Role of Land-Surface Changes in Arctic Summer Warming

While the Budyko framework has been a simple and convenient tool to assess runoff (Q) responses to climatic and surface changes, it has been unclear how parameters of a Budyko function represent the vertical land-atmosphere interactions. Here, we explicitly derived a two-parameter equation by correcting a boundary condition of the Budyko hypothesis. The correction enabled for the Budyko function to reflect the evaporative demand (Ep) that actively responds to soil moisture deficiency. The derived two-parameter function suggests that four physical variables control surface runoff; namely, precipitation (P), potential evaporation (Ep), wet-environment evaporation (Ew), and the catchment properties (n). We linked the derived Budyko function to a definitive complementary evaporation principle, and assessed the relative elasticities of Q to climatic and land surface changes. Results showed that P is the primary control of runoff changes in most of river basins across the world, but its importance declined with climatological aridity. In arid river basins, the catchment properties play a major role in changing runoff, while changes in Ep and Ew seem to exert minor influences on Q changes. It was also found that the two-parameter Budyko function can capture unusual negative correlation between the mean annual Q and Ep. This work suggests that at least two parameters are required for a Budyko function to properly describe the vertical interactions between the land and the atmosphere.

Download Full-text

Evaluating the role of soil physical properties on simulated land-atmosphere interactions over South Africa using coupled atmosphere-hydrological modeling

10.5194/egusphere-egu21-9418 ◽

2021 ◽

Author(s):

Zhenyu Zhang ◽

Patrick Laux ◽

Joël Arnault ◽

Jianhui Wei ◽

Jussi Baade ◽

...

Keyword(s):

South Africa ◽

Physical Properties ◽

Land Degradation ◽

Land Surface ◽

Soil Physical Properties ◽

Near Surface ◽

Soil Database ◽

Global Soil ◽

The Impact

Land degradation with its direct impact on vegetation, surface soil layers and land surface albedo, has great relevance with the climate system. Assessing the climatic and ecological effects induced by land degradation requires a precise understanding of the interaction between the land surface and atmosphere. In coupled land-atmosphere modeling, the low boundary conditions impact the thermal and hydraulic exchanges at the land surface, therefore regulates the overlying atmosphere by land-atmosphere feedback processes. However, those land-atmosphere interactions are not convincingly represented in coupled land-atmosphere modeling applications. It is partly due to an approximate representation of hydrological processes in land surface modeling. Another source of uncertainties relates to the generalization of soil physical properties in the modeling system. This study focuses on the role of the prescribed physical properties of soil in high-resolution land surface-atmosphere simulations over South Africa. The model used here is the hydrologically-enhanced Weather Research and Forecasting (WRF-Hydro) model. Four commonly used global soil datasets obtained from UN Food and Agriculture Organization (FAO) soil database, Harmonized World Soil Database (HWSD), Global Soil Dataset for Earth System Model (GSDE), and SoilGrids dataset, are incorporated within the WRF-Hydro experiments for investigating the impact of soil information on land-atmosphere interactions. The simulation results of near-surface temperature, skin temperature, and surface energy fluxes are presented and compared to observational-based reference dataset. It is found that simulated soil moisture is largely influenced by soil texture features, which affects its feedback to the atmosphere.

Download Full-text

Clarifying the role of radiative mechanisms in the spatio-temporal changes of land surface temperature across the Horn of Africa

Remote Sensing of Environment ◽

10.1016/j.rse.2018.11.024 ◽

2019 ◽

Vol 221 ◽

pp. 210-224 ◽

Cited By ~ 11

Author(s):

Temesgen Alemayehu Abera ◽

Janne Heiskanen ◽

Petri Pellikka ◽

Miina Rautiainen ◽

Eduardo Eiji Maeda

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temporal Changes ◽

Horn Of Africa ◽

Spatio Temporal

Download Full-text

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

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.

Download Full-text

Thermal and moisture response to land surface changes across different ecosystems over Heilong-Amur River Basin

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.151799 ◽

2021 ◽

pp. 151799

Author(s):

Tingxiang Liu ◽

Lingxue Yu ◽

Kun Bu ◽

Jiuchun Yang ◽

Fengqin Yan ◽

...

Keyword(s):

River Basin ◽

Land Surface ◽

Amur River Basin ◽

Amur River ◽

Surface Changes

Download Full-text

Improvement of Clustering Methods for Modelling Abrupt Land Surface Changes in Satellite Image Fusions

Remote Sensing ◽

10.3390/rs11151759 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1759 ◽

Cited By ~ 4

Author(s):

Detang Zhong ◽

Fuqun Zhou

Keyword(s):

Land Surface ◽

Satellite Image ◽

Weighted Average ◽

Clustering Methods ◽

Fusion Model ◽

Dynamic Prediction ◽

New Methods ◽

Satellite Sensors ◽

Spectral Mixing ◽

Surface Changes

A key challenge in developing models for the fusion of surface reflectance data across multiple satellite sensors is ensuring that they apply to both gradual vegetation phenological dynamics and abrupt land surface changes. To better model land cover spatial and temporal changes, we proposed previously a Prediction Smooth Reflectance Fusion Model (PSRFM) that combines a dynamic prediction model based on the linear spectral mixing model with a smoothing filter corresponding to the weighted average of forward and backward temporal predictions. One of the significant advantages of PSRFM is that PSRFM can model abrupt land surface changes either through optimized clusters or the residuals of the predicted gradual changes. In this paper, we expanded our approach and developed more efficient methods for clustering. We applied the new methods for dramatic land surface changes caused by a flood and a forest fire. Comparison of the model outputs showed that the new methods can capture the land surface changes more effectively. We also compared the improved PSRFM to two most popular reflectance fusion algorithms: Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced version of STARFM (ESTARFM). The results showed that the improved PSRFM is more effective and outperforms STARFM and ESTARFM both visually and quantitatively.

Download Full-text