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  • Author or Editor: Benjamin S. Felzer x
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Mingkai Jiang
,
Benjamin S. Felzer
, and
Dork Sahagian

Abstract

The proper understanding of precipitation variability, seasonality, and predictability are important for effective environmental management. Precipitation and its associated extremes vary in magnitude and duration both spatially and temporally, making it one of the most challenging climate parameters to predict on the basis of global and regional climate models. Using information theory, an improved understanding of precipitation predictability in the conterminous United States over the period of 1949–2010 is sought based on a gridded monthly precipitation dataset. Predictability is defined as the recurrent likelihood of patterns described by the metrics of magnitude variability and seasonality. It is shown that monthly mean precipitation and duration-based dry and wet extremes are generally highly variable in the east compared to those in the west, while the reversed spatial pattern is observed for intensity-based wetness indices except along the Pacific Northwest coast. It is thus inferred that, over much of the U.S. landscape, variations of monthly mean precipitation are driven by the variations in precipitation occurrences rather than the intensity of infrequent heavy rainfall. It is further demonstrated that precipitation seasonality for means and extremes is homogeneously invariant within the United States, with the exceptions of the West Coast, Florida, and parts of the Midwest, where stronger seasonality is identified. A proportionally higher role of variability in regulating precipitation predictability is demonstrated. Seasonality surpasses variability only in parts of the West Coast. The quantified patterns of predictability for precipitation means and extremes have direct applications to those phenomena influenced by climate periodicity, such as biodiversity and ecosystem management.

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Michael C. MacCracken
,
Eric J. Barron
,
David R. Easterling
,
Benjamin S. Felzer
, and
Thomas R. Karl

In support of the U.S. National Assessment of the Potential Consequences of Climate Variability and Change, climate scenarios were prepared to serve as the basis for evaluating the vulnerability of environmental and societal systems to changes projected for the twenty-first century. Since publication of the results of the assessment at the end of 2000, the National Research Council's report Climate Change Science: An Analysis of Some Key Questions, and the U.S. government's U.S. Climate Action Report—2002 have both relied on the assessment's findings. Because of the importance of these findings, it is important to directly address questions regarding the representativeness and usefulness of the model-based projections on which the findings were based. In particular, criticisms have focused on whether the climate models that were relied upon adequately represented twentieth-century conditions and whether their projections of conditions for the twenty-first century were outliers. Reexamination of the approach used in developing and evaluating the climate scenarios indicates that the results from the two primary climate modeling groups that were relied upon allowed the generation of climate scenarios that span much of the range of possible future climatic conditions projected by the larger set of model simulations, which was compiled for the IPCCs Third Assessment Report. With the set of models showing increasing agreement in their simulations of twentieth-century trends in climate and of projected changes in climate on subcontinental to continental scales, the climate scenarios that were generated seem likely to provide a plausible representation of the types of climatic conditions that could be experienced during the twenty-first century. Warming, reduced snow cover, and more intense heavy precipitation events were projected by all models, suggesting such changes are quite likely. However, significant differences remain in the projection of changes in precipitation and of the regional departures in climate from the larger-scale patterns. For this reason, evaluating potential impacts using climate scenarios based on models exhibiting different regional responses is a necessary step to ensuring a representative analysis. Utilizing an even more encompassing set of scenarios in the future could help move from mainly qualitative toward more certain and quantitative conclusions.

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Andrei P. Sokolov
,
David W. Kicklighter
,
Jerry M. Melillo
,
Benjamin S. Felzer
,
C. Adam Schlosser
, and
Timothy W. Cronin

Abstract

The impact of carbon–nitrogen dynamics in terrestrial ecosystems on the interaction between the carbon cycle and climate is studied using an earth system model of intermediate complexity, the MIT Integrated Global Systems Model (IGSM). Numerical simulations were carried out with two versions of the IGSM’s Terrestrial Ecosystems Model, one with and one without carbon–nitrogen dynamics.

Simulations show that consideration of carbon–nitrogen interactions not only limits the effect of CO2 fertilization but also changes the sign of the feedback between the climate and terrestrial carbon cycle. In the absence of carbon–nitrogen interactions, surface warming significantly reduces carbon sequestration in both vegetation and soil by increasing respiration and decomposition (a positive feedback). If plant carbon uptake, however, is assumed to be nitrogen limited, an increase in decomposition leads to an increase in nitrogen availability stimulating plant growth. The resulting increase in carbon uptake by vegetation exceeds carbon loss from the soil, leading to enhanced carbon sequestration (a negative feedback). Under very strong surface warming, however, terrestrial ecosystems become a carbon source whether or not carbon–nitrogen interactions are considered.

Overall, for small or moderate increases in surface temperatures, consideration of carbon–nitrogen interactions result in a larger increase in atmospheric CO2 concentration in the simulations with prescribed carbon emissions. This suggests that models that ignore terrestrial carbon–nitrogen dynamics will underestimate reductions in carbon emissions required to achieve atmospheric CO2 stabilization at a given level. At the same time, compensation between climate-related changes in the terrestrial and oceanic carbon uptakes significantly reduces uncertainty in projected CO2 concentration.

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