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

Abstract

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.

Antarctic Atmospheric River Climatology and Precipitation Impacts

“…Here, we also observe little difference in results between the AR catalogs forced with ERA‐5 and MERRA‐2 meaning that our AR detection algorithm is robust and minimally sensitive to the choice of reanalysis. Future work attributing impacts to AR activity should take into account uncertainty in the AR detector and the range of AR frequency from various detection products may affect results (O'Brien et al., 2020).…”

Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment

“…Recent work involving manual identification of ARs by experts (Prabhat et al, 2021;O'Brien, Risser, et al, 2020) suggests that the spread in AR algorithm behavior is linked to differences in opinion about what does and does not constitute an AR. O' Brien, Risser, et al (2020) show that this spread in subjective opinion projects directly on to quantitative differences in the sign of the correlation coefficient between an El Niño index and global AR count.…”

“…Even though ARDTs are often initially designed with different purposes in mind, Payne et al (2020) demonstrate that there is overlap in what they are ultimately used to study. The community has recently started to recognize that uncertainty associated with the numerical definition of ARs may have important implications for our understanding of ARs and their changes in a future warmer world (Guan et al, 2018;Huning et al, 2017;Lora et al, 2020;Newman et al, 2012;O'Brien, Payne, et al, 2020;O'Brien, Risser, et al, 2020;Ralph, Wilson, et al, 2019;Rutz et al, 2019;Shields et al, 2018;Shields, Rosenbloom, et al, 2019;Shields, Rutz, et al, 2019) The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) was launched by members of the AR research community in order to systematically assess the impact of this uncertainty on our scientific understanding (Shields et al, 2018). The First ARTMIP Workshop (Shields, Rutz, et al, 2019) defined a multitier experimental design focusing on uncertainty in the observational record (Tier 1; Rutz et al, 2019), and uncertainty in AR variability and change (Tier 2).…”

Constraining and Characterizing the Size of Atmospheric Rivers: A Perspective Independent From the Detection Algorithm

“…In this study, we use the AR detection results from three different ARTMIP methods (Rutz et al., 2019; Shields et al., 2018): CASCADE_BARD_v1 (O'Brien et al., 2020), Lora_global (Lora et al., 2017), and Mundhenk_v3 (Mundhenk et al., 2016). Employing these three different detection algorithms allows us to broadly sample ARs in the North Pacific Ocean.…”

“…Following O'Brien et al. (2020), to avoid the large contiguous regions of high IVT near the tropics associated with the intertropical convergence zone (ITCZ), we spatially filter the IVT field as where IVT′( x , y ) is the filtered IVT field, x and y are the longitude and latitude, respectively, and Δ y is half‐width at half‐maximum of the filter. We use Δ y = 15°, which effectively damps the IVT to zero within the ITCZ.…”