Kathy Hibbard

The next generation of scenarios for climate change research and assessment

Advances in the science and observation of climate change are providing a clearer understanding of the inherent variability of Earth’s climate system and its likely response to human and natural influences. The implications of climate change for the environment and society will depend not only on the response of the Earth system to changes in radiative forcings, but also on how humankind responds through changes in technology, economies, lifestyle and policy. Extensive uncertainties exist in future forcings of and responses to climate change, necessitating the use of scenarios of the future to explore the potential consequences of different response options. To date, such scenarios have not adequately examined crucial possibilities, such as climate change mitigation and adaptation, and have relied on research processes that slowed the exchange of information among physical, biological and social scientists. Here we describe a new process for creating plausible scenarios to investigate some of the most challenging and important questions about climate change confronting the global community.

Sustainability or Collapse: What Can We Learn from Integrating the History of Humans and the Rest of Nature?

Abstract Understanding the history of how humans have interacted with the rest of nature can help clarify the options for managing our increasingly interconnected global system. Simple, deterministic relationships between environmental stress and social change are inadequate. Extreme drought, for instance, triggered both social collapse and ingenious management of water through irrigation. Human responses to change, in turn, feed into climate and ecological systems, producing a complex web of multidirectional connections in time and space. Integrated records of the co-evolving human-environment system over millennia are needed to provide a basis for a deeper understanding of the present and for forecasting the future. This requires the major task of assembling and integrating regional and global historical, archaeological, and paleoenvironmental records. Humans cannot predict the future. But, if we can adequately understand the past, we can use that understanding to influence our decisions and to create a better, more sustainable and desirable future.

GLOBAL POTENTIAL NET PRIMARY PRODUCTION PREDICTED FROM VEGETATION CLASS, PRECIPITATION, AND TEMPERATURE

Net primary production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approximately 5600 global data points with observed mean annual NPP, land cover class, precipitation, and temperature were compiled. Precipitation was better correlated with NPP than temperature, and it explained much more of the variability in mean annual NPP for grass- or shrub-dominated systems (r2 = 0.68) than for tree-dominated systems (r2 = 0.39). For a given precipitation level, tree-dominated systems had significantly higher NPP (approximately 100-150 g C m(-2) yr(-1)) than non-tree-dominated systems. Consequently, previous empirical models developed to predict NPP based on precipitation and temperature (e.g., the Miami model) tended to overestimate NPP for non-tree-dominated systems. Our new model developed at the National Center for Ecological Analysis and Synthesis (the NCEAS model) predicts NPP for tree-dominated systems based on precipitation and temperature; but for non-tree-dominated systems NPP is solely a function of precipitation because including a temperature function increased model error for these systems. Lower NPP in non-tree-dominated systems is likely related to decreased water and nutrient use efficiency and higher nutrient loss rates from more frequent fire disturbances. Late 20th century aboveground and total NPP for global potential native vegetation using the NCEAS model are estimated to be approximately 28 Pg and approximately 46 Pg C/yr, respectively. The NCEAS model estimated an approximately 13% increase in global total NPP for potential vegetation from 1901 to 2000 based on changing precipitation and temperature patterns.

Observing terrestrial ecosystems and the carbon cycle from space.

Terrestrial ecosystem and carbon cycle feedbacks will significantly impact future climate, but their responses are highly uncertain. Models and tipping point analyses suggest the tropics and arctic/boreal zone carbon-climate feedbacks could be disproportionately large. In situ observations in those regions are sparse, resulting in high uncertainties in carbon fluxes and fluxes. Key parameters controlling ecosystem carbon responses, such as plant traits, are also sparsely observed in the tropics, with the most diverse biome on the planet treated as a single type in models. We analyzed the spatial distribution of in situ data for carbon fluxes, stocks and plant traits globally and also evaluated the potential of remote sensing to observe these quantities. New satellite data products go beyond indices of greenness and can address spatial sampling gaps for specific ecosystem properties and parameters. Because environmental conditions and access limit in situ observations in tropical and arctic/boreal environments, use of space-based techniques can reduce sampling bias and uncertainty about tipping point feedbacks to climate. To reliably detect change and develop the understanding of ecosystems needed for prediction, significantly, more data are required in critical regions. This need can best be met with a strategic combination of remote and in situ data, with satellite observations providing the dense sampling in space and time required to characterize the heterogeneity of ecosystem structure and function.

A strategy for climate change stabilization experiments

Climate models used for climate change projections are on the threshold of including much greater biological and chemical detail than previous models. Today, standard climate models (referred to generically as atmosphere-ocean general circulation models, or AOGCMs) include components that simulate the coupled atmosphere, ocean, land, and sea ice. Some modeling centers are now incorporating carbon cycle models into AOGCMs in a move toward an Earth system model (ESM) capability. Additional candidate components to include in ESMs are aerosols, chemistry, ice sheets, and dynamic vegetation [e.g., Cox et al., 2000; Friedlingstein et al., 2006].

Investigating the nexus of climate, energy, water, and land at decision-relevant scales: the Platform for Regional Integrated Modeling and Analysis (PRIMA)

The Platform for Regional Integrated Modeling and Analysis (PRIMA) is an innovative modeling system developed at Pacific Northwest National Laboratory (PNNL) to simulate interactions among natural and human systems at scales relevant to regional decision making. PRIMA brings together state-of-the-art models of regional climate, hydrology, agriculture and land use, socioeconomics, and energy systems using a flexible coupling approach. Stakeholder decision support needs underpin the application of the platform to regional issues, and an uncertainty characterization process is used to identify robust decisions. The platform can be customized to inform a variety of complex questions, such as how a policy in one sector might affect the ability to meet climate mitigation targets or adaptation goals in another sector. Current numerical experiments focus on the eastern United States, but the framework is designed to be regionally flexible. This paper provides a high-level overview of PRIMA’s functional capabilities and describes some key challenges and opportunities associated with integrated regional modeling.

Initiative to quantify terrestrial carbon sources and sinks

Questions related to the distribution and spatio-temporal dynamics of the terrestrial carbon fluxes are at the core of current scientific and policy debates. In recent years, the primary concern has been the increasing CO2 content in the atmosphere, its effect on climate, and the associated role of terrestrial ecosystems in mitigating the increase and impact of climate change. However, terrestrial carbon dynamics is also closely related to biodiversity land degradation, and other pressing policy and assessment questions. Yet at the global level, no system in place now can provide quantitative information about carbon sources and sinks systematically, reliably, and accurately.

A modeling study of coastal inundation induced by storm surge, sea-level rise, and subsidence in the Gulf of Mexico

Abstract The northern coasts of the Gulf of Mexico (GoM) are highly vulnerable to the direct threats of climate change, such as hurricane-induced storm surge, and such risks are exacerbated by land subsidence and global sea-level rise. This paper presents an application of a coastal storm surge model to study the coastal inundation process induced by tide and storm surge, and its response to the effects of land subsidence and sea-level rise in the northern Gulf coast. The unstructured-grid finite-volume coastal ocean model was used to simulate tides and hurricane-induced storm surges in the GoM. Simulated distributions of co-amplitude and co-phase lines for semi-diurnal and diurnal tides are in good agreement with previous modeling studies. The storm surges induced by four historical hurricanes (Rita, Katrina, Ivan, and Dolly) were simulated and compared to observed water levels at National Oceanic and Atmospheric Administration tide stations. Effects of coastal subsidence and future global sea-level rise on coastal inundation in the Louisiana coast were evaluated using a “change of inundation depth” parameter through sensitivity simulations that were based on a projected future subsidence scenario and 1-m global sea-level rise by the end of the century. Model results suggested that hurricane-induced storm surge height and coastal inundation could be exacerbated by future global sea-level rise and subsidence, and that responses of storm surge and coastal inundation to the effects of sea-level rise and subsidence are highly nonlinear and vary on temporal and spatial scales.

Aggregation of Species Properties for Biogeochemical Modeling: Empirical Results

In many biogeochemical models, plant species are aggregated such that only one generic plant type is represented. If multiple species are present, each “species” or functional type is represented as a collection of physiological traits. We recently have been exploring the physiological responses of co-occurring plant species of a variety of growth forms to determine how many separate physiological types are needed in order to capture the dynamics of net primary production, decomposition, carbon (C) storage and nitrogen (N) availability. We measured species-level photosynthetic responses, canopy light extinction, leaf N and lignin, and soil nutrient processes. In both the prairie and the shrub-savanna we found that photosynthetic responses scale with light within the plant canopy such that for calculation of C gain, the canopy can be modeled as a single unit, regardless of the vertical distribution of species. Nutrient cycling and C storage are, however, very different in herbaceous areas compared to wooded areas, such that shrub-dominated areas have different soil C levels and N mineralization rates from grassland areas. In Texas, shrub-dominated areas have higher soil C and N mineralization. The dominant woody plant in the Texas site is an N fixer. Significant areal expansion of this plant, documented since the mid-1800s, has undoubtedly affected regional patterns of N cycling and net primary production (NPP) (similar to results of Vitousek and Walker, 1989, from Hawaii). The dominant woody plant in Kansas is not a fixer and invasion (which is locally common due to fire suppression) results in losses of N availability and stored C. We suggest that some ecosystem processes may be modeled successfully with highly aggregated models, particularly models of processes that are highly constrained by environmental factors. Other processes will require detailed specification of physiological traits at the species or population level. The degree of species aggregation in ecosystem models should be regarded as a research problem rather than a quasi-ideological problem.

Northern Eurasia in the global Earth system

Northern Eurasia is undergoing significant changes associated with warming climate and with socioeconomic changes during the entire twentieth century. Climatic changes over this vast landmass interact and affect the rate of global change through atmospheric circulation and through strong biogeophysical and biogeochemical couplings. Current and future interactions and feedbacks to the global system of this carbon- rich, cold-region component of the Earth system remain to a large extent unknown. The Northern Eurasia Earth Science Partnership Initiative (NEESPI; http://neespi.org), an international and interdisciplinary program, was established to address these issues. NEESPIs overarching science question is, How do we develop our predictive capability of terrestrial ecosystems dynamics over northern Eurasia for the 21st century to support global projections as well as informed decision-making and numerous practical applications in the region? Since 2004, more than 100 international research projects have joined NEESPI, and the initiative has been endorsed by several international programs and projects.