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  • Author or Editor: James S. Risbey x
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James S. Risbey
and
Milind Kandlikar

The problem of detection of climate change and attribution of causes of change has been formalized as a series of discrete probability judgements in an expert elicitation protocol. Here results are presented from the protocol for 19 experts, highlighting areas of convergence and divergence among experts. There is broad agreement among the experts that the global mean surface air temperature, vertical pattern of temperature change, geographical pattern of temperature change, and changes in diurnal temperature are the important lines of evidence for climate change detection and attribution. For the global mean and vertical pattern lines of evidence, the majority of experts (90%) reject the null hypothesis (no climate change) at the 5% significance level, thereby lending strong support to detection of climate change. For these lines of evidence the median probability of detection at the 5% significance level across experts exceeds 0.9. For the geographical pattern and diurnal cycle lines of evidence, there is far less agreement and fewer than half the experts support detection at even the 10% level of significance. On attribution there is a broad consensus that greenhouse forcing is responsible for about half the warming in global mean temperature in the past century. This result is fairly robust to uncertainties assessed in the relevant forcings by this set of experts. For the other lines of evidence, greenhouse forcing makes smaller fractional contributions with more spread among expert assessments. The near consensus of the experts on detection of climate change and attribution to greenhouse gases rests on the evidence of change in global mean surface air temperature. For the other lines of evidence, there is either significant expert disagreement on detection (the geographical pattern and diurnal cycle), or attribution of change is predominantly to causes other than greenhouse gas forcing (the vertical pattern).

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Stephan Lewandowsky
,
James S. Risbey
, and
Naomi Oreskes

Abstract

There has been much recent published research about a putative “pause” or “hiatus” in global warming. We show that there are frequent fluctuations in the rate of warming around a longer-term warming trend, and that there is no evidence that identifies the recent period as unique or particularly unusual. In confirmation, we show that the notion of a pause in warming is considered to be misleading in a blind expert test. Nonetheless, the most recent fluctuation about the longer-term trend has been regarded by many as an explanatory challenge that climate science must resolve. This departs from long-standing practice, insofar as scientists have long recognized that the climate fluctuates, that linear increases in CO2 do not produce linear trends in global warming, and that 15-yr (or shorter) periods are not diagnostic of long-term trends. We suggest that the repetition of the “warming has paused” message by contrarians was adopted by the scientific community in its problem-solving and answer-seeking role and has led to undue focus on, and mislabeling of, a recent fluctuation. We present an alternative framing that could have avoided inadvertently reinforcing a misleading claim.

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Michael R. Grose
,
James S. Risbey
,
Mitchell T. Black
, and
David J. Karoly
Full access
Michael R. Grose
,
Mitchell Black
,
James S. Risbey
,
Peter Uhe
,
Pandora K. Hope
,
Karsten Haustein
, and
Dann Mitchell
Full access
Carly R. Tozer
,
James S. Risbey
,
Michael Grose
,
Didier P. Monselesan
,
Dougal T. Squire
,
Amanda S. Black
,
Doug Richardson
,
Sarah N. Sparrow
,
Sihan Li
, and
David Wallom
Free access
Kirsten L. Findell
,
Rowan Sutton
,
Nico Caltabiano
,
Anca Brookshaw
,
Patrick Heimbach
,
Masahide Kimoto
,
Scott Osprey
,
Doug Smith
,
James S. Risbey
,
Zhuo Wang
,
Lijing Cheng
,
Leandro B. Diaz
,
Markus G. Donat
,
Michael Ek
,
June-Yi Lee
,
Shoshiro Minobe
,
Matilde Rusticucci
,
Frederic Vitart
, and
Lin Wang

Abstract

The World Climate Research Programme (WCRP) envisions a world “that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind.” This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction, and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization.

Open access
Christopher J. White
,
Daniela I. V. Domeisen
,
Nachiketa Acharya
,
Elijah A. Adefisan
,
Michael L. Anderson
,
Stella Aura
,
Ahmed A. Balogun
,
Douglas Bertram
,
Sonia Bluhm
,
David J. Brayshaw
,
Jethro Browell
,
Dominik Büeler
,
Andrew Charlton-Perez
,
Xandre Chourio
,
Isadora Christel
,
Caio A. S. Coelho
,
Michael J. DeFlorio
,
Luca Delle Monache
,
Francesca Di Giuseppe
,
Ana María García-Solórzano
,
Peter B. Gibson
,
Lisa Goddard
,
Carmen González Romero
,
Richard J. Graham
,
Robert M. Graham
,
Christian M. Grams
,
Alan Halford
,
W. T. Katty Huang
,
Kjeld Jensen
,
Mary Kilavi
,
Kamoru A. Lawal
,
Robert W. Lee
,
David MacLeod
,
Andrea Manrique-Suñén
,
Eduardo S. P. R. Martins
,
Carolyn J. Maxwell
,
William J. Merryfield
,
Ángel G. Muñoz
,
Eniola Olaniyan
,
George Otieno
,
John A. Oyedepo
,
Lluís Palma
,
Ilias G. Pechlivanidis
,
Diego Pons
,
F. Martin Ralph
,
Dirceu S. Reis Jr.
,
Tomas A. Remenyi
,
James S. Risbey
,
Donald J. C. Robertson
,
Andrew W. Robertson
,
Stefan Smith
,
Albert Soret
,
Ting Sun
,
Martin C. Todd
,
Carly R. Tozer
,
Francisco C. Vasconcelos Jr.
,
Ilaria Vigo
,
Duane E. Waliser
,
Fredrik Wetterhall
, and
Robert G. Wilson

Abstract

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

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