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Geopotential heights of constant pressure surfaces are retrieved from GPS radio occultation data. In order to assess accuracy, a subset of data obtained by GPS/MET during spring and summer 1995 are compared to the output of the ECMWF global model...
The global carbon cycle is experiencing continued perturbations via increases in atmospheric carbon concentrations, which are partly reduced by terrestrial biosphere and ocean carbon uptake. Greenhouse gas satellites have been shown to be useful in retrieving atmospheric carbon concentrations and observing surface and atmospheric CO2 seasonal-to-interannual variations. However, limited attention has been placed on using satellite column CO2 retrievals to evaluate surface CO2 fluxes from the terrestrial biosphere without advanced inversion models at low latency. Such applications could be useful to monitor, in near real time, biosphere carbon fluxes during climatic anomalies like drought, heatwaves, and floods, before more complex terrestrial biosphere model outputs and/or advanced inversion modelling estimates become available. Here, we explore the ability of Orbiting Carbon Observatory-2 (OCO-2) column-averaged dry air CO2 (XCO2) retrievals to directly detect and estimate terrestrial biosphere CO2 flux anomalies using a simple mass-balance approach. An initial global analysis of surface–atmospheric CO2 coupling and transport conditions reveals that the western US, among a handful of other regions, is a feasible candidate for using XCO2 for detecting terrestrial biosphere CO2 flux anomalies. Using the CarbonTracker model reanalysis as a test bed, we first demonstrate that a well-established mass-balance approach can estimate monthly surface CO2 flux anomalies from XCO2 enhancements in the western United States. The method is optimal when the study domain is spatially extensive enough to account for atmospheric mixing and has favorable advection conditions with contributions primarily from one background region. We find that errors in individual soundings reduce the ability of OCO-2 XCO2 to estimate more frequent, smaller surface CO2 flux anomalies. However, we find that OCO-2 XCO2 can often detect and estimate large surface flux anomalies that leave an imprint on the atmospheric CO2 concentration anomalies beyond the retrieval error/uncertainty associated with the observations. OCO-2 can thus be useful for low-latency monitoring of the monthly timing and magnitude of extreme regional terrestrial biosphere carbon anomalies.
The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud-climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions 'How does the Earth system respond to forcing?' and 'What are the origins and consequences of systematic model biases?' and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity. A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming? CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. 1. How well do clouds and other relevant variables simulated by models agree with observations? 2. What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models? 3. Which models have the most credible representations of processes relevant to the simulation of clouds? 4. How do clouds and their changes interact with other elements of the climate system?
For given climatic rates of precipitation and potential evaporation, the land surface hydrology parameterizations of atmospheric general circulation models will maintain soil-water storage conditions that balance the moisture input and output. The surface relative soil saturation for such climatic conditions serves as a measure of the land surface parameterization state under a given forcing. The equilibrium value of this variable for alternate parameterizations of land surface hydrology are determined as a function of climate and the sensitivity of the surface to shifts and changes in climatic forcing are estimated.
Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.
Actual observations of the solar spectral irradiance are extremely limited on climate time scales; therefore, various empirical models use solar proxies to reconstruct the actual output of the Sun over long time scales. The Flare Irradiance Spectral Model (FISM) is an empirical model of the solar irradiance spectrum from 0.1 to 190 nm at 1 nm spectral resolution and on a I-minute time cadence. The goal of FISM is to provide accurate solar spectral irradiances over the vacuum ultraviolet (VUV: 0-200 nm) range as input for ionospheric and thermospheric. A brief overview of the proxies used in the FISM model will be given, and also discussed is how the Solar Dynamics Observatory (SDO) EUV Variability Experiment (EVE) will contribute to improving FISM estimates and its accuracies. Also presented will be a discussion of other solar irradiance proxies and measurements, and their associated uncertainties, used for solar spectral reconstructions.
A multivariate statistical clustering technique--based on the k-means algorithm of Hartigan has been used to extract patterns of climatological significance from 200 years of general circulation model (GCM) output. Originally developed and implemented on a Beowulf-style parallel computer constructed by Hoffman and Hargrove from surplus commodity desktop PCs, the high performance parallel clustering algorithm was previously applied to the derivation of ecoregions from map stacks of 9 and 25 geophysical conditions or variables for the conterminous U.S. at a resolution of 1 sq km. Now applied both across space and through time, the clustering technique yields temporally-varying climate regimes predicted by transient runs of the Parallel Climate Model (PCM). Using a business-as-usual (BAU) scenario and clustering four fields of significance to the global water cycle (surface temperature, precipitation, soil moisture, and snow depth) from 1871 through 2098, the authors' analysis shows an increase in spatial area occupied by the cluster or climate regime which typifies desert regions (i.e., an increase in desertification) and a decrease in the spatial area occupied by the climate regime typifying winter-time high latitude perma-frost regions. The patterns of cluster changes have been analyzed to understand the predicted variability in the water cycle on global and continental scales. In addition, representative climate regimes were determined by taking three 10-year averages of the fields 100 years apart for northern hemisphere winter (December, January, and February) and summer (June, July, and August). The result is global maps of typical seasonal climate regimes for 100 years in the past, for the present, and for 100 years into the future. Using three-dimensional data or phase space representations of these climate regimes (i.e., the cluster centroids), the authors demonstrate the portion of this phase space occupied by the land surface at all points in space and time. Any single spot on the globe will exist in one of these climate regimes at any single point in time. By incrementing time, that same spot will trace out a trajectory or orbit between and among these climate regimes (or atmospheric states) in phase (or state) space. When a geographic region enters a state it never previously visited, a climatic change is said to have occurred. Tracing out the entire trajectory of a single spot on the globe yields a 'manifold' in state space representing the shape of its predicted climate occupancy. This sort of analysis enables a researcher to more easily grasp the multivariate behavior of the climate system.
Practitioners of climate change prediction base many of their future climate scenarios on General Circulation Models (GCM's), each model with differing assumptions and parameter requirements. For representing the atmosphere, GCM's typically contain equations for calculating motion of particles, thermodynamics and radiation, and continuity of water vapor. Hydrology and heat balance are usually included for continents, and sea ice and heat balance are included for oceans. The current issue of this journal contains a paper by Van Blarcum et al. (1995) that predicts runoff from nine high-latitude rivers under a doubled CO2 atmosphere. The paper is important since river flow is an indicator variable for climate change. The authors show that precipitation will increase under the imposed perturbations and that owing to higher temperatures earlier in the year that cause the snow pack to melt sooner, runoff will also increase. They base their simulations on output from a GCM coupled with an interesting water routing scheme they have devised. Climate change models have been linked to other models to predict deforestation.
This paper describes the datasets that were used to implement an AgMIP Regional Integrated Assessment for the Nioro region of Senegal to assess the potential impacts of climate change on the principal agricultural system in the Senegal peanut basin and to assess adaptations of that system to climate change under current as well as future climate and socio-economic conditions. This dataset includes the Representative Agricultural Pathways developed for Nioro from 2000-2050; the climate data that were used to implement crop yield simulations; the data that were used to parameterize the DSSAT and APSIM crop models, including historical climate data and future climate scenarios; and the data that were used to parameterize the Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOAMD) economic simulation model, as well as simulated model outputs.
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional trends in temperature, precipitation, and other climate characteristics and have been one of the dominant drivers of decadal trends in Asian and African precipitation. These and other influences on regional climate from changes in aerosol emissions are expected to continue and potentially strengthen in the coming decades. However, a combination of large uncertainties in emission pathways, radiative forcing, and the dynamical response to forcing makes anthropogenic aerosol a key factor in the spread of near-term climate projections, particularly on regional scales, and therefore an important one to constrain. For example, in terms of future emission pathways, the uncertainty in future global aerosol and precursor gas emissions by 2050 is as large as the total increase in emissions since 1850. In terms of aerosol effective radiative forcing, which remains the largest source of uncertainty in future climate change projections, CMIP6 models span a factor of 5, from −0.3 to −1.5 W m−2. Both of these sources of uncertainty are exacerbated on regional scales. The Regional Aerosol Model Intercomparison Project (RAMIP) will deliver experiments designed to quantify the role of regional aerosol emissions changes in near-term projections. This is unlike any prior MIP, where the focus has been on changes in global emissions and/or very idealised aerosol experiments. Perturbing regional emissions makes RAMIP novel from a scientific standpoint and links the intended analyses more directly to mitigation and adaptation policy issues. From a science perspective, there is limited information on how realistic regional aerosol emissions impact local as well as remote climate conditions. Here, RAMIP will enable an evaluation of the full range of potential influences of realistic and regionally varied aerosol emission changes on near-future climate. From the policy perspective, RAMIP addresses the burning question of how local and remote decisions affecting emissions of aerosols influence climate change in any given region. Here, RAMIP will provide the information needed to make direct links between regional climate policies and regional climate change. RAMIP experiments are designed to explore sensitivities to aerosol type and location and provide improved constraints on uncertainties driven by aerosol radiative forcing and the dynamical response to aerosol changes. The core experiments will assess the effects of differences in future global and regional (Africa and the Middle East, East Asia, North America and Europe, and South Asia) aerosol emission trajectories through 2051, while optional experiments will test the nonlinear effects of varying emission locations and aerosol types along this future trajectory. All experiments are based on the shared socioeconomic pathways and are intended to be performed with 6th Climate Model Intercomparison Project (CMIP6) generation models, initialised from the CMIP6 historical experiments, to facilitate comparisons with existing projections. Requested outputs will enable the analysis of the role of aerosol in near-future changes in, for example, temperature and precipitation means and extremes, storms, and air quality.
Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single-moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a midlatitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.
Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. The combination of reliable cloud microphysics and radar reflectivity may constrain radiative transfer models used in satellite simulators during future missions, including EarthCARE and the NASA Global Precipitation Measurement. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a mid latitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.
We present results from several connected efforts to apply machine learning methods to estimates of seasonal agricultural production anomalies around the world. First, we apply the XGBoost Random Forest method to fit emulators that mimic global crop models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI). These are the same models used in the agricultural sector simulations of the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). These emulators use 8 climate variables split across 5 sub-seasonal representations of the growing season for each ½ degree grid cell around the world for maize, wheat, rice and soybeans. Emulators are useful for estimating conditions that have not already been simulated by GGCMI (e.g., in a seasonal prediction model) and also to diagnose model differences and capabilities. For example, emulators of the pDSSAT maize model tend to be more reliant on mean temperatures than the LPJmL model, and few models have strong responses to cold extremes. Second, we use a similar XGBoost approach to fit empirical models for national production data for the top 20 producing countries according to the United Nations Food and Agricultural Organization (FAO). Models utilize both climate observations and the GGCM models as predictors, resulting in skillful models for many (but not all) top producing-countries. The patterns of climate and crop model features selected indicate regions and systems that are better or worse simulated by the GGCMs. For example, information in cold extreme predictors is often combined with GGCM output predictors to provide sensitivity that models may underrepresent.
Agriculture in Sub-Saharan Africa (SSA) is experiencing climate change-related effects that call for integrated regional assessments, yet capacity for these assessments has been low. The Agricultural Model Intercomparison and Improvement Project (AgMIP) is advancing research on integrated regional assessments of climate change that include climate, crop, and economic modeling and analysis. Through AgMIP, regional integrated assessments are increasingly gaining momentum in SSA, and multi-institutional regional research teams (RRTs) centered in East, West, and Southern· Africa are generating new information on climate change impacts and adaptation in selected agricultural systems. The research in Africa is organized into four RRTs and a coordination team. Each of the RRTs in SSA is composed of scientists from the Consultative Group of International Agricultural Research (CGIAR) institutions, National Agriculture Research institutes (NARs), and universities consisting of experts in crop and economic modeling, climate, and information technology. Stakeholder involvement to inform specific agricultural systems to be evaluated, key outputs, and the representative agricultural pathways (RAPs), is undertaken at two levels: regional and national, in order to contribute to decision making at these levels. Capacity building for integrated assessment (lA) is a key component that is undertaken continuously through interaction with experts in regional and SSA-wide workshops, and through joint creation of tools. Many students and research affiliates have been identified and entrained as part of capacity building in IA. Bi-monthly updates on scholarly publications in climate change in Africa also serve as a vehicle for knowledge-sharing. With 60 scientists already trained and actively engaged in IA and over 80 getting monthly briefs on the latest information on climate change, a climate-informed community of experts is gradually taking shape in SSA. (See Part 2, Appendices 3-5 in this volume for AgMIP Regional Workshop reports.)
Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution in response to different climate scenarios and assess the mass loss that would contribute to future sea level rise. However, there is currently no consensus on estimates of the future mass balance of the ice sheet, primarily because of differences in the representation of physical processes, forcings employed and initial states of ice sheet models. This study presents results from ice flow model simulations from 13 international groups focusing on the evolution of the Antarctic ice sheet during the period 2015–2100 as part of the Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climate model results. Simulations of the Antarctic ice sheet contribution to sea level rise in response to increased warming during this period varies between -7:8 and 30.0 cm of sea level equivalent (SLE) under Representative Concentration Pathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment with constant climate conditions and should therefore be added to the mass loss contribution under climate conditions similar to present-day conditions over the same period. The simulated evolution of the West Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica ass change varies between -6.1 and 8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighing the increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelf collapse, here assumed to be caused by large amounts of liquid water ponding at the surface of ice shelves, yields an additional simulated mass loss of 28mm compared to simulations without ice shelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, the calibration of these melt rates based on oceanic conditions taken outside of ice shelf cavities and the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario based on two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared to simulations done under present-day conditions for the two CMIP5 forcings used and display limited mass gain in East Antarctica.
Climate and environments of the mid-Pliocene warm period (3.264 to 3.025 Ma) have been extensively studied.Whilst numerical models have shed light on the nature of climate at the time, uncertainties in their predictions have not been systematically examined. The Pliocene Model Intercomparison Project quantifies uncertainties in model outputs through a coordinated multi-model and multi-mode data intercomparison. Whilst commonalities in model outputs for the Pliocene are clearly evident, we show substantial variation in the sensitivity of models to the implementation of Pliocene boundary conditions. Models appear able to reproduce many regional changes in temperature reconstructed from geological proxies. However, data model comparison highlights that models potentially underestimate polar amplification. To assert this conclusion with greater confidence, limitations in the time-averaged proxy data currently available must be addressed. Furthermore, sensitivity tests exploring the known unknowns in modelling Pliocene climate specifically relevant to the high latitudes are essential (e.g. palaeogeography, gateways, orbital forcing and trace gasses). Estimates of longer-term sensitivity to CO2 (also known as Earth System Sensitivity; ESS), support previous work suggesting that ESS is greater than Climate Sensitivity (CS), and suggest that the ratio of ESS to CS is between 1 and 2, with a "best" estimate of 1.5.
We have evaluated two methods of simulating the seasonal cycle of snow over sea ice in and around the Arctic: The NCAR global climate model CCM3, with its standard snow hydrology, and the snow pack model SNTHERM, forced with hourly atmospheric output from CCM3. A new dataset providing dates for the onset of snow melt over Arctic sea ice provides a means for assessing basin-wide how well the models simulate melt onset, but contains no information on how long it then takes for all the snow to melt. Use of data from the SHEBA site provides very detailed information on the behavior of the snow before and during the melt season, but only for a very limited area. Russian drift data provide climatological data on the seasonal cycle of snow water equivalent and snow density, over multi-year sea ice in the central Arctic basin. These datasets are used to compare the two modeling methods, and to see if use of the more physically-realistic SNTHERM provides any significant improvements. Conclusions obtained so far include: 1. Both CCM3 and CCM3/SNTHERM do a good job overall of matching the onset of snow melt dataset; although CCM3/SNTHERM consistently trends to underestimate the date and CCM3 to overestimate it. 2. SHEBA and ice drift data for the Arctic show that CCM3/ SNTHERM does a better job than CCM3 at simulating the total melt period. 3. Ice drift snow density and accumulation data suggest that while providing superior results, CCM3/SNTHERM may still suffer from overly vigorous melting. 4. Both the large-scale atmospheric forcing and snow pack physical processes are important in proper simulation of the snow seasonal cycle. Ongoing work includes further diagnosis of CCM3/SNTHERM, use of more observational datasets, especially from marginal seas in the pan-Arctic, and full coupling of SNTHERM into CCM3 (work to date has all been off-line simulations).
Reference or standard atmospheric models have long been used for design and mission planning of various aerospace systems. The NASA/Marshall Space Flight Center (MSFC) Global Reference Atmospheric Model (GRAM) was developed in response to the need for a design reference atmosphere that provides complete global geographical variability, and complete altitude coverage (surface to orbital altitudes) as well as complete seasonal and monthly variability of the thermodynamic variables and wind components. A unique feature of GRAM is that, addition to providing the geographical, height, and monthly variation of the mean atmospheric state, it includes the ability to simulate spatial and temporal perturbations in these atmospheric parameters (e.g. fluctuations due to turbulence and other atmospheric perturbation phenomena). A summary comparing GRAM features to characteristics and features of other reference or standard atmospheric models, can be found Guide to Reference and Standard Atmosphere Models. The original GRAM has undergone a series of improvements over the years with recent additions and changes. The software program is called Earth-GRAM2007 to distinguish it from similar programs for other bodies (e.g. Mars, Venus, Neptune, and Titan). However, in order to make this Technical Memorandum (TM) more readable, the software will be referred to simply as GRAM07 or GRAM unless additional clarity is needed. Section 1 provides an overview of the basic features of GRAM07 including the newly added features. Section 2 provides a more detailed description of GRAM07 and how the model output generated. Section 3 presents sample results. Appendices A and B describe the Global Upper Air Climatic Atlas (GUACA) data and the Global Gridded Air Statistics (GGUAS) database. Appendix C provides instructions for compiling and running GRAM07. Appendix D gives a description of the required NAMELIST format input. Appendix E gives sample output. Appendix F provides a list of available parameters to enable the user to generate special output. Appendix G gives an example and guidance on incorporating GRAM07 as a subroutine in other programs such as trajectory codes or orbital propagation routines.