scholarly journals Ability of a land surface model to predict climate induced changes in northern Russian river runoff during the 21st century

2015 ◽  
Vol 371 ◽  
pp. 59-64 ◽  
Author(s):  
O. N. Nasonova ◽  
Y. M. Gusev ◽  
E. M. Volodin ◽  
E. E. Kovalev
Keyword(s):  
River Runoff ◽  
Land Surface ◽  
Climate Model ◽  
Greenhouse Gas Emission ◽  
Global Climate ◽  
Global Climate Model ◽  
Land Surface Model ◽  
Climatic Conditions ◽  
Surface Model ◽  
Russian River

Abstract. The objective of the present study is application of the land surface model SWAP to project climate change impact on northern Russian river runoff up to 2100 using meteorological projections from the atmosphere–ocean global climate model INMCM4.0. The study was performed for the Northern Dvina River and the Kolyma River characterized by different climatic conditions. The ability of both models to reproduce the observed river runoff was investigated. To apply SWAP for hydrological projections, the robustness of the model was evaluated. The river runoff projections up to 2100 were calculated for two greenhouse gas emission scenarios: RCP8.5 and RCP4.5 prepared for the phase five of the Coupled Model Intercomparison Project (CMIP5). For each scenario, several runoff projections were obtained using different models (INMCM4.0 and SWAP) and different post-processing techniques for correcting biases in meteorological forcing data. Differences among the runoff projections obtained for the same emission scenario and the same period illustrate uncertainties resulted from application of different models and bias-correcting techniques.

scholarly journals The effect of GCM biases on global runoff simulations of a land surface model

2017 ◽  
Author(s):  
Lamprini V. Papadimitriou ◽  
Aristeidis G. Koutroulis ◽  
Manolis G. Grillakis ◽  
Ioannis K. Tsanis
Keyword(s):  
Bias Correction ◽  
Land Surface ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. Global Climate Model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However most state-of-art hydrological models require more forcing variables, additionally to precipitation and temperature, such as radiation, humidity, air pressure and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the land surface model JULES set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four Effect Categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

scholarly journals The effect of GCM biases on global runoff simulations of a land surface model

2017 ◽  
Vol 21 (9) ◽  
pp. 4379-4401 ◽  
Author(s):  
Lamprini V. Papadimitriou ◽  
Aristeidis G. Koutroulis ◽  
Manolis G. Grillakis ◽  
Ioannis K. Tsanis
Keyword(s):  
Bias Correction ◽  
Land Surface ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

High-Resolution Regional Climate Simulations of Arctic Hydroclimatic Change

2021 ◽  
Author(s):  
Andrew Newman ◽  
Yifan Cheng ◽  
Keith Musselman ◽  
Anthony Craig ◽  
Sean Swenson ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Water Cycle ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Land Surface Model ◽  
The Arctic ◽  
Surface Model ◽  
Systems Model

<p>The Arctic has warmed during the recent observational record and is projected to keep warming through the end of the 21<sup>st</sup> century in nearly every future emissions scenario and global climate model. This will drive continued thawing of permafrost-rich soils, alter the partitioning of rain versus snow events, and greatly affectthe water cycle and land-surface processes across the Arctic. However, previous analyses of these impacts using dynamical models have relied on global climate model output or relatively coarse regional climate model simulations. In the coarse simulations, projections of changes to the water cycle and land-surface processes in areas of complex orography and high land-surface heterogeneity, which are characteristic of many regions in the Arctic, may thus be limited. </p><p>Here, we discuss recent work examining high-resolution regional climate simulations over Alaska and NW Canada. Completed and upcoming simulations have been and will be run at a 4 km grid spacing, which is sufficient to resolve orography across this region’s mountain ranges. The initial simulation results are very encouraging and show the regional climate model yields a realistic representation of the seasonal and spatial evolution of precipitation, temperature, and snowpack compared to previous studies across Alaska and other Arctic regions. A paired future climate simulation uses the Pseudo-Global Warming (PGW) approach, where the end of century ensemble mean monthly climate perturbations (CMIP5 RCP8.5) are used to incorporate the thermodynamic effects of future warming into the present-day climate as represented by ERA-Interim reanalysis data. Changes in major components of the hydroclimate (e.g. precipitation, temperature, snowfall, snowpack) are projected to sometimes be significant in this future scenario. For example, the seasonal snow cover in some regions is projected to mostly disappear. However, there are also projected increases in snowpack in historically very cold areas (e.g. high elevations) that are able to stay cold enough in the future to support snowfall and snowpack.</p><p>Finally, we will present a new effort to couple an advanced land-surface model, the Community Terrestrial Systems Model (CTSM), within the Regional Arctic Systems Model (RASM) in an effort to better represent complex land-surface and subsurface (e.g. permafrost, streamflow availability timing and temperatures) processes for climate change impact studies. CTSM is a complex physically based land-surface model that is able to represent multiple snow layers, a complex canopy, and multiple soil layers including organic matter and frozen soils, which enables us to explicitly represent spatial variability in the regional hydroclimate and land states (e.g. permafrost) at relatively high spatial resolutions relative to other simulations (4 km land and atmosphere grids).  Successful coupling of CTSM within RASM has been completed and we will discuss some preliminary land-atmosphere coupled test results.</p>

scholarly journals An Urban Parameterization for a Global Climate Model. Part I: Formulation and Evaluation for Two Cities

2008 ◽  
Vol 47 (4) ◽  
pp. 1038-1060 ◽  
Author(s):  
K. W. Oleson ◽  
G. B. Bonan ◽  
J. Feddema ◽  
M. Vertenstein ◽  
C. S. B. Grimmond
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Surface ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Model ◽  
Land Use Land Cover ◽  
Community Land Model

Abstract Urbanization, the expansion of built-up areas, is an important yet less-studied aspect of land use/land cover change in climate science. To date, most global climate models used to evaluate effects of land use/land cover change on climate do not include an urban parameterization. Here, the authors describe the formulation and evaluation of a parameterization of urban areas that is incorporated into the Community Land Model, the land surface component of the Community Climate System Model. The model is designed to be simple enough to be compatible with structural and computational constraints of a land surface model coupled to a global climate model yet complex enough to explore physically based processes known to be important in determining urban climatology. The city representation is based upon the “urban canyon” concept, which consists of roofs, sunlit and shaded walls, and canyon floor. The canyon floor is divided into pervious (e.g., residential lawns, parks) and impervious (e.g., roads, parking lots, sidewalks) fractions. Trapping of longwave radiation by canyon surfaces and solar radiation absorption and reflection is determined by accounting for multiple reflections. Separate energy balances and surface temperatures are determined for each canyon facet. A one-dimensional heat conduction equation is solved numerically for a 10-layer column to determine conduction fluxes into and out of canyon surfaces. Model performance is evaluated against measured fluxes and temperatures from two urban sites. Results indicate the model does a reasonable job of simulating the energy balance of cities.

scholarly journals Simulating Hydrological Impacts under Climate Change: Implications from Methodological Differences of a Pan European Assessment

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1331 ◽  
Author(s):  
Aristeidis Koutroulis ◽  
Lamprini Papadimitriou ◽  
Manolis Grillakis ◽  
Ioannis Tsanis ◽  
Klaus Wyser ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Model ◽  
Climate Projections ◽  
European Continent ◽  
Hydrological Changes ◽  
Hydrological Impacts

The simulation of hydrological impacts in a changing climate remains one of the main challenges of the earth system sciences. Impact assessments can be, in many cases, laborious processes leading to inevitable methodological compromises that drastically affect the robustness of the conclusions. In this study we examine the implications of different CMIP5-based regional and global climate model ensembles for projections of the hydrological impacts of climate change. We compare results from three different assessments of hydrological impacts under high-end climate change (RCP8.5) across Europe, and we focus on how methodological differences affect the projections. We assess, as systematically as possible, the differences in runoff projections as simulated by a land surface model driven by three different sets of climate projections over the European continent at global warming of 1.5 °C, 2 °C and 4 °C relative to pre-industrial levels, according to the RCP8.5 concentration scenario. We find that these methodological differences lead to considerably different outputs for a number of indicators used to express different aspects of runoff. We further use a number of new global climate model experiments, with an emphasis on high resolution, to test the assumption that many of the uncertainties in regional climate and hydrological changes are driven predominantly by the prescribed sea surface temperatures (SSTs) and sea-ice concentrations (SICs) and we find that results are more sensitive to the choice of the atmosphere model compared to the driving SSTs. Finally, we combine all sources of information to identify robust patterns of hydrological changes across the European continent.

Quantifying the biologically possible range of steady-state soil and surface climates with climate model simulations

Biologia ◽  
2006 ◽  
Vol 61 (19) ◽  
Author(s):  
Axel Kleidon
Keyword(s):  
Surface Energy ◽  
Land Surface ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Climatic Conditions ◽  
Terrestrial Biosphere ◽  
Form And Function ◽  
Climate Model Simulations ◽  
Vegetation Form

AbstractThe terrestrial biosphere shapes the exchange fluxes of energy and mass at the land surface. The diversity of plant form and functioning can potentially result in a wide variety of possible climatic conditions at the land surface and in the soil, which in turn feed back to more or less suitable conditions for terrestrial productivity. Here, I use sensitivity simulations to vegetation form and functioning with a global climate model to quantify this possible range of steady-states (“PROSS”) of the surface energy-and mass balances. The surface energy-and water balances over land are associated with substantial sensitivity to vegetation parameters, with precipitation varying by more than a factor of 2, and evapotranspiration by a factor of 5. This range in biologically possible climatic conditions is associated with drastically different levels of vegetation productivity. Optimum conditions for maximum productivity are close to the simulated climate of present-day conditions. These results suggest the conclusions that (a) climate does not determine vegetation form and function, but merely constrains it, and (b) the emergent climatic conditions at the land surface seem to be close to optimal for the functioning of the terrestrial biosphere.

scholarly journals Reinitialised versus continuous regional climate simulations using ALARO-0 coupled to the land surface model SURFEXv5

2017 ◽  
Vol 10 (1) ◽  
pp. 223-238 ◽  
Author(s):  
Julie Berckmans ◽  
Olivier Giot ◽  
Rozemien De Troch ◽  
Rafiq Hamdi ◽  
Reinhart Ceulemans ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Global Climate Model ◽  
Regional Climate ◽  
Land Surface Model ◽  
Strong Interactions ◽  
Surface Model ◽  
Alternative Approach

Abstract. Dynamical downscaling in a continuous approach using initial and boundary conditions from a reanalysis or a global climate model is a common method for simulating the regional climate. The simulation potential can be improved by applying an alternative approach of reinitialising the atmosphere, combined with either a daily reinitialised or a continuous land surface. We evaluated the dependence of the simulation potential on the running mode of the regional climate model ALARO coupled to the land surface model Météo-France SURFace EXternalisée (SURFEX), and driven by the ERA-Interim reanalysis. Three types of downscaling simulations were carried out for a 10-year period from 1991 to 2000, over a western European domain at 20 km horizontal resolution: (1) a continuous simulation of both the atmosphere and the land surface, (2) a simulation with daily reinitialisations for both the atmosphere and the land surface and (3) a simulation with daily reinitialisations of the atmosphere while the land surface is kept continuous. The results showed that the daily reinitialisation of the atmosphere improved the simulation of the 2 m temperature for all seasons. It revealed a neutral impact on the daily precipitation totals during winter, but the results were improved for the summer when the land surface was kept continuous. The behaviour of the three model configurations varied among different climatic regimes. Their seasonal cycle for the 2 m temperature and daily precipitation totals was very similar for a Mediterranean climate, but more variable for temperate and continental climate regimes. Commonly, the summer climate is characterised by strong interactions between the atmosphere and the land surface. The results for summer demonstrated that the use of a daily reinitialised atmosphere improved the representation of the partitioning of the surface energy fluxes. Therefore, we recommend using the alternative approach of the daily reinitialisation of the atmosphere for the simulation of the regional climate.

QA4SM – An online tool for satellite soil moisture data validation

2021 ◽  
Author(s):  
Samuel Scherrer ◽  
Wolfgang Preimesberger ◽  
Monika Tercjak ◽  
Zoltan Bakcsa ◽  
Alexander Boresch ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Global Climate ◽  
European Space Agency ◽  
Land Surface Model ◽  
Surface Model ◽  
Space Applications ◽  
Online Tool ◽  
Soil Moisture Data

<p>To validate satellite soil moisture products and compare their quality with other products, standardized, fully traceable validation methods are required. The QA4SM (Quality Assurance for Soil Moisture; ) free online validation tool provides an easy-to-use implementation of community best practices and requirements set by the Global Climate Observing System and the Committee on Earth Observation Satellites. It sets the basis for a community wide standard for validation studies.</p><p>QA4SM can be used to preprocess, intercompare, store, and visualise validation results. It uses state-of-the-art open-access soil moisture data records such as the European Space Agency’s Climate Change Initiative (ESA CCI) and the Copernicus Climate Change Services (C3S) soil moisture datasets, as well as single-sensor products, e.g. H-SAF Metop-A/B ASCAT surface soil moisture, SMOS-IC, and SMAP L3 soil moisture. Non-satellite data include in-situ data from the International Soil Moisture Network (ISMN: ), as well as land surface model or reanalysis products, e.g. ERA5 soil moisture.</p><p>Users can interactively choose temporal or spatial subsets of the data and apply filters on quality flags. Additionally, validation of anomalies and application of different scaling methods are possible. The tool provides traditional validation metrics for dataset pairs (e.g. correlation, RMSD) as well as triple collocation metrics for dataset triples. All results can be visualised on the webpage, downloaded as figures, or downloaded in NetCDF format for further use. Archiving and publishing features allow users to easily store and share validation results. Published validation results can be cited in reports and publications via DOIs.</p><p>The new version of the service provides support for high-resolution soil moisture products (from Sentinel-1), additional datasets, and improved usability.</p><p>We present an overview and examples of the online tool, new features, and give an outlook on future developments.</p><p><em>Acknowledgements: This work was supported by the QA4SM & QA4SM-HR projects, funded by the Austrian Space Applications Programme (FFG).</em></p>

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