Impacts of land cover data quality on regional climate simulations

2009 ◽  
Vol 30 (13) ◽  
pp. 1942-1953 ◽  
Author(s):  
Elif Sertel ◽  
Alan Robock ◽  
Cankut Ormeci
Keyword(s):  
Land Cover ◽  
Data Quality ◽  
Regional Climate ◽  
Land Cover Data ◽  
Climate Simulations

scholarly journals MODIS land cover uncertainty in regional climate simulations

2017 ◽  
Vol 49 (11-12) ◽  
pp. 4047-4059 ◽  
Author(s):  
Xue Li ◽  
Joseph P. Messina ◽  
Nathan J. Moore ◽  
Peilei Fan ◽  
Ashton M. Shortridge
Keyword(s):  
Land Cover ◽  
Regional Climate ◽  
Climate Simulations

Impact of different and time-varying land cover data sets in a regional climate model on regional and local climate over Europe

2020 ◽  
Author(s):  
Mingyue Zhang ◽  
Jürgen Helmert ◽  
Merja Tölle
Keyword(s):  
Land Cover ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Data Sets ◽  
Time Varying ◽  
Data Set ◽  
Local Climate ◽  
Land Cover Data ◽  
Simulation Results

<p>According to IPCC, Land use and Land Cover (LC) changes have a key role to adapt and mitigate future climate change aiming to stabilize temperature rise up to 2°C. Land surface change at regional scale is associated to global climate change, such as global warming. It influences the earth’s water and energy cycles via influences on the heat, moisture and momentum transfer, and on the chemical composition of the atmosphere. These effects show variations due to different LC types, and due to their spatial and temporal resolutions.  Thus, we incorporate a new time-varying land cover data set based on ESACCI into the regional climate model COSMO-CLM(v5.0). Further, the impact on the regional and local climate is compared to the standard operational LC data of GLC2000 and GlobCover 2009. Convection-permitting simulations with the three land cover data sets are performed at 0.0275° horizontal resolution over Europe for the time period from 1992 to 2015.</p><p>Overall, the simulation results show comparable agreement to observations. However, the simulation results based on GLC2000 and GlobCover 2009 (with 23 LC types) LC data sets show a fluctuation of 0.5K in temperature and 5% of precipitation. Even though the LC is classified into the same types, the difference in LC distribution and fraction leads to variations in climate simulation results. Using all of the 37 LC types of the ESACCI-LC data set show noticeable differences in distribution of temperature and precipitation compared to the simulations with GLC2000 and GlobCover 2009. Especially in forest areas, slight differences of the plant cover type (e.g. Evergreen or Deciduous) could result in up to 10% differences (increase or decrease) in temperature and precipitation over the simulation domain. Our results demonstrate how LC changes as well as different land cover type effect regional climate. There is need for proper and time-varying land cover data sets for regional climate model studies. The approach of including ESACCI-LC data set into regional climate model simulations also improved the external data generation system.</p><p>We anticipate this research to be a starting point for involving time-varying LC data sets into regional climate models. Furthermore, it will give us a possibility to quantify the effect of time-varying LC data on regional climate accurately.</p><p><strong>Acknowledgement</strong>:</p><p>1: Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the Federal Ministry of Education and Research in Germany (BMBF). We acknowledge the funding of the German Research Foundation (DFG) through grant NR. 401857120.</p><p>2: Appreciation for the support of Jürg Luterbacher and Eva Nowatzki.</p><p> </p>

scholarly journals Urban Effects on Regional Climate: A Case Study in the Phoenix and Tucson “Sun Corridor”

2016 ◽  
Vol 20 (20) ◽  
pp. 1-25 ◽  
Author(s):  
Zhao Yang ◽  
Francina Dominguez ◽  
Hoshin Gupta ◽  
Xubin Zeng ◽  
Laura Norman
Keyword(s):  
Land Cover ◽  
Urban Expansion ◽  
Regional Climate ◽  
Wrf Model ◽  
Urban Canopy ◽  
Mountainous Regions ◽  
Land Cover Data ◽  
Lateral Boundary Conditions ◽  
Regional Reanalysis ◽  
The Phoenix

Abstract Land-use and land-cover change (LULCC) due to urban expansion alter the surface albedo, heat capacity, and thermal conductivity of the surface. Consequently, the energy balance in urban regions is different from that of natural surfaces. To evaluate the changes in regional climate that could arise because of projected urbanization in the Phoenix–Tucson corridor, Arizona, this study applied the coupled WRF Model–Noah–Urban Canopy Model (UCM; which includes a detailed urban radiation scheme) to this region. Land-cover changes were represented using land-cover data for 2005 and projections to 2050, and historical North American Regional Reanalysis (NARR) data were used to specify the lateral boundary conditions. Results suggest that temperature changes will be well defined, reflecting the urban heat island (UHI) effect within areas experiencing LULCC. Changes in precipitation are less robust but seem to indicate reductions in precipitation over the mountainous regions northeast of Phoenix and decreased evening precipitation over the newly urbanized area.

scholarly journals A Sensitivity Assessment of COSMO-CLM to Different Land Cover Schemes in Convection-Permitting Climate Simulations over Europe

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1595
Author(s):  
Mingyue Zhang ◽  
Merja H. Tölle ◽  
Eva Hartmann ◽  
Elena Xoplaki ◽  
Jürg Luterbacher
Keyword(s):  
Land Cover ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Regional Climate ◽  
Plant Cover ◽  
Horizontal Resolution ◽  
Area Index ◽  
Climate Simulations ◽  
Simulation Results ◽  
Land Cover Maps

The question of how sensitive the regional and local climates are to different land cover maps and fractions is important, as land cover affects the atmospheric circulation via its influence on heat, moisture, and momentum transfer, as well as the chemical composition of the atmosphere. In this study, we used three independent land cover data sets, GlobCover 2009, GLC2000 and ESACCI-LC, as the lower boundary of the regional climate model COSMO-CLM (Consortium for Small Scale Modeling in Climate Mode, v5.0-clm15) to perform convection-permitting regional climate simulations over the large part of Europe covering the years 1999 and 2000 at a 0.0275° horizontal resolution. We studied how the sensitivity of the impacts on regional and local climates is represented by different land cover maps and fractions, especially between warm (summer) and cold (winter) seasons. We show that the simulated regional climate is sensitive to different land cover maps and fractions. The simulated temperature and observational data are generally in good agreement, though with differences between the seasons. In comparison to winter, the summer simulations are more heterogeneous across the study region. The largest deviation is found for the alpine area (−3 to +3 °C), which might be among different reasons due to different classification systems in land cover maps and orographical aspects in the COSMO-CLM model. The leaf area index and plant cover also showed different responses based on various land cover types, especially over the area with high vegetation coverage. While relating the differences of land cover fractions and the COSMO-CLM simulation results (the leaf area index, and plant coverage) respectively, the differences in land cover fractions did not necessarily lead to corresponding bias in the simulation results. We finally provide a comparative analysis of how sensitive the simulation outputs (temperature, leaf area index, plant cover) are related to different land cover maps and fractions. The different regional representations of COSMO-CLM indicate that the soil moisture, atmospheric circulation, evaporative demand, elevation, and snow cover schemes need to be considered in the regional climate simulation with a high horizontal resolution.

scholarly journals SPATIAL DATA QUALITY EVALUATION FOR LAND COVER CLASSIFICATION APPROACHES

2020 ◽  
Vol V-3-2020 ◽  
pp. 681-687
Author(s):  
M. Salhab ◽  
A. Basiri
Keyword(s):  
Land Cover ◽  
Data Quality ◽  
Spatial Data ◽  
Land Cover Classification ◽  
Training Dataset ◽  
Global Land Cover ◽  
Spatial Data Quality ◽  
Land Cover Data ◽  
Data Gaps ◽  
Global Land

Abstract. Data gaps and poor data quality may lead to flawed conclusions and data-driven policies and decisions, such as the measurement of Sustainable Development Goals progress. This is particularly important for land cover data, as an essential source of data for a wide range of applications and real-world challenges including climate change mitigation, food security planning, resource allocation and mobilization. While global land cover datasets are available, their usability is limited by their coarse spatial and temporal resolutions. Furthermore, having a good understanding of the fitness for the purpose is imperative. This paper compares two datasets from a spatial data quality perspective: (1) a global land cover map, and (2) a fit-for-purpose training dataset that is generated using visual inspection of very high-resolution satellite data. The latter dataset is created using Google Earth Engine (GEE), a cloud-based computing platform and data repository. We systematically evaluate the two datasets from spatial data quality (SDQ) perspective using the Analytic Hierarchy Process (AHP) to prioritise the criteria, i.e. SDQ. To validate the results, land cover classifications are conducted using both datasets, also within GEE. Based on the results of the SDQ evaluation and land cover classification, we find that the second training dataset significantly outperformed the global land cover maps. Our study also shows that cloud-based computing platforms and publicly available data repositories can provide an effective approach to filling land cover data gaps in data-scarce regions.

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