scholarly journals Pan-Antarctic map of near-surface permafrost temperatures at 1 km<sup>2</sup> scale

2019 ◽  
Author(s):  
Jaroslav Obu ◽  
Sebastian Westermann ◽  
Gonçalo Vieira ◽  
Andrey Abramov ◽  
Megan Balks ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Reanalysis Data ◽  
Strong Wind ◽  
Near Surface ◽  
Ground Temperatures ◽  
Permafrost Temperature ◽  
Snow Conditions ◽  
Almost All

Abstract. Permafrost is present under almost all of the Antarctic’s ice-free areas but little is known about spatial variations of permafrost temperatures outside a few areas with established ground temperature measurements. We modelled a temperature at the top of the permafrost (TTOP) for all the ice-free areas of Antarctic mainland and Antarctic Islands at 1 km2 resolution during 2000–2017. The model was driven by remotely-sensed land surface temperatures and down-scaled ERA-Interim climate reanalysis data and subgrid permafrost variability was simulated by variable snow cover. The results were validated against in-situ measured ground temperatures from 40 permafrost boreholes and the resulting root mean square error was 1.9 °C. The lowest near-surface permafrost temperature of −33 °C was modelled at Mount Markham in Queen Elizabeth Range in the Transantarctic Mountains. This is the lowest permafrost temperature on Earth according to the modelling results on global scale. The temperatures were most commonly modelled between −23 and −18 °C for mountainous areas rising above the Antarctic Ice Sheet and between −14 and −8 °C for coastal areas. The model performance was good where snow conditions were modelled realistically but errors of up to 4 °C can occur at sites with strong wind-driven redistribution of snow.

scholarly journals Pan-Antarctic map of near-surface permafrost temperatures at 1 km<sup>2</sup> scale

2020 ◽  
Vol 14 (2) ◽  
pp. 497-519 ◽  
Author(s):  
Jaroslav Obu ◽  
Sebastian Westermann ◽  
Gonçalo Vieira ◽  
Andrey Abramov ◽  
Megan Ruby Balks ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Reanalysis Data ◽  
Strong Wind ◽  
Near Surface ◽  
Permafrost Temperature ◽  
Scale Modelling ◽  
Almost All ◽  
The Antarctic

Abstract. Permafrost is present within almost all of the Antarctic's ice-free areas, but little is known about spatial variations in permafrost temperatures except for a few areas with established ground temperature measurements. We modelled a temperature at the top of the permafrost (TTOP) for all the ice-free areas of the Antarctic mainland and Antarctic islands at 1 km2 resolution during 2000–2017. The model was driven by remotely sensed land surface temperatures and downscaled ERA-Interim climate reanalysis data, and subgrid permafrost variability was simulated by variable snow cover. The results were validated against in situ-measured ground temperatures from 40 permafrost boreholes, and the resulting root-mean-square error was 1.9 ∘C. The lowest near-surface permafrost temperature of −36 ∘C was modelled at Mount Markham in the Queen Elizabeth Range in the Transantarctic Mountains. This is the lowest permafrost temperature on Earth, according to global-scale modelling results. The temperatures were most commonly modelled between −23 and −18 ∘C for mountainous areas rising above the Antarctic Ice Sheet and between −14 and −8 ∘C for coastal areas. The model performance was good where snow conditions were modelled realistically, but errors of up to 4 ∘C occurred at sites with strong wind-driven redistribution of snow.

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.

Factors influencing permafrost temperatures across tree line in the uplands east of the Mackenzie Delta, 2004–20101This article is one of a series of papers published in this CJES Special Issue on the theme of Fundamental and applied research on permafrost in Canada.2Polar Continental Shelf Contribution 03611.

2012 ◽  
Vol 49 (8) ◽  
pp. 877-894 ◽  
Author(s):  
M.J. Palmer ◽  
C.R. Burn ◽  
S.V. Kokelj
Keyword(s):  
Snow Depth ◽  
Applied Research ◽  
Abrupt Change ◽  
Late Winter ◽  
Mackenzie Delta ◽  
Near Surface ◽  
Ground Temperatures ◽  
Winter Snow ◽  
Shrub Tundra ◽  
Snow Conditions

Air and near-surface ground temperatures, late-winter snow conditions, and characteristics of the vegetation cover and soil were measured across the forest–tundra transition in the uplands east of the Mackenzie Delta, Northwest Territories, in 2004–2010. Mean late-winter snow depth decreased northward from 73 cm in the subarctic boreal forest near Inuvik to 22 cm in low-shrub tundra. Annual near-surface ground temperatures decreased northward by 0.1–0.3 °C/km near the northern limit of trees, in association with an abrupt change in snow depth. The rate decreased to 0.01–0.06 °C/km in the tundra. The freezing season is twice as long as the thawing season in the region, so measured differences in the regional ground thermal regime were dominated by the contrast in winter surface conditions between forest and tundra.

scholarly journals Required sampling-density of ground-based soil moisture and brightness temperature observations for calibration/validation of L-band satellite observations based on a virtual reality

2019 ◽  
Author(s):  
Shaoning Lv ◽  
Bernd Schalge ◽  
Pablo Saavedra Garfias ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Confidence Level ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Microwave Emission ◽  
Near Surface ◽  
Temperature Observations ◽  
Better Than

Abstract. Microwave remote sensing is the most promising tool for monitoring global-scale near-surface soil moisture distributions. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are made to evaluate their soil moisture products via ground observations, forward microwave transfer simulation, and retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional terrestrial system model TerrSysMP and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify sampling distance required for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling resolution of better than 6 km is required to validate the targeted accuracy of 0.04 cm3/cm3 (70 % confidence level) in SMOS and SMAP over typical midlatitude European regions. The minimum sampling resolution depends on the land-surface inhomogeneity and the meteorological situation, which influence the soil moisture patterns, and ranges from about 7 km to 17 km for a 70 % confidence level for a typical year. At the minimum sampling resolution for a 70 % confidence level also the accuracy of footprint-averaged brightness temperature estimates is equal or better than 15 K/10 K for H/V polarization. Estimates strongly deteriorate with sparser sampling densities, e.g., at 3/9 km with 3/5 sampling sites the confidence level of derived footprint estimates can reach about 0.5–0.6 for soil moisture which is much less than the standard 0.7 requirements for ground measurements. The representativeness of ground-based soil moisture and brightness temperature observations – and thus their required minimum sampling densities – are only weakly correlated in space and time. This study provides a basis for a better understanding of sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

scholarly journals Evaluating the Atibaia River Hydrology using JULES6.1

2021 ◽  
Author(s):  
Hsi-Kai Chou ◽  
Ana Maria Heuminski de Avila ◽  
Michaela Bray
Keyword(s):  
Land Surface ◽  
Environmental Changes ◽  
Water Flux ◽  
Model Performance ◽  
Reanalysis Data ◽  
Agricultural Activity ◽  
Multiple Sources ◽  
Metropolitan Regions ◽  
River Hydrology

Abstract. Land surface models such as the Joint UK Land Environment Simulator (JULES) are increasingly used for hydrological assessments because of their state-of-the-art representation of physical processes and versatility. Unlike statistical models and AI models, the JULES model simulates the physical water flux under given meteorological conditions, allowing us to understand and investigate the cause and effect of environmental processes changes. Here we explore the possibility of this approach using a case study in the Atibaia river basin, which serves as a major water supply for metropolitan regions of Campinas and São Paulo, Brazil. The watershed is suffering increasing hydrological risks, which could be attributed to environmental changes, such as urbanization and agricultural activity. The increasing risks highlight the importance to evaluate the land surface processes of the watershed systematically. We explore the use of local precipitation collection complement with multiple sources of global reanalysis data to simulate the basin hydrology. Our results show that the coarse resolution of rainfall data is the main reason to reduce model performance. Despite this shortcoming, key hydrological fluxes in the basin can be represented by the JULES model simulations.

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

2016 ◽  
Vol 9 (3) ◽  
pp. 1073-1085 ◽  
Author(s):  
Sojung Park ◽  
Seon Ki Park
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Surface Condition ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Vegetation Types ◽  
Area Index ◽  
Almost All ◽  
Vegetation Effect

Abstract. Snow-covered surface albedo varies depending on many factors, including snow grain size, snow cover thickness, snow age, forest shading factor, etc., and its parameterization 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 vegetation types in East Asia during winter have the minimum values 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 activeness, 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 implementing 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 approximately 69 %.

scholarly journals Global land-surface evaporation estimated from satellite-based observations

2010 ◽  
Vol 7 (5) ◽  
pp. 8479-8519 ◽  
Author(s):  
D. G. Miralles ◽  
T. R. H. Holmes ◽  
R. A. M. De Jeu ◽  
J. H. Gash ◽  
A. G. C. A. Meesters ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Surface Conductance ◽  
Vegetation Density ◽  
Climate Conditions ◽  
Eddy Covariance Measurements ◽  
One Year ◽  
Daily Evaporation

Abstract. This paper outlines a new methodology to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale, with a 0.25 degree spatial resolution. Central to this approach is the use of the Priestley and Taylor (PT) evaporation model. Because the PT equation is driven by net radiation, this strategy avoids the need to specify surface fields of variables, such as the surface conductance, which cannot be detected directly from space. Key distinguishing features are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the use of a detailed rainfall interception module. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (R = 0.84, N = 43) and show a negligible bias (−1.5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of R = 0.84, still lower than the R = 0.91 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented.

Advancements in the mesoscale Hydrologic Model at the global scale

2021 ◽  
Author(s):  
Oldrich Rakovec ◽  
Maren Kaluza ◽  
Rohini Kumar ◽  
Robert Schweppe ◽  
Pallav Shrestha ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Hydrologic Model ◽  
Seasonal Forecasts ◽  
Ongoing Research ◽  
Surface Models ◽  
Forecasting System ◽  
Best Parameter

&lt;p&gt;This study synthesizes the advancements made in the setup of the mesoscale Hydrologic Model (mHM; [1,2,3]) at the global scale. Underlying vegetation and geophysical characteristics are provided at &amp;#8776;200m, while the mHM simulates water fluxes and states between 10 km and 100 km spatial resolution. The meteorologic forcing data are derived from the readily available, near-real time ERA-5 dataset [4]. The total of 50 global parameters of the Multiscale Parameter Regionalization (MPR) are constrained in two modes: (1) streamflow only across 3054 gauges, and (2) streamflow across 3054 gauges and simultaneously with FLUXNET ET and GRACE TWSA across 258 domains consisting of &amp;#8776;10&amp;#176; x 10&amp;#176; blocks. Model performance is finally evaluated against a range of observed and reference data since 1985.&amp;#160;&lt;/p&gt;&lt;p&gt;The single best parameter set evaluated across 3054 GRDC global streamflow station yield median performance of 0.47 daily KGE (0.55 monthly KGE). This performance varies strongly between continents. For example, median daily KGE across Europe is around 0.55 (N basins=972) and across northern America around 0.5 (N basins=1264). So far, the worst model performance is observed across Africa, with median KGE of 0 (N basins=202), using the same globally constrained parameter set. The deterioration of model performance based on seamless parameterization can be explained by the quality of the underlying data, which corresponds to areas, where water balance closure error is the biggest. Additionally, missed model processes play an important role as well. Finally, there remains a large gap between the onsite calibrations and global calibrations and ongoing research is being done to narrow down these differences. This work is the fundament for building skillful global seasonal forecasting system ULYSSES [6], which provides hindcasts and operational seasonal forecasts of hydrologic variables using four state of the art hydrologic/land surface models with lead time of 6 months.&lt;/p&gt;&lt;ul&gt;&lt;li&gt;[1] https://www.ufz.de/mhm&lt;/li&gt; &lt;li&gt;[2] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008WR007327&lt;/li&gt; &lt;li&gt;[3] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR012195&lt;/li&gt; &lt;li&gt;[4] https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803&lt;/li&gt; &lt;li&gt;[5] https://www.ufz.de/ulysses&lt;/li&gt; &lt;/ul&gt;

Evaluating Enhanced Hydrological Representations in Noah LSM over Transition Zones: Implications for Model Development

2009 ◽  
Vol 10 (3) ◽  
pp. 600-622 ◽  
Author(s):  
Enrique Rosero ◽  
Zong-Liang Yang ◽  
Lindsey E. Gulden ◽  
Guo-Yue Niu ◽  
David J. Gochis
Keyword(s):  
Land Surface ◽  
Soil Column ◽  
Model Development ◽  
Model Performance ◽  
Superior Performance ◽  
Surface Model ◽  
Multiple Model ◽  
Near Surface ◽  
Transition Zones ◽  
Soil Wetness

Abstract The authors introduce and compare the performance of the unified Noah land surface model (LSM) and its augments with physically based, more conceptually realistic hydrologic parameterizations. Forty-five days of 30-min data collected over nine sites in transition zones are used to evaluate (i) their benchmark, the standard Noah LSM release 2.7 (STD); (ii) a version equipped with a short-term phenology module (DV); and (iii) one that couples a lumped, unconfined aquifer model to the model soil column (GW). Their model intercomparison, enhanced by multiobjective calibration and model sensitivity analysis, shows that, under the evaluation conditions, the current set of enhancements to Noah fails to yield significant improvement in the accuracy of simulated, high-frequency, warm-season turbulent fluxes, and near-surface states across these sites. Qualitatively, the versions of DV and GW implemented degrade model robustness, as defined by the sensitivity of model performance to uncertain parameters. Quantitatively, calibrated DV and GW show only slight improvement in the skill of the model over calibrated STD. Then, multiple model realizations are compared to explicitly account for parameter uncertainty. Model performance, robustness, and fitness are quantified for use across varied sites. The authors show that the least complex benchmark LSM (STD) remains as the most fit version of the model for broad application. Although GW typically performs best when simulating evaporative fraction (EF), 24-h change in soil wetness (ΔW30), and soil wetness, it is only about half as robust as STD, which also performs relatively well for all three criteria. GW’s superior performance results from bias correction, not from improved soil moisture dynamics. DV performs better than STD in simulating EF and ΔW30 at the wettest site, because DV tends to enhance transpiration and canopy evaporation at the expense of direct soil evaporation. This same model structure limits performance at the driest site, where STD performs best. This dichotomous performance suggests that the formulations that determine the partitioning of LE flux need to be modified for broader applicability. Thus, this work poses a caveat for simple “plug and play” of functional modules between LSMs and showcases the utility of rigorous testing during model development.

scholarly journals Global land-surface evaporation estimated from satellite-based observations

2011 ◽  
Vol 15 (2) ◽  
pp. 453-469 ◽  
Author(s):  
D. G. Miralles ◽  
T. R. H. Holmes ◽  
R. A. M. De Jeu ◽  
J. H. Gash ◽  
A. G. C. A. Meesters ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Vegetation Density ◽  
Climate Conditions ◽  
New Strategy ◽  
Interception Loss ◽  
One Year ◽  
Daily Evaporation

Abstract. This paper outlines a new strategy to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale and 0.25 degree spatial resolution. Central to this methodology is the use of the Priestley and Taylor (PT) evaporation model. The minimalistic PT equation combines a small number of inputs, the majority of which can be detected from space. This reduces the number of variables that need to be modelled. Key distinguishing features of the approach are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the detailed estimation of rainfall interception loss. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (R=0.80, N=43) and present a low average bias (−5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of R=0.83, still lower than the R=0.90 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented.

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