of modeling peat fires and tropical deforestation fires. Before the 1850s, all models show only a weak trend in global fire emissions, which is consistent with the multi-source merged historical reconstructions used as input data for CMIP6. On the other hand, the trends are quite different among DGVMs for the 20th century, with some models showing an increase and others a decrease in fire emissions, mainly as a result of the discrepancy in their simulated responses to human population density change and land use and land cover change (LULCC). Our study provides an important dataset for further development of regional and global multi-source merged historical reconstructions, analyses of the historical changes in fire emissions and their uncertainties, and quantification of the role of fire emissions in the Earth system. It also highlights the importance of accurately modeling the responses of fire emissions to LULCC and population density change in reducing uncertainties in historical reconstructions of fire emissions and providing more reliable future projections.