scholarly journals Pan-Arctic linkages between snow accumulation and growing-season air temperature, soil moisture and vegetation

2013 ◽  
Vol 10 (11) ◽  
pp. 7575-7597 ◽  
Author(s):  
K. A. Luus ◽  
Y. Gel ◽  
J. C. Lin ◽  
R. E. J. Kelly ◽  
C. R. Duguay
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Regional Scale ◽  
Surface Characteristics ◽  
Growing Season ◽  
Snow Accumulation ◽  
Snow Season ◽  
Air Temperatures ◽  
Land Surface Characteristics

Abstract. Arctic field studies have indicated that the air temperature, soil moisture and vegetation at a site influence the quantity of snow accumulated, and that snow accumulation can alter growing-season soil moisture and vegetation. Climate change is predicted to bring about warmer air temperatures, greater snow accumulation and northward movements of the shrub and tree lines. Understanding the responses of northern environments to changes in snow and growing-season land surface characteristics requires: (1) insights into the present-day linkages between snow and growing-season land surface characteristics; and (2) the ability to continue to monitor these associations over time across the vast pan-Arctic. The objective of this study was therefore to examine the pan-Arctic (north of 60° N) linkages between two temporally distinct data products created from AMSR-E satellite passive microwave observations: GlobSnow snow water equivalent (SWE), and NTSG growing-season AMSR-E Land Parameters (air temperature, soil moisture and vegetation transmissivity). Due to the complex and interconnected nature of processes determining snow and growing-season land surface characteristics, these associations were analyzed using the modern nonparametric technique of alternating conditional expectations (ACE), as this approach does not impose a predefined analytic form. Findings indicate that regions with lower vegetation transmissivity (more biomass) at the start and end of the growing season tend to accumulate less snow at the start and end of the snow season, possibly due to interception and sublimation. Warmer air temperatures at the start and end of the growing season were associated with diminished snow accumulation at the start and end of the snow season. High latitude sites with warmer mean annual growing-season temperatures tended to accumulate more snow, probably due to the greater availability of water vapor for snow season precipitation at warmer locations. Regions with drier soils preceding snow onset tended to accumulate greater quantities of snow, likely because drier soils freeze faster and more thoroughly than wetter soils. Understanding and continuing to monitor these linkages at the regional scale using the ACE approach can allow insights to be gained into the complex response of Arctic ecosystems to climate-driven shifts in air temperature, vegetation, soil moisture and snow accumulation.

scholarly journals Pan-Arctic linkages between snow accumulation and growing season air temperature, soil moisture and vegetation

2013 ◽  
Vol 10 (1) ◽  
pp. 1747-1791
Author(s):  
K. A. Luus ◽  
Y. Gel ◽  
J. C. Lin ◽  
R. E. J. Kelly ◽  
C. R. Duguay
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Regional Scale ◽  
Surface Characteristics ◽  
Growing Season ◽  
Snow Accumulation ◽  
Snow Season ◽  
Air Temperatures ◽  
Land Surface Characteristics

Abstract. Arctic field studies have indicated that the air temperature, soil moisture and vegetation at a site influence the quantity of snow accumulated, and that snow accumulation can alter growing season soil moisture and vegetation. Climate change is predicted to bring about warmer air temperatures, greater snow accumulation and northward movements of the shrub and tree lines. Understanding the response of northern environments to changes in snow and growing season land surface characteristics requires: (1) insights into the present-day linkages between snow and growing season land surface characteristics; and (2) the ability to continue to monitor these associations over time across the vast pan-Arctic. The objective of this study was therefore to examine the pan-Arctic (north of 60° N) linkages between two temporally distinct data products created from AMSR-E satellite passive microwave observations: GlobSnow snow water equivalent, and NTSG (growing season air temperature, soil moisture and vegetation transmissivity). Due to the complex and interconnected nature of processes determining snow and growing season land surface characteristics, these associations were analyzed using the modern non-parametric technique of Alternating Conditional Expectations (ACE), as this approach does not impose a predefined analytic form. Findings indicate that regions with lower vegetation transmissivity (more biomass) at the start and end of the growing season tend to accumulate less snow at the start and end of the snow season, possibly due to interception and shading. Warmer air temperatures at the start and end of the growing season were associated with diminished snow accumulation at the start and end of the snow season. High latitude sites with warmer mean annual growing season temperatures tended to accumulate more snow, probably due to the greater availability of water vapor for snow season precipitation at warmer locations. Regions with drier soils preceding snow onset tended to accumulate greater quantities of snow, likely because drier soils freeze faster and more thoroughly than wetter soils. Understanding and continuing to monitor these linkages at the regional scale using the ACE approach can allow insights to be gained into the complex response of Arctic ecosystems to climate-driven shifts in air temperature, vegetation, soil moisture and snow accumulation.

scholarly journals Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

2021 ◽  
Vol 13 (1) ◽  
pp. 133
Author(s):  
Hao Sun ◽  
Yajing Cui
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Feed Forward Neural Network ◽  
Comparative Performance ◽  
Geographical Factors

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

scholarly journals Seasonal dynamics of methane emissions from a subarctic fen in the Hudson Bay Lowlands

2013 ◽  
Vol 10 (7) ◽  
pp. 4465-4479 ◽  
Author(s):  
K. L. Hanis ◽  
M. Tenuta ◽  
B. D. Amiro ◽  
T. N. Papakyriakou
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Growing Season ◽  
Near Surface ◽  
Growing Seasons ◽  
Air Temperatures ◽  
Soil Temperatures ◽  
Surface Soil Temperature ◽  
Filling Method ◽  
Highly Correlated

Abstract. Ecosystem-scale methane (CH4) flux (FCH4) over a subarctic fen at Churchill, Manitoba, Canada was measured to understand the magnitude of emissions during spring and fall shoulder seasons, and the growing season in relation to physical and biological conditions. FCH4 was measured using eddy covariance with a closed-path analyser in four years (2008–2011). Cumulative measured annual FCH4 (shoulder plus growing seasons) ranged from 3.0 to 9.6 g CH4 m−2 yr−1 among the four study years, with a mean of 6.5 to 7.1 g CH4 m−2 yr−1 depending upon gap-filling method. Soil temperatures to depths of 50 cm and air temperature were highly correlated with FCH4, with near-surface soil temperature at 5 cm most correlated across spring, fall, and the shoulder and growing seasons. The response of FCH4 to soil temperature at the 5 cm depth and air temperature was more than double in spring to that of fall. Emission episodes were generally not observed during spring thaw. Growing season emissions also depended upon soil and air temperatures but the water table also exerted influence, with FCH4 highest when water was 2–13 cm below and lowest when it was at or above the mean peat surface.

scholarly journals Characterizing Surface and Air Temperature in the Baltic Sea Coastal Area Using Remote Sensing Techniques and Gis

2016 ◽  
Vol 23 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Andrzej Chybicki ◽  
Marcin Kulawiak ◽  
Zbigniew Łubniewski
Keyword(s):  
Surface Temperature ◽  
Coastal Zone ◽  
Air Temperature ◽  
Land Surface ◽  
Vegetation Indices ◽  
Indirect Comparison ◽  
Surface Characteristics ◽  
Comparison Method ◽  
Effective Emissivity ◽  
Water Vapour Content

Abstract Estimation of surface temperature using multispectral imagery retrieved from satellite sensors constitutes several problems in terms of accuracy, accessibility, quality and evaluation. In order to obtain accurate results, currently utilized methods rely on removing atmospheric fluctuations in separate spectral windows, applying atmospheric corrections or utilizing additional information related to atmosphere or surface characteristics like atmospheric water vapour content, surface effective emissivity correction or transmittance correction. Obtaining accurate results of estimation is particularly critical for regions with fairly non-uniform distribution of surface effective emissivity and surface characteristics such as coastal zone areas. The paper presents the relationship between retrieved land surface temperature, air temperature, sea surface temperature and vegetation indices (VI) calculated based on remote observations in the coastal zone area. An indirect comparison method between remotely estimated surface temperature and air temperature using LST/VI feature space characteristics in an operational Geographic Information System is also presented.

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

scholarly journals Simulating the Effects of Land Surface Characteristics on Planetary Boundary Layer Parameters for a Modeled Landfalling Tropical Cyclone

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 138
Author(s):  
Yu Wang ◽  
Corene J. Matyas
Keyword(s):  
Tropical Cyclone ◽  
Land Surface ◽  
Friction Velocity ◽  
Inner Core ◽  
Sensible Heat Flux ◽  
Surface Characteristics ◽  
Near Surface ◽  
Land Surfaces ◽  
Rain Rates ◽  
Land Surface Characteristics

This study examined whether varying moisture availability and roughness length for the land surface under a simulated Tropical Cyclone (TC) could affect its production of precipitation. The TC moved over the heterogeneous land surface of the southeastern U.S. in the control simulation, while the other simulations featured homogeneous land surfaces that were wet rough, wet smooth, dry rough, and dry smooth. Results suggest that the near-surface atmosphere was modified by the changes to the land surface, where the wet cases have higher latent and lower sensible heat flux values, and rough cases exhibit higher values of friction velocity. The analysis of areal-averaged rain rates and the area receiving low and high rain rates shows that simulations having a moist land surface produce higher rain rates and larger areas of low rain rates in the TC’s inner core. The dry and rough land surfaces produced a higher coverage of high rain rates in the outer regions. Key differences among the simulations happened as the TC core moved over land, while the outer rainbands produced more rain when moving over the coastline. These findings support the assertion that the modifications of the land surface can influence precipitation production within a landfalling TC.

The Incidence of Climatic Conditions Favourable to Coffee Berry Disease in Kenya

1971 ◽  
Vol 7 (4) ◽  
pp. 303-314 ◽  
Author(s):  
J. M. Waller
Keyword(s):  
Air Temperature ◽  
Spore Germination ◽  
Growing Season ◽  
Climatic Conditions ◽  
Tree Canopy ◽  
Disease Development ◽  
Spore Dispersal ◽  
Coffee Berry ◽  
Coffee Berry Disease ◽  
Air Temperatures

SUMMARYClimatic conditions affecting the development of CBD are assessed by measuring wetness within the tree canopy and air temperature. Saturation of the tree canopy, necessary for spore dispersal, occurs most frequently at the tops of trees and the duration of wetness permitting spore germination is most prolonged at night. Night air temperatures are closest to berry temperatures and are important in assessing infection periods. Disease development in 1968 and 1969 was related to the number of infection periods during the growing season. Polythene tree covers which kept trees sufficiently dry to stop disease development were used in determining infection at different times of the year.

Changing sensitivity of global vegetation productivity to hydro-climate drivers

2021 ◽  
Author(s):  
Wantong Li ◽  
Matthias Forkel ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Sophia Walther ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Growing Season ◽  
Climatic Conditions ◽  
Terrestrial Vegetation ◽  
Spatial And Temporal Patterns ◽  
Vegetation Productivity ◽  
Area Index ◽  
Global Vegetation

<p>Terrestrial vegetation couples the global water, energy and carbon exchange between the atmosphere and the land surface. Thereby, vegetation productivity is determined by a multitude of energy- and water-related variables. While the emergent sensitivity of productivity to these variables has been inferred from Earth observations, its temporal evolution during the last decades is unclear, as well as potential changes in response to trends in hydro-climatic conditions. In this study, we analyze the changing sensitivity of global vegetation productivity to hydro-climate conditions by using satellite-observed vegetation indices (i.e. NDVI) at the monthly timescale from 1982–2015. Further, we repeat the analysis with simulated leaf area index and gross primary productivity from the TRENDY vegetation models, and contrast the findings with the observation-based results. We train a random forest model to predict anomalies of productivity from a comprehensive set of hydro-meteorological variables (temperature, solar radiation, vapor pressure deficit, surface and root-zone soil moisture and precipitation), and to infer the sensitivity to each of these variables. By training models from temporal independent subsets of the data we detect the evolution of sensitivity across time. Results based on observations show that vegetation sensitivity to energy- and water-related variables has significantly changed in many regions across the globe. In particular we find decreased (increased) sensitivity to temperature in very warm (cold) regions. Thereby, the magnitude of the sensitivity tends to differ between the early and late growing seasons. Likewise, we find changing sensitivity to root-zone soil moisture with increases predominantly in the early growing season and decreases in the late growing season. For better understanding the mechanisms behind the sensitivity changes, we analyse land-cover changes, hydro-climatic trends, and abrupt disturbances (e.g. drought, heatwave events or fires could result in breaking points of sensitivity evolution in the local interpretation). In summary, this study sheds light on how and where vegetation productivity changes its response to the drivers under climate change, which can help to understand possibly resulting changes in spatial and temporal patterns of land carbon uptake.</p>

Sign in / Sign up

Export Citation Format

Share Document