Justin M. Glisan

Bio: Justin M. Glisan is an academic researcher from Iowa State University. The author has contributed to research in topics: Precipitation & Weather Research and Forecasting Model. The author has an hindex of 7, co-authored 11 publications receiving 241 citations.

Self-organizing map analysis of widespread temperature extremes in Alaska and Canada

Abstract: This paper demonstrates how self-organizing maps (SOMs) can be used to evaluate the large-scale environment, in particular the synoptic circulation associated with widespread temperature extremes. The paper provides details on how SOMs are created, how they can be used to understand extreme events, and lessons learned in applying this methodology for extremes analysis. Using a SOM can be helpful in understanding the underlying physical processes that control extreme events, and how the extremes and the processes that control them may change in time or differ across space. Examples of widespread daily temperature extremes in 4 regions: 2 each in Alaska and in northern Canada during winter (December, January, and February) for 1989−2007 are presented to illustrate the application of the methodology. For the regions studied, the size of the domain over which the synoptic circulation was defined—in particular using a smaller domain focused on particular regions—and a greater number of classes to represent the archetypical synoptic patterns for the regions, give the best relationship between synoptic circulation and extremes. The results are most robust for the Alaskan domains and less so for the Canadian domains, leading to the conclusion that further study is warranted to better understand extremes in the Canadian regions.

Effects of Spectral Nudging in WRF on Arctic Temperature and Precipitation Simulations

Abstract: Spectral (interior) nudging is a way of constraining a model to be more consistent with observed behavior. However, such control over model behavior raises concerns over how much nudging may affect unforced variability and extremes. Strong nudging may reduce or filter out extreme events since nudging pushes the model toward a relatively smooth, large-scale state. The question then becomes: what is the minimum spectral nudging needed to correct biases while not limiting the simulation of extreme events? To determine this, case studies were performed using a six-member ensemble of the Pan-Arctic Weather Research and Forecasting model (WRF) with varying spectral nudging strength, using WRF’s standard nudging as a reference point. Two periods were simulated, one in a cold season (January 2007) and one in a warm season (July 2007).Precipitation and 2-m temperature were analyzed to determine how changing spectral nudging strength impacts temperature and precipitation extremes and selected percentiles. R...

Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX)

Abstract: The ability of state-of-the-art regional climate models to simulate cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic-CORDEX initiative. Some models employ large-scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble are compared with the results forced by four reanalyses (ERA-Interim, National Centers for Environmental Prediction-Climate Forecast System Reanalysis, National Aeronautics and Space Administration-Modern-Era Retrospective analysis for Research and Applications Version 2, and Japan Meteorological Agency-Japanese 55-year reanalysis) in winter and summer for 1981-2010 period. In addition, we compare cyclone statistics between ERA-Interim and the Arctic System Reanalysis reanalyses for 2000-2010. Biases in cyclone frequency, intensity, and size over the Arctic are also quantified. Variations in cyclone frequency across the models are partly attributed to the differences in cyclone frequency over land. The variations across the models are largest for small and shallow cyclones for both seasons. A connection between biases in the zonal wind at 200 hPa and cyclone characteristics is found for both seasons. Most models underestimate zonal wind speed in both seasons, which likely leads to underestimation of cyclone mean depth and deep cyclone frequency in the Arctic. In general, the regional climate models are able to represent the spatial distribution of cyclone characteristics in the Arctic but models that employ large-scale spectral nudging show a better agreement with ERA-Interim reanalysis than the rest of the models. Trends also exhibit the benefits of nudging. Models with spectral nudging are able to reproduce the cyclone trends, whereas most of the nonnudged models fail to do so. However, the cyclone characteristics and trends are sensitive to the choice of nudged variables. (Less)

WRF summer extreme daily precipitation over the CORDEX Arctic

Abstract: We analyze daily precipitation extremes produced by a six-member ensemble of the Pan-Arctic Weather Research and Forecasting (WRF) that simulated 19 years on the Coordinated Regional Climate Downscaling Experiment (CORDEX) Arctic domain for the Arctic summer. Attention focuses on four North American analysis regions defined using climatological records, regional weather patterns, and geographical/topographical features. We compare simulated extremes with those occurring at corresponding observing stations in the U.S. National Climate Data Center's Global Summary of the Day. Our analysis focuses on variations in features of the extremes such as magnitudes, spatial scales, and temporal regimes between regions. Using composites of extreme events, we also analyze the processes producing these extremes, comparing circulation, pressure, temperature, and humidity fields from the ERA-Interim reanalysis and the model output. Although the model's extreme precipitation is low compared to the observed one, the physical behavior in the reanalysis leading to observed extremes is simulated in the model. In particular, the reanalysis and the model both show the importance of moisture advection against topography for producing most of the extreme daily precipitation events in summer. In contrast, parts of Arctic western Canada also have a substantial contribution from convective precipitation, which is not seen in the other regions analyzed. The analysis establishes the physical credibility of the simulations for extreme behavior. It also highlights the utility of the model for extracting behaviors that are not easily discernible from the observations such as convective precipitation.

Synoptic Conditions During Wintertime Temperature Extremes in Alaska

Abstract: The large-scale atmospheric state associated with widespread wintertime warm and cold extremes in southern Alaska was identified using 1989 to 2007 European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-I) data. Extremes were defined as days with the coldest and warmest 1% of daily temperatures. Widespread extreme events were identified for days when at least 25 50 km grid cells in the study domain met the extreme temperature criteria. A total of 55 cold and 74 warm extreme days were identified in 19 winters. Composites of the atmospheric state from 5days before through the day of the extreme events were analyzed to assess the large-scale atmospheric state associated with the extremes. The method of self-organizing maps (SOMs) was used to identify the range of sea level pressure (SLP) patterns present in the ERA-I December–February data, and these SLP patterns were then used to stratify the extreme days by their large-scale atmospheric circulation. Composites for all warm or cold extreme days showed less intense features than those for specific SLP patterns. In all of the composites temperature advection, strongest at 700 hPa, and anomalous longwave radiation were the primary factors that led to the extreme events. The anomalous downwelling longwave radiationwas due to either reduced cloud cover, during cold extremes, or to increased cloud cover, during warm extremes. The SOM composites provided additional insight into the temporal evolution of the extreme days and highlighted different portions of southern Alaska most likely to experience temperature extremes for a given SOM SLP pattern.

More Evidence Linking Arctic Amplification to Extreme Weather in Mid-Latitudes

Abstract: [1] Arctic amplification (AA) – the observed enhanced warming in high northern latitudes relative to the northern hemisphere – is evident in lower-tropospheric temperatures and in 1000-to-500 hPa thicknesses. Daily fields of 500 hPa heights from the National Centers for Environmental Prediction Reanalysis are analyzed over N. America and the N. Atlantic to assess changes in north-south (Rossby) wave characteristics associated with AA and the relaxation of poleward thickness gradients. Two effects are identified that each contribute to a slower eastward progression of Rossby waves in the upper-level flow: 1) weakened zonal winds, and 2) increased wave amplitude. These effects are particularly evident in autumn and winter consistent with sea-ice loss, but are also apparent in summer, possibly related to earlier snow melt on high-latitude land. Slower progression of upper-level waves would cause associated weather patterns in mid-latitudes to be more persistent, which may lead to an increased probability of extreme weather events that result from prolonged conditions, such as drought, flooding, cold spells, and heat waves.

Atmospheric Science: An Introductory Survey

Abstract: John M Wallace and Peter V Hobbs London: Academic 1977 pp xvii + 467 price £12.80 This is a comprehensive textbook for undergraduate courses in atmospheric physics. It contains general physical meteorology (atmospheric hydrostatics, cloud physics, radioactive transfer and thermodynamics), some selected topics of special interest (aerosol physics, aeronomy and physical climatology) and dynamic meteorology describing and interpreting large scale atmospheric motions.

Regional Dynamical Downscaling and the CORDEX Initiative

Abstract: We review the challenges and future perspectives of regional climate model (RCM), or dynamical downscaling, activities. Among the main technical issues in need of better understanding are those of selection and sensitivity to the model domain and resolution, techniques for providing lateral boundary conditions, and RCM internal variability. The added value (AV) obtained with the use of RCMs remains a central issue, which needs more rigorous and comprehensive analysis strategies. Within the context of regional climate projections, large ensembles of simulations are needed to better understand the models and characterize uncertainties. This has provided an impetus for the development of the Coordinated Regional Downscaling Experiment (CORDEX), the first international program offering a common protocol for downscaling experiments, and we discuss how CORDEX can address the key scientific challenges in downscaling research. Among the main future developments in RCM research, we highlight the development of cou...

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

Abstract: This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

Coordinated Global and Regional Climate Modeling

Abstract: A new approach of coordinated global and regional climate modeling is presented. It is applied to the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) and its parent global climate model CanESM2. CanRCM4 was developed specifically to downscale climate predictions and climate projections made by its parent global model. The close association of a regional climate model (RCM) with a parent global climate model (GCM) offers novel avenues of model development and application that are not typically available to independent regional climate modeling centers. For example, when CanRCM4 is driven by its parent model, driving information for all of its prognostic variables is available (including aerosols and chemical species), significantly improving the quality of their simulation. Additionally, CanRCM4 can be driven by its parent model for all downscaling applications by employing a spectral nudging procedure in CanESM2 designed to constrain its evolution to follow any ...