scholarly journals A 16-year record (2002–2017) of permafrost, active layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River Delta, northern Siberia: an opportunity to validate remote sensing data and land surface, snow, and permafrost models

2018 ◽  
Author(s):  
Julia Boike ◽  
Jan Nitzbon ◽  
Katharina Anders ◽  
Mikhail Grigoriev ◽  
Dmitry Bolshiyanov ◽  
...  
Keyword(s):  
Energy Balance ◽  
Observational Data ◽  
Land Surface ◽  
Laser Scanning ◽  
Landscape Heterogeneity ◽  
The Arctic ◽  
Annual Rainfall ◽  
Data Set ◽  
Northern Siberia ◽  
Research Site

Abstract. Most of the world's permafrost is located in the Arctic, where its frozen organic carbon con-tent makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere is a positive feed-back mechanism that has the potential for climate warming. It is therefore particularly im-portant to understand the links between the energy balance, which can vary rapidly over hour-ly to annual time scales, and permafrost condition, which changes slowly on decadal to cen-tennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, pro-cessing and data quality control. Additional data include a high-resolution digital terrain mod-el (DTM) obtained from terrestrial LiDAR laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active lay-er soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon center, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modeling. For the record from 1998 to 2017, the average mean annual air temperature was − 12.3 °C, with mean monthly temperature of the warmest month (July) recorded as 9.5 °C and for the coldest month (February) − 32.7 °C. The average annual rainfall was 169 mm. The depth of zero annual amplitude niveau is at 20.8 m, and has warmed from − 9.1 °C in 2006 to − 7.7 °C in 2017. The presented data are available in the supplementary material of this paper and through the PANGAEA website (https://doi.org/10.1594/PANGAEA.891142).

scholarly journals A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models

2019 ◽  
Vol 11 (1) ◽  
pp. 261-299 ◽  
Author(s):  
Julia Boike ◽  
Jan Nitzbon ◽  
Katharina Anders ◽  
Mikhail Grigoriev ◽  
Dmitry Bolshiyanov ◽  
...  
Keyword(s):  
Energy Balance ◽  
Observational Data ◽  
Active Layer ◽  
Land Surface ◽  
The Arctic ◽  
Annual Rainfall ◽  
Organic Carbon Content ◽  
Data Set ◽  
Northern Siberia ◽  
Research Site

Abstract. Most of the world's permafrost is located in the Arctic, where its frozen organic carbon content makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere, as well as snow cover changes, are positive feedback mechanisms that have the potential for climate warming. It is therefore particularly important to understand the links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost conditions, which changes slowly on decadal to centennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, processing, and data quality control. Furthermore, we present a merged data set of the parameters, which were measured from 1998 onwards. Additional data include a high-resolution digital terrain model (DTM) obtained from terrestrial lidar laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active-layer soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon centre, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modelling. For the record from 1998 to 2017, the average mean annual air temperature was −12.3 ∘C, with mean monthly temperature of the warmest month (July) recorded as 9.5 ∘C and for the coldest month (February) −32.7 ∘C. The average annual rainfall was 169 mm. The depth of zero annual amplitude is at 20.75 m. At this depth, the temperature has increased from −9.1 ∘C in 2006 to −7.7 ∘C in 2017. The presented data are freely available through the PANGAEA (https://doi.org/10.1594/PANGAEA.891142) and Zenodo (https://zenodo.org/record/2223709, last access: 6 February 2019) websites.

scholarly journals A 20-year record (1998–2017) of permafrost, active layer, and meteorological conditions at a High Arctic permafrost research site (Bayelva, Spitsbergen): an opportunity to validate remote sensing data and land surface, snow, and permafrost models

2017 ◽  
Author(s):  
Julia Boike ◽  
Inge Juszak ◽  
Stephan Lange ◽  
Sarah Chadburn ◽  
Eleanor Burke ◽  
...  
Keyword(s):  
Energy Balance ◽  
High Resolution ◽  
Observational Data ◽  
Digital Elevation Model ◽  
Positive Feedback ◽  
The Arctic ◽  
Data Sets ◽  
Data Set ◽  
Digital Elevation ◽  
Elevation Model

Abstract. Most permafrost is located in the Arctic, where frozen organic carbon makes it an important component of the global climate system. Despite the fact that the Arctic climate changes more rapidly than the rest of the globe, observational data density in the region is low. Permafrost thaw and carbon release to the atmosphere are a positive feedback mechanism that can exacerbate climate warming. This positive feedback functions via changing land-atmosphere energy and mass exchanges. There is thus a great need to understand links between the energy balance, which can vary rapidly over hourly to annual time scales, and permafrost, which changes slowly over long time periods. This understanding thus mandates long-term observational data sets. Such a data set is available from the Bayelva Site at Ny-Ålesund, Svalbard, where meteorology, energy balance components and subsurface observations have been made for the last 20 years. Additional data include a high resolution digital elevation model and a panchromatic image. This paper presents the data set produced so far, explains instrumentation, calibration, processing and data quality control, as well as the sources for various resulting data sets. The resulting data set is unique in the Arctic and serves a baseline for future studies. Since the data provide observations of temporally variable parameters that mitigate energy fluxes between permafrost and atmosphere, such as snow depth and soil moisture content, they are suitable for use in integrating, calibrating and testing permafrost as a component in Earth System Models. The data set also includes a high resolution digital elevation model that can be used together with the snow physical information for snow pack modeling. The presented data are available in the supplementary material for this paper and through the PANGAEA website ( https://doi.pangaea.de/10.1594/PANGAEA.880120).

scholarly journals A 20-year record (1998–2017) of permafrost, active layer and meteorological conditions at a high Arctic permafrost research site (Bayelva, Spitsbergen)

2018 ◽  
Vol 10 (1) ◽  
pp. 355-390 ◽  
Author(s):  
Julia Boike ◽  
Inge Juszak ◽  
Stephan Lange ◽  
Sarah Chadburn ◽  
Eleanor Burke ◽  
...  
Keyword(s):  
Time Series ◽  
Energy Balance ◽  
Observational Data ◽  
Positive Feedback ◽  
Global Climate ◽  
Feedback Mechanism ◽  
The Arctic ◽  
Data Sets ◽  
Data Quality Control ◽  
Data Set

Abstract. Most permafrost is located in the Arctic, where frozen organic carbon makes it an important component of the global climate system. Despite the fact that the Arctic climate changes more rapidly than the rest of the globe, observational data density in the region is low. Permafrost thaw and carbon release to the atmosphere are a positive feedback mechanism that can exacerbate global warming. This positive feedback functions via changing land–atmosphere energy and mass exchanges. There is thus a great need to understand links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost, which changes slowly over long time periods. This understanding thus mandates long-term observational data sets. Such a data set is available from the Bayelva site at Ny-Ålesund, Svalbard, where meteorology, energy balance components and subsurface observations have been made for the last 20 years. Additional data include a high-resolution digital elevation model (DEM) that can be used together with the snow physical information for snowpack modeling and a panchromatic image. This paper presents the data set produced so far, explains instrumentation, calibration, processing and data quality control, as well as the sources for various resulting data sets. The resulting data set is unique in the Arctic and serves as a baseline for future studies. The mean permafrost temperature is −2.8 °C, with a zero-amplitude depth at 5.5 m (2009–2017). Since the data provide observations of temporally variable parameters that mitigate energy fluxes between permafrost and atmosphere, such as snow depth and soil moisture content, they are suitable for use in integrating, calibrating and testing permafrost as a component in earth system models.The presented data are available in the Supplement for this paper (time series) and through the PANGAEA and Zenodo data portals: time series (https://doi.org/10.1594/PANGAEA.880120, https://zenodo.org/record/1139714) and HRSC-AX data products (https://doi.org/10.1594/PANGAEA.884730, https://zenodo.org/record/1145373).

scholarly journals Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

2013 ◽  
Vol 7 (3) ◽  
pp. 2333-2372
Author(s):  
E. Kantzas ◽  
M. Lomas ◽  
S. Quegan ◽  
E. Zakharova
Keyword(s):  
Land Surface ◽  
Field Data ◽  
Climate Models ◽  
Optimization Techniques ◽  
The Arctic ◽  
Surface Model ◽  
Land Surface Models ◽  
Snow Density ◽  
Data Set ◽  
Surface Models

Abstract. An increasing number of studies have demonstrated the significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled, earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both the systems processes and its initial state. This study focuses on snow-related variables and extensively utilizes a historical data set (1966–1996) of field snow measurements acquired across the extend of the Former Soviet Union (FSU) to evaluate a range of simulated snow metrics produced by a variety of land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific issues in simulating snow dynamics such as magnitude and timings of SWE as well as evolution of snow density. We further employ the field snow measurements alongside novel and model-independent methodologies to extract for the first time (i) a fresh snow density value (57–117 kg m–3) for the region and (ii) mean monthly snowpack sublimation estimates across a grassland-dominated western (November–February) [9.2, 6.1, 9.15, 15.25] mm and forested eastern sub-sector (November–March) [1.53, 1.52, 3.05, 3.80, 12.20] mm; we subsequently use the retrieved values to assess relevant model outputs. The discussion session consists of two parts. The first describes a sensitivity study where field data of snow depth and snow density are forced directly into the surface heat exchange formulation of a land surface model to evaluate how inaccuracies in simulating snow metrics affect important modeled variables and carbon fluxes such as soil temperature, thaw depth and soil carbon decomposition. The second part showcases how the field data can be assimilated with ready-available optimization techniques to pinpoint model issues and improve their performance.

scholarly journals The Arctic Summer Cloud Ocean Study (ASCOS): overview and experimental design

2014 ◽  
Vol 14 (6) ◽  
pp. 2823-2869 ◽  
Author(s):  
M. Tjernström ◽  
C. Leck ◽  
C. E. Birch ◽  
J. W. Bottenheim ◽  
B. J. Brooks ◽  
...  
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Cloud Condensation Nuclei ◽  
The Arctic ◽  
Data Set ◽  
Arctic Clouds ◽  
Pack Ice ◽  
Ocean Study ◽  
Arctic Summer

Abstract. The climate in the Arctic is changing faster than anywhere else on earth. Poorly understood feedback processes relating to Arctic clouds and aerosol–cloud interactions contribute to a poor understanding of the present changes in the Arctic climate system, and also to a large spread in projections of future climate in the Arctic. The problem is exacerbated by the paucity of research-quality observations in the central Arctic. Improved formulations in climate models require such observations, which can only come from measurements in situ in this difficult-to-reach region with logistically demanding environmental conditions. The Arctic Summer Cloud Ocean Study (ASCOS) was the most extensive central Arctic Ocean expedition with an atmospheric focus during the International Polar Year (IPY) 2007–2008. ASCOS focused on the study of the formation and life cycle of low-level Arctic clouds. ASCOS departed from Longyearbyen on Svalbard on 2 August and returned on 9 September 2008. In transit into and out of the pack ice, four short research stations were undertaken in the Fram Strait: two in open water and two in the marginal ice zone. After traversing the pack ice northward, an ice camp was set up on 12 August at 87°21' N, 01°29' W and remained in operation through 1 September, drifting with the ice. During this time, extensive measurements were taken of atmospheric gas and particle chemistry and physics, mesoscale and boundary-layer meteorology, marine biology and chemistry, and upper ocean physics. ASCOS provides a unique interdisciplinary data set for development and testing of new hypotheses on cloud processes, their interactions with the sea ice and ocean and associated physical, chemical, and biological processes and interactions. For example, the first-ever quantitative observation of bubbles in Arctic leads, combined with the unique discovery of marine organic material, polymer gels with an origin in the ocean, inside cloud droplets suggests the possibility of primary marine organically derived cloud condensation nuclei in Arctic stratocumulus clouds. Direct observations of surface fluxes of aerosols could, however, not explain observed variability in aerosol concentrations, and the balance between local and remote aerosols sources remains open. Lack of cloud condensation nuclei (CCN) was at times a controlling factor in low-level cloud formation, and hence for the impact of clouds on the surface energy budget. ASCOS provided detailed measurements of the surface energy balance from late summer melt into the initial autumn freeze-up, and documented the effects of clouds and storms on the surface energy balance during this transition. In addition to such process-level studies, the unique, independent ASCOS data set can and is being used for validation of satellite retrievals, operational models, and reanalysis data sets.

scholarly journals Limitations and improvements of the energy balance closure with reference to experimental data measured over a maize field

Atmósfera ◽  
2015 ◽  
Vol 27 (4) ◽  
pp. 335-352
Author(s):  
DANIELE MASSERONI ◽  
CHIARA CORBARI ◽  
MARCO MANCINI
Keyword(s):  
Energy Balance ◽  
Land Surface ◽  
Turbulent Flux ◽  
Comprehensive Evaluation ◽  
Energy Balance Closure ◽  
Statistical Regression ◽  
Flux Footprint ◽  
Data Set ◽  
Available Energy ◽  
Maize Field

The use of energy fluxes data to validate land surface models requires that energy balance closure conservationis satisfied, but usually this condition is not verified when the available energy is bigger than the sumof turbulent vertical fluxes. In this work, a comprehensive evaluation of energy balance closure problems isperformed on a 2012 data set from Livraga obtained by a micrometeorological eddy covariance station locatedin a maize field in the Po Valley. Energy balance closure is calculated by statistical regression of turbulentenergy fluxes and soil heat flux against available energy. Generally, the results indicate a lack of closure witha mean imbalance in the order of 20%. Storage terms are the main reason for the unclosed energy balance butalso the turbulent mixing conditions play a fundamental role in reliable turbulent flux estimations. Recentlyintroduced in literature, the energy balance problem has been studied as a scale problem. A representativesource area for each flux of the energy balance has been analyzed and the closure has been performed infunction of turbulent flux footprint areas. Surface heterogeneity and seasonality effects have been studied to understand the influence of canopy growth on the energy balance closure. High frequency data have beenused to calculate co-spectral and ogive functions, which suggest that an averaging period of 30 min may misstemporal scales that contribute to the turbulent fluxes. Finally, latent and sensible heat random error estimationsare computed to give information about the measurement system and turbulence transport deficiencies

Hydroclimatological evaluation of CLM5 simulations using multiple data sources for land-atmosphere interaction studies over Scandinavia

2020 ◽  
Author(s):  
Yeliz A. Yılmaz ◽  
Lena M. Tallaksen ◽  
Frode Stordal
Keyword(s):  
Observational Data ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Surface Model ◽  
Data Sets ◽  
Earth System ◽  
Data Set ◽  
Cold Environments ◽  
Community Land Model ◽  
Terrestrial Water

<p>Arctic amplification leads to rapid changes in the terrestrial water and energy balances at high northern latitudes. Advances in Earth System Models (ESMs) is improving our understanding of the underlying feedback mechanisms leading to these changes. The representation of the land surface in ESMs is essential to simulate and understand changes at the global and regional scales. The latest version of the land component of the Norwegian Earth System Model (NorESM), namely the Community Land Model (CLM5), has received substantial new implementations to help simulate the land surface processes in cold environments. At the same time, the behaviour of offline CLM5 simulations and new observational data sets have not been systematically compared over Scandinavian regions. In this study, we run the CLM5 model at relatively high resolution (0.25 degrees) over Scandinavia (including Svalbard) for 15 years between 2002 and 2016. We evaluate the water and energy budget components of CLM5 using several reanalyses and satellite-based observational data sets. In particular, we use monthly model outputs and compare with the satellite retrievals from GRACE, MODIS, AMSR2, and AMSR-E, and reanalysis data sets from ERA5, GLDAS, and MERRA-2. As an additional data source, we use the local‐scale measurements obtained from the Finse Eco-Hydrological Observatory (Finse EcHO) at 1200 m a.s.l, and the high-Arctic research site at Bayelva near Ny-Ålesund, Svalbard. Our investigation is focused on several variables including terrestrial water storage, snow water equivalent, turbulent fluxes, net radiation, and skin temperature. The results indicate that the perceived performance of the land surface model (CLM5) depends strongly on the reference observational data set. Regional discrepancies between data sets, particularly for Svalbard, prompts further investigation of the underlying sources of uncertainty. The results of this evaluation provide a valuable source of information for future studies in the region, particularly in the Land-ATmosphere Interactions in Cold Environments (LATICE) project, which focuses on cold region land surface dynamics, integrating across observational systems, laboratory experiments, field, and modeling efforts.</p><p>Acknowledgement : This study is conducted under the LATICE strategic research initiative funded by the Faculty of Mathematics and Natural Sciences at the University of Oslo, and the project EMERALD (294948) funded by the Research Council of Norway.</p>

scholarly journals Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

2014 ◽  
Vol 8 (2) ◽  
pp. 487-502
Author(s):  
E. Kantzas ◽  
S. Quegan ◽  
M. Lomas ◽  
E. Zakharova
Keyword(s):  
Land Surface ◽  
Field Data ◽  
Snow Water Equivalent ◽  
Optimization Techniques ◽  
The Arctic ◽  
Surface Model ◽  
Land Surface Models ◽  
Snow Density ◽  
Data Set ◽  
Surface Models

Abstract. An increasing number of studies have demonstrated significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled Earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both system processes and its initial state. This study focuses on snow-related variables and makes extensive use of a historical data set (1966–1996) of field snow measurements acquired across the extent of the former Soviet Union to evaluate a range of simulated snow metrics produced by several land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific failings in simulating snowpack properties such as magnitude, inter-annual variability, timings of snow water equivalent and evolution of snow density. We develop novel and model-independent methodologies that use the field snow measurements to extract the values of fresh snow density and snowpack sublimation, and exploit them to assess model outputs. By directly forcing the surface heat exchange formulation of a land surface model with field data on snow depth and snow density, we evaluate how inaccuracies in simulating snow metrics affect soil temperature, thaw depth and soil carbon decomposition. We also show how field data can be assimilated into models using optimization techniques in order to identify model defects and improve model performance.

scholarly journals The Arctic Summer Cloud-Ocean Study (ASCOS): overview and experimental design

2013 ◽  
Vol 13 (5) ◽  
pp. 13541-13652 ◽  
Author(s):  
M. Tjernström ◽  
C. Leck ◽  
C. E. Birch ◽  
B. J. Brooks ◽  
I. M. Brooks ◽  
...  
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
The Arctic ◽  
Data Set ◽  
Arctic Clouds ◽  
Low Level ◽  
Pack Ice ◽  
Ocean Study ◽  
Arctic Summer

Abstract. The climate in the Arctic is changing faster than anywhere else on Earth. Poorly understood feedback processes relating to Arctic clouds and aerosol-cloud interactions contribute to a poor understanding of the present changes in the Arctic climate system, and also to a large spread in projections of future climate in the Arctic. The problem is exacerbated by the paucity of research-quality observations in the central Arctic. Improved formulations in climate models require such observations, which can only come from measurements in-situ in this difficult to reach region with logistically demanding environmental conditions. The Arctic Summer Cloud-Ocean Study (ASCOS) was the most extensive central Arctic Ocean expedition with an atmospheric focus during the International Polar Year (IPY) 2007–2008. ASCOS focused on the study of the formation and life cycle of low-level Arctic clouds. ASCOS departed from Longyearbyen on Svalbard on 2 August and returned on 9 September 2008. In transit into and out of the pack ice, four short research stations were undertaken in the Fram Strait; two in open water and two in the marginal ice zone. After traversing the pack-ice northward an ice camp was set up on 12 August at 87°21' N 01°29' W and remained in operation through 1 September, drifting with the ice. During this time extensive measurements were taken of atmospheric gas and particle chemistry and physics, mesoscale and boundary-layer meteorology, marine biology and chemistry, and upper ocean physics. ASCOS provides a unique interdisciplinary data set for development and testing of new hypotheses on cloud processes, their interactions with the sea ice and ocean and associated physical, chemical, and biological processes and interactions. For example, the first ever quantitative observation of bubbles in Arctic leads, combined with the unique discovery of marine organic material, polymer gels with an origin in the ocean, inside cloud droplets suggest the possibility of primary marine organically derived cloud condensation nuclei in Arctic stratocumulus clouds. Direct observations of surface fluxes of aerosols could, however, not explain observed variability in aerosol concentrations and the balance between local and remote aerosols sources remains open. Lack of CCN was at times a controlling factor in low-level cloud formation, and hence for the impact of clouds on the surface energy budget. ASCOS provided detailed measurements of the surface energy balance from late summer melt into the initial autumn freeze-up, and documented the effects of clouds and storms on the surface energy balance during this transition. In addition to such process-level studies, the unique, independent ASCOS data set can and is being used for validation of satellite retrievals, operational models, and reanalysis data sets.

scholarly journals Development of a 10-year (2001–2010) 0.1° data set of land-surface energy balance for mainland China

2014 ◽  
Vol 14 (23) ◽  
pp. 13097-13117 ◽  
Author(s):  
X. Chen ◽  
Z. Su ◽  
Y. Ma ◽  
S. Liu ◽  
Q. Yu ◽  
...  
Keyword(s):  
Energy Balance ◽  
High Resolution ◽  
Surface Energy ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Water Cycle ◽  
Surface Radiation ◽  
Wave Radiation ◽  
The Tibetan Plateau ◽  
Data Set

Abstract. In the absence of high-resolution estimates of the components of surface energy balance for China, we developed an algorithm based on the surface energy balance system (SEBS) to generate a data set of land-surface energy and water fluxes on a monthly timescale from 2001 to 2010 at a 0.1 × 0.1° spatial resolution by using multi-satellite and meteorological forcing data. A remote-sensing-based method was developed to estimate canopy height, which was used to calculate roughness length and flux dynamics. The land-surface flux data set was validated against "ground-truth" observations from 11 flux tower stations in China. The estimated fluxes correlate well with the stations' measurements for different vegetation types and climatic conditions (average bias = 11.2 Wm−2, RMSE = 22.7 Wm−2). The quality of the data product was also assessed against the GLDAS data set. The results show that our method is efficient for producing a high-resolution data set of surface energy flux for the Chinese landmass from satellite data. The validation results demonstrate that more accurate downward long-wave radiation data sets are needed to be able to estimate turbulent fluxes and evapotranspiration accurately when using the surface energy balance model. Trend analysis of land-surface radiation and energy exchange fluxes revealed that the Tibetan Plateau has undergone relatively stronger climatic change than other parts of China during the last 10 years. The capability of the data set to provide spatial and temporal information on water-cycle and land–atmosphere interactions for the Chinese landmass is examined. The product is free to download for studies of the water cycle and environmental change in China.

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