Impacts of Fractional Snow Cover on Surface Air Temperature in the NCAR Community Atmosphere Model (NCAR-CAM2)

2004 ◽  
Author(s):  
Zong-Liang Yang ◽  
Guo-Yue Niu ◽  
Qianru Zeng
Keyword(s):  
Snow Cover ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Community Atmosphere Model ◽  
Fractional Snow Cover ◽  
Atmosphere Model

scholarly journals Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States

2014 ◽  
Vol 15 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Jiarui Dong ◽  
Mike Ek ◽  
Dorothy Hall ◽  
Christa Peters-Lidard ◽  
Brian Cosgrove ◽  
...  
Keyword(s):  
United States ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Probability Of Detection ◽  
Joint Analysis ◽  
Snow Cover Area ◽  
Fractional Snow Cover ◽  
Retrieval Errors

Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.

scholarly journals Contribution of SST change to multidecadal global and continental surface air temperature trends between 1910 and 2013

2019 ◽  
Vol 54 (3-4) ◽  
pp. 1295-1313
Author(s):  
Yidan Xu ◽  
Jianping Li ◽  
Cheng Sun ◽  
Xiaopei Lin ◽  
Hailong Liu ◽  
...  
Keyword(s):  
Air Temperature ◽  
North Atlantic ◽  
Surface Air Temperature ◽  
Western Pacific ◽  
Multidecadal Variability ◽  
The North ◽  
Temperature Trends ◽  
Community Atmosphere Model ◽  
Community Earth System Model ◽  
Goddard Institute

AbstractThe global mean surface air temperature (GMST) shows multidecadal variability over the period of 1910–2013, with an increasing trend. This study quantifies the contribution of hemispheric surface air temperature (SAT) variations and individual ocean sea surface temperature (SST) changes to the GMST multidecadal variability for 1910–2013. At the hemispheric scale, both the Goddard Institute for Space Studies (GISS) observations and the Community Earth System Model (CESM) Community Atmosphere Model 5.3 (CAM5.3) simulation indicate that the Northern Hemisphere (NH) favors the GMST multidecadal trend during periods of accelerated warming (1910–1945, 1975–1998) and cooling (1940–1975, 2001–2013), whereas the Southern Hemisphere (SH) slows the intensity of both warming and cooling processes. The contribution of the NH SAT variation to the GMST multidecadal trend is higher than that of the SH. We conduct six experiments with different ocean SST forcing, and find that all the oceans make positive contributions to the GMST multidecadal trend during rapid warming periods. However, only the Indian, North Atlantic, and western Pacific oceans make positive contributions to the GMST multidecadal trend between 1940 and 1975, whereas only the tropical Pacific and the North Pacific SSTs contribute to the GMST multidecadal trend between 2001 and 2013. The North Atlantic and western Pacific oceans have important impacts on modulating the GMST multidecadal trend across the entire 20th century. Each ocean makes different contributions to the SAT multidecadal trend of different continents during different periods.

scholarly journals Simulating Springtime Temperature Patterns in the Community Atmosphere Model Coupled to the Community Land Model Using Prognostic Leaf Area

2004 ◽  
Vol 17 (23) ◽  
pp. 4531-4540 ◽  
Author(s):  
Samuel Levis ◽  
Gordon B. Bonan
Keyword(s):  
Leaf Area ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Dynamic Coupling ◽  
Model Simulations ◽  
Community Land Model ◽  
Leaf Emergence ◽  
Community Atmosphere Model ◽  
Surface Climate ◽  
Atmosphere Model

Abstract Observations show that emergence of foliage in springtime slows surface air temperature warming as a result of greater transpiration. Model simulations with the Community Atmosphere Model coupled to the Community Land Model confirm that evapotranspiration contributes to this pattern and that this pattern occurs more reliably with prognostic leaf area as opposed to prescribed leaf area. With prescribed leaf area, leaves emerge independent of prevailing environmental conditions, which may preclude photosynthesis from occurring. In contrast, prognostic leaf area ensures that leaves emerge when conditions are favorable for photosynthesis, and thus transpiration. These results reveal a dynamic coupling between the atmosphere and vegetation in which the observed reduction in the springtime warming trend only occurs when photosynthesis, stomatal conductance, and leaf emergence are synchronized with the surface climate.

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.

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

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 Climate change in the High Mountain Asia in CMIP6

2021 ◽  
Vol 12 (4) ◽  
pp. 1061-1098
Author(s):  
Mickaël Lalande ◽  
Martin Ménégoz ◽  
Gerhard Krinner ◽  
Kathrin Naegeli ◽  
Stefan Wunderle
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Relative Bias ◽  
The Other ◽  
Surface Elevation ◽  
High Mountain ◽  
Model Biases ◽  
Snow Cover Extent

Abstract. Climate change over High Mountain Asia (HMA, including the Tibetan Plateau) is investigated over the period 1979–2014 and in future projections following the four Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The skill of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models is estimated for near-surface air temperature, snow cover extent and total precipitation, and 10 of them are used to describe their projections until 2100. Similarly to previous CMIP models, this new generation of general circulation models (GCMs) shows a mean cold bias over this area reaching −1.9 [−8.2 to 2.9] ∘C (90 % confidence interval) in comparison with the Climate Research Unit (CRU) observational dataset, associated with a snow cover mean overestimation of 12 % [−13 % to 43 %], corresponding to a relative bias of 52 % [−53 % to 183 %] in comparison with the NOAA Climate Data Record (CDR) satellite dataset. The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1.5 [0.3 to 2.9] mm d−1, corresponding to a relative bias of 143 % [31 % to 281 %], but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation. The models with the best performance for temperature are not necessarily the most skillful for the other variables, and there is no clear relationship between model resolution and model skill. This highlights the need for a better understanding of the physical processes driving the climate in this complex topographic area, as well as for further parameterization developments adapted to such areas. A dependency of the simulated past trends on the model biases is found for some variables and seasons; however, some highly biased models fall within the range of observed trends, suggesting that model bias is not a robust criterion to discard models in trend analysis. The HMA median warming simulated over 2081–2100 with respect to 1995–2014 ranges from 1.9 [1.2 to 2.7] ∘C for SSP1-2.6 to 6.5 [4.9 to 9.0] ∘C for SSP5-8.5. This general warming is associated with a relative median snow cover extent decrease from −9.4 % [−16.4 % to −5.0 %] to −32.2 % [−49.1 % to −25.0 %] and a relative median precipitation increase from 8.5 % [4.8 % to 18.2 %] to 24.9 % [14.4 % to 48.1 %] by the end of the century in these respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces, excluding the Arctic area. Seasonal temperature, snow cover and precipitation changes over HMA show a linear relationship with the global surface air temperature (GSAT), except for summer snow cover which shows a slower decrease at strong levels of GSAT.

Ice-Sheet Initiation and Climatic Influences of Expanded Snow Cover in Arctic Canada

1978 ◽  
Vol 10 (2) ◽  
pp. 141-149 ◽  
Author(s):  
Larry D. Williams
Keyword(s):  
Snow Cover ◽  
Temperature Change ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Ice Sheet ◽  
Canadian Arctic ◽  
Surface Air Temperature ◽  
Laurentide Ice Sheet ◽  
Climate Variables ◽  
Summer Insolation

It has been suggested that the Laurentide Ice Sheet originated with extensive perennial snow cover, and that the snow cover affected climate so as to aid ice-sheet development. In this study, a large increase in extent of October 1st snow cover in the Canadian Arctic from 1967–1970 to 1971–1975 is compared to changes in October means of other climate variables. Over the area of snow-cover expansion, mean surface air temperature decreased by up to 3°C, mean 500-mbar height was lowered by over 60 m, and precipitation was increased by up to a factor of two. These effects, if applied to the entire summer, together with the temperature change computed by Shaw and Donn for a Northern Hemisphere summer insolation minimum (the Milankovich effect), can account for glacierization of the Central Canadian Arctic.

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