Assessing the Impact of Land Use Changes on Regional Climate over Europe †
Department of Meteorology and Climatology, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
†
Presented at the 17th International Conference on Meteorology, Climatology, and Atmospheric Physics—COMECAP 2025, Nicosia, Cyprus, 29 September–1 October 2025.
Environ. Earth Sci. Proc. 2025, 35(1), 53; https://doi.org/10.3390/eesp2025035053
Published: 27 September 2025
Abstract
Anthropogenic alterations of the land surface through activities such as agriculture, forestry and urban development represent important human-induced forcings on the Earth’s climate system. This study, conducted in the framework of the UpClim project, employs the non-hydrostatic WRF-ARW v4.5.1 model forced by ERA5 reanalysis data to assess the impact of land use changes (LUCs) on the European climate. The study aims to quantify the effects of LUCs over the EURO-CORDEX domain at 0.11° resolution during 1980–1985 by comparing simulations with transient land use forcing against a control run with static land use.
1. Introduction
Land cover changes as a result of human land use activities represent a significant climate forcing. These changes modify the surface energy balance, thus significantly affecting the climate system, particularly the atmospheric temperature, moisture and precipitation [1] and especially the likelihood, intensity and duration of many extreme events [2] due to changes in evapotranspiration and sensible heat fluxes. Although these effects range from local- and regional-scale to sub-continental- and global-scale [1], land use and land cover change forcings have not been sufficiently integrated into climate change projections using regional climate models (RCMs). Consequently, there is a systematic missing forcing agent related to land use change and the assessment of its impact on regional climate. In this work realistic land use change experiments on the European continental scale are conducted as transient simulations using a regional climate model, driven by ERA5 reanalysis [3]. The land use forcing [4] was based on a high-resolution land use and land cover dataset under the LUCAS framework, a Flagship Pilot Study of the Coordinated Regional Downscaling (CORDEX) under the auspices of the World Climate Change Program. Transient regional climate simulations including land use changes were compared to simulations applying a static land use map corresponding to the year 2015, following the official EURO-CORDEX simulation protocol [5]. Additionally, both simulations were compared against the E-OBS v31.0e observational data [6].
2. Materials and Methods
2.1. LUCAS Land Use Change (LUC) Dataset
The LUCAS land use and land cover change dataset version 1.1 provides annual land use land cover maps for Europe at 0.1° resolution from 1950 to 2100 [4]. Designed as input to state-of–the-art RCMs, it enables the analysis of realistic LUC impacts on past and future climates. The plant functional type distribution for the year 2015 is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset, while land use changes from the Land-Use Harmonization 2 dataset [7] are integrated via a developed land use translator to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. Between 1950 and 2015, the dataset includes a reduction in cropland alongside an expansion of irrigated cropland, afforestation in mountainous areas, and increased urbanization. The extent and magnitude of these changes are considerable and likely to impact simulated European climate patterns. The dataset also records historical shifts in the broadleaf/needleleaf forest ratio, but does not distinguish between irrigation methods, which may have differential climate effects [4].
2.2. The Weather Research and Forecasting (WRF) Model
The Weather Research and Forecasting (WRF) Model [8,9] is a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting applications. The WRF model has been widely used as a regional climate model [10] and is an official model-member of the Ensemble Design Matrix of CMIP6/EURO-CORDEX [11]. In this study, the non-hydrostatic WRF model with the Advanced Research dynamic solver (WRF-ARW v4.5.1) and some additional modifications and improvements in the NoahMP land use model [12] was utilized. Concerning the static LULC information in WRF, the MODIS classes are used as the default land use dataset, and the LUCAS-LUC plant functional types are translated into MODIS categories.
3. Results
3.1. Near-Surface Air Temperature
According to Figure 1 both simulations exhibit a negative temperature bias relative to the E-OBS dataset, particularly during winter, when the seasonal mean temperature bias reaches up to −2.1C over Eastern Europe (Table 1, Table 2, Table 3, Table 4). During spring and autumn, a negative bias is still evident in Europe, although of a lower magnitude compared to winter. In contrast, summer is characterized by a positive temperature bias across Europe. The seasonal temperature difference between LUCAS_eval and CORDEX_eval simulations remains generally low throughout the year, i.e., <0.5 °C as a regional seasonal average (Table 1).
3.2. Precipitation
As illustrated in Figure 2, both simulations exhibit a positive precipitation bias compared to the E-OBS dataset throughout the year. The highest seasonal mean precipitation bias is observed in the MD region during spring with a magnitude of 1.3 mm (Table 5, Table 6, Table 7, Table 8). Between the LUCAS_eval and CORDEX_eval simulations, the seasonal precipitation difference remains relatively small, as a regional average.
4. Conclusions
The main purpose of this study is to assess the impact of land use change over Europe for the period 1980–1985. The analysis is based on two climate model simulations over the official EURO-CORDEX domain at 0.11° resolution (EUR-11), using reanalysis forcing (ERA5). One simulation (CORDEX_eval) has static land use information as in the official WRF-EURO-CORDEX CMIP6 simulations, while the second one (LUCAS_eval) uses a land use change dataset at annual intervals. The analysis shows that relative to the E-OBS observational data, both simulations exhibit biases in temperature, namely cold/warm in winter/summer, and precipitation, i.e., wet for all seasons. The differences between the two simulations are relatively small as a regional average but become higher on a local level. An important note is that over regions where the E-OBS datasets are sparse [13] (e.g., the Alps or Greece), the uncertainty in the bias estimation is larger.
Author Contributions
Conceptualization, E.K. and S.K.; methodology, S.E.P., S.K. and E.K.; software and analysis, S.E.P.; validation, S.E.P.; investigation, S.K. and S.E.P.; resources, E.K.; data curation, S.K. and S.E.P.; writing—original draft preparation, S.E.P.; writing—review and editing, E.K. and S.K.; visualization, S.E.P.; supervision, E.K. and S.K.; project administration, E.K.; funding acquisition, E.K. All authors have read and agreed to the published version of the manuscript.
Funding
The research project is implemented in the framework of the H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union –NextGenerationEU (H.F.R.I. Project Number: 14696).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The ERA-5 data are freely available from the Copernicus Climate Change Service (C3S) (https://cds.climate.copernicus.eu/datasets). The Weather Research and Forecasting (WRF) code is available at https://www2.mmm.ucar.edu/wrf/OnLineTutorial/index.php, and the LUCAS LUC historical land use and land cover change dataset for Europe can be found at https://www.wdc-climate.de/ui/entry?acronym=LUC_hist_EU_v1.1. The E-OBS data are freely available from the Copernicus Climate Change Service (C3S) (https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php#datafiles). All of these sources were last accessed on 21 May 2025.
Acknowledgments
The authors acknowledge the support of the Greek Research and Technology Network (GRNET) High Performance Computing (HPC) infrastructure for providing the computational resources of AUTH-simulations (under project IDs pr016029_thin and pr017036_thin) and the AUTH Scientific Computing Center for technical support. We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (https://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu). All of these sources were last accessed on 21 May 2025.
Conflicts of Interest
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
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Figure 1.
Seasonal bias of temperature (°C) for the period “1980–1985”. LUCAS_eval refers to the ERA5-driven transient LULC simulation, while CORDEX_eval is the control run with static land. The first column refers to the bias of LUCAS_eval (WRF minus E-OBS dataset), the second to the bias of CORDEX_eval, and the third to the difference between the LUCAS_eval and CORDEX_eval simulations.
Figure 1.
Seasonal bias of temperature (°C) for the period “1980–1985”. LUCAS_eval refers to the ERA5-driven transient LULC simulation, while CORDEX_eval is the control run with static land. The first column refers to the bias of LUCAS_eval (WRF minus E-OBS dataset), the second to the bias of CORDEX_eval, and the third to the difference between the LUCAS_eval and CORDEX_eval simulations.
Figure 2.
Seasonal bias of precipitation (mm) for the period “1980–1985”. LUCAS_eval refers to the transient LULC simulation driven by ERA5 reanalysis data, while CORDEX_eval is the control run with static LULCs. The first column refers to the bias of LUCAS_eval (WRF minus E-OBS dataset) and the second to the bias of CORDEX_eval, and the third refers to the difference between the LUCAS_eval and CORDEX_eval simulations.
Figure 2.
Seasonal bias of precipitation (mm) for the period “1980–1985”. LUCAS_eval refers to the transient LULC simulation driven by ERA5 reanalysis data, while CORDEX_eval is the control run with static LULCs. The first column refers to the bias of LUCAS_eval (WRF minus E-OBS dataset) and the second to the bias of CORDEX_eval, and the third refers to the difference between the LUCAS_eval and CORDEX_eval simulations.
Table 1.
Mean seasonal bias of temperature (°C) in Europe for the “1980–1985” period during winter. LUCAS_eval refers to the transient LUC simulation driven by ERA5 reanalysis data, while CORDEX_eval is the control run with static LUCs. AL: Alps, BI: British Isles, EA: Eastern Europe, FR: France, IP: Iberian Peninsula, MD: Mediterranean, ME: Mid-Europe, SC: Scandinavia, GR: Greece.
Table 1.
Mean seasonal bias of temperature (°C) in Europe for the “1980–1985” period during winter. LUCAS_eval refers to the transient LUC simulation driven by ERA5 reanalysis data, while CORDEX_eval is the control run with static LUCs. AL: Alps, BI: British Isles, EA: Eastern Europe, FR: France, IP: Iberian Peninsula, MD: Mediterranean, ME: Mid-Europe, SC: Scandinavia, GR: Greece.
| Winter (DJF) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Dataset | AL | BI | EA | FR | IP | MD | ME | SC | GR |
| LUCAS_eval—E-OBS | −1.4 | −1.1 | −2.1 | −1.3 | −0.8 | −1.4 | −1.9 | −0.6 | −1.2 |
| CORDEX_eval—E-OBS | −1.5 | −0.9 | −2.1 | −1.4 | −0.8 | −1.1 | −2.1 | −0.4 | −0.8 |
| LUCAS_eval—CORDEX_eval | 0.0 | −0.2 | 0.0 | 0.1 | 0.0 | −0.2 | 0.2 | −0.3 | −0.5 |
Table 2.
The same as Table 1 for spring.
Table 2.
The same as Table 1 for spring.
| Spring (MAM) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Dataset | AL | BI | EA | FR | IP | MD | ME | SC | GR |
| LUCAS_eval—E-OBS | −0.5 | −0.6 | −0.4 | −0.3 | −0.5 | −0.3 | −0.4 | −0.8 | −0.2 |
| CORDEX_eval—E-OBS | −0.5 | −0.5 | −0.3 | −0.1 | −0.3 | −0.1 | −0.3 | −0.9 | −0.1 |
| LUCAS_eval—CORDEX_eval | −0.1 | −0.1 | −0.1 | −0.2 | −0.2 | −0.2 | −0.1 | 0.2 | −0.1 |
Table 3.
The same as Table 1 for summer.
Table 3.
The same as Table 1 for summer.
| Summer (JJA) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Dataset | AL | BI | EA | FR | IP | MD | ME | SC | GR |
| LUCAS_eval—E-OBS | 0.8 | −0.3 | 0.6 | 0.4 | 0.8 | 0.9 | 0.2 | 0.2 | 0.5 |
| CORDEX_eval—E-OBS | 0.9 | −0.2 | 0.7 | 0.6 | 0.8 | 0.8 | 0.4 | 0.3 | 0.1 |
| LUCAS_eval—CORDEX_eval | −0.1 | 0.0 | −0.1 | −0.2 | 0.0 | 0.2 | −0.2 | −0.1 | 0.4 |
Table 4.
The same as Table 1 for autumn.
Table 4.
The same as Table 1 for autumn.
| Autumn (SON) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Dataset | AL | BI | EA | FR | IP | MD | ME | SC | GR |
| LUCAS_eval—E-OBS | −0.1 | −0.8 | −0.5 | −0.4 | −0.2 | −0.4 | −0.6 | −1.1 | −0.6 |
| CORDEX_eval—E-OBS | −0.1 | −0.7 | −0.4 | −0.3 | −0.1 | −0.2 | −0.5 | −0.7 | −0.3 |
| LUCAS_eval—CORDEX_eval | 0.0 | −0.1 | −0.1 | −0.1 | −0.1 | −0.1 | −0.1 | −0.4 | −0.3 |
Table 5.
Mean seasonal bias of precipitation (mm) and relative bias (%; in brackets) in Europe for the “1980–1985” period during winter. LUCAS_eval refers to the transient LUC simulation driven by ERA5 reanalysis data, while CORDEX_eval is the control run with static LUCs. AL: Alps, BI: British Isles, EA: Eastern Europe, FR: France, IP: Iberian Peninsula, MD: Mediterranean, ME: Mid-Europe, SC: Scandinavia, GR: Greece.
Table 5.
Mean seasonal bias of precipitation (mm) and relative bias (%; in brackets) in Europe for the “1980–1985” period during winter. LUCAS_eval refers to the transient LUC simulation driven by ERA5 reanalysis data, while CORDEX_eval is the control run with static LUCs. AL: Alps, BI: British Isles, EA: Eastern Europe, FR: France, IP: Iberian Peninsula, MD: Mediterranean, ME: Mid-Europe, SC: Scandinavia, GR: Greece.
| Winter (DJF) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Dataset | AL | BI | EA | FR | IP | MD | ME | SC | GR |
| LUCAS_eval—E-OBS | 0.4 (19%) | 0.1 (9%) | 0.6 (50%) | 0.3 (14%) | 0.6 (51%) | 1.0 (50%) | 0.3 (20%) | 0.4 (31%) | 1.2 (52%) |
| CORDEX_eval—E-OBS | 0.5 (22%) | 0.1 (11%) | 0.7 (59%) | 0.3 (15%) | 0.8 (59%) | 1.2 (55%) | 0.3 (21%) | 0.4 (32%) | 1.4 (58%) |
| LUCAS_eval—CORDEX_eval | −0.1 (−3%) | −0.1 (−1%) | −0.1 (−5%) | 0.0 (−1%) | −0.1 (−5%) | −0.2 (−3%) | 0.0 (−1%) | 0.0 (0%) | −0.3 (−4%) |
Table 6.
The same as Table 5 for spring.
Table 6.
The same as Table 5 for spring.
| Spring (MAM) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Dataset | AL | BI | EA | FR | IP | MD | ME | SC | GR |
| LUCAS_eval—E-OBS | 1.0 (34%) | 0.7 (33%) | 0.9 (61%) | 0.5 (22%) | 0.9 (55%) | 1.3 (80%) | 0.4 (20%) | 0.6 (47%) | 1.1 (71%) |
| CORDEX_eval—E-OBS | 1.0 (35%) | 0.8 (36%) | 0.8 (58%) | 0.6 (25%) | 0.9 (53%) | 1.1 (74%) | 0.5 (27%) | 0.5 (44%) | 1.1 (74%) |
| LUCAS_eval—CORDEX_eval | −0.1 (−0%) | −0.1 (−2%) | 0.1 (3%) | −0.1 (−2%) | 0.1 (2%) | 0.1 (5%) | −0.1 (−5%) | 0.0 (4%) | 0.0 (−1%) |
Table 7.
The same as Table 5 for summer.
Table 7.
The same as Table 5 for summer.
| Summer (JJA) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Dataset | AL | BI | EA | FR | IP | MD | ME | SC | GR |
| LUCAS_eval—E-OBS | 0.5 (17%) | 0.5 (22%) | 0.8 (36%) | 0.3 (14%) | 0.8 (211%) | 0.8 (513%) | 0.5 (25%) | 0.9 (38%) | 1.1 (680%) |
| CORDEX_eval—E-OBS | 0.5 (19%) | 0.3 (16%) | 0.8 (37%) | 0.3 (17%) | 0.7 (174%) | 1.0 (485%) | 0.3 (14%) | 0.7 (30%) | 1.4 (598%) |
| LUCAS_eval—CORDEX_eval | 0.0 (0%) | 0.1 (5%) | 0.0 (0%) | 0.0 (−1%) | 0.1 (14%) | −0.2 (−3%) | 0.3 (10%) | 0.2 (8%) | −0.3 (−3%) |
Table 8.
The same as Table 5 for autumn.
Table 8.
The same as Table 5 for autumn.
| Autumn (SON) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Dataset | AL | BI | EA | FR | IP | MD | ME | SC | GR |
| LUCAS_eval—E-OBS | 0.3 (11%) | 0.1 (5%) | 0.4 (31%) | 0.1 (4%) | 0.6 (40%) | 0.5 (29%) | 0.4 (22%) | 0.4 (19%) | 1.0 (73%) |
| CORDEX_eval—E-OBS | 0.3 (9%) | 0.1 (6%) | 0.4 (34%) | 0.1 (2%) | 0.5 (35%) | 0.7 (43%) | 0.3 (17%) | 0.5 (22%) | 1.1 (80%) |
| LUCAS_eval—CORDEX_eval | 0.1 (2%) | 0.0 (0%) | 0.0 (−2) | 0.0 (2%) | 0.1 (4%) | −0.3 (−9%) | 0.1 (5%) | −0.1 (−2%) | −0.1 (−5%) |
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Paschou, S.E.; Kartsios, S.; Katragkou, E. Assessing the Impact of Land Use Changes on Regional Climate over Europe. Environ. Earth Sci. Proc. 2025, 35, 53. https://doi.org/10.3390/eesp2025035053
AMA Style
Paschou SE, Kartsios S, Katragkou E. Assessing the Impact of Land Use Changes on Regional Climate over Europe. Environmental and Earth Sciences Proceedings. 2025; 35(1):53. https://doi.org/10.3390/eesp2025035053
Chicago/Turabian StylePaschou, Sofia Eirini, Stergios Kartsios, and Eleni Katragkou. 2025. "Assessing the Impact of Land Use Changes on Regional Climate over Europe" Environmental and Earth Sciences Proceedings 35, no. 1: 53. https://doi.org/10.3390/eesp2025035053
APA StylePaschou, S. E., Kartsios, S., & Katragkou, E. (2025). Assessing the Impact of Land Use Changes on Regional Climate over Europe. Environmental and Earth Sciences Proceedings, 35(1), 53. https://doi.org/10.3390/eesp2025035053
