scholarly journals Implications on atmospheric dynamics and the effect on black carbon transport into the Eurasian Arctic based on the choice of land surface model schemes and reanalysis data in model simulations with WRF

2016 ◽  
Author(s):  
Carolina Cavazos Guerra ◽  
Axel Lauer ◽  
Andreas B. Herber ◽  
Tim M. Butler ◽  
Annette Rinke ◽  
...  
Keyword(s):  
Atmospheric Circulation ◽  
Land Surface ◽  
Land Surface Model ◽  
Reanalysis Data ◽  
Atmospheric Dynamics ◽  
The Arctic ◽  
Surface Model ◽  
Vertical Profiles ◽  
Model Simulations ◽  
Arctic Atmosphere

Abstract. A realistic simulation of physical and dynamical processes in the Arctic atmosphere and its feedbacks with the surface conditions is still a challenge for state-of-the-art Arctic climate models. This is of critical importance because studies of, for example, transport of pollutants from middle latitudes into the Arctic rely on the skill of the model in correctly representing atmospheric circulation including the key mechanisms and pathways of pollutant transport. In this work the performance of the Weather Research and Forecast model (WRF) with two land surface model schemes (Noah and NoahMP) and two reanalysis data sets for creation of lateral boundary conditions (ERA-interim and ASR) is evaluated focusing on meteorological surface properties and atmospheric dynamics. This includes the position and displacement of the polar dome and other features characterizing atmospheric circulation associated to sea ice maxima/minima extent within the Eurasian Arctic. The model simulations analyzed are carried out at 15-km horizontal resolution over a period of five years (2008 to 2012). The WRF model simulations are evaluated against surface meteorological data from automated weather stations and vertical profiles from radiosondes. Results show that the model is able to reproduce the main features of the atmospheric dynamics and vertical structure of the Arctic atmosphere reasonably well. The influence of the choice of the reanalyses used as initial and lateral boundary condition and of the LSM on the model results is complex and no combination is found to be clearly superior in all variables analyzed. The model results show that a more sophisticated formulation of land surface processes does not necessarily lead to significant improvements in the model results. This suggests that other factors such as the decline of the Arctic sea ice, stratosphere-troposphere interactions, atmosphere-ocean interaction, and boundary layer processes are also highly important and can have a significant influence on the model results. The “best” configuration for simulating Arctic meteorology and processes most relevant for pollutant transport (ASR + NoahMP) is then used in a simulation with WRF including aerosols and chemistry (WRF-Chem) to simulate black carbon (BC) concentrations in and around the Arctic and to assess the role of the modeled atmospheric circulation in the simulated BC concentrations inside the Arctic domain. Results from simulations with chemistry are evaluated against aerosol optical depth from several Aeronet stations and BC concentrations and particle number concentrations from several stations from the EBAS database. The results with WRF-Chem show a strong dependency of the simulated BC concentration on the modeled meteorology and the transport of the pollutants around our domain. The results also show that biases in the modeled BC concentrations can also be related to the emission data. Significant improvements of the models and of our understanding of the impact of anthropogenic BC emissions on the Arctic strongly depends on the availability of suitable, long-term observational data of concentrations of BC and particulate matter, vertical profiles of temperature and humidity and wind.

scholarly journals Future projection of greenhouse gas emissions due to permafrost degradation using a simple numerical scheme with a global land surface model

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tokuta Yokohata ◽  
Kazuyuki Saito ◽  
Akihiko Ito ◽  
Hiroshi Ohno ◽  
Katsumasa Tanaka ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Land Surface ◽  
Ghg Emissions ◽  
Land Surface Model ◽  
Land Development ◽  
The Arctic ◽  
Surface Model ◽  
Permafrost Degradation ◽  
Model Simulations ◽  
Dynamic Degradation

Abstract The Yedoma layer, a permafrost layer containing a massive amount of underground ice in the Arctic regions, is reported to be rapidly thawing. In this study, we develop the Permafrost Degradation and Greenhouse gasses Emission Model (PDGEM), which describes the thawing of the Arctic permafrost including the Yedoma layer due to climate change and the greenhouse gas (GHG) emissions. The PDGEM includes the processes by which high-concentration GHGs (CO2 and CH4) contained in the pores of the Yedoma layer are released directly by dynamic degradation, as well as the processes by which GHGs are released by the decomposition of organic matter in the Yedoma layer and other permafrost. Our model simulations show that the total GHG emissions from permafrost degradation in the RCP8.5 scenario was estimated to be 31-63 PgC for CO2 and 1261-2821 TgCH4 for CH4 (68th percentile of the perturbed model simulations, corresponding to a global average surface air temperature change of 0.05–0.11 °C), and 14-28 PgC for CO2 and 618-1341 TgCH4 for CH4 (0.03–0.07 °C) in the RCP2.6 scenario. GHG emissions resulting from the dynamic degradation of the Yedoma layer were estimated to be less than 1% of the total emissions from the permafrost in both scenarios, possibly because of the small area ratio of the Yedoma layer. An advantage of PDGEM is that geographical distributions of GHG emissions can be estimated by combining a state-of-the-art land surface model featuring detailed physical processes with a GHG release model using a simple scheme, enabling us to consider a broad range of uncertainty regarding model parameters. In regions with large GHG emissions due to permafrost thawing, it may be possible to help reduce GHG emissions by taking measures such as restraining land development.

scholarly journals Impacts of a Parameterization Deficiency on Offline and Coupled Land Surface Model Simulations

2003 ◽  
Vol 4 (5) ◽  
pp. 901-914 ◽  
Author(s):  
Yuqiong Liu ◽  
Luis A. Bastidas ◽  
Hoshin V. Gupta ◽  
Soroosh Sorooshian
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Simulations

scholarly journals Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia

Heliyon ◽  
2019 ◽  
Vol 5 (9) ◽  
pp. e02469 ◽  
Author(s):  
Achenafi Teklay ◽  
Yihun T. Dile ◽  
Dereje H. Asfaw ◽  
Haimanote K. Bayabil ◽  
Kibruyesfa Sisay
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Lake Tana ◽  
Model Simulations ◽  
Lake Tana Basin

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

2017 ◽  
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.

scholarly journals Evaluating Multiple WRF Configurations and Forcing over the Northern Patagonian Icecap (NPI) and Baker River Basin

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 815
Author(s):  
Marcelo Somos-Valenzuela ◽  
Francisco Manquehual-Cheuque
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Short Wave ◽  
Reanalysis Data ◽  
Wave Radiation ◽  
Surface Model ◽  
Nwp Model

The use of numerical weather prediction (NWP) model to dynamically downscale coarse climate reanalysis data allows for the capture of processes that are influenced by land cover and topographic features. Climate reanalysis downscaling is useful for hydrology modeling, where catchment processes happen on a spatial scale that is not represented in reanalysis models. Selecting proper parameterization in the NWP for downscaling is crucial to downscale the climate variables of interest. In this work, we are interested in identifying at least one combination of physics in the Weather Research Forecast (WRF) model that performs well in our area of study that covers the Baker River Basin and the Northern Patagonian Icecap (NPI) in the south of Chile. We used ERA-Interim reanalysis data to run WRF in twenty-four different combinations of physics for three years in a nested domain of 22.5 and 4.5 km with 34 vertical levels. From more to less confident, we found that, for the planetary boundary layer (PBL), the best option is to use YSU; for the land surface model (LSM), the best option is the five-Layer Thermal, RRTM for longwave, Dudhia for short wave radiation, and Thompson for the microphysics. In general, the model did well for temperature (average, minimum, maximum) for most of the observation points and configurations. Precipitation was good, but just a few configurations stood out (i.e., conf-9 and conf-10). Surface pressure and Relative Humidity results were not good or bad, and it depends on the statistics with which we evaluate the time series (i.e., KGE or NSE). The results for wind speed were inferior; there was a warm bias in all of the stations. Once we identify the best configuration in our experiment, we run WRF for one year using ERA5 and FNL0832 climate reanalysis. Our results indicate that Era-interim provided better results for precipitation. In the case of temperature, FNL0832 gave better results; however, all of the models’ performances were good. Therefore, working with ERA-Interim seems the best option in this region with the physics selected. We did not experiment with changes in resolution, which may have improved results with ERA5 that has a better spatial and temporal resolution.

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

2017 ◽  
Vol 17 (13) ◽  
pp. 8371-8394 ◽  
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 ◽  
Atmospheric Methane ◽  
Multiple Sources ◽  
Atmospheric Measurements ◽  
Methane Cycle

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, in particular freshwater emissions which are often overlooked in methane modelling. 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. In particular, freshwater systems play a decisive part in summer, representing on average between 11 and 26 % of the simulated Arctic methane signal at the sites. 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 mixing ratios. The closest agreement with the observations is reached when using the two wetland models which have emissions peaking in August–September, while all others reach their maximum in June–July. Such phasing provides an interesting constraint on wetland models which still have large uncertainties at present. 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 methane. 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.

scholarly journals ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

2018 ◽  
Vol 22 (6) ◽  
pp. 3515-3532 ◽  
Author(s):  
Clement Albergel ◽  
Emanuel Dutra ◽  
Simon Munier ◽  
Jean-Christophe Calvet ◽  
Joaquin Munoz-Sabater ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Model Simulations ◽  
In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

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