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Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.

Our success with environmental prediction could be considered among humankind’s most remarkable developments over the past 50 years. It protects lives and property, and helps us advance the well-being of society. Vast changes have occurred recently to the complexity and scope of our weather enterprise. Given its importance to society, there is reason to optimize our approaches for advancing its scope and capabilities. This essay highlights three points that may help facilitate this optimization, with an overarching suggestion to more overtly embrace a systems (engineering) approach. 1) Continued emphasis should be placed on advancing Earth system science as the foundational knowledge that advances weather prediction as well as the more holistic scope of environmental prediction (EP). 2) The complexity and coupling of the social, programmatic, observation, modeling, analytic, and interdisciplinary landscapes within the EP enterprise suggest adding a system engineering perspective/approach to further optimize outcomes and limit vulnerabilities. 3) The consideration of the enterprise as a data to information flow problem highlights opportunities and focal points to leverage that could help to advance the societal benefits derived from the EP enterprise. A generalized, highly simplified systems perspective on the advancement of Earth science and environmental prediction is offered by framing a simple equation involving the synthesis of observations, models, and programmatics that in turn yield science and applications benefits. Simplifications and derivatives of this equation are used to distill challenges and opportunities for further advancing enterprise benefits and to motivate considerations of scope and priorities related to our community’s decadal survey(s).

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

The NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight satellites was successfully launched into low Earth orbit on 15 December 2016. Each satellite carries a radar receiver that measures GPS signals scattered from the surface. Wind speed over the ocean is determined from distortions in the signal caused by wind-driven surface roughness. GPS operates at a sufficiently low frequency to allow for propagation through all precipitation, including the extreme rain rates present in the eyewall of tropical cyclones. The spacing and orbit of the satellites were chosen to optimize frequent sampling of tropical cyclones. In this study, we characterize the CYGNSS ocean surface wind speed measurements by their uncertainty, dynamic range, sensitivity to precipitation, spatial resolution, spatial and temporal sampling, and data latency. The current status of each of these properties is examined and potential future improvements are discussed. In addition, examples are given of current science investigations that make use of the data.

The Observing System Research and Predictability Experiment (THORPEX) was a 10-yr, international research program organized by the World Meteorological Organization’s World Weather Research Program. THORPEX was motivated by the need to accelerate the rate of improvement in the accuracy of 1-day to 2-week forecasts of high-impact weather for the benefit of society, the economy, and the environment. THORPEX, which took place from 2005 to 2014, was the first major international program focusing on the advancement of global numerical weather prediction systems since the Global Atmospheric Research Program, which took place almost 40 years earlier, from 1967 through 1982. The scientific achievements of THORPEX were accomplished through bringing together scientists from operational centers, research laboratories, and the academic community to collaborate on research that would ultimately advance operational predictive skill. THORPEX included an unprecedented effort to make operational products readily accessible to the broader academic research community, with community efforts focused on problems where challenging science intersected with the potential to accelerate improvements in predictive skill. THORPEX also collaborated with other major programs to identify research areas of mutual interest, such as topics at the intersection of weather and climate. THORPEX research has 1) increased our knowledge of the global-to-regional influences on the initiation, evolution, and predictability of high-impact weather; 2) provided insight into how predictive skill depends on observing strategies and observing systems; 3) improved data assimilation and ensemble forecast systems; 4) advanced knowledge of high-impact weather associated with tropical and polar circulations and their interactions with midlatitude flows; and 5) expanded society’s use of weather information through applied and social science research.

The objective of the Observations for Model Intercomparison Projects (Obs4MIPs) is to provide observational data to the climate science community, which is analogous (in terms of variables, temporal and spatial frequency, and periods) to output from the 5th phase of the World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project (CMIP5) climate model simulations. The essential aspect of the Obs4MIPs methodology is that it strictly follows the CMIP5 protocol document when selecting the observational datasets. Obs4MIPs also provides documentation that describes aspects of the observational data (e.g., data origin, instrument overview, uncertainty estimates) that are of particular relevance to scientists involved in climate model evaluation and analysis. In this paper, we focus on the activities related to the initial set of satellite observations, which are being carried out in close coordination with CMIP5 and directly engage NASA's observational (e.g., mission and instrument) science teams. Having launched Obs4MIPs with these datasets, a broader effort is also briefly discussed, striving to engage other agencies and experts who maintain datasets, including reanalysis, which can be directly used to evaluate climate models. Different strategies for using satellite observations to evaluate climate models are also briefly summarized.

AFFILIATIONS: FeRRaRo anD WaliseR—Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California; GleckleR anD tayloR—Lawrence Livermore National Laboratory, Livermore, California; eyRinG—Deutsches Zentrum fur Luftund Raumfahrt, Institut fur Physik der Atmosphare, Oberpfaffenhofen, Germany CORRESPONDING AUTHOR: Robert Ferraro, Jet Propulsion Laboratory, MS 301-330, 4800 Oak Grove Dr., Pasadena, CA 91109-8099 E-mail: robert.d.ferraro@jpl.nasa.gov

The representation of tropical convection remains a serious challenge to the skillfulness of our weather and climate prediction systems. To address this challenge, the World Climate Research Programme (WCRP) and The Observing System Research and Predictability Experiment (THORPEX) of the World Weather Research Programme (WWRP) are conducting a joint research activity consisting of a focus period approach along with an integrated research framework tailored to exploit the vast amounts of existing observations, expanding computational resources, and the development of new, high-resolution modeling frameworks. The objective of the Year of Tropical Convection (YOTC) is to use these constructs to advance the characterization, modeling, parameterization, and prediction of multiscale tropical convection, including relevant two-way interactions between tropical and extratropical systems. This article highlights the diverse array of scientifically interesting and socially important weather and climate events associated with the WCRP–WWRP/THORPEX YOTC period of interest: May 2008–April 2010. Notable during this 2-yr period was the change from cool to warm El Niño– Southern Oscillation (ENSO) states and the associated modulation of a wide range of smaller time- and space-scale tropical convection features. This period included a near-record-setting wet North American monsoon in 2008 and a very severe monsoon drought in India in 2009. There was also a plethora of tropical wave activity, including easterly waves, the Madden–Julian oscillation, and convectively coupled equatorial wave interactions. Numerous cases of high-impact rainfall events occurred along with notable features in the tropical cyclone record. The intent of this article is to highlight these features and phenomena, and in turn promote their interrogation via theory, observations, and models in concert with the YOTC program so that improved understanding and pre- dictions of tropical convection can be afforded.

The Year of Tropical Convection (YOTC) project recognizes that major improvements are needed in how the tropics are represented in climate models. Tropical convection is organized into multiscale precipitation systems with an underlying chaotic order. These organized systems act as building blocks for meteorological events at the intersection of weather and climate (time scales up to seasonal). These events affect a large percentage of the world's population. Much of the uncertainty associated with weather and climate derives from incomplete understanding of how meteorological systems on the mesoscale (~1–100 km), synoptic scale (~1,000 km), and planetary scale (~10,000 km) interact with each other. This uncertainty complicates attempts to predict high-impact phenomena associated with the tropical atmosphere, such as tropical cyclones, the Madden–Julian oscillation, convectively coupled tropical waves, and the monsoons. These and other phenomena influence the extratropics by migrating out of the tropics and by the remote effects of planetary waves, including those generated by the MJO. The diurnal and seasonal cycles modulate all of the above. It will be impossible to accurately predict climate on regional scales or to comprehend the variability of the global water cycle in a warmer world without comprehensively addressing tropical convection and its interactions across space and time scales.

No Abstract available.

The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group (MJOWG) has taken steps to promote the adoption of a uniform diagnostic and set of skill metrics for analyzing and assessing dynamical forecasts of the MJO. Here we describe the framework and initial implementation of the approach using real-time forecast data from multiple operational numerical weather prediction (NWP) centers. The objectives of this activity are to provide a means to i) quantitatively compare skill of MJO forecasts across operational centers, ii) measure gains in forecast skill over time by a given center and the community as a whole, and iii) facilitate the development of a multimodel forecast of the MJO. The MJO diagnostic is based on extensive deliberations among the MJOWG in conjunction with input from a number of operational centers and makes use of the MJO index of Wheeler and Hendon. This forecast activity has been endorsed by the Working Group on Numerical Experimentation (WGNE), the international body th...

A multi-scale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The GCM allows global coverage and the CRM allows explicit simulation of cloud processes and their interactions with radiation and surface processes. A new MMF has been developed that is based the Goddard finite volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved .surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger, propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using conventional cloud parameterizations. Both MMFs also produce a precipitation bias in the western Pacific during Northern Hemisphere summer. However, there are also notable differences between two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF overestimates global rainfall because of its oceanic component. Some critical issues associated with the Goddard MMF are presented in this paper.

Weather prediction is typically concerned with lead times of hours to days, while seasonal-to-interannual climate prediction is concerned with lead times of months to seasons. Recently, there has been growing interest in 'subseasonal' forecasts---those that have lead times on the order of weeks (e.g., Schubert et al. 2002; Waliser et al. 2003; Waliser et al. 2005). The basis for developing and exploiting subseasonal predictions largely resides with phenomena such as the Pacific North American (PNA) pattern, the North Atlantic oscillation (NAO), the Madden-Julian Oscillation (MJO), mid-latitude blocking, and the memory associated with soil moisture, as well as modeling techniques that rely on both initial conditions and slowly varying boundary conditions (e.g., tropical Pacific SST). An outgrowth of this interest has been the development of an Experimental MJO Prediction Project (EMPP). Th project provides real-time weather and climate information and predictions for a variety of applications, broadly encompassing the subseasonal weather-climate connection. Th focus is on the MJO because it represents a repeatable, low-frequency phenomenon. MJO's importance among the subseasonal phenomena is very similar to that of El Nino-Southern Oscillation(ENSO) among the interannual phenomena. This note describes the history and objectives of EMPP, its status,capabilities, and plans.

The objective of this study is to estimate the limit of dynamical predictability of the Madden–Julian oscillation (MJO). Ensembles of “twin” predictability experiments were carried out with the NASA Goddard Laboratory for the Atmospheres (GLA) atmospheric general circulation model (AGCM) using specified annual cycle SSTs. Initial conditions were taken from a 10-yr control simulation during periods of strong MJO activity identified via extended empirical orthogonal function (EOF) analysis of 30–90-day bandpassed tropical rainfall. From this analysis, 15 cases were chosen when the MJO convective center was located over the Indian Ocean, Maritime Continent, western Pacific Ocean, and central Pacific Ocean, respectively, making 60 MJO cases in total. In addition, 15 cases were selected that exhibited very little to no MJO activity. Two different sets of small random perturbations were added to these 75 initial states. Simulations were then performed for 90 days from each of these 150 perturbed initial conditi...