Search Results

You are looking at 1 - 8 of 8 items for

  • Author or Editor: Peter B. Gibson x
  • Refine by Access: All Content x
Clear All Modify Search
Peter B. Gibson and Nicolas J. Cullen

Abstract

Even in locations endowed with excellent wind resources, the intermittent nature of wind is perceived as a barrier to reliable generation. However, recent studies have demonstrated that electrically interconnecting wind farms in a meteorologically oriented network can reduce supply variability and the observed frequency of zero-generation conditions. In this study a 5-yr synthetic dataset of 15 wind farms is utilized to investigate the benefits to supply reliability from wind farm interconnection in New Zealand. An examination is carried out primarily through a synoptic climatology framework, hypothesizing that benefits to reliability are primarily related to the degree to which wind farms are influenced differently by the synoptic-scale circulation. Using a weather-typing approach and composite analysis, regionality is observed in the linkages between synoptic-scale circulation and wind resources, particularly between wind farms located in the far northern and far southern regions of the country. Subsequently, and as compared with all other possible combinations, supply reliability is observed to be optimized in a network that includes wind farms connected between far northern and far southern regions, under which the frequency of hours with zero generation is almost eliminated. It is likely that the frequency of hours with zero generation could be further reduced on the basis of a more extensive meteorologically based selection of wind data from a greater number of locations. It is suggested that these findings should be taken into consideration in future planning and site selection of wind farm projects in New Zealand.

Full access
Sarah E. Perkins and Peter B. Gibson
Full access
Hamish D. Prince, Nicolas J. Cullen, Peter B. Gibson, Jono Conway, and Daniel G. Kingston

Abstract

The occurrence of extreme precipitation events in New Zealand regularly results in devastating impacts to the local society and environment. An automated atmospheric river (AR) detection technique (ARDT) is applied to construct a climatology (1979–2019) of extreme midlatitude moisture fluxes conducive to extreme precipitation. A distinct seasonality exists in AR occurrence aligning with seasonal variations in the midlatitude jet streams. The formation of the Southern Hemisphere winter split jet enables AR occurrence to persist through all seasons in northern regions of New Zealand, while southern regions of the country exhibit a substantial (50%) reduction in AR occurrence as the polar jet shifts southward during the cold season. ARs making landfall on the western coast of New Zealand (90% of all events) are characterized by a dominant northwesterly moisture flux associated with a distinct dipole pressure anomaly, with low pressure to the southwest and high pressure to the northeast of New Zealand. Precipitation totals during AR events increase with AR rank (five-point scale) throughout the country, with the most substantial increase on the windward side of the Southern Alps (South Island). The largest events (rank 5 ARs) produce 3-day precipitation totals exceeding 1000 mm. ARs account for up to 78% of total precipitation and up to 94% of extreme precipitation on the west coast of the South Island. Assessment of the multiscale atmospheric processes associated with AR events governing extreme precipitation in the Southern Alps of New Zealand should remain a priority given their hydrological significance and impact on people and infrastructure.

Open access
Peter B. Gibson, Andrew J. Pitman, Ruth Lorenz, and Sarah E. Perkins-Kirkpatrick

Abstract

Understanding the physical drivers of heat waves is essential for improving short-term forecasts of individual events and long-term projections of heat waves under climate change. This study provides the first analysis of the influence of the large-scale circulation on Australian heat waves, conditional on the land surface conditions. Circulation types, sourced from reanalysis, are used to characterize the different large-scale circulation patterns that drive heat wave events across Australia. The importance of horizontal temperature advection is illustrated in these circulation patterns, and the pattern occurrence frequency is shown to reorganize through different modes of climate variability. It is further shown that the relative likelihood of a particular synoptic situation being associated with a heat wave is strongly modulated by the localized partitioning of available energy between surface sensible and latent heat fluxes (as measured through evaporative fraction) in many regions in reanalysis data. In particular, a several-fold increase in the likelihood of heat wave day occurrence is found during days of reduced evaporative fraction under favorable circulation conditions. The atmospheric circulation and land surface conditions linked to heat waves in reanalysis were then examined in the context of CMIP5 climate model projections. Large uncertainty was found to exist for many regions, especially in terms of the direction of future land surface changes and in terms of the magnitude of atmospheric circulation changes. Efforts to constrain uncertainty in both atmospheric and land surface processes in climate models, while challenging, should translate to more robust regional projections of heat waves.

Full access
Peter B. Gibson, Duane E. Waliser, Huikyo Lee, Baijun Tian, and Elias Massoud

Abstract

Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.

Full access
Peter B. Gibson, Duane E. Waliser, Bin Guan, Michael J. DeFlorio, F. Martin Ralph, and Daniel L. Swain

Abstract

Persistent winter ridging events are a consistent feature of meteorological drought across the western and southwestern United States. In this study, a ridge detection algorithm is developed and applied on daily geopotential height anomalies to track and quantify the diversity of individual ridge characteristics (e.g., position, frequency, magnitude, extent, and persistence). Three dominant ridge types are shown to play important, but differing, roles for influencing the location of landfalling atmospheric rivers (ARs), precipitation, and subsequently meteorological drought. For California, a combination of these ridge types is important for influencing precipitation deficits on daily through seasonal time scales, indicating the various pathways by which ridging can induce drought. Furthermore, both the frequency of ridge types and reduced AR activity are necessary features for explaining drought variability on seasonal time scales across the western and southwestern regions. The three ridge types are found to be associated in different ways with various remote drivers and modes of variability, highlighting possible sources of subseasonal-to-seasonal (S2S) predictability. A comparison between ridge types shows that anomalously large and persistent ridging events relate to different Rossby wave trains across the Pacific with different preferential upstream locations of tropical heating. For the “South-ridge” type, centered over the Southwest, a positive trend is found in both the frequency and persistence of these events across recent decades, likely contributing to observed regional drying. These results illustrate the utility of feature tracking for characterizing a wider range of ridging features that collectively influence precipitation deficits and drought.

Free access
Meredith A. Fish, James M. Done, Daniel L. Swain, Anna M. Wilson, Allison C. Michaelis, Peter B. Gibson, and F. Martin Ralph

Abstract

Successive atmospheric river (AR) events—known as AR families—can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impacts that often stem from AR families, the large-scale environments and mechanisms associated with these compound events remain poorly understood. In this work, a new reanalysis-based 39-yr catalog of 248 AR family events affecting California between 1981 and 2019 is introduced. Nearly all (94%) of the interannual variability in AR frequency is driven by AR family versus single events. Using k-means clustering on the 500-hPa geopotential height field, six distinct clusters of large-scale patterns associated with AR families are identified. Two clusters are of particular interest due to their strong relationship with phases of El Niño–Southern Oscillation (ENSO). One of these clusters is characterized by a strong ridge in the Bering Sea and Rossby wave propagation, most frequently occurs during La Niña and neutral ENSO years, and is associated with the highest cluster-average precipitation across California. The other cluster, characterized by a zonal elongation of lower geopotential heights across the Pacific basin and an extended North Pacific jet, most frequently occurs during El Niño years and is associated with lower cluster-average precipitation across California but with a longer duration. In contrast, single AR events do not show obvious clustering of spatial patterns. This difference suggests that the potential predictability of AR families may be enhanced relative to single AR events, especially on subseasonal to seasonal time scales.

Restricted access
Christopher J. White, Daniela I. V. Domeisen, Nachiketa Acharya, Elijah A. Adefisan, Michael L. Anderson, Stella Aura, Ahmed A. Balogun, Douglas Bertram, Sonia Bluhm, David J. Brayshaw, Jethro Browell, Dominik Büeler, Andrew Charlton-Perez, Xandre Chourio, Isadora Christel, Caio A. S. Coelho, Michael J. DeFlorio, Luca Delle Monache, Francesca Di Giuseppe, Ana María García-Solórzano, Peter B. Gibson, Lisa Goddard, Carmen González Romero, Richard J. Graham, Robert M. Graham, Christian M. Grams, Alan Halford, W. T. Katty Huang, Kjeld Jensen, Mary Kilavi, Kamoru A. Lawal, Robert W. Lee, David MacLeod, Andrea Manrique-Suñén, Eduardo S. P. R. Martins, Carolyn J. Maxwell, William J. Merryfield, Ángel G. Muñoz, Eniola Olaniyan, George Otieno, John A. Oyedepo, Lluís Palma, Ilias G. Pechlivanidis, Diego Pons, F. Martin Ralph, Dirceu S. Reis Jr., Tomas A. Remenyi, James S. Risbey, Donald J. C. Robertson, Andrew W. Robertson, Stefan Smith, Albert Soret, Ting Sun, Martin C. Todd, Carly R. Tozer, Francisco C. Vasconcelos Jr., Ilaria Vigo, Duane E. Waliser, Fredrik Wetterhall, and Robert G. Wilson

Abstract

The subseasonal-to-seasonal (S2S) predictive timescale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this timescale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a ‘knowledge-value’ gap, where a lack of evidence and awareness of the potential socio-economic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development – demonstrating both skill and utility across sectors – this dialogue can be used to help promote and accelerate the awareness, value and co-generation of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting timescale.

Full access