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Kristopher M. Bedka
and
Konstantin Khlopenkov

associated with OTs given that most exhibit a clear BT minimum that extends across several 4-km pixels. Resampling also dampens small-scale BT variability in convective anvils that could confuse the pattern-recognition scheme and lead to false detection. MODIS 1-km-resolution VIS imagery is also input to the detection algorithm, which also matches the resolution of current and historical GEO VIS data. All resampling of imagery described in this paper is implemented by using a resampling function over a

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Martin Bergemann
,
Christian Jakob
, and
Todd P. Lane

priori assumptions are met (see section 3 for the heuristics used here) and the remaining part of the image can be considered as the detected pattern. We start the pattern detection by applying the rainfall intensity threshold and converting the rainfall data to a binary image. Areas less than the threshold are set to 0 and the ones above to 1. After converting the rainfall data to a binary image, small holes within the rainfall areas (1-domains) are closed. Closing of small holes within a

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DáithíA. Stone
,
Myles R. Allen
,
Frank Selten
,
Michael Kliphuis
, and
Peter A. Stott

EBM parameters. Now that we have estimated the responses of the GCM to individual forcings, we can proceed with the standard detection and attribution methodology ( Allen and Tett 1999 ). Under this, we express the observed temperature response pattern T obs as a linear sum of the simulated responses determined for each forcing ( T i ) plus a residual ( υ 0 ): Here β i is the scaling factor corresponding to the response to forcing i that is to be estimated in the regression. This relational

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Kate Marvel
,
Mark Zelinka
,
Stephen A. Klein
,
Céline Bonfils
,
Peter Caldwell
,
Charles Doutriaux
,
Benjamin D. Santer
, and
Karl E. Taylor

structure ( Santer et al. 2003 , 2013 ). In this study we use these properties to perform the first formal detection and attribution study on observed cloud trends. This requires that we first consider a number of related questions: Can we identify the fingerprints of external forcing on model cloud properties, and if so, are they distinct from patterns that arise from internal variability alone? How long an observational record is theoretically required to ensure detection of an externally forced

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Qiaohong Sun
,
Francis Zwiers
,
Xuebin Zhang
, and
Jun Yan

-Young and Zhang 2020 ), and in some regions, including notably dry and wet regions ( Donat et al. 2016 ), the high latitudes of the Northern Hemisphere ( Groisman et al. 2005 ; Westra et al. 2013 ), central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia ( Sun et al. 2021 ). Previous detection and attribution analyses ( Min et al. 2011 ; Zhang et al. 2013 ; Li et al. 2017 ; Dong et al. 2020 ; Kirchmeier

Open access
Kate Marvel
,
Michela Biasutti
,
Céline Bonfils
,
Karl E. Taylor
,
Yochanan Kushnir
, and
Benjamin I. Cook

. D&A toolkit In this paper, we will use a standard set of “fingerprinting” and signal detection methods presented in, for example, Santer et al. (2005 , 2011 , 2013) . 1) Fingerprinting The fingerprint F ( ϕ ) of climate change is the spatial pattern that characterizes the climate system response to external forcing ( Allen and Stott 2003 ; Gillett et al. 2002 ; Hegerl et al. 1996 ; Stott et al. 2000 ; Tett et al. 2002 ). Following, for example, Hasselmann (1993) and Santer et al

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Russell Blackport
,
John C. Fyfe
, and
Benjamin D. Santer

estimates for the role of anthropogenic aerosol forcing ( Gillett et al. 2013 ; Lott et al. 2013 ; Swart et al. 2018 ). One of the main goals of this study is to determine if applying the same detection and attribution method across these three different variables leads to more consistent results for the estimated strength of the anthropogenic aerosol fingerprint. After first analyzing the temporal and spatial patterns of the model-predicted TMT, ST, and OHC fingerprints, we then check to see if we

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Benjamin D. Santer
,
Stephen Po-Chedley
,
Nicole Feldl
,
John C. Fyfe
,
Qiang Fu
,
Susan Solomon
,
Mark England
,
Keith B. Rodgers
,
Malte F. Stuecker
,
Carl Mears
,
Cheng-Zhi Zou
,
Céline J. W. Bonfils
,
Giuliana Pallotta
,
Mark D. Zelinka
,
Nan Rosenbloom
, and
Jim Edwards

1. Introduction Detection and attribution (D&A) studies seek to disentangle human and natural influences on Earth’s climate. This research made a significant contribution to the recent finding that human influence on climate is unequivocal ( IPCC 2021 ). Pattern-based “fingerprint” methods are a key element of D&A research ( Hasselmann 1979 ; North et al. 1995 ; Hegerl et al. 1996 ; Santer et al. 1996 ; Tett et al. 1996 ; Stott et al. 2000 ; Barnett et al. 2005 ). The initial

Free access
Laurent Terray
,
Lola Corre
,
Sophie Cravatte
,
Thierry Delcroix
,
Gilles Reverdin
, and
Aurélien Ribes

associated with quasi-uniform tropical SST warming ( Xie et al. 2010 ). Despite common features among projected spatial patterns of SST change, deviations from uniform tropical SST warming differ between models leading to large uncertainty with regard to the future distribution of tropical precipitation and evaporation changes. Furthermore, early detection and attribution of these changes is also hampered by the difficulty and lack of long-term freshwater flux observations over the oceans and their high

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Ioannis Cheliotis
,
Elsa Dieudonné
,
Hervé Delbarre
,
Anton Sokolov
,
Egor Dmitriev
,
Patrick Augustin
,
Marc Fourmentin
,
François Ravetta
, and
Jacques Pelon

Jussieu site. Thermal patterns are expected to occur during fair cumuli weather; hence the sunshine duration, the atmospheric pressure, and the light wind conditions are appropriate parameters to indicate their development. Furthermore, the detection of precipitation events reveals the cases when the estimation of the ABL height via the lidar observations were not applicable. Figure 5 illustrates the minimum and maximum temperatures, the daily values of sunshine in hours, the daily accumulated

Open access