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Peter Morgan
and
Uwe Radok

Abstract

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Joseph G. Alfieri
,
Peter D. Blanken
,
David Smith
, and
Jack Morgan

Abstract

Grassland environments constitute approximately 40% of the earth’s vegetated surface, and they play a key role in a number of processes linking the land surface with the atmosphere. To investigate these linkages, a variety of techniques, including field and modeling studies, are required. Using data collected at the Central Plains Experimental Range (CPER) in northeastern Colorado from 25 March to 10 November 2004, this study compares two common ways of measuring turbulent fluxes of latent heat, sensible heat, and carbon dioxide in the field: the eddy covariance (EC) and Bowen ratio energy balance (BREB) methods. The turbulent fluxes measured by each of these methods were compared in terms of magnitude and seasonal behavior and were combined to calculate eddy diffusivities and examine turbulent transport. Relative to the EC method, the BREB method tended to overestimate the magnitude of the sensible heat, latent heat, and carbon dioxide fluxes. As a result, substantial differences in both the diurnal pattern and long-term magnitudes of the water and carbon budgets were apparent depending on which method was used. These differences arise from (i) the forced closure of the surface energy balance and (ii) the assumption of similarity between the eddy diffusivities required by the BREB method. An empirical method was developed that allows the BREB and EC datasets to be reconciled; this method was tested successfully using data collected at the CPER site during 2005. Ultimately, however, the BREB and EC methods show important differences that must be recognized and taken into account when analyzing issues related to the energy, water, or carbon cycles.

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James S. Risbey
,
Peter J. Lamb
,
Ron L. Miller
,
Michael C. Morgan
, and
Gerard H. Roe

Abstract

A set of regional climate scenarios is constructed for two study regions in North America using a combination of GCM output and synoptic–dynamical reasoning. The approach begins by describing the structure and components of a climate scenario and identifying the dynamical determinants of large-scale and regional climate. Expert judgement techniques are used to categorize the tendencies of these elements in response to increased greenhouse forcing in climate model studies. For many of the basic dynamical elements, tendencies are ambiguous, and changes in sign (magnitude, position) can usually be argued in either direction. A set of climate scenarios is produced for winter and summer, emphasizing the interrelationships among dynamical features, and adjusting GCM results on the basis of known deficiences in GCM simulations of the dynamical features. The scenarios are qualitative only, consistent with the level of precision afforded by the uncertainty in understanding of the dynamics, and in order to provide an outline of the reasoning and chain of contingencies on which the scenarios are based. The three winter scenarios outlined correspond roughly to a north–south displacement of the stationary wave pattern, to an increase in amplitude of the pattern, and to a shift in phase of the pattern. These scenarios illustrate that small changes in the dynamics can lead to large changes in regional climate in some regions, while other regions are apparently insensitive to some of the large changes in dynamics that can be plausibly hypothesized. The dynamics of summer regional climate changes are even more difficult to project, though thermodynamic considerations allow some more general conclusions to be reached in this season. Given present uncertainties it is difficult to constrain regional climate projections.

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Christopher A. Fiebrich
,
Cynthia R. Morgan
,
Alexandria G. McCombs
,
Peter K. Hall Jr.
, and
Renee A. McPherson

Abstract

Mesoscale meteorological data present their own challenges and advantages during the quality assurance (QA) process because of their variability in both space and time. To ensure data quality, it is important to perform quality control at many different stages (e.g., sensor calibrations, automated tests, and manual assessment). As part of an ongoing refinement of quality assurance procedures, meteorologists with the Oklahoma Mesonet continually review advancements and techniques employed by other networks. This article’s aim is to share those reviews and resources with scientists beginning or enhancing their own QA program. General QA considerations, general automated tests, and variable-specific tests and methods are discussed.

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Peter J. Walton
,
Morgan B. Yarker
,
Michel D. S. Mesquita
, and
Friederike E. L. Otto

Abstract

Globally, decision-makers are increasingly using high-resolution climate models to support policy and planning; however, many of these users do not have the knowledge needed to use them appropriately. This problem is compounded by not having access to quality learning opportunities to better understand how to apply the models and interpret results. This paper discusses and proposes an educational framework based on two independent online courses on regional climate modeling, which addresses the accessibility issue and provides guidance to climate science professors, researchers, and institutions who want to create their own online courses.

The role of e-learning as an educational tool is well documented, highlighting the benefits of improved personal efficiency through “anywhere, anytime” learning with the flexibility to support professional development across different sectors. In addition, improved global Internet means increased accessibility. However, e-learning’s function as a tool to support understanding of atmospheric physics and high-resolution climate modeling has not been widely discussed. To date, few courses, if any, support understanding that takes full advantage of e-learning best practices.

There is a growing need for climate literacy to help inform decision-making on a range of scales, from individual households to corporate CEOs. And while there is a plethora of climate information online, educational theory suggests that people need to be guided in how to convert this information into applicable knowledge.

Here, we present how the experience of the courses we designed and ran independent of each other, both engaging learners with better understanding benefits and limitations of regional climate modeling, lead to a framework of designing e-learning for climate modeling.

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