A Comprehensive Intermediate-Term Drought Evaluation System and Evaluation of Climate Data Products over the Conterminous United States

Xue, Zeyu; Ullrich, P. A.

doi:10.1175/jhm-d-20-0314.1

Cited by 5 publications

(5 citation statements)

References 80 publications

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“…Collections of metrics should consist of distinct metrics, should be comprehensive for the need or interest, and should leverage all available observations of sufficient quality. Techniques like principal feature analysis or principal component analysis may be used to identify relationships between metrics within collections (Xue & Ullrich, 2021).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Metrics can be used to perform analysis in a uniform, standardized, comparable, and reproducible way, for purposes such as informing model development via identification of areas where model skill is lacking, or evaluating the degree to which physical processes and phenomena are represented (Coburn & Pryor, 2021; Ekstrom et al, 2018; Pryor & Schoof, 2019, 2020; A. Srivastava et al, 2020; Xue & Ullrich, 2021; Zarzycki et al, 2021). Metrics can also quantify the degree to which a data set is credible or “fit for purpose” for particular applications (Briley et al, 2020; Jagannathan et al, 2020).…”

Section: Features and Uses Of Metricsmentioning

confidence: 99%

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Metrics as tools for bridging climate science and applications

Reed

Goldenson

Grotjahn

et al. 2022

WIREs Climate Change

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In climate science and applications, the term "metric" is used to describe the distillation of complex, multifaceted evaluations to summarize the overall quality of a model simulation, or other data product, and/or as a means to quantify some response to climate change. Metrics provide insights into the fidelity of processes and outcomes from climate models and can assist with both differentiating models' representation of variables or processes and informing whether models are "fit for purpose." Metrics can also provide a valuable reference point for co-production of knowledge between climate scientists and climate impact practitioners. Although continued metric developments enable model developers to better understand the impacts of decisions made in the model design process, metrics also have implications for the characterization of uncertainty and facilitating analyses of underlying physical processes. As a result, comprehensive evaluation with multiple metrics enhances usability of climate information by both scientific and stakeholder communities. This paper presents examples of insights gained from the development and appropriate use of metrics, and provides examples of how metrics can be used to engage with stakeholders and inform decision-making.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Features and Uses Of Metricsmentioning

confidence: 99%

Metrics as tools for bridging climate science and applications

Reed

Goldenson

Grotjahn

et al. 2022

WIREs Climate Change

View full text Add to dashboard Cite

show abstract

“…In all cases, perturbations are computed for each calendar month (i.e., each of January and February, etc., has its own climatological perturbation) and linearly interpolated in time (between the middle of 2 consecutive months). CESM1 is employed here since it is a high-quality model with demonstrable performance over the NEUS (Kay et al, 2015;Swann et al, 2016;Sillmann et al, 2013;Karmalkar et al, 2019;Xue and Ullrich, 2021a). Further, the CESM1 initial condition ensemble contains 40 ensemble members that reasonably capture the internal variability, and all necessary meteorological variables for this study are available from the model output.…”

Section: Methodology and Modified Forcingsmentioning

confidence: 99%

Sensitivity of the pseudo-global warming method under flood conditions: a case study from the northeastern US

Xue,

Ullrich,

Leung

2023

Hydrol. Earth Syst. Sci.

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Abstract. Intensified extreme precipitation and corresponding floods are the most relevant consequences of climate change over the northeastern US (NEUS). To evaluate the impacts of climate change or certain climate perturbations on future extreme weather events which are dynamically similar to historic analogs, the pseudo-global warming (PGW) method has been frequently employed; however, this method lacks precise definition and guidelines, thus limiting its application. More specifically, three key questions related to the application of the PGW method remain unanswered: at what spatial scale should climate perturbations be applied? Among the different meteorological variables available, which ones should be perturbed? And will PGW projections vary significantly when different perturbations are applied? To address these questions, we examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments with varied perturbation spatial scales and choice of perturbed meteorological variables. The results show that the projections of precipitation and other essential variables at the regional mean scale are consistent across the PGW simulations, with a relative difference of much less than 10 %; however, different perturbation modifications can cause significant displacements of the storm events being simulated. Several previously assumed advantages of modifying only the temperature at regional mean scale, such as the preservation of geostrophic balance, do not appear to hold. Also, for these experiments, we find the regional mean perturbation produces a positive precipitation bias because it ignores the land–ocean warming contrast, which is a robust regional response to global warming. Overall, PGW experiments with perturbations from temperature or the combination of temperature and wind at the grid point scale are both recommended, depending on the research questions. The first approach can isolate the spatially dependent thermodynamic impact, and the latter incorporates both the thermodynamic and dynamic impacts.

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“…has its own climatological perturbation), and linearly interpolated in time. CESM1 is employed here since it is a high-quality model with demonstrable performance over the NEUS (Kay et al, 2015;Swann et al, 2016;Sillmann et al, 2013;Karmalkar et al, 2019;Xue and Ullrich, 2021a). Further, its initial condition ensemble contains forty instances, allowing us to mitigate noise from internal variability, and providing all necessary meteorological variables for this study.…”

Section: Methodology and Modified Forcingsmentioning

confidence: 99%

Sensitivity of the pseudo-global warming method under flood conditions: A case study from the Northeastern U.S.

Xue

Ullrich²

2022

Preprint

Self Cite

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Abstract. Intensified extreme precipitation and resulting flooding are among the most impactful consequences of climate change, especially over the northeastern US (NEUS). To project and understand the impacts of climate change (or related climate perturbations) on extreme weather events as they may occur in the future, the Pseudo-Global Warming (PGW) method has been employed with great success. However, it has never been ascertained to what degree the conclusions from PGW studies are sensitive to the design of the PGW experiment. Consequently, three key questions related to the application of the PGW method remain unanswered: At what spatial scale should climate perturbations be applied? Among the different meteorological variables available, which should be perturbed? And will PGW projections vary significantly under different experiment designs? To begin to address these questions, we examine the sensitivity and robustness of conclusions drawn from the PGW method over NEUS by conducting multiple PGW experiments. The results show that the projections of precipitation and other essential variables are consistent at the regional mean scale, with a relative difference of much less than 10\\%; however, different experimental designs nonetheless cause significant displacements among storm events. Several previously assumed advantages of modifying temperature at the regional mean scale do not appear to hold, such as the preservation of geostrophic balance. Also, we find the regional mean perturbation produces a positive precipitation bias due to overestimated warming over the ocean. Overall, PGW experiments with perturbations from temperature or the combination of temperature and wind at the gridpoint scale are both recommended, depending on the research target. The first approach can isolate the spatially-dependent thermodynamic impact, and the latter incorporates both the thermodynamic and dynamic impacts.

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A Comprehensive Intermediate-Term Drought Evaluation System and Evaluation of Climate Data Products over the Conterminous United States

Cited by 5 publications

References 80 publications

Metrics as tools for bridging climate science and applications

Metrics as tools for bridging climate science and applications

Sensitivity of the pseudo-global warming method under flood conditions: a case study from the northeastern US

Sensitivity of the pseudo-global warming method under flood conditions: A case study from the Northeastern U.S.

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