scholarly journals Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain

2012 ◽  
Vol 6 (1) ◽  
pp. 51-69 ◽  
Author(s):  
S. Hachem ◽  
C. R. Duguay ◽  
M. Allard
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Ground Surface ◽  
Surface Heterogeneity ◽  
The Arctic ◽  
Near Surface ◽  
The North ◽  
Continuous Permafrost ◽  
Spatial Estimates ◽  
Promising Source

Abstract. Obtaining high resolution records of surface temperature from satellite sensors is important in the Arctic because meteorological stations are scarce and widely scattered in those vast and remote regions. Surface temperature is the primary climatic factor that governs the existence, spatial distribution and thermal regime of permafrost which is a major component of the terrestrial cryosphere. Land Surface (skin) Temperatures (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to ground-based near-surface air (Tair) and ground surface temperature (GST) measurements obtained from 2000 to 2008 at herbaceous and shrub tundra sites located in the continuous permafrost zone of Northern Québec, Nunavik, Canada, and of the North Slope of Alaska, USA. LSTs (temperatures at the surface materials-atmosphere interface) are found to be better correlated with Tair (1–3 m above the ground) than with available GST (3–5 cm below the ground surface). As Tair is most often used by the permafrost community, this study focused on this parameter. LSTs are in stronger agreement with Tair during the snow cover season than in the snow free season. Combining Aqua and Terra LST-Day and LST-Nigh acquisitions into a mean daily value provides a large number of LST observations and a better overall agreement with Tair. Comparison between mean daily LSTs and mean daily Tair, for all sites and all seasons pooled together yields a very high correlation (R = 0.97; mean difference (MD) = 1.8 °C; and standard deviation of MD (SD) = 4.0 °C). The large SD can be explained by the influence of surface heterogeneity within the MODIS 1 km2 grid cells, the presence of undetected clouds and the inherent difference between LST and Tair. Retrieved over several years, MODIS LSTs offer a great potential for monitoring surface temperature changes in high-latitude tundra regions and are a promising source of input data for integration into spatially-distributed permafrost models.

scholarly journals Comparison of MODIS-derived land surface temperatures with near-surface soil and air temperature measurements in continuous permafrost terrain

2011 ◽  
Vol 5 (3) ◽  
pp. 1583-1625 ◽  
Author(s):  
S. Hachem ◽  
C. R. Duguay ◽  
M. Allard
Keyword(s):  
Surface Temperature ◽  
Soil Temperature ◽  
Air Temperature ◽  
High Latitude ◽  
Land Surface ◽  
Temperature Measurements ◽  
Near Surface ◽  
Recent Climate ◽  
Continuous Permafrost ◽  
Promising Source

Abstract. In Arctic and sub-Arctic regions, meteorological stations are scattered and poorly distributed geographically; they are mostly located along coastal areas and are often unreachable by road. Given that high-latitude regions are the ones most significantly affected by recent climate warming, there is a need to supplement existing meteorological station networks with spatially continuous measurements such as those obtained by spaceborne platforms. In particular, land surface (skin) temperature (LST) retrieved from satellite sensors offer the opportunity to utilize remote sensing technology to obtain a consistent coverage of a key parameter for climate, permafrost, and hydrological research. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms offers the potential to provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS were compared to ground-based near-surface air and soil temperature measurements obtained at herbaceous and shrub tundra sites located in the continuous permafrost zone of northern Québec, Canada, and the North Slope of Alaska, USA. LST values were found to be better correlated with near-surface air temperature (1–2 m above the ground) than with soil temperature (3–5 cm below the ground) measurements. A comparison between mean daily air temperature from ground-based station measurements and mean daily MODIS LST, calculated from daytime and nighttime temperature values of both Terra and Aqua acquisitions, for all sites and all seasons pooled together reveals a high correlation between the two sets of measurements (R>0.93 and mean difference of −1.86 °C). Mean differences ranged between −0.51 °C and −5.13 °C due to the influence of surface heterogeneity within the MODIS 1 km2 grid cells at some sites. Overall, it is concluded that MODIS offers a great potential for monitoring surface temperature changes in high-latitude tundra regions and provides a promising source of input data for integration into spatially-distributed permafrost models.

scholarly journals Using MODIS Land Surface Temperatures for Permafrost Thermal Modeling in Beiluhe Basin on the Qinghai-Tibet Plateau

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4200 ◽  
Author(s):  
Anyuan Li ◽  
Caichu Xia ◽  
Chunyan Bao ◽  
Guoan Yin
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Ground Surface ◽  
Tibet Plateau ◽  
Active Layer Thickness ◽  
Permafrost Region ◽  
Thermal Dynamics ◽  
Near Surface ◽  
Warm Bias ◽  
Qinghai Tibet Plateau

It is essential to monitor the ground temperature over large areas to understand and predict the effects of climate change on permafrost due to its rapid warming on the Qinghai-Tibet Plateau (QTP). Land surface temperature (LST) is an important parameter for the energy budget of permafrost environments. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products are especially valuable for detecting permafrost thermal dynamics across the QTP. This study presents a comparison of MODIS-LST values with in situ near-surface air temperature (Ta), and ground surface temperature (GST) obtained from 2014 to 2016 at five sites in Beiluhe basin, a representative permafrost region on the QTP. Furthermore, the performance of the thermal permafrost model forced by MODIS-LSTs was studied. Averaged LSTs are found to strongly correlated with Ta and GST with R2 values being around 0.9. There is a significant warm bias (4.43–4.67 °C) between averaged LST and Ta, and a slight warm bias (0.67–2.66 °C) between averaged LST and GST. This study indicates that averaged MODIS-LST is supposed to be a useful data source for permafrost monitoring. The modeled ground temperatures and active-layer thickness have a good agreement with the measurements, with a difference of less than 1.0 °C and 0.4 m, respectively.

scholarly journals Effects of groundwater pumping on ground surface temperature: A regional modeling study in the North China Plain

2020 ◽  
Author(s):  
You-Kuan Zhang ◽  
Chen Yang ◽  
Xiaofan Yang
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
North China Plain ◽  
North China ◽  
Ground Surface ◽  
Temporal Variations ◽  
Heat Fluxes ◽  
Ground Surface Temperature ◽  
The North ◽  
The North China Plain

<p>It is recognized that groundwater (GW) may play an important role in the subsurface–land-surface–atmosphere system and that pumping of GW may affect soil moisture which in turn influences local weather and climate through land-atmosphere interactions. In this study effects of GW pumping on ground surface temperature (GST) in the North China Plain (NCP) were investigated with a coupled ParFlow.CLM model of subsurface and land-surface processes and their interactions. The model was validated using the water and energy fluxes reported in previous studies and from the JRA-55 reanalysis. Numerical experiments were designed to examine the impacts of GW pumping and irrigation on GST. Results show significant effects of GW pumping on GST in the NCP. Generally, the subsurface acts as a buffer to temporal variations in heat fluxes at the land-surface, but long-term pumping can gradually weaken this buffer, resulting in increases in the spatio-temporal variability of GST, as exemplified by hotter summers and colder winters. Considering that changes of water table depth (WTD) can significantly affect land surface heat fluxes when WTD ranges between 1–10 m, the 0.5 m/year increase of WTD simulated by the model due to pumping can continue to raise GST for about 20 years from the pre-pumping WTD in the NCP. The increase of GST is expected to be faster initially and gradually slow down. The findings from this study may implicate similar GST increases may occur in other regions with GW depletion.</p>

scholarly journals Toward the Detection of Permafrost Using Land-Surface Temperature Mapping

2020 ◽  
Vol 12 (4) ◽  
pp. 695 ◽  
Author(s):  
Jigjidsurengiin Batbaatar ◽  
Alan R. Gillespie ◽  
Ronald S. Sletten ◽  
Amit Mushkin ◽  
Rivka Amit ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Arid Regions ◽  
Surface Soil ◽  
Surface Heterogeneity ◽  
Proof Of Concept ◽  
Near Surface ◽  
Freeze And Thaw ◽  
Infrared Data

Permafrost is degrading under current warming conditions, disrupting infrastructure, releasing carbon from soils, and altering seasonal water availability. Therefore, it is important to quantitatively map the change in the extent and depth of permafrost. We used satellite images of land-surface temperature to recognize and map the zero curtain, i.e., the isothermal period of ground temperature during seasonal freeze and thaw, as a precursor for delineating permafrost boundaries from remotely sensed thermal-infrared data. The phase transition of moisture in the ground allows the zero curtain to occur when near-surface soil moisture thaws or freezes, and also when ice-rich permafrost thaws or freezes. We propose that mapping the zero curtain is a precursor to mapping permafrost at shallow depths. We used ASTER and a MODIS-Aqua daily afternoon land-surface temperature (LST) timeseries to recognize the zero curtain at the 1-km scale as a “proof of concept.” Our regional mapping of the zero curtain over an area around the 7000 m high volcano Ojos del Salado in Chile suggests that the zero curtain can be mapped over arid regions of the world. It also indicates that surface heterogeneity, snow cover, and cloud cover can hinder the effectiveness of our approach. To be of practical use in many areas, it may be helpful to reduce the topographic and compositional heterogeneity in order to increase the LST accuracy. The necessary finer spatial resolution to reduce these problems is provided by ASTER (90 m).

scholarly journals Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

2015 ◽  
Vol 12 (8) ◽  
pp. 7665-7687 ◽  
Author(s):  
C. L. Pérez Díaz ◽  
T. Lakhankar ◽  
P. Romanov ◽  
J. Muñoz ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Balance Model ◽  
Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

scholarly journals Revisiting the trend in the occurrences of the “warm Arctic–cold Eurasian continent” temperature pattern

2020 ◽  
Vol 20 (22) ◽  
pp. 13753-13770
Author(s):  
Lejiang Yu ◽  
Shiyuan Zhong ◽  
Cuijuan Sui ◽  
Bo Sun
Keyword(s):  
Surface Temperature ◽  
Atlantic Multidecadal Oscillation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Temperature Pattern ◽  
Self Organizing Map ◽  
Central Pacific ◽  
The North ◽  
Eurasian Continent ◽  
Increasing Trend

Abstract. The recent increasing trend of “warm Arctic, cold continents” has attracted much attention, but it remains debatable as to what forces are behind this phenomenon. Here, we revisited surface temperature variability over the Arctic and the Eurasian continent by applying the self-organizing-map (SOM) technique to gridded daily surface temperature data. Nearly 40 % of the surface temperature trends are explained by the nine SOM patterns that depict the switch to the current warm Arctic–cold Eurasia pattern at the beginning of this century from the reversed pattern that dominated the 1980s and 1990s. Further, no cause–effect relationship is found between the Arctic sea ice loss and the cold spells in the high-latitude to midlatitude Eurasian continent suggested by earlier studies. Instead, the increasing trend in warm Arctic–cold Eurasia pattern appears to be related to the anomalous atmospheric circulations associated with two Rossby wave trains triggered by rising sea surface temperature (SST) over the central North Pacific and the North Atlantic oceans. On interdecadal timescale, the recent increase in the occurrences of the warm Arctic–cold Eurasia pattern is a fragment of the interdecadal variability of SST over the Atlantic Ocean as represented by the Atlantic Multidecadal Oscillation (AMO) and over the central Pacific Ocean.

The limitations of in situ data for validating satellite-derived spatio-temporal ocean products.

2021 ◽  
Author(s):  
Rory Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack
Keyword(s):  
Surface Temperature ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Bias Reduction ◽  
Data Validation ◽  
Near Surface ◽  
In Situ Data ◽  
Spatio Temporal ◽  
Image Pixels

<p>The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.</p><p>For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.</p><p>A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.</p><p>Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.</p>

NMME-based Assessment of Prediction Skills of US Summertime Droughts and Pluvials: Role of Near-surface Temperature Prediction Skill

2021 ◽  
Author(s):  
Jonghun Kam ◽  
Sungyoon Kim ◽  
Joshua Roundy
Keyword(s):  
Surface Temperature ◽  
Climate Extremes ◽  
Prediction Skill ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Anomaly Correlation ◽  
The North ◽  
Anomaly Correlation Coefficient ◽  
Precipitation Anomalies

<p>This study used the North American Multi-Model Ensemble (NMME) system to understand the role of near surface temperature in the prediction skill for US climate extremes. In this study, the forecasting skill was measured by anomaly correlation coefficient (ACC) between the observed and forecasted precipitation (PREC) or 2-meter air temperature (T2m) over the contiguous United States (CONUS) during 1982–2012. The strength of the PREC-T2m coupling was measured by ACC between observed PREC and T2m or forecasted PREC and T2m over the CONUS. This study also assessed the NMME forecasting skill for the summers of 2004 (spatial anomaly correlation between PREC and T2m: 0.05), 2011 (-0.65), and 2012 (-0.60) when the PREC-T2m coupling is weaker or stronger than the 1982–2012 climatology (ACC:-0.34). This study found that most of the NMME models show stronger (negative) PREC-T2m coupling than the observed coupling, indicating that they fail to reproduce interannual variability of the observed PREC-T2m coupling. Some NMME models with skillful prediction for T2m show the skillful prediction of the precipitation anomalies and US droughts in 2011 and 2012 via strong PREC-T2m coupling despite the fact that the forecasting skill is year-dependent and model-dependent. Lastly, we explored how the forecasting skill for SSTs over north Pacific and Atlantic Oceans affects the forecasting skill for T2m and PREC over the US. The findings of this study suggest a need for the selective use of the current NMME seasonal forecasts for US droughts and pluvials.</p>

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