Real-time extraction of the Madden-Julian oscillation using empirical mode decomposition and statistical forecasting with a VARMA model

A simple guide to the new technique of empirical mode decomposition (EMD) in a meteorological-climate forecasting context is presented. A single application of EMD to a time series essentially acts as a local high-pass filter. Hence, successive applications can be used to produce a bandpass filter that is highly efficient at extracting a broadband signal such as the Madden-Julian Oscillation (MJO). The basic EMD method is adapted to minimize end effects, such that it is suitable for use in real time. The EMD process is then used to efficiently extract the MJO signal from gridded time series of outgoing longwave radiation (OLR) data. A range of statistical models from the general class of vector autoregressive moving average (VARMA) models was then tested for their suitability in forecasting the MJO signal, as isolated by the EMD. A VARMA (5, 1) model was selected and its parameters determined by a maximum likelihood method using 17 yr of OLR data from 1980 to 1996. Forecasts were then made on the remaining independent data from 1998 to 2004. These were made in real time, as only data up to the date the forecast was made were used. The median skill of forecasts was accurate (defined as an anomaly correlation above 0.6) at lead times up to 25 days

Tropical mid-tropospheric CO_2 variability driven by the Madden–Julian oscillation

Carbon dioxide (CO_2) is the most important anthropogenic greenhouse gas in the present-day climate. Most of the community focuses on its long-term (decadal to centennial) behaviors that are relevant to climate change, but there are relatively few discussions of its higher-frequency forms of variability, and none regarding its subseasonal distribution. In this work, we report a large-scale intraseasonal variation in the Atmospheric Infrared Sounder CO_2 data in the global tropical region associated with the Madden–Julian oscillation (MJO). The peak-to-peak amplitude of the composite MJO modulation is ~1 ppmv, with a standard error of the composite mean < 0.1 ppmv. The correlation structure between CO2 and rainfall and vertical velocity indicate positive (negative) anomalies in CO_2 arise due to upward (downward) large-scale vertical motions in the lower troposphere associated with the MJO. These findings can help elucidate how faster processes can organize, transport, and mix CO_2 and provide a robustness test for coupled carbon–climate models

NASA Goddard Giovanni Support for YOTC

The fundamental challenges to overcoming our shortcomings in understanding and modeling/predicting tropical convection have been twofold: I) the need to represent the broad range of scales applicable to the tropical organization problem (i.e. cumulus to planetary), and II) the lack of observations that adequately and simultaneously characterize this broad range of scales and that also provide three-dimensional information on thermodynamic, radiative and dynamical interactions, including cloud microphysical processes. In regards to the second challenge, it should be stressed that this problem will not be solved through the production and examination of one or a few high quality long-term records of fundamental quantities (e.g., SST, water vapor, cloud fraction). Rather, an alternative and more comprehensive paradigm is needed, one that integrates the multitude of applicable resources and measures of tropical convection in a manner that that can be better utilized by the diagnostic, modeling and forecasting communities to more completely and coherently focus on the problem.Because the goal of YOTC involves examining a scientifically complex, multi-scale process , rather than documenting the characteristics of a single parameter (e.g., SST, cloud cover), YOTC has an IOP perspective that targets a period, May 2008 April 2010, long enough to encompass many cases of tropical convection activity in many of its most challenging yet influential forms. This includes mesoscale and synoptic variability, easterly waves and hurricanes, convectively coupled waves, the MJO and the culmination of these in terms of the monsoon, their interactions with the extra-tropics, and mean characteristics such as tropical-to-subtropical transitions. The YOTC time period and length are driven in part by the following: 1) keeping the multi-sensor/multi-platform and model-analyses data sets and associated infrastructure manageable, 2) facilitating a focused effort by the research and operational communities on a specific scientific problem, and 3) capitalizing on the recent key additions to the armada of satellites (e.g., CloudSat and CALIPSO). The proposed dissemination framework for the YOTC satellite data archive is based on the Giovanni system. Giovanni is a web-based application developed by the NASA Goddard Earth Science (GES) Data and Information Service Center (DISC) that provides a simple and intuitive way to visualize, analyze, and access/download vast amounts of Earth science remote sensing data. A prototype YOTC Giovanni System (hereafter YOTC-GS) is in the process of being developed. YOTC-GS will provide access to level 2 (i.e. swath level data) and/or level 3 (i.e. gridded/mapped data) forms of satellite data, the choice or both depending on what is appropriate and relevant. The former is needed and better suited for detailed process examination, exploiting the highest temporal-spatial resolutions available and comparison to regional cloud-system resolving model / cloud resolving model (CSRM/CRM) model output. The latter is needed and more well suited for examination of phenomena, conditions and processes on large to global scales, and for comparisons to global model analyses, prediction and simulation output

Real-time localised forecasting of the Madden-Julian Oscillation using neural network models

Existing statistical forecast models of the Madden-Julian Oscillation (MJO) are generally of very low order and predict the evolution of a small number (typically two) of principal components (PCs). While such models are skilful up to 25 days lead time, by design they only predict the very largest-scale features of the MJO. Here we present a higher-order MJO statistical forecast model that is able to predict MJO variability on smaller, more localised scales, that will be of more direct benefit to national weather agencies and regional government planning. The model is based on daily outgoing long-wave radiation (OLR) data that are intraseasonally filtered using a recently developed technique of empirical mode decomposition that can be used in real time. A standard truncated PC analysis is then used to isolate the maximum amount of variance in a finite number of modes. The evolution of these modes is then forecast using a neural network model, which does not suffer from the parametrisation problems of other statistical forecast techniques when applied to a higher number of modes. Compared to a standard 2-PC model, the higher-order PC model showed improved skill over the whole MJO domain, with substantial improvements over the western Pacific, Arabian Sea, Bay of Bengal, South China Sea and Phillipine Sea

NASA Downscaling Project

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA-2 reanalyses were used to drive the NU-WRF regional climate model and a GEOS-5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000-2010. The results of these experiments were compared to observational datasets to evaluate the output

The effect of the Madden-Julian Oscillation on station rainfall and river level in the Fly River system, Papua New Guinea

The Madden-Julian oscillation (MJO) is the dominant mode of intraseasonal variability in tropical rainfall on the large scale, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. The Fly River system, Papua New Guinea, is one of the wettest regions on Earth and is at the heart of the MJO envelope. A 16 year time series of daily precipitation at 15 stations along the river system exhibits strong MJO modulation in rainfall. At each station, the difference in rainfall rate between active and suppressed MJO conditions is typically 40% of the station mean. The spread of rainfall between individual MJO events was small enough such that the rainfall distributions between wet and dry phases of the MJO were clearly separated at the catchment level. This implies that successful prediction of the large-scale MJO envelope will have a practical use for forecasting local rainfall. In the steep topography of the New Guinea Highlands, the mean and MJO signal in station precipitation is twice that in the satellite Tropical Rainfall Measuring Mission 3B42HQ product, emphasizing the need for ground-truthing satellite-based precipitation measurements. A clear MJO signal is also present in the river level, which peaks simultaneously with MJO precipitation input in its upper reaches but lags the precipitation by approximately 18 days on the flood plains

Vertical Moist Thermodynamic Structure and Spatial–Temporal Evolution of the MJO in AIRS Observations

The atmospheric moisture and temperature profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit on the NASA Aqua mission, in combination with the precipitation from the Tropical Rainfall Measuring Mission (TRMM), are employed to study the vertical moist thermodynamic structure and spatial–temporal evolution of the Madden–Julian oscillation (MJO). The AIRS data indicate that, in the Indian Ocean and western Pacific, the temperature anomaly exhibits a trimodal vertical structure: a warm (cold) anomaly in the free troposphere (800–250 hPa) and a cold (warm) anomaly near the tropopause (above 250 hPa) and in the lower troposphere (below 800 hPa) associated with enhanced (suppressed) convection. The AIRS moisture anomaly also shows markedly different vertical structures as a function of longitude and the strength of convection anomaly. Most significantly, the AIRS data demonstrate that, over the Indian Ocean and western Pacific, the enhanced (suppressed) convection is generally preceded in both time and space by a low-level warm and moist (cold and dry) anomaly and followed by a low-level cold and dry (warm and moist) anomaly. The MJO vertical moist thermodynamic structure from the AIRS data is in general agreement, particularly in the free troposphere, with previous studies based on global reanalysis and limited radiosonde data. However, major differences in the lower-troposphere moisture and temperature structure between the AIRS observations and the NCEP reanalysis are found over the Indian and Pacific Oceans, where there are very few conventional data to constrain the reanalysis. Specifically, the anomalous lower-troposphere temperature structure is much less well defined in NCEP than in AIRS for the western Pacific, and even has the opposite sign anomalies compared to AIRS relative to the wet/dry phase of the MJO in the Indian Ocean. Moreover, there are well-defined eastward-tilting variations of moisture with height in AIRS over the central and eastern Pacific that are less well defined, and in some cases absent, in NCEP. In addition, the correlation between MJO-related midtropospheric water vapor anomalies and TRMM precipitation anomalies is considerably more robust in AIRS than in NCEP, especially over the Indian Ocean. Overall, the AIRS results are quite consistent with those predicted by the frictional Kelvin–Rossby wave/conditional instability of the second kind (CISK) theory for the MJO

Atmospheric response to observed intraseasonal tropical sea surface temperature anomalies

The major tropical convective and circulation features of the intraseasonal or Madden-Julian Oscillation (MJO) are simulated as a passive response to observed MJO sea surface temperature (SST) anomalies in an atmospheric general circulation model (AGCM), strengthening the case for ocean-atmosphere interactions being central to MJO dynamics. However, the magnitude of the surface fluxes diagnosed from the MJO cycle in the AGCM, that would feed back onto the ocean in a coupled system, are much weaker than in observations. The phasing of the convective-dynamical model response to the MJO SST anomalies and the associated surface flux anomalies is too fast compared to observations of the (potentially) coupled system, and would act to damp the SST anomalies

Vertical Moist Thermodynamic Structure of the Madden–Julian Oscillation in Atmospheric Infrared Sounder Retrievals: An Update and a Comparison to ECMWF Interim Re-Analysis

The large-scale vertical moist thermodynamic structure of the Madden–Julian oscillation (MJO) was documented using the first 2.5 yr (2002–05) of version 4 atmospheric specific humidity and temperature profiles from the Atmospheric Infrared Sounder (AIRS). In this study, this issue is further examined using currently available 7-yr version 5 AIRS data (2002–09) to test its dependence on the AIRS data record lengths, AIRS retrieval versions, and MJO event selection and compositing methods employed. The results indicate a strong consistency of the large-scale vertical moist thermodynamic structure of the MJO between different AIRS data record lengths (2.5 vs 7 yr), different AIRS retrieval versions (4 vs 5), and different MJO analysis methods [the extended empirical orthogonal function (EEOF) method vs the multivariate empirical orthogonal function (MEOF) method]. The large-scale vertical moist thermodynamic structures of the MJO between the AIRS retrievals and the ECMWF Interim Re-Analysis (ERA-Interim) products are also compared. The results indicate a much better agreement of the MJO vertical structure between AIRS and ERA-Interim than with the NCEP–NCAR reanalysis, although a significant difference exists in the magnitude of moisture anomalies between ERA-Interim and AIRS. This characterization of the vertical moist thermodynamic structure of the MJO by AIRS and ERA-Interim offers a useful observation-based metric for general circulation model diagnostics

Modulation of Atlantic Aerosols by the Madden-Julian Oscillation

Much like the better-known EI Nino-Southern Oscillation, the Madden-Julian Oscillation (MJO) is a global-scale atmospheric phenomenon. The MJO involves periodic, systematic changes in the distribution of clouds and precipitation over the western Pacific and Indian oceans, along with differences in wind intensity over even more extensive areas, including the north and subtropical Atlantic Ocean. The lead authors of this paper developed a sophisticated mathematical technique for mapping the spatial and temporal behavior of changes in the atmosphere produced by the MJO. In a previous paper, we applied this technique to search for modulation of airborne particle amount in the eastern hemisphere associated with the "wet" (cloudy) vs. "dry" phases of the MJO. The study used primarily AVHRR, MODIS, and TOMS satellite-retrieved aerosol amount, but concluded that other factors, such as cloud contamination of the satellite signals, probably dominated the observed variations. The current paper looks at MJO modulation of desert dust transport eastward across the Atlantic from northern Africa, a region much less subject to systematic cloud contamination than the eastern hemisphere areas studied previously. In this case, a distinct aerosol signal appears, showing that dust is transported westward much more effectively during the MJO phase that favors westward-flowing wind, and such transport is suppressed when the MJO reduces these winds. Aside form the significant achievement in identifying such an effect, the result implies that an important component of global dust transport can be predicted based on the phase of the MJO. As a consequence, the impact of airborne dust on storm development in the Atlantic, and on dust deposition downwind of the desert sources, can also be predicted and more accurately modeled