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Michael B. Richman and Peter J. Lamb

Abstract

This paper presents the results of climatic pattern analyses of three- and seven-day summer (May–August) rainfall totals for the central United States. A range of eigenvectorial methods is applied to 1949–80 data for a regularly spaced network of 402 stations that extends from the Rocky to the Appalachian Mountains and from the Gulf Coast to the Canadian border. The major objectives are to quantitatively assess the sensitivity of eigenvectorial results to several parameters that have hitherto been the subject of considerable qualitative concern, and to identify the potential applications of those results.

The entire domain variance fractions cumulatively explained by a) the first 10 correlation-based unrotated Principal Components (PCs) and b) the 10 orthogonally rotated (VARIMAX criterion) PCs derived from them are identical for the same data. They vary between 35–47 percent depending on the data time scale and form, being higher for seven- than three-day totals and further enhanced when those totals are square-root (especially) and log10 transformed. The (highly contrasting) sets of unrotated and VARIMAX PC spatial loading patterns are invariant with respect to data time scale and form. They receive strong statistical support from analyses performed on subsets of the data, their covariance- and cross-products-based equivalents, counterpart common factor patterns, and (for VARIMAX) an obliquely rotated (Hanis–Kaiser Case II B′B criterion) PC analysis. The unrotated PC loading patterns very closely resemble the set that Buell claimed would tend to characterize a domain of the present rectangular shape, irrespective of the meteorological parameter treated. They receive little physical support from analyses performed separately for subareas of the domain or from comparison with the interstation correlation matrix from which they are derived. The VARIMAX PC loading patterns, in contrast, derive strong physical support from those verifications. Each of these patterns emphasizes a relatively strong anomaly in a different part of the domain; they collectively yield a regionalization of the domain into 10 subareas within which three- and seven-day summer rainfall tends to be spatially coherent. The regionalization is suggested to be of considerable potential utility for crop-yield modeling, short-range weather prediction, and research into climatic variation and change.

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J. D. Fuentes, D. D. Baldocchi, and B. Lamb
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David L. Montroy, Michael B. Richman, and Peter J. Lamb

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Most investigations of relationships between tropical Pacific sea surface temperature anomaly (SSTA) events and regional climate patterns have assumed the teleconnections to be linear, whereby the climate patterns associated with cold SSTA events are considered to be similar in structure and morphology but opposite in sign to those linked to warm SSTA events. In contrast, and motivated by early evidence of nonlinearity in the above regard, this study identifies characteristic (i.e., composite) calendar monthly central and eastern North American precipitation patterns separately for warm and cold SSTA events in different regions of the tropical Pacific (central, eastern, west-central“horseshoe,” far western) identified through principal component analysis. The precipitation anomaly patterns are computed from an approximately 1° lat–long set of monthly station data for 1950–92. Their robustness and nonlinearity are established using local, regional, and field statistical significance tests and a variance analysis.

This combination of unique SSTA analyses, resulting composite selection, and characteristic precipitation anomaly determination from a fine-resolution dataset increases our understanding of tropical Pacific–North American precipitation teleconnections in several respects. First, significant linkages to the two SSTA modes related to traditional warm and cold events (central and eastern tropical Pacific) are identified for all months except September and October, with all exhibiting some nonlinear characteristics. The most important of those nonlinearities involve associations with eastern tropical Pacific SSTAs, which affect precipitation near the southern Atlantic and Gulf of Mexico coasts (dry for cold Novembers), around the Great Lakes and in the Ohio River valley (dry, warm, January–February), in the southeastern United States (dry, warm, July–August), and across the northern Great Plains (dry, warm, November–January). Conversely, the regions confirmed to have essentially linear associations with traditional warm and cold events include the Gulf of Mexico coast (positive relation with eastern tropical Pacific, January–March), Ohio River valley (negative, central tropical Pacific, February), and mid-Atlantic coast (negative, eastern tropical Pacific, July–August). However, only nonlinear precipitation teleconnections are associated with SSTAs in tropical Pacific regions largely unrelated to ENSO. These principally involve anomalously dry conditions in much of the eastern half of the United States during January–March and in the central United States in July–October (warm SSTAs in west-central tropical Pacific horseshoe), and in a strip from Texas to New England in January and along the central gulf coast and lower Mississippi valley in April (warm SSTAs in far western tropical Pacific). The results thus demonstrate the sensitivity of central and eastern North American precipitation teleconnections to the location and extent of tropical Pacific SSTAs. In the appendix, the present results are also compared to the observed climate anomalies during the 1997–98 El Niño event.

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D. Finn, B. Lamb, M. Y. Leclerc, S. Lovejoy, S. Pecknold, and D. Schertzer

Abstract

A codimension multifractal methodology was used to analyze and to model scalar concentration fluctuations within sulfur hexafluoride tracer gas plumes from a line source in atmospheric surface-layer flows. Correspondence was exhibited between the double trace moments parameters α and C 1 of the codimension methodology and the experimentally measured plume concentration characteristics of peak-to-mean ratio and concentration fluctuation intensity. Data series were generated using an extremal Levy, stochastic multifractal model, with the experimental α and C 1 as inputs. Uncertainties in experimentally determined plume characteristic values overlapped the uncertainties in model-simulated values. The utility of the procedure includes 1) characterizing the state of scalar turbulent mixing, 2) helping to evaluate and to model hazardous plume concentrations, and 3) being able to estimate the probability of realizing extreme events at timescales of observation beyond or at magnitudes in excess of those present in the actual observations.

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Zewdu T. Segele, Michael B. Richman, Lance M. Leslie, and Peter J. Lamb

Abstract

An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June–September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble.

For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed–predicted correlation r (SSClim) being +0.81 (65%) for 1970–2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society.

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Hamish A. Ramsay, Lance M. Leslie, Peter J. Lamb, Michael B. Richman, and Mark Leplastrier

Abstract

This study investigates the role of large-scale environmental factors, notably sea surface temperature (SST), low-level relative vorticity, and deep-tropospheric vertical wind shear, in the interannual variability of November–April tropical cyclone (TC) activity in the Australian region. Extensive correlation analyses were carried out between TC frequency and intensity and the aforementioned large-scale parameters, using TC data for 1970–2006 from the official Australian TC dataset. Large correlations were found between the seasonal number of TCs and SST in the Niño-3.4 and Niño-4 regions. These correlations were greatest (−0.73) during August–October, immediately preceding the Australian TC season. The correlations remain almost unchanged for the July–September period and therefore can be viewed as potential seasonal predictors of the forthcoming TC season. In contrast, only weak correlations (<+0.37) were found with the local SST in the region north of Australia where many TCs originate; these were reduced almost to zero when the ENSO component of the SST was removed by partial correlation analysis. The annual frequency of TCs was found to be strongly correlated with 850-hPa relative vorticity and vertical shear of the zonal wind over the main genesis areas of the Australian region. Furthermore, correlations between the Niño SST and these two atmospheric parameters exhibited a strong link between the Australian region and the Niño-3.4 SST. A principal component analysis of the SST dataset revealed two main modes of Pacific Ocean SST variability that match very closely with the basinwide patterns of correlations between SST and TC frequencies. Finally, it is shown that the correlations can be increased markedly (e.g., from −0.73 to −0.80 for the August–October period) by a weighted combination of SST time series from weakly correlated regions.

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E. Pattey, R. L. Desjardins, H. Westberg, B. Lamb, and T. Zhu

Abstract

Daytime isoprene emissions were measured over a black spruce forest in Saskatchewan (Canada) during the Boreal Ecosystem Atmosphere Study of 1994. The relaxed eddy-accumulation (REA) technique was used to measure isoprene fluxes in parallel with gradient measurements, which are required for using the gradient transport (GT) theory. The average isoprene flux was 2.29 mg C m−2 h−1 in late July and decreased to 0.54 mg C m−2 h−1 in early September. The senescent needles and lower ambient air temperature were most likely the cause of the lower isoprene emissions measured in September. A relationship of isoprene flux with air temperature was derived at the canopy scale because canopy temperature is not readily available. High isoprene emissions were observed at temperatures above 25°C. These were most likely in relation to thermoprotection of photosynthesis. The diurnal trends measured by GT and REA were similar. Isoprene fluxes measured using GT were 63% lower than those using REA. The underestimation resulted from having the lower GT inlet in the roughness sublayer, in which the flux–gradient relationships are not valid. Measuring the gradient at about two canopy height, with a reduced spacing from 10 to 3 m, would reduce the underestimation of the scalar flux, but it would also reduce the isoprene concentration differences by five to six times compared to those obtained with the REA technique. Previously, black spruce has been assigned to the highest emitting class of spruce forest for inventory purposes;however, the authors’ results suggest that the boreal zone black spruce should be assigned a standard emission rate of 6 rather than 18 μg C g−1 h−1.

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J. D. Fuentes, M. Lerdau, R. Atkinson, D. Baldocchi, J. W. Bottenheim, P. Ciccioli, B. Lamb, C. Geron, L. Gu, A. Guenther, T. D. Sharkey, and W. Stockwell

Nonmethane hydrocarbons are ubiquitous trace atmospheric constituents yet they control the oxidation capacity of the atmosphere. Both anthropogenic and biogenic processes contribute to the release of hydrocarbons to the atmosphere. In this manuscript, the state of the science concerning biosynthesis, transport, and chemical transformation of hydrocarbons emitted by the terrestrial biosphere is reviewed. In particular, the focus is on isoprene, monoterpenes, and oxygenated hydrocarbons. The generated science during the last 10 years is reviewed to explain and quantify hydrocarbon emissions from vegetation and to discern impacts of biogenic hydrocarbons on local and regional atmospheric chemistry. Furthermore, the physiological and environmental processes controlling biosynthesis and production of hydrocarbon compounds are reported on. Many advances have been made on measurement and modeling approaches developed to quantify hydrocarbon emissions from leaves and forest ecosystems. A synthesis of the atmospheric chemistry of biogenic hydrocarbons and their role in the formation of oxidants and aerosols is presented. The integration of biogenic hydrocarbon kinetics and atmospheric physics into mathematical modeling systems is examined to assess the contribution of biogenic hydrocarbons to the formation of oxidants and aerosols, thereby allowing us to study their impacts on the earth's climate system and to develop strategies to reduce oxidant precursors in affected regions.

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Edward G. Patton, Thomas W. Horst, Peter P. Sullivan, Donald H. Lenschow, Steven P. Oncley, William O. J. Brown, Sean P. Burns, Alex B. Guenther, Andreas Held, Thomas Karl, Shane D. Mayor, Luciana V. Rizzo, Scott M. Spuler, Jielun Sun, Andrew A. Turnipseed, Eugene J. Allwine, Steven L. Edburg, Brian K. Lamb, Roni Avissar, Ronald J. Calhoun, Jan Kleissl, William J. Massman, Kyaw Tha Paw U, and Jeffrey C. Weil

The Canopy Horizontal Array Turbulence Study (CHATS) took place in spring 2007 and is the third in the series of Horizontal Array Turbulence Study (HATS) experiments. The HATS experiments have been instrumental in testing and developing subfilterscale (SFS) models for large-eddy simulation (LES) of planetary boundary layer (PBL) turbulence. The CHATS campaign took place in a deciduous walnut orchard near Dixon, California, and was designed to examine the impacts of vegetation on SFS turbulence. Measurements were collected both prior to and following leafout to capture the impact of leaves on the turbulence, stratification, and scalar source/sink distribution. CHATS utilized crosswind arrays of fast-response instrumentation to investigate the impact of the canopy-imposed distribution of momentum extraction and scalar sources on SFS transport of momentum, energy, and three scalars. To directly test and link with PBL parameterizations of canopy-modified turbulent exchange, CHATS also included a 30-m profile tower instrumented with turbulence instrumentation, fast and slow chemical sensors, aerosol samplers, and radiation instrumentation. A highresolution scanning backscatter lidar characterized the turbulence structure above and within the canopy; a scanning Doppler lidar, mini sodar/radio acoustic sounding system (RASS), and a new helicopter-observing platform provided details of the PBL-scale flow. Ultimately, the CHATS dataset will lead to improved parameterizations of energy and scalar transport to and from vegetation, which are a critical component of global and regional land, atmosphere, and chemical models. This manuscript presents an overview of the experiment, documents the regime sampled, and highlights some preliminary key findings.

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Jielun Sun, Steven P. Oncley, Sean P. Burns, Britton B. Stephens, Donald H. Lenschow, Teresa Campos, Russell K. Monson, David S. Schimel, William J. Sacks, Stephan F. J. De Wekker, Chun-Ta Lai, Brian Lamb, Dennis Ojima, Patrick Z. Ellsworth, Leonel S. L. Sternberg, Sharon Zhong, Craig Clements, David J. P. Moore, Dean E. Anderson, Andrew S. Watt, Jia Hu, Mark Tschudi, Steven Aulenbach, Eugene Allwine, and Teresa Coons

A significant fraction of Earth consists of mountainous terrain. However, the question of how to monitor the surface–atmosphere carbon exchange over complex terrain has not been fully explored. This article reports on studies by a team of investigators from U.S. universities and research institutes who carried out a multiscale and multidisciplinary field and modeling investigation of the CO2 exchange between ecosystems and the atmosphere and of CO2 transport over complex mountainous terrain in the Rocky Mountain region of Colorado. The goals of the field campaign, which included ground and airborne in situ and remote-sensing measurements, were to characterize unique features of the local CO2 exchange and to find effective methods to measure regional ecosystem–atmosphere CO2 exchange over complex terrain. The modeling effort included atmospheric and ecological numerical modeling and data assimilation to investigate regional CO2 transport and biological processes involved in ecosystem–atmosphere carbon exchange. In this report, we document our approaches, demonstrate some preliminary results, and discuss principal patterns and conclusions concerning ecosystem–atmosphere carbon exchange over complex terrain and its relation to past studies that have considered these processes over much simpler terrain.

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