scholarly journals Potential Predictability of North American Surface Temperature. Part I: Information-Based versus Signal-To-Noise-Based Metrics

2014 ◽  
Vol 27 (4) ◽  
pp. 1578-1599 ◽  
Author(s):  
Y. Tang ◽  
D. Chen ◽  
X. Yan
Keyword(s):  
Surface Temperature ◽  
Interannual Variability ◽  
Air Temperature ◽  
North American ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Multiple Model ◽  
Signal To Noise ◽  
Potential Predictability ◽  
Geographical Variations

Abstract In this study, the potential predictability of the North American (NA) surface air temperature was explored using information-based predictability framework and Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) multiple model ensembles. Emphasis was put on the comparison of predictability measured by information-based metrics and by the conventional signal-to-noise ratio (SNR)-based metrics. Furthermore, the potential predictability was optimally decomposed into different modes by maximizing the predictable information (equivalent to the maximum of SNR), from which the most predictable structure was extracted and analyzed. It was found that the conventional SNR-based metrics underestimate the potential predictability, in particular in these areas where the predictable signals are relatively weak. The most predictable components of the NA surface air temperature can be characterized by the interannual variability mode and the long-term trend mode. The former is inherent to tropical Pacific sea surface temperature (SST) forcing such as El Niño–Southern Oscillation (ENSO), whereas the latter is closely associated with the global warming. The amplitude of the two modes has geographical variations in different seasons. On this basis, the possible physical mechanisms responsible for the predictable mode of interannual variability and its potential benefits to the improvement of seasonal climate prediction were discussed.

Potential predictability of Arabian peninsula summer surface air temperature in the North American multimodel ensemble

2019 ◽  
Vol 53 (7-8) ◽  
pp. 4249-4266 ◽  
Author(s):  
Muhammad Azhar Ehsan ◽  
Fred Kucharski ◽  
Mansour Almazroui ◽  
Muhammad Ismail ◽  
Michael K. Tippett
Keyword(s):  
Air Temperature ◽  
North American ◽  
Arabian Peninsula ◽  
Surface Air Temperature ◽  
Multimodel Ensemble ◽  
Potential Predictability ◽  
The North ◽  
North American Multimodel Ensemble

Diagnosing Sources of U.S. Seasonal Forecast Skill

2006 ◽  
Vol 19 (13) ◽  
pp. 3279-3293 ◽  
Author(s):  
X. Quan ◽  
M. Hoerling ◽  
J. Whitaker ◽  
G. Bates ◽  
T. Xu
Keyword(s):  
Air Temperature ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Diagnostic Methods ◽  
Sea Surface ◽  
Empirical Methods ◽  
Spatial Coverage ◽  
Sst Anomalies

Abstract In this study the authors diagnose the sources for the contiguous U.S. seasonal forecast skill that are related to sea surface temperature (SST) variations using a combination of dynamical and empirical methods. The dynamical methods include ensemble simulations with four atmospheric general circulation models (AGCMs) forced by observed monthly global SSTs from 1950 to 1999, and ensemble AGCM experiments forced by idealized SST anomalies. The empirical methods involve a suite of reductions of the AGCM simulations. These include uni- and multivariate regression models that encapsulate the simultaneous and one-season lag linear connections between seasonal mean tropical SST anomalies and U.S. precipitation and surface air temperature. Nearly all of the AGCM skill in U.S. precipitation and surface air temperature, arising from global SST influences, can be explained by a single degree of freedom in the tropical SST field—that associated with the linear atmospheric signal of El Niño–Southern Oscillation (ENSO). The results support previous findings regarding the preeminence of ENSO as a U.S. skill source. The diagnostic methods used here exposed another skill source that appeared to be of non-ENSO origins. In late autumn, when the AGCM simulation skill of U.S. temperatures peaked in absolute value and in spatial coverage, the majority of that originated from SST variability in the subtropical west Pacific Ocean and the South China Sea. Hindcast experiments were performed for 1950–99 that revealed most of the simulation skill of the U.S. seasonal climate to be recoverable at one-season lag. The skill attributable to the AGCMs was shown to achieve parity with that attributable to empirical models derived purely from observational data. The diagnostics promote the interpretation that only limited advances in U.S. seasonal prediction skill should be expected from methods seeking to capitalize on sea surface predictors alone, and that advances that may occur in future decades could be readily masked by inherent multidecadal fluctuations in skill of coupled ocean–atmosphere systems.

scholarly journals An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST

2021 ◽  
Vol 56 (1-2) ◽  
pp. 635-650 ◽  
Author(s):  
Qingxiang Li ◽  
Wenbin Sun ◽  
Xiang Yun ◽  
Boyin Huang ◽  
Wenjie Dong ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Temperature Trends ◽  
Global Mean

scholarly journals Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

2015 ◽  
Vol 12 (8) ◽  
pp. 7665-7687 ◽  
Author(s):  
C. L. Pérez Díaz ◽  
T. Lakhankar ◽  
P. Romanov ◽  
J. Muñoz ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Balance Model ◽  
Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

scholarly journals Influence of ENSO on North American subseasonal surface air temperature variability

2021 ◽  
Vol 2 (2) ◽  
pp. 395-412
Author(s):  
Patrick Martineau ◽  
Hisashi Nakamura ◽  
Yu Kosaka
Keyword(s):  
North America ◽  
Air Temperature ◽  
North American ◽  
Surface Air Temperature ◽  
Tropical Pacific ◽  
La Niña ◽  
Western North America ◽  
The North ◽  
Subseasonal Variability ◽  
La Nina

Abstract. The wintertime influence of tropical Pacific sea surface temperature (SST) variability on subseasonal variability is revisited by identifying the dominant mode of covariability between 10–60 d band-pass-filtered surface air temperature (SAT) variability over the North American continent and winter-mean SST over the tropical Pacific. We find that the El Niño–Southern Oscillation (ENSO) explains a dominant fraction of the year-to-year changes in subseasonal SAT variability that are covarying with SST and thus likely more predictable. In agreement with previous studies, we find a tendency for La Niña conditions to enhance the subseasonal SAT variability over western North America. This modulation of subseasonal variability is achieved through interactions between subseasonal eddies and La Niña-related changes in the winter-mean circulation. Specifically, eastward-propagating quasi-stationary eddies over the North Pacific are more efficient in extracting energy from the mean flow through the baroclinic conversion during La Niña. Structural changes of these eddies are crucial to enhance the efficiency of the energy conversion via amplified downgradient heat fluxes that energize subseasonal eddy thermal anomalies. The enhanced likelihood of cold extremes over western North America is associated with both an increased subseasonal SAT variability and the cold winter-mean response to La Niña.

Surface air–soil temperature relationship and shallow soil thermal regime: a case study using a soil temperature observational dataset for Spain.

2021 ◽  
Author(s):  
Camilo Melo Aguilar ◽  
Fidel González Rouco ◽  
Norman Steinert ◽  
Elena García Bustamante ◽  
Felix García Pereira ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Soil Temperature ◽  
Air Temperature ◽  
Thermal Regime ◽  
Ground Surface ◽  
Surface Air Temperature ◽  
Environmental Variable ◽  
Chemical Processes ◽  
Strong Connection ◽  
Shallow Subsurface

<p>The land-atmosphere interactions via the energy and water exchanges at the ground surface generally translate into a strong connection between the surface air temperature (SAT) and the ground surface temperature (GST). In turn, the surface temperature affects the amount of heat flowing into the soil, thus controlling the subsurface temperature profile. As soil temperature (ST) is a key environmental variable that controls various physical, biological and chemical processes, understanding the relationship between SAT and GST and STs is important.</p><p>In situ ST measurements represent the most adequate source of information to evaluate the distribution of temperature in soils and to address its influence on soil biological and chemical processes as well as on climate feedbacks. However, ST observations are scarce both in space and time. Therefore, the development of ST observational datasets is of great interest to promote analyses regarding the soil thermodynamics and the response to atmospheric warming.</p><p>We have developed a quality-controlled dataset of Soil Temperature Observations for Spain (SoTOS). The ST data are obtained from the Spanish meteorological agency (AEMET), including ST at different layers down to a depth of 1 m (i.e., 0.05, 0.1, 0.2, 0.5 and 1 m depth) for 39 observatories for the 1985–2018 period. Likewise, 2m air temperature has also been included for the same 39 sites.</p><p>SoTOS is employed to evaluate the shallow subsurface thermal regime and the SAT–GST relationship on interannual to multidecadal timescales. The results show that thermal conduction is the main heat transfer mechanism that controls the distribution of soil temperatures in the shallow subsurface. Regarding the SAT-GST relationship, there is a strong connection between SAT and GST. However, the SAT–GST coupling may be disrupted on seasonal to multidecadal timescales due to variations in the surface energy balance in response to decreasing soil moisture conditions over the last decade at some SoTOS sites. This results in larger GST warming relative to SAT. Such a response may have implications for climate studies that assume a strong connection between SAT and GST such as air temperature estimations from remote sensing products or even for palaeoclimatic analyses.</p>

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.

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