Impact of land surface roughness on downslope windstorm modelling in the Arctic

Abstract. Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest in the estimation of the surface turbulent flux parameterisation from passive remote sensing leads to an increasing development of models, and the common use of simple semi-empirical formulations to estimate surface roughness. Over complex surface land cover, these approaches would benefit from the combined use of passive remote sensing and land surface structure measurements from Light Detection And Ranging (LIDAR) techniques. Following early studies based on LIDAR profile data, this paper explores the use of imaging LIDAR measurements for the estimation of the aerodynamic roughness length over a heterogeneous landscape of the Heihe river basin, a typical inland river basin in the northwest of China. The point cloud obtained from multiple flight passes over an irrigated farmland area were used to separate the land surface topography and the vegetation canopy into a Digital Elevation Model (DEM) and a Digital Surface Model (DSM) respectively. These two models were then incorporated in two approaches: (i) a strictly geometrical approach based on the calculation of the plan surface density and the frontal surface density to derive a geometrical surface roughness; (ii) a more aerodynamic approach where both the DEM and DSM are introduced in a Computational Fluid Dynamics model (CFD). The inversion of the resulting 3-D wind field leads to a fine representation of the aerodynamic surface roughness. Examples of the use of these three approaches are presented for various wind directions together with a cross-comparison of results on heterogeneous land cover and complex roughness element structures.

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Halogenated Greenhouse Gases Made Global Warming Primarily

10.21203/rs.3.rs-1230021/v1 ◽

2022 ◽

Author(s):

Qing-Bin Lu

Keyword(s):

Sea Ice ◽

Greenhouse Gases ◽

Land Surface ◽

Southern Oscillation ◽

Theoretical Calculations ◽

Global Climate ◽

Surface Air Temperature ◽

The Arctic ◽

Sea Ice Extent ◽

Snow Cover Extent

Abstract Time-series observations of global lower stratospheric temperature (GLST), global land surface air temperature (LSAT), global mean surface temperature (GMST), sea ice extent (SIE) and snow cover extent (SCE), together with observations reported in Paper I, combined with theoretical calculations of GLSTs and GMSTs, have provided strong evidence that ozone depletion and global climate changes are dominantly caused by human-made halogen-containing ozone-depleting substances (ODSs) and greenhouse gases (GHGs) respectively. Both GLST and SCE have become constant since the mid-1990s and GMST/LSAT has reached a peak since the mid-2000s, while regional continued warmings at the Arctic coasts (particularly Russia and Alaska) in winter and spring and at some areas of Antarctica are observed and can be well explained by a sea-ice-loss warming amplification mechanism. The calculated GMSTs by the parameter-free warming theory of halogenated GHGs show an excellent agreement with the observed GMSTs after the natural El Niño southern oscillation (ENSO) and volcanic effects are removed. These results provide a convincing mechanism of global climate change and will make profound changes in our understanding of atmospheric processes. This study also emphasizes the critical importance of continued international efforts in phasing out all anthropogenic halogenated ODSs and GHGs.

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

The Cryosphere Discussions ◽

10.5194/tcd-7-2333-2013 ◽

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.

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An assessment of the carbon balance of arctic tundra: comparisons among observations, process models, and atmospheric inversions

Biogeosciences Discussions ◽

10.5194/bgd-9-4543-2012 ◽

2012 ◽

Vol 9 (4) ◽

pp. 4543-4594 ◽

Cited By ~ 13

Author(s):

A. D. McGuire ◽

T. R. Christensen ◽

D. Hayes ◽

A. Heroult ◽

E. Euskirchen ◽

...

Keyword(s):

Land Surface ◽

Arctic Tundra ◽

Process Models ◽

Atmospheric Co2 ◽

The Arctic ◽

Uncertainty Estimates ◽

Effective Transfer ◽

And Inversion ◽

Process Based Models ◽

Observation Networks

Abstract. Although arctic tundra has been estimated to cover only 8% of the global land surface, the large and potentially labile carbon pools currently stored in tundra soils have the potential for large emissions of carbon (C) under a warming climate. These emissions as radiatively active greenhouse gases in the form of both CO2 and CH4 could amplify global warming. Given the potential sensitivity of these ecosystems to climate change and the expectation that the Arctic will experience appreciable warming over the next century, it is important to assess whether responses of C exchange in tundra regions are likely to enhance or mitigate warming. In this study we compared analyses of C exchange of Arctic tundra between 1990–1999 and 2000–2006 among observations, regional and global applications of process-based terrestrial biosphere models, and atmospheric inversion models. Syntheses of the compilation of flux observations and of inversion model results indicate that the annual exchange of CO2 between arctic tundra and the atmosphere has large uncertainties that cannot be distinguished from neutral balance. The mean estimate from an ensemble of process-based model simulations suggests that arctic tundra acted as a sink for atmospheric CO2 in recent decades, but based on the uncertainty estimates it cannot be determined with confidence whether these ecosystems represent a weak or a strong sink. Tundra was 0.6 °C warmer in the 2000s compared to the 1990s. The central estimates of the observations, process-based models, and inversion models each identify stronger sinks in the 2000s compared with the 1990s. Similarly, the observations and the applications of regional process-based models suggest that CH4 emissions from arctic tundra have increased from the 1990s to 2000s. Based on our analyses of the estimates from observations, process-based models, and inversion models, we estimate that arctic tundra was a sink for atmospheric CO2 of 110 Tg C yr−1 (uncertainty between a sink of 291 Tg C yr−1 and a source of 80 Tg C yr−1) and a source of CH4 to the atmosphere of 19 Tg C yr−1 (uncertainty between sources of 8 and 29 Tg C yr−1). The suite of analyses conducted in this study indicate that it is clearly important to reduce uncertainties in the observations, process-based models, and inversions in order to better understand the degree to which Arctic tundra is influencing atmospheric CO2 and CH4 concentrations. The reduction of uncertainties can be accomplished through (1) the strategic placement of more CO2 and CH4 monitoring stations to reduce uncertainties in inversions, (2) improved observation networks of ground-based measurements of CO2 and CH4 exchange to understand exchange in response to disturbance and across gradients of hydrological variability, and (3) the effective transfer of information from enhanced observation networks into process-based models to improve the simulation of CO2 and CH4 exchange from arctic tundra to the atmosphere.

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On the role of Eurasian autumn snow cover in dynamical seasonal predictions

10.5194/ems2021-192 ◽

2021 ◽

Author(s):

Paolo Ruggieri ◽

Marianna Benassi ◽

Stefano Materia ◽

Daniele Peano ◽

Constantin Ardilouze ◽

...

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Land Surface ◽

General Circulation ◽

Dominant Role ◽

General Circulation Models ◽

The Arctic ◽

Seasonal Forecasts ◽

Eurasian Snow

<p>Seasonal climate predictions leverage on many predictable or persistent components of the Earth system that can modify the state of the atmosphere and of relant weather related variable such as temprature and precipitation. With a dominant role of the ocean, the land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between land surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere both locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been investigated and documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of Autumn Eurasian snow in recent dynamical seasonal forecasts is therefore unclear. In this study we assess the role of Eurasian snow cover in a set of 5 operational seasonal forecast system characterized by a large ensemble size and a high atmospheric and oceanic resolution. Results are compemented with a set of targeted idealised simulations with atmospheric general circulation models forced by different snow cover conditions. Forecast systems reproduce realistically regional changes of the surface energy balance associated with snow cover variability. Retrospective forecasts and idealised sensitivity experiments converge in identifying a coherent change of the circulation in the Northern Hemisphere. This is compatible with a lagged but fast feedback from the snow to the Arctic Oscillation trough a tropospheric pathway.</p>

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The CIMR mission and its unique capabilities for soil moisture sensing

10.5194/egusphere-egu21-9484 ◽

2021 ◽

Author(s):

Maria Piles ◽

Roberto Fernandez-Moran ◽

Luis Gómez-Chova ◽

Gustau Camps-Valls ◽

Dara Entekhabi ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Spatial Scales ◽

Microwave Radiometer ◽

Critical Time ◽

Global Scale ◽

The Arctic ◽

Temporal Perspective ◽

Microwave Radiometers ◽

Moisture Sensing

<p>The Copernicus Imaging Microwave Radiometer (CIMR) mission is currently being developed as a High Priority Copernicus Mission to support the Integrated European Policy for the Arctic. Due to its measurement characteristics, CIMR has exciting capabilities to enable a unique set of land surface products and science applications at a global scale. These characteristics go beyond what previous microwave radiometers (e.g. AMSR series, SMAP and SMOS) provide, and therefore allow for entirely new approaches to the estimation of bio-geophysical products from brightness temperature observations. Most notably, CIMR channels (L-,C-,X-,Ka-,Ku-bands) are very well fit for the simultaneous retrieval of soil moisture and vegetation properties, like biomass and moisture of different plant components such as leaves, stems or trunks. Also, the distinct spatial resolution of each frequency band allows for the development of approaches to cascade information and obtain these properties at multiple spatial scales. From a temporal perspective, CIMR has a higher revisit time than previous L-band missions dedicated to soil moisture monitoring (about 1 day global, sub-daily at the poles). This improved temporal resolution could allow resolving critical time scales of water processes, which is relevant to better model and understand land-atmosphere exchanges and feedbacks. In this presentation, new opportunities for soil moisture remote sensing made possible by the CIMR mission, as well as synergies and cross-sensor opportunities will be discussed.&#160;&#160;</p>

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Simulation of permafrost and seasonal thaw depth in the JULES land surface scheme

The Cryosphere ◽

10.5194/tc-5-773-2011 ◽

2011 ◽

Vol 5 (3) ◽

pp. 773-790 ◽

Cited By ~ 59

Author(s):

R. Dankers ◽

E. J. Burke ◽

J. Price

Keyword(s):

Land Surface ◽

The Arctic ◽

Soil Parameters ◽

Active Layer Thickness ◽

Organic Carbon Content ◽

Land Surface Scheme ◽

Spatial Coverage ◽

Soil Temperatures ◽

Thaw Depth ◽

Surface Scheme

Abstract. Land surface models (LSMs) need to be able to simulate realistically the dynamics of permafrost and frozen ground. In this paper we evaluate the performance of the LSM JULES (Joint UK Land Environment Simulator), the stand-alone version of the land surface scheme used in Hadley Centre climate models, in simulating the large-scale distribution of surface permafrost. In particular we look at how well the model is able to simulate the seasonal thaw depth or active layer thickness (ALT). We performed a number of experiments driven by observation-based climate datasets. Visually there is a very good agreement between areas with permafrost in JULES and known permafrost distribution in the Northern Hemisphere, and the model captures 97% of the area where the spatial coverage of the permafrost is at least 50%. However, the model overestimates the total extent as it also simulates permafrost where it occurs sporadically or only in isolated patches. Consistent with this we find a cold bias in the simulated soil temperatures, especially in winter. However, when compared with observations on end-of-season thaw depth from around the Arctic, the ALT in JULES is generally too deep. Additional runs at three sites in Alaska demonstrate how uncertainties in the precipitation input affect the simulation of soil temperatures by affecting the thickness of the snowpack and therefore the thermal insulation in winter. In addition, changes in soil moisture content influence the thermodynamics of soil layers close to freezing. We also present results from three experiments in which the standard model setup was modified to improve physical realism of the simulations in permafrost regions. Extending the soil column to a depth of 60 m and adjusting the soil parameters for organic content had relatively little effect on the simulation of permafrost and ALT. A higher vertical resolution improves the simulation of ALT, although a considerable bias still remains. Future model development in JULES should focus on a dynamic coupling of soil organic carbon content and soil thermal and hydraulic properties, as well as allowing for sub-grid variability in soil types.

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The influence of climate and hydrological variables on opposite anomaly in active-layer thickness between Eurasian and North American watersheds

The Cryosphere ◽

10.5194/tc-7-631-2013 ◽

2013 ◽

Vol 7 (2) ◽

pp. 631-645 ◽

Cited By ~ 29

Author(s):

H. Park ◽

J. Walsh ◽

A. N. Fedorov ◽

A. B. Sherstiukov ◽

Y. Iijima ◽

...

Keyword(s):

Layer Thickness ◽

Active Layer ◽

North American ◽

Land Surface ◽

Snow Depth ◽

The Arctic ◽

Surface Model ◽

Active Layer Thickness ◽

Hydrological Variables ◽

Permafrost Regions

Abstract. This study not only examined the spatiotemporal variations of active-layer thickness (ALT) in permafrost regions during 1948–2006 over the terrestrial Arctic regions experiencing climate changes, but also identified the associated drivers based on observational data and a simulation conducted by a land surface model (CHANGE). The focus on the ALT extends previous studies that have emphasized ground temperatures in permafrost regions. The Ob, Yenisey, Lena, Yukon, and Mackenzie watersheds are foci of the study. Time series of ALT in Eurasian watersheds showed generally increasing trends, while the increase in ALT in North American watersheds was not significant. However, ALT in the North American watersheds has been negatively anomalous since 1990 when the Arctic air temperature entered into a warming phase. The warming temperatures were not simply expressed to increases in ALT. Since 1990 when the warming increased, the forcing of the ALT by the higher annual thawing index (ATI) in the Mackenzie and Yukon basins has been offset by the combined effects of less insulation caused by thinner snow depth and drier soil during summer. In contrast, the increasing ATI together with thicker snow depth and higher summer soil moisture in the Lena contributed to the increase in ALT. The results imply that the soil thermal and moisture regimes formed in the pre-thaw season(s) provide memory that manifests itself during the summer. The different ALT anomalies between Eurasian and North American watersheds highlight increased importance of the variability of hydrological variables.

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Detectability of Arctic methane sources at six sites performing continuous atmospheric measurements

10.5194/acp-2017-169 ◽

2017 ◽

Cited By ~ 1

Author(s):

Thibaud Thonat ◽

Marielle Saunois ◽

Philippe Bousquet ◽

Isabelle Pison ◽

Zeli Tan ◽

...

Keyword(s):

Land Surface ◽

Transport Model ◽

Land Surface Model ◽

Arctic Region ◽

The Arctic ◽

Surface Model ◽

Multiple Sources ◽

Atmospheric Measurements ◽

Methane Cycle ◽

Methane Budget

Abstract. Understanding the recent evolution of methane emissions in the Arctic is necessary to interpret the global methane cycle. Emissions are affected by significant uncertainties and are sensitive to climate change, leading to potential feedbacks. A polar version of the CHIMERE chemistry-transport model is used to simulate the evolution of tropospheric methane in the Arctic during 2012, including all known regional anthropogenic and natural sources. CHIMERE simulations are compared to atmospheric continuous observations at six measurement sites in the Arctic region. In winter, the Arctic is dominated by anthropogenic emissions; emissions from continental seepages and oceans, including from the East Siberian Arctic Shelf, can contribute significantly in more limited areas. In summer, emissions from wetland and freshwater sources dominate across the whole region. The model is able to reproduce the seasonality and synoptic variations of methane measured at the different sites. We find that all methane sources significantly affect the measurements at all stations at least at the synoptic scale, except for biomass burning; this indicates the relevance of continuous observations to gain a mechanistic understanding of Arctic methane sources. Sensitivity tests reveal that the choice of the land surface model used to prescribe wetland emissions can be critical in correctly representing methane concentrations. Also testing different freshwater emission inventories leads to large differences in modelled methane. Attempts to include methane sinks (OH oxidation and soil uptake) reduced the model bias relative to observed atmospheric CH4. The study illustrates how multiple sources, having different spatiotemporal dynamics and magnitudes, jointly influence the overall Arctic methane budget, and highlights ways towards further improved assessments.

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Accounting for Surface Roughness Scattering in the Characterization of Forest Litter with Ground-Penetrating Radar

Remote Sensing ◽

10.3390/rs11070828 ◽

2019 ◽

Vol 11 (7) ◽

pp. 828

Author(s):

Frédéric André ◽

François Jonard ◽

Mathieu Jonard ◽

Harry Vereecken ◽

Sébastien Lambot

Keyword(s):

Surface Roughness ◽

Ground Penetrating Radar ◽

Land Surface ◽

Forest Litter ◽

Relative Dielectric Permittivity ◽

In Situ Experiment ◽

In Situ Experiments ◽

Ground Penetrating

Accurate characterization of forest litter is of high interest for land surface modeling and for interpreting remote sensing observations over forested areas. Due to the large spatial heterogeneity of forest litter, scattering from litter layers has to be considered when sensed using microwave techniques. Here, we apply a full-waveform radar model combined with a surface roughness model to ultrawideband ground-penetrating radar (GPR) data acquired above forest litter during controlled and in situ experiments. For both experiments, the proposed modeling approach successfully described the radar data, with improvements compared to a previous study in which roughness was not directly accounted for. Inversion of the GPR data also provided reliable estimates of the relative dielectric permittivity of the recently fallen litter (OL layer) and of the fragmented litter in partial decomposition (OF layer) with, respectively, averaged values of 1.35 and 3.8 for the controlled experiment and of 3.9 and 7.5 for the in situ experiment. These results show the promising potentialities of GPR for efficient and non-invasive characterization of forest organic layers.

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