Climate Model Uncertainty and Trend Detection in Regional Sea Level Projections: A Review

Carson, Mark; Lyu, Kewei; Richter, Kristin; Becker, Mélanie; Domingues, Catia M.; Han, Weiqing; Zanna, Laure

doi:10.1007/s10712-019-09559-3

Cited by 15 publications

(13 citation statements)

References 106 publications

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“…In other words, the LFCIV is an oceanic driver of coastal sea-level changes in the turbulent regime, which may compete with the external drivers examined in Woodworth et al (2019) and with the ocean-atmosphere coupled modes presented by Han et al (2019). More importantly, the random character of LFCIV suggests that it actually constitutes a source of uncertainty for the detection and attribution of coastal sea-level trends, in addition to those simulated by climate models (Carson et al 2019), and that these uncertainties should also be taken into account for coastal sea-level projections (see Jevrejeva et al 2019).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Trends of Coastal Sea Level Between 1993 and 2015: Imprints of Atmospheric Forcing and Oceanic Chaos

Penduff

Llovel

et al. 2019

Surv Geophys

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The observation and simulation of the variability of coastal sea level are impacted by various uncertainties, such as measurement errors and sampling biases, unresolved processes, and model and forcing biases. Ocean model simulations suggest that another uncertainty should be taken into account for the attribution of sea-level changes. Global ocean simulations indeed show that resolving mesoscale turbulence (even partly) promotes the emergence of low-frequency (LF) chaotic intrinsic variability (CIV) which causes substantial random fluctuations of sea level up to multiple decades in eddy-active regions of the world ocean. This random LFCIV is superimposed on the atmospherically forced (or simply "forced") fluctuations, which are directly controlled by the atmospheric variability. We show from a large ensemble of global oceanic hindcasts that this multi-decadal LFCIV leaves a substantial imprint on the long-term trends (1993-2015) of coastal sea level: over 17-20% of the global ocean coastal area, in particular along the coastlines of the northwestern Pacific and Indian Oceans, and around the Gulf of Mexico, random sealevel trends may blur their atmospherically forced counterpart, such that simulated (and potentially observed) coastal sea-level trends cannot be unambiguously attributed to atmospheric or anthropic causes. The steric and manometric sea-level change contributions of these uncertainties are discussed, suggesting that they mostly come from the manometric sea-level trends near the coasts.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Trends of Coastal Sea Level Between 1993 and 2015: Imprints of Atmospheric Forcing and Oceanic Chaos

Penduff

Llovel

et al. 2019

Surv Geophys

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“…Meanwhile, climate change projections for the XXI century that are obtained from different models are inherently uncertain. The main sources of uncertainty, which were analysed and summarized in several studies (e.g., [15][16][17]19,27,28]), are as follows: uncertainty in greenhouse gas emission scenarios, natural climate variability and climate models' uncertainties. Since we focus on climate system simulations, the climate models' uncertainties are of primary concern.…”

Section: Notes On Outdoor Environmental Ergonomics Through the Prism Of Climate Changementioning

confidence: 99%

Mathematical Modelling of Climate Change and Variability in the Context of Outdoor Ergonomics

2021

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The current climate change, unlike previous ones, is caused by human activity and is characterized by an unprecedented rate of increase in the near-surface temperature and an increase in the frequency and intensity of hazardous weather and climate events. To survive, society must be prepared to implement adaptation strategies and measures to mitigate the negative effects of climate change. This requires, first of all, knowledge of how the climate will change in the future. To date, mathematical modelling remains the only method and effective tool that is used to predict the climate system’s evolution under the influence of natural and anthropogenic perturbations. It is important that mathematics and its methods and approaches have played a vital role in climate research for several decades. In this study, we examined some mathematical methods and approaches, primarily, mathematical modelling and sensitivity analysis, for studying the Earth’s climate system, taking into account the dependence of human health on environmental conditions. The essential features of stochastic climate models and their application for the exploration of climate variability are examined in detail. As an illustrative example, we looked at the application of a low-order energy balance model to study climate variability. The effects of variations in feedbacks and the climate system’s inertia on the power spectrum of global mean surface temperature fluctuations that characterized the distribution of temperature variance over frequencies were estimated using a sensitivity analysis approach. Our confidence in the obtained results was based on the satisfactory agreement between the theoretical power spectrum that was derived from the energy balance model and the power spectrum that was obtained from observations and coupled climate models, including historical runs of the CMIP5 models.

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“…Accurate measurements and analytics of global rainfall are crucial for advancing our understanding of the climate system [9] and human society. Furthermore, reanalyzed precipitation products contain uncertainties introduced by the following aspects: the inter-model indeterminacies due to the discrepancies in the responses from different models under a warming climate, and internal variability of a climate model due to its own uncertainty components [10]. A solid analysis of the trends and variations in the observed precipitation characteristics can be utilized to mitigate these potential uncertainties by integrating these trends to the models [11,12].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data

et al. 2021

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The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by −0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends.

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Climate Model Uncertainty and Trend Detection in Regional Sea Level Projections: A Review

Cited by 15 publications

References 106 publications

Trends of Coastal Sea Level Between 1993 and 2015: Imprints of Atmospheric Forcing and Oceanic Chaos

Trends of Coastal Sea Level Between 1993 and 2015: Imprints of Atmospheric Forcing and Oceanic Chaos

Mathematical Modelling of Climate Change and Variability in the Context of Outdoor Ergonomics

Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data

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