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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
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NASA NTRS2005

A Regional, Integrated Monitoring System for the Hydrology of the Pan-Arctic Land Mass

Work under this NASA contract developed a system for monitoring and historical analysis of the major components of the pan-Arctic terrestrial water cycle. It is known as Arctic-RIMS (Regional Integrated Hydrological Monitoring System for the Pan-Arctic Landmass). The system uses products from EOS-era satellites, numerical weather prediction models, station records and other data sets in conjunction with an atmosphere-land surface water budgeting scheme. The intent was to compile operational (at 1-2 month time lags) gridded fields of precipitation (P), evapotranspiration (ET), P-ET, soil moisture, soil freeze/thaw state, active layer thickness, snow extent and its water equivalent, soil water storage, runoff and simulated discharge along with estimates of non-closure in the water budget. Using "baseline" water budgeting schemes in conjunction with atmospheric reanalyses and pre-EOS satellite data, water budget fields were conjunction with atmospheric reanalyses and pre-EOS satellite data, water budget fields were compiled to provide historical time series. The goals as outlined in the original proposal can be summarized as follows: 1) Use EOS data to compile hydrologic products for the pan-Arctic terrestrial regions including snowcover/snow water equivalent (SSM/A MODIS, AMSR) and near-surface freeze/thaw dynamics (Sea Winds on QuikSCAT and ADEOS I4 SSMI and AMSR). 2) Implement Arctic-RIMS to use EOS data streams, allied fields and hydrologic models to produce allied outputs that fully characterize pan-Arctic terrestrial and aerological water budgets. 3) Compile hydrologically-based historical products providing a long-term baseline of spatial and temporal variability in the water cycle.

Serreze, Mark↗
NASA NTRS2008

Modern Era Retrospective-analysis for Research and Applications (MERRA) Global Water and Energy Budgets

The Modern. Era Retrospective-analysis for Research and Applications (MERRA) reanalyses has produced several years of data, on the way to a completing. the 1979-present modern satellite era. Here, we present a preliminary evaluation of those years currently available, includin g comparisons with the existing long reanalyses (ERA40, JRA25 and NCE P I and II) as well as with global data sets for the water and energy cycle Time series shows that the MERRA budgets can change with some of the variations in observing systems. We will present all terms of the budgets in MERRA including the time rates of change and analysis increments (tendency due to the analysis of observations)

Bosilovich, Michael G.↗
NASA NTRS2009

Modern Era Retrospective-Analysis for Research and Applications

The Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalyses has produced several years of data, on the way to a. completing the 1979-present modern satellite era. Here, we present a preliminary evaluation of those years currently available, including comparisons with the existing long reanalyses (ERA40, JPA25 and NCEP I and II) as well as with global data sets for the water and energy cycle. Time series shows that the MERRA budgets can change with some of the variations in observing systems. We will present all terms of the budgets in MERRA including the time rates of change and analysis increments (tendency due to the analysis of observations).

Bosilovich, Michael G.↗
NASA NTRS2009

Modern Era Retrospective-analysis for Research and Applications (MERRA) Global Water and Energy Budgets

In the Summer of 2009, NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) will have completed 28 years of global satellite data analyses. Here, we characterize the global water and energy budgets of MERRA, compared with available observations and the latest reanalyses. In this analysis, the climatology of the global average components are studied as well as the separate land and ocean averages. In addition, the time series of the global averages are evaluated. For example, the global difference of precipitation and evaporation generally shows the influence of water vapor observations on the system. Since the observing systems change in time, especially remotely sensed observations of water, significant temporal variations can occur across the 28 year record. These then are also closely connected to changes in the atmospheric energy and water budgets. The net imbalance of the energy budget at the surface can be large and different signs for different reanalyses. In MERRA, the imbalance of energy at the surface tends to improve with time being the smallest during the most recent and abundant satellite observations.

Bosilovich, Michael G.↗
NASA NTRS2010

Evaluation of the Modern Era Retrospective-Analysis for Research and Applications (MERRA) Global Water and Energy Budgets

The Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalyses has completed 27 years of data) soon to be caught up to present. Here) we present an evaluation of those years currently available) including comparisons with the existing long reanalyses (ERA40) JRA25 and NCEP I and II) as well as with global data sets for the water and energy cycle. Time series shows that the MERRA budgets can change with some of the variations in observing systems, but that the magnitude of energy imbalance in the system is improved with more observations. We will present all terms of the budgets in MERRA including the time rates of change and analysis increments (tendency due to the analysis of observations).

Bosilovich, Michael G.↗
NASA NTRS2010

A Comparison of MERRA and NARR Reanalysis Datasets with the DOE ARM SGP Continuous Forcing data

In this study, the atmospheric state, precipitation, cloud fraction, and radiative fluxes from Modern Era Retrospective-analysis for Research and Applications (MERRA) and North American Regional Reanalysis (NARR) are collected and compared with the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) continuous forcing during the period 1999-2001. For the atmospheric state, the three datasets have excellent agreement for the horizontal wind components and air temperature. NARR and ARM have generally good agreement for humidity, except for several biases in the PBL and in the upper troposphere. MERRA, on the other hand, suffers from a year-round negative bias in humidity except for the month of June. For the vertical pressure velocity, significant differences exist with the largest biases occurring during the spring upwelling and summer downwelling periods. Although NARR and MERRA share many resemblances to each other, ARM outperforms these reanalyses in terms of correlation with cloud fraction. Because the ARM forcing is constrained by observed precipitation that gives the adequate mass, heat, and moisture budgets, much of the precipitation (specifically during the late spring/early summer) is caused by smaller-scale forcing that is not captured by the reanalyses. Both NARR and MERRA capture the seasonal variation of CF observed by ARM radar-lidar and GOES with high correlations (0.92-0.78), but having negative biases of 14% and 3%, respectively. Compared to the ARM observations, MERRA shows a better agreement for both SW and LW fluxes except for LW-down (due to a negative bias in water vapor), NARR has significant positive bias for SW-down and negative bias for LW-down under clear- and all-sky conditions . The NARR biases result from a combination of too few clouds and a lack of sufficient extinction by aerosols and water vapor in the atmospheric column. The results presented here represent only one location for a limited time period, and more comparisons at different locations and longer time period are needed.

Kennedy, Aaron D.↗
NASA NTRS2010

The Effect of Satellite Observing System Changes on MERRA Water and Energy Fluxes

Because reanalysis data sets offer state variables and fluxes at regular space / time intervals, atmospheric reanalyses have become a mainstay of the climate community for diagnostic purposes and for driving offline ocean and land models. Although one weakness of these data sets is the susceptibility of the flux products to uncertainties because of shortcomings in parameterized model physics, another issue, perhaps less appreciated, is the fact that continual but discreet changes in the evolving observational system, particularly from satellite sensors, may also introduce artifacts in the time series of quantities. In this paper we examine the ability of the NASA MERRA (Modern Era Retrospective Analysis for Research and Applications) and other recent reanalyses to determine variability in the climate system over the satellite record (approximately the last 30 years). In particular we highlight the effect on the reanalysis of discontinuities at the junctures of the onset of passive microwave imaging (Special Sensor Microwave Imager) in late 1987 as well as improved sounding and imaging with the Advanced Microwave Sounding Unit, AMSU-A, in 1998. We first examine MERRA fluxes from the perspective of how physical modes of variability (e.g. ENSO events, Pacific Decadal Variability) are contaminated by artificial step-like trends induced by the onset of new moisture data these two satellite observing systems. Secondly, we show how Redundancy Analysis, a statistical regression methodology, is effective in relating these artifact signals in the moisture and temperature analysis increments to their presence in the physical flux terms (e.g. precipitation, radiation). This procedure is shown to be effective greatly reducing the artificial trends in the flux quantities.

Robertson, Franklin R.↗
NASA NTRS2010

MERRA: Progress, Applications, and Challenges for the Future

This presentation gives an overview of the results from the Modern Era Retrospective-Analysis for Research and Applications (MERRA) project, an atmospheric global reanalysis, recently completed at the NASA/Global Modeling and Assimilation Office (GMAO). Comparisons are made with previous and other recent reanalyses to highlight improvements as well as remaining challenges regarding the quality of the reanalyses on diurnal to decadal and longer time scales. Several examples will be presented that highlight some of the strengths of the MERRA product in addressing issues of climate variability on subseasonal to interannual time scales.

Schubert, Siegfried D.↗
NASA NTRS2011

The Effect of Satellite Observing System Changes on MERRA Water and Energy Fluxes

Because reanalysis data sets offer state variables and fluxes at regular space / time intervals, atmospheric reanalyses have become a mainstay of the climate community for diagnostic purposes and for driving offline ocean and land models. Although one weakness of these data sets is the susceptibility of the flux products to uncertainties because of shortcomings in parameterized model physics, another issue, perhaps less appreciated, is the fact that continual but discreet changes in the evolving observational system, particularly from satellite sensors, may also introduce artifacts in the time series of quantities. In this paper we examine the ability of the NASA MERRA (Modern Era Retrospective Analysis for Research and Applications) and other recent reanalyses to determine variability in the climate system over the satellite record (approx. the last 30 years). In particular we highlight the effect on the reanalysis of discontinuities at the junctures of the onset of passive microwave imaging (Special Sensor Microwave Imager) in late 1987 and, more prominently, with improved sounding and imaging with the Advanced Microwave Sounding Unit, AMSU-A, in 1998. We first examine MERRA fluxes from the perspective of how physical modes of variability (e.g. ENSO events, Pacific Decadal Variability) are contained by artificial step-like trends induced by the onset of new moisture data these two satellite observing systems. Secondly, we show how Redundancy Analysis, a statistical regression methodology, is effective in relating these artifact signals in the moisture and temperature analysis increments to their presence in the physical flux terms (e.g. precipitation, radiation). This procedure is shown to be effective greatly reducing the artificial trends in the flux quantities.

Robertson, Franklin R.↗
NASA NTRS2011

Global Intercomparison of 12 Land Surface Heat Flux Estimates

A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993-1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/ Mosaic/Noah). An intercomparison of the global latent heat flux (Q(sub le)) annual means shows a spread of approx 20 W/sq m (all-product global average of approx 45 W/sq m). A similar spread is observed for the sensible (Q(sub h)) and net radiative (R(sub n)) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Q(sub le)and Q(sub h) absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Q(sub le) differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Q(sub le) and R(sub n) were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

Jimenez, C.↗
NASA NTRS2013

On the Reprocessing and Reanalysis of Observations for Climate

The long observational record is critical to our understanding of the Earths climate, but most observing systems were not developed with a climate objective in mind. As a result, tremendous efforts have gone into assessing and reprocessing the data records to improve their usefulness in climate studies. The purpose of this paper is to both review recent progress in reprocessing and reanalyzing observations, and to summarize the challenges that must be overcome in order to improve our understanding of climate and variability. Reprocessing improves data quality through more scrutiny and improved retrieval techniques for individual observing systems, while reanalysis merges many disparate observations with models through data assimilation, yet both aim to provide an climatology of Earth processes. Many challenges remain, such as tracking the improvement of processing algorithms and limited spatial coverage. Reanalyses have fostered significant research, yet reliable global trends in many physical fields are not yet attainable, despite significant advances in data assimilation and numerical modeling. Oceanic reanalyses have made significant advances in recent years, but will only be discussed here in terms of progress toward integrated Earth system analyses. Climate data sets are generally adequate for process studies and large-scale climate variability. Communication of the strengths, limitations and uncertainties of reprocessed observations and reanalysis data, not only among the community of developers, but also with the extended research community, including the new generations of researchers and the decision makers is crucial for further advancement of the observational data records. It must be emphasized that careful investigation of the data and processing methods are required to use the observations appropriately.

Uncertainty↗
NASA NTRS2014

Impact of Land Surface Initialization Approach on Subseasonal Forecast Skill: a Regional Analysis in the Southern Hemisphere

The authors use a sophisticated coupled land-atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%-20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia.

Precipitation↗
NASA NTRS2015

Sensitivity of Simulated Global Ocean Carbon Flux Estimates to Forcing by Reanalysis Products

Reanalysis products from MERRA, NCEP2, NCEP1, and ECMWF were used to force an established ocean biogeochemical model to estimate air-sea carbon fluxes (FCO2) and partial pressure of carbon dioxide (pCO2) in the global oceans. Global air-sea carbon fluxes and pCO2 were relatively insensitive to the choice of forcing reanalysis. All global FCO2 estimates from the model forced by the four different reanalyses were within 20% of in situ estimates (MERRA and NCEP1 were within 7%), and all models exhibited statistically significant positive correlations with in situ estimates across the 12 major oceanographic basins. Global pCO2 estimates were within 1% of in situ estimates with ECMWF being the outlier at 0.6%. Basin correlations were similar to FCO2. There were, however, substantial departures among basin estimates from the different reanalysis forcings. The high latitudes and tropics had the largest ranges in estimated fluxes among the reanalyses. Regional pCO2 differences among the reanalysis forcings were muted relative to the FCO2 results. No individual reanalysis was uniformly better or worse in the major oceanographic basins. The results provide information on the characterization of uncertainty in ocean carbon models due to choice of reanalysis forcing.

MERRA↗
NASA NTRS2018

Using Reanalysis in Crop Monitoring and Forecasting Systems

Weather observations are essential for crop monitoring and forecasting but they are not always available and in some cases they have limited spatial representativeness. Thus, reanalyses represent an alternative source of information to be explored. In this study, we assess the feasibility of reanalysis-based crop monitoring and forecasting by using the system developed and maintained by the European Commission- Joint Research Centre, its gridded daily meteorological observations, the biased-corrected reanalysis AgMERRA and the ERA-Interim reanalysis. We focus on Europe and on two crops, wheat and maize, in the period 1980-2010 under potential and water-imited conditions. In terms of inter-annual yield correlation at the country scale, the reanalysis-driven systems show a very good performance for both wheat and maize (with correlation values higher than 0.6 in almost all EU28 countries) when compared to the observations-driven system. However, significant yield biases affect both crops. All simulations show similar correlations with respect to the FAO reported yield time series. These findings support the integration of reanalyses in current crop monitoring and forecasting systems and point to the emerging opportunities linked to the coming availability of higher-resolution reanalysis updated at near real time.

Reanalysis↗
NASA NTRS2018

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO S2S Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite Sea Surface Salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (National Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF ( Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive) V4 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by the satellite at approximately 1 centimeter) and in situ measurements (typically measured by Argo floats at 3 meters). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from the GMAO S2S reanalyses that assimilates/withholds along-track (L2) SSS. In particular, we contrast forecasts of the overestimated 2014 El Nino, the big 2015 El Nino, and the minor 2016 La Nina. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrades the mixed layer depth leading to more accurate coupled air/sea interaction.

Hackert, E.↗
NASA NTRS2018

Consequences of Different Air-Sea Feedbacks on Ocean Using MITgcm and MERRA-2 Forcing: Implications for Coupled Data Assimilation Systems

Ocean surface flux estimates from atmospheric and oceanic reanalyses contain errors that compensate for inaccuracies in the respective atmosphere and ocean models used to generate these reanalyses. A conundrum for climate studies is the discrepancy between surface fluxes that minimize model-data differences for an atmosphere-only model vs surface fluxes that minimize model-data differences for an ocean model. As a first step towards a consistent coupled ocean-atmosphere data-assimilation (DA) system, we compare surface net heat flux from a state-of-the-art atmospheric reanalysis, the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to net heat flux from a state-of-the-art ocean state estimate, the Estimating the Circulation and Climate of the Ocean Version 4 (ECCO-v4). The possible impacts of the MERRA-2 and ECCO-v4 air-sea net heat flux difference in a coupled DA system were assessed using a set of experiments designed to imitate different “flavors” of a coupled DA system in an ocean-only setup. This was done by forcing the ECCO-v4 underlying ocean model - the Massachusetts Institute of Technology general circulation model (MITgcm) - with different sets of MERRA-2 fields and utilizing different forcing methods. By doing so we were able to turn off different air-sea feedbacks which, in a coupled DA setup, are partially muted by the constraining observations. The set of experiments, therefore, represents a range of active feedbacks in different “flavors” of coupled data-assimilation systems. For the period 1992–2011, MERRA-2 net heat flux has a global mean difference of -4.9 Wm(exp -2) relative to ECCO-v4. When MERRA-2 surface fields are used to force MITgcm, imbalances in the energy and the hydrological cycles of MERRA-2, which are directly related to the fact that MERRA-2 was created without an interactive ocean, propagate to the ocean. The experiment in which MITgcm is forced with MERRA-2 fluxes (MERRA-2-flux experiment) results in a 2.5°C global mean Sea Surface Temperature (SST) cooling, a 1m reduction in global mean sea level, and other drastic changes in the large scale ocean circulation relative to those resulting when the MITgcm is forced with the optimized ECCO-v4 net heat flux (the ECCO-v4 experiment itself). When MITgcm is forced with MERRA-2 state variables (MERRA-2-state experiment), the SST is somewhat restored to the observed SST, but the errors are shifted to the water cycle, resulting in a global mean sea level increase of 2.7 m. To further explore the pros and cons of these two approaches, we introduce a new intermediate forcing method in which the ocean is forced with turbulent fluxes but has a long wave feedback. This method, unlike MERRA-2 state, preserves the MERRA-2 water and salinity cycles, and it reduces the SST error compared to the MERRA-2-flux experiment, but the SST is not as good as that in the MERRA-2-state experiment. Our results have implications for ocean-model forcing recipes and clearly reveal the undesirable consequences of limiting the feedbacks in either these types of experiments or in coupled DA.

Ehud Strobach↗
NASA NTRS2019

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO Seasonal Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite sea surface salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (North American Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF (Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive satellite) V4.1 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by satellite at approximately 1-centimeter depth) and in situ measurements (typically measured by Argo floats at 3-meters depth). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from GMAO S2S spring reanalyses that assimilate/withhold along-track (L2) SSS. In particular, we contrast forecasts of the big 2015 El Nino, the 2017 La Nina and the 2018 weak El Nino. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrade the mixed layer depth leading to more accurate coupled air/sea interaction. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast forecasts initialized with the benefit of these two satellite SSS observation types. We assess the impact of gridded satellite sea surface salinity observations on dynamical ENSO forecasts for the big 2015 El Nino.

Hackert, E.↗
NASA NTRS2019

The Effects of a 1998 Observing System Change on MERRA-2-Based Ozone Profile Simulations

Model simulations of ozone (O3) driven by meteorological reanalyses are useful for filling observational gaps and interpreting observed O3 variability and trends. However, the transport circulation of reanalysis products is impacted by changes to the observing system (the data assimilated into the reanalyses). We examine the impacts of these changes on simulated O3 from two models, Global Modeling Initiative (GMI) Chemistry Transport Model (GMI CTM) and Modern‐Era Retrospective Analysis for Research Applications version 2 (MERRA‐2) GMI Replay (M2 GMI Replay) simulation, using observations from global ozonesondes (>50,000 profiles) and satellites from 1980 to 2016. Both models are constrained by meteorology from the NASA MERRA‐2 reanalysis, and both use versions of NASA's GMI chemical mechanism. We focus on an observing system change affecting simulated O3 after 1998, associated with the assimilation of temperature and humidity data from new microwave profiling satellites. A large post‐1998 O3 increase, mainly confined to 15–20‐km altitude, of ~10 Dobson units (DU) in midlatitudes occurs in the GMI CTM, worsening the bias compared to observations. In contrast, an increase in M2 GMI Replay simulation O3 of ~10 DU is observed only near −60° latitude, reducing the bias compared to observations. The GMI CTM O3 high biases display a Quasi‐Biennial Oscillation (QBO)‐like periodicity that result from excessive transport from the tropical stratosphere to the midlatitude lower stratosphere during the QBO westerly phase. We quantify O3 discontinuities caused by MERRA‐2 observing system changes and demonstrate how the MERRA‐2 Global Modeling Initiative Replay simulation dampens the effects of these changes and QBO‐driven artifacts on simulated lower stratospheric and total O3. We caution against using simulations driven by a reanalysis to derive multidecadal O3 trends, especially prior to 1998.

Replay↗