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NASA NTRS1995

Technical report series on global modeling and data assimilation. Volume 6: A multiyear assimilation with the GEOS-1 system: Overview and results

The Data Assimilation Office (DAO) at Goddard Space Flight Center has produced a multiyear global assimilated data set with version 1 of the Goddard Earth Observing System Data Assimilation System (GEOS-1 DAS). One of the main goals of this project, in addition to benchmarking the GEOS-1 system, was to produce a research quality data set suitable for the study of short-term climate variability. The output, which is global and gridded, includes all prognostic fields and a large number of diagnostic quantities such as precipitation, latent heating, and surface fluxes. Output is provided four times daily with selected quantities available eight times per day. Information about the observations input to the GEOS-1 DAS is provided in terms of maps of spatial coverage, bar graphs of data counts, and tables of all time periods with significant data gaps. The purpose of this document is to serve as a users' guide to NASA's first multiyear assimilated data set and to provide an early look at the quality of the output. Documentation is provided on all the data archives, including sample read programs and methods of data access. Extensive comparisons are made with the corresponding operational European Center for Medium-Range Weather Forecasts analyses, as well as various in situ and satellite observations. This document is also intended to alert users of the data about potential limitations of assimilated data, in general, and the GEOS-1 data, in particular. Results are presented for the period March 1985-February 1990.

Suarez, Max J.↗

NASA NTRS1999

Parallelization of the Physical-Space Statistical Analysis System (PSAS)

Atmospheric data assimilation is a method of combining observations with model forecasts to produce a more accurate description of the atmosphere than the observations or forecast alone can provide. Data assimilation plays an increasingly important role in the study of climate and atmospheric chemistry. The NASA Data Assimilation Office (DAO) has developed the Goddard Earth Observing System Data Assimilation System (GEOS DAS) to create assimilated datasets. The core computational components of the GEOS DAS include the GEOS General Circulation Model (GCM) and the Physical-space Statistical Analysis System (PSAS). The need for timely validation of scientific enhancements to the data assimilation system poses computational demands that are best met by distributed parallel software. PSAS is implemented in Fortran 90 using object-based design principles. The analysis portions of the code solve two equations. The first of these is the "innovation" equation, which is solved on the unstructured observation grid using a preconditioned conjugate gradient (CG) method. The "analysis" equation is a transformation from the observation grid back to a structured grid, and is solved by a direct matrix-vector multiplication. Use of a factored-operator formulation reduces the computational complexity of both the CG solver and the matrix-vector multiplication, rendering the matrix-vector multiplications as a successive product of operators on a vector. Sparsity is introduced to these operators by partitioning the observations using an icosahedral decomposition scheme. PSAS builds a large (approx. 128MB) run-time database of parameters used in the calculation of these operators. Implementing a message passing parallel computing paradigm into an existing yet developing computational system as complex as PSAS is nontrivial. One of the technical challenges is balancing the requirements for computational reproducibility with the need for high performance. The problem of computational reproducibility is well known in the parallel computing community. It is a requirement that the parallel code perform calculations in a fashion that will yield identical results on different configurations of processing elements on the same platform. In some cases this problem can be solved by sacrificing performance. Meeting this requirement and still achieving high performance is very difficult. Topics to be discussed include: current PSAS design and parallelization strategy; reproducibility issues; load balance vs. database memory demands, possible solutions to these problems.

Larson, J. W.↗

NASA NTRS1999

Middle Atmosphere Transport Properties of Assimilated Datasets

One of the most compelling reasons for performing data assimilation in the middle atmosphere is to obtain global, balanced datasets for studies of trace gas transport and chemistry. This is a major motivation behind the Goddard Earth observation System-Data Assimilation System (GEOS-DAS). Previous studies have shown that while this and other data assimilation systems can generally obtain good estimates of the extratropical rotational velocity field, the divergent part of the dynamical field is deficient; this impacts the "residual circulation" and leads to spurious trace gas transport on seasonal and interannual timescales. These problems are impacted by the quality and the method of use of the observational data and by deficiencies in the atmospheric general circulation model. Whichever the cause at any place and time, the "solution" is to introduce non-physical forcing terms into the system (the so-called incremental analysis updates); these can directly (thermal) or indirectly (mechanical) affect the residual circulation. This paper will illustrate how the divergent circulation is affected by deficiencies in both observations and models. Theoretical considerations will be illustrated with examples from the GEOS-DAS and from simplified numerical experiments. These are designed to isolate known problems, such as the inability of models to sustain a quasi-biennial oscillation and sparse observational constraints on tropical dynamics, or radiative inconsistencies in the presence of volcanic aerosols.

Pawson, Steven↗

NASA NTRS1999

Middle Atmospheric Transport Properties of Assimilated Datasets

Pawson, Steven↗

NASA NTRS1999

Improving Global Reanalyses and Short-Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a 1+1D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the 1+1D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+1D scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSW rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Hou, Arthur Y.↗

NASA NTRS2000

Performance of the GEOS-3/Terra Data Assimilation System in the Northern Stratospheric Winter 1999/2000

As part of NASA's support for the Terra satellite, which became operational in January 2000, the Data Assimilation Office introduced a new version of the GEOS data assimilation system (DAS) in November 1999. This system, GEOS-3/Terra, differs from its predecessor in several ways, notably through an increase in horizontal resolution (from 2-by-2.5 degrees to 1-by-1 degree), a slightly lower upper boundary (0.1 instead of 0.01hPa) with fewer levels (48 as opposed to 70), and substantial changes to the tropospheric physics package. This paper will address the performance of the GEOS-3/Terra DAS in the stratosphere. it focusses on the analyses (produced four times daily) and the five-day forecasts (produced twice daily). These were important for the meteorological support of the SAGE-3 Ozone Loss and Validation Experiment, based in Kiruna, Northern Sweden, in the winter of 1999/2000. It is shown that the analyses of basic meteorological fields (temperature, geopotential height, and horizontal wind) are in good agreement with those from other centers. The analyses captured the cold polar vortex which persisted through most of the winter. It is shown that forecasts (up to five days) tend to have a warm bias, which is important for the prediction of polar stratospheric clouds, which are triggered by temperatures of 195K (or lower). The importance of accurate upper tropospheric forecasts in predicting the stratospheric flow is highlighted in the context of the evolution of the shape of the stratospheric polar vortex. A prominent blocking high in the Atlantic region in January was an important factor determining the shape of the distorted lower stratospheric vortex; the predictive skill of these features was strongly coupled in the GEOS-3/Terra system.

Pawson, S.↗

NASA NTRS2000

Improving Global Reanalyses and Short Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a "1+1"D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the "1+1"D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+lD scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSM/I rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Hou, Arthur Y.↗

NASA NTRS2000

Using TRMM Field Campaign Data for Assessing GEOS Forecast and Assimilation Products

The wealth of in-situ measurements gathered during Tropical Rain Measuring Mission (TRMM) field campaigns over a wide range of tropical conditions constitute an important data source for evaluating the quality of global model forecasts and assimilated datasets. In this study we use selected observations of cloud microphysics and atmospheric sounding from TEFLUN-1998, SCEMEX-1998, and TRMMLBA-1999 to examine the assimilation and forecast fields produced by the operational GEOS-3 (Goddard Earth Observing System - version 3) global data assimilation system (DAS) and a new finite-volume DAS under development at the Data Assimilation Office. Additionally, TRMM field campaign measurements are used to verify the impact of assimilating rainfall and moisture data derived from TRMM Microwave Imager and Special Sensor Microwave/Imager instruments on the GEOS analysis. We will also explore issues concerning the 'error of representativeness' in using in-situ observations of quantities with large spatial and temporal variability such as precipitation for validating gridded global data products.

Hou, Arthur Y.↗

NASA NTRS2001

The Impact of British Airways Wind Observations on the Goddard Earth Observing System Analyses and Forecasts

British Airways flight data recorders can provide valuable meteorological information, but they are not available in real-time on the Global Telecommunication System. Information from the flight recorders was used in the Global Aircraft Data Set (GADS) experiment as independent observations to estimate errors in wind analyses produced by major operational centers. The GADS impact on the Goddard Earth Observing System Data Assimilation System (GEOS DAS) analyses was investigated using GEOS-1 DAS version. Recently, a new Data Assimilation System (fvDAS) has been developed at the Data Assimilation Office, NASA Goddard. Using fvDAS , the, GADS impact on analyses and forecasts was investigated. It was shown the GADS data intensify wind speed analyses of jet streams for some cases. Five-day forecast anomaly correlations and root mean squares were calculated for 300, 500 hPa and SLP for six different areas: Northern and Southern Hemispheres, North America, Europe, Asia, USA These scores were obtained as averages over 21 forecasts from January 1998. Comparisons with scores for control experiments without GADS showed a positive impact of the GADS data on forecasts beyond 2-3 days for all levels at the most areas.

Rukhovets, Leonid↗

NASA NTRS2002

Regional Data Assimilation Using a Stretched-Grid Approach and Ensemble Calculations

The global variable resolution stretched grid (SG) version of the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) incorporating the GEOS SG-GCM (Fox-Rabinovitz 2000, Fox-Rabinovitz et al. 2001a,b), has been developed and tested as an efficient tool for producing regional analyses and diagnostics with enhanced mesoscale resolution. The major area of interest with enhanced regional resolution used in different SG-DAS experiments includes a rectangle over the U.S. with 50 or 60 km horizontal resolution. The analyses and diagnostics are produced for all mandatory levels from the surface to 0.2 hPa. The assimilated regional mesoscale products are consistent with global scale circulation characteristics due to using the SG-approach. Both the stretched grid and basic uniform grid DASs use the same amount of global grid-points and are compared in terms of regional product quality.

Fox-Rabinovitz, M. S.↗

NASA NTRS2002

Sensitivity of Middle Atmospheric Analyses to the Representation of Gravity-Wave Drag in the DAO's Data Assimilation System

The damping of mesoscale gravity waves has important effects on the global circulation, structure, and composition of the atmosphere. A number of assimilation and forecast experiments have been conducted to examine the sensitivity of meteorological analyses and forecasts to the representation of gravity wave impacts in a data assimilation system (DAS). The experiments were conducted with the Finite-Volume (FV) DAS developed at NASA's Data Assimilation Office (DAO), The main purpose of this research is to determine the optimal combination of wave number, phase speed, wavelength, etc. for representing gravity-wave drag (GWD) in FVDAS. The GWD included in FVDAS includes a spectrum of waves, as would be forced by topography and transient motions (e.g., convection) in the troposphere. The sensitivity experiments are performed by modifying several parameters, such as the number of waves allowed, their wavelength, the background stress amplitude, etc. The results show that the assimilated fields are very sensitive to the number of gravity waves represented in the system, especially at high latitudes of the middle and upper stratosphere and mesosphere in winter. The analyzed stratopause temperature varies by up to 10K when the GWD scheme is modified from a multiple-wave scheme (using a stationary wave and waves with phase speeds of 10, 20, 30 and 40 m/s in each direction) to a single, stationary wave. Insight into the reality of the various versions of the GWD can be obtained by examining the "Observation minus Forecast" residuals from the FVDAS.

Li, Shuhua↗

NASA NTRS2002

The GEOS Retrospective Data Assimilation System: The 6-hour lag case

The fixed-lag Kalman smoother (FLKS) has been proposed as a framework to construct data assimilation procedures capable of producing high-quality climate research datasets. Fixed-lag Kalman smoother-based systems, referred to as retrospective data assimilation systems, are an extension to three-dimensional filtering procedures with the added capability of incorporating observations not only in the past and present time of the estimate, but also at future times. A variety of simplifications are necessary to render retrospective assimilation procedures practical. In this article, we present an FLKS-based retrospective data assimilation system implementation for the Goddard Earth Observing System (GOES) Data Assimilation System (DAS). The practicality of this implementation comes from the practicality of its underlying (filter) analysis system, i.e., the physical-space statistical analysis system (PSAS). The behavior of two schemes is studied here. The first retrospective analysis (RA) scheme is designed simply to update the regular PSAS analyses with observations available at times ahead of the regular analysis times. Although our GEOS DAS implementation is general, results are only presented for when observations 6-hours ahead of the analysis time are used to update the PSAS analyses and thereby to calculate the so-called lag-1 retrospective analyses. Consistency tests for this RA scheme show that the lag-1 retrospective analyses indeed have better 6-hour predictive skills than the predictions from the regular analyses. This motivates the introduction of the second retrospective analysis scheme which, at each analysis time, uses the 6-hour retrospective analysis to replace the first-guess normally used in the PSAS analysis, and therefore allows the calculation of a revised (filter) PSAS analysis. Since in this scheme the lag-1 retrospective analyses influence the filter results, this procedure is referred to as the retrospective-based iterative analysis (RIA) scheme. Results from the RIA scheme indicate its potential for improving the overall quality of the assimilation.

Zhu, Yan-Qiu↗

NASA NTRS2002

Climatology, Natural Cycles, and Modes of Interannual Variability of the Great Plains Low-Level Jet as Assimilated by the GEOS-1 Data Analysis System

Despite the fact that the low-level jet of the southern Great Plains (the GPLLJ) of the U.S. is primarily a nocturnal phenomenon that virtually vanishes during the daylight hours, it is one of the most persistent and stable features of the low-level continental flow during the warm-season months, May through August. We have first used significant-level data to validate the skill of the GEOS-1 Data Assimilation System (DAS) in realistically detecting this jet and inferring its structure and evolution. We have then carried out a 15-year reanalysis with the GEOS-1 DAS to determine and validate its climatology and mean diurnal cycle and to study its interannual variability. Interannual variability of the GPLLJ is much smaller than mean diurnal and random intraseasonal variability and comparable in magnitude, but not location, to mean seasonal variability. There are three maxima of interannual low-level meridional flow variability of the GPLLJ over the upper Great Plains, southeastern Texas, and the western Gulf of Mexico. Cross-sectional profiles of mean southerly wind through the Texas maximum remain relatively stable and recognizable from year to year with only its eastward flank showing significant variability. This variability, however, exhibits a distinct, biennial oscillation during the first six years of the reanalysis period and only then. Each of the three variability maxima corresponds to a spatially coherent, jet-like pattern of low-level flow interannual variability. There are three prominent modes of interannual. variability. These include the intermittent biennial oscillation (IBO), local to the Texas maximum. Its signal is evident in surface pressure, surface temperature, ground wetness and upper air flow, as well. A larger-scale continental convergence pattern (CCP) of covariance, exhibiting strong anti-correlation between the flow near the Texas and the upper Great Plains variability maxima, is revealed only when the IBO is removed from the interannual time series. A third, subtropical mode of covariance is associated with the Gulf of Mexico variability maximum. Significant interannual anti-correlations of the southeasterly flow over the Arizona/New Mexico region with the CCP and the subtropical mode are enhanced when restricted to the month of July. These anti-correlations may relate to an observed out-of-phase precipitation relationship between the Great Plains and the southwestern U.S.. The typical duration of interannual low-level meridional wind anomalies within a given season increases over the continent with decreasing latitude from two to three weeks over the upper Great Plains to six to seven weeks over eastern Texas.

Helfand, H. M.↗

NASA NTRS2002

Assimilation Experiments of One-Dimensional Variational Analyses with GPS/MET Refractivity

The GPS/MET 1995 experiment demonstrated the feasibility of remote soundings of the Earth's atmosphere using the GPS radio occultation technique. Subsequent studies assessed the information content of such measurements. Several groups have attempted to assimilate the GPS radio occultation measurements into a global Data Assimilation System (DAS). These attempts followed in general the methodology proposed by Eyre, and range from die act four-dimensional variational assimilation of bending angles to assimilation of inverted profiles of temperature and/or humidity. We present here an hybrid approach. We attempted to take advantage of the accuracy of current, weather models to constrain one-dimensional variational analyses using GPS refractivity. Retrieved profiles of atmospheric parameters were then assimilated like other types of observations in a global DAS for issuing the next forecast period.

Poli, Paul↗

NASA NTRS2002

Configuration and Intraseasonal Duration of Interannual Anomalies of the Great Plains Low-Level Jet

Despite the fact that the low-level jet of the southern Great Plains (the GPLLJ) of the U.S. is primarily a nocturnal phenomenon that virtually vanishes during the daylight hours, it is one of the most persistent and stable climatological features of the low-level continental flow during the warm-season months, May through August. We have used significant-level data to validate the skill of the GEOS-1 Data Assimilation System (DAS) in realistically detecting this jet and inferring its structure and evolution. We have then carried out a 15-year reanalysis with the GEOS-1 DAS to determine its climatology and mean diurnal cycle and to study its interannual variability. Interannual anomalies of the meridional flow associated with the GPLLJ are much smaller than the mean diurnal fluctuations, than random intraseasonal anomalies, and than the mean wind itself. There are three maxima of low-level meridional flow variance over the Great Plains and the Gulf of Mexico: a 1.2 m2 s-2 peak over the southeast Texas, to the east and south of the mean velocity peak, a 1.0 m2 s-2 peak over the western Gulf of Mexico, and a .8 m2 s-2 peak over the upper Great Plains (UGP), near the Nebraska/South Dakota border. Each of the three variance maxima corresponds to a spatially coherent, jet-like pattern of low-level flow interannual variability. There are also three dominant modes of interannual variability corresponding to the three variance maxima, but not in a simple one-to-one relationship. Cross-sectional profiles of mean southerly wind over Texas remain relatively stable and recognizable from year to year with only its eastward flank showing significant variability. This variability, however, exhibits a distinct, biennial oscillation during the first six to seven years of the reanalysis period and only then. This intermittent biennial oscillation (IBO, one of the three modes discussed in the previous paragraph) in the lowlevel flow is restricted to the region surrounding eastern Texas and is also evident in the NCEP/NCAR reanalysis data set from about 1978 to 1985 or 1986 and again from 1995 to 2000. It is evident as well in surface pressure in both the GEOS-1 and NCEP/NCAR sets. The interannual anomalies do not necessarily persist uniformly throughout an entire season, but can fluctuate from one part of the season to the next. To estimate the characteristic sub-seasonal time scales for coherence of these fluctuations, we have taken the weekly anomaly of low-level wind at each point of the domain from the climatological average for that given point and that given week of the season and computed the covariance of its fluctuations over all weeks and over all years with the weekly climatological anomaly of the meridional wind at each of the three reference points discussed above. The typical duration of a coherent interannual anomaly within a given warm season increases with decreasing latitude from 2 to 3 weeks over the UGP, to 6 to 7 weeks over eastern Texas. Coherence over the western Gulf of Mexico is intermediate between the two with a typical duration of 4 to 5 weeks. There appears to be evidence that the interannual anomalies over Texas the Gulf propagate to the UGP after a week and those over the Gulf propagate there after 2 to 3 weeks. There also appears to be some reverse propagation of interannual anomalies over the UGP to Texas and to the Gulf after a period of about one week. The interannual anomalies in southerly flow over eastern Texas seem to correlate well with interannual anomalies of surface temperature and (negative) ground wetness and over western Texas.

Helfand, H. M.↗

NASA NTRS2003

A Global Aerosol Model Forecast for the ACE-Asia Field Experiment

We present the results of aerosol forecast during the Aerosol Characterization Experiment (ACE-Asia) field experiment in spring 2001, using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and the meteorological forecast fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The aerosol model forecast provides direct information on aerosol optical thickness and concentrations, enabling effective flight planning, while feedbacks from measurements constantly evaluate the model, making successful model improvements. We verify the model forecast skill by comparing model predicted total aerosol extinction, dust, sulfate, and SO2 concentrations with those quantities measured by the C-130 aircraft during the ACE-Asia intensive operation period. The GEOS DAS meteorological forecast system shows excellent skills in predicting winds, relative humidity, and temperature for the ACE-Asia experiment area as well as for each individual flight, with skill scores usually above 0.7. The model is also skillful in forecast of pollution aerosols, with most scores above 0.5. The model correctly predicted the dust outbreak events and their trans-Pacific transport, but it constantly missed the high dust concentrations observed in the boundary layer. We attribute this missing dust source to the desertification regions in the Inner Mongolia Province in China, which have developed in recent years but were not included in the model during forecasting. After incorporating the desertification sources, the model is able to reproduce the observed high dust concentrations at low altitudes over the Yellow Sea. Two key elements for a successful aerosol model forecast are correct source locations that determine where the emissions take place, and realistic forecast winds and convection that determine where the aerosols are transported. We demonstrate that our global model can not only account for the large-scale intercontinental transport, but also produce the small-scale spatial and temporal variations that are adequate for aircraft measurements planning.

Chin, Mian↗

NASA NTRS2002

The Effect of Transport and Circulation Differences on Stratospheric Ozone Recovery in Two 35-year Three-dimensional Simulations

The NASA Global Modeling Initiative (GMI) has completed two 35-year simulations with WMO future baseline boundary conditions that simulate increasing N2O and CH4 emissions and decreasing organic chlorine and bromine emissions. Simulations were done with the GMI offline chemistry and transport model using 1) 1 year of winds from the Finite-Volume General Circulation Model (FV-GCM), repeated for the 35 years, and 2) 1 year of winds from the Finite-Volume Data Assimilation System (FV-DAS), repeated for 35-years. The simulations have full stratospheric chemistry. To understand differences in simulated ozone recoveries, basic transport and circulation differences between these models are evaluated. The distribution of mean age of stratospheric air in the FV-GCM run agrees well with observations in the lower stratosphere but the FV-DAS ages are generally too low. This implies circulation and mixing differences that will affect the distributions of other trace species such as CH4, NO, and the organic halogens, all of which are responding to changing boundary conditions and are involved in ozone loss. Realism of model transport is evaluated, with particular attention given to regions and seasons where ozone recovery is expected. Preliminary results indicate increasing ozone trends in the lowermost stratosphere in summer and in the Antarctic and Arctic lower stratosphere in winter and spring.

Bhartia, P. K.↗

NASA NTRS2003

Evaluating the Credibility of Transport Processes in the Global Modeling Initiative 3D Model Simulations of Ozone Recovery

The Global Modeling Initiative has integrated two 35-year simulations of an ozone recovery scenario with an offline chemistry and transport model using two different meteorological inputs. Physically based diagnostics, derived from satellite and aircraft data sets, are described and then used to evaluate the realism of temperature and transport processes in the simulations. Processes evaluated include barrier formation in the subtropics and polar regions, and extratropical wave-driven transport. Some diagnostics are especially relevant to simulation of lower stratospheric ozone, but most are applicable to any stratospheric simulation. The temperature evaluation, which is relevant to gas phase chemical reactions, showed that both sets of meteorological fields have near climatological values at all latitudes and seasons at 30 hPa and below. Both simulations showed weakness in upper stratospheric wave driving. The simulation using input from a general circulation model (GMI(sub GCM)) showed a very good residual circulation in the tropics and northern hemisphere. The simulation with input from a data assimilation system (GMI(sub DAS)) performed better in the midlatitudes than at high latitudes. Neither simulation forms a realistic barrier at the vortex edge, leading to uncertainty in the fate of ozone-depleted vortex air. Overall, tracer transport in the offline GMI(sub GCM) has greater fidelity throughout the stratosphere than the GMI(sub DAS).

Strahan, Susan E.↗