Maximizing ENSO as a source of western US hydroclimate predictability

Patricola, Christina M.; O’Brien, John P.; Risser, Mark D.; Rhoades, Alan M.; O’Brien, T. A.; Ullrich, P. A.; Stone, Dáithí A.; Collins, William D.

doi:10.1007/s00382-019-05004-8

Cited by 63 publications

(103 citation statements)

References 103 publications

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“…5a and b) has reduced the amount of water stored as spring snowpack (Mote et al, 2018). This declining capability of mountains to act as natural reservoirs is a key response to climate warming (Rhoades et al, 2018a). It has also led to more frequent warm snow drought conditions (Hatchett and McEvoy, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Many historically snow-dominated mountains in the western US, particularly those with mild winter temperatures, are undergoing declines in snow accumulation (Mote et al, 2018). Projections for the 21st century suggest continued warming and snowpack declines (Rhoades et al, 2018a). Because of downstream dependence on snow-derived water resources and susceptibility to flooding from snowmelt events, California is an ideal location for examining the changes in historical precipitation partitioning.…”

Section: Introductionmentioning

confidence: 99%

“…This effect is pronounced in lower elevations, i.e., warmer regions of watersheds where snow cover tends to be shallower and more ephemeral. The effects of a warming climate on snowpack vary, with the greatest sensitivity found in warm snow climates located near the climatological rainsnow transition elevation (Howat and Tulaczyk, 2005;Mote et al, 2005), predisposing these regions to warming-induced hydrologic vulnerability (Huning and AghaKouchak, 2018;Klos et al, 2014;Rhoades et al, 2018a). Changes in rainsnow partitioning and their manifestation on water storage in spring snowpack are thus of paramount importance for guiding changes in water resource management operations intended to enhance water supply reliability.…”

Section: Introductionmentioning

confidence: 99%

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Technical note: Precipitation-phase partitioning at landscape scales to regional scales

Lynn¹,

Cuthbertson²,

He³

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Water management throughout the western United States largely relies on the partitioning of cool season mountain precipitation into rain and snow, particularly snow as it maximizes available water for warm season use. Recent studies indicate a shift toward increased precipitation falling as rain, which is consistent with a warming climate. An approach is presented to estimate precipitation-phase partitioning across landscapes from 1948 to the present by combining fine-scale gridded precipitation data with coarse-scale freezing level and precipitation data from an atmospheric reanalysis. A marriage of these data sets allows for a new approach to estimate spatial patterns and trends in precipitation partitioning over elevational and latitudinal gradients in major water supply basins. This product is used in California as a diagnostic indicator of changing precipitation phase across mountain watersheds. Results show the largest increases in precipitation falling as rain during the past 70 years in lower elevation watersheds located within the climatological rain–snow transition regions of northern California during spring. Further development of the indicator can inform adaptive water management strategy development and implementation in the face of a changing climate.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Technical note: Precipitation-phase partitioning at landscape scales to regional scales

Lynn¹,

Cuthbertson²,

He³

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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“…We argue that the differences in correlation coefficient between the restrictive and permissive EGIDs likely results from differences in the degree to which tropical moisture anomalies are filtered among the EGIDs. Patricola et al (2019) show that strong El Niño events are associated with positive IVT anomalies in much of the tropics and a separate band of positive anomalies in the midlatitudes (around 30 • latitude; their Figure 11). The positive IVT anomalies in the tropics would have no effect on the subset of AR detector parameters with high values of ∆y, since these values would be aggressively filtered.…”

Section: Uncertainty In the Relationship Between Enso And Ar Countmentioning

confidence: 93%

Detection of Atmospheric Rivers with Inline Uncertainty Quantification: TECA-BARD v1.0

O’Brien¹,

Risser²,

Loring³

et al. 2020

Preprint

Self Cite

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Abstract. It has become increasingly common for researchers to utilize methods that identify weather features in climate models. There is an increasing recognition that the uncertainty associated with choice of detection method may affect our scientific understanding. For example, results from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) indicate that there are a broad range of plausible atmospheric river (AR) detectors, and that scientific results can depend on the algorithm used. There are similar examples from the literature on extratropical cyclones and tropical cyclones. It is therefore imperative to develop detection techniques that explicitly quantify the uncertainty associated with the detection of events. We seek to answer the question: given a ‘plausible’ AR detector, how does uncertainty in the detector quantitatively impact scientific results? We develop a large dataset of global AR counts, manually identified by a set of 8 researchers with expertise in atmospheric science, which we use to constrain parameters in a novel AR detection method. We use a Bayesian framework to sample from the set of AR detector parameters that yield AR counts similar to the expert database of AR counts; this yields a set of 'plausible' AR detectors from which we can assess quantitative uncertainty. This probabilistic AR detector has been implemented in the Toolkit for Extreme Climate Analysis (TECA), which allows for efficient processing of petabyte-scale datasets. We apply the TECA Bayesian AR Detector, TECA-BARD v1.0, to the MERRA2 reanalysis and show that the sign of the correlation between global AR count and El Nino Southern Oscillation depends on the set of parameters used.

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“…Many of the existing AR studies have considered uncertainty in the underlying datasets, such as uncertainty associated with choice of reanalysis and climate models (Gao et al, 2015;Payne and Magnusdottir, 2015;Warner et al, 2015;6144 T. A. O'Brien et al: AR detection with UQ: TECA-BARD v1.0.1 Espinoza et al, 2018;Gershunov et al, 2017Gershunov et al, , 2019aRalph et al, 2019b;Payne et al, 2020), and a few have considered AR detector uncertainty in the observational record of ARs (Guan and Waliser, 2015a;Ralph et al, 2019b). Studies based on ARTMIP have started to explore uncertainty with respect to AR detection, and the uncertainty is larger than many in the community had anticipated (Shields et al, 2018(Shields et al, , 2019bChen et al, 2018;Rutz et al, 2019;Shields et al, 2019a;Chen et al, 2019;Ralph et al, 2019b;Payne et al, 2020).…”

Section: The Importance Of Uncertainty In Feature Detectionmentioning

confidence: 99%

Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1

et al. 2020

Self Cite

View full text Add to dashboard Cite

Abstract. It has become increasingly common for researchers to utilize methods that identify weather features in climate models. There is an increasing recognition that the uncertainty associated with choice of detection method may affect our scientific understanding. For example, results from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) indicate that there are a broad range of plausible atmospheric river (AR) detectors and that scientific results can depend on the algorithm used. There are similar examples from the literature on extratropical cyclones and tropical cyclones. It is therefore imperative to develop detection techniques that explicitly quantify the uncertainty associated with the detection of events. We seek to answer the following question: given a “plausible” AR detector, how does uncertainty in the detector quantitatively impact scientific results? We develop a large dataset of global AR counts, manually identified by a set of eight researchers with expertise in atmospheric science, which we use to constrain parameters in a novel AR detection method. We use a Bayesian framework to sample from the set of AR detector parameters that yield AR counts similar to the expert database of AR counts; this yields a set of “plausible” AR detectors from which we can assess quantitative uncertainty. This probabilistic AR detector has been implemented in the Toolkit for Extreme Climate Analysis (TECA), which allows for efficient processing of petabyte-scale datasets. We apply the TECA Bayesian AR Detector, TECA-BARD v1.0.1, to the MERRA-2 reanalysis and show that the sign of the correlation between global AR count and El Niño–Southern Oscillation depends on the set of parameters used.

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Maximizing ENSO as a source of western US hydroclimate predictability

Cited by 63 publications

References 103 publications

Technical note: Precipitation-phase partitioning at landscape scales to regional scales

Technical note: Precipitation-phase partitioning at landscape scales to regional scales

Detection of Atmospheric Rivers with Inline Uncertainty Quantification: TECA-BARD v1.0

Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1

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