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James N. Marquis, Adam C. Varble, Paul Robinson, T. Connor. Nelson, and Katja Friedrich

Abstract

Data from scanning radars, radiosondes, and vertical profilers deployed during three field campaigns are analyzed to study interactions between cloud-scale updrafts associated with initiating deep moist convection and the surrounding environment. Three cases are analyzed in which the radar networks permitted dual-Doppler wind retrievals in clear air preceding and during the onset of surface precipitation. These observations capture the evolution of: i) the mesoscale and boundary layer flow, and ii) low-level updrafts associated with deep moist convection initiation (CI) events yielding sustained or short-lived precipitating storms.

The elimination of convective inhibition did not distinguish between sustained and unsustained CI events, though the vertical distribution of convective available potential energy may have played a role. The clearest signal differentiating the initiation of sustained versus unsustained precipitating deep convection was the depth of the low-level horizontal wind convergence associated with the mesoscale flow feature triggering CI, a sharp surface wind shift boundary or orographic upslope flow. The depth of the boundary layer relative to the height of the LFC failed to be a consistent indicator of CI potential. Widths of the earliest detectable low-level updrafts associated with sustained precipitating deep convection were ~3-5 km, larger than updrafts associated with surrounding boundary layer turbulence (~1-3-km wide). It is hypothesized that updrafts of this larger size are important for initiating cells to survive the destructive effects of buoyancy dilution via entrainment.

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Katja Friedrich, Jeffrey R. French, Sarah A. Tessendorf, Melinda Hatt, Courtney Weeks, Robert M. Rauber, Bart Geerts, Lulin Xue, Roy M. Rasmussen, Derek R. Blestrud, Melvin L. Kunkel, Nicholas Dawson, and Shaun Parkinson

Abstract

The spatial distribution and magnitude of snowfall resulting from cloud seeding with silver iodide (AgI) is closely linked to atmospheric conditions, seeding operations, and dynamical, thermodynamical, and microphysical processes. Here, microphysical processes leading to ice and snow production are analyzed in orographic clouds for three cloud seeding events, each with light or no natural precipitation and well-defined, traceable seeding lines. Airborne and ground-based radar observations are linked to in-situ cloud and precipitation measurements to determine the spatiotemporal evolution of ice initiation, particle growth, and snow fallout in seeded clouds. These processes and surface snow amounts are explored as particle plumes evolve from varying amounts of AgI released, and within changing environmental conditions, including changes in liquid water content (LWC) along and downwind of the seeding track, wind speed, and shear. More AgI did not necessarily produce more liquid equivalent snowfall (LESnow). The greatest amount of LESnow, largest area covered by snowfall, and highest peak snowfall produced through seeding occurred on the day with the largest and most widespread occurrence of supercooled drizzle, highest wind shear, and greater LWC along and downwind of the seeding track. The day with the least supercooled drizzle and the lowest LWC downwind of the seeding track produced the smallest amount of LESnow through seeding. The stronger the wind, the farther away the snowfall occurred from the seeding track.

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Robert Palmer, David Whelan, David Bodine, Pierre Kirstetter, Matthew Kumjian, Justin Metcalf, Mark Yeary, Tian-You Yu, Ramesh Rao, John Cho, David Draper, Stephen Durden, Stephen English, Pavlos Kollias, Karen Kosiba, Masakazu Wada, Joshua Wurman, William Blackwell, Howard Bluestein, Scott Collis, Jordan Gerth, Aaron Tuttle, Xuguang Wang, and Dusan Zrnic
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Yuechun Wang and Steven M. Quiring

Abstract

The evidence shows that soil moisture has an important influence on North American Monsoon (NAM) precipitation. This study evaluates the local and nonlocal feedbacks of soil moisture on summer (June - September) precipitation in the NAM region using observational data. We applied a multivariate statistical method known as the Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) to control for internal atmospheric variability and sea surface temperature (SST) forcings so that we could isolate the impact of soil moisture feedbacks on NAM precipitation. Our results identify feedback pathways between soil moisture and precipitation in the NAM region and in the Southern Rocky Mountains (SRM) region. Wet soils in the SRM result in lower-than-normal local surface temperature, weaker water vapor transport from the eastern Pacific and the Gulf of California (GOC), and less monsoon precipitation. Precipitation over the U.S. Great Plains also significantly increases when there are wet soils in the SRM. This occurs due to an enhanced water vapor influx into this region. On the other hand, anomalously wet soils in the NAM region increase NAM precipitation by enhancing local moist static energy and increasing the strength of the monsoonal circulation. Our observational results using SGEFA agree well with previous numerical modeling studies. This study highlights the critical role of land-atmosphere interactions for understanding NAM variability.

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Tara Howatt, Stephanie Waterman, and Tetjana Ross

Abstract

Turbulence plays a key role in many oceanic processes, but a shortage of turbulence observations impedes its exploration. Parameterizations of turbulence applied to readily-available CTD data can be useful in expanding our understanding of the space-time variability of turbulence. Typically tested and applied to shipboard data, these parameterizations have not been rigorously tested on data collected by underwater gliders, which show potential to observe turbulence in conditions that ships cannot. Using data from a 10-day glider survey in a coastal shelf environment, we compare estimates of turbulent dissipation from the finescale parameterization and Thorpe scale method to those estimated from microstructure observations collected on the same glider platform. We find that the finescale parameterization captures the magnitude and statistical distribution of dissipation, but cannot resolve spatiotemporal features in this relatively shallow water depth. In contrast, the Thorpe scale method more successfully characterizes the spatiotemporal distribution of turbulence; however, the magnitude of dissipation is overestimated, largely due to limitations on the detectable density overturn size imposed by the typical glider CTD sampling frequency of 0.5 Hz and CTD noise. Despite these limitations, turbulence parameterizations provide a viable opportunity to use CTD data collected by the multitude of gliders sampling the ocean to develop greater insight into the space-time variability of ocean turbulence and the role of turbulence in oceanic processes.

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Yunji Zhang, Xingchao Chen, and Yinghui Lu

Abstract

There are ongoing efforts to establish an ensemble data assimilation and prediction system for tropical cyclones based on the FV3 (finite-volume cubed-sphere) dynamic core with the capability to assimilate satellite all-sky infrared and microwave observations. To complement the system developments and improve our understanding of the assimilation of all-sky infrared and microwave observations, this study assesses their potential impacts on the analysis of Hurricane Harvey (2017) through examinations of the structure and dynamics of the ensemble-based correlations as well as single observation data assimilation experiments, using an ensemble forecast generated by a global-to-regional nested FV3-based model. It is found that different infrared and microwave channels are sensitive to different types of hydrometeors within different layers of the atmosphere, and the correlations vanish beyond 200 km in the region covered by cloud or abundant hydrometeors. The spatial correlations between brightness temperatures and model states will adjust the structure and intensity of the hurricane in the model so that the simulated hurricane will better fit the “observed” brightness temperatures. In general, these results show how assimilating infrared and microwave together can improve the analyses of tropical cyclone intensity and structure, which may lead to improved intensity forecasts.

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Yann Y. Planton, Jérôme Vialard, Eric Guilyardi, Mathieu Lengaigne, and Michael J. McPhaden

Abstract

Unusually high western Pacific oceanic heat content often leads to El Niño about 1 year later, while unusually low heat content leads to La Niña. Here, we investigate if El Niño Southern Oscillation (ENSO) predictability also depends on the initial state recharge, and discuss the underlying mechanisms. To that end, we use the CNRM-CM5 model, which has a reasonable representation of the main observed ENSO characteristics, asymmetries and feedbacks. Observations and a 1007-years long CNRM-CM5 simulation indicate that discharged states evolve more systematically into La Niña events than recharged states into neutral states or El Niño events. We ran 70-members ensemble experiments in a perfect-model setting, initialized in boreal fall from either recharged or discharged western Pacific heat content, sampling the full range of corresponding ENSO phases. Predictability measures based both on spread and signal-to-noise ratio confirm that discharged states yield a more predictable ENSO outcome one year later than recharged states. As expected from recharge oscillator theory, recharged states evolve into positive central Pacific sea surface temperature anomalies in boreal spring, inducing stronger and more variable Westerly Wind Event activity and a fast growth of the ensemble spread during summer and fall. This also enhances the positive wind stress feedback in fall, but the effect is offset by changes in thermocline and heat flux feedbacks. The state-dependent component of westerly wind events is thus the most likely cause for the predictability asymmetry in CNRM-CM5, although changes in the low-frequency wind stress feedback may also contribute.

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Ivana ALEKSOVSKA, Laure RAYNAUD, Robert FAIVRE, François BRUN, and Marc RAYNAL

Abstract

Agriculture is a highly weather-dependent activity, climatic conditions impact both directly crop growth and indirectly diseases and pests developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatio-temporal scales, are coupled to a temperature-driven developmental model for grape vine moth in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric post-processing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to sub-seasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required.

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David C. Eisenhauer

Abstract

This paper presents a case study of how boundary objects were deployed to support a collaborative knowledge production process that resulted in the creation of climate change knowledge usable to municipal governments in the New Jersey shore region. In doing so, a case is made that boundary objects are useful throughout the collaborative process in overcoming ambiguity and disagreement. This points to boundary objects possessing a wider array of capabilities than frequently theorized in the climate policy literature. Effectively designing and using boundary objects, however, requires carefully considering how they interface and interact with one another.

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Ruud Sperna Weiland, Karin van der Wiel, Frank Selten, and Dim Coumou

Abstract

Persistent hot-dry or cold-wet summer weather can have significant impacts on agriculture, health and the environment. For North-Western Europe, these weather regimes are typically linked to, respectively, blocked or zonal jetstream states. The fundamental dynamics underlying these circulation states are still poorly understood. Edward Lorenz postulated that summer circulation may be either fully or almost intransitive, implying that part of the phase space (capturing circulation variability) cannot be reached within one specific summer. If true, this would have major implications for the predictability of summer weather and our understanding of the drivers of interannual variability of summer weather. Here, we test the two Lorenz hypotheses (i.e. fully or almost intransitive) for European summer circulation, capitalising on a newly-available, very large ensemble (2000 years) of present-day climate data in the fully-coupled global climate model EC-Earth. Using Self-Organising Maps, we quantify the phase space of summer circulation and the trajectories through phase space in unprecedented detail. We show that, based on Markov assumptions, the summer circulation is strongly dependent on its initial state in early summer with the atmospheric memory ranging from 28 days up to ~45 days. The memory is particularly long if the initial state is either a blocked or a zonal flow state. Furthermore, we identify two groups of summers which are characterised by distinctly different trajectories through phase space, and which prefer either a blocked or zonal circulation state, respectively. These results suggest that intransitivity is indeed a fundamental property of the atmosphere and an important driver of interannual variability.

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