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J. L. Kinter III, M. J. Fennessy, V. Krishnamurthy, and L. Marx

Abstract

Recent decadal regime shifts in the large-scale circulation of the tropical atmosphere are examined using analyses and independent observations of the circulation and precipitation. Comparisons between reanalysis products and independent observations suggest that the shifts that are apparent and significant in the reanalysis products may be artifacts of changes in the observing system and/or the data assimilation procedures.

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Lakshmi Krishnamurthy, Gabriel A. Vecchi, Rym Msadek, Andrew Wittenberg, Thomas L. Delworth, and Fanrong Zeng

Abstract

This study investigates the seasonality of the relationship between the Great Plains low-level jet (GPLLJ) and the Pacific Ocean from spring to summer, using observational analysis and coupled model experiments. The observed GPLLJ and El Niño–Southern Oscillation (ENSO) relation undergoes seasonal changes with a stronger GPLLJ associated with La Niña in boreal spring and El Niño in boreal summer. The ability of the GFDL Forecast-Oriented Low Ocean Resolution (FLOR) global coupled climate model, which has the high-resolution atmospheric and land components, to simulate the observed seasonality in the GPLLJ–ENSO relationship is assessed. The importance of simulating the magnitude and phase locking of ENSO accurately in order to better simulate its seasonal teleconnections with the Intra-Americas Sea (IAS) is demonstrated. This study explores the mechanisms for seasonal changes in the GPLLJ–ENSO relation in model and observations. It is hypothesized that ENSO affects the GPLLJ variability through the Caribbean low-level jet (CLLJ) during the summer and spring seasons. These results suggest that climate models with improved ENSO variability would advance our ability to simulate and predict seasonal variations of the GPLLJ and their associated impacts on the United States.

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V. Krishnamurthy, Cristiana Stan, David A. Randall, Ravi P. Shukla, and James L. Kinter III

Abstract

The simulation of the South Asian monsoon by a coupled ocean–atmosphere model with an embedded cloud-resolving model is analyzed on intraseasonal and interannual time scales. The daily modes of variability in the superparameterized Community Climate System Model, version 3 (SP-CCSM), are compared with those in observation, the superparameterized Community Atmospheric Model, version 3 (SP-CAM3), and the control simulation of CCSM (CT-CCSM) with conventional parameterization of convection. The CT-CCSM fails to simulate the observed intraseasonal oscillations but is able to generate the atmospheric El Niño–Southern Oscillation (ENSO) mode, although with regular biennial variability. The dominant modes of variability extracted from daily anomalies of outgoing longwave radiation, precipitation, and low-level horizontal wind in SP-CCSM consist of two intraseasonal oscillations and two seasonally persisting modes, in good agreement with observation. The most significant observed features of the intraseasonal oscillations correctly simulated by the SP-CCSM are the northward propagation of convection, precipitation, and circulation as well as the eastward and westward propagations. The observed spatial structure and the periods of the oscillations are also well captured by the SP-CCSM, although with lesser magnitude. The SP-CCSM is able to simulate the chaotic variability and spatial structure of the seasonally persisting atmospheric ENSO mode, while the evidence for the Indian Ocean dipole mode is inconclusive. The SP-CAM3 simulates two intraseasonal oscillations and the atmospheric ENSO mode. However, the intraseasonal oscillations in SP-CAM3 do not show northward propagation while their periods and spatial structures are not comparable to observation. The results of this study indicate the necessity of coupled models with sufficiently realistic cloud parameterizations.

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Y. L. Pichugina, R. M. Banta, T. Bonin, W. A. Brewer, A. Choukulkar, B. J. McCarty, S. Baidar, C. Draxl, H. J. S. Fernando, J. Kenyon, R. Krishnamurthy, M. Marquis, J. Olson, J. Sharp, and M. Stoelinga

Abstract

Annually and seasonally averaged wind profiles from three Doppler lidars were obtained from sites in the Columbia River basin of east-central Oregon and Washington, a major region of wind-energy production, for the Second Wind Forecast Improvement Project (WFIP2) experiment. The profile data are used to quantify the spatial variability of wind flows in this area of complex terrain, to assess the HRRR–NCEP model’s ability to capture spatial and temporal variability of wind profiles, and to evaluate model errors. Annually averaged measured wind speed differences over the 70-km extent of the lidar measurements reached 1 m s−1 within the wind-turbine rotor layer, and 2 m s−1 for 200–500 m AGL. Stronger wind speeds in the lowest 500 m occurred at sites higher in elevation, farther from the river, and farther west—closer to the Cascade Mountain barrier. Validating against the lidar data, the HRRR model underestimated strong wind speeds (>12 m s−1) and, consequently, their frequency of occurrence, especially at the two lowest-elevation sites, producing annual low biases in rotor-layer wind speed of 0.5 m s−1. The RMSE between measured and modeled winds at all sites was about 3 m s−1 and did not degrade significantly with forecast lead time. The nature of the model errors was different for different seasons. Moreover, although the three sites were located in the same basin terrain, the nature of the model errors was different at each site. Thus, if only one of the sites had been instrumented, different conclusions would have been drawn as to the major sources of model error, depending on where the measurements were made.

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Liwei Jia, Xiaosong Yang, Gabriel A. Vecchi, Richard G. Gudgel, Thomas L. Delworth, Anthony Rosati, William F. Stern, Andrew T. Wittenberg, Lakshmi Krishnamurthy, Shaoqing Zhang, Rym Msadek, Sarah Kapnick, Seth Underwood, Fanrong Zeng, Whit G. Anderson, Venkatramani Balaji, and Keith Dixon

Abstract

This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

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G. A. Vecchi, T. Delworth, R. Gudgel, S. Kapnick, A. Rosati, A. T. Wittenberg, F. Zeng, W. Anderson, V. Balaji, K. Dixon, L. Jia, H.-S. Kim, L. Krishnamurthy, R. Msadek, W. F. Stern, S. D. Underwood, G. Villarini, X. Yang, and S. Zhang

Abstract

Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux adjustment.” A suite of 12-month duration retrospective forecasts is performed over the 1981–2012 period, after initializing the climate model to observationally constrained conditions at the start of each forecast period, using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basinwide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally aggregated regional TC activity months in advance are feasible.

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Robert M. Banta, Yelena L. Pichugina, W. Alan Brewer, Aditya Choukulkar, Kathleen O. Lantz, Joseph B. Olson, Jaymes Kenyon, Harindra J. S. Fernando, Raghu Krishnamurthy, Mark J. Stoelinga, Justin Sharp, Lisa S. Darby, David D. Turner, Sunil Baidar, and Scott P. Sandberg

Abstract

Ground-based Doppler-lidar instrumentation provides atmospheric wind data at dramatically improved accuracies and spatial/temporal resolutions. These capabilities have provided new insights into atmospheric flow phenomena, but they also should have a strong role in NWP model improvement. Insight into the nature of model errors can be gained by studying recurrent atmospheric flows, here a regional summertime diurnal sea breeze and subsequent marine-air intrusion into the arid interior of Oregon–Washington, where these winds are an important wind-energy resource. These marine intrusions were sampled by three scanning Doppler lidars in the Columbia River basin as part of the Second Wind Forecast Improvement Project (WFIP2), using data from summer 2016. Lidar time–height cross sections of wind speed identified 8 days when the diurnal flow cycle (peak wind speeds at midnight, afternoon minima) was obvious and strong. The 8-day composite time–height cross sections of lidar wind speeds are used to validate those generated by the operational NCEP–HRRR model. HRRR simulated the diurnal wind cycle, but produced errors in the timing of onset and significant errors due to a premature nighttime demise of the intrusion flow, producing low-bias errors of 6 m s−1. Day-to-day and in the composite, whenever a marine intrusion occurred, HRRR made these same errors. The errors occurred under a range of gradient wind conditions indicating that they resulted from the misrepresentation of physical processes within a limited region around the measurement locations. Because of their generation within a limited geographical area, field measurement programs can be designed to find and address the sources of these NWP errors.

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H. J. S. Fernando, J. Mann, J. M. L. M. Palma, J. K. Lundquist, R. J. Barthelmie, M. Belo-Pereira, W. O. J. Brown, F. K. Chow, T. Gerz, C. M. Hocut, P. M. Klein, L. S. Leo, J. C. Matos, S. P. Oncley, S. C. Pryor, L. Bariteau, T. M. Bell, N. Bodini, M. B. Carney, M. S. Courtney, E. D. Creegan, R. Dimitrova, S. Gomes, M. Hagen, J. O. Hyde, S. Kigle, R. Krishnamurthy, J. C. Lopes, L. Mazzaro, J. M. T. Neher, R. Menke, P. Murphy, L. Oswald, S. Otarola-Bustos, A. K. Pattantyus, C. Veiga Rodrigues, A. Schady, N. Sirin, S. Spuler, E. Svensson, J. Tomaszewski, D. D. Turner, L. van Veen, N. Vasiljević, D. Vassallo, S. Voss, N. Wildmann, and Y. Wang

Abstract

A grand challenge from the wind energy industry is to provide reliable forecasts on mountain winds several hours in advance at microscale (∼100 m) resolution. This requires better microscale wind-energy physics included in forecasting tools, for which field observations are imperative. While mesoscale (∼1 km) measurements abound, microscale processes are not monitored in practice nor do plentiful measurements exist at this scale. After a decade of preparation, a group of European and U.S. collaborators conducted a field campaign during 1 May–15 June 2017 in Vale Cobrão in central Portugal to delve into microscale processes in complex terrain. This valley is nestled within a parallel double ridge near the town of Perdigão with dominant wind climatology normal to the ridges, offering a nominally simple yet natural setting for fundamental studies. The dense instrument ensemble deployed covered a ∼4 km × 4 km swath horizontally and ∼10 km vertically, with measurement resolutions of tens of meters and seconds. Meteorological data were collected continuously, capturing multiscale flow interactions from synoptic to microscales, diurnal variability, thermal circulation, turbine wake and acoustics, waves, and turbulence. Particularly noteworthy are the extensiveness of the instrument array, space–time scales covered, use of leading-edge multiple-lidar technology alongside conventional tower and remote sensors, fruitful cross-Atlantic partnership, and adaptive management of the campaign. Preliminary data analysis uncovered interesting new phenomena. All data are being archived for public use.

Open access