scholarly journals Time scales of the European surface air temperature variability: The role of the 7–8 year cycle

2016 ◽  
Vol 43 (2) ◽  
pp. 902-909 ◽  
Author(s):  
Nikola Jajcay ◽  
Jaroslav Hlinka ◽  
Sergey Kravtsov ◽  
Anastasios A. Tsonis ◽  
Milan Paluš
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Temperature Variability

scholarly journals Diagnosis of Multiyear Predictability on Continental Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Optimization Method ◽  
First Year ◽  
Temperature And Precipitation ◽  
Regional Prediction ◽  
Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

scholarly journals Impact of Subsurface Temperature Variability on Surface Air Temperature Variability: An AGCM Study

2008 ◽  
Vol 9 (4) ◽  
pp. 804-815 ◽  
Author(s):  
Sarith P. P. Mahanama ◽  
Randal D. Koster ◽  
Rolf H. Reichle ◽  
Max J. Suarez
Keyword(s):  
Surface Temperature ◽  
Soil Temperature ◽  
Air Temperature ◽  
Seasonal Cycle ◽  
Surface Air Temperature ◽  
Temperature Variability ◽  
Climate System ◽  
Boreal Summer ◽  
Temperature Anomalies ◽  
Subsurface Soil

Abstract Anomalous atmospheric conditions can lead to surface temperature anomalies, which in turn can lead to temperature anomalies in the subsurface soil. The subsurface soil temperature (and the associated ground heat content) has significant memory—the dissipation of a temperature anomaly may take weeks to months—and thus subsurface soil temperature may contribute to the low-frequency variability of energy and water variables elsewhere in the system. The memory may even provide some skill to subseasonal and seasonal forecasts. This study uses three long-term AGCM experiments to isolate the contribution of subsurface soil temperature variability to variability elsewhere in the climate system. The first experiment consists of a standard ensemble of Atmospheric Model Intercomparison Project (AMIP)-type simulations in which the subsurface soil temperature variable is allowed to interact with the rest of the system. In the second experiment, the coupling of the subsurface soil temperature to the rest of the climate system is disabled; that is, at each grid cell, the local climatological seasonal cycle of subsurface soil temperature (as determined from the first experiment) is prescribed. Finally, a climatological seasonal cycle of sea surface temperature (SST) is prescribed in the third experiment. Together, the three experiments allow the isolation of the contributions of variable SSTs, interactive subsurface soil temperature, and chaotic atmospheric dynamics to meteorological variability. The results show that allowing an interactive subsurface soil temperature does, indeed, significantly increase surface air temperature variability and memory in most regions. In many regions, however, the impact is negligible, particularly during boreal summer.

scholarly journals Spatially coherent late Holocene Antarctic Peninsula surface air temperature variability

Geology ◽  
2018 ◽  
Vol 46 (12) ◽  
pp. 1071-1074 ◽  
Author(s):  
Dan J. Charman ◽  
Matthew J. Amesbury ◽  
Thomas P. Roland ◽  
Jessica Royles ◽  
Dominic A. Hodgson ◽  
...  
Keyword(s):  
Air Temperature ◽  
Antarctic Peninsula ◽  
Late Holocene ◽  
Surface Air Temperature ◽  
Temperature Variability

scholarly journals Influence of Climate on Emergency Department Visits for Syncope: Role of Air Temperature Variability

PLoS ONE ◽  
2011 ◽  
Vol 6 (7) ◽  
pp. e22719 ◽  
Author(s):  
Andrea Galli ◽  
Franca Barbic ◽  
Marta Borella ◽  
Giorgio Costantino ◽  
Francesca Perego ◽  
...  
Keyword(s):  
Emergency Department ◽  
Air Temperature ◽  
Temperature Variability ◽  
Emergency Department Visits
Sign in / Sign up

Export Citation Format

Share Document