Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX Flagship Pilot Study Land Use and Climate Across Scales (LUCAS) models – Part 2: The role of changing vegetation

International audienceAbstract. Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX Flagship Pilot Study Land Use and Climate Across Scales (LUCAS) models – Part 2: The role of changing vegetation

Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect

Seasonal snow cover plays a major role in the climate system of the Northern Hemisphere via its effect on land surface albedo and fluxes. In climate models the parameterization of interactions between snow and atmosphere remains a source of uncertainty and biases in the representation of local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled with different land surface models to simulate snow–atmosphere interactions over Europe in winter and spring. We use a previously defined index, the snow-albedo sensitivity index (SASI), to quantify the radiative forcing associated with snow cover anomalies. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over middle and high latitudes in Europe. The choice of parameterizations, and primarily the choice of the land surface model, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover accurately. The degree of agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that, during this time period, the choice of the land surface model is more critical for the representation of SASI than the atmospheric model

CORDEX-WRF v1.3: development of a module for the Weather Research and Forecasting (WRF) model to support the CORDEX community

International audienceThe Coordinated Regional Climate Downscaling Experiment (CORDEX) is a scientific effort of the World Climate Research Program (WRCP) for the coordination of regional climate initiatives. In order to accept an experiment, CORDEX provides experiment guidelines, specifications of regional domains, and data access and archiving. CORDEX experiments are important to study climate at the regional scale, and at the same time, they also have a very prominent role in providing regional climate data of high quality. Data requirements are intended to cover all the possible needs of stakeholders and scientists working on climate change miti-gation and adaptation policies in various scientific communities. The required data and diagnostics are grouped into different levels of frequency and priority, and some of them even have to be provided as statistics (minimum, maximum, mean) over different time periods. Most commonly, scientists need to post-process the raw output of regional climate models, since the latter was not originally designed to meet the specific CORDEX data requirements. This post-processing procedure includes the computation of diagnos-tics, statistics, and final homogenization of the data, which is often computationally costly and time-consuming. Therefore , the development of specialized software and/or code is required. The current paper presents the development of a specialized module (version 1.3) for the Weather Research and Forecasting (WRF) model capable of outputting the required CORDEX variables. Additional diagnostic variables not required by CORDEX, but of potential interest to the regional climate modeling community, are also included in the module. "Generic" definitions of variables are adopted in order to overcome the model and/or physics parameterization dependence of certain diagnostics and variables, thus facilitating a robust comparison among simulations. The module is computationally optimized, and the output is divided into different priority levels following CORDEX specifications (Core, Tier 1, and additional) by selecting precompilation flags. This implementation of the module does not add a significant extra cost when running the model; for example, the addition of the Core variables slows the model time step by less than a 5 %. The use of the module reduces the requirements of disk storage by about a 50 %. The module performs Published by Copernicus Publications on behalf of the European Geosciences Union. 1030 L. Fita et al.: WRF module for CORDEX output neither additional statistics over different periods of time nor homogenization of the output data

Afforestation impact on soil temperature in regional climate model simulations over Europe

International audienceAbstract. In the context of the first phase of the Coordinated Regional Climate Downscaling Experiment in the European domain (EURO-CORDEX) flagship plot study on Land Use and Climate Across Scales (LUCAS), we investigate the biophysical impact of afforestation on the seasonal cycle of soil temperature over the European continent with an ensemble of 10 regional climate models. For this purpose, each ensemble member performed two idealized land cover experiments in which Europe is covered either by forests or grasslands. The multi-model mean exhibits a reduction of the annual amplitude of soil temperature (AAST) due to afforestation over all European regions, although this is not a robust feature among the models. In the Mediterranean, the spread of simulated AAST response to afforestation is between −4 and +2 ∘C at 1 m below the ground, while in Scandinavia the inter-model spread ranges from −7 to +1 ∘C. We show that the large range in the simulated AAST response is due to the representation of the summertime climate processes and is largely explained by inter-model differences in leaf area index (LAI), surface albedo, cloud fraction and soil moisture, when all combined into a multiple linear regression. The changes in these drivers essentially determine the ratio between the increased radiative energy at surface (due to lower albedo in forests) and the increased sum of turbulent heat fluxes (due to mixing-facilitating characteristics of forests), and consequently decide the changes in soil heating with afforestation in each model. Finally, we pair FLUXNET sites to compare the simulated results with observation-based evidence of the impact of forest on soil temperature. In line with models, observations indicate a summer ground cooling in forested areas compared to open lands. The vast majority of models agree with the sign of the observed reduction in AAST, although with a large variation in the magnitude of changes. Overall, we aspire to emphasize the biophysical effects of afforestation on soil temperature profile with this study, given that changes in the seasonal cycle of soil temperature potentially perturb crucial biochemical processes. Robust knowledge on biophysical impacts of afforestation on soil conditions and its feedbacks on local and regional climate is needed in support of effective land-based climate mitigation and adaption policies

Afforestation impact on soil temperature in regional climate model simulations over Europe

In the context of the first phase of the Coordinated Regional Climate Downscaling Experiment in the European domain (EURO-CORDEX) flagship plot study on Land Use and Climate Across Scales (LUCAS), we investigate the biophysical impact of afforestation on the seasonal cycle of soil temperature over the European continent with an ensemble of 10 regional climate models. For this purpose, each ensemble member performed two idealized land cover experiments in which Europe is covered either by forests or grasslands. The multi-model mean exhibits a reduction of the annual amplitude of soil temperature (AAST) due to afforestation over all European regions, although this is not a robust feature among the models. In the Mediterranean, the spread of simulated AAST response to afforestation is between −4 and +2 ∘C at 1 m below the ground, while in Scandinavia the inter-model spread ranges from −7 to +1 ∘C. We show that the large range in the simulated AAST response is due to the representation of the summertime climate processes and is largely explained by inter-model differences in leaf area index (LAI), surface albedo, cloud fraction and soil moisture, when all combined into a multiple linear regression. The changes in these drivers essentially determine the ratio between the increased radiative energy at surface (due to lower albedo in forests) and the increased sum of turbulent heat fluxes (due to mixing-facilitating characteristics of forests), and consequently decide the changes in soil heating with afforestation in each model. Finally, we pair FLUXNET sites to compare the simulated results with observation-based evidence of the impact of forest on soil temperature. In line with models, observations indicate a summer ground cooling in forested areas compared to open lands. The vast majority of models agree with the sign of the observed reduction in AAST, although with a large variation in the magnitude of changes. Overall, we aspire to emphasize the biophysical effects of afforestation on soil temperature profile with this study, given that changes in the seasonal cycle of soil temperature potentially perturb crucial biochemical processes. Robust knowledge on biophysical impacts of afforestation on soil conditions and its feedbacks on local and regional climate is needed in support of effective land-based climate mitigation and adaption policies

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX Flagship Pilot Study Land Use and Climate Across Scales (LUCAS) models – Part 2: The role of changing vegetation

Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe

Land-atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models -Part 1: Evaluation of the snow-albedo effect

International audienceAbstract. Seasonal snow cover plays a major role in the climate system of the Northern Hemisphere via its effect on land surface albedo and fluxes. In climate models the parameterization of interactions between snow and atmosphere remains a source of uncertainty and biases in the representation of local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled with different land surface models to simulate snow–atmosphere interactions over Europe in winter and spring. We use a previously defined index, the snow-albedo sensitivity index (SASI), to quantify the radiative forcing associated with snow cover anomalies. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over middle and high latitudes in Europe. The choice of parameterizations, and primarily the choice of the land surface model, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover accurately. The degree of agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that, during this time period, the choice of the land surface model is more critical for the representation of SASI than the atmospheric model

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX Flagship Pilot Study Land Use and Climate Across Scales (LUCAS) models – Part 2: The role of changing vegetation

Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe

Biogeophysical impacts of forestation in Europe: first results from the LUCAS (Land Use and Climate Across Scales) regional climate model intercomparison

The Land Use and Climate Across Scales Flagship Pilot Study (LUCAS FPS) is a coordinated community effort to improve the integration of land use change (LUC) in regional climate models (RCMs) and to quantify the biogeophysical effects of LUC on local to regional climate in Europe. In the first phase of LUCAS, nine RCMs are used to explore the biogeophysical impacts of re-/afforestation over Europe: two idealized experiments representing respectively a non-forested and a maximally forested Europe are compared in order to quantify spatial and temporal variations in the regional climate sensitivity to forestation. We find some robust features in the simulated response to forestation. In particular, all models indicate a year-round decrease in surface albedo, which is most pronounced in winter and spring at high latitudes. This results in a winter warming effect, with values ranging from +0.2 to +1 K on average over Scandinavia depending on models. However, there are also a number of strongly diverging responses. For instance, there is no agreement on the sign of temperature changes in summer with some RCMs predicting a widespread cooling from forestation (well below −2 K in most regions), a widespread warming (around +2 K or above in most regions) or a mixed response. A large part of the inter-model spread is attributed to the representation of land processes. In particular, differences in the partitioning of sensible and latent heat are identified as a key source of uncertainty in summer. Atmospheric processes, such as changes in incoming radiation due to cloud cover feedbacks, also influence the simulated response in most seasons. In conclusion, the multi-model approach we use here has the potential to deliver more robust and reliable information to stakeholders involved in land use planning, as compared to results based on single models. However, given the contradictory responses identified, our results also show that there are still fundamental uncertainties that need to be tackled to better anticipate the possible intended or unintended consequences of LUC on regional climates.ISSN:2190-4987ISSN:2190-497