Influence of December snow cover over North America on January surface air temperature over the midlatitude Asia

2019 ◽  
Vol 40 (1) ◽  
pp. 572-584
Author(s):  
Jingyi Li ◽  
Fei Li ◽  
Shengping He ◽  
Huijun Wang ◽  
Yvan J. Orsolini
Keyword(s):  
North America ◽  
Snow Cover ◽  
Air Temperature ◽  
Surface Air Temperature

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.

North Hemispheric winter air temperature variability and its linkage to North Pacific and North Atlantic SST modes?

2020 ◽  
Author(s):  
Binhe Luo ◽  
Dehai Luo ◽  
Aiguo Dai ◽  
Lixin Wu
Keyword(s):  
North America ◽  
Air Temperature ◽  
North Atlantic ◽  
North Pacific ◽  
Wave Train ◽  
Surface Air Temperature ◽  
Eastern North America ◽  
The North ◽  
The North Atlantic ◽  
The North Pacific

<p>Winter surface air temperature (SAT) over North America exhibits pronounced variability on sub-seasonal-to-interdecadal timescales, but its causes are not fully understood. Here observational and reanalysis data from 1950-2017 are analyzed to investigate these causes. Detrended daily SAT data reveals a known warm-west/cold-east (WWCE) dipole over midlatitude North America and a cold-north/warm-south (CNWS) dipole over eastern North America. It is found that while the North Pacific blocking (PB) is important for the WWCE and CNWS dipoles, they also depend on the phase of the North Atlantic Oscillation (NAO). When a negative-phase NAO (NAO-) concurs with PB, the WWCE dipole is enhanced (compared with the PB alone case) and it also leads to a warm north/cold south dipole anomaly in eastern North America; but when PB occurs with a positive-phase NAO (NAO<sup>+</sup>), the WWCE dipole weakens and the CNWS dipole is enhanced. In particular, the WWCE dipole is favored by a combination of eastward-displaced PB and NAO<sup>-</sup> that form a negative Arctic Oscillation. Furthermore, a WWCE dipole can form over midlatitude North America when PB occurs together with southward-displaced NAO<sup>+</sup>.The PB events concurring with NAO<sup>-</sup> (NAO<sup>+</sup>) and SAT WWCE (CNWS) dipole are favored by the El Nio-like (La Nia-like) SST mode, though related to the North Atlantic warm-cold-warm (cold-warm-cold) SST tripole pattern. It is also found that the North Pacific mode tends to enhance the WWCE SAT dipole through increasing PB-NAO<sup>-</sup> events and producing the WWCE SAT dipole component related to the PB-NAO<sup>+</sup> events because the PB and NAO<sup>+</sup> form a more zonal wave train in this case.</p>

scholarly journals Seasonal Variation and Spatial Patterns of the Atmospheric Component of the Pacific Decadal Oscillation

2013 ◽  
Vol 26 (5) ◽  
pp. 1575-1594 ◽  
Author(s):  
Catrin M. Mills ◽  
John E. Walsh
Keyword(s):  
United States ◽  
North America ◽  
Air Temperature ◽  
Pacific Decadal Oscillation ◽  
Southern Oscillation ◽  
Grid Point ◽  
Southeastern United States ◽  
Surface Air Temperature ◽  
The Pacific ◽  
Difference Fields

Abstract The Pacific decadal oscillation (PDO) is an El Niño–Southern Oscillation (ENSO)-like climate oscillation that varies on multidecadal and higher-frequency scales, with a sea surface temperature (SST) dipole in the Pacific. This study addresses the seasonality, vertical structure, and across-variable relationships of the local North Pacific and downstream North American atmospheric signal of the PDO. The PDO-based composite difference fields of 500-mb geopotential height, surface air temperature, sea level pressure, and precipitation vary not only across seasons, but also from one calendar month to another within a season, although month-to-month continuity is apparent. The most significant signals occur in western North America and in the southeastern United States, where a positive PDO is associated with negative heights, consistent with underlying temperatures in the winter. In summer, a negative precipitation signal in the southeastern United States associated with a positive PDO phase is consistent with a ridge over the region. When an annual harmonic is fit to the 12 monthly surface air temperature differences at each grid point, the PDO temperature signal peaks in winter in most of North America, while a peak in summer occurs in the southeastern United States. Approximately 25% of the variance of the PDO index is accounted for by ENSO. Atmospheric composite differences based on a residual (ENSO linearly removed) PDO index have many similarities to those of the full PDO signal.

scholarly journals An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

2012 ◽  
Vol 6 (4) ◽  
pp. 3317-3348 ◽  
Author(s):  
C. Brutel-Vuilmet ◽  
M. Ménégoz ◽  
G. Krinner
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Linear Correlation ◽  
Large Scale ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Snow Cover Extent ◽  
Global Mean

Abstract. The 20th century seasonal Northern Hemisphere land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a~reduced spring snow cover extent over the 1979–2005 is underestimated. We show that this is linked to the simulated Northern Hemisphere extratropical land warming trend over the same period, which is underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between hemispheric seasonal spring snow cover extent and boreal large-scale annual mean surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear correlation between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the sensitivity of the Northern Hemisphere spring snow cover to global mean surface air temperature changes is underestimated at present because of the underestimate of the boreal land temperature change amplification.

Impact of Land Initial States Uncertainty on Subseasonal Surface Air Temperature Prediction in CFSv2 Reforecasts

2020 ◽  
Vol 21 (9) ◽  
pp. 2101-2121 ◽  
Author(s):  
Chul-Su Shin ◽  
Paul A. Dirmeyer ◽  
Bohua Huang ◽  
Subhadeep Halder ◽  
Arun Kumar
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Air Temperature ◽  
Land Surface ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Prediction Skill ◽  
Temperature Prediction ◽  
Initial States ◽  
Air Temperature Prediction

AbstractThe NCEP CFSv2 ensemble reforecasts initialized with different land surface analyses for the period of 1979–2010 have been conducted to assess the effect of uncertainty in land initial states on surface air temperature prediction. The two observation-based land initial states are adapted from the NCEP CFS Reanalysis (CFSR) and the NASA GLDAS-2 analysis; atmosphere, ocean, and ice initial states are identical for both reforecasts. This identical-twin experiment confirms that the prediction skill of surface air temperature is sensitive to the uncertainty of land initial states, especially in soil moisture and snow cover. There is no distinct characteristic that determines which set of the reforecasts performs better. Rather, the better performer varies with the lead week and location for each season. Estimates of soil moisture between the two land initial states are significantly different with an apparent north–south contrast for almost all seasons, causing predicted surface air temperature discrepancies between the two sets of reforecasts, particularly in regions where the magnitude of initial soil moisture difference lies in the top quintile. In boreal spring, inconsistency of snow cover between the two land initial states also plays a critical role in enhancing the discrepancy of predicted surface air temperature from week 5 to week 8. Our results suggest that a reduction of the uncertainty in land surface properties among the current land surface analyses will be beneficial to improving the prediction skill of surface air temperature on subseasonal time scales. Implications of a multiple land surface analysis ensemble are also discussed.

scholarly journals On the Role of Snow Cover in Depressing Air Temperature

2008 ◽  
Vol 47 (7) ◽  
pp. 2008-2022 ◽  
Author(s):  
Thomas L. Mote
Keyword(s):  
Boundary Layer ◽  
North America ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Depth ◽  
Cloud Cover ◽  
Effect Of Temperature ◽  
Temperature Changes ◽  
Temperature Depression

Abstract This study empirically examines the role of snow depth on the depression of air temperature after controlling for effect of temperature changes above the boundary layer. In addition, this study examines the role of cloud cover, solar elevation angle, and maximum snow-covered albedo on the temperature depression due to snow cover. The work uses a new dataset of daily, gridded snow depth, snowfall, and maximum and minimum temperatures for North America from 1960 to 2000 in conjunction with 850-hPa temperature data for the same period from the NCEP–NCAR reanalysis, version 1. The 850-hPa temperatures are used as a control to remove the effect of temperature changes above the boundary layer on surface air temperatures. Findings from an analysis of variance demonstrate that snow cover can result in daily maximum (minimum) temperature depressions on average of 4.5°C (2.6°C) for snow depths greater than 10 cm over the grasslands of central North America, but temperature depressions average only 1.2°C (1.1°C) overall. The temperature depression of snow cover is shown to be reduced by increased cloud cover and decreased maximum albedo, which is indicative of denser forest cover. The role of snow melting on temperature depression is further explored by comparing days with maximum temperatures above or below freezing.

Forecasting North America Winter Surface Air Temperature Using Machine Learning Methods

2020 ◽  
Author(s):  
Qifeng Qian ◽  
Xiaojing Jia ◽  
Hai Lin
Keyword(s):  
Machine Learning ◽  
North America ◽  
Air Temperature ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

<p>Two machine learning (ML) models (Support Vector Regression and Extreme Gradient Boosting; SVR and XGBoost hereafter) have been developed to perform seasonal forecast for the winter (December–January–February, DJF) surface air temperature (SAT) in North America (NA) in this study. The seasonal forecast skills of the two ML models are evaluated in a cross-validated fashion. Forecast results from one Linear Regression (LR and hereafter) model and two Canadian dynamic climate models are used for the purpose of a comparison. In the take-one-out hindcast experiment, the two ML models and the LR model show reasonable seasonal forecast skills for the winter SAT in NA. Comparing to the two Canadian dynamic models, the two ML models and the LR model have better forecast skill for the winter SAT over the central NA which mainly get contribution of a skillful forecast of the second Empirical Orthogonal Function (EOF) mode of winter SAT over NA. In general, the SVR model and XGBoost model hindcasts show better forecast performances than LR model. However, the LR model shows less dependence on the size of the training dataset than SVR and XGBoost models. In the real forecast experiments during the period 2011-2017, compared to the two Canadian dynamic climate models, the two ML models clearly improve the forecast skill of winter SAT over northern and central NA. The results of this study suggest that ML models may provide real-time supplementary forecast tools to improve the forecast skill and may operationally facilitate the seasonal forecast of the winter climate of NA. </p>

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