Downscaling of Climate Information

Mearns, Linda O.; Bukovsky, Melissa; Pryor, S. C.; Magaña, Víctor

doi:10.1007/978-3-319-03768-4_5

Cited by 8 publications

(6 citation statements)

References 206 publications

(211 reference statements)

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“…Dynamical downscaling techniques use physics-based models or regional climate models (RCMs) constrained to a smaller region and are driven by GCM projections at the boundaries to produce higher resolution projections. The NA-CORDEX data set corresponds to a collection of high-resolution projections from different GCM-RCM combinations and emissions scenarios (Mearns et al, 2014). Figure S1 shows a detailed comparison of the methodological steps of the process followed to produce each data set.…”

Section: Publicly Available Downscaled Us Climate Projections Data Setsmentioning

confidence: 99%

Uncertainties in Future U.S. Extreme Precipitation From Downscaled Climate Projections

Lopez-Cantu

Prein

Samaras

2020

Geophysical Research Letters

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Impacts modelers and stakeholders use publicly available data sets of downscaled climate projections to assess and design infrastructure for changes in future rainfall extremes. If differences across data sets exist, infrastructure resilience decisions could change depending on which single data set is used. We assess changes in U.S. rainfall extremes from 2044-2099 compared with 1951-2005 based on 227 projections under RCP4.5 and RCP8.5 from five widely used data sets. We show there are large differences in the change magnitude and its spatial structure between data sets. At the continental scale, the data sets show different increases, with high-end extremes (e.g. 100-year event) generally increasing more (between 10% and 50%) than low-end extremes (e.g. 5-year). These differences largely contribute to the overall uncertainty for small average recurrence intervals (ARIs) extremes (2-to 10-year), while uncertainties due to short record length dominate large ARIs (25-to 100-year). The results indicate that robust infrastructure planning should consider these uncertainties to enable resilient infrastructure under climate change. Plain Language SummaryObserved extreme rainfall magnitudes have increased since 1950, and climate model projections indicate that these increases will continue throughout the 21st century in many areas in the US. Adapting and designing infrastructure for climate change requires future extreme rainfall projections at high resolutions. Global climate model (GCM) output resolution is generally much coarser, and methods have been developed to create relevant climate information at regional scales. Multiple open data sets exist that provide downscaled projections of future rainfall extremes. We analyze changes in future daily extremes using five widely used data sets in climate change impacts assessments and decision making. We found large differences between data sets in how much and where extreme events will intensify by the end of the 21st century. This and other sources of uncertainty need to be considered when designing resilient infrastructure.

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Section: Publicly Available Downscaled Us Climate Projections Data Setsmentioning

confidence: 99%

Uncertainties in Future U.S. Extreme Precipitation From Downscaled Climate Projections

Lopez-Cantu

Prein

Samaras

2020

Geophysical Research Letters

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“…Improving simulation of regional climates is a high priority for climate science and a justification for use of limited area models (regional climate models, RCMs) and/or adaptive grid or stretch grid global models [ Mearns et al ., ]. Achieving improvements in the description of regional (and subregional) climates is predicated on evaluation of, and attribution of, current model skill and uncertainty in simulations of contemporary and future climates.…”

Section: Background and Motivationmentioning

confidence: 99%

Evaluation of near‐surface temperature, humidity, and equivalent temperature from regional climate models applied in type II downscaling

Pryor

Schoof

2016

JGR Atmospheres

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Atmosphere-surface interactions are important components of local and regional climates due to their key roles in dictating the surface energy balance and partitioning of energy transfer between sensible and latent heat. The degree to which regional climate models (RCMs) represent these processes with veracity is incompletely characterized, as is their ability to capture the drivers of, and magnitude of, equivalent temperature (T e ). This leads to uncertainty in the simulation of near-surface temperature and humidity regimes and the extreme heat events of relevance to human health, in both the contemporary and possible future climate states. Reanalysis-nested RCM simulations are evaluated to determine the degree to which they represent the probability distributions of temperature (T), dew point temperature (T d ), specific humidity (q) and T e over the central U.S., the conditional probabilities of T d |T, and the coupling of T, q, and T e to soil moisture and meridional moisture advection within the boundary layer (adv(T e )). Output from all RCMs exhibits discrepancies relative to observationally derived time series of near-surface T, q, T d , and T e , and use of a single layer for soil moisture by one of the RCMs does not appear to substantially degrade the simulations of near-surface T and q relative to RCMs that employ a four-layer soil model. Output from MM5I exhibits highest fidelity for the majority of skill metrics applied herein, and importantly most realistically simulates both the coupling of T and T d , and the expected relationships of boundary layer adv(T e ) and soil moisture with near-surface T and q. Citation:Pryor, S. C., and J. T. Schoof (2016), Evaluation of near-surface temperature, humidity, and equivalent temperature from regional climate models applied in type II downscaling,

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“…Internal variability uncertainty results from processes that can constructively or destructively interfere, for a short period of time, with long‐term trends associated with anthropogenic climate change, for example, variability associated with the El Niño Southern Oscillation (ENSO). Downscaling of climate projections (Mearns et al ., ), sometimes necessary for projections to be relevant at local scales (Wilby and Fowler, ), can also add uncertainty or error (Pielke and Wilby, ). However, it may be possible to reduce model uncertainty by identifying a subset of models of relatively higher integrity (e.g., Brekke et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Assessment of uncertainty in multi‐model means of downscaled South Florida precipitation for projected (2019–2099) climate

Infanti

Kirtman

Aumen

et al. 2019

Intl Journal of Climatology

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South Florida resource management, particularly the Everglades restoration effort, is beginning to consider projections of precipitation from multiple climate models for decision‐making. Because precipitation changes can significantly affect the Everglades ecosystem, characterization of precipitation projection uncertainty is important for resource management decisions, and reduction of uncertainty is desired for better decision‐making. Though uncertainty of precipitation projections has been characterized for many regions, uncertainty has not been sufficiently quantified for South Florida. This study builds upon prior results for projected Florida precipitation by considering recent climate model simulations, seasonal and spatial information, and uncertainty quantification and reduction. We identify the multi‐model mean change in South Florida precipitation and characterize the uncertainty of 37 statistically downscaled Coupled Model Intercomparison Project Phase 5 models. For 2019–2045, there is a likely (over 60% of ensemble members) increase in South Florida annual mean precipitation owing to a likely to very likely (near 90% of ensemble members) increase in dry season (November, December, and January) precipitation, while wet season (June, July, and August) shows a more likely than not (over 50% of ensemble members) decrease in precipitation in the southern region and increase in precipitation in the northern region of South Florida. As South Florida agencies are on the verge of including precipitation projections in their upcoming planning horizon, this information will aid South Florida climate data users in decisions influenced by future rainfall.

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Downscaling of Climate Information

Cited by 8 publications

References 206 publications

Uncertainties in Future U.S. Extreme Precipitation From Downscaled Climate Projections

Uncertainties in Future U.S. Extreme Precipitation From Downscaled Climate Projections

Evaluation of near‐surface temperature, humidity, and equivalent temperature from regional climate models applied in type II downscaling

Assessment of uncertainty in multi‐model means of downscaled South Florida precipitation for projected (2019–2099) climate

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