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Matthew Newman, Gilbert P. Compo, and Michael A. Alexander

Abstract

Variability of the Pacific decadal oscillation (PDO), on both interannual and decadal timescales, is well modeled as the sum of direct forcing by El Niño–Southern Oscillation (ENSO), the “reemergence” of North Pacific sea surface temperature anomalies in subsequent winters, and white noise atmospheric forcing. This simple model may be taken as a null hypothesis for the PDO, and may also be relevant for other climate integrators that have been previously related to the PDO.

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M. M. Hurwitz, P. A. Newman, L. D. Oman, and A. M. Molod

Abstract

This study is the first to identify a robust El Niño–Southern Oscillation (ENSO) signal in the Antarctic stratosphere. El Niño events between 1979 and 2009 are classified as either conventional “cold tongue” events (positive SST anomalies in the Niño-3 region) or “warm pool” events (positive SST anomalies in the Niño-4 region). The 40-yr ECMWF Re-Analysis (ERA-40), NCEP, and Modern Era Retrospective–Analysis for Research and Applications (MERRA) meteorological reanalyses are used to show that the Southern Hemisphere stratosphere responds differently to these two types of El Niño events. Consistent with previous studies, cold tongue events do not impact temperatures in the Antarctic stratosphere. During warm pool El Niño events, the poleward extension and increased strength of the South Pacific convergence zone favor an enhancement of planetary wave activity during September–November. On average, these conditions lead to higher polar stratospheric temperatures and a weakening of the Antarctic polar jet in November and December, as compared with neutral ENSO years. The phase of the quasi-biennial oscillation (QBO) modulates the stratospheric response to warm pool El Niño events; the strongest planetary wave driving events are coincident with the easterly phase of the QBO.

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V. Aquila, L. D. Oman, R. Stolarski, A. R. Douglass, and P. A. Newman

Abstract

Observations have shown that the mass of nitrogen dioxide decreased at both southern and northern midlatitudes in the year following the eruption of Mt. Pinatubo, indicating that the volcanic aerosol had enhanced nitrogen dioxide depletion via heterogeneous chemistry. In contrast, the observed ozone response showed a northern midlatitude decrease and a small southern midlatitude increase. Previous simulations that included an enhancement of heterogeneous chemistry by the volcanic aerosol but no other effect of this aerosol produce ozone decreases in both hemispheres, contrary to observations. The authors’ simulations show that the heating due to the volcanic aerosol enhanced both the tropical upwelling and Southern Hemisphere extratropical downwelling. This enhanced extratropical downwelling, combined with the time of the eruption relative to the phase of the Brewer–Dobson circulation, increased Southern Hemisphere ozone via advection, counteracting the ozone depletion due to heterogeneous chemistry on the Pinatubo aerosol.

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Matthew Newman, Andrew T. Wittenberg, Linyin Cheng, Gilbert P. Compo, and Catherine A. Smith
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Naoki Mizukami, Martyn P. Clark, Ethan D. Gutmann, Pablo A. Mendoza, Andrew J. Newman, Bart Nijssen, Ben Livneh, Lauren E. Hay, Jeffrey R. Arnold, and Levi D. Brekke

Abstract

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

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Pablo A. Mendoza, Martyn P. Clark, Naoki Mizukami, Andrew J. Newman, Michael Barlage, Ethan D. Gutmann, Roy M. Rasmussen, Balaji Rajagopalan, Levi D. Brekke, and Jeffrey R. Arnold

Abstract

The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.

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P. Klein, T. A. Bonin, J. F. Newman, D. D. Turner, P. B. Chilson, C. E. Wainwright, W. G. Blumberg, S. Mishra, M. Carney, E. P. Jacobsen, S. Wharton, and R. K. Newsom

Abstract

This paper presents an overview of the Lower Atmospheric Boundary Layer Experiment (LABLE), which included two measurement campaigns conducted at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site in Oklahoma during 2012 and 2013. LABLE was conducted as a collaborative effort between the University of Oklahoma (OU), the National Severe Storms Laboratory, Lawrence Livermore National Laboratory (LLNL), and the ARM program. LABLE can be considered unique in that it was designed as a multiphase, low-cost, multiagency collaboration. Graduate students served as principal investigators and took the lead in designing and conducting experiments aimed at examining boundary layer processes.

The main objective of LABLE was to study turbulent phenomena in the lowest 2 km of the atmosphere over heterogeneous terrain using a variety of novel atmospheric profiling techniques. Several instruments from OU and LLNL were deployed to augment the suite of in situ and remote sensing instruments at the ARM site. The complementary nature of the deployed instruments with respect to resolution and height coverage provides a near-complete picture of the dynamic and thermodynamic structure of the atmospheric boundary layer. This paper provides an overview of the experiment including 1) instruments deployed, 2) sampling strategies, 3) parameters observed, and 4) student involvement. To illustrate these components, the presented results focus on one particular aspect of LABLE: namely, the study of the nocturnal boundary layer and the formation and structure of nocturnal low-level jets. During LABLE, low-level jets were frequently observed and they often interacted with mesoscale atmospheric disturbances such as frontal passages.

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V. Eyring, N. R. P. Harris, M. Rex, T. G. Shepherd, D. W. Fahey, G. T. Amanatidis, J. Austin, M. P. Chipperfield, M. Dameris, P. M. De F. Forster, A. Gettelman, H. F. Graf, T. Nagashima, P. A. Newman, S. Pawson, M. J. Prather, J. A. Pyle, R. J. Salawitch, B. D. Santer, and D. W. Waugh

Accurate and reliable predictions and an understanding of future changes in the stratosphere are major aspects of the subject of climate change. Simulating the interaction between chemistry and climate is of particular importance, because continued increases in greenhouse gases and a slow decrease in halogen loading are expected. These both influence the abundance of stratospheric ozone. In recent years a number of coupled chemistry–climate models (CCMs) with different levels of complexity have been developed. They produce a wide range of results concerning the timing and extent of ozone-layer recovery. Interest in reducing this range has created a need to address how the main dynamical, chemical, and physical processes that determine the long-term behavior of ozone are represented in the models and to validate these model processes through comparisons with observations and other models. A set of core validation processes structured around four major topics (transport, dynamics, radiation, and stratospheric chemistry and microphysics) has been developed. Each process is associated with one or more model diagnostics and with relevant datasets that can be used for validation. This approach provides a coherent framework for validating CCMs and can be used as a basis for future assessments. Similar efforts may benefit other modeling communities with a focus on earth science research as their models increase in complexity.

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J. P. Taylor, W. L Smith, V. Cuomo, A. M. Larar, D. K. Zhou, C. Serio, T. Maestri, R. Rizzi, S. Newman, P. Antonelli, S. Mango, P. Di Girolamo, F. Esposito, G. Grieco, D. Summa, R. Restieri, G. Masiello, F. Romano, G. Pappalardo, G. Pavese, L. Mona, A. Amodeo, and G. Pisani

The international experiment called the European Aqua Thermodynamic Experiment (EAQUATE) was held in September 2004 in Italy and the United Kingdom to validate Aqua satellite Atmospheric Infrared Sounder (AIRS) radiance measurements and derived products with certain groundbased and airborne systems useful for validating hyperspectral satellite sounding observations. A range of flights over land and marine surfaces were conducted to coincide with overpasses of the AIRS instrument on the Earth Observing System Aqua platform. Direct radiance evaluation of AIRS using National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed-Interferometer (NAST-I) and the Scanning High-Resolution Infrared Sounder has shown excellent agreement. Comparisons of level-2 retrievals of temperature and water vapor from AIRS and NAST-I validated against high-quality lidar and dropsonde data show that the 1-K/l-km and 10%/1-km requirements for temperature and water vapor (respectively) are generally being met. The EAQUATE campaign has proven the need for synergistic measurements from a range of observing systems for satellite calibration/validation and has paved the way for future calibration/validation activities in support of the Infrared Atmospheric Sounding Interferometer on the European Meteorological Operational platform and Cross-Track Infrared Sounder on the U.S. NPOESS Prepatory Project platform.

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Annarita Mariotti, Cory Baggett, Elizabeth A. Barnes, Emily Becker, Amy Butler, Dan C. Collins, Paul A. Dirmeyer, Laura Ferranti, Nathaniel C. Johnson, Jeanine Jones, Ben P. Kirtman, Andrea L. Lang, Andrea Molod, Matthew Newman, Andrew W. Robertson, Siegfried Schubert, Duane E. Waliser, and John Albers

Abstract

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.

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