This paper describes a new open-source software framework for automated pointwise feature tracking that is applicable to a wide array of climate datasets using either structured or unstructured ...grids. Common climatological pointwise features include tropical cyclones, extratropical cyclones and tropical easterly waves. To enable support for a wide array of detection schemes, a suite of algorithmic kernels have been developed that capture the core functionality of algorithmic tracking routines throughout the literature. A review of efforts related to pointwise feature tracking from the past 3 decades is included. Selected results using both reanalysis datasets and unstructured grid simulations are provided.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
This study applies a sensitivity analysis (SA) technique (the Morris method, MM) to an automated Lagrangian tropical cyclone (TC) tracking algorithm used on gridded climate data. MM demonstrates the ...ability to screen for input parameters defining TCs (such as minimum intensity and lifetime) that contribute significantly to sensitivity in output metrics (such as storm count). The SA is performed by tracking TCs in four different reanalyses. Tracked TC trajectories are compared to a pointwise observational record. Results show that using thermally integrated metrics for isolating TC warm cores is superior to single‐temperature levels. Input thresholds defining TC vortex strength during tracking contribute the most variance in all output metrics. Integrated output metrics (such as accumulated cyclone energy) are less variable than “counting” metrics such as TC frequency. MM greatly reduces the computational requirements for tracker optimization, with tracked TCs demonstrating better hit and false alarm rates than previous studies.
Key Points
Sensitivity analysis applied to an objective TC tracker screens for input thresholds of importance efficiently, allowing for optimization
Thresholds defining vortex intensity are key players in driving sensitivity of TC output metrics
Integrated output metrics demonstrate less variability over the same range of inputs compared to TC count
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Simulated historical precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and ...Indices. The model indices are evaluated against corresponding indices from the CPC unified gauge-based analyses of precipitation over seven geographical regions across the contiguous US (CONUS). The regions assessed match those in recent US National Climate Assessment Reports. To estimate observational uncertainty, precipitation indices for three other observational datasets (HadEx2, Livneh and PRISM) are evaluated against the CPC analyses. Both the moderate and extreme mean precipitation intensities are overestimated over the western CONUS and underestimated in the areas of the Central Great Plains (CGP) in most CMIP6 models tested. Most CMIP6 models overestimate the mean and variability of wet spell durations and underestimate the mean and variability of dry spell durations across the CONUS. Biases in interannual variability of most of the indices have similar patterns to those in corresponding mean biases. The median and interquartile model spreads in CMIP6 model biases are clearly smaller than those in CMIP5 model biases for wet spell durations. Multimodel medians of CMIP6 (CMIP6-MMM) and CMIP5 (CMIP5-MMM) have similar biases in climatology and variability but biases tend to be smaller in CMIP6-MMM. Depending on the index, extreme precipitation is slightly better in parts of the eastern half of the CONUS in CMIP6-MMM, otherwise, the biases in climatology and variability are similar to CMIP5-MMM. CMIP6-MMM performs better than individual models and even observational datasets in some cases. Differences between observational datasets for most indices are comparable to the CMIP6 interquartile model spread. The better-performing observational and model datasets are different in different parts of the CONUS.
•CMIP6 historical precipitation extremes are assessed over 7 contiguous US regions.•Similar but smaller biases are in CMIP6 multimodel medians than CMIP5 models tested.•A CMIP6 multimodel median performs better overall than any individual model tested.•Observational uncertainty has size like the interquartile model spread in most cases.•Models and observations that do well in one region are not always good elsewhere.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This article describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a ...comprehensive set of metrics is defined to systematically compare and contrast products with one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or for regional domains. Simple-to-visualize “scorecards” allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher-resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic Ocean TC frequency. Upstream refinement over northern Africa offers little benefit in simulating storm occurrence, but spatial genesis patterns are improved. We also show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on the version of the Morrison–Gettelman microphysics package that is used.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study examines the linear orthogonal modes associated with monthly precipitation in the northeastern United States, from CESM1 LENS (35 ensemble members, 1979–2005) and two reanalysis datasets ...(ERA5, 1950–2018 and NOAA-CIRES-DOE 20CRv3, 1950–2015). Calendar months are aggregated together, and any linear trends in data are removed. Using region-averaged precipitation anomaly time series and monthly anomalies for several global 2D atmospheric fields, a linear orthogonal decomposition method is implemented to iteratively extract time series (based on field and geographic location) of absolute maximum correlation. Linear modes associated with this method are then projected onto the full set of 2D fields to provide physical insight into the mechanisms involved in generating precipitation. In this region, the first mode is associated with vapor transport from the Atlantic seaboard, the second mode is characterized by westward vapor transport associated with extratropical cyclones, and the third mode captures vapor transport from the Gulf of Mexico during the fall and winter. However, the third mode is less robust in the spring and summer. Results are generally consistent across the datasets, and applying multiple linear regression with the linear modes to predict the precipitation anomalies produces R-squared values of around 0.54–0.65 for CESM1 LENS, and around 0.58–0.88 for reanalysis, with the lowest values generally in the spring and late summer. The influence of low-frequency climate variability on the modes is considered for CESM1 LENS, and the modes in late winter can be predicted with some success via a combination of several, prominent large-scale teleconnection patterns.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The design of accurate, conservative, consistent, and monotone operators for remapping scalar fields between computational grids on the sphere has been a persistent issue for global modeling groups. ...This problem is especially pronounced when mapping between distinct discretizations (such as finite volumes or finite elements). To this end, this paper provides a novel unified mathematical framework for the development of linear remapping operators. This framework is then applied in the development of high-order conservative, consistent, and monotone linear remapping operators from a finite-element discretization to a finite-volume discretization. The resulting scheme is evaluated in the context of both idealized and operational simulations and shown to perform well for a variety of problems.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
This paper presents a new atmospheric dynamical core which uses a high-order upwind finite-volume scheme of Godunov type for discretizing the non-hydrostatic equations of motion on the sphere under ...the shallow-atmosphere approximation. The model is formulated on the cubed-sphere in order to avoid polar singularities. An operator-split Runge–Kutta–Rosenbrock scheme is used to couple the horizontally explicit and vertically implicit discretizations so as to maintain accuracy in time and space and enforce a global CFL condition which is only restricted by the horizontal grid spacing and wave speed. The Rosenbrock approach is linearly implicit and so requires only one matrix solve per column per time step. Using a modified version of the low-speed AUSM+-up Riemann solver allows us to construct the vertical Jacobian matrix analytically, and so significantly improve the model efficiency. This model is tested against a series of typical atmospheric flow problems to verify accuracy and consistency. The test results reveal that this approach is stable, accurate and effective at maintaining sharp gradients in the flow.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Abstract
This paper extends on the first part of this series by describing four examples of 2D linear maps that can be constructed in accordance with the theory of the earlier work. The focus is ...again on spherical geometry, although these techniques can be readily extended to arbitrary manifolds. The four maps include conservative, consistent, and (optionally) monotone linear maps (i) between two finite-volume meshes, (ii) from finite-volume to finite-element meshes using a projection-type approach, (iii) from finite-volume to finite-element meshes using volumetric integration, and (iv) between two finite-element meshes. Arbitrary order of accuracy is supported for each of the described nonmonotone maps.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Climate change will impact western USA water supplies by shifting precipitation from snow to rain and driving snowmelt earlier in the season. However, changes at the regional-to-mountain scale is ...still a major topic of interest. This study addresses the impacts of climate change on mountain snowpack by assessing historical and projected variable-resolution (VR) climate simulations in the community earth system model (VR-CESM) forced by prescribed sea-surface temperatures along with widely used regional downscaling techniques, the coupled model intercomparison projects phase 5 bias corrected and statistically downscaled (CMIP5-BCSD) and the North American regional climate change assessment program (NARCCAP). The multi-model RCP8.5 scenario analysis of winter season SWE for western USA mountains indicates by 2040-2065 mean SWE could decrease −19% (NARCCAP) to −38% (VR-CESM), with an ensemble median change of −27%. Contrary to CMIP5-BCSD and NARCCAP, VR-CESM highlights a more pessimistic outcome for western USA mountain snowpack in latter-parts of the 21st century. This is related to temperature changes altering the snow-albedo feedback, snowpack storage, and precipitation phase, but may indicate that VR-CESM resolves more physically consistent elevational effects lacking in statistically downscaled datasets and teleconnections that are not captured in limited area models. Overall, VR-CESM projects by 2075–2100 that average western USA mountain snowfall decreases by −30%, snow cover by −44%, SWE by −69%, and average surface temperature increase of +5.0
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C. This places pressure on western USA states to preemptively invest in climate adaptation measures such as alternative water storage, water use efficiency, and reassess reservoir storage operations.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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•There are many types of actionable climate information and many ways to use it.•Typologies that map the landscape of climate information and its use are lacking.•We empirically ...derive typologies using 4-years of co-production engagements.•Such typologies can reduce the time and cost of actionable knowledge generation.
Developing actionable climate information and integrating it into decision-making are two crucial elements for promoting effective societal responses to climate change. However, what constitutes actionable climate information, and how it is used, varies based on the actors, systems, and scales that are relevant to specific decisions. Yet, the terms ‘actionable climate information’ or ‘use of climate information’ are used abstractly. There is a lack of holistic understanding of the various types of information that can be deemed as usable by different users, and the different ways in which they may be used in decision-making. Typologies or generalizable categorizations can help both knowledge producers and users to better envision the entire landscape of climate information and its uses and can help to reduce the time and cost of actionable knowledge production. Through systematic coding and analysis of ∼ 4 years of co-production engagements between climate scientists and resource managers, this paper presents empirically derived typologies of actionable climate information and its use, and explores whether certain uses are better informed by specific types of climate information. These typologies provide a valuable starting point for climate information producers, users, and boundary spanners working on climate-informed resource management, to reduce some of the time-intensive elements of the process.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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