Abstract In this study, climate model simulations are evaluated with regard to the wind energy resource in Germany. Since accurate determination of local wind requires a high resolution, we consider ...simulations of Convection Permitting Regional Climate Models (CP-RCMs) with 3km resolution. We analysed RCP8.5 scenario simulations carried out i) within the EUCP project using the RCM REMO in convection permitting mode for the Central European domain (CEU-3) and ii) within the CORDEX FPS-Convection project that provides a model ensemble of CP-RCMs for the Pan-Alpine region (ALP-3). The models are forced with RCMs at intermediate resolution which are by itself driven with General Circulation Models (GCMs) from the Coupled Model Intercomparison Project – Phase 5 (CMIP5) for the two time slices 1996-2005 (’historical’) and 2041-2050 (‘near-future’). Wind energy yield is calculated at 100 m height by using 3 MW wind turbine parameters and a height correction method. The results are compared with observations from four different met-masts at different locations. The RCMs at intermediate resolution are closer to observations than the CP-RCM REMO while the ensemble of CORDEX FPS-Convection reveals that the difference between model and observations is highly model dependent. A significant climate trend in the comparison of ’historical’ to ‘near future’ cannot be seen for Germany in both the EUCP and CORDEX FPS-Convection simulations.
The aim of this study was to develop an advanced parameterization of the snow-free land surface albedo for climate modelling describing the temporal variation of surface albedo as a function of ...vegetation phenology on a monthly time scale. To estimate the effect of vegetation phenology on snow-free land surface albedo, remotely sensed data products from the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra platform measured during 2001 to 2004 are used. The snow-free surface albedo variability is determined by the optical contrast between the vegetation canopy and the underlying soil surface. The MODIS products of the white-sky albedo for total shortwave broad bands and the fraction of absorbed photosynthetically active radiation (FPAR) are analysed to separate the vegetation canopy albedo from the underlying soil albedo. Global maps of pure soil albedo and pure vegetation albedo are derived on a 0.5° regular latitude/longitude grid, re-sampling the high-resolution information from remote sensing-measured pixel level to the model grid scale and filling up gaps from the satellite data. These global maps show that in the northern and mid-latitudes soils are mostly darker than vegetation, whereas in the lower latitudes, especially in semi-deserts, soil albedo is mostly higher than vegetation albedo. The separated soil and vegetation albedo can be applied to compute the annual surface albedo cycle from monthly varying leaf area index. This parameterization is especially designed for the land surface scheme of the regional climate model REMO and the global climate model ECHAM5, but can easily be integrated into the land surface schemes of other regional and global climate models.
Abstract We describe the first effort within the Coordinated Regional Climate Downscaling Experiment–Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of ...twenty-first-century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over nine CORDEX domains at ∼25-km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, and energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors, and climate service activities. The future of the CORDEX-CORE initiative is also discussed.
Irrigation is a crucial land use practice to adapt agriculture to unsuitable climate and soil conditions. Aiming to improve the growth of plants, irrigation modifies the soil condition, which causes ...atmospheric effects and feedbacks through land–atmosphere interaction. These effects can be quantified with numerical climate models, as has been done in various studies. It could be shown that irrigation effects, such as air temperature reduction and humidity increase, are well understood and should not be neglected on local and regional scales. However, there is a lack of studies including the role of vegetation in the altered land–atmosphere interaction. With the increasing resolution of numerical climate models, these detailed processes have a chance to be better resolved and studied. This study aims to analyze the effects of irrigation on land–atmosphere interaction, including the effects and feedbacks of vegetation. We developed a new parameterization for irrigation, implemented it into the REgional climate MOdel (REMO2020), and coupled it with the interactive MOsaic-based VEgetation module (iMOVE). Following this new approach of a separate irrigated fraction, the parameterization is suitable as a subgrid parameterization for high-resolution studies and resolves irrigation effects on land, atmosphere, and vegetation. Further, the parameterization is designed with three different water application schemes in order to analyze different parameterization approaches and their influence on the representation of irrigation effects. We apply the irrigation parameterization for southwestern Europe including the Mediterranean region at a 0.11∘ horizontal resolution for hot extremes. The simulation results are evaluated in terms of the consistency of physical processes. We found direct effects of irrigation, like a changed surface energy balance with increased latent and decreased sensible heat fluxes, and a surface temperature reduction of more than −4 K as a mean during the growing season. Further, vegetation reacts to irrigation with direct effects, such as reduced water stress, but also with feedbacks, such as a delayed growing season caused by the reduction of the near-surface temperature. Furthermore, the results were compared to observational data, showing a significant bias reduction in the 2 m mean temperature when using the irrigation parameterization.
Assessing multiple climatic and non-climatic variables affecting one region at the same time is a crucial aspect to support climate adaptation action. This publication presents a method to display ...relevant measures of any three adaptation relevant parameters (or optionally their projected future changes) at once on a map by allocating them to multiple transparency levels of the three primary colors of additive color mixing (red, green, and blue). The overlay of information allows the combined assessment of the regional exposures. The method is demonstrated by two examples based on an ensemble of regional climate projections analyzed for 1.5 °C, 2 °C, and 3 °C global warming periods. The first example shows the increasing number of people at risk for summer climate extremes under 1.5 °C, 2 °C, and 3 °C global warming by combining projected increases in tropical nights and summer intense precipitation days with today’s population density. Under 3 °C global warming, many heavily populated areas across Europe are affected by both heat stress and summer precipitation extremes, whereas under 1.5 °C global warming, heat stress regions are restricted to southern Europe and the large settlements along the Eastern Mediterranean coast. A second example combines daily mean and minimum and maximum summer temperatures and highlights the regional expansion and the increasing robustness of projected mean summer warming with rising global warming levels, as well as the regional day to night differences of the warming signal.
The concept of plant functional types (PFTs) is shown to be beneficial in representing the complexity of plant characteristics in land use and climate change studies using regional climate models ...(RCMs). By representing land use and land cover (LULC) as functional traits, responses and effects of specific plant communities can be directly coupled to the lowest atmospheric layers. To meet the requirements of RCMs for realistic LULC distribution, we developed a PFT dataset for Europe (LANDMATE PFT Version 1.0; http://doi.org/10.26050/WDCC/LM_PFT_LandCov_EUR2015_v1.0, Reinhart et al., 2021b). The dataset is based on the high-resolution European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset and is further improved through the additional use of climate information. Within the LANDMATE – LAND surface Modifications and its feedbacks on local and regional cliMATE – PFT dataset, satellite-based LULC information and climate data are combined to create the representation of the diverse plant communities and their functions in the respective regional ecosystems while keeping the dataset most flexible for application in RCMs. Each LULC class of ESA-CCI is translated into PFT or PFT fractions including climate information by using the Holdridge life zone concept. Through consideration of regional climate data, the resulting PFT map for Europe is regionally customized. A thorough evaluation of the LANDMATE PFT dataset is done using a comprehensive ground truth database over the European continent. The assessment shows that the dominant LULC types, cropland and woodland, are well represented within the dataset, while uncertainties are found for some less represented LULC types. The LANDMATE PFT dataset provides a realistic, high-resolution LULC distribution for implementation in RCMs and is used as a basis for the Land Use and Climate Across Scales (LUCAS) Land Use Change (LUC) dataset which is available for use as LULC change input for RCM experiment set-ups focused on investigating LULC change impact.
Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperatures (i.e. heat and cold) may increase the risk of MI. The relationship between health impacts and ...climate is complex and influenced by a multitude of climatic, environmental, socio-demographic and behavioural factors. Here, we present a machine learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany, between 1998 and 2015. Multivariable predictors include weather and climate, air pollution (PM10, NO, NO2, SO2 and O3), surrounding vegetation and demographic data. We tested the following ML regression algorithms: decision tree, random forest, multi-layer perceptron, gradient boosting and ridge regression. The models are able to predict the total annual number of MIs reasonably well (adjusted R2=0.62–0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approach provides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes.
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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.
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