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Charles Jones

Abstract

California receives most of the annual precipitation during the boreal winter season. Additionally, large spatial and temporal variations in the total rainfall amounts are observed. This study investigates the occurrence of extreme precipitation events in California and the modulation by the Madden–Julian oscillation (MJO). Three questions are investigated. 1) Are extreme precipitation events in California more likely to occur during active MJO than inactive periods? 2) In what phase of the MJO life cycle are extreme events more likely? 3) Are interannual variations in the frequency of extreme events in California related to interannual variations of the MJO?

Daily totals derived from gridded hourly station data are used to define extreme precipitation events from January 1958 to December 1996. Outgoing longwave radiation from polar orbiting satellites (1979–96) and zonal component of the wind at 200 hPa and 850 hPa from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis (1958–96) are used to describe the life cycle of the oscillation and its interannual variability. The results indicate that the frequency of extreme events are more common when tropical activity associated with the MJO is high, as opposed to periods of quiescent phases of the oscillation. Second, a slight preference for a higher number of events is observed when convective anomalies are located in the Indian Ocean. In this situation, low-level westerly and easterly wind anomalies are observed over the Indian and western Pacific Oceans, respectively. The analysis of the interannual variability in the amplitude of the MJO and the occurrence of extreme events over California indicates no direct and systematic relationships with the number of extreme events.

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Charles Jones

Abstract

This paper presents a new empirical model to simulate the Madden–Julian oscillation (MJO), which is the most prominent mode of tropical intraseasonal variability. Zonal wind components at 850 and 200 hPa from reanalysis (1948–2007) and outgoing longwave radiation from satellites (1979–2007) are used to identify MJOs and characterize their statistical properties.

The temporal variability of the MJO is represented with a nine-state first-order Markov chain in which state 0 represents quiescent days and states 1–8 are phases of the MJO when it is active. Transition probabilities are estimated based on the historical record of MJO events, and sensitivity tests were performed to evaluate the best estimates for a homogeneous model. Once the model simulates time series of phase transitions, composites of convective and circulation anomalies determine the spatial structure of the events. The amplitudes of the MJOs are stochastically generated with an amplitude factor that has a Gaussian frequency distribution.

MJO events generated by the homogeneous stochastic model occur irregularly in time and can appear as single isolated events or sequences of successive MJOs. The MJO in the model can have different eastward propagations and the zonal scale is consistent with the observations. The simulated MJOs have different durations (30–90 days), and each event can be stronger or weaker than the mean composite according to a normal distribution. The results show that the homogeneous stochastic model simulates the irregularity of the MJO and model biases are small. Possible applications and future extensions of the homogeneous stochastic model are discussed.

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Charles Jones and Jimy Dudhia

Abstract

The Madden–Julian oscillation (MJO) is an important source of predictability. The boreal 2004/05 winter is used as a case study to conduct predictability experiments with the Weather Research and Forecasting (WRF) Model. That winter season was characterized by an MJO event, weak El Niño, strong North Atlantic Oscillation, and extremely wet conditions over the contiguous United States (CONUS). The issues investigated are as follows: 1) growth of forecast errors in the tropics relative to the extratropics, 2) propagation of forecast errors from the tropics to the extratropics, 3) forecast error growth on spatial scales associated with MJO and non-MJO variability, and 4) the relative importance of MJO and non-MJO tropical variability on predictability of precipitation over CONUS.

Root-mean-square errors in forecasts of normalized eddy kinetic energy (NEKE) (200 hPa) show that errors in initial conditions in the tropics grow faster than in the extratropics. Potential predictability extends out to about 4 days in the tropics and 9 days in the extratropics. Forecast errors in the tropics quickly propagate to the extratropics, as demonstrated by experiments in which initial conditions are only perturbed in the tropics. Forecast errors in NEKE (200 hPa) on scales related to the MJO grow slower than in non-MJO variability over localized areas in the tropics and short lead times. Potential predictability of precipitation extends to 1–5 days over most of CONUS but to longer leads (7–12 days) over regions with orographic precipitation in California. Errors in initial conditions on small scales relative to the MJO quickly grow, propagate to the extratropics, and degrade forecast skill of precipitation.

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Charles Jones and Bryan C. Weare

Abstract

This paper examines whether or not low-level moisture convergence and surface latent heat flux act as forcing mechanisms of the Madden and Julian oscillation (MJO), as it is proposed by the theories of wave-CISK (conditional instability of the second kind) and evaporation-wind feedback. The mean brightness temperature of cloudy pixels at 11 μm, obtained from five years of International Satellite Cloud Climatology Project data, is used as a proxy for tropical convective activity. Five years of European Centre for Medium-Range Weather Forecasts analyses are used to estimate surface latent heat fluxes and moisture divergence integrated in the low levels of the troposphere.

Spectral analysis of latent heat fluxes over the Indian and Pacific Oceans shows significant spectral peaks in the frequency band of the MJO. These peaks are due mainly to the oscillation in the surface wind speed rather than in the specific humidity difference. Principal component analysis and tagged correlation patterns of filtered time series 20–70 days are used to investigate the relationships between anomalies in convection, surface latent heat fluxes, and low-level moisture divergence. The correlation patterns show that negative anomalies of latent heat fluxes are systematically observed to the east, whereas positive anomalies are observed to the west of the region of convection. Positive anomalies of surface latent heat flux tag time variations in convection by about 4 days. This result contrasts with the basic requirement of the evaporation-wind feedback theory, which claims that evaporation anomalies are positive on the eastern side of the convective region. In contrast, tag correlation patterns indicate that the region of maximum low-level moisture convergence is located to the east of the region of convection, and low-level moisture convergence leads time variations in convective activity by about 2 days. This observational result supports the frictional wave-CISK theory as a mechanism of the MJO.

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Charles C. Meek and Barclay G. Jones

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Leila M. V. Carvalho and Charles Jones

Abstract

Global warming has been linked to systematic changes in North and South America's climates and may severely impact the North American monsoon system (NAMS) and South American monsoon system (SAMS). This study examines interannual-to-decadal variations and changes in the low-troposphere (850 hPa) temperature (T850) and specific humidity (Q850) and relationships with daily precipitation over the tropical Americas using the NCEP–NCAR reanalysis, the Climate Forecast System Reanalysis (CFSR), and phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations for two scenarios: “historic” and high-emission representative concentration pathway 8.5 (RCP8.5). Trends in the magnitude and area of the 85th percentiles were distinctly examined over North America (NA) and South America (SA) during the peak of the respective monsoon season. The historic simulations (1951–2005) and the two reanalyses agree well and indicate that significant warming has occurred over tropical SA with a remarkable increase in the area and magnitude of the 85th percentile in the last decade (1996–2005). The RCP8.5 CMIP5 ensemble mean projects an increase in the T850 85th percentile of about 2.5°C (2.8°C) by 2050 and 4.8°C (5.5°C) over SA (NA) by 2095 relative to 1955. The area of SA (NA) with T850 ≥ the 85th percentile is projected to increase from ~10% (15%) in 1955 to ~58% (~33%) by 2050 and ~80% (~50%) by 2095. The respective increase in the 85th percentile of Q850 is about 3 g kg−1 over SAMS and NAMS by 2095. CMIP5 models project variable changes in daily precipitation over the tropical Americas. The most consistent is increased rainfall in the intertropical convergence zone in December–February (DJF) and June–August (JJA) and decreased precipitation over NAMS in JJA.

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Leila M. V. Carvalho and Charles Jones

Abstract

A simple, fully automated, and efficient method to determine the structural properties and evolution (tracking) of cloud shields of convective systems (CS) is described. The method, which is based on the maximum spatial correlation tracking technique (MASCOTTE), is a new alternative to the existent techniques available for studies that monitor the evolution of CS using satellite images. MASCOTTE provides as CS structural properties the following parameters: mean and variance of brightness temperature, horizontal area, perimeter, minimum brightness temperature, fractional convective area, center of gravity, and fragmentation. The fragmentation parameter has the potential to monitor the evolution of the CS. A new way of estimating the orientation and eccentricity of CS is proposed and is based on the empirical orthogonal function analysis of CS pixel coordinates. The method includes an accurate detection of splitting and merging of convective systems, which is a critical step in the automated satellite CS life cycle determination. Based on the magnitudes of the spatial correlation between consecutive satellite images and the changes in horizontal areas of CS, MASCOTTE provides a simple and skillful technique to track the evolution of CS life cycles. The MASCOTTE methodology is applied to infrared satellite images during seven consecutive days of the Wet-Season Atmospheric Mesoscale Campaign of the Large-Scale Biosphere–Atmosphere Experiment and ground validation experiment of the Tropical Rainfall Measuring Mission in the Brazilian state of Rondônia in the Amazon basin. The results indicate that MASCOTTE is a valuable approach to understanding the variability of CS.

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Charles Jones, Pete Peterson, and Catherine Gautier

Abstract

A new methodology for deriving monthly averages of surface specific humidity (Q a) and air temperature (T a) is described. Two main aspects characterize the new approach. First, remotely sensed parameters, total precipitable water (W), and sea surface temperature (SST) are used to derive Q a and T a. Second, artificial neural networks (ANN) are employed to find transfer functions relating the input (W, SST) and output (Q a and T a) parameters. Input data consist of nearly six years (January 1988–November 1993) of monthly averages of total precipitable water from Special Sensor Microwave/Imager data and sea surface temperature analysis from the National Centers for Environmental Prediction. Surface marine observations of Q a and T a are used to develop and evaluate the new methodology.

The performance of the algorithm is measured with surface marine observations not used in the development phase. Higher seasonally dependent discrepancies between Q a and T a derived from the new method and in situ data are observed in regions such as the Kuroshio and Gulf Stream currents. After removal of systematic biases, the new method indicates that the combination of W and SST as input parameters and the ANN algorithm provides an interesting alternative for deriving monthly averaged surface parameters. The global mean bias in Q a is 0.010 ± 0.23 g kg−1 over most oceanic areas, whereas root-mean-square (rms) differences are 0.77 ± 0.39 g kg−1. Likewise, the global mean bias and rms in T a are on the order of −7.3 × 10−5 ± 0.27°C and 0.72 ± 0.38°C, respectively.

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Charles Jones, Duane E. Waliser, and Catherine Gautier

Abstract

The Madden–Julian oscillation (MJO) involves pronounced variations in convection and large-scale circulation throughout the tropical troposphere. In addition, the MJO is also related to dynamic and thermodynamic variability near the surface and the upper ocean. This study uses observational data to characterize the changes in surface heat fluxes and sea surface temperature (SST) during the life cycle of the MJO.

Variations in convective activity are described with outgoing longwave radiation (OLR) during the period January 1985 through September 1994. International Satellite Cloud Climatology Project data (January 1985–April 1991) and European Centre for Medium-Range Weather Forecasts surface analyses (January 1985–December 1994) are used to derive surface fluxes of net shortwave radiation (SW), latent heat (E), their difference (Q = SW − E), and SST.

The spatial patterns of OLR, SW, E, Q, and SST anomalies reveal that the region of positive OLR anomalies that precede the occurrence of enhanced convection is associated with positive SW and negative E anomalies, which result in positive Q anomalies. The prevailing conditions in the region of positive Q anomalies favor the development of positive SST anomalies, which lead to variations of enhanced convection. In contrast the region of negative OLR anomalies is associated with negative SW and positive E anomalies. These conditions induce negative Q anomalies, which favor the formation of negative SST anomalies. The above results suggest a possible feedback between the oscillation and intraseasonal variations in SST and this may be an important mechanism for numerical simulations of the life cycle of the MJO.

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Charles C. Meek and Barclay G. Jones

Abstract

A statistical analysis has been made of individual particle transport in a homogeneous, turbulent fluid flow. Expressions for dispersion, correlation coefficients, and turbulent energy content have been obtained. In the course of the development two parameters were found to characterize particulate transport, one of which relates to inertial effects acting on the particle, while the other describes the effects of crossing trajectories. As in previous studies by others, crossing-trajectories effects are found to he of particular importance; inertial effects, however, even for heavy particles, are not insignificant. Comparison of theoretical predictions with experimental data shows good agreement.

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