Volume Two, Number One
Summer 1998

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GAIM Project Scientist

GAIM is joined by Dr. Kathy Hibbard, who arrived this Spring as the GAIM Project Scientist. She comes to us from the University of Montana where she worked in a post-doctoral position with Steve Running on his Biome-BGC ecosystem model and the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP).

Kathy received her B.S. and M.S. at Colorado State University. She worked at NASA/Ames Research Center on projects that involved the application of remotely sensed data to ecological questions before moving to Texas A&M where she worked with Steve Archer and Dave Schimel on her Ph.D. entitled: "Landscape Patterns of Carbon and Nitrogen Dynamics in a Subtropical Savanna: Observations and Models." During her studies, she received the NASA Graduate Student Research Program Fellowship, as well as the Sid Kyle Rangeland Ecology Fellowship. Kathy has published papers on a broad range of topics including ecological applications for remotely sensed data, avian energetics, and contributed to VEMAP-Phase I papers.

Kathy holds a position of Research Scientist at the University of New Hampshire, and in addition to her responsibilities with GAIM modelling and data efforts, she continues to explore and publish her own independent research in ecosystem science and modelling.

Her experience in terrestrial ecosystems makes it natural for Kathy to take an active role in the Gross Primary Production Data Initiative (GPPDI) and the next phase of our carbon model intercomparison activity, the Ecosystem Model-Data Intercomparison (EMDI). She is also involved in the Transcom chemical atmospheric transport project, the Fluxnet program, and is working to link these various efforts in order to facilitate coupled earth-system model development.

We are pleased to welcome Kathy to GAIM and look forward to working with her during this important phase of GAIM synthesis and integration of IGBP research!


New GAIM PLAN

by Dork Sahagian and Berrien Moore III

The GAIM Task Force, in conjunction with the GAIM Office, the Core Projects, and the SC-IGBP has formulated a new GAIM PLAN to meet the needs of the IGBP research community as it evolves with the generation of new scientific results. While the details are still being finalized, all have agreed with the basic ideas, and these serve as a basis for an integrated approach to the upcoming IGBP synthesis. The full GAIM PLAN will be published as part of the IGBP or GAIM report Series.

In its initial stages, GAIM concentrated on certain key issues concerning the Carbon Cycle and aspects of the coupling between terrestrial ecosystem and climate. This start-up phase aimed to develop and test programmatic techniques and model development for tractable cross-cutting problems which could then be used as smaller-scale examples of the larger Earth System issues which GAIM must ultimately face. The theme of the carbon cycle was selected because of the relative maturity of research in the relevant individual disciplines. With the success of several model development projects and model intercomparison activities, GAIM is now poised, on the basis of the GAIM PLAN, to extend its analysis to broader issues which will be encountered in global biogeochemical models. GAIM will move now to its role as integrator of IGBP science.

GAIM will focus on Tasks which cut across the realms of the Core Projects. In addition, It will examine theoretical and practical modelling techniques which will enable the effective coupling of subsystem models and the development and evaluation of Earth System Models. The broadly cross-cutting issues and projects described in the GAIM PLAN are aimed at providing some of these resources, and are part of a continuous re-evaluation of GAIM science, implementation and organizational structure. Each of the activities in the GAIM PLAN are directed toward integration of IGBP scientific results.

Integration of IGBP Science

As Earth subsystem models develop to a more robust level, GAIM is preparing to enter an integrative phase. This will involve the establishment of techniques for coupling and integration of biogeochemical subsystem models in preparation for the construction of integrated prognostic biogeochemical models. Such integration will involve coordination with each of the IGBP Core Projects. The integration program will have three aspects, each contributing to the overall objective of developing the modelling capacity which will ensure the achievement of GAIM's goals. The first will be at the subsystem level, where GAIM activities will be designed to bring developing subsystem models into boundary compatibility. This will be done through modelling workshops involving intercomparisons of like subsystem models, and intercomparisons involving coupling between adjacent subsystem models (which must match boundary conditions and fluxes). The second segment will be at the system level, where simple Earth system models are compared to highlight differences in coupling techniques, inter-element fluxes, and sensitivity studies to reveal the differences between models of the relative importance of individual system parameters. The third segment will be at the IGBP Core Project level, where GAIM will work with Core Project modelling teams to help facilitate inter-subsystem coordination. In addition, GAIM will act as a connection between IGBP and its sister organizations, WCRP and IHDP. GAIM will organize workshops with inter-programme modelling teams to facilitate such connections.

Subsystem models

Subsystem integration involves four key linkages: land-atmosphere, ocean-atmosphere, atmospheric physics with atmospheric chemistry, and land-ocean. These subsystems have different space and time scales (which themselves depend upon what is being tracked) and are often quite stiff as linked systems and therefore difficult in perturbation experiments. There are also greatly differing degrees of parameterization with little understanding as to effects. Important linkage experiments, however, can NOW be done: a) the ocean carbon model inter-comparison project (OCMIP) has linked atmosphere GCM's (mainly as drivers) with ocean GCM's containing carbon chemistry and crude biology; b) the terrestrial carbon models (NPP Efforts) are being driven partially by GCM results; and c) GCM Atmosphere transport codes are being explored for tackling the chemistry connection.

Subsystem models are being developed at present with a variety of structures and emphases. While each model is taken to represent the processes within a biogeochemical subsystem, the analytical and numerical formulations are widely disparate, and often lead to significant differences in model results. A fundamental issue is the development of subsystem models in such a way that the boundary conditions and fluxes for each will be compatible with each of the others. This compatibility is defined in terms of the ability of each model to provide the necessary input to define the boundary conditions needed to most efficiently run the others. For example, the boundary between the terrestrial and marine biogeochemical systems involves physical and chemical conditions and fluxes which are so complex that no single subsystem model presently accounts for all. Thus, matching boundary fluxes at the boundary would be impossible unless carefully coordinated during the model development phase.

As each of the subsystems becomes better understood and models converge on realistic values of output parameters, it will be timely to couple compatible models to form a more complete Earth system model. While it is not necessary to assume (and not possible to mandate) that all models of a particular subsystem will have identical input/output parameterizations, it is essential the each model be coupled only to other subsystem models with compatible parameterizations. Thus we envision the emergence of a suite of coupled models, each with consistent coupling and interactions between model components, but each based on a different style of process formulation. The parallel development of coupled Earth system models has several advantages. The most important is that because no single model (even an integrated Earth system model based on compatibly coupled subsystem models) accounts for all processes and interactions in the Earth system, each model will necessarily result in slight differences in inter-component fluxes and sensitivities. This will set the stage for Earth system model intercomparison which will highlight the relative importance of the various processes, interactions, and feedbacks between subsystems modelled by each of the integrated models. Such intercomparisons should ultimately lead to modified integrated models which correctly account for the interactions to which the Earth system is most sensitive, while becoming unburdened from those to which it is demonstrably insensitive.

Models of the various aspects of the Earth System are each associated with some uncertainties (errors). The uncertainties arise from 1) incomplete theoretical or mechanistic understanding of Earth System processes, 2) inaccurate formulation of processes with in the modelled subsystem (e.g. ocean circulation and trace gas transport), and 3) inaccurate or simplistic boundary conditions. The first two issues are being addressed through various model development and intercomparison projects (e.g. NPP, OCMIP). The latter source of uncertainty is a more complex problem and bears on subsystem model coupling. Because gas, water, and energy exchange between subsystems (e.g. ocean, atmosphere) determines their respective modelled boundary conditions, model coupling can lead to more accurate modelled fluxes across the boundary. However, coupling introduces its own uncertainties and sources of error. It is thus necessary to consider the effect of model coupling to TOTAL uncertainty. The obvious goal is to develop Earth System models in which this total uncertainty is reduced to levels less than the individual uncertainties of the subsystem models with prescribed boundary conditions.

System level

It is the ultimate mission of the GAIM Task Force to promote the development of integrated models of the Earth's biogeochemical system for eventual linking to the physical climate system studied through WCRP as well as the societal systems studied through IHDP. Simple models of the Earth system already exist, but they are not sufficiently robust to incorporate the detailed subsystem models being developed throughout the IGBP. It will nevertheless be instructive to examine such simple holistic models because some features which emerge may help identify and thus forestall potential problems in developing more comprehensive models on the basis of subsystem model coupling. Important insights can be gained from existing simple models of the Earth system, so we will build on these simple models in two ways:

  1. An organized simple but total Earth System Model approach that raises difficult system dynamic issues (chaos, feedbacks, parameterization sensitivities, policy effects, etc.), and
  2. An effort to collect and document existing models of key features of the Earth System (e.g. Carbon Cycle) that could run on a PC (or run over the WWW). The purpose of the former is to highlight key scientific issues that may be lost in the large model efforts while the purpose of the latter is outreach and education.

In order to assess the validity of Earth system models, it is critical to understand the sensitivity of the system to each of the input data. Heuristic and mathematical models are becoming developed to a point now where it is appropriate to consider model sensitivity. It will be necessary to conduct model sensitivity analyses of dynamic vegetation models, ocean carbon cycle models, GCMs, and hydrologic models as well as for simple Earth system models with respect to the various input climate and ecological data.

In the next several years, GAIM will begin to address some of the more theoretical issues involved in complex model development, coupling and evaluation. These issues are not necessarily limited to Earth System applications, nor are they being addressed primarily by Earth scientists. GAIM will explore the existing theory and focus its efforts on development of applications for Earth System Modelling. Examples of issues to be addressed in this aspect of GAIM include: schemes for quantifying and comparing coupled and uncoupled model result uncertainties, determining the minimum necessary resolution for model validation data, inverse methods for applying model validation data, quantifying system responses to subsystem-level and system-level perturbations, and sensitivity studies of coupled systems.

IGBP Core Project Integration

Each of the IGBP Core Projects is developing models of the appropriate biogeochemical subsystems. Once these are completed, it will be GAIM's task to promote the coupling of the various subsystem models and the development of integrated Earth system models. Model coupling will require advance planning so that it will be possible to most effectively match boundary conditions and fluxes. Thus, input and output data sets will need to be assessed and standardized, model temporal and spatial resolutions will have to be matched or scaled where necessary, and common numerical protocols will need to be defined so that the necessary parameters will flow through one subsystem model to the next. GAIM will work closely with the Core Projects to ensure that subsystem model linkages can be made smoothly.

The development of Earth System Models is a complex problem, to which the extensive resources of various institutions will be applied. The GAIM Task Force will not compete with these efforts, but rather will encourage and complement the efforts, as the Task Force is composed of key scientists from these leading institutions world-wide. The composition of the Task Force is determined by the scientific issue being addressed, and will continue to evolve in response to the development of new Earth System Models. As such, GAIM will provide a means for planning and coordination between these various institutional efforts.

The IGBP Core Projects are organized in such a fashion to encourage interactions and collaborations between scientists specializing in each of the Earth's biogeochemical subsystems. As such, the framework is already in place for organization of collaborative and intercomparison activities which should lead most effectively toward meaningfully coupled models. GAIM will need to work closely with each of the IGBP Core Project modelling teams to help direct the modelling efforts in a direction which will result in the most efficient coupling possible.

Scientific Questions

The new GAIM PLAN is based on a set of fundamental questions. (These questions encompass the scope of the original "6 key research questions" indicated in IGBP Report 28, 1994.) These questions are for the most part too general and broad to answer directly. They are based on a set of basic observations which must be explained if we are to understand key aspects of the Earth System. The process of seeking answers to the Fundamental Questions should fill the gaps in our understanding of the connections in the "Bretherton Diagram" as well as provide the framework for constructing reliable global prognostic biogeochemical models, GAIM's ultimate goal. As such, the Fundamental Questions address the outstanding gaps in our understanding of the linkages between the various part of the Earth System as represented by the Core Projects. The fundamental questions are each composed of a set of sub-questions which are at a more tractable scope given existing scientific research programs. While even the sub-questions are too broad to be answered by the results of individual research projects, the coordinated collaboration of research groups throughout IGBP (and beyond) can effectively address them. The narrower scope of the sub-questions will provide results in the nearer-term, making related research more readily fundable in the still somewhat disciplinary funding climate of national agencies worldwide. While inroads are being made to ensure adequate funding of ambitious interdisciplinary research such as that with which GAIM has been charged, there are a number of important issues which can be addressed now in the more "traditional" framework.

The four Fundamental Questions and their sub-questions will be addressed by GAIM research directly in some cases, and by Core Projects in others. Individual research projects will generally encompass only part of each question. The results of investigations of all sub-questions together will be used to address each of the Fundamental Questions . For the next several years, this will be accomplished through workshops on selected topics which will promote the synthesis and integration of IGBP scientific results in order to address the Fundamental Questions and ultimately lead to the development of a suite of global prognostic biogeochemical models. For some subquestions, a specific research plan has been formulated and preliminary work is already underway. In contrast, some other questions should be regarded as a "call to arms" for GAIM and the rest of IGBP in conjunction with the international global change research community.

The sub-questions within the Fundamental Questions follow a general pattern including a foundation based on "paleo" (1A, 2A...), then issues related to understanding ongoing system-level processes (1B, 2B...), and finally questions aimed toward developing prognostic capabilities (1C, 2C...). Each of these sub-questions will require the integration of a number of distinct research projects. Some of these projects will be conducted by GAIM, while others will be done by the Core Projects and other programs (e.g. WCRP, IHDP). Each project will be designed to address a specific sub-question, and will require development of theory, models, and datasets throughout IGBP Core Projects and framework activities. GAIM will take the lead in coordinating project results by convening the appropriate intercomparisons, syntheses, etc. This will entail a much greater level of interaction between GAIM and the Core Projects at all levels of activity than there has been in the past. In part, the ability to address this level of issues has been made possible by scientific advances within the Core Projects over the last several years, and the successful integration of IGBP science will depend on greater interaction with and between the Core Projects.

Overarching Vision

In seeking to develop a prognostic capability for the Earth's biogeochemical system that could be linked with the physical-climate system, we pose a set of fundamental questions about the interplay between biogeochemistry and climate. Each of these questions represents a plane of knowledge that cuts across the Earth system; moreover, each of these questions is motivated by observations and they are posed as a search for coherent explanations of observations of global environmental change. In large part, the IGBP Core Projects in various combinations are already working toward the answers to some aspects of these questions, and the GAIM PLAN is designed to work closely with the Core Projects on the corresponding aspects of these issues. The various sub-issues of each Fundamental Question represent a bridge between the answers we seek in order to develop prognostic biogeochemical models and the results which are available from specific disciplinary research projects.

The Fundamental Questions are based on a set of fundamental observations. We seek coherent explanations for these observations. While the observations listed below only partially represent the already observed changes in the Earth System, a coherent explanation should also account for additional observations and future data sets.
The GAIM Task Force was formed as an overarching framework activity of the IGBP for coordinating and stimulating different multi-disciplinary research components that can be synthesized to formulate an integrated view of the Earth System. The Goal of GAIM is to advance the study of the coupled dynamics of the Earth system using as tools both data and models. The challenge to GAIM is to initiate activities that will lead to the rapid development and application of a suite of Global Prognostic Biogeochemical Models. These global biogeochemical models would subsequently be linked, partly through hydrological coupling, to General Circulation Models (GCMs), thereby providing models of the Earth system
In order to make progress toward its goal, the Task Force must:
  • analyze current models and data
  • interpret the capability of current models and experimental programs against the demands for knowledge
  • advance and synthesize our understanding of the global biogeochemical cycles and their links to the hydrological cycle and more generally to the physical-climate system as a whole

Much of the progress to date in modelling specific components within the global biogeochemical subsystem sets the context for modelling activities within the various IGBP Core Projects. GAIM recognizes, supports, and will benefit from these efforts. GAIM activity is by definition cross-cutting; therefore, the activities of GAIM intersect fundamentally with all the Core Projects as well as with the World Climate Research Program (WCRP) and International Human Dimensions Programme (IHDP). Top


Global Net Primary Productivity (NPP) Update

by Kathy Hibbard

In the first GAIM newsletter (Winter 1997) the current status of the Potsdam Institut für Klimafolgenforschung (PIK)/ net primary productivity (NPP) model intercomparison reported the development of standardized input datasets for climate (temperature, precipitation, solar radiation), soil texture, a stochastic weather generator (WGEN), the NOAA Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) and fraction of intercepted photosynthetically active radiation (FPAR) FASIR dataset. In addition, preliminary reports included the development of an average global net primary productivity (NPP) map (Figure 1).

Global annual NPP, averaged across all the models was highest (>1000 gC m-2) in the humid tropics (Amazonia, Central Africa, Southeast Asia), where both temperature and moisture requirements were fully satisfied for photosynthesis to occur (Figure 1). The coefficient of variation (Fig. 2a) was less than 15% for most areas, therefore, it was reasonable to assume that the broad features of the average global map was a comprehensive representation of the NPP fluxes estimated comparably by the different models. The spatial distribution of NPP was reflected by the variability, or the standard deviation (Figure 2b), and was large where NPP was high and small where NPP was low.

The highest standard deviation occurred at the borders of productive vegetation types, possibly due to different input vegetation datasets (with different boundaries near ecotones). In the previous GAIM newsletter, it was also suggested that differences in biome assignment/definitions produced variable NPP predictions at ecotone boundaries.

Further analyses revealed that the assumption of homogeneous vegetation within a gridcell resulted in abrupt differences in simulated variables (e.g. NPP, FPAR) at ecotone boundaries as biome paramaterizations suddenly changed. Differences in vegetation maps used and their associated parameters influenced differences in seasonal leaf area index (LAI), FPAR, and NPP among the models at least as much as the differences in model assumptions about ecosphysiology. Future activities may resolve differences in the spatial pattern of vegetation structure among models by: (1) increasing the spatial resolution of vegetation characteristics with the use of NDVI data (e.g. for SDBM, CASA, TURC, GLO-PEM, KGBM); (2) representation of ecosystems at the gridcell-level as a mosaic of plant-functional types rather than a single dominant vegetation type; or (4) incorporate a stochastic vegetation type initialization from the biogeography component of a dynamic vegetation model (e.g. gap model in HYBRID, BIOME3, DOLY).

The use of different spatial distributions of vegetation among models did not,however, account for the observed discrepancies in the correlations between annual NPP and NDVI, nor was there a clear trend in the relationships between NPP and NDVI between models that could be credited to the use of actual vegetation (CARAIB and CASA), NDVI-based seasonal canopy dynamics (GLO-PEM and TURC), or potential vegetation (all other models) at either the global, or biome scale. These results support previous findings that correlations between NDVI and annual NPP were not significantly different in BIOME-BGC, CENTURY, and TEM regardless of the inclusion or exclusion of land use, suggesting that land use changes may be masked when data are aggregated over large regions, such as in this study. However, the strong influence of vegetation distributions over NPP estimates at ecotones suggested that it is crucial to use a unified vegetation classification in order to understand how ecosystem structure and function may change along environmental gradients or transects.

While different vegetation inputs may not have contributed significantly to the variability in annual NPP with respect to the NDVI, model assumptions with regard to canopy dynamics and vegetation structure within a biome type strongly influenced the estimates of seasonal NPP from gross primary productivity (GPP) and plant, or autotrophic respiration (RA). Models that translated leaf-level processes at fine (daily or sub-daily) temporal resolutions to the canopy tended to predict relatively larger seasonal variations in NPP (e.g. HYBRID, SIB2) than those who assumed that the vegetation acts as a single 'box' (e.g. TEM) (Figure 3). This led to an effort to resolve complex model interactions in space and time, not only to gain insight into NPP predictions, but to further understand the mechanistic processes at work within the models.

CLIMATE

Regional analyses for the boreal and tropical evergreen forests highlighted the importance of model assumptions regarding the influence of climate on simulated NPP. For instance, model sensitivity to temperature and solar radiation during dry periods in tropical evergreen forests produced widely scattered NPP estimates and were the direct result from variable model assumptions about the influence of moisture availability (e.g. rooting depth) throughout the year. Additionally, model assumptions regarding snowpack, permafrost, and soil temperature for boreal evergreen forests were important climatic influences on predicting NPP.

Strong correlations among the seasonal changes in NPP with solar radiation, temperature and FPAR in boreal evergreen and temperate deciduous forested suggested that the influence of seasonal canopy NPP changes (phenology) may be partially accounted for in models that only included the influence of climatic variables on NPP. In addition, comparable correlations between seasonal NPP and APAR/LUE in these ecosystems suggests a general consensus among models with respect to the influence of canopy summergreen phenology. In contrast, the relationships between seasonal NPP and APAR/LUE for savanna and tropical evergreen forests were weak, indicating that simulated phenology and canopy radiation dynamics were not as important in these biomes, however, soil moisture status may provide a better indicator into NPP cycles in raingreen, or drought-deciduous ecosystems. Modeled NPP was more sensitive to increased temperatures and interactions between warming and drying periods than the NDVI, suggesting that factors not included in the model formulations, such as deeply-rooted plants that are able to obtain water even during months of drought were contributing to the NDVI signal.

WATER

A simple scalar defined as the difference between annual precipitation and potential evapotranspiration was developed to estimate the effect of water availability on simulated NPP. While the water balance coefficient (WBC) did not account for the seasonality of precipitation or soil moisture, insight was provided into the methodologies incorporated by the different models to calculate a water budget. For instance, the smooth increase of NPP with the increasing WBC in models with physiological controls over ET and NPP suggested that upper limits on ecosystem productivity were controlled by a water deficit to plants. Recent studies have shown that vegetation productivity is the result of the interaction of several environmental factors and we should not expect a generic dependence between a single environmental control and NPP, however, water availability is the dominant constraint on NPP over a larger part of the globe (52%) relative to any other environmental factor. However, differences in modeled sensitivity to one or more climate variables (temperature, precipitation, solar radiation) may not be distinguishable when climate is not limiting or when values are averaged over the year. However, similarities between model estimates may disappear when climate varies from the average condition, either as a change in annual averages or as an increase in seasonal amplitude.

LIGHT

There was a consistent, 28% discrepancy between simulated an satellite-derived global APAR for two possible reasons: (1) the NDVI-derived FPAR may have been underestimated as satellite data pre-processing algorithms (atmospheric correction, cloud removal) decreases the remotely-sensed signal; (2) simulated FPAR was dependent on LAI which may have been overestimated as model formulations did not always include the whole range of possible constraints (moisture, temperature, nutrient) on simulated LAI. In addition, the use of potential versus actual land-cover to derive FPAR may have contributed to the differences between simulated and the NDVI-derived FPAR as potential vegetation maps did not incorporate land-use (agriculture, land clearing, urbanization) effects. However, agricultural systems can contain dense canopies, comparable to potential vegetation, resulting in comparable APAR values. Crop phenology, or the timing of planting and removal as detected by the satellite-derived LAI can be greatly different from potential LAI maps. Therefore, the use of potential versus actual land cover maps may be critical to predicting the seasonal fluctuations in LAI and APAR as well as NPP. With the exception of GLO-PEM, the models reasonably simulated 'realistic' ranges in light use efficiency (LUE) at both the individual grid cell and zonal (latitudinal) spatial scales.

The relative importance of APAR and LUE on NPP was analyzed globally and spatially by latitude and individual gridcell. Light use efficiency was computed annually as the simulated carbon gain relative to absorbed light, or NPP/APAR. Spatial variability in simulated NPP was primarily accounted for by APAR, whereas LUE accounted for much of the NPP differences among models. However, APAR and LUE offset each other such that global NPP values fell within the range of 'generally accepted values'. It was suggested that models may perhaps, be implicitly calibrated to 'commonly accepted values'.

THE FUTURE

Currently, there are no available global data sets that describe the seasonal changes in ecosystem structure and function that can be used to compare models with. If the NDVI can be used to develop phenological models on large scales and to constrain annual NPP estimates under contemporary conditions, then confidence in estimates made by non-satellite, climate-driven biogeography and biogeochemistry models under climate change scenarios can increase. The availability of net ecosystem exchange (NEE) measurements from measurement networks including EUROFLUX, Ameriflux, the NASA field campaigns in the boreal forests (BOREAS) and in the tropics (LBA), and improved vegetation indices from satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS), will improve our ability to predict primary productivity of terrestrial ecosystems. In addition, the Global Primary Productivity Data Initiative (GPPDI) has developed an extensive dataset from global NPP measurements that can be incorporated in the next step, which is clearly to begin model evaluation efforts with observed datasets.
Figure 1. Annual net primary production (g Cm-2yr-1) estimated as the average of all model NPP estimates.

NPP fig1

Figure 2. Spatial distribution of the variability in NPP estimates among the models as represented by (a) the coefficient of variance of model NPP estimated in a grid cell; and (b) the standard deviation of model NPP estimated in a grid cell. The coefficient of variance is determined by dividing the standard deviation by the mean of the model NPP estimates within a grid cell.

NPP fig2

Figure 3. Comparison of seasonal variations in global net primary production among models. Net primary productivity is driven by light and constrained by water, temperature, and nutrient availability. Of the seventeen participating models for this intercomparison, the most commonly simulated constraints included light and/or moisture availability, with six of seventeen models including nitrogen limitations. Four of the six models that applied nitrogen constraints estimated lower global NPP than all other models. Four of the six models which incorporated a vapor pressure deficit estimated higher global NPP than those using other approaches to describe the influence of moisture on NPP, suggesting that NPP estimates were sensitive to the modeling strategy for the effect of water stress on NPP. One of the two models using vapor pressure deficit that predicted a lower global NPP (DOLY) also included nitrogen constraints on NPP. Here, we summarize the analyses that focused on global and regional spatial as well as annual and seasonal temporal scales in the general context of climate, water, and light limitations on net primary productivity

NPP fig3

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Incorporating Human Dimensions in Earth System Models


AGU Special Session, May 1998
by Dork Sahagian

Scientists from a wide variety of disciplines, including atmospheric physics, biogeochemistry, public policy, ecology, sociology, medicine, and economics gathered in May for a special Union session at the American Geophysical Union (AGU) meeting in Boston to discuss ways to incorporate socioeconomic factors into developing models of the Earth System. This merging of disparate research communities was aimed toward clarifying specific issues such as model integration, societal feedback on biogeochemical systems, and the causes, processes and consequences of policy changes on global environmental systems.

Throughout the international research community, there is a growing recognition of the importance of global change research and the critical next step of incorporating societal systems into the "equation." From the perspective of Global Change, the Earth System can be divided into physical climate, biogeochemical, and human components. While there are strong and growing links between physical climate and biogeochemical research, the links to the human dimension have largely been ignored. This is partly because the social, economic and political sciences are comprised of a different scholarly community than the physical and biogeochemical sciences. In addition, modelling the complexities of modern human society is an exceedingly difficult task, particularly when future projections are involved. However, human activity is a major (and perhaps dominant) driver for global change. In order to develop biogeochemical models, it is critical to accurately account for changes in the driving forces stemming from human activity. While this is not yet possible, some socio-economic models exist now, and others are currently being developed. On the basis of evolving drivers to the system, their development and coupling to physical climate and biogeochemical models will ultimately enable the modeling community to make more reliable estimates of future impact of global change and to assess possible alternative mitigation and adaptation strategies.

This special AGU session was a preliminary attempt to bring together researchers from the physical, biogeochemical, and human dimensions arenas to explore the way ahead. After a summary by Dork Sahagian and Larry Kohler (IHDP Executive Director), presentations were made on series of topics. Talks opened with a challenge posed to the community which was followed by a wide-ranging series of presentations regarding specific modelling approaches to issues such as climate change, human health, land use, biogeochemical cycles and general models of human behavior. The afternoon highlighted policy issues especially related to climate change and Post-Kyoto implementation challenges and culminated in a panel discussion focusing on some of the broader issues of complex model coupling, development of prognostic capabilities, policy implications, and suggestions on ways for a united research community to proceed. The panel included:

Rosina Bierbaum, Acting Associate
  Director for Environment, Office of
  Science and Technology Policy,
 Executive Office of the President (of the U.S.);

Anthony Janetos, Program Scientist,
 Program of land Use and Land Cover
 Change, NASA Headquarters;

Larry Kohler, Executive Director of
 the International Human Dimensions Programme

The session identified some of the re search community's capabilities as well as its weaknesses and needs for future development. In particular, there was a notable lack of data sets available for linking social and biogeochemical systems. Hopefully, as IHDP and its linkages with IGBP develop further, these data gaps will be addressed. Another critical area of linkage is in modelling. It became apparent that very different approaches are used to model social and biogeochemical systems. While the biogeochemical models are diagnostic and have reasonable prognostic potential, social models are more descriptive and most often applied to local populations. This limitation of social models makes it difficult to couple, much less integrate with global biogeochemical models, but is understandable given the complexity and fragmentation of socio - political and economic systems, many of which are not easily classified within traditional geographical boundaries. In the future, as the global economy becomes itself more integrated due to increased communications and trade capabilities (i.e. globalization) , some of the economic aspects of social fragmentation may be reduced, making it possible to develop and integrate more robust socio - economic components within Earth System models. Similarly, the increased focus on the development of regional models may provide new opportunities for the GLOBAL Earth system models and the LOCAL socio-political-economic models to come together at the regional level.

While the AGU Special Session on Incorporating Human Dimensions in Earth System Models raised more questions than it answered, we can look forward to increased cooperation and collaboration between the socioeconomic and biogeochemical research communities to seek the answers.

Incorporating Human Dimensions in Earth System Models

Introduction (Sahagian and Kohler)

Kaufmann; Needed: A Truly Interdisciplinary Approach for Assessing Global Climate Change.

Malone & Rayner; Human Choice and Climate Change: Incorporating Social Complexity in Models of Human Behavior for Integrated Assessment.

Epstien; Emerging Infectious Diseases and Global Change.

Ramankutty & Foley; Incorporating Land Use into a Global Biosphere Model.

Haberl; Indicators for Society's Metabolism and the Colonization of Nature as Link Between Natural and Social Sciences.

Ver, Hoover, Mackenzie & Lerman; Industrial Ecology of the C-N-P-S Global and Regional Biogeochemical Cycles.

Shortle & Kocagil; Assessing the Impacts of Climate Change on Waterborne Diseases.

Kheshgi & Jain; Reduction of the Atmospheric Concentration of Methane as a Strategic Response Option to Global Climate Change.

Dowlatabadi; The Human Dimensions of Climate Policy Design: an Integrated Assessment Approach.

Toth; Environmental Targets in Integrated Assessments: The Tolerable Windows Approach.

Yohe & Toth; Adaptation and Tolerable Climate Change.

Padovani; Natural Disaster Reduction Issues at the Science/Policy Interface.

Bierbaum; Science and Policy for the Environment: The First National Assessment of the Consequences of Climate Change for the United States.

Jacoby; Human-Climate Interactions in Multi-Gas Accounting Systems.

Edmonds, Kim, MacCracken, Sands & Wise; Kyoto-The Morning After: An Integrated Assessment Analysis of a Protocol in Progress.

Panel Discussion (Larry Kohler, IHDP; Rosina Bierbaum, Executive Office of the President, Anthony Janetos, NASA.)

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OCMIP-2 In Progress

by J. C. Orr(1) and R. G. Najjar(2)

Thirteen modeling groups are currently participating in the second phase of the Ocean Carbon-Cycle Model Intercomparison Project (1998-2000). During the first months of OCMIP-2, common protocols for simulations have been finalized and the first simulations have begun. As model output becomes available and OCMIP-2 simulations continue, results will be analyzed centrally at IPSL and PSU. The first OCMIP-2 workshop, to be held in Paris in May 1999, will provide an opportunity for modeling groups to discuss comparison of results from these first simulations, which focus on the natural carbon cycle, its anthropogenic perturbation, and tracer validations for CFC-11, CFC-12, and C-14.

Simultaneously, an effort has been devoted to developing a new OCMIP Web site: http://www.ipsl.jussieu.fr/OCMIP/ in order to provide an archive for model boundary conditions and protocols and to facilitate planning and discussion. That same site also contains other information useful to OCMIP modelers as well as those with a more general interest in OCMIP.

  1. LSCE/CEA-CNRS Saclay, Bt. 709 L'Orme, F-91191, Gif-sur-Yvette Cedex, France Institut Pierre Simon Laplace (IPSL), Paris, France
  2. Dept. of Meteorology, The Pennsylvania State University (PSU), University Park, PA 16802, USA Top

Global Primary Production Data Initiative (GPPDI)

by Kathy Hibbard

Progress in simulating the global carbon cycle is inhibited by the paucity of adequate observational data for model parameterization and verification. Net primary productivity (NPP) integrates plant-atmosphere exchange of carbon dioxide (CO2) through the assimilation and synthesis of inorganic carbon (CO2) into organic plant biomass, and is widely simulated by many terrestrial ecosystem and biogeography models. The objective of the IBGP Global Primary Production Data Initiative (GPPDI) is to develop data sets of NPP and associated variables that can be used to develop and verify models of NPP at the global scale. In addition, it is anticipated that the products of the GPPDI will contribute to a number of other activities including, but not limited to: the EOS product validation program, VEMAP, the IGBP Global Change in Terrestrial Ecosystems Long-Term Ecological Modeling (LEMA) network, SCOPE forests and grasslands activities, as well as the European Union agricultural monitoring program. The challenge is to take extensive but incomplete datasets and make them usable for analyses and models by estimating total NPP in a consistent manner.

Two Working Groups, organized under the GPPDI and co-funded by the National Center for Ecological Analysis and Synthesis (NCEAS) and IGBP-DIS are addressing this challenge. Working Group I (WG1) met December 10-14, 1997 at NCEAS where empirical rules for estimating total NPP from partial measurements were developed for grassland, boreal forest, and tropical rainforest biome types. Working Group II (WGII) met February 17-22, 1998 at NCEAS, where rules developed in WG1 were applied to scaling questions from point measurements to surface grid (up to 0.5°) scales. A third working group (WGIII) is scheduled to meet in October, 1998 to apply rules and scaling algorithms developed in the first two working groups to regional transects throughout the globe.

Three classes of site-level NPP were recognized by the GPPDI: Class 1 sites include total (above- and below-ground) NPP on an annual time scale, plus the driving variables needed for some of all of the various total NPP models developed. Sites where CO2 fluxes (eddy-covariance tower sites), C-14 pulse labeling or root rhizotron data have been collected significantly improve the data. Class 2 sites do not include total system NPP measurements (e.g. above-ground only), however, direct or surrogate measures of important components of NPP (generally above-ground), also the ancillary data needed to drive NPP models are available. Class 3 sites contain data sets that are lacking measurements of the variables needed to drive NPP models (e.g. climate, soils). Primary use of Class 3 sites is to assess the representativeness of Class 1 and Class 2 data in a global context. The working groups focused on three biome types: forests, grasslands, and crops with the forest group subdivided into boreal and tropical forests. Grassland datasets originate from the range literature and included the Great Plains (U.S.A), Queensland Grasslands (Australia), and China. Datasets from croplands are currently limited to the midwestern corn/soy crops of the United States.

The boreal forest group highlighted the importance of large scale structural controls on NPP such as disturbance (fires, insect outbreak), and the subsequent influence on a forests' age-state, or age structure. Mixed age class forests can confound NPP estimates by models where stand age is important, however, for large scale NPP predictions, the boreal contains large patches of even-age stands. The boreal forest group identified approximately 20 Class 1 sites in Alaska, the BOREAS project, Canada, perhaps Sweden, and Russia. In general, the data suggested a robust predictor for below-ground net primary productivity was a simple, linear function of above-ground productivity (BGNPP ~ 0.74 * AGNPP). The caveat was presented, however, that forest inventory data is biased towards productive plots, and only the larger, harvestable trees are measured, and not the understory (e.g. sassafrass in the southeastern United States).

The tropical forest group identified few sites that included measurements of below-ground productivity. It was estimated that there currently exist approximately 42 Class 1 and Class 2 sites globally. There appears to be a good relationship between litterfall and above- ground biomass increment, however, there were no relationships between climatic variables and observed forest productivity (stem increment). In contrast with the large-tree bias in boreal forests, there is a bias in tropical forests favoring small- to mid-sized trees due to difficulties in the removal of large trees. It is the larger-sized trees in tropical forests however, that often account for 30% or greater of total stand biomass.

There is a large database with peak standing biomass available for global grasslands, however, there are few below ground estimates. There are perhaps two grassland sites that have measured root turnover for up to two years, and there appears to be no relationship between above- and below-ground peak standing biomass. In addition, there was no consistent relationships found between peak standing biomass and the timing, regional (north vs. south), or functionality (e.g. C3 vs. C4) of grassland sites. However, above-ground peak standing biomass in grasslands (Great Plains, U.S.A.) appears to follow a precipitation gradient.

Two crop models (CERES-Maize and GLYCIM) were used to undertake sensitivity analyses of harvest index, shoot root ratio and grain to total production ratios in response to rainfall and variations in total NPP in corn and soybean. Realistic climate data sets for Iowa were used. Corn was found to have an almost constant ear weight/total weight and shoot/root weight ratio, irrespective of total production. Soybean seed/total weight ratio increased with seed weight and shoot/root ratio declined with precipitation. Both seed weight and precipitation are available and so the possibility of modeling these responses using available forcing variables exists.

Additional topics covered at the working groups included discussions focused on biophysical (e.g. climate, soils) datasets available for model input as well as scaling input data from points to a surface gridcell up to 1.0°. Input datasets for NPP models include biophysical (e.g. climate, soils), and potential versus actual (remotely sensed) vegetation maps. Datasets currently in use to simulate NPP at variable spatial and temporal scales were identified. For instance, global (e.g. Leemans and Cramer 1993, Piper et al. 1996) and continental (Kittel et al.1995) climate databases as well as models, (e.g. MTCLIM; Hungerford et al. 1989 and PRISM;Daly et al. 1994) are widely used. Current techniques in scaling algorithims can be identified as one of three types: (1) classify and multiply, or the more commonly referred to method of 'paint-by-numbers'; (2) simulate and aggregate (or aggregate and simulate); and (3) spatial statistics or geostatistical techniques. The most common technique in use today is the classify and multiply as it is simple. However, problems arise when scaling non-linear processes in a linear fashion.

The results of the two working groups are being prepared in the form of nine journal articles over the following topics: (1) forest inventory estimates of net primary productivity; (2) crop yield statistics to estimate net primary productivity; (3) grassland NPP; (4) implications of scaling point (<=0.5o) to gridscale (up to 1.0°) biophysical datsaets; and (5) landscape stratification coupled with site NPP data. It was agreed that an additional, or third workshop would be productive to: (a) review the first NPP datasets by authors and representative modelers from the papers listed above (b) specify additional sites and data for further application of the NCEAS/GPPDI methodologies developed; and (c) prepare for the application of the developed regional datasets to the next model intercomparison.

References

Daly, C., R. P. Neilson, and D. L. Phillips, 1994, A statistical-topographic model for mapping climatological precipitation over mountainous terrain: Journal of Applied Meteorology, v. 31, p. 159-166.

Hungerford, R. D., R. R. Nemani, S. W. Running, and J. C. Coughlan, 1989, MTCLIM: A mountain microclimate simulation model, United States Department of Agriculture, Forest Service.

Kittel, T. G. F., N.A. Rosenbloom, T.H. Painter, D.S. Schimel, and VEMAP Modeling Participants, 1995, The VEMAP integrated database for modeling United States ecosystem/vegetation sensitivity to climate change: Journal of Biogeography, v. 22, p. 857-862. Top


IGBP-DIS Wetland Data Activity

by Martine Michou

As a follow-up to the IGBP wetlands workshop led by GAIM in 1996, IGBP-DIS held a planning meeting (10 March, 1998; Toulouse) to organize an activity directed toward assembling the data sets necessary for IGBP wetlands research. The global inventory and characterization of wetlands has been mentioned several times as being an item of priority by IGBP Core Projects scientists. These data sets are required for instance to quantify the importance of wetlands as a source of methane and other biogenic trace gases. Such a need was first identified in the IGBP Report No 30 "IGBP Global Modelling and Data Activities 1994-1998". It was once again emphasized during the latest IGBP-DIS Scientific Steering Committee meeting held in March 1997. New remote sensing techniques and improved capabilities to deal with complex global data sets encourage IGBP-DIS to consider this parameter as a strong candidate for the IGBP-DIS exercise.

Jean-Paul Malingreau, chairman of the IGBP-DIS Scientific Steering Committee, opened the meeting and welcomed participants, noting the wide range of interests represented. D. Sahagian first presented the outcomes of the IGBP Wetland Workshop held in May 1996 with representatives from the GAIM, IGBP-DIS, BAHC, IGAC and LUCC IGBP Core Projects. A full meeting report is available on the WWW, at http://www.gaim.unh.edu/wetlands.html. After his presentation of the May 1996 Workshop, D. Sahagian insisted that future initiatives to assess wetland areas should focus on the seasonally and episodically inundated areas in addition to the "traditional" permanently flooded wetlands. In such initiatives, wetland extent should be expressed in terms of hectare-days (or equivalent units). He then indicated that a complete wetland inventory could be compiled using the following types of information:

  1. Ramsar Convention database
  2. Global/Continental scale inventories
  3. National inventories
  4. ONC charts
  5. Swamp marks on local topographic maps of 1;250,000 scale
  6. Swamp marks on local topographic maps of 1:50,000 scale
  7. AVHRR type RS data classified using expert local knowledge
  8. AVHRR type RS data classified using about four types of swamp marks from local 1:100,000 maps
  9. Passive microwave of surface flooded area when the wetlands are dry
  10. Passive microwave of surface flooded area when the wetlands are wet
  11. Active microwave estimate of surface flooded area when the wetlands are dry
  12. Active microwave estimate of surface flooded area when the wetlands are wet
  13. Optical (TM probably) classified with local knowledge input
  14. Optical (TM probably) classified using the digitized swamp marks only
  15. a) hybrid of AVHRR and TM
    b) passive microwave and TM
    c) active microwave and TM

Finally, Sahagian proposed an Action Plan for discussion, as follows:

Short-term activities should consist of the:

M. Finlayson presented the three ERISS wetland projects that could be of interest to the IGBP-DIS Wetland data initiative. These projects cover both local, regional and international scales, and their main aims are to provide information for management purposes.

C. Baker presented the RHIER Wetland Ecosystems Research Group research activities. These include the development of functional assessment procedures for the European wetland ecosystems, which should enable the characterization and prediction of functioning of wetlands, as well as the development of a functional classification. The landscape context appears to be an important parameter. Geomorphic, hydrologic, ecologic, pedologic and land use information are used in this functional assessment.

M. Tamura presented wetland research projects on progress at NIES. The first consists of monitoring and mapping of wetlands by remote sensing techniques (on-going project since 1993). The final objective is to develop a global wetland map. The second is observation of soil and vegetation in Siberia from satellites data (on going project since 1993). Objectives are (1) to classify ecosystems in West Siberian wetlands, using both coarse and high-resolution sensor data, and (2) to estimate methane emissions from wetland areas by combining the results of ecosystem classifications and ground flux measurements.

A. Rosenqvist presented the results of a research activity conducted in collaboration with the Instituto National de Pesquisas de Amazonia (Brazil). This project aimed at investigating inundation patterns and related biological and geochemical processes in the Jau River Basin in Central Amazonia. A CH4 emission model based on identification of inundated areas by SAR data was developed, and outputs of this model over a 3-year period confirmed significant CH4 emissions (2000-4000 tonnes/year for a 200km2 area).

G. Degrandi presented progress made using remote sensing techniques for mapping forests over tropical areas, combining ERS-1 SAR and other satellite data. Measurements of flooded forests appear promising.

O. Arino presented ESA potential contribution to the IGBP-DIS Wetland data project. Both the ERS satellites (launch of ERS-1 in 1991), and the ENVISAT satellite (launch planned at the end of 1999) could be a valuable source of data for the wetland community. In addition, some new sensors on ENVISAT (e.g., the MERIS sensor) appear promising for wetland scientists.

M. Maiden reported on the various data sets available from NASA which could be considered when inventorying wetland areas:

Maiden then described the NASA/University of Maryland Coastal Marsh project that will produce a data base containing health information about all coastal marshes along the Southeast coast of the U.S. Finally, she noted that NASA Earth Science Information Partners are being established. These partners will produce and publish information and products, and provide services in support of the Earth system science. Collaboration between the IGBP-DIS Wetland initiative and these Partners could be highly beneficial.

Participants at the Wetland Data planning meeting agreed that the highest order requirement for IGBP-DIS is to provide the community with a global so-called "base-line" digital data set. This data set should delineate wetlands, identified simply as "areas covered by vegetation and water during some period of the annual cycle", at a relatively "low resolution" (from a few hundred meters to a kilometer). This data set should also include information on the timing of the floods over a year.

The "base-line" and "fast-track" product should make use of existing data sources, both non-remote and remote sensing sources. Integration of such multi-source information will serve as a prototyping exercise for IGBP-DIS. Characterization of the various data sources, for example in terms of data quality, will need careful attention.

It was recommended that the IGBP-DIS Office get in touch with the RAMSAR Convention Bureau as a start to gather non-satellite source wetland data. A first "gross" assessment of the extent of wetlands from non-remote sensing sources and/or from low resolution optical remote sensing sources would minimize efforts in acquiring and processing SAR data.

Meeting participants agreed that the following remote sensing data sets should be considered as candidates for the global "fast track" product:

Meeting participants also recommended that the capabilities of the following sensors be further investigated, and that a decision to make use of their data be taken within six months:

JP. Malingreau asked meeting participants if they were willing to continue after this meeting to support the IGBP-DIS wetland initiative, being a member of the IGBP-DIS wetland group. This working group should provide recommendations for the implementation phase of the data set development process, and then guide the data development. All participants agreed to act as members of the working group. Sahagian expressed J. Melack's interest in leading this working group (co-convener of the original IGBP Wetlands workshop). Malingreau welcomed this offer, and announced that the working group leader would be nominated by the IGBP-DIS Scientific Steering Committee the next day. He also described that the working group should include thematic specialists, Earth Observation Space Agencies representatives, preferably data users, IGBP Core Project and IGBP-DIS representatives. Top


Transcom Update

by Kathy Hibbard and Scott Denning

Much of our current understanding about the global carbon cycle has come from observing the changes in atmospheric CO2 concentrations (Keeling 1986) over time. Time series (e.g., Mauna Loa record) provides insight into the seasonal cycle as well as global source/sink and interannual variations (Conway et al. 1994, Francey et al. 1995, Keeling et al. 1995). Additionally, existing flask networks (e.g. CMDL, CSIRO, etc.) provide information about the spatial structure, or distribution of atmospheric CO2. For example, a disproportionate amount of fossil fuel emissions occur in the northern hemisphere, and a large terrestrial CO2 sink is required to explain the weak observed North-South gradient (e.g. Tans et al. 1990). However, an accurate quantitative interpretation of the spatial structure requires models of trace gas transport.

Chemical tracer transport models (CTMs) have been used to study atmospheric CO2 since the early 1980's (c.f. Fung et al. 1983, Heimann and Keeling 1989, Tans et al 1989, Enting and Mansbridge 1991). The models can be characterized by the mechanisms they incorporate to transport tracers horizontally and vertically across the globe. One class of CTM's transport CO2 using offline analyzed winds from weather forecast centers or Global Circulation Models (GCMs), others actually calculate tracer concentrations with a GCM internal to the transport model. Results in the literature have shown that there are considerable model-dependent differences in simulating the global movement of atmospheric tracers (Law et al. 1996) .

The Atmospheric Tracer Transport Model Intercomparison Project (TransCom) is part of a larger GAIM research program focused on the development of coupled ecosystem-atmosphere models that describe the time evolution of trace gases with changing climate and changes in anthropogenic forcing. TransCom was formed following the 4th International CO2 Conference in Carquerianne, France to compare global CO2 models and quantify the differences among the simulations. Atmospheric chemical tracer transport models (CTMs) serve three crucial functions in the development, testing, and validation of global Earth system models:

  1. predictions of trace gas fluxes at the Earth's surface may be used to drive CTMs and the resulting simulations of atmospheric concentrations may be compared to observations to test Earth system models;
  2. trace gas fluxes at the surface may be calculated from observations of atmospheric concentration via an "inversion" of the data with a CTM, improving process-level understanding and directly validating Earth system models; and
  3. simulation of the fate and temporal evolution of reactive trace gases such as methane (CH4) and nitrous oxide (N2O) requires a detailed atmospheric chemistry module in Earth system models, which includes both transport and chemical transformation.

The goal of TransCom is to quantify and diagnose the uncertainty in inversion calculations of the global carbon budget that result from errors in the simulated transport. An important source of uncertainty in these calculations is the simulated transport itself, which varies among the many transport models used by the community. TransCom investigators have conducted a series of 3-dimensional tracer model intercomparison experiments which are intended to (1) quantify the degree of uncertainty in current carbon budget estimates that results from uncertainty in model transport; (2) identify the specific sources of uncertainties in the models; and (3) identify key areas to focus future transport model development and improvements in the global observing system that will reduce the uncertainty in carbon budget inversion calculations.

Initial model intercomparisons of the annual mean CO2 signal from fossil fuel burning and the seasonal cycle inferred from terrestrial ecosystems were presented at the First GAIM Science Conference in Garmisch-Partenkirchen, October 1995. A second experiment, involving simulation of SF6 was coordinated by Scott Denning at Colorado State University and the University of California at Santa Barbara, U.S.A. Analysis of these results is ongoing, and a third experiment is being planned, which will involve CTM intercomparisons of global carbon budgets calculated by inversion modeling.

The first phase of TransCom compared model performance for two salient features of atmospheric CO2: the annual mean north-south gradient (dominated by fossil fuel emissions), and the seasonal cycle (dominated by exchange with terrestrial ecosystems). Twelve modeling groups from 4 continents participated, many of which have been used extensively in carbon cycle research. The experimental design for both the annual and seasonal simulations consisted of model runs for at least three years from an initial atmosphere with uniform CO2, providing sufficient time for the model atmosphere to establish an "equilibrium" (annually repeating) concentration distributions. Each set of model results were normalized such that the January global three-dimensional mean was zero. Results showed a surprising degree of variance among models with regard to meridional north-south gradient at the surface, and especially aloft (Figure 1).
Figure 1. Zonal mean annual mixing ratio of CO2 response to fossil fuel emissions as simulated by 12 transport models for (a) surface and (b) 200 mb pressure level. The value at the south pole has been subtracted. Note that the CSIRO9, GFDL-GCTM, and ANU models behaved differently from the others.

figure1

While the annual mean biospheric source was specified as zero everywhere (though certainly non-zero at particular times of the year), this is not true of the spatial distribution of annual mean concentration resulting from this source (Figure 2).Ê In some of the models, the interaction of seasonal variations in transport with the seasonal biospheric source produced strong non-zero annual mean concentrations in the northern hemisphere.Ê This appears to be due to seasonal covariance between the CO2 exchange with the biosphere and vertical mixing in the models.This phenomenon has come to be known as the "rectifier" effect (Denning et al. 1995). Model agreement was strongest at the surface over ocean regions, and broke down considerably over land and in the upper troposphere, where there are very few observations. Some models were clearly outliers, but it was impossible to assess any degree of individual model accuracy because the technology does not yet exist to measure concentrations of CO2 in the real atmosphere that are due to specific processes (fossil fuel and/or vegetation).
Figure 2. Annual mean zonal mean surface CO2 mixing ratio response to purely seasonal exchange with the terrestrial biosphere as simulated by 12 atmospheric tracer transport models.

figure2

To understand the performance of the various models with respect to the inter-hemispheric gradients of passive tracers, TransCom needed to move beyond the simulations of unobserved (and unobservable) fossil fuel CO2 to a tracer that is well observed and whose atmospheric budget is not complicated by missing sinks. This required a tracer with well-documented concentrations around the world, with a quantifiable emissions field, and preferably with insignificant sinks. Previous studies have used CFCs for this purpose, but since the Montreal Protocols were implemented, the emissions of CFCs have been declining so rapidly that the concentration field has been out of equilibrium with the emissions field, making the observations difficult to interpret. Instead, sulfur hexafluoride (SF6), a non-reactive anthropogenic tracer was chosen which is released primarily from electrical distribution equipment with a spatial pattern characteristic of fossil fuel emissions [Maiss et al. 1996]. The advantages of SF6 are that it has no sinks and therefore has a smoothly increasing time series which is easy to interpret, and that it is now measured at a relatively large number of stations around the world [Crutzen et al., 1998; Geller et al., 1997; Maiss et al., 1996]. Because the emissions and concentration field for SF6 are much better known than for CO2, the results of this "calibration experiment" were used to evaluate the realism of the large-scale inter-hemispheric transport characteristics of each model in a context for which the "right answer" was known. In addition, the calibration experiment included the calculation of transport diagnostics designed to help elucidate the mechanisms by which the various models produced their different tracer distributions.

Results from Phase II indicated closer model agreement than in Phase 1, partly due to a slightly different suite of models, and perhaps partly reflecting model improvement. One very significant conclusion is that the differences among the simulated Northern-Southern hemispheric gradients across models (which is used to interpret the strength of the meridional CO2 sink in inverse calculations) depends very strongly on the details of the sub-grid scale vertical mixing. Again, the models could be classified into two categories: the first simulated relatively weak vertical gradients over the northern extra tropics (SF6 source region), whereas the second group of models simulated stronger vertical gradients over the source region. This breakdown is likely the result of different model formulations with regard to how transport by convection and diffusion is parameterized at the subgrid scale (Denning et al. in press). Additional mechanistic details and results from TransCom 2 are still being analyzed.

Analysis of the changing distribution of trace gases can yield estimates of surface fluxes on large spatial scales (a "top-down" or "inverse" approach). The "top-down" or "inversion" approach has been widely used to study sources and sinks of atmospheric CO2 (Enting and Mansbridge 1989, Heimann and Keeling 1989, Tans et al 1989, 1990, Ciais et al. 1995). The next phase of TransCom will involve intercomparison of inversion calculations of the carbon budget of the atmosphere, with the objective of quantifying the uncertainty in such calculations that arises directly from uncertainty in the simulated transport. Computation of the contemporary carbon budget of the atmosphere using the suite of calibrated and improved models will provide both more reliable estimates of the terrestrial sink and a better set of tracer transport models for future research.

In March, 1998, a core group from TransCom met to discuss the experimental design for the inversion, or "top-down" simulations that would identify sources of atmospheric CO2. Some groups have more experience with model development and inversion calculations, and it was decided that the project would benefit from a three-tiered approach for group participation in the experiment: (Level 1) a minimal set of CTM integrations with maximum support from a central coordinator; (Level 2) a more involved set of basis functions and CTM integrations, preserving the central control of the inversion calculation; and (Level 3) complete flexibility with respect to both the structure of the source/sink functions and inversion methods. Submission of the Level 1 output would be required of all participants; Level 2 and Level 3 participation would be optional. Groups wishing to participate in Levels 2 or 3 would need to use the same CTM in all cases. Details of the planning session can be found at the TransCom homepage: http://dendrus.atmos.colostate.edu/transcom.

At this time, TransCom and GAIM would like to extend an open invitation to participate in this next exciting phase of TransCom. Participants at the planning meeting suggested that a workshop be held in December, 1998, prior to AGU in the San Francisco Bay Area to introduce TransCom 3 to the larger research community, seek comment on a proposed experimental design, and facilitate participation through data dissemination and tutorials. For further information, please contact Kevin Gurney at: keving@atmos.colostate.edu

References

Conway, T.J., P. Tans, L. Waterman, K. Thoning, D. Buanerkitzis, K. Masarie, and N. Zhang. 1994. Evidence for interannual variability of the carbon cycle from the NOAA/CMDL global air sampling network. Journal of Geophysical Research, 99D: 22831-22855.

Crutzen, P., T. Rockmann, C. Brenninkmeijer, and P. Neeb. 1998. Ozonolysis of nonmethane hydrocarbons as a source of the observed mass independent oxygen isotope enrichment on tropospheric CO. Journal of Geophysical Research 103:1463-1470.

Denning, A.S.,G.J. Collatz, C.G. Zhang, D.A. Randall, et al. 1996. Simulations of terrestrial carbon metabolism and atmospheric in a General Circulation Model. 1. Surface Carbon Fluxes. Tellus, Series B-Chemical and Physical Meteorology 48:521-542.

Denning, A.S., M. Holzer, K. Gurney, M. Heimann, R. Law, P. Rayner, I. Fung, and others. Three-Dimensional transport and concentration of SF6: A model intercomparison study (TransCom 2), Tellus, in press.

Enting, I., and J. Mansbridge. 1991. Latitudinal distribution of sources and sinks of CO2: Results of an inversion study. Tellus 43B:156-170.

Francey, R.J., P. Tans, C. Allison, I. Enting, J. White, and M. Trolier. 1995. Changes in oceanic and terrestrial carbon uptake since 1982. Nature 373:326-330.

Fung, I., K. Prentice, E. Mathews, J. Lerner, and G. Russell. 1983. Three-dimensional tracer model study of atmospheric CO2: Response to seasonal exchanges with the terrestrial biosphere. Journal of Geophysical Research 88:1281-1294.

Geller, L.S., J.W. Elkins, J.M. Lobert, A.D. Hurst and others. 1997. Troposhperic SF6: Observed latitudinal distribution and trends, derived emissions and interhemispheric exchange time. Geophysical Research Letters 24:675-678.

Heimann, M., and C. Keeling. 1989. A three-dimensional model of atmospheric CO2 transport based on observed winds: 2. Model description and simulated tracer experiments. In: Aspects of Climate Variability in the Pacific and Western Americas, (D.H. Peterson, ed.). 55:237-275, 305-363.

Keeling, C.D. 1986. Atmospheric CO2 concentrations. Mauna Loa Observatory, Hawaii 1958-1986. NDP-001/R1. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN.

Keeling, C., T. Whorf, M. Wahlen, and J.V.D. Plicht. 1995. Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980. Nature 375:666-670.

Law, R., P. Rayner, A. Denning, D. Erickson, I.Y. Fung, M. Heimann, S. Piper, M. Ramonet, S. T. Aguchi, J. Taylor, C. Trudinger, and I. Watterson. 1996. Variations in modeled atmospheric transport of carbon dioxide and the consequences for CO2 inversions. Global Biogeochemical Cycles 10:783-796.

Maiss, M., L.P. Steele, R.J. Francey, P.J. Fraser, and others. 1996. Sulfur HexafluorideÑA Powerful New Atmospheric Tracer. Atmospheric Environments 30:1621-1629.

Tans, P., T. Conway, and T. Nakazawa.1989. Latitudinal distribution of the sources and sinks of atmospheric carbon dioxide derived from surface observations and an atmospheric transport model, Journal of Geophysical Research 94:5151-5172.

Tans, P. P., I. Y. Fung, and T. Takahashi. 1990. Observational constraints on the global atmospheric CO2 budget. Science 247:1431-1438. Top


GAIM PLAN Fundamental Questions

  1. What controls the partitioning of the major biogeochemical elements in the Earth System?
    What are the patterns and processes by which C, N, P, S, Fe and other biologically important elements are partitioned among the major active reservoirs (vegetation and soils, atmosphere, continental water, coastal zone, open ocean)?

    1. What changes in elemental partitioning were associated with sea-level changes and other factors during glacial-interglacial cycles and how did these changes interact with marine and terrestrial productivity?
    2. What processes control horizontal transport of biogeochemically active species (CO2, CH4, P, S, N, etc.) above, at, and below the Earth's surface?
      (What is the stoichiometry of riverine fluxes today and how has this changed due to human activity? - Continental Aquatic Systems)
      (What is the effect of horizontal atmospheric transport of trace gases on global atmospheric composition? - Transcom)
      (What is the role of ocean circulation in redistributing CO2 and other trace gases, and how does this affect ocean-atmosphere gas exchange? - OCMIP)
    3. How will changing climate and land use alter the couplings between biogeochemical cycles of different elements?


  2. How do changes in ecosystems interact with the physical climate system?
    What processes determine how climate change affects marine and terrestrial ecosystems, and what are the potential climate feedbacks due to these processes?

    1. What have been the impacts of climate changes on marine and terrestrial ecosystems during the past 200 ka, and what have been the feedback effects on the physical atmosphere/ocean system?
    2. What is the role of ecosystem level processes (growth, competition, disturbance, mortality, decomposition, soil organic matter dynamics, migration) on the broad-scale structure of the biosphere? To what extent may plant population processes accelerate or delay climate- or CO2-related changes in the distribution of vegetation?
    3. How will future natural and anthropogenic changes in ecosystems and their interactions with climate affect the Earth System? In particular, what are the likely consequences of future land-use changes for the climate of the next 200 years?


  3. How do changes in the radiatively and chemically active gas composition of the atmosphere interact with the physical climate system?
    What controls atmospheric composition and what feedbacks exist between trace gases and terrestrial/marine sources and sinks?

    1. What have been the causes and consequences of natural atmospheric composition changes during the past 200 ka? (Paleo Trace Gas and Aerosol Initiative)
    2. What controls the sources and sinks of CO2, CH4, N2O, NOx, NMHC and CO in the biosphere and how are changes in climate likely to impact on the atmospheric concentrations of these gases? How can we explain the observed variability and trends of atmospheric aerosols, CO2, CH4, N2O and tropospheric O3 during recent decades?
    3. How would we expect future climate changes to interact with atmospheric trace gas composition, and what will be the consequences for scenarios covering the next 200 years?


  4. Given our understanding of the couplings among physical and biogeochemical aspects of the Earth system, what will be the nature of its future interactions with human activities?
    How will the nature of anthropogenic influences on the Earth System change in response to global change and how will the perception of environmental impacts alter human activities?

    1. Can rapid climate change events, like those that have happened during ice ages and deglaciations, also be triggered by human alterations of the Earth system?
    2. What are the likely relative magnitudes of the climatic effects of different anthropogenic drivers of global change, e.g. land use changes versus fossil fuel burning?
    3. How will an increased understanding of anthropogenic alterations of the Earth System affect future land use and emissions policy? What socio-economic factors will modulate enactment of and adherence to such policy, and to what extent do existing socio-economic conditions constrain future policy design, magnitude, implementation and time frame?



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