Sensitivity of European Temperature to Albedo Parameterization in the Regional Climate Model COSMO-CLM Linked to Extreme Land Use Changes

Previous studies based on observations and models are uncertain about the biophysical impact of af- and deforestation in the northern hemisphere mid-latitude summers, and show either a cooling or warming. The magnitude and direction is still uncertain. In this study, the effect of three different albedo parameterizations in the regional climate model COSMO-CLM (v5.09) is examined performing afforestation experiments at 0.44° horizontal resolution across the EURO-CORDEX domain during 1986-2015. Idealized de- and af-forestation simulations are compared to a simulation with no land cover change. Emphasis is put on the impact of changes in radiation and turbulent fluxes. A clear latitudinal pattern is found, which results partly due to the strong land cover conversion from forest- to grassland in the high latitudes and open land to forest conversion in mid-latitudes. Afforestation warms the climate in winter, and strongest in mid-latitudes. Results are indifferent in summer owing to opposing albedo and evapotranspiration effects of comparable size but different sign. Thus, the net effect is small for summer. Depending on the albedo parameterization in the model, the temperature effect can turn from cooling to warming in mid-latitude summers. The summer warming due to deforestation to grassland is up to 3°C higher than due to afforestation. The cooling by grass or warming by forest is in magnitude comparable and small in winter. The strength of the described near-surface temperature changes depends on the magnitude of the individual biophysical changes in the specific background climate conditions of the region. Thus, the albedo parameterization need to account for different vegetation types. Furthermore, we found that, depending on the region, the land use change effect is more important than the model uncertainty due to albedo parameterization. This is important information for model development

Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula

peer reviewedVegetation models for climate adaptation and mitigation strategies require spatially high-resolution climate input data in which the error with respect to observations has been previously corrected. To quantify the impact of bias correction, we examine the effects of quantile-mapping bias correction on the climate change signal (CCS) of climate, extremes, and biological variables from the convective regional climate model COSMO-CLM and two dynamic vegetation models (LPJ-GUESS and CARAIB). COSMO-CLM was driven and analyzed at 3 km horizontal resolution over Central Europe (CE) and the Iberian Peninsula (IP) for the transient period 1980–2070 under the RCP8.5 scenario. Bias-corrected and uncorrected climate simulations served as input to run the dynamic vegetation models over Wallonia. Main result of the impact of bias correction on the climate is a reduction of seasonal absolute precipitation by up to −55% with respect to uncorrected simulations. Yet, seasonal climate changes of precipitation and also temperature are marginally affected by bias correction. Main result of the impact of bias correction on changes in extremes is a robust spatial mean CCS of climate extreme indices over both domains. Yet, local biases can both over- and underestimate changes in these indices and be as large as the raw CCS. Changes in extremely wet days are locally enhanced by bias correction by more than 100%. Droughts in southern IP are exacerbated by bias correction, which increases changes in consecutive dry days by up to 14 days/year. Changes in growing season length in CE are affected by quantile mapping due to local biases ranging from 24 days/year in western CE to −24 days/year in eastern CE. The increase of tropical nights and summer days in both domains is largely affected by bias correction at the grid scale because of local biases ranging within ±14 days/year. Bias correction of this study strongly reduces the precipitation amount which has a strong impact on the results of the vegetation models with a reduced vegetation biomass and increases in net primary productivity. Nevertheless, there are large differences in the results of the two applied vegetation models.Multisectoral analysis of climate and land use change impacts on pollinators, plant diversity and crops yields (MAPPY)Projets multilatéraux de recherche (PINT-MULTI

Impact of Deforestation on Land–Atmosphere Coupling Strength and Climate in Southeast Asia

Southeast Asia (SEA) is a deforestation hotspot. A thorough understanding of the accompanying biogeophysical consequences is crucial for sustainable future development of the region’s ecosystem functions and society. In this study, data from ERA-Interim driven simulations conducted with the state-of-the-art regional climate model COSMO-CLM (CCLM; version 4.8.17) at 14 km horizontal resolution are analyzed over SEA for the period from 1990 to 2004, and during El Niño–Southern Oscillation (ENSO) events for November to March. A simulation with large-scale deforested land cover is compared to a simulation with no land cover change. In order to attribute the differences due to deforestation to feedback mechanisms, the coupling strength concept is applied based on Pearson correlation coefficients. The correlations were calculated based on 10-day means between the latent heat flux and maximum temperature, the latent and sensible heat flux, and the latent heat flux and planetary boundary layer height. The results show that the coupling strength between land and atmosphere increased for all correlations due to deforestation. This implies a strong impact of the land on the atmosphere after deforestation. Differences in environmental conditions due to deforestation are most effective during La Niña years. The strength of La Nina events on the region is reduced as the impact of deforestation on the atmosphere with drier and warmer conditions superimpose this effect. The correlation strength also intensified and shifted towards stronger coupling during El Niño events for both Control and Grass simulations. However, El Niño years have the potential to become even warmer and drier than during usual conditions without deforestation. This could favor an increase in the formation of tropical cyclones. Whether deforestation will lead to a permanent transition to agricultural production increases in this region cannot be concluded. Rather, the impact of deforestation will be an additional threat besides global warming in the next decades due to the increase in the occurrence of multiple extreme events. This may change the type and severity of upcoming impacts and the vulnerability and sustainability of our society

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

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

Impact of Environmental Conditions on Grass Phenology in the Regional Climate Model COSMO-CLM

Feedbacks of plant phenology to the regional climate system affect fluxes of energy, water, CO2, biogenic volatile organic compounds as well as canopy conductance, surface roughness length, and are influencing the seasonality of albedo. We performed simulations with the regional climate model COSMO-CLM (CCLM) at three locations in Germany covering the period 1999 to 2015 in order to study the sensitivity of grass phenology to different environmental conditions by implementing a new phenology module. We provide new evidence that the annually-recurring standard phenology of CCLM is improved by the new calculation of leaf area index (LAI) dependent upon surface temperature, day length, and water availability. Results with the new phenology implemented in the model show a significantly higher correlation with observations than simulations with the standard phenology. The interannual variability of LAI improves the representation of vegetation in years with extremely warm winter/spring (e.g., 2007) or extremely dry summer (e.g., 2003) and shows a more realistic growth period. The effect of the newly implemented phenology on atmospheric variables is small but tends to be positive. It should be used in future applications with an extension on more plant functional type

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

Evaluation of Alpine-Mediterranean precipitation events in convection-permitting regional climate models using a set of tracking algorithms

International audienceWe here inter-compare four different tracking algorithms by applying them onto the precipitation fields of an ensemble of convection-permitting regional climate models (cpRCMs) and on high-resolution observational datasets of precipitation. The domain covers the Alps and the northern Mediterranean and thus we here analyse heavy precipitation events, that are renowned for causing hydrological hazards. In this way, this study is both, an inter-comparison of tracking algorithms as well as an evaluation study of cpRCMs in the Lagrangian frame of reference. The tracker inter-comparison is performed by comparison of two case studies as well as of climatologies of cpRCMs and observations. We find that that all of the trackers produce qualitatively equal results concerning characteristic track properties. This means that, despite of quantitative differences, equivalent scientific conclusions would be drawn. This result suggests that all trackers investigated are reliable analysis tools of atmospheric research. With respect to the model ensemble evaluation, we find an encouraging performance of cpRCMs in comparison to radar-based observations. In particular prominent hotspots of heavy precipitation events are well-reproduced by the models. In general most characteristic properties of precipitation events have positive biases. Assuming the under-catchment of precipitation in observations in a domain of such complex orography, this result is to be expected. Only the mean area of tracks is underestimated, while their duration is overestimated. Mean precipitation rate is estimated well, while maximum precipitation rate is overestimated. Furthermore, geometrical and rain volume are overestimated. We find that models overestimate the occurrence of precipitation events over all mountain chains, whereas over plain terrain in summer precipitation events are seen underestimated. This suggests that, despite the convection-permitting resolution, thermally driven thunderstorms are either not triggered or their dynamics still under-resolved. Eventually we find that biases in the spatio-temporal properties of precipitation events appear reduced when evaluating cpRCMs against Doppler radar-based and rain gauge-adjusted observational datasets of comparable spatial resolution, strengthening their role in evaluation studies