MJO Simulation Diagnostics

The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the MJO in climate and weather systems, current global circulation models (GCMs) exhibit considerable shortcomings in representing this phenomenon. These shortcomings have been documented in a number of multimodel comparison studies over the last decade. However, diagnosis of model performance has been challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of variance and correlation to more sophisticated space–time spectral and empirical orthogonal function analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.

Biosensor-based diagnostics of contaminated groundwater: assessment and remediation strategy

Shallow groundwater beneath a former airfield site in southern England has been heavily contaminated with a wide range of chlorinated solvents. The feasibility of using bacterial biosensors to complement chemical analysis and enable cost-effective, and focussed sampling has been assessed as part of a site evaluation programme. Five different biosensors, three metabolic (Vibrio fischeri, Pseudomonas fluorescens 10568 and Escherichia coli HB101) and two catabolic (Pseudomonas putida TVA8 and E. coli DH5alpha), were employed to identify areas where the availability and toxicity of pollutants is of most immediate environmental concern. The biosensors used showed different sensitivities to each other and to the groundwater samples tested. There was generally a good agreement with chemical analyses. The potential efficacy of remediation strategies was explored by coupling sample manipulation to biosensor tests. Manipulation involved sparging and charcoal treatment procedures to simulate remediative engineering solutions. Sparging was sufficient at most locations. (C) 2004 Elsevier Ltd. All rights reserved.

Public values for badgers, bovine TB reduction and management strategies

This paper applies an attribute-based stated choice experiment approach to estimate the value that society places on changes to the size of the badger population in England and Wales. The study was undertaken in the context of a rising incidence of bovine tuberculosis (bTB) in cattle and the government's review of current bTB control policy. This review includes consideration of culling badgers to reduce bTB in cattle, since badgers are thought to be an important wildlife reservoir for the disease. The design of the CE involved four attributes (size of badger population, cattle slaughtered due to bTB, badger management strategy and household tax) at four levels with eight choice sets of two alternatives presented to respondents. Telephone interviews were undertaken with over 400 respondents, which elicited their attitudes and preferences concerning badgers, bTB in cattle and badger management strategies. The study estimated a willingness to pay of 0.10 pound per household per year per 100,000 badgers and 1.52 pound per household per year per 10,000 cattle slaughtered due to bTB which aggregated to 22 per badger and 3298 pound per bTB slaughtered animal for all households in England and Wales. Management strategy toward badgers had a very high valuation, highlighting the emotive issue of badger culling for respondents and the importance of government policy towards badgers.

Lanthanide complexes of 2-hydroxynicotinic acid: synthesis, luminescence properties and the crystal structures of [Ln(HnicO)(2)(mu-HnicO)(H2O)] center dot nH(2)O (Ln = Tb, Eu)

New lanthanide complexes of 2-hydroxynicotinic acid (H(2)nicO) [Ln(HnicO)(2)(mu-HnicO)(H2O)] (.) nH(2)O (Ln = Eu, Gd, Tb, Er, Tm) were prepared. The crystal structures of the [Tb(HnicO)(2)(g-HnicO)(H2O)] (.) 1.75H(2)O(1) and [Eu(HniCO)(2)(mu-HnicO)(H2O)] (.) 1.25H(2)O (2) complexes were determined by X-ray diffraction. The 2-hydroxynicotinate ligand coordinates through O,O-chelation to the lanthanide(III) ions as shown by X-ray diffraction and the infrared, Raman and NMR spectroscopy results. Photoluminescence measurements were performed for the Eu(III) and Tb(III) complexes. Lifetimes of 0.592 +/- 0.007 and 0.113 +/- 0.002 ms were determined for the Eu3+ and Tb3+ emitting states D-5(0) and D-5(4), respectively. A value around 30% was found for the D-5(0) quantum efficiency. The energy transfer mechanisms between the lanthanide ions and the ligands are discussed and compared with those observed in similar complexes involving the 3-hydroxypicolinate ligand based on the luminescence of the respective Gd3+-based complexes. (C) 2003 Published by Elsevier Ltd.

Regularized logistic models for probabilistic forecasting and diagnostics

Logistic models are studied as a tool to convert dynamical forecast information (deterministic and ensemble) into probability forecasts. A logistic model is obtained by setting the logarithmic odds ratio equal to a linear combination of the inputs. As with any statistical model, logistic models will suffer from overfitting if the number of inputs is comparable to the number of forecast instances. Computational approaches to avoid overfitting by regularization are discussed, and efficient techniques for model assessment and selection are presented. A logit version of the lasso (originally a linear regression technique), is discussed. In lasso models, less important inputs are identified and the corresponding coefficient is set to zero, providing an efficient and automatic model reduction procedure. For the same reason, lasso models are particularly appealing for diagnostic purposes.

Understanding advances in the simulation of intraseasonal variability in the ECMWF model. Part II: the application of process-based diagnostics

In Part I of this study it was shown that moving from a moisture-convergent- to a relative-humidity-dependent organized entrainment rate in the formulation for deep convection was responsible for significant advances in the simulation of the Madden – Julian Oscillation (MJO) in the ECMWF model. However, the application of traditional MJO diagnostics were not adequate to understand why changing the control on convection had such a pronounced impact on the representation of the MJO. In this study a set of process-based diagnostics are applied to the hindcast experiments described in Part I to identify the physical mechanisms responsible for the advances in MJO simulation. Increasing the sensitivity of the deep convection scheme to environmental moisture is shown to modify the relationship between precipitation and moisture in the model. Through dry-air entrainment, convective plumes ascending in low-humidity environments terminate lower in the atmosphere. As a result, there is an increase in the occurrence of cumulus congestus, which acts to moisten the mid troposphere. Due to the modified precipitation – moisture relationship more moisture is able to build up, which effectively preconditions the tropical atmosphere for the t ransition t o d eep convection. R esults from this study suggest that a tropospheric moisture control on convection is key to simulating the interaction between the convective heating and the large-scale wave forcing associated with the MJO.

Using transport diagnostics to understand chemistry climate model ozone simulations

We use observations of N2O and mean age to identify realistic transport in models in order to explain their ozone predictions. The results are applied to 15 chemistry climate models (CCMs) participating in the 2010 World Meteorological Organization ozone assessment. Comparison of the observed and simulated N2O, mean age and their compact correlation identifies models with fast or slow circulations and reveals details of model ascent and tropical isolation. This process‐oriented diagnostic is more useful than mean age alone because it identifies models with compensating transport deficiencies that produce fortuitous agreement with mean age. The diagnosed model transport behavior is related to a model’s ability to produce realistic lower stratosphere (LS) O3 profiles. Models with the greatest tropical transport problems compare poorly with O3 observations. Models with the most realistic LS transport agree more closely with LS observations and each other. We incorporate the results of the chemistry evaluations in the Stratospheric Processes and their Role in Climate (SPARC) CCMVal Report to explain the range of CCM predictions for the return‐to‐1980 dates for global (60°S–60°N) and Antarctic column ozone. Antarctic O3 return dates are generally correlated with vortex Cly levels, and vortex Cly is generally correlated with the model’s circulation, although model Cl chemistry and conservation problems also have a significant effect on return date. In both regions, models with good LS transport and chemistry produce a smaller range of predictions for the return‐to‐1980 ozone values. This study suggests that the current range of predicted return dates is unnecessarily broad due to identifiable model deficiencies.

A closer look at chaotic advection in the stratosphere: part II: statistical diagnostics

Statistical diagnostics of mixing and transport are computed for a numerical model of forced shallow-water flow on the sphere and a middle-atmosphere general circulation model. In particular, particle dispersion statistics, transport fluxes, Liapunov exponents (probability density functions and ensemble averages), and tracer concentration statistics are considered. It is shown that the behavior of the diagnostics is in accord with that of kinematic chaotic advection models so long as stochasticity is sufficiently weak. Comparisons with random-strain theory are made.

Portmanteau model diagnostics and tests for nonlinearity: a comparative Monte Carlo study of two alternative methods

This paper employs an extensive Monte Carlo study to test the size and power of the BDS and close return methods of testing for departures from independent and identical distribution. It is found that the finite sample properties of the BDS test are far superior and that the close return method cannot be recommended as a model diagnostic. Neither test can be reliably used for very small samples, while the close return test has low power even at large sample sizes