Top PDF The response of Arctic vegetation to the summer climate: relation between shrub cover, NDVI, surface albedo and temperature

The response of Arctic vegetation to the summer climate: relation between shrub cover, NDVI, surface albedo and temperature

The response of Arctic vegetation to the summer climate: relation between shrub cover, NDVI, surface albedo and temperature

Team 2003 ). These similar-sized study areas are located on the North Slope of Alaska (center at 70 ◦ 0  N, 154 ◦ 0  W), on the Yamal peninsula, NW-Siberia (center at 69 ◦ 3  N, 70 ◦ 0  E) and in the Queen Maud Gulf Migratory Bird Sanctuary, Nunavut, Canada (center at 67 ◦ 0  N, 101 ◦ 3  W) (figure 1 ). Hereafter, the study areas are referred to as YAK, ALA, YAM and NUN, respectively. All four areas are located within bioclimatic subzones D (summer warmth index (SWI) 12–20 ◦ C) and E (SWI 20–35 ◦ C) and represent CAVM classes barren tundra, graminoid tundra, shrub tundra and wetlands (figure 1 ). Substrate soil chemistry is slightly acidic to circumneutral (pH 5.5–7) in mostly all parts of the four areas (Walker et al 2005 ). MODIS albedo, NDVI, and land cover data were obtained from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) for biogeochemical dynamics using the MODIS subsetting tool ( http://daac.ornl.gov/ MODIS/modis.shtml ), accessed January, February and July 2011. Sinusoidal projected data were acquired for the four low-Arctic tundra areas (figure 1 ) for the period 2000–10. We used the MODIS NDVI product (MOD13Q1, 250 m spatial resolution, 16-days composites produced every 16 days) to compile annual maximum NDVI (maxNDVI) maps by selecting the maximum NDVI from the 16-days composites for each pixel. (Holben 1986 ). NDVI data were quality-filtered by the MODIS subsetting tool, excluding snow- and cloud- covered pixels.
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Potential Arctic tundra vegetation shifts in response to changing temperature, precipitation and permafrost thaw

Potential Arctic tundra vegetation shifts in response to changing temperature, precipitation and permafrost thaw

Climate change in the Arctic region affects tundra vege- tation composition. The northernmost tundra is dominated by mosses and lichens due to the extremely low summer temperatures. Southwards, with increasing summer temper- atures, graminoids and dwarf shrubs increase in abundance (Walker et al., 2005). Climate change influences the tundra vegetation in multiple ways. Warming experiments in tundra ecosystems showed an increase in graminoids and deciduous shrubs in response to raised temperatures, while mosses and lichens and the overall species diversity decreased (Walker et al., 2006). Shrubs have been observed to expand with on- going temperature increase, presumably due to the increased availability of nutrients in the warmer soil (Tape et al., 2006; Myers-Smith et al., 2011). Several tree and shrub species, in- cluding dwarf birches (Betula glandulosa and Betula nana), willows (Salix spp.), juniper (Juniperus nana) and green alder (Alnus viridis), have expanded and increased in abun- dance in the Arctic as a response to climatic warming (Sturm et al., 2001; Tape et al., 2006; Hallinger et al., 2010; El- mendorf et al., 2012). However, besides climate, other fac- tors such as herbivory, soil moisture and soil nutrient avail- ability affect shrub growth as well, and it is therefore com- plex predicting the expansion of shrubs in the Arctic region (Myers-Smith et al., 2011, 2015). Increased abundance of shrubs might have important consequences for permafrost feedbacks. For example, an increase in low shrubs might slow down permafrost thaw as a result of the shadow they cast on the soil (Blok et al., 2010). However, tall shrubs may increase atmospheric heating and permafrost thawing due to their lower albedo (Bonfils et al., 2012)
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A Geobotanical Analysis Of Circumpolar Arctic Vegetation, Climate, And Substrate

A Geobotanical Analysis Of Circumpolar Arctic Vegetation, Climate, And Substrate

70 values correlate well with ground characteristics o f arctic vegetation and can be used to distinguish between vegetation types (Hope et al., 1993; Stow et al., 1993). Most studies comparing arctic NDVI and temperature have looked at change over time, focusing on the effects o f anthropogenic climate change. Myneni et al. (1997), Bogaert et al. (2002), Jia et al. (2003), Zhou et al. (2003) and Goetz et al. (2005) all found increases in arctic NDVI related to increases in temperature over time. There have been questions as to whether these results were an artifact o f the satellite record due to orbit degradation and changes in sensors between satellites (Fung, 1997; Kaufmann et al., 2000). Ground studies have been able to document changes in shrub cover in some areas (Tape et al., 2006), but have had difficulty measuring large-scale changes in vegetation cover in the Arctic (Callaghan, 2005). A few studies have looked for effects in the opposite direction: the influence o f arctic and boreal vegetation on surface temperatures (Hope et al., 2005) (Kaufman et al., 2003), but in the Arctic the effect is much stronger in the other direction, with summer temperatures determining NDVI values (Kaufman et al., 2003). Changes in arctic NDVI with latitude have been correlated with bioclimate zones (Raynolds et al., 2006) and on the North Slope o f Alaska with total summer warmth (Jia et al., 2002).
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Analyzing The Spatial And Temporal Variability Of Vegetation Cover And Land Surface Temperature Over Ghana From 2005 To 2015

Analyzing The Spatial And Temporal Variability Of Vegetation Cover And Land Surface Temperature Over Ghana From 2005 To 2015

The Normalized Difference Vegetation Index (NDVI) is an indicator ranging between -0.3 and 1.0 that quantifies vegetation by means of measuring the difference between the near-infrared (NIR) which is the wavelength that is strongly reflected by vegetation (Lillesand et al 2004) and red (R) which is also the wavelength that is highly absorbed by vegetation (Lillesand et al 2004; Brown 2015; Didan et al, 2015). When harnessed, NDVI possesses the capability of analysing remotely sensed measurements to assess whether or not the object (material) under observation contains live green vegetation. Because the spectral behaviour of foliage, soil, rocks and mineral components across the electromagnetic spectrum are known, it is then possible to obtain NDVI information by placing emphasis on the near-infrared (NIR) and red (R) bands of the sensor (Lillesand et al 2004). Therefore, the magnitude of the difference between the near- infrared and the red reflectance, then determines the amount of vegetation.
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Investigation of Remote Sensing Derived Surface Temperature and Normalized Difference Vegetation Index and Implications for Land Cover Classification

Investigation of Remote Sensing Derived Surface Temperature and Normalized Difference Vegetation Index and Implications for Land Cover Classification

Land cover data for the project area was obtained from the Africover Land Cover Classification and Mapping project-Kenya aggregate land cover-at a scale of 1:100,000. The original land cover was interpreted from Landsat imagery (Bands 4,3,2) acquired in the year 1999 (Geonetwork, 2012). The purpose of the Africover project is to produce a digital georeferenced land cover database for the whole of Africa (FAO, 1997). Based upon the international standard land cover classification system, Africover was created from Landsat TM imagery, aerial photography, and field observations. The 16 major land cover classes (figure 6) identified in the study area are: Artificial and Natural Waterbodies, Built Area, Non-built Area, Bare Area, Sparse Vegetation, Grassland, Herbaceous, Shrubland, Thicket, Woodland, Forest, Aquatic or frequently flooded Graminoid (mainly rice) Crop, Herbaceous Crop, Shrub Crop, and Tree Crop. Areas mapped with more than one of the 16 major cover types were not included in this study. Therefore, of the original 34,115 square kilometers in the scene, only 13,057 (38.3%) are single land cover types and were used in this
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Arctic Alaskan Shrub Growth, Distribution, And Relationships To Landscape Processes And Climate During The 20Th Century

Arctic Alaskan Shrub Growth, Distribution, And Relationships To Landscape Processes And Climate During The 20Th Century

Statistics Transect was considered the sample unit when comparing site characteristics, and the distribution of each environmental variable within the expanding or stagnant category was tested for normality. In four cases (E and S mineral soil C, alder leaf %C and 815N) the distribution failed all four normality tests (Shapiro-Wilk, Jarque-Bera, Anderson- Darling, and Lilliefors), so one outlier from four environmental variables was removed and normality was satisfied. Transect averages and standard error were calculated and T- tests applied to determine significant differences between expanding and stagnant shrub patches. Paired T-tests were used to compare temperature loggers deployed at the 8 Nimiuktuk transects. When calculating mean thaw depth, 120 cm was assigned to locations where permafrost was >120 cm (beyond the length of the probe) or rocks prohibited measurement. In all stagnant shrub patches, and in only three expanding locations, pronounced microtopography (tussocks) convinced us to use temperature and moisture probes on both high and low microsites. In these cases the high and low values were averaged. Unless otherwise mentioned, means ± standard error are reported.
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MODIS Derived Arctic Land Surface Temperature Trends

MODIS Derived Arctic Land Surface Temperature Trends

MOD11A1 (Terra) and MYD11AI (Aqua) Level-3 Version 5 datasets are in HDF-EOS format and data structure. The kelvin data layer is a 5-by-5 degree gran- ule at 1-km posting sinusoidal grid [12]. We extract day- time (AM and PM) temperatures with the highest quality flag (most reliable) beginning on 5 March 2000 (Terra) and 8 July 2002 (Aqua). Accuracy of the retrieval land- surface temperature is at 1-kelvin level [13,14]. Diur- nal-average trends of MODIS land-surface temperatures with near-ground air temperatures and shallow sub-sur- face soil temperatures show consistent and high correla- tion [15].
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Arctic shrubification mediates the impacts of warming climate on changes to tundra vegetation

Arctic shrubification mediates the impacts of warming climate on changes to tundra vegetation

Our predictions also demonstrate that species groups can be expected to show a wide variety of responses to warming and shrubification depending on species ’ life-history characteristics. The varying outcomes between species groups might also reveal some mechanisms behind the impacts. For example, the increased richness of boreal plant species, and the decreased richness and high extinction rate of arctic plant species due to the warming may be caused by ‘thermophilization’, i.e. warmer climate favouring warm-adapted species [e.g. 61]. Warming also increa- ses the richness of shrubs and graminoids at the cost of forbs [see also 65 ], which might reflect the different resource acquisition strategies of species [ 31, 66 ]. Shrubi fication decreases the species richness of arctic plants and graminoids more heavily than the boreal plants and forbs, respectively. These outcomes might reflect varying competitive abilities between the spe- cies groups [e.g. 30, 31]. For example, taller boreal plants are superior light-competitors compared to low-growing arctic plants. Contradictorily, the turn- over and extinction rates are ampli fied more strongly by shrubi fication for boreal than arctic species, and for graminoids than forbs. This might, instead of an eco- logical rationale, be explained by spatial associations: the estimates of shrub cover and tree canopy are the highest at low elevations with higher ratios of boreal species and graminoids.
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The thermodynamic structure of summer Arctic stratocumulus and the dynamic coupling to the surface

The thermodynamic structure of summer Arctic stratocumulus and the dynamic coupling to the surface

Abstract. The vertical structure of Arctic low-level clouds and Arctic boundary layer is studied, using observations from ASCOS (Arctic Summer Cloud Ocean Study), in the central Arctic, in late summer 2008. Two general types of cloud structures are examined: the “neutrally stratified” and “stably stratified” clouds. Neutrally stratified are mixed- phase clouds where radiative-cooling near cloud top pro- duces turbulence that generates a cloud-driven mixed layer. When this layer mixes with the surface-generated turbulence, the cloud layer is coupled to the surface, whereas when such an interaction does not occur, it remains decoupled; the lat- ter state is most frequently observed. The decoupled clouds are usually higher compared to the coupled; differences in thickness or cloud water properties between the two cases are however not found. The surface fluxes are also very similar for both states. The decoupled clouds exhibit a bi- modal thermodynamic structure, depending on the depth of the sub-cloud mixed layer (SCML): clouds with shallower SCMLs are disconnected from the surface by weak inver- sions, whereas those that lay over a deeper SCML are as- sociated with stronger inversions at the decoupling height. Neutrally stratified clouds generally precipitate; the evapora- tion/sublimation of precipitation often enhances the decou- pling state. Finally, stably stratified clouds are usually lower, geometrically and optically thinner, non-precipitating liquid- water clouds, not containing enough liquid to drive efficient mixing through cloud-top cooling.
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Hydroecological response of arctic rivers to climate change

Hydroecological response of arctic rivers to climate change

24 habitat conditions for growth and development than meltwater-dominated systems (Prowse et al., 2006). The dual effects of nutrient loading and improved habitat quality for organisms in high-latitude rivers may result in substantial changes in particular ecosystem processes associated with increased primary production and microbial respiration (Lecerf and Richardson, 2010). In particular, variation in nutrient uptake rates in response to climate change has important management implications for reducing nutrient loading and regulating water quality in downstream areas (Alexander et al., 2007). Small headwater rivers play a key role in this function because they comprise over 80% of total drainage network length and have high processing rates relative to larger channels downstream (Alexander et al., 2000; Peterson et al., 2001; Craig et al., 2008). While there are few studies on nutrient uptake in headwater Arctic rivers (but see Wollheim et al., 2001; Scott et al., 2010), the extreme cold, highly unstable channels, and limited pool of bioavailable nutrients (Friberg et al., 2001; Hodson et al., 2002; Huryn et al., 2005; Petrone et al., 2006; Holmes et al., 2008; Blaen et al., in press) that characterise many meltwater-dominated high-latitude systems suggest that contemporary rates of nutrient uptake are low in these environments (Scott et al., 2010). In the context of projected warming in polar regions, increased autotrophy and microbial activity may lead to a subsequent intensification in nutrient uptake and rates of retention in Arctic rivers. Moreover, these processes could be further enhanced by the deepening of thaw bulbs below main river channels associated with permafrost melting (Zarnetske et al., 2007; Brosten et al., 2009). Hyporheic transient storage areas, where waters are stationary relative to the main channel, are thought to be important zones of biogeochemical processing, but may be limited in depth by permafrost layers in high-latitude areas (Edwardson et al., 2003; Greenwald et al., 2008; Merck et al., 2012).
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Land surface albedo and vegetation feedbacks enhanced the millennium drought in south east Australia

Land surface albedo and vegetation feedbacks enhanced the millennium drought in south east Australia

A number of these studies have focused on how the surface feedbacks affect the development in particular locations or drought events. For instance, Oglesby and Erickson (1989) used a GCM to examine the influence of soil moisture on drought in North America, also finding a positive feedback that enhances drought conditions. Hong and Kalnay (2000) used an RCM to investigate the role of local feedbacks in the development of the Texas, USA, drought in 1998. They found that the surface feedbacks were responsible for up to 30 % of the precipitation deficit during the drought. Schu- bert et al. (2004) investigated causes of the North American Dust Bowl drought in the 1930s. They attributed 50 % of the precipitation deficit to soil moisture–precipitation feedbacks. Zaitchik et al. (2007) examined the surface influence on a drought that occurred in the Middle East in 1999. Using a regional climate model (RCM), they found that vegetation and albedo changes had clear effects on the surface fluxes and planetary boundary layer (PBL) growth, but limited im- pact on the precipitation decrease (up to 4 %) compared to a normal year. Wu and Zhang (2013) performed an RCM in- vestigation of soil moisture feedback on the 1999 drought in northern China, finding that the feedback accounted for up to 50 % of the precipitation decline in some places. Zaitchik et al. (2012) investigated the surface feedback on the southern Great Plains, USA, drought of 2006. They found that the pre- cipitation decline during drought development increased by ∼ 10 % due to the feedback. Finally, Meng et al. (2014a, b) examined the role of changes in surface albedo and surface vegetation on the development of the 2002 drought in south- east Australia. They found that the precipitation reduction was enhanced by up to 20 and 10 % due to surface albedo and vegetation changes, respectively. Importantly, they identified differences in timescales over which changes in vegetation occur compared to changes in soil moisture or albedo, with the relatively slow vegetation changes tending to dampen the positive soil moisture–precipitation feedback. All of these studies showed that the land surface–precipitation feedbacks play an important role in drought development. However, the strength of this role is both space and time dependent.
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Regional melt-pond fraction and albedo of thin Arctic first-year drift ice in late summer

Regional melt-pond fraction and albedo of thin Arctic first-year drift ice in late summer

The relatively small spatial scale of a typical pond system, typically few tens to thousands of m 2 (e.g., Tschudi et al., 2001; Perovich et al., 2002b; Hohenegger et al., 2012), large intersite variability in melt-pond coverage and the overcast conditions prevailing in the summer Arctic promote the use of low-altitude airborne methods for studying the morpho- logical and optical properties of the sea-ice cover. Although remote sensing of summer sea ice utilizing various satellite- based sensors has made considerable progress throughout the last decades (e.g., Markus et al., 2003; Rösel et al., 2012; Tschudi et al., 2008; Kim et al., 2013), these aerial surveys can provide valuable high-resolution validation data for the emerging algorithms. Combining the spatial data on surface types with in situ measurements of incident/reflected solar ra- diation (albedo) and turbulent heat fluxes for different types of surfaces may in turn provide estimates of the regional- scale surface energy balance of sea ice. A number of such studies have been conducted in the past with a focus on spa- tial and temporal evolution of fractional melt-pond cover- age, pond-size probability density (e.g., see Perovich et al., 2002b, for a review), and their relationship with the pre-melt surface topography (Derksen et al., 1997; Eicken et al., 2004; Petrich et al., 2012) and surface albedo. Depending on the instrumentation setup used, the spatial ranges covered varied from tens of meters to hundreds of kilometers, on the order of the typical scale of a GCM grid cell.
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Sensitivity of Pliocene Arctic climate to orbital forcing, atmospheric CO2 and sea ice albedo parameterisation

Sensitivity of Pliocene Arctic climate to orbital forcing, atmospheric CO2 and sea ice albedo parameterisation

Salzmann, U., Dolan, A., Haywood, A., Chan, W.-L., Voss, J., Hill, D., Abe-Ouchi, A., Otto- Bliesner, B., Bragg, F., Chandler, M., Contoux, C., Dowsett, H., Jost, A., Kamae, Y., Lohmann, G., Lunt, D., Pickering, S., Pound, M., Ramstein, G., Rosenbloom, N., Sohl, L., Stepanek, C., Ueda, H., Zhang, Z., 2013. Challenges in quantifying Pliocene terrestrial warming revealed by data-model discord. Nature Climate Change 3 (1), 969–974.

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Near-surface meteorology during the Arctic Summer Cloud Ocean Study (ASCOS):  evaluation of reanalyses and global climate models

Near-surface meteorology during the Arctic Summer Cloud Ocean Study (ASCOS): evaluation of reanalyses and global climate models

ate conditions from the ASCOS period, these simulations were completed using the CCPP (Climate Change Prediction Program)-ARM (Atmospheric Radiation Measurement) Pa- rameterizations Testbed (CAPT, Phillips et al., 2004). CAPT utilizes operational analyses from numerical weather pre- diction centers to initialize CAM5 and produce short-term forecasts. In this instance, the European Centre for Medium- Range Weather Forecasts (ECMWF) Year of Tropical Con- vection (YOTC) analysis was used to initialize forecasts within CAPT. The analysis data are interpolated from the finer-resolution analysis grid of 0.150 ◦ and 91 levels to the CAM5 grids using procedures outlined in Boyle et al. (2005). These procedures use a slightly different interpolation ap- proach for each of the dynamic state variables (i.e., horizon- tal winds, temperature, specific humidity and surface pres- sure), along with careful adjustments to account for the dif- ference in representation of the earth’s topography between models. A series of 6-day hindcasts were initialized every day at 00:00 UTC from the ECMWF analysis for the en- tire YOTC period from 1 May 2008 to 30 April 2010. Only the atmospheric winds, temperature and moisture are initial- ized, while the rest of the initial variables (land and atmo- sphere) come from an additional ECMWF-nudged run of the same model. Skin surface temperature and sea ice are pre- scribed using the NOAA optimum interpolation (OI) sea sur- face temperature (SST) V2. These data are weekly means on a 1 ◦ × 1 ◦ grid and are interpolated in time from weekly to the model time step. Since the model has a spin-up period to adjust to ECMWF conditions, the ASCOS time series are created by concatenating hours 24–48 from each hindcast.
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Ecosystem carbon dynamics differ between tundra shrub types in the western Canadian Arctic

Ecosystem carbon dynamics differ between tundra shrub types in the western Canadian Arctic

Shrub expansion at high latitudes has been implicated in driving vegetation ‘ greening ’ trends and may partially offset CO 2 emissions from warming soils. However, we do not yet know how Arctic shrub expansion will impact ecosystem carbon (C) cycling and this limits our ability to forecast changes in net C storage and resulting climate feedbacks. Here we quantify the allocation of photosynthate between different ecosystem components for two common deciduous Arctic shrubs, both of which are increasing in abundance in the study region; green alder (Alnus viridis (Chaix) DC.) and dwarf birch (Betula glandulosa Michx., B.). Using 13 C isotopic labelling, we show that carbon use efficiency (i.e. the fraction of gross photosynthesis remaining after subtracting respiration) in peak growing season is similar between the two shrubs (56 ± 12% for A. viridis, 59 ± 6% for B. glandulosa), but that biomass production efficiency (plant C uptake allocated to biomass production, per unit gross photosynthesis) is 56 ± 14% for A. viridis, versus 31 ± 2% for B. glandulosa. A significantly greater proportion of recent photosynthate is allocated to woody biomass in A. viridis dominated plots (27 ±
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The Influence of Autumnal Eurasian Snow Cover on Climate and Its Link with Arctic Sea Ice Cover

The Influence of Autumnal Eurasian Snow Cover on Climate and Its Link with Arctic Sea Ice Cover

Fig. 1 : Normalized squared covariance (NSC, contours, in %) for the first MCA mode between observed SLP and Eurasian snow cover, for each month in the atmosphere. The lag is positive when the snow cover leads SLP. The gray shading provides the level of statistical significance for NSC. The plus symbols indicate the atmospheric month and time lag where the level of significance for the correlation (R) is below 5%.

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Evidence for fire in the Pliocene Arctic in response to amplified temperature

Evidence for fire in the Pliocene Arctic in response to amplified temperature

Current rates of warming in the Canadian Arctic are now roughly triple the rate of global warming (Bush and Lem- men, 2019). Since 1850, global land surface temperatures have increased by approximately 1.0 ◦ C, whereas circum- Arctic land surface temperatures have increased by > 2.0 ◦ C (Jones and Moberg, 2003; Francis and Skific, 2015). Such Arctic amplification of temperatures has also occurred dur- ing other warm climate anomalies in Earth’s past. Paleo- climate records from the Arctic indicate that the change in Arctic summer temperatures during past global warm peri- ods was 3–4 times larger than global temperature change (Miller et al., 2010). While earth system models (ESMs) have been able to provide fairly accurate predictions of the mod- ern amplification of Arctic temperatures hitherto observed for some time (Marshall et al., 2014), they have only recently implemented mechanisms that simulate Arctic amplification of temperature for past warm periods such as the Pliocene (2.6–5.3) with a convincing pattern of seasonality (Zheng et al., 2019). The success of earlier models at capturing mod- ern warming, contrasted with the additions needed to simu- late the Pliocene Arctic temperatures, suggest that the array of fast and slow feedback mechanisms have not fully mani- fested themselves for the modern Arctic, and perhaps there are still further feedback mechanisms we are yet to under- stand and implement in climate models.
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Parameterization of the snow-covered surface albedo in the Noah-MP Version 1.0 by implementing vegetation effects

Parameterization of the snow-covered surface albedo in the Noah-MP Version 1.0 by implementing vegetation effects

Abstract. Snow-covered surface albedo varies depending on many factors, including snow grain size, snow cover thick- ness, snow age, forest shading factor, etc., and its parameter- ization is still under great uncertainty. For the snow-covered surface condition, albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the Noah land surface model with multiple physics options (Noah-MP), almost all vegeta- tion types in East Asia during winter have the minimum val- ues of leaf area index (LAI) and stem area index (SAI), which are too low and do not consider the vegetation types. Because LAI and SAI are represented in terms of photosynthetic ac- tiveness, stem and trunk in winter are not well represented with only these parameters. We found that such inadequate representation of the vegetation effect is mainly responsible for the large positive bias in calculating the winter surface albedo in the Noah-MP. In this study, we investigated the vegetation effect on the snow-covered surface albedo from observations and improved the model performance by im- plementing a new parameterization scheme. We developed new parameters, called leaf index (LI) and stem index (SI), which properly manage the effect of vegetation structure on the snow-covered surface albedo. As a result, the Noah-MP’s performance in the winter surface albedo has significantly improved – the root mean square error is reduced by approx- imately 69 %.
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The effects of additional black carbon on the albedo of Arctic sea ice: variation with sea ice type and snow cover

The effects of additional black carbon on the albedo of Arctic sea ice: variation with sea ice type and snow cover

To understand the degree to which black carbon in sea ice may affect surface albedo, knowledge of snow depth over sea ice and its variation seasonally and spatially is essen- tial. Snow depth measurements over sea ice have been re- ported both from ground measurements (e.g. Warren et al., 1999; Massom et al., 2001) and more recently through satel- lite and airborne measurements (e.g Kanagaratnam et al., 2007; Kwok and Cunningham, 2008; Kwok et al., 2011; Galin et al., 2012). Two studies provide an overview of snow thicknesses over sea ice. Warren et al. (1999) present a com- prehensive data set of Arctic Ocean snow cover from mea- surements of snow depth and density over 37 yr at the So- viet drifting stations, while Massom et al. (2001), using data collected over 10 yr, review snow thickness and snow type of Antarctic snow on sea ice. Arctic sea ice is mostly free of snow during the second half of July and all of August. Therefore during these months black carbon in sea ice would affect surface albedo. Snow thickness reaches a maximum in the Arctic in May, when the average depth is 34.4 cm (War- ren et al., 1999). In Antarctica mean snow thickness varies both seasonally and regionally due to differences in precip- itation regimes and the age of the underlying ice (Massom et al., 2001). In March, in East Antarctica, 20 % of the sea ice is predominately snow free, and less than 10 % of the snow cover is thicker than 10 cm. By August (winter), snow thick- ness is typically 10–20 cm, but 10 % of the sea ice remains snow free (Massom et al., 2001). Although snow on sea ice would appear to predominately mask the effects of black car- bon in sea ice in both the Antarctic and Arctic, the effect of black carbon on albedo of sea ice is important for a few of months of the year, in both the Antarctic and Arctic. These months would be following a period of snow melt over sea ice where black carbon may be concentrated onto the sea ice surface from meltwater (Grenfell et al., 2002). Doherty et al. (2010) measured spatial variation of black carbon through sea ice cores taken on sea ice in the southern Canadian basin, suggesting black carbon is concentrated near the surface fol- lowing snowmelt. Further work on the distribution of black carbon in sea ice would be useful. The months of bare sea ice would also coincide with higher surface irradiance owing to smaller solar zenith angles, so melting may be exacerbated, as more radiation is absorbed by black carbon.
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Global Spatial Relationship between Land Use Land Cover and Land Surface Temperature

Global Spatial Relationship between Land Use Land Cover and Land Surface Temperature

Land Surface Temperature (LST) and Land Use Land Cover (LULC) are the principal aspects of climate and environment studies. The object of the study is to assess spatial relationship between LST and remote sensing LULC indices at the global and continental scale. Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua daytime LST and eight LULC MODIS indices of 2018 prepared and processed using Earth Engine Code Editor. R squared and significance of the relationship values of randomly selected points computed in R program. The research observed the relationship between examined indices and LST is significant at the 0.001 level. Normalized Difference Water Index (NDWI) and Normalized Difference Snow Index (DSI) are the dominant drivers of LST in the world, Asia and North America. In Australia and Africa, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are the dominant drivers of LST. Albedo and Normalized Difference Soil Index (NDSI) have superior in Central America. In South America and Europe, the dominant driver of LST is NDWI. Relationship between albedo and LST is moderate inverse on a global scale. Observed relationship between LST and examined vegetation indices is positive in Europe and North America while inverse in Australia and Africa. All observed relationship between Normalized Difference Built-up Index (NDBI) and LST are positive. Association observed between NDSI and LST is positive in Australia, Africa and Central America.
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