Goddard Earth Observing System Version Research Articles

Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part II): Cloud Coverage

This is the second part of a study on how temporal sampling frequency affects satellite retrievals in support of the Deep Space Climate Observatory (DSCOVR) mission. Continuing from Part 1, which looked at Earth’s radiation budget, this paper presents the effect of sampling frequency on DSCOVR-derived cloud fraction. The output from NASA’s Goddard Earth Observing System version 5 (GEOS-5) Nature Run is used as the “truth”. The effect of temporal resolution on potential DSCOVR observations is assessed by subsampling the full Nature Run data. A set of metrics, including uncertainty and absolute error in the subsampled time series, correlation between the original and the subsamples, and Fourier analysis have been used for this study. Results show that, for a given sampling frequency, the uncertainties in the annual mean cloud fraction of the sunlit half of the Earth are larger over land than over ocean. Analysis of correlation coefficients between the subsamples and the original time series demonstrates that even though sampling at certain longer time intervals may not increase the uncertainty in the mean, the subsampled time series is further and further away from the “truth” as the sampling interval becomes larger and larger. Fourier analysis shows that the simulated DSCOVR cloud fraction has underlying periodical features at certain time intervals, such as 8, 12, and 24 h. If the data is subsampled at these frequencies, the uncertainties in the mean cloud fraction are higher. These results provide helpful insights for the DSCOVR temporal sampling strategy.

Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part I): Earth’s Radiation Budget

Satellites always sample the Earth-atmosphere system in a finite temporal resolution. This study investigates the effect of sampling frequency on the satellite-derived Earth radiation budget, with the Deep Space Climate Observatory (DSCOVR) as an example. The output from NASA’s Goddard Earth Observing System Version 5 (GEOS-5) Nature Run is used as the truth. The Nature Run is a high spatial and temporal resolution atmospheric simulation spanning a two-year period. The effect of temporal resolution on potential DSCOVR observations is assessed by sampling the full Nature Run data with 1-h to 24-h frequencies. The uncertainty associated with a given sampling frequency is measured by computing means over daily, monthly, seasonal and annual intervals and determining the spread across different possible starting points. The skill with which a particular sampling frequency captures the structure of the full time series is measured using correlations and normalized errors. Results show that higher sampling frequency gives more information and less uncertainty in the derived radiation budget. A sampling frequency coarser than every 4 h results in significant error. Correlations between true and sampled time series also decrease more rapidly for a sampling frequency less than 4 h.

Enhance parallel input/output with cross-bundle aggregation

The exponential growth of computing power on leadership scale computing platforms imposes grand challenge to scientific applications’ input/output (I/O) performance. To bridge the performance gap between computation and I/O, various parallel I/O libraries have been developed and adopted by computer scientists. These libraries enhance the I/O parallelism by allowing multiple processes to concurrently access the shared data set. Meanwhile, they are integrated with a set of I/O optimization strategies such as data sieving and two-phase I/O to better exploit the supplied bandwidth of the underlying parallel file system. Most of these techniques are optimized for the access on a single bundle of variables generated by the scientific applications during the I/O phase, which is stored in the form of file. Few of these techniques focus on cross-bundle I/O optimizations. In this article, we investigate the potential benefit from cross-bundle I/O aggregation. Based on the analysis of the I/O patterns of a mission-critical scientific application named the Goddard Earth Observing System, version 5 (GEOS-5), we propose a Bundle-based PARallel Aggregation (BPAR) framework with three partitioning schemes to improve its I/O performance as well as the I/O performance of a broad range of other scientific applications. Our experiment result reveals that BPAR can deliver 2.1× I/O performance improvement over the baseline GEOS-5, and it is very promising in accelerating scientific applications’ I/O performance on various computing platforms.

Assessing the tangent linear behaviour of common tracer transport schemes and their use in a linearised atmospheric general circulation model

The linearity of a selection of common advection schemes is tested and examined with a view to their use in the tangent linear and adjoint versions of an atmospheric general circulation model. The schemes are tested within a simple offline one-dimensional periodic domain as well as using a simplified and complete configuration of the linearised version of NASA's Goddard Earth Observing System version 5 (GEOS-5). All schemes which prevent the development of negative values and preserve the shape of the solution are confirmed to have non-linear behaviour. The piecewise parabolic method (PPM) with certain flux limiters, including that used by default in GEOS-5, is found to support linear growth near the shocks. This property can cause the rapid development of unrealistically large perturbations within the tangent linear and adjoint models. It is shown that these schemes with flux limiters should not be used within the linearised version of a transport scheme. The results from tests using GEOS-5 show that the current default scheme (a version of PPM) is not suitable for the tangent linear and adjoint model, and that using a linear third-order scheme for the linearised model produces better behaviour. Using the third-order scheme for the linearised model improves the correlations between the linear and non-linear perturbation trajectories for cloud liquid water and cloud liquid ice in GEOS-5.

Use of the CALIOP vertical feature mask for evaluating global aerosol models

Abstract. We use observations from the space-based Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to evaluate global aerosol distributions simulated in the NASA Modern Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero). We focus particularly on an evaluation of aerosol types, using the CALIOP vertical feature mask (VFM) algorithm, and look especially at Saharan dust distributions during July 2009. MERRAero consists of an aerosol simulation produced in the Goddard Earth Observing System version 5 (GEOS-5) Earth system model and incorporates assimilation of MODIS-derived aerosol optical thickness (AOT) to constrain column aerosol loadings. For comparison to the CALIOP VFM we construct two synthetic VFMs using the MERRAero aerosol distributions: a CALIOP-like VFM in which we simulate the total attenuated backscatter and particle depolarization ratio from the MERRAero output and pass those into the CALIOP VFM typing algorithm (MERRAero-CALIOP), and an extinction-based VFM in which we use the MERRAero-simulated species-resolved extinction to map the MERRAero species to the CALIOP VFM types (MERRAero-Extinction). By comparing the MERRAero-CALIOP VFM to CALIOP VFM, we can diagnose the aerosol transport and speciation in MERRAero. By comparing the MERRAero-CALIOP and MERRAero-Extinction-simulated VFM, we perform a simple observing system experiment (OSE), which is useful for identifying limitations of the CALIOP VFM algorithm itself. We find that, despite having our column AOT constrained by MODIS, comparison to the CALIOP VFM reveals a greater occurrence of dusty aerosol layers in our MERRAero-CALIOP VFM due to errors in MERRAero aerosol speciation. Additionally, we find that the CALIOP VFM algorithm is challenged when classifying aerosol features when multiple aerosol types are present, as our application of the CALIOP VFM algorithm to MERRAero aerosol distributions classified marine-dominated aerosol layers with low aerosol loadings as polluted dust when the contribution of dust to the total extinction was low.

Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis

Abstract. A radiative transfer interface has been developed to simulate the UV aerosol index (AI) from the NASA Goddard Earth Observing System version 5 (GEOS-5) aerosol assimilated fields. The purpose of this work is to use the AI and aerosol absorption optical depth (AAOD) derived from the Ozone Monitoring Instrument (OMI) measurements as independent validation for the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Since AI is dependent on aerosol concentration, optical properties and altitude of the aerosol layer, we make use of complementary observations to fully diagnose the model, including AOD from the Multi-angle Imaging SpectroRadiometer (MISR), aerosol retrievals from the AErosol RObotic NETwork (AERONET) and attenuated backscatter coefficients from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission to ascertain potential misplacement of plume height by the model. By sampling dust, biomass burning and pollution events in 2007 we have compared model-produced AI and AAOD with the corresponding OMI products, identifying regions where the model representation of absorbing aerosols was deficient. As a result of this study over the Saharan dust region, we have obtained a new set of dust aerosol optical properties that retains consistency with the MODIS AOD data that were assimilated, while resulting in better agreement with aerosol absorption measurements from OMI. The analysis conducted over the southern African and South American biomass burning regions indicates that revising the spectrally dependent aerosol absorption properties in the near-UV region improves the modeled-observed AI comparisons. Finally, during a period where the Asian region was mainly dominated by anthropogenic aerosols, we have performed a qualitative analysis in which the specification of anthropogenic emissions in GEOS-5 is adjusted to provide insight into discrepancies observed in AI comparisons.

Assimilation of Freeze–Thaw Observations into the NASA Catchment Land Surface Model

Abstract The land surface freeze–thaw (F/T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, an F/T assimilation algorithm was developed for the NASA Goddard Earth Observing System, version 5 (GEOS-5), modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F/T state in the GEOS-5 Catchment land surface model. The F/T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F/T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F/T observations. The assimilation of perfect (error free) F/T observations reduced the root-mean-square errors (RMSEs) of surface temperature and soil temperature by 0.206° and 0.061°C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7% and 3.1%, respectively). For a maximum classification error CEmax of 10% in the synthetic F/T observations, the F/T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178° and 0.036°C, respectively. For CEmax = 20%, the F/T assimilation still reduces the RMSE of model surface temperature estimates by 0.149°C but yields no improvement over the model soil temperature estimates. The F/T assimilation scheme is being developed to exploit planned F/T products from the NASA Soil Moisture Active Passive (SMAP) mission.

The global structure of upper troposphere‐lower stratosphere ozone in GEOS‐5: A multiyear assimilation of EOS Aura data

AbstractEight years of ozone measurements retrieved from the Ozone Monitoring Instrument and the Microwave Limb Sounder, both on the EOS Aura satellite, have been assimilated into the Goddard Earth Observing System Version 5 (GEOS‐5) data assimilation system. This study evaluates this assimilated product, highlighting its potential for science. The impact of observations on the GEOS‐5 system is explored by examining the spatial distribution of the observation‐minus‐forecast statistics. Independent data are used for product validation. The correlation of the lower stratospheric (the tropopause to 50 hPa) ozone column with ozonesondes is 0.99 and the (high) bias is 0.5%, indicating the success of the assimilation in reproducing the ozone variability in that layer. The upper tropospheric (500 hPa to the tropopause) assimilated ozone column is about 10% lower than the ozonesonde column, but the correlation is still high (0.87). The assimilation is shown to realistically capture the sharp cross‐tropopause gradient in ozone mixing ratio. Occurrence of transport‐driven low ozone laminae in the assimilation system is similar to that obtained from the High Resolution Dynamics Limb Sounder (HIRDLS) above the 400 K potential temperature surface, but the assimilation produces fewer laminae than seen by HIRDLS below that surface. Although the assimilation produces about 25% fewer occurrences per day during the 3 years of HIRDLS data, the interannual variability is captured correctly. This data‐driven assimilated product is complementary to ozone fields generated from chemistry and transport models. Applications include study of the radiative forcing by ozone and tracer transport near the tropopause.

The Major Stratospheric Sudden Warming of January 2013: Analyses and Forecasts in the GEOS-5 Data Assimilation System

Abstract The major stratospheric sudden warming (SSW) of 6 January 2013 is examined using output from the NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System version 5 (GEOS-5) near-real-time data assimilation system (DAS). GEOS-5 analyses showed that the SSW of January 2013 was a major warming by 1200 UTC 6 January, with a wave-2 vortex-splitting pattern. Upward wave activity flux from the upper troposphere (~23 December 2012) displaced the ~10-hPa polar vortex off the pole in a wave-1 pattern, enabling the poleward advection of subtropical values of Ertel potential vorticity (EPV) into a developing anticyclonic circulation region. While the polar vortex subsequently split (wave-2 pattern) the wave-2 forcing [upward Eliassen–Palm (EP) flux] was smaller than what was found in recent wave-2, SSW events, with most of the forcing located in the Pacific hemisphere. Investigation of a rapidly developing tropospheric weather system over the North Atlantic on 28–29 December 2012 showed strong transient upward wave activity flux from the storm with influences up to 10 hPa; however, the Pacific hemisphere wave forcing remained dominate at this time. Results from the GEOS-5 five-day forecasts showed that the forecasts accurately predicted the major SSW of January 2013. The overall success of the 5-day forecasts provides motivation to produce regular 10-day forecasts with GEOS-5, to better support studies of stratosphere–troposphere interaction.

Sensitivity of Tropical Cyclones to Parameterized Convection in the NASA GEOS-5 Model

Abstract The sensitivity of tropical cyclones (TCs) to changes in parameterized convection is investigated to improve the simulation of TCs in the North Atlantic. Specifically, the impact of reducing the influence of the Relaxed Arakawa–Schubert (RAS) scheme-based parameterized convection is explored using the Goddard Earth Observing System version 5 (GEOS-5) model at 0.25° horizontal grid spacing. The years 2005 and 2006, characterized by very active and inactive hurricane seasons, respectively, are selected for simulation. A reduction in parameterized deep convection results in an increase in TC activity (e.g., TC number and longer life cycle) to more realistic levels compared to the baseline control configuration. The vertical and horizontal structure of the strongest simulated hurricane shows the maximum wind speed greater than 60 m s−1 and the minimum sea level pressure reaching ~940 mb, which are never achieved by the control configuration. The radius of the maximum wind of ~50 km, the location of the warm core exceeding 10°C, and the horizontal compactness of the hurricane center are all quite realistic without any negatively affecting the atmospheric mean state. This study reveals that an increase in the threshold of minimum entrainment suppresses parameterized deep convection by entraining more dry air into the typical plume. This leads to cooling and drying at the mid to upper troposphere, along with the positive latent heat flux and moistening in the lower troposphere. The resulting increase in conditional instability provides an environment that is more conducive to TC vortex development and upward moisture flux convergence by dynamically resolved moist convection, thereby increasing TC activity.

Tropospheric delay ray tracing applied in VLBI analysis

AbstractTropospheric delay modeling error continues to be one of the largest sources of error in VLBI (very long baseline interferometry) analysis. For standard operational solutions, we use the VMF1 elevation‐dependent mapping functions derived from European Centre for Medium‐Range Weather Forecasts data. These mapping functions assume that tropospheric delay at a site is azimuthally symmetric. As this assumption is not true, we have instead determined the ray trace delay along the signal path through the troposphere for each VLBI quasar observation. We determined the troposphere refractivity fields from the pressure, temperature, specific humidity, and geopotential height fields of the NASA Goddard Space Flight Center Goddard Earth Observing System version 5 numerical weather model. When applied in VLBI analysis, baseline length repeatabilities were improved compared with using the VMF1 mapping function model for 72% of the baselines and site vertical repeatabilities were better for 11 of 13 sites during the 2 week CONT11 observing period in September 2011. When applied to a larger data set (2011–2013), we see a similar improvement in baseline length and also in site position repeatabilities for about two thirds of the stations in each of the site topocentric components.

An updated treatment of soil texture and associated hydraulic properties in a global land modeling system

AbstractThe advent of new data sets describing soil texture and associated soil properties offers the promise of improved hydrological simulation. Here we describe the composition of a new soil texture data set and its implementation into a specific land surface modeling system, namely, the Catchment land surface model (LSM) of the NASA Goddard Earth Observing System version 5 (GEOS‐5) modeling and assimilation framework. First, global soil texture composites are generated using data from the Harmonized World Soil Database version 1.21 (HWSD1.21) and the State Soil Geographic (STATSGO2) project, with explicit consideration of different levels of organic material. Then, the LSM's soil parameters are upgraded using the new texture data, with hydraulic parameters derived for the more extensive set of texture classes using pedotransfer functions. Other changes to the LSM parameters are included to further support simulations at increasingly fine resolutions. A suite of simulations with the original and new parameter versions shows modest yet significant improvements in the Catchment LSM's simulation of soil moisture and surface hydrological fluxes. The revised LSM parameters will be used for the forthcoming Soil Moisture Active Passive (SMAP) soil moisture assimilation product.

A Global Climatology of Outgoing Longwave Spectral Cloud Radiative Effect and Associated Effective Cloud Properties

Abstract Longwave (LW) spectral flux and cloud radiative effect (CRE) are important for understanding the earth’s radiation budget and cloud–radiation interaction. Here, the authors extend their previous algorithms to collocated Atmospheric Infrared Sounder (AIRS) and Cloud and the Earth’s Radiant Energy System (CERES) observations over the entire globe and show that the algorithms yield consistently good performances for measurements over both land and ocean. As a result, the authors are able to derive spectral flux and CRE at 10-cm−1 intervals over the entire LW spectrum from all currently available collocated AIRS and CERES observations. Using this multiyear dataset, they delineate the climatology of spectral CRE, including the far IR, over the entire globe as well as in different climate zones. Furthermore, the authors define two quantities, IR-effective cloud-top height (CTHeff) and cloud amount (CAeff), based on the monthly-mean spectral (or band by band) CRE. Comparisons with cloud fields retrieved by the CERES–Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm indicate that, under many circumstances, the CTHeff and CAeff can be related to the physical retrievals of CTH and CA and thus can enhance understandings of model deficiencies in LW radiation budgets and cloud fields. Using simulations from the GFDL global atmosphere model, version 2 (AM2); NASA’s Goddard Earth Observing System, version 5 (GEOS-5); and Environment Canada’s Canadian Centre for Climate Modelling and Analysis (CCCma) Fourth Generation Canadian Atmospheric General Circulation Model (CanAM4) as case studies, the authors further demonstrate the merits of the CTHeff and CAeff concepts in providing insights on global climate model evaluations that cannot be obtained solely from broadband LW flux and CRE comparisons.

On the Role of SST Forcing in the 2011 and 2012 Extreme U.S. Heat and Drought: A Study in Contrasts

Abstract This study compares the extreme heat and drought that developed over the United States in 2011 and 2012 with a focus on the role of sea surface temperature (SST) forcing. Experiments with the NASA Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model show that the winter/spring response over the United States to the Pacific SST is remarkably similar for the two years despite substantial differences in the tropical Pacific SST. As such, the pronounced winter and early spring temperature differences between the two years (warmth confined to the south in 2011 and covering much of the continent in 2012) primarily reflect differences in the contributions from the Atlantic and Indian Oceans, with both acting to cool the east and upper Midwest during 2011, while during 2012 the Indian Ocean reinforced the Pacific-driven, continental-wide warming and the Atlantic played a less important role. During late spring and summer of 2011, the tropical Pacific SST forced a continued warming and drying over the southern United States, though considerably weaker than observed. Nevertheless, the observed 2011 anomalies fall well within the model’s intraensemble spread. In contrast, the observed rapid development of intense heat and drying over the central United States during June and July 2012 falls on the margins of the model’s intraensemble spread, with the response to the SST giving little indication that 2012 would produce record-breaking precipitation deficits and heat. A diagnosis of the 2012 observed circulation anomalies shows that the most extreme heat and drought was tied to the development of a stationary Rossby wave and an associated anomalous upper-tropospheric high maintained by weather transients.

Uncertainty quantification of GEOS-5 L-band radiative transfer model parameters using Bayesian inference and SMOS observations

Uncertainties in L-band (1.4GHz) microwave radiative transfer modeling (RTM) affect the simulation of brightness temperatures (Tb) over land and the inversion of satellite-observed Tb into soil moisture retrievals. In particular, accurate estimates of the microwave soil roughness, vegetation optical depth and scattering albedo for large-scale applications are difficult to obtain from field studies and often lack an estimate of uncertainty. Here, a Markov Chain Monte Carlo (MCMC) simulation method is used to determine satellite-scale estimates of RTM parameters and their posterior uncertainty by minimizing the misfit between long-term averages and standard deviations of simulated and observed Tb at multiple incidence angles, at horizontal and vertical polarizations, and for morning and evening overpasses. Tb simulations are generated with the land model component of the Goddard Earth Observing System (version 5) and confronted with Tb observations from the Soil Moisture Ocean Salinity satellite mission. The maximum a posteriori density (MAP) parameter values reduce the root-mean-square differences between observed and simulated long-term Tb averages and standard deviations to 3.4K and 2.3K, respectively. The relative uncertainty of the posterior RTM parameter estimates is typically less than 25% of the MAP parameter value, whereas it exceeds 100% for literature-based prior parameter estimates. It is also shown that the parameter values estimated through Particle Swarm Optimization are in close agreement with those obtained from MCMC simulation. The MCMC results for the RTM parameter values and the uncertainties presented herein are directly relevant to the need for accurate Tb modeling in global land data assimilation systems.

Modifications of the quasi‐biennial oscillation by a geoengineering perturbation of the stratospheric aerosol layer

AbstractThis paper examines the impact of geoengineering via stratospheric sulfate aerosol on the quasi‐biennial oscillation (QBO) using the NASA Goddard Earth Observing System version 5 Chemistry Climate Model. We performed four 30 year simulations with a continuous injection of sulfur dioxide on the equator at 0° longitude. The four simulations differ by the amount of sulfur dioxide injected (5 Tg/yr and 2.5 Tg/yr) and the altitude of the injection (16 km–25 km and 22 km–25 km). We find that such an injection dramatically alters the quasi‐biennial oscillation, prolonging the phase of easterly shear with respect to the control simulation. This is caused by the increased aerosol heating and associated warming in the tropical lower stratosphere and higher residual vertical velocity. In the case of maximum perturbation, i.e., highest stratospheric aerosol burden, the lower tropical stratosphere is locked into a permanent westerly QBO phase.

Evaluation of GEOS-5 sulfur dioxide simulations during the Frostburg, MD 2010 field campaign

Abstract. Sulfur dioxide (SO2) is a major atmospheric pollutant with a strong anthropogenic component mostly produced by the combustion of fossil fuel and other industrial activities. As a precursor of sulfate aerosols that affect climate, air quality, and human health, this gas needs to be monitored on a global scale. Global climate and chemistry models including aerosol processes along with their radiative effects are important tools for climate and air quality research. Validation of these models against in-situ and satellite measurements is essential to ascertain the credibility of these models and to guide model improvements. In this study, the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) module running on-line inside the Goddard Earth Observing System version 5 (GEOS-5) model is used to simulate aerosol and SO2 concentrations. Data taken in November 2010 over Frostburg, Maryland during an SO2 field campaign involving ground instrumentation and aircraft are used to evaluate GEOS-5 simulated SO2 concentrations. Preliminary data analysis indicated the model overestimated surface SO2 concentration, which motivated the examination of the specification of SO2 anthropogenic emission rates. As a result of this analysis, a revision of anthropogenic emission inventories in GEOS-5 was implemented, and the vertical placement of SO2 sources was updated. Results show that these revisions improve the model agreement with observations locally and in regions outside the area of this field campaign. In particular, we use the ground-based measurements collected by the United States Environmental Protection Agency (US EPA) for the year 2010 to evaluate the revised model simulations over North America.

The GOddard SnoW Impurity Module (GOSWIM) for the NASA GEOS-5 Earth System Model: Preliminary Comparisons with Observations in Sapporo, Japan

The snow darkening module evaluating dust, black carbon, and organic carbon depositions on mass and albedo has been developed for the NASA Goddard Earth Observing System, Version 5 (GEOS-5) Earth System Model, as the GOddard SnoW Impurity Module (GOSWIM). GOSWIM consists of the snow albedo scheme from a previous study (Yasunari et al. 2011) with updates and a newly developed mass concentration scheme, using aerosol depositions from the chemical transport model (GOCART) in GEOS-5. Compared to observations at Sapporo, the numerical experiments, forced by observation-based meteorology and aerosol depositions from GOES-5, better simulated the seasonal migration of snow depth, albedos, and impurities of dust, BC, and OC in the snow surface. However, the magnitude of the impurities is underestimated, compared to the sporadic snow impurity measurements. Increasing the deposition rates of dust and BC could explain the differences on the snow darkening effect between observation and simulation. Ignoring BC deposition can possibly lead to an extension of snow cover duration in Sapporo for four days. Comparing the off-line GOSWIM and the GEOS-5 global simulations, we found that determining better local precipitation and deposition rates of the aerosols are key factors in generating better GOSWIM snow darkening simulation in NASA GEOS-5.

Remote sensing of surface visibility from space: A look at the United States East Coast

Measurement of surface visibility is important for the management of air quality, human health, and transportation. Currently, visibility measurements are only available through ground-based instrumentation, such as the Automated Surface Observing System (ASOS), and therefore lack spatial coverage. In analogy to the recent work of using satellite-based aerosol optical depth (AOD) to derive surface dry aerosol mass concentration at continental-to-global scale for cloud-free conditions, this study evaluates the potential of AOD retrieved from the MODerate Resolution Imaging Spectroradiometer (MODIS) for deriving surface visibility. For this purpose of evaluation the truncated (up to ∼16 km or 10 miles) and discrete (at the interval no less than 0.4 km or 1/4 mile) visibility data from hourly operational weather reports are not suitable, and the ASOS-measured one-minute raw surface extinction coefficient (bext) values have to be used. Consequently, a method for quality control on the bext data is first developed to eliminate frequent problems such as extraneous points, poor calibration, and bad formatting, after which reliable bext data are obtained to estimate the surface visibility. Subsequent analysis of the AOD and bext relationship on the East Coast of the United States reveals their average linear correlation coefficient (R) of 0.61 for all twelve (2000–2011) years of data at 32 ASOS stations, with the highest R value in summer and the lowest values in fall and winter. Incorporation of the Goddard Earth Observing System, Version 5 (GEOS-5) modeled vertical profile of aerosols into the derivation of visibility from AOD is evaluated for two methods, one scaling the modeled surface bext with the ratio of MODIS AOD to the modeled AOD, and another scaling the ratio of modeled AOD in the boundary layer to total columnar AOD with the MODIS AOD and assuming well-mixed aerosol extinction in the boundary layer. Analysis with three summers (2003–2004, 2006) of available GEOS-5 data and ASOS data reveals that the second method is superior, and generates a regression model that, after independent evaluation for summer 2005, is found to be statistically robust with R of 0.70 and a mean bias of 0.32 km in derived visibility. This study is among the first to demonstrate the potential of using satellite-based aerosol product over land to operationally derive surface visibility.