Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Ryan L Sriver"'
Publikováno v:
PLoS ONE, Vol 13, Iss 2, p e0190641 (2018)
Many institutions worldwide are considering how to include uncertainty about future changes in sea-levels and storm surges into their investment decisions regarding large capital infrastructures. Here we examine how to characterize deeply uncertain c
Externí odkaz:
https://doaj.org/article/811c885561214d6b978d91450f101b61
Autor:
David C. Lafferty, Ryan L. Sriver
Publikováno v:
npj Climate and Atmospheric Science, Vol 6, Iss 1, Pp 1-13 (2023)
Abstract Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach. Here, we perform a variance decom
Externí odkaz:
https://doaj.org/article/e22c5d62632b45fca7a9615199756eb8
Autor:
David C. Lafferty, Ryan L. Sriver
Publikováno v:
npj Climate and Atmospheric Science, Vol 7, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/2920875347c0488e90877d308fa4d987
Autor:
Vivek Srikrishnan, David C. Lafferty, Tony E. Wong, Jonathan R. Lamontagne, Julianne D. Quinn, Sanjib Sharma, Nusrat J. Molla, Jonathan D. Herman, Ryan L. Sriver, Jennifer F. Morris, Ben Seiyon Lee
Publikováno v:
Earth's Future, Vol 10, Iss 8, Pp n/a-n/a (2022)
Abstract Simulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct
Externí odkaz:
https://doaj.org/article/42bb6c2d10b541bea2e5f7d750710654
Publikováno v:
Earth's Future, Vol 7, Iss 6, Pp 677-690 (2019)
Abstract Reduced complexity climate models are useful tools for quantifying decision‐relevant uncertainties, given their flexibility, computational efficiency, and suitability for large‐ensemble frameworks necessary for statistical estimation usi
Externí odkaz:
https://doaj.org/article/d6ea1e99b62844eea0fcdfdb410033a9
Autor:
Hui Li, Ryan L. Sriver
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 10, Iss 1, Pp 165-186 (2018)
Abstract High‐resolution Atmosphere General Circulation Models (AGCMs) are capable of directly simulating realistic tropical cyclone (TC) statistics, providing a promising approach for TC‐climate studies. Active air‐sea coupling in a coupled mo
Externí odkaz:
https://doaj.org/article/fd36911a5d9849bc9258a2d7d790d663
Autor:
David C Lafferty, Ryan L. Sriver
Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach. Here, we perform a variance decomposition
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::efc2741eeada2d99f460dd5f3c4e7a75
https://doi.org/10.22541/essoar.168286894.44910061/v1
https://doi.org/10.22541/essoar.168286894.44910061/v1
Autor:
Ryan L. Sriver, Iman Haqiqi, Robert E. Nicholas, D. C. Lafferty, Thomas W. Hertel, Klaus Keller
Publikováno v:
Communications Earth & Environment, Vol 2, Iss 1, Pp 1-10 (2021)
Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9af1889b1ea85195a37cb73ab6feefc5
https://doaj.org/article/b44eae854a2a40f7af7844b31b055a33
https://doaj.org/article/b44eae854a2a40f7af7844b31b055a33
Autor:
Benjamin Aaron Vega-Westhoff, Ryan L. Sriver, Steven J. Smith, Corinne Hartin, Adria K. Schwarber
Publikováno v:
Earth System Dynamics, Vol 10, Pp 729-739 (2019)
Simple climate models (SCMs) are numerical representations of the Earth's gas cycles and climate system. SCMs are easy to use and computationally inexpensive, making them an ideal tool in both scientific and decision-making contexts (e.g., complex cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b90ec9fe9d73fa2d02f3a5dedb606ef
https://doi.org/10.5194/esd-10-729-2019
https://doi.org/10.5194/esd-10-729-2019
Publikováno v:
Earth's Future, Vol 7, Iss 6, Pp 677-690 (2019)
Reduced complexity climate models are useful tools for quantifying decision‐relevant uncertainties, given their flexibility, computational efficiency, and suitability for large‐ensemble frameworks necessary for statistical estimation using resamp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03456e434748b090617e0b1f0bf46deb
https://doi.org/10.1029/2018ef001082
https://doi.org/10.1029/2018ef001082