Natural and anthropogenic methane emissions in West Siberia estimated using a wetland inventory, GOSAT and a regional tower network

2020 ◽  
Author(s):  
Shamil Maksyutov ◽  
Motoki Sasakawa ◽  
Rajesh Janardanan ◽  
Fenjuan Wang ◽  
Aki Tsuruta ◽  
...  
Keyword(s):  
High Resolution ◽  
West Siberia ◽  
Methane Emissions ◽  
Inverse Model ◽  
Anthropogenic Emissions ◽  
Taiga Zone ◽  
Tracer Transport ◽  
Bottom Up ◽  
Near Surface

<p>West Siberia contributes a large fraction of Russian methane emissions, with both natural emissions from peatlands and anthropogenic emissions by oil and gas industries. To quantify anthropogenic emissions with atmospheric observations and inventories, we must better understand the natural wetland emissions.  We combine high-resolution wetland mapping based on Landsat data for whole West Siberian lowland with a database of in situ flux measurements to derive bottom-up wetland emission estimates. We use a global high-resolution methane flux inversion based on a Lagrangian-Eulerian coupled tracer transport model to estimate methane emissions in West Siberia using atmospheric methane data collected at the Siberian GHG monitoring network JR-STATION, ZOTTO, data by the global in situ network and GOSAT satellite observations. High-resolution prior fluxes were prepared for anthropogenic emissions (EDGAR), biomass burning (GFAS), and wetlands (VISIT). A global high-resolution wetland emission dataset was constructed using 0.5-degree monthly emission data simulated by the VISIT model and wetland area fraction map by the Global Lake and Wetlands Database (GLWD). We estimate biweekly flux corrections to prior flux fields for 2010 to 2015. The inverse model optimizes corrections to two categories of fluxes: anthropogenic and natural (wetlands). Based on fitting the model simulations to the observations, the inverse model provides upward corrections to West Siberian anthropogenic emissions in winter and wetland emissions in summer. The use of high-resolution atmospheric transport in the flux inversion, when compared to low-resolution transport modeling, enables a better fit to observations in winter, when anthropogenic emissions dominate variability of the near-surface methane concentration. We estimate 15% higher anthropogenic emissions than EDGAR v.4.3.2 inventory for whole Russia, with most of the correction attributed to West Siberia and the European part of Russia. Comparison of the inversion estimates with the bottom-up wetland emission inventory for West Siberia suggests a need to adjust the wetland emissions to match observed north-south gradient of emissions with higher emissions in the southern taiga zone.</p>

scholarly journals Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM – FLEXPART coupled transport model and its adjoint

2020 ◽  
Author(s):  
Shamil Maksyutov ◽  
Tomohiro Oda ◽  
Makoto Saito ◽  
Rajesh Janardanan ◽  
Dmitry Belikov ◽  
...  
Keyword(s):  
High Resolution ◽  
Transport Model ◽  
Dispersion Model ◽  
Surface Flux ◽  
Particle Dispersion ◽  
Anthropogenic Emissions ◽  
Coupled Transport ◽  
Tracer Transport ◽  
Flux Corrections

Abstract. We developed a high-resolution surface flux inversion system based on the global Lagrangian–Eulerian coupled tracer transport model composed of National Institute for Environmental Studies Transport Model (NIES-TM) and FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM-FLEXPART-variational) as it applies variational optimisation to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve best fit to atmospheric CO2 data collected by the global in-situ network, as a necessary step towards capability of estimating anthropogenic CO2 emissions. We employ the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate the surface flux footprints of CO2 observations at a 0.1° × 0.1° spatial resolution. The LPDM is coupled to a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator is being implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in-situ CO2 data included in the Observation Package (ObsPack) dataset. High-resolution prior flux fields were prepared using Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange and Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and separate simulation of CO2 fluxes at daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for terrestrial biosphere was scaled proportionally to the monthly mean Gross Primary Production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and vegetation productivity gradients. The resulting flux estimates improve fit to the observations at continuous observations sites, reproducing both the seasonal variation and short-term concentration variability, including high CO2 concentration events associated with anthropogenic emissions. The use of high-resolution atmospheric transport in global CO2 flux inversion has the advantage of better resolving the transport from the mix of the anthropogenic and biospheric sources in densely populated continental regions and shows potential for better separation between fluxes from terrestrial ecosystems and strong localised sources such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as the posterior fit is better than that by the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker only for a fraction of the monitoring sites, mostly at coastal and island locations experiencing mix of background and local flux signals.

scholarly journals Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations

2020 ◽  
Vol 12 (3) ◽  
pp. 375 ◽  
Author(s):  
Rajesh Janardanan ◽  
Shamil Maksyutov ◽  
Aki Tsuruta ◽  
Fenjuan Wang ◽  
Yogesh K. Tiwari ◽  
...  
Keyword(s):  
United States ◽  
High Resolution ◽  
The United States ◽  
Methane Emissions ◽  
Inverse Model ◽  
Anthropogenic Emissions ◽  
Observation Data ◽  
Scale Analysis ◽  
Uncertainty Range ◽  
Natural Emissions

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries.

scholarly journals Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint

2021 ◽  
Vol 21 (2) ◽  
pp. 1245-1266
Author(s):  
Shamil Maksyutov ◽  
Tomohiro Oda ◽  
Makoto Saito ◽  
Rajesh Janardanan ◽  
Dmitry Belikov ◽  
...  
Keyword(s):  
High Resolution ◽  
Transport Model ◽  
Dispersion Model ◽  
Surface Flux ◽  
Particle Dispersion ◽  
Anthropogenic Emissions ◽  
Coupled Transport ◽  
Tracer Transport ◽  
Flux Corrections

Abstract. We developed a high-resolution surface flux inversion system based on the global Eulerian–Lagrangian coupled tracer transport model composed of the National Institute for Environmental Studies (NIES) transport model (TM; collectively NIES-TM) and the FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM–FLEXPART-variational) as it applies a variational optimization to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve the best fit to atmospheric CO2 data collected by the global in situ network as a necessary step towards the capability of estimating anthropogenic CO2 emissions. We employed the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate surface flux footprints of CO2 observations at a spatial resolution of 0.1∘×0.1∘. The LPDM is coupled with a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator was implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in situ CO2 data included in the Observation Package (ObsPack) data set. High-resolution prior flux fields were prepared using the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, the Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange, and the Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and a separate simulation of CO2 fluxes at a daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for the terrestrial biosphere was scaled proportionally to the monthly mean gross primary production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and fine-scale vegetation productivity gradients. The resulting flux estimates improved the fit to the observations taken at continuous observation sites, reproducing both the seasonal and short-term concentration variabilities including high CO2 concentration events associated with anthropogenic emissions. The use of a high-resolution atmospheric transport in global CO2 flux inversions has the advantage of better resolving the transported mixed signals from the anthropogenic and biospheric sources in densely populated continental regions. Thus, it has the potential to achieve better separation between fluxes from terrestrial ecosystems and strong localized sources, such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as our posterior fit was better than that of the National Oceanic and Atmospheric Administration (NOAA)'s CarbonTracker for only a fraction of the monitoring sites, i.e. mostly at coastal and island locations where background and local flux signals are mixed.

Country scale analysis of natural and anthropogenic methane emissions with a high-resolution inverse model using GOSAT and ground-based observations

2020 ◽  
Author(s):  
Rajesh Janardanan ◽  
Shamil Maksyutov ◽  
Aki Tsuruta ◽  
Fenjuan Wang ◽  
Yogesh Tiwari ◽  
...  
Keyword(s):  
United States ◽  
High Resolution ◽  
The United States ◽  
Methane Emissions ◽  
Inverse Model ◽  
Anthropogenic Emissions ◽  
Observation Data ◽  
Scale Analysis ◽  
Uncertainty Range ◽  
Natural Emissions

&lt;p&gt;Here, we present the results of a global high-resolution inversion study of methane emissions and their analysis for the large emitting countries. We employ a global high-resolution inverse model to optimize CH&lt;sub&gt;4&lt;/sub&gt; emissions using Greenhouse gas Observing Satellite (GOSAT) and surface observation data over the 2011-2017 period for the two main source categories of anthropogenic and natural emissions. As prior emissions, we used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission, scaled by country to match the national emissions reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimate a global total anthropogenic and natural methane emissions of 340.9 Tg CH&lt;sub&gt;4&lt;/sub&gt; yr&lt;sup&gt;-1&lt;/sup&gt; and 232.5 Tg CH&lt;sub&gt;4&lt;/sub&gt; yr&lt;sup&gt;-1&lt;/sup&gt;, respectively. This agrees with recent Global Carbon Project (GCP) estimates of 357 and 215 Tg CH&lt;sub&gt;4&lt;/sub&gt; yr&lt;sup&gt;-1&lt;/sup&gt;, respectively. Country-scale analysis of the estimated anthropogenic emissions shows that for all the top-emitting countries, differences with their respective nationally reported inventories are within the uncertainty range of the inventories. Large emitting countries such as China, Russia and the United States have mean estimated anthropogenic emission of 45.7&amp;#177;8.6, 31.9&amp;#177;7.8 and 29.8&amp;#177;7.8 Tg CH&lt;sub&gt;4&lt;/sub&gt; yr&lt;sup&gt;-1 &lt;/sup&gt;respectively. For natural emissions, we estimate large emissions for Brazil (39.8&amp;#177;12.4 Tg CH&lt;sub&gt;4&lt;/sub&gt; yr&lt;sup&gt;-1&lt;/sup&gt;), the United States (25.9&amp;#177;8.3 Tg CH&lt;sub&gt;4&lt;/sub&gt; yr&lt;sup&gt;-1&lt;/sup&gt;), Russia (13.2&amp;#177;9.3 Tg CH&lt;sub&gt;4&lt;/sub&gt; yr&lt;sup&gt;-1&lt;/sup&gt;), India (12.3&amp;#177;6.4 Tg CH&lt;sub&gt;4&lt;/sub&gt; yr&lt;sup&gt;-1&lt;/sup&gt;), and Canada (12.2&amp;#177;5.1 Tg CH&lt;sub&gt;4&lt;/sub&gt; yr&lt;sup&gt;-1&lt;/sup&gt;). In both emission categories, natural and anthropogenic, the major emitting countries all had model corrections to their emissions that were within the uncertainty range of the inventories and the inverse model uncertainty. As a special case, we evaluate anthropogenic emissions estimated for India (24.2&amp;#177;5.3 Tg yr&lt;sup&gt;-1&lt;/sup&gt;) with aircraft observation data over urban regions over India. On average, the optimized profiles showed a better match with the observations compared to the prior profile confirming improved estimates by the model for India.&lt;/p&gt;

scholarly journals Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories

2019 ◽  
Vol 11 (21) ◽  
pp. 2489 ◽  
Author(s):  
Fenjuan Wang ◽  
Shamil Maksyutov ◽  
Aki Tsuruta ◽  
Rajesh Janardanan ◽  
Akihiko Ito ◽  
...  
Keyword(s):  
High Resolution ◽  
Methane Emission ◽  
Statistical Data ◽  
Trace Gases ◽  
Inverse Model ◽  
Anthropogenic Emissions ◽  
Atmospheric Research ◽  
Assimilation System ◽  
Gosat Satellite ◽  
Country Specific

We present a global 0.1° × 0.1° high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010–2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH4 yr−1 for Russia, 12.8 to 14.3 Tg CH4 yr−1 for temperate South America, and 43.2 to 44.9 Tg CH4 yr−1 for contiguous USA, and the values decrease from 54 to 51.3 Tg CH4 yr−1 for China, 26.2 to 25.5 Tg CH4 yr−1 for Europe, and by 12.4 Tg CH4 yr−1 for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC.

scholarly journals Methane emissions in the United States, Canada, and Mexico: Evaluation of national methane emission inventories and sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH<sub>4</sub> ObsPack) and satellite (GOSAT) atmospheric observations

2021 ◽  
Author(s):  
Xiao Lu ◽  
Daniel J. Jacob ◽  
Haolin Wang ◽  
Joannes D. Maasakkers ◽  
Yuzhong Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
United States ◽  
Inverse Analysis ◽  
The United States ◽  
Methane Emissions ◽  
Anthropogenic Emissions ◽  
Emission Inventories ◽  
South Central ◽  
The Us

Abstract. We quantify methane emissions and their 2010–2017 trends by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), Environment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecologia y Cambio Climatico (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC), and thus serves as an evaluation of these inventories in terms of their magnitudes and trends. Emissions are optimized with a Gaussian mixture model (GMM) at 0.5° × 0.625° resolution and for individual years. Optimization is done analytically using log-normal error forms. This yields closed-form statistics of error estimates and information content on the posterior (optimized) estimates, allows better representation of the high tail of the emission distribution, and enables construction of a large ensemble of inverse solutions using different observations and assumptions. We find that GOSAT and in situ observations are largely consistent and complementary in the optimization of methane emissions for North America. Mean 2010–2017 anthropogenic emissions from our base GOSAT + in situ inversion, with ranges from the inversion ensemble, are 36.9 (32.5–37.8) Tg a−1 for CONUS, 5.3 (3.6–5.7) Tg a−1 for Canada, and 6.0 (4.7–6.1) Tg a−1 for Mexico. These are higher than the most recent reported national inventories of 26.0 Tg a−1 for the US (EPA), 4.0 Tg a−1 for Canada (ECCC), and 5.0 Tg a−1 for Mexico (INECC). The correction in all three countries is largely driven by a factor of 2 underestimate in emissions from the oil sector with major contributions from the south-central US, western Canada, and southeast Mexico. Total CONUS anthropogenic emissions in our inversion peak in 2014, in contrast to the EPA report of a steady decreasing trend over 2010–2017. This reflects combined effects of increases in emissions from the oil and landfill sectors, decrease from the gas, and flat emissions from the livestock and coal sectors. We find decreasing trends in Canadian and Mexican anthropogenic methane emissions over the 2010–2017 period, mainly driven by oil and gas emissions. Our best estimates of mean 2010–2017 wetland emissions are 8.4 (6.4–10.6) Tg a−1 for CONUS, 9.9 (7.8–12.0) Tg a−1 for Canada, and 0.6 (0.4–0.6) Tg a−1 for Mexico. Wetland emissions in CONUS show an increasing trend of 2.6 (1.7–3.8) % a−1 over 2010–2017 correlated with precipitation.

scholarly journals Evaluation of the coupled high-resolution atmospheric chemistry model system MECO(n) using in situ and MAX-DOAS NO<sub>2</sub> measurements

2021 ◽  
Vol 14 (7) ◽  
pp. 5241-5269
Author(s):  
Vinod Kumar ◽  
Julia Remmers ◽  
Steffen Beirle ◽  
Joachim Fallmann ◽  
Astrid Kerkweg ◽  
...  
Keyword(s):  
High Resolution ◽  
Atmospheric Chemistry ◽  
Temporal Variability ◽  
Model System ◽  
West Germany ◽  
Optical Absorption Spectroscopy ◽  
Anthropogenic Emissions ◽  
Spatial And Temporal Variability ◽  
Daily Variability

Abstract. We present high spatial resolution (up to 2.2×2.2 km2) simulations focussed over south-west Germany using the online coupled regional atmospheric chemistry model system MECO(n) (MESSy-fied ECHAM and COSMO models nested n times). Numerical simulation of nitrogen dioxide (NO2) surface volume mixing ratios (VMRs) are compared to in situ measurements from a network with 193 locations including background, traffic-adjacent and industrial stations to investigate the model's performance in simulating the spatial and temporal variability of short-lived chemical species. We show that the use of a high-resolution and up-to-date emission inventory is crucial for reproducing the spatial variability and resulted in good agreement with the measured VMRs at the background and industrial locations with an overall bias of less than 10 %. We introduce a computationally efficient approach that simulates diurnal and daily variability in monthly-resolved anthropogenic emissions to resolve the temporal variability of NO2. MAX-DOAS (Multiple AXis Differential Optical Absorption Spectroscopy) measurements performed at Mainz (49.99∘ N, 8.23∘ E) were used to evaluate the simulated tropospheric vertical column densities (VCDs) of NO2. We propose a consistent and robust approach to evaluate the vertical distribution of NO2 in the boundary layer by comparing the individual differential slant column densities (dSCDs) at various elevation angles. This approach considers details of the spatial heterogeneity and sensitivity volume of the MAX-DOAS measurements while comparing the measured and simulated dSCDs. The effects of clouds on the agreement between MAX-DOAS measurements and simulations have also been investigated. For low elevation angles (≤8∘), small biases in the range of −14 % to +7 % and Pearson correlation coefficients in the range of 0.5 to 0.8 were achieved for different azimuth directions in the cloud-free cases, indicating good model performance in the layers close to the surface. Accounting for diurnal and daily variability in the monthly-resolved anthropogenic emissions was found to be crucial for the accurate representation of time series of measured NO2 VMR and dSCDs and is particularly critical when vertical mixing is suppressed, and the atmospheric lifetime of NO2 is relatively long.

Temperature monitoring in mountain regions using reanalyses: Lessons from the Alps

2020 ◽  
Author(s):  
Simon C. Scherrer ◽  
Sven Kotlarski
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Reanalysis Data ◽  
Swiss Alps ◽  
Mountain Regions ◽  
Near Surface ◽  
The Alps ◽  
Regional Reanalysis ◽  
High Elevations

&lt;p&gt;The monitoring of near-surface temperature is a fundamental task of climatology that remains especially challenging in mountain regions. Here we assess the regional monitoring capabilities of modern reanalysis products in the well-monitored northern Swiss Alps during the last 20 to almost 60 years. Monthly and seasonal 2 m air temperature (T2m) anomalies of the global ERA5 and the three regional reanalysis products HARMONIE, MESCAN-SURFEX and COSMO-REA6 are evaluated against high quality in situ observational data for a low elevation (foothills) mean, and a high elevation (Alpine) mean. All reanalysis products show a good year-round performance for the foothills with the global reanalysis ERA5 showing the best overall performance. The high-resolution regional reanalysis COSMO-REA6 clearly performs best for the Alpine mean, especially in winter. Most reanalysis data sets show deficiencies at high elevations in winter and considerably overestimate recent T2m trends in winter. This stresses the fact that even in the most recent decades utmost care is required when using reanalysis data for near-surface temperature trend assessments in mountain regions. Our results indicate that a high-resolution model topography is an important prerequisite for an adequate monitoring of winter T2m using reanalysis data at high elevations in the Alps. Assimilating T2m remains challenging in highly complex terrain. The remaining shortcomings of modern reanalyses also highlight the continued need for a reliable and dense in situ observational monitoring network in mountain regions.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Analysis of the horizontal two-dimensional near-surface structure of a winter tornadic vortex using high-resolution in situ wind and pressure measurements

2015 ◽  
Vol 120 (12) ◽  
pp. 5879-5894 ◽  
Author(s):  
Ryohei Kato ◽  
Kenichi Kusunoki ◽  
Eiichi Sato ◽  
Wataru Mashiko ◽  
Hanako Y. Inoue ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Structure ◽  
Pressure Measurements ◽  
Two Dimensional ◽  
Near Surface

scholarly journals Near-surface diurnal warming simulations: validation with high resolution profile measurements

2012 ◽  
Vol 9 (6) ◽  
pp. 3851-3878
Author(s):  
B. Scanlon ◽  
G. A. Wick ◽  
B. Ward
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Wave Breaking ◽  
Solar Absorption ◽  
Near Surface ◽  
Annual Cycles ◽  
Diurnal Warming ◽  
In Situ Methods ◽  
Critical Requirement

Abstract. Sea surface temperature (SST) is an important property for governing the exchange of energy between the ocean and the atmosphere. Common in-situ methods of measuring SST often require a cool-skin and warm-layer adjustment in the presence of diurnal warming effects. A critical requirement for an ocean sub-model is that it can simulate the change in SST over diurnal, seasonal, and annual cycles. In this paper we use high-resolution near-surface profiles of SST to validate simulated near-surface temperature profiles from a modified version of the Kantha and Clayson 1-D mixed layer model. Additional model enhancements such as the incorporation of a parameterisation of turbulence generated by wave breaking and a solar absorption model are also validated. The model simulations show a strong variability in highly stratified conditions, with different models providing the best results depending on the specific criteria and conditions. In general, the models with enhanced wave breaking effects tended to underestimate the temperature profile measurements while the more coarse baseline and blended approaches produced the most accurate comparisons with the in-situ SST data.

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