scholarly journals An application of statistical downscaling to estimate surface air temperature in Japan

2002 ◽  
Vol 107 (D10) ◽  
pp. ACL 14-1-ACL 14-10 ◽  
Author(s):  
Naoko Oshima ◽  
Hisashi Kato ◽  
Shinji Kadokura
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Surface Air Temperature

scholarly journals A statistical downscaling method for daily air temperature in data-sparse, glaciated mountain environments

2015 ◽  
Vol 8 (3) ◽  
pp. 579-593 ◽  
Author(s):  
M. Hofer ◽  
B. Marzeion ◽  
T. Mölg
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
A Priori ◽  
Surface Air Temperature ◽  
Reanalysis Data ◽  
Data Availability ◽  
Skill Assessment ◽  
Selection Strategy ◽  
Near Surface ◽  
Sd Model

Abstract. This study presents a statistical downscaling (SD) method for high-altitude, glaciated mountain ranges. The SD method uses an a priori selection strategy of the predictor (i.e., predictor selection without data analysis). In the SD model validation, emphasis is put on appropriately considering the pitfalls of short observational data records that are typical of high mountains. An application example is shown, with daily mean air temperature from several sites (all in the Cordillera Blanca, Peru) as target variables, and reanalysis data as predictors. Results reveal strong seasonal variations of the predictors' performance, with the maximum skill evident for the wet (and transitional) season months January to May (and September), and the lowest skill for the dry season months June and July. The minimum number of observations (here, daily means) required per calendar month to obtain statistically significant skill ranges from 40 to 140. With increasing data availability, the SD model skill tends to increase. Applied to a choice of different atmospheric reanalysis predictor variables, the presented skill assessment identifies only air temperature and geopotential height as significant predictors for local-scale air temperature. Accounting for natural periodicity in the data is vital in the SD procedure to avoid spuriously high performances of certain predictors, as demonstrated here for near-surface air temperature. The presented SD procedure can be applied to high-resolution, Gaussian target variables in various climatic and geo-environmental settings, without the requirement of subjective optimization.

scholarly journals The Greenland ice sheet: modelling the surface mass balance from GCM output with a new statistical downscaling technique

2013 ◽  
Vol 7 (3) ◽  
pp. 3163-3207 ◽  
Author(s):  
M. Geyer ◽  
D. Salas Y Melia ◽  
E. Brun ◽  
M. Dumont
Keyword(s):  
Mass Balance ◽  
Air Temperature ◽  
Statistical Downscaling ◽  
Ice Sheet ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Greenland Ice Sheet ◽  
Surface Mass Balance ◽  
Near Surface ◽  
Surface Mass

Abstract. The aim of this study is to derive a realistic estimation of the Surface Mass Balance (SMB) of the Greenland ice sheet (GrIS) through statistical downscaling of Global Coupled Model (GCM) outputs. To this end, climate simulations performed with the CNRM-CM5.1 Atmosphere-Ocean GCM within the CMIP5 (Coupled Model Intercomparison Project phase 5) framework are used for the period 1850–2300. From the year 2006, two different emission scenarios are considered (RCP4.5 and RCP8.5). Simulations of SMB performed with the detailed snowpack model Crocus driven by CNRM-CM5.1 surface atmospheric forcings serve as a reference. On the basis of these simulations, statistical relationships between total precipitation, snow-ratio, snowmelt, sublimation and near-surface air temperature are established. This leads to the formulation of SMB variation as a function of temperature variation. Based on this function, a downscaling technique is proposed in order to refine 150 km horizontal resolution SMB output from CNRM-CM5.1 to a 15 km resolution grid. This leads to a much better estimation of SMB along the GrIS margins, where steep topography gradients are not correctly represented at low-resolution. For the recent past (1989–2008), the integrated SMB over the GrIS is respectively 309 and 243 Gt yr–1 for raw and downscaled CNRM-CM5.1. In comparison, the Crocus snowpack model forced with ERA-Interim yields a value of 245 Gt yr–1. The major part of the remaining discrepancy between Crocus and downscaled CNRM-CM5.1 SMB is due to the different snow albedo representation. The difference between the raw and the downscaled SMB tends to increase with near-surface air temperature via an increase in snowmelt.

scholarly journals Seasonally changing contribution of sea ice and snow cover to uncertainty in multi-decadal Eurasian surface air temperature trends based on CESM simulations

2021 ◽  
Author(s):  
Zhaomin Ding ◽  
Renguang Wu
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Western Siberia ◽  
Russian Far East ◽  
Far East ◽  
Surface Air Temperature ◽  
The Caspian Sea ◽  
Sea Ice Concentration ◽  
Northern Siberia ◽  
The Impact

AbstractThis study investigates the impact of sea ice and snow changes on surface air temperature (SAT) trends on the multidecadal time scale over the mid- and high-latitudes of Eurasia during boreal autumn, winter and spring based on a 30-member ensemble simulations of the Community Earth System Model (CESM). A dynamical adjustment method is used to remove the internal component of circulation-induced SAT trends. The leading mode of dynamically adjusted SAT trends is featured by same-sign anomalies extending from northern Europe to central Siberia and to the Russian Far East, respectively, during boreal spring and autumn, and confined to western Siberia during winter. The internally generated component of sea ice concentration trends over the Barents-Kara Seas contributes to the differences in the thermodynamic component of internal SAT trends across the ensemble over adjacent northern Siberia during all the three seasons. The sea ice effect is largest in autumn and smallest in winter. Eurasian snow changes contribute to the spread in dynamically adjusted SAT trends as well around the periphery of snow covered region by modulating surface heat flux changes. The snow effect is identified over northeast Europe-western Siberia in autumn, north of the Caspian Sea in winter, and over eastern Europe-northern Siberia in spring. The effects of sea ice and snow on the SAT trends are realized mainly by modulating upward shortwave and longwave radiation fluxes.

scholarly journals Subseasonal relationship between Arctic and Eurasian surface air temperature

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hye-Jin Kim ◽  
Seok-Woo Son ◽  
Woosok Moon ◽  
Jong-Seong Kug ◽  
Jaeyoung Hwang
Keyword(s):  
Air Temperature ◽  
Diabatic Heating ◽  
Surface Air Temperature ◽  
Significant Negative Correlation ◽  
Cold Season ◽  
Reanalysis Data ◽  
Necessary Condition ◽  
Sea Level Pressure ◽  
Blocking High ◽  
Pressure Anomaly

AbstractThe subseasonal relationship between Arctic and Eurasian surface air temperature (SAT) is re-examined using reanalysis data. Consistent with previous studies, a significant negative correlation is observed in cold season from November to February, but with a local minimum in late December. This relationship is dominated not only by the warm Arctic-cold Eurasia (WACE) pattern, which becomes more frequent during the last two decades, but also by the cold Arctic-warm Eurasia (CAWE) pattern. The budget analyses reveal that both WACE and CAWE patterns are primarily driven by the temperature advection associated with sea level pressure anomaly over the Ural region, partly cancelled by the diabatic heating. It is further found that, although the anticyclonic anomaly of WACE pattern mostly represents the Ural blocking, about 20% of WACE cases are associated with non-blocking high pressure systems. This result indicates that the Ural blocking is not a necessary condition for the WACE pattern, highlighting the importance of transient weather systems in the subseasonal Arctic-Eurasian SAT co-variability.

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