NASA Goddard Earth Observing Research Articles

Enhancing long-term trend simulation of the global tropospheric hydroxyl (TOH) and its drivers from 2005 to 2019: a synergistic integration of model simulations and satellite observations

Abstract. The tropospheric hydroxyl (TOH) radical is a key player in regulating oxidation of various compounds in Earth's atmosphere. Despite its pivotal role, the spatiotemporal distributions of OH are poorly constrained. Past modeling studies suggest that the main drivers of OH, including NO2, tropospheric ozone (TO3), and H2O(v), have increased TOH globally. However, these findings often offer a global average and may not include more recent changes in diverse compounds emitted on various spatiotemporal scales. Here, we aim to deepen our understanding of global TOH trends for more recent years (2005–2019) at 1×1°. To achieve this, we use satellite observations of HCHO and NO2 to constrain simulated TOH using a technique based on a Bayesian data fusion method, alongside a machine learning module named the Efficient CH4-CO-OH (ECCOH) configuration, which is integrated into NASA's Goddard Earth Observing System (GEOS) global model. This innovative module helps efficiently predict the convoluted response of TOH to its drivers and proxies in a statistical way. Aura Ozone Monitoring Instrument (OMI) NO2 observations suggest that the simulation has high biases for biomass burning activities in Africa and eastern Europe, resulting in a regional overestimation of up to 20 % in TOH. OMI HCHO primarily impacts the oceans, where TOH linearly correlates with this proxy. Five key parameters, i.e., TO3, H2O(v), NO2, HCHO, and stratospheric ozone, can collectively explain 65 % of the variance in TOH trends. The overall trend of TOH influenced by NO2 remains positive, but it varies greatly because of the differences in the signs of anthropogenic emissions. Over the oceans, TOH trends are primarily positive in the Northern Hemisphere, resulting from the upward trends in HCHO, TO3, and H2O(v). Using the present framework, we can tap the power of satellites to quickly gain a deeper understanding of simulated TOH trends and biases.

New Insights Into the Role of Atmospheric Transport and Mixing on Column and Surface Concentrations of NO2 at a Coastal Urban Site

AbstractWe use a multi‐year record of Pandora‐derived NO2 total column abundance in Boston to examine the influence of atmospheric transport on column NO2 and its surface concentrations during the warm season in a coastal urban environment. We derive tropospheric NO2 estimates from the total column with a measurement‐model fusion approach using near‐real‐time estimates of stratospheric NO2 from NASA's Goddard Earth Observing System Composition Forecast model system and find the average influence of stratospheric NO2 at this urban site can be 30%–70% depending on season and time of day. Sea breeze days tend to exhibit rapid temporal variability in the column that which can go in the opposite direction of changes in surface NO2 concentrations. By comparing tropospheric NO2 with surface concentrations, we constrain the role of boundary layer entrainment processes in the evolution of surface NO2 concentrations, while highlighting the value of column measurements in identifying sea breeze frontal dynamics. We estimate an apparent equal mixing layer height of NO2 and infer that surface NOx emissions remain concentrated near the surface regardless of atmospheric stability regime. When comparing the Pandora‐ to TROPOMI‐derived column NO2 measurements, we find that sea breeze days present a unique challenge likely due to higher spatial heterogeneity in NO2 and the meteorology involved that is not well represented in retrieval inputs. Our observations provide new insights into column and surface variability of NO2 which will be relevant to interpreting geostationary observations, especially in coastal urban locations.

Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System

Abstract. Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorologically relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks to months is an area of active research and development. NASA's Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1–3-month lead times in the HMA region, including a portion of the Indian subcontinent, during the retrospective forecast period, 1981–2016. To assess forecast skill, we evaluate 2 m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to the similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from an anomaly correlation of Ranom=0.18 for precipitation to Ranom=0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are evaluated against independent observations; results for the 1-month forecast skill range from Ranom=0.13 for snow water equivalent to Ranom=0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system's ability to forecast HMA hydrometeorology.

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast.

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods rely on either chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product, NASA's Goddard Earth Observing System "Composition Forecasting" (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next 5 days at a 1 km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next 2 days had an overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following 3 forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with a validation normalized mean bias close to 0, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

Communicating respiratory health risk among children using a global air quality index.

Air pollution poses a serious threat to children's respiratory health around the world. Satellite remote-sensing technology and air quality models can provide pollution data on a global scale, necessary for risk communication efforts in regions without ground-based monitoring networks. Several large centers, including NASA, produce global pollution forecasts that may be used alongside air quality indices to communicate local, daily risk information to the public. Here we present a health-based, globally applicable air quality index developed specifically to reflect the respiratory health risks among children exposed to elevated outdoor air pollution. Additive, excess-risk air quality indices were developed using 51 different coefficients derived from time-series health studies evaluating the impacts of ambient fine particulate matter, nitrogen dioxide, and ozone on children's respiratory morbidity outcomes. A total of four indices were created which varied based on whether or not the underlying studies controlled for co-pollutants and in the adjustment of excess risks of individual pollutants. Combined with historical estimates of air pollution provided globally at a 25×25km2 spatial resolution from the NASA's Goddard Earth Observing System composition forecast (GEOS-CF) model, each of these indices were examined in a global sample of 664 small and 140 large cities for study year 2017. Adjusted indices presented the most normal distributions of locally-scaled index values, which has been shown to improve associations with health risks, while indices based on coefficients controlling for co-pollutants had little effect on index performance. We provide the steps and resources need to apply our final adjusted index at the local level using freely-available forecasting data from the GEOS-CF model, which can provide risk communication information for cities around the world to better inform individual behavior modification to best protect children's respiratory health.

Investigating the potential of a global precipitation forecast to inform landslide prediction

Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infrastructure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA's Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.

Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems

Abstract. The ability to monitor and understand natural and anthropogenic variability in atmospheric carbon dioxide (CO2) is a growing need of many stakeholders across the world. Systems that assimilate satellite observations, given their short latency and dense spatial coverage, into high-resolution global models are valuable, if not essential, tools for addressing this need. A notable drawback of modern assimilation systems is the long latency of many vital input datasets; for example, inventories, in situ measurements, and reprocessed remote-sensing data can trail the current date by months to years. This paper describes techniques for bias-correcting surface fluxes derived from satellite observations of the Earth's surface to be consistent with constraints from inventories and in situ CO2 datasets. The techniques are applicable in both short-term forecasts and retrospective simulations, thus taking advantage of the coverage and short latency of satellite data while reproducing the major features of long-term inventory and in situ records. Our approach begins with a standard collection of diagnostic fluxes which incorporate a variety of remote-sensing driver data, viz. vegetation indices, fire radiative power, and nighttime lights. We then apply an empirical sink so that global budgets of the diagnostic fluxes match given atmospheric and oceanic growth rates for each year. This step removes coherent, systematic flux errors that produce biases in CO2 which mask the signals an assimilation system hopes to capture. Depending on the simulation mode, the empirical sink uses different choices of atmospheric growth rates: estimates based on observations in retrospective mode and projections based on seasonal forecasts of sea surface temperature in forecasting mode. The retrospective fluxes, when used in simulations with NASA's Goddard Earth Observing System (GEOS), reproduce marine boundary layer measurements with comparable skill to those using fluxes from a modern inversion system. The forecasted fluxes show promising accuracy in their application to the analysis of changes in the carbon cycle as they occur.

Application of NASA-Unified WRF model to carbon dioxide simulation- model development and evaluation

The NASA-Unified Weather Research and Forecasting model has been coupled with the Carnegie-Ames-Stanford Approach biogeochemical model/Global Fire Emissions Database and the Parameterized Chemistry Transport Model (NU-WRF-CASA) to simulate CO2 transport and variability at fine spatial resolutions, which is anticipated to assist in better understanding the mechanisms and processes controlling carbon sources/sinks and in reducing uncertainty in carbon-climate interactions. NU-WRF-CASA is designed to operate in an internally consistent manner in which NASA's Goddard Earth Observing System Model, version 5, can be applied to drive the entire coupled modeling system. A 3-year simulation was carried out for North America to evaluate NU-WRF-CASA's performance under various regions and environmental conditions. Observational CO2 data from tower, flask, aircraft, and satellite measurements were used for model evaluations. The results showed that NU-WRF-CASA correctly distributed CO2 fluxes and reproduced spatial-temporal CO2 transport and variability reasonably well with overall bias within 1 ppmv.

Chemical Mechanisms and Their Applications in the Goddard Earth Observing System (GEOS) Earth System Model.

NASA's Goddard Earth Observing System (GEOS) Earth System Model (ESM) is a modular, general circulation model (GCM), and data assimilation system (DAS) that is used to simulate and study the coupled dynamics, physics, chemistry, and biology of our planet. GEOS is developed by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. It generates near‐real‐time analyzed data products, reanalyses, and weather and seasonal forecasts to support research targeted to understanding interactions among Earth System processes. For chemistry, our efforts are focused on ozone and its influence on the state of the atmosphere and oceans, and on trace gas data assimilation and global forecasting at mesoscale discretization. Several chemistry and aerosol modules are coupled to the GCM, which enables GEOS to address topics pertinent to NASA's Earth Science Mission. This paper describes the atmospheric chemistry components of GEOS and provides an overview of its Earth System Modeling Framework (ESMF)‐based software infrastructure, which promotes a rich spectrum of feedbacks that influence circulation and climate, and impact human and ecosystem health. We detail how GEOS allows model users to select chemical mechanisms and emission scenarios at run time, establish the extent to which the aerosol and chemical components communicate, and decide whether either or both influence the radiative transfer calculations. A variety of resolutions facilitates research on spatial and temporal scales relevant to problems ranging from hourly changes in air quality to trace gas trends in a changing climate. Samples of recent GEOS chemistry applications are provided.

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.

Assessing the magnitude of CO2 flux uncertainty in atmospheric CO2 records using products from NASA's Carbon Monitoring Flux Pilot Project

AbstractNASA's Carbon Monitoring System Flux Pilot Project (FPP) was designed to better understand contemporary carbon fluxes by bringing together state‐of‐the art models with remote sensing data sets. Here we report on simulations using NASA's Goddard Earth Observing System Model, version 5 (GEOS‐5) which was used to evaluate the consistency of two different sets of observationally informed land and ocean fluxes with atmospheric CO2 records. Despite the observation inputs, the average difference in annual terrestrial biosphere flux between the two land (NASA Ames Carnegie‐Ames‐Stanford‐Approach (CASA) and CASA‐Global Fire Emissions Database version 3 (GFED)) models is 1.7 Pg C for 2009–2010. Ocean models (NASA's Ocean Biogeochemical Model (NOBM) and Estimating the Circulation and Climate of the Ocean Phase II (ECCO2)‐Darwin) differ by 35% in their global estimates of carbon flux with particularly strong disagreement in high latitudes. Based upon combinations of terrestrial and ocean fluxes, GEOS‐5 reasonably simulated the seasonal cycle observed at Northern Hemisphere surface sites and by the Greenhouse gases Observing SATellite (GOSAT) while the model struggled to simulate the seasonal cycle at Southern Hemisphere surface locations. Though GEOS‐5 was able to reasonably reproduce the patterns of XCO2 observed by GOSAT, it struggled to reproduce these aspects of Atmospheric Infrared Sounder observations. Despite large differences between land and ocean flux estimates, resulting differences in atmospheric mixing ratio were small, typically less than 5 ppm at the surface and 3 ppm in the XCO2 column. A statistical analysis based on the variability of observations shows that flux differences of these magnitudes are difficult to distinguish from inherent measurement variability, regardless of the measurement platform.

Modeling the influences of aerosols on pre-monsoon circulation and rainfall over Southeast Asia

Abstract. We conduct several sets of simulations with a version of NASA's Goddard Earth Observing System, version 5, (GEOS-5) Atmospheric Global Climate Model (AGCM) equipped with a two-moment cloud microphysical scheme to understand the role of biomass burning aerosol (BBA) emissions in Southeast Asia (SEA) in the pre-monsoon period of February–May. Our experiments are designed so that both direct and indirect aerosol effects can be evaluated. For climatologically prescribed monthly sea surface temperatures, we conduct sets of model integrations with and without biomass burning emissions in the area of peak burning activity, and with direct aerosol radiative effects either active or inactive. Taking appropriate differences between AGCM experiment sets, we find that BBA affects liquid clouds in statistically significantly ways, increasing cloud droplet number concentrations, decreasing droplet effective radii (i.e., a classic aerosol indirect effect), and locally suppressing precipitation due to a deceleration of the autoconversion process, with the latter effect apparently also leading to cloud condensate increases. Geographical re-arrangements of precipitation patterns, with precipitation increases downwind of aerosol sources are also seen, most likely because of advection of weakly precipitating cloud fields. Somewhat unexpectedly, the change in cloud radiative effect (cloud forcing) at surface is in the direction of lesser cooling because of decreases in cloud fraction. Overall, however, because of direct radiative effect contributions, aerosols exert a net negative forcing at both the top of the atmosphere and, perhaps most importantly, the surface, where decreased evaporation triggers feedbacks that further reduce precipitation. Invoking the approximation that direct and indirect aerosol effects are additive, we estimate that the overall precipitation reduction is about 40% due to the direct effects of absorbing aerosols, which stabilize the atmosphere and reduce surface latent heat fluxes via cooler land surface temperatures. Further refinements of our two-moment cloud microphysics scheme are needed for a more complete examination of the role of aerosol–convection interactions in the seasonal development of the SEA monsoon.

Catastrophe-Concept-Based Cumulus Parameterization: Correction of Systematic Errors in the Precipitation Diurnal Cycle over Land in a GCM

Abstract The onset of cumulus convection in a grid column is a catastrophe, also known as a subcritical instability. Accordingly, in designing a cumulus parameterization scheme the onset of cumulus convection requires that a parameter crosses a critical value and the termination of cumulus convection requires that the same or a different parameter crosses a different critical value. Once begun, cumulus convection continues to exist, regardless of whether the onset criterion is still met, until the termination criterion is met. Also, the intensity of cumulus precipitation is related to how far the state is from the termination, not the onset, criterion. The cumulus parameterization schemes currently in use in GCMs, however, treat the onset of cumulus convection as a supercritical instability; namely, convection is turned on when a parameter exceeds a critical value and is turned off when the same parameter falls below the same critical value. Also, the intensity of cumulus precipitation is related to how far this critical value has been exceeded. Among the adverse consequences of the supercritical-instability-concept-based cumulus parameterization schemes are that over relatively flat land the precipitation peak occurs around noon—4–6 h too soon—and that the amplitude of the precipitation diurnal cycle is too weak. Based on the above-mentioned concept, a new cumulus parameterization scheme was designed by taking advantage of the existing infrastructure of the relaxed Arakawa–Schubert scheme (RAS), but replacing RAS's guiding principle with the catastrophe concept. Test results using NASA's Goddard Earth Observing System GCM, version 5 (GEOS-5), show dramatic improvement in the phase and amplitude of the precipitation diurnal cycle over relatively flat land.

Connections between the Spring Breakup of the Southern Hemisphere Polar Vortex, Stationary Waves, and Air–Sea Roughness

Abstract A robust connection between the drag on surface-layer winds and the stratospheric circulation is demonstrated in NASA's Goddard Earth Observing System Chemistry–Climate Model (GEOSCCM). Specifically, an updated parameterization of roughness at the air–sea interface, in which surface roughness is increased for moderate wind speeds (4–20 m s−1), leads to a decrease in model biases in Southern Hemispheric ozone, polar cap temperature, stationary wave heat flux, and springtime vortex breakup. A dynamical mechanism is proposed whereby increased surface roughness leads to improved stationary waves. Increased surface roughness leads to anomalous eddy momentum flux convergence primarily in the Indian Ocean sector (where eddies are strongest climatologically) in September and October. The localization of the eddy momentum flux convergence anomaly in the Indian Ocean sector leads to a zonally asymmetric reduction in zonal wind and, by geostrophy, to a wavenumber-1 stationary wave pattern. This tropospheric stationary wave pattern leads to enhanced upward wave activity entering the stratosphere. The net effect is an improved Southern Hemisphere vortex: the vortex breaks up earlier in spring (i.e., the spring late-breakup bias is partially ameliorated) yet is no weaker in midwinter. More than half of the stratospheric biases appear to be related to the surface wind speed biases. As many other chemistry–climate models use a similar scheme for their surface-layer momentum exchange and have similar biases in the stratosphere, the authors expect that results from GEOSCCM may be relevant for other climate models.

An analysis of the impact of convective parameter sensitivity on simulated global atmospheric CO distributions

[1] In an effort to better understand how uncertainty in simulated convection propagates into simulations of global trace gas distributions, we have constructed an eight-member ensemble of simulations using NASA's Goddard Earth Observing System Version 5 (GEOS-5) general circulation model (GCM). The ensemble was created by perturbing parameters in the model's moist physics schemes found to strongly influence the magnitude of convective mass flux. Globally, ensemble spreads in column CO are typically small (less than 4% of the mean column value) and, in many areas, are not significantly different from internal model variability. The largest ensemble spreads are found near source regions and outflow pathways. At the majority of remote surface monitoring sites, the annual mean ensemble spread is less than 5%, indicating that these locations, which are often the basis of inversion studies, are relatively insensitive to uncertainty in the representation of convection. We also examine in greater detail two simulations in which the magnitude of convective mass flux is significantly altered. Changes to convective parameters strongly influence grid-scale vertical and turbulent transport processes in addition to convective mass flux. Despite large differences in the magnitude of convective mass fluxes, this compensating behavior by other model processes results in comparable atmospheric residence times in the two simulations and largely similar global CO distributions. The results indicate that convective mass flux is strongly related to other vertical transport processes in a GCM and cannot be viewed as entirely separate. Future studies of the role of convective transport need to consider the relationship between convective and total mass flux.

Influence of the 2006 Indonesian biomass burning aerosols on tropical dynamics studied with the GEOS‐5 AGCM

The direct and semidirect effects of aerosols produced by Indonesian biomass burning (BB) during August–November 2006 on tropical dynamics have been examined using NASA's Goddard Earth Observing System, version 5, (GEOS‐5) atmospheric general circulation model (AGCM). Simulations were driven by two sets of aerosol forcing fields calculated offline by a separate aerosol transport model, one that included Indonesian BB aerosol emissions and one that did not. In order to separate the influence of the aerosols from internal model variability, the means of two ten‐member ensembles were compared. Diabatic heating from BB aerosols increased temperatures over Indonesia between 150 and 400 hPa. The higher temperatures resulted in strong increases in upward grid‐scale vertical motion, which increased water vapor and CO over Indonesia. In October, the largest increases in water vapor were found in the midtroposphere (∼25%) while the largest increases in CO occurred just below the tropopause (80 ppbv or ∼50%). Diabatic heating from the Indonesian BB aerosols caused CO to increase by 9% throughout the tropical tropopause layer in November and 5% in the lower stratosphere in December. The results demonstrate that aerosol heating plays an important role in the transport of BB pollution and troposphere‐to‐stratosphere transport. Changes in vertical motion and cloudiness induced by aerosol heating can also alter the transport and phase of water vapor in the upper troposphere/lower stratosphere.

Stratospheric transport using 6‐h‐averaged winds from a data assimilation system

Stratospheric transport calculated using assimilated winds has been shown to be too fast in many cases, because of excessive mixing and an overstrong residual circulation. It is shown that the use of 6‐h‐averaged wind fields instead of instantaneous analyses can substantially reduce this problem for NASA's Goddard Earth Observing System version 4 (GEOS‐4) sequential data assimilation system. Two examples are used to illustrate impacts in an off‐line chemistry transport model. An age‐of‐air computation shows that the stratosphere becomes substantially older when time‐averaged winds are used, yet still not as old as that determined from observations. An ozone assimilation experiment reveals improvements in the spatial structure of assimilated ozone, better agreement with independent observations, and a 40–60% reduction in observation‐minus‐forecast residuals. The averaged meteorological fields have also been incorporated in the on‐line transport computations: This is equivalent to using a 6‐h square‐wave filter at the analysis times. Sub‐6‐h noise in the system is substantially reduced with this approach. Two additional examples of transport are shown. A simulation of 2004–2005 shows that the descent of N2O in the Arctic polar vortex is represented more accurately than in previous studies. A tape recorder signal is evident in the tropical moisture; it ascends about 30% faster than that in the real atmosphere. In summary, the use of 6‐h‐averaged winds substantially improves the transport characteristics of the assimilated data, although the circulation remains too fast.