Scientific Highlights


La Niņa Summit: Review of the Causes and Consequences of Cold Events

This event was convened by Michael Glantz in Boulder, Colorado, from 15-17 July 1998. The purpose for convening such a meeting was to identify what is known, what is not known, and what societies need to know about cold events in order to forecast their onset, growth, and decay several months in advance and to prepare for their societal impacts. (The terms "La Niņa" and "cold event" are used interchangeably.) An executive summary of the deliberations and presentations of the participants went on line in late September 1998. The full report has been on line since mid-October 1998. Both reports are available in hard copy. The workshop was supported by the United Nations U (Tokyo), NCAR, the United Nations Environment Programme (UNEP), and the NSF. The workshop was coordinated by an ESIG team led by D. Jan Stewart. Stewart was assisted by Baat Enosh (U Colorado-Boulder), Hanna Gilbert (U Colorado-Boulder) and Ben Rasmussen (Carleton College).


Prediction in the Earth Sciences: Use and Misuse by Policy-Makers

Pielke, Dan Sarewitz (Geological Society of America [GSA], Retired), Radford Byerly (retired, US House Science and Technology Committee), and Dale Jamieson (Carleton College) continued an NSF-sponsored project on the use and misuse of predictive earth science by policy-makers. Major financial and intellectual resources in the earth sciences are now focused on trying to predict the behavior of natural and human-induced environmental phenomena. Such efforts reflect a demand by policy-makers for predictive information that can help guide political decision-making on controversial environmental issues that include negotiation of international environmental treaties, disposal of radioactive waste, and control of development in areas prone to natural disasters. However, neither policy-makers nor scientists possess the information necessary for understanding if, how, and when research focusing on prediction can be productively applied to the policy-making process. Whereas timely, policy-relevant predictions may help policy-makers respond to some environmental problems, the misapplication of prediction research to policy problems can undermine policy goals, waste scarce financial and intellectual resources, and undermine the credibility of the scientific enterprise.

This project convened a major workshop in Estes Park, Colorado, in September 1998 that brought together scientists, policy-makers, and policy analysts to develop, present, and integrate case histories in predictive earth science research (past and ongoing). The workshop focused on the delineation of usable principles and criteria that can help policy-makers judge the potential value of scientific prediction for different types of political and social problems related to the environment and will thus contribute to the design of science and environmental policies that are fiscally responsible, scientifically efficient, and socially constructive. Toward this end, a significant component of this project will be the dissemination of workshop results to the relevant scientific and policy-making communities through publications and presentations.


U.S./Canada Transboundary Water Resources and Climate Change

Kathleen Miller and Linda Mearns completed work on a project for NAFTA's Commission for Environmental Cooperation (CEC) on the impacts of climate variability and possible climate change on transboundary freshwater resources in North America. The full project, which involved collaboration among researchers at NCAR, U Arizona, National Autonomous University of Mexico, and Environment Canada researchers at U British Columbia and U Waterloo provided the Commission with a comprehensive literature review and assessment of climate impacts and policy implications for a set of major transboundary rivers along the US/Canadian and US/Mexican borders.

Miller and Mearns wrote the overall introduction for the report and collaborated fully with the Canadian team on the survey of US/Canada border-region impacts, as well as on an extensive, focused assessment of impacts on the Columbia River system. The portions of the report contributed by this US/Canadian collaboration included a description of the current state of knowledge regarding the impacts of climate on streamflows, lake levels, water quality, and other salient characteristics of transboundary water resources along the US-Canadian border, along with assessments of the vulnerabilities of natural resource systems and water-dependent human activities to these climate impacts. The Columbia River Basin study focused on the potential implications of climate change for management of ongoing conflicts between hydropower production and other uses of the river, including protection of salmon populations. Work on this part of the project used the results of two regional climate model runs (control and doubled CO2) for the Pacific Northwest segment of the full domain, which included the western two-thirds of the US. Miller and Stewart Cohen (Environment Canada) are currently collaborating on an article for journal submission based on the Columbia River case study.


Climate Change Surprises

Glantz received funding from the Department of Energy (via Argonne National Laboratory) to collaborate with David Streets of Argonne on a project originally designed to define a taxonomy of global climate change "surprises." The earth's climate is, in many ways, an unpredictable physical system. Abrupt as well as slow-onset "surprises" often cause major disruptions to society in terms of loss of capital, natural resources, and human life. The results of this project will be available to those researchers involved in integrated assessment model development activities related to climate change, as well as to those interested in the policy aspects of the climate change issue. The project was finalized in FY98. Due to the demand for the project's final report, additional funds were obtained from DoE in order to reprint it in FY99 for more widespread distribution.


Normalized Hurricane Damages in the US: 1925-1995

Previous research into long-term trends in hurricane-caused damage along the US coast had suggested that economic damage has been rapidly increasing within the last two decades, even after considering inflation. However, to best capture the year-to-year variability in tropical cyclone damage, consideration must also be given to two additional factors: coastal population changes and changes in wealth. Both population and wealth have increased dramatically over the last several decades, and these factors act to enhance the recent hurricane damages preferentially over those that occurred previously. More appropriate trends in US hurricane damages can be calculated when a normalization of the damages is done that takes into account inflation, changes in coastal population, and wealth. With the normalization factored in, the trend of increasing damage amounts in recent decades disappears. Instead, substantial multidecadal variations in normalized damages are observed: the 1970s and 1980s actually saw less damages incurred than in the preceding few decades. Only during the early 1990s does damage approach the high level of impact seen in the period from the 1940s through the 1960s, showing that what has been observed recently is not unprecedented. Over the long term, the average annual impact of damages in the continental US is about $4.8 billion (1995 $) -- substantially more than previous estimates. Of these damages, over 83% are accounted for by hurricanes classified as intense (Saffir-Simpson Category 3, 4, and 5), yet these make up only 21 percent of the US-landfalling tropical cyclones.


Forecast Value Web Site

Shelly Knight (U Colorado-Boulder), in collaboration with Richard Katz, developed a web site that categorizes recent case studies of the value of weather and climate forecasts. This site is intended to supplement Economic Value of Weather and Climate Forecasts, a book edited by Katz and the late Allan Murphy (Oregon State U). The scope of the site is limited to prescriptive studies that obtain quantitative estimates of forecast value. The same categorization scheme is employed as in the book. Recent case studies are primarily from the agricultural sector, and several focus on El Niño/Southern Oscillation-related forecasts.


Nested Regional Climate Change Scenario

Linda Mearns, along with Larry McDaniel, Elena Tsvetsinskaya (U Nebraska-Lincoln), Theo Mavromatis (U Florida-Gainesville), William Easterling (Pennsylvania State U) and Cynthia Hays (U Nebraska-Lincoln), completed work on a four-year NIGEC (National Institute for Global Environmental Change) project, "Development of a Nested Regional Climate Change Scenario with an Application to Crop Models." The project involved regional climate modeling with RegCM2 by Filippo Giorgi (Salam International Centre for Theoretical Physics, Trieste, Italy) and Christine Shields (CGD), detailed climate model evaluation, and application to crop models. Each stage of the work focused on some kind of uncertainty analysis of: (1) spatial scale of climate change; (2) the type of downscaling technique; (3) effect of scale differences on agricultural impacts; and (4) choice of impact model. Numerous publications have resulted from this work (see FY97 and FY98 ASDR publications lists).

(1) A high-resolution climate change scenario (control and doubled carbon dioxide) was formed using the RegCM2 at 50-km grid point spacing of the western two-thirds of the United States. A coarse resolution scenario was formed from the output of the CSIRO general circulation model, which provided the boundary conditions for the regional model runs.

(2) The climate change scenario from the regional model was compared with a semi-empirical downscaling (SDS) method. The comparison indicates that the climate changes in the RegCM2 are more pronounced than those of the SDS. Also, different directions of change in precipitation were found between the two methods.

(3) We apply the two different scales of climate change scenarios to the EPIC corn, wheat, and soybean crop models for the GCM grid boxes in the central Great Plains. We found that the different scale scenarios produced substantial differences in the impacts of climate change on these agricultural crops (see figures).

(4) We went on to compare the results from EPIC corn and wheat models for parts of the Great Plains with those of the CERES corn and wheat models. We found that the CERES model produced very different changes in yield from those of the EPIC model (see figures).


Climate Variability and Agriculture in the Southeast US

Work on two overlapping three-year projects (NASA/USEPA/USDA) has continued by Mearns, Katz, McDaniel, Tsvetsinskaya, Mavromatis, Gregory Carbone (U South Carolina), Bette Walker-Shea (U Nebraska), and William Easterling (Pennsylvania State U). Regional climate modeling, conditioned stochastic modeling, and transient coupled GCM runs form the basis of three different types of climate change scenarios. Remote sensing, crop and economic modeling, and spatial scaling analysis make up the other elements of the projects.

Major accomplishments in the projects this year have included: (i) Development of a seasonal observed daily temperature and precipitation data set for the entire Southeastern US, gridded on a .5-degree grid; (ii) Development of two different soils data bases; (iii) Detailed site validation of wheat, soybean, sorghum, and corn CERES models; (iv) Advanced work on remotely sensed AVHRR and SPOT data; (v) Analysis of observed temperature and precipitation data sets in the Southeast US for development of stochastic models conditioned on ENSO phases (see section on stochastic modeling for further details); (vi) Generation of the second set of five-year control and doubled CO2 runs over the Southeast US with the Regional Climate Model; (vii) Preliminary multi-year runs of most crop models over the full domain, and comparison with county yields. The interannual variability of the spatial distribution of observed yields is well-reproduced by the models, especially for adjacent years when the variability is high.


   

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