scholarly journals Global Mean Surface Temperature Response to Large‐Scale Patterns of Variability in Observations and CMIP5

2019 ◽  
Vol 46 (4) ◽  
pp. 2232-2241 ◽  
Author(s):  
Jules B. Kajtar ◽  
Matthew Collins ◽  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Timothy J. Osborn ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Large Scale ◽  
Temperature Response ◽  
Global Mean Surface Temperature ◽  
Global Mean

scholarly journals A review of progress towards understanding the transient global mean surface temperature response to radiative perturbation

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Masakazu Yoshimori ◽  
Masahiro Watanabe ◽  
Hideo Shiogama ◽  
Akira Oka ◽  
Ayako Abe-Ouchi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Temperature Response ◽  
Global Mean Surface Temperature ◽  
Radiative Perturbation ◽  
Global Mean

scholarly journals Nonlocal Effects Dominate the Global Mean Surface Temperature Response to the Biogeophysical Effects of Deforestation

2019 ◽  
Vol 46 (2) ◽  
pp. 745-755 ◽  
Author(s):  
Johannes Winckler ◽  
Quentin Lejeune ◽  
Christian H. Reick ◽  
Julia Pongratz
Keyword(s):  
Surface Temperature ◽  
Temperature Response ◽  
Nonlocal Effects ◽  
Global Mean Surface Temperature ◽  
Global Mean

scholarly journals An interaction network perspective on the relation between patterns of sea surface temperature variability and global mean surface temperature

2013 ◽  
Vol 4 (2) ◽  
pp. 743-783 ◽  
Author(s):  
A. Tantet ◽  
H. A. Dijkstra
Keyword(s):  
Community Structure ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Southern Oscillation ◽  
Function Analysis ◽  
Interaction Network ◽  
Sea Surface ◽  
Global Mean Surface Temperature ◽  
Global Mean

Abstract. On interannual-to-multidecadal time scales variability in sea surface temperature appears to be organized in large-scale spatiotemporal patterns. In this paper, we investigate these patterns by studying the community structure of interaction networks constructed from sea surface temperature observations. Much of the community structure as well as the first neighbour maps can be interpreted using known dominant patterns of variability, such as the El Niño/Southern Oscillation and the Atlantic Multidecadal Oscillation and teleconnections. The community detection method allows to overcome some shortcomings of Empirical Orthogonal Function analysis or composite analysis and hence provides additional information with respect to these classical analysis tools. The community analysis provides also new insight into the relationship between patterns of sea surface temperature and the global mean surface temperature (GMST). On the decadal-to-multidecadal time scale, we show that only two communities (Indian Ocean and North Atlantic) determine most of the GMST variability.

scholarly journals 2-D Empirical Mode Decomposition on the sphere, application to the spatial scales of surface temperature variations

2008 ◽  
Vol 5 (1) ◽  
pp. 405-435 ◽  
Author(s):  
N. Fauchereau ◽  
S. Sinclair ◽  
G. Pegram
Keyword(s):  
Surface Temperature ◽  
Empirical Mode Decomposition ◽  
Large Scale ◽  
Spatial Scales ◽  
Two Dimensions ◽  
Temperature Variations ◽  
Mode Decomposition ◽  
Global Mean Surface Temperature ◽  
Global Mean ◽  
Global Data

Abstract. The Empirical Mode Decomposition (EMD) is applied here in two dimensions over the sphere to demonstrate its potential as a data-driven method of separating the different scales of spatial variability in a geophysical (climatological/meteorological) field. After a brief description of the basics of the EMD in 1 then 2 dimensions, the principles of its application on the sphere are explained, in particular via the use of a zonal equal area partitioning. The EMD is first applied to a artificial dataset, demonstrating its capability in extracting the different (known) scales embedded in the field. The decomposition is then applied to a global mean surface temperature dataset, and we show qualitatively that it extracts successively larger scales of temperature variations related for example to the topographic and large-scale, solar radiation forcing. We propose that EMD can be used as a global data-adaptative filter, which will be useful in analyzing geophysical phenomena that arise as the result of forcings at multiple spatial scales.

scholarly journals Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

2021 ◽  
Author(s):  
Philip G. Sansom ◽  
Donald Cummins ◽  
Stefan Siegert ◽  
David B Stephenson
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Surface Temperature ◽  
Climate Model ◽  
Future Projections ◽  
Perfect Model ◽  
Global Mean Surface Temperature ◽  
Time Series Approach ◽  
Energy Balance Models ◽  
Global Mean

Abstract Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

scholarly journals Holocene global mean surface temperature, a multi-method reconstruction approach

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Darrell Kaufman ◽  
Nicholas McKay ◽  
Cody Routson ◽  
Michael Erb ◽  
Christoph Dätwyler ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Global Mean Surface Temperature ◽  
Global Mean

scholarly journals Comparing variability and trends in observed and modelled global-mean surface temperature

2010 ◽  
Vol 37 (16) ◽  
pp. n/a-n/a ◽  
Author(s):  
John C. Fyfe ◽  
Nathan P. Gillett ◽  
David W. J. Thompson
Keyword(s):  
Surface Temperature ◽  
Global Mean Surface Temperature ◽  
Global Mean
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