Cooperative Institute for Research in the Atmosphere

Abstract. The present paper reviews existing knowledge with regard to Organic Aerosol (OA) of importance for global climate modelling and defines critical gaps needed to reduce the involved uncertainties. All pieces required for the representation of OA in a global climate model are sketched out with special attention to Secondary Organic Aerosol (SOA): The emission estimates of primary carbonaceous particles and SOA precursor gases are summarized. The up-to-date understanding of the chemical formation and transformation of condensable organic material is outlined. Knowledge on the hygroscopicity of OA and measurements of optical properties of the organic aerosol constituents are summarized. The mechanisms of interactions of OA with clouds and dry and wet removal processes parameterisations in global models are outlined. This information is synthesized to provide a continuous analysis of the flow from the emitted material to the atmosphere up to the point of the climate impact of the produced organic aerosol. The sources of uncertainties at each step of this process are highlighted as areas that require further studies.

ABSTRACT Land cover changes ( LCCs ) play an important role in the climate system. Research over recent decades highlights the impacts of these changes on atmospheric temperature, humidity, cloud cover, circulation, and precipitation. These impacts range from the local‐ and regional‐scale to sub‐continental and global‐scale. It has been found that the impacts of regional‐scale LCC in one area may also be manifested in other parts of the world as a climatic teleconnection. In light of these findings, this article provides an overview and synthesis of some of the most notable types of LCC and their impacts on climate. These LCC types include agriculture, deforestation and afforestation, desertification, and urbanization. In addition, this article provides a discussion on challenges to, and future research directions in, assessing the climatic impacts of LCC .

Abstract An intermediate-complexity, quasi–physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are distributed: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, surface pressure, and precipitation. To produce these distributions, MicroMet assumes that at least one value of each of the following meteorological variables are available for each time step, somewhere within, or near, the simulation domain: air temperature, relative humidity, wind speed, wind direction, and precipitation. These variables are collected at most meteorological stations. For the incoming solar and longwave radiation, and surface pressure, either MicroMet can use its submodels to generate these fields, or it can create the distributions from observations as part of a data assimilation procedure. MicroMet includes a preprocessor component that analyzes meteorological data, then identifies and corrects potential deficiencies. Since providing temporally and spatially continuous atmospheric forcing data for terrestrial models is a core objective of MicroMet, the preprocessor also fills in any missing data segments with realistic values. Data filling is achieved by employing a variety of procedures, including an autoregressive integrated moving average calculation for diurnally varying variables (e.g., air temperature). To create the distributed atmospheric fields, spatial interpolations are performed using the Barnes objective analysis scheme, and subsequent corrections are made to the interpolated fields using known temperature–elevation, wind–topography, humidity–cloudiness, and radiation–cloud–topography relationships.

Abstract SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowpack melt. Conceptually, SnowModel includes the first-order physics required to simulate snow evolution within each of the global snow classes (i.e., ice, tundra, taiga, alpine/mountain, prairie, maritime, and ephemeral). The required model inputs are 1) temporally varying fields of precipitation, wind speed and direction, air temperature, and relative humidity obtained from meteorological stations and/or an atmospheric model located within or near the simulation domain; and 2) spatially distributed fields of topography and vegetation type. SnowModel’s ability to simulate seasonal snow evolution was compared against observations in both forested and nonforested landscapes. The model closely reproduced observed snow-water-equivalent distribution, time evolution, and interannual variability patterns.

Abstract In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ∼20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth–density–SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within ±8 cm of the measured values, with most estimates falling much closer.

NASA's Black Marble nighttime lights product suite (VNP46) is available at 500 m resolution since January 2012 with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF effects; (3) geometric-optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Initial validation results are presented together with example case studies illustrating the scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full range of retrieval conditions.

The Cooperative Atmosphere–Surface Exchange Study—1999 (CASES-99) refers to a field experiment carried out in southeast Kansas during October 1999 and the subsequent program of investigation. Comprehensive data, primarily taken during the nighttime but typically including the evening and morning transition, supports data analyses, theoretical studies, and state-of-the-art numerical modeling in a concerted effort by participants to investigate four areas of scientific interest. The choice of these scientific topics is motivated by both the need to delineate physical processes that characterize the stable boundary layer, which are as yet not clearly understood, and the specific scientific goals of the investigators. Each of the scientific goals should be largely achievable with the measurements taken, as is shown with preliminary analysis within the scope of three of the four scientific goals. Underlying this effort is the fundamental motivation to eliminate deficiencies in surface layer and turbulent diffusion parameterizations in atmospheric models, particularly where the Richardson number exceeds 0.25. This extensive nocturnal boundary layer (NBL) dataset is available to the scientific community at large, and the CASES-99 participants encourage all interested parties to utilize it. These preliminary analyses show that during nights where weak (< 2 m s−1) surface winds and strong static stability near the surface (exceeding 150 C km−1 to 20 m AGL) might otherwise indicate essentially nonturbulent conditions, that various, sometimes undefined, atmospheric phenomena can generate significant turbulent mixing, and therefore significant turbulent fluxes. In many cases, a jet structure will form in the NBL between 50 and 200 m AGL, resulting in strong shear between the surface and jet maximum. Consequently, though surface winds are weak, turbulence can be a significant feature in the stable NBL. Further, contrary to some previous work studying nocturnal jets over the Great Plains, the wind direction in the jet is often influenced by an inertial oscillation and seldom confined to the southerly quadrant (e.g., the Great Plains low-level jet).

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Rapid development of agriculture and fossil fuel combustion greatly increased US reactive nitrogen emissions to the atmosphere in the second half of the 20th century, resulting in excess nitrogen deposition to natural ecosystems. Recent efforts to lower nitrogen oxides emissions have substantially decreased nitrate wet deposition. Levels of wet ammonium deposition, by contrast, have increased in many regions. Together these changes have altered the balance between oxidized and reduced nitrogen deposition. Across most of the United States, wet deposition has transitioned from being nitrate-dominated in the 1980s to ammonium-dominated in recent years. Ammonia has historically not been routinely measured because there are no specific regulatory requirements for its measurement. Recent expansion in ammonia observations, however, along with ongoing measurements of nitric acid and fine particle ammonium and nitrate, permit new insight into the balance of oxidized and reduced nitrogen in the total (wet + dry) US nitrogen deposition budget. Observations from 37 sites reveal that reduced nitrogen contributes, on average, ∼65% of the total inorganic nitrogen deposition budget. Dry deposition of ammonia plays an especially key role in nitrogen deposition, contributing from 19% to 65% in different regions. Future progress toward reducing US nitrogen deposition will be increasingly difficult without a reduction in ammonia emissions.

The mean absolute error of the official tropical cyclone (TC) intensity forecasts from the National Hurricane Center (NHC) and the Joint Typhoon Warning Center (JTWC) shows limited evidence of improvement over the past two decades. This result has sometimes erroneously been used to conclude that little or no progress has been made in the TC intensity guidance models. This article documents statistically significant improvements in operational TC intensity guidance over the past 24 years (1989–2012) in four tropical cyclone basins (Atlantic, eastern North Pacific, western North Pacific, and Southern Hemisphere). Errors from the best available model have decreased at 1%–2% yr−1 at 24–72 h, with faster improvement rates at 96 and 120 h. Although these rates are only about one-third to one-half of the rates of reduction of the track forecast models, most are statistically significant at the 95% level. These error reductions resulted from improvements in statistical–dynamical intensity models and consensus techniques that combine information from statistical–dynamical and dynamical models. The reason that the official NHC and JTWC intensity forecast errors have decreased slower than the guidance errors is because in the first half of the analyzed period, their subjective forecasts were more accurate than any of the available guidance. It is only in the last decade that the objective intensity guidance has become accurate enough to influence the NHC and JTWC forecast errors.

The Cloud Profiling Radar, the sole science instrument of the CloudSat Mission, is a 94-GHz nadir-looking radar that measures the power backscattered by hydrometeors (clouds and precipitation) as a function of distance from the radar. This instrument has been acquiring global time series of vertical cloud structures since June 2, 2006. In this paper, an overview of the radar performance and status, to date, is provided together with a description of the basic data products and the surface clutter rejection algorithm introduced for the Release 04 data product release.

Abstract. Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100 000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25 %, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20 % over land and 40 % over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.

While the formation pathways and thermodynamic properties of inorganic species (e.g., sulphate) in atmospheric aerosols are well understood, many more uncertainties exist about organics. In the present study we present oxidation pathways of organic gas phase species that lead to low volatility organic compounds (C 2 ‐C 6 dicarboxylic acids, pyruvic acid) in both the aqueous and gas phases. This mechanism is implemented in a cloud parcel model initialized with pure (NH 4 ) 2 SO 4 particles in 10 discrete sizes. Under clean continental conditions a few cloud processing cycles produce a total organic mass addition of ∼150 ng m −3 . Individual resuspended aerosol size classes contain significant organic fractions, sometimes higher than 50%. These are likely upper bound estimates of organic mass production. In a polluted, i.e., SO 2 ‐rich scenario, about 400 ng m −3 organic material is produced after about eight cloud cycles. Since the initial conditions in this latter case favor significant production of sulphate, the organic fraction of the aerosol mass after cloud processing represents a much lower percentage of the total aerosol mass. Oxalic, glutaric, adipic, and pyruvic acids are the main contributors to the organic fraction in both cases. In agreement with observations, the oxalate fraction in processed particles exceeds the fractions of other dicarboxylic acids since it represents an end product in the oxidation of several organic gas phase species. The study suggests that cloud processing may act as a significant source of small dicarboxylic acids, some fraction of which can be retained in the aerosol phase upon drop evaporation.

Abstract Vertical wind shear and storm motion are two of the most important factors contributing to rainfall asymmetries in tropical cyclones (TCs). Global TC rainfall structure, in terms of azimuthal distribution and asymmetries relative to storm motion, has been previously described using the Tropical Rainfall Measuring Mission Microwave Imager rainfall estimates. The mean TC rainfall distribution and the wavenumber-1 asymmetry vary with storm intensity and geographical location among the six oceanic basins. This study uses a similar approach to investigate the relationship between the structure of TC rainfall and the environmental flow by computing the rainfall asymmetry relative to the vertical wind shear. The environmental vertical wind shear is defined as the difference between the mean wind vectors of the 200- and 850-hPa levels over an outer region extending from the radius of 200–800 km around the storm center. The wavenumber-1 maximum rainfall asymmetry is downshear left (right) in the Northern (Southern) Hemisphere. The rainfall asymmetry decreases (increases) with storm intensity (shear strength). The rainfall asymmetry maximum is predominantly downshear left for shear values &amp;gt; 7.5 m s−1. Large asymmetries are usually observed away from the TC centers. As TC intensity increases, the asymmetry maximum shifts upwind to the left. The analysis is further extended to examine the storm motion and the vertical wind shear and their collective effects on TC rainfall asymmetries. It is found that the vertical wind shear is a dominant factor for the rainfall asymmetry when shear is &amp;gt;5 m s−1. The storm motion–relative rainfall asymmetry in the outer rainband region is comparable to that of shear relative when the shear is &amp;lt;5 m s−1, suggesting that TC translation speed becomes an important factor in the low shear environment. The overall TC rainfall asymmetry depends on the juxtaposition and relative magnitude of the storm motion and environmental shear vectors in all oceanic basins.

In the spring of 1985 an interagency consortium of federal land management agencies and the Environmental Protection Agency established the Interagency Monitoring of Protected Visual Environments (IMPROVE) network to assess visibility and aerosol monitoring for the purpose of tracking spatial and temporal trends of visibility and visibility‐impairing particles in rural areas. The program was initiated with 20 monitoring sites and was expanded to 165 sites between 2000 and 2003. This paper reports on fine aerosol data collected in the year 2001 at 143 sites. The major fine (d p &lt; 2.5 μm) particle aerosol species, sulfates, nitrates, organics, light‐absorbing carbon, and wind‐blown dust, and coarse gravimetric mass are monitored, and at some sites, light scattering and/or extinction are measured. Sulfates, carbon, and crustal material are responsible for most of the fine mass at the majority of locations throughout the United States, while at sites in southern California and the midwestern United States, nitrates can contribute significantly. In the eastern United States, sulfates contribute between 50 and 60% of the fine mass. Sulfate concentrations tend to be highest in the summer months while organic concentrations can be high in the spring, summer, or fall seasons, depending upon fire‐related emissions. However, at the two urban sites, Phoenix, Arizona, and Puget Sound, Washington, organics peak during the winter months. Nitrate concentrations also tend to be highest during the winter months. During the spring months in many areas of the western United States, fine soil can contribute as much as 40% of fine mass. The temporal changes in soil concentration that occur simultaneously over much of the western United States including the Rocky Mountain region suggest a large source region, possibly long‐range transport of Asian dust.

A method for retrieving precipitation over the ocean using spaceborne W‐band (94 GHz) radar is introduced and applied to the CloudSat Cloud Profiling Radar. The method is most applicable to stratiform‐type precipitation. Measurements of radar backscatter from the ocean surface are combined with information about surface wind speed and sea surface temperature to derive the path‐integrated attenuation through precipitating cloud systems. The scattering and extinction characteristics of raindrops are modeled using a combination of Mie theory (for raindrops) and the discrete dipole approximation (for ice crystals and melting snow), and a model of the melting layer is implemented to represent the transition between ice and liquid water. Backward Monte Carlo modeling is used to model multiple scattering from precipitating hydrometeors between the radar and ocean surface, which is shown to be significant for precipitation rates exceeding 3–5 mm h −1 , particularly when precipitating ice is present. An uncertainty analysis is presented and the algorithm is applied to near‐global CloudSat observations and compared with other near‐global precipitation sources. In the tropics, CloudSat tends to underestimate the heaviest precipitation. It is found that in the middle latitudes, however, CloudSat observes precipitation more often and with greater resulting accumulation than other spaceborne sensors.

Abstract. This synthesis discusses the emissions of carbon dioxide from fossil-fuel combustion and cement production. While much is known about these emissions, there is still much that is unknown about the details surrounding these emissions. This synthesis explores our knowledge of these emissions in terms of why there is concern about them; how they are calculated; the major global efforts on inventorying them; their global, regional, and national totals at different spatial and temporal scales; how they are distributed on global grids (i.e., maps); how they are transported in models; and the uncertainties associated with these different aspects of the emissions. The magnitude of emissions from the combustion of fossil fuels has been almost continuously increasing with time since fossil fuels were first used by humans. Despite events in some nations specifically designed to reduce emissions, or which have had emissions reduction as a byproduct of other events, global total emissions continue their general increase with time. Global total fossil-fuel carbon dioxide emissions are known to within 10 % uncertainty (95 % confidence interval). Uncertainty on individual national total fossil-fuel carbon dioxide emissions range from a few percent to more than 50 %. This manuscript concludes that carbon dioxide emissions from fossil-fuel combustion continue to increase with time and that while much is known about the overall characteristics of these emissions, much is still to be learned about the detailed characteristics of these emissions.

The impact of giant and ultragiant cloud condensation nuclei (>5-μm radius) on drizzle formation in stratocumuli is investigated within a number of modeling frameworks. These include a simple box model of collection, a trajectory ensemble model (comprising an ensemble of Lagrangian parcel models), a 2D eddy-resolving model, and a 3D large-eddy simulation model. Observed concentrations of giant cloud condensation nuclei (GCCN) over the ocean at ambient conditions indicate that 20-μm radius haze particles exist in concentrations of between 10−4 and 10−2 cm−3, depending on ambient wind speed and seastate. It is shown that these concentrations are sufficient to move a nonprecipitating stratocumulus into a precipitating state at typical cloud condensation nucleus (CCN) concentrations of 50 to 250 cm−3, with higher concentrations of GCCN being required at higher CCN concentrations. However, at lower CCN concentrations, drizzle is often active anyway and the addition of GCCN has little impact. At high CCN concentrations, drizzle development is slow and GCCN have the greatest potential for enhancing the collection process. Thus, although drizzle production decreases with increasing CCN concentration, the relative impact of GCCN increases with increasing CCN concentration. It is also shown that in the absence of GCCN, a shift in the modal radius of the CCN distribution to larger sizes suppresses drizzle because larger modal radii enable the activation of larger droplet number concentrations. Finally, calculations of the impact of GCCN on cloud optical properties are performed over a range of parameter space. Results indicate that the presence of GCCN moderates the effect of CCN on optical properties quite significantly. In the absence of GCCN, an increase in CCN from 50 to 150 cm−3 results in a threefold increase in albedo; when GCCN exist at a concentration of 10−3 cm−3, the increase in albedo is only twofold. Thus the variable presence of GCCN represents yet another uncertainty in estimating the influence of anthropogenic activity on climate.

Abstract. The Orbiting Carbon Observatory-3 (OCO-3) is NASA's next instrument dedicated to extending the record of the dry-air mole fraction of column carbon dioxide (XCO2) and solar-induced fluorescence (SIF) measurements from space. The current schedule calls for a launch from the Kennedy Space Center no earlier than April 2019 via a Space-X Falcon 9 and Dragon capsule. The instrument will be installed as an external payload on the Japanese Experimental Module Exposed Facility (JEM-EF) of the International Space Station (ISS) with a nominal mission lifetime of 3 years. The precessing orbit of the ISS will allow for viewing of the Earth at all latitudes less than approximately 52∘, with a ground repeat cycle that is much more complicated than the polar-orbiting satellites that so far have carried all of the instruments capable of measuring carbon dioxide from space. The grating spectrometer at the core of OCO-3 is a direct copy of the OCO-2 spectrometer, which was launched into a polar orbit in July 2014. As such, OCO-3 is expected to have similar instrument sensitivity and performance characteristics to OCO-2, which provides measurements of XCO2 with precision better than 1 ppm at 3 Hz, with each viewing frame containing eight footprints approximately 1.6 km by 2.2 km in size. However, the physical configuration of the instrument aboard the ISS, as well as the use of a new pointing mirror assembly (PMA), will alter some of the characteristics of the OCO-3 data compared to OCO-2. Specifically, there will be significant differences from day to day in the sampling locations and time of day. In addition, the flexible PMA system allows for a much more dynamic observation-mode schedule. This paper outlines the science objectives of the OCO-3 mission and, using a simulation of 1 year of global observations, characterizes the spatial sampling, time-of-day coverage, and anticipated data quality of the simulated L1b. After application of cloud and aerosol prescreening, the L1b radiances are run through the operational L2 full physics retrieval algorithm, as well as post-retrieval filtering and bias correction, to examine the expected coverage and quality of the retrieved XCO2 and to show how the measurement objectives are met. In addition, results of the SIF from the IMAP–DOAS algorithm are analyzed. This paper focuses only on the nominal nadir–land and glint–water observation modes, although on-orbit measurements will also be made in transition and target modes, similar to OCO-2, as well as the new snapshot area mapping (SAM) mode.

emissions over the last decades have affected atmospheric composition on a regional and global scale with large impact on air quality, atmospheric deposition and the radiative forcing of sulfate aerosols. Reproduction of historical atmospheric pollution levels based on global aerosol models and emission changes is crucial to prove that such models are able to predict future scenarios. Here, we analyze consistency of trends in observations of sulfur components in air and precipitation from major regional networks and estimates from six different global aerosol models from 1990 until 2015. There are large interregional differences in the sulfur trends consistently captured by the models and observations, especially for North America and Europe. Europe had the largest reductions in sulfur emissions in the first part of the period while the highest reduction came later in North America and East Asia. The uncertainties in both the emissions and the representativity of the observations are larger in Asia. However, emissions from East Asia clearly increased from 2000 to 2005 followed by a decrease, while in India a steady increase over the whole period has been observed and modelled. The agreement between a bottom-up approach, which uses emissions and process-based chemical transport models, with independent observations gives an improved confidence in the understanding of the atmospheric sulfur budget.