Abstract
The European Union has set ambitious CO
2
reduction targets, stimulating renewable energy production and accelerating deployment of offshore wind energy in northern European waters, mainly ...the North Sea. With increasing size and clustering, offshore wind farms (OWFs) wake effects, which alter wind conditions and decrease the power generation efficiency of wind farms downwind become more important. We use a high-resolution regional climate model with implemented wind farm parameterizations to explore offshore wind energy production limits in the North Sea. We simulate near future wind farm scenarios considering existing and planned OWFs in the North Sea and assess power generation losses and wind variations due to wind farm wake. The annual mean wind speed deficit within a wind farm can reach 2–2.5 ms
−1
depending on the wind farm geometry. The mean deficit, which decreases with distance, can extend 35–40 km downwind during prevailing southwesterly winds. Wind speed deficits are highest during spring (mainly March–April) and lowest during November–December. The large-size of wind farms and their proximity affect not only the performance of its downwind turbines but also that of neighboring downwind farms, reducing the capacity factor by 20% or more, which increases energy production costs and economic losses. We conclude that wind energy can be a limited resource in the North Sea. The limits and potentials for optimization need to be considered in climate mitigation strategies and cross-national optimization of offshore energy production plans are inevitable.
This study compares the direct and semi-direct aerosol effects of different annual cycles of tropospheric aerosol loads for Europe from 1950 to 2009 using the regional climate model COSMO-CLM, which ...is laterally forced by reanalysis data and run using prescribed, climatological aerosol optical properties. These properties differ with respect to the analysis strategy and the time window, and are then used for the same multi-decadal period. Five simulations with different aerosol loads and one control simulation without any tropospheric aerosols are integrated and compared. Two common limitations of our simulation strategy, to fully assess direct and semi-direct aerosol effects, are the applied observed sea surface temperatures and sea ice conditions, and the lack of short-term variations in the aerosol load. Nevertheless, the impact of different aerosol climatologies on common regional climate model simulations can be assessed. The results of all aerosol-including simulations show a distinct reduction in solar irradiance at the surface compared with that in the control simulation. This reduction is strongest in the summer season and is balanced primarily by a weakening of turbulent heat fluxes and to a lesser extent by a decrease in longwave emissions. Consequently, the seasonal mean surface cooling is modest. The temperature profile responses are characterized by a shallow near-surface cooling and a dominant warming up to the mid-troposphere caused by aerosol absorption. The resulting stabilization of stratification leads to reduced cloud cover and less precipitation. A decrease in cloud water and ice content over Central Europe in summer possibly reinforce aerosol absorption and thus strengthen the vertical warming. The resulting radiative forcings are positive. The robustness of the results was demonstrated by performing a simulation with very strong aerosol forcing, which lead to qualitatively similar results. A distinct added value over the default aerosol setup of Tanré et al. (
1984
) was found in the simulations with more recent aerosol data sets for solar irradiance. The improvements are largest under low cloud conditions, while overestimated cloud cover in all setups causes a common underestimation of low and medium values of solar irradiance. In addition, the prevalent cold bias in the COSMO-CLM is reduced in winter and spring when using updated aerosol data. Our results emphasize the importance of semi-direct aerosol effects, especially over Central Europe in terms of changes in turbulent fluxes and changes in cloud properties. We also suggest to replace the default Tanré et al. (
1984
) aerosol climatology with more recent and realistic data sets. Thereby, a better model performance in comparison to observations can be achieved, or the masking of model shortcomings due to a too strong direct aerosol forcing thus far is prevented.
In this study, we analyse the uncertainty of the effect of enhanced greenhouse gas conditions on windiness projected by an ensemble of regional model simulations driven by the same global control and ...climate change simulations. These global conditions, representative for 1961-1990 and 2071-2100, were prepared by the Hadley Centre based on the IPCC SRES/A2 scenario. The basic data sets consist of simulated daily maximum and daily mean wind speed fields (over land) from the PRUDENCE data archive at the Danish Meteorological Institute. The main focus is on the results from the standard 50 km-resolution runs of eight regional models. The best parameter for determining possible future changes in extreme wind speeds and possible change in the number of storm events is maximum daily wind speed. It turned out during this study that the method for calculating maximum daily wind speed differs among the regional models. A comparison of simulated winds with observations for the control period shows that models without gust parameterisation are not able to realistically capture high wind speeds. The two models with gust parametrization estimate an increase of up to 20% of the number of storm peak (defined as gusts > 8 Bft in this paper) events over Central Europe in the future. In order to use a larger ensemble of models than just the two with gust parameterisation, we also look at the 99th percentile of daily mean wind speed. We divide Europe into eight sub-regions (e.g., British Isles, Iberian Peninsula, NE Europe) and investigate the inter-monthly variation of wind over these regions as well as differences between today's climate and a possible future climate. Results show differences and similarities between the sub-regions in magnitude, spread, and seasonal tendencies. The model ensemble indicates a possible increase in future mean daily wind speed during winter months, and a decrease during autumn in areas influenced by North Atlantic extra-tropical cyclones. PUBLICATION ABSTRACT
A new regional climate projection ensemble has been created for the Australasia region as part of the World Climate Research Programs Coordinated Regional Downscaling Experiment (CORDEX). The ...CORDEX-Australasia ensemble is the largest regional climate projection ensemble ever created for the region. It is a 20-member ensemble made by 6 regional climate models downscaling 11 global climate models. Overall the ensemble produces a good representation of recent climate. Consistent biases within the ensemble include an underestimation of the diurnal temperature range and an underestimation of precipitation across much of southern Australia. Under a high emissions scenario projected temperature changes by the end of the twenty-first century reach ~ 5 K in the interior of Australia with smaller increases found toward the coast. Projected precipitation changes are towards drying, particularly in the most populated areas of the southwest and southeast of the continent. The projected precipitation change is very seasonal with summer projected to see little change leaning toward an increase. These results provide a foundation enabling future studies of regional climate changes, climate change impacts, and adaptation options for Australia.
The ability of regional climate models (RCMs) to accurately simulate current and future climate is increasingly important for impact assessment. This is the first evaluation of all reanalysis-driven ...RCMs within the CORDEX Australasia framework four configurations of the Weather Forecasting and Research (WRF) model, and single configurations of COSMO-CLM (CCLM) and the Conformal-Cubic Atmospheric Model (CCAM) to simulate the historical climate of Australia (1981–2010) at 50 km resolution. Simulations of near-surface maximum and minimum temperature and precipitation were compared with gridded observations at annual, seasonal, and daily time scales. The spatial extent, sign, and statistical significance of biases varied markedly between the RCMs. However, all RCMs showed widespread, statistically significant cold biases in maximum temperature which were the largest during winter. This bias exceeded − 5 K for some WRF configurations, and was the lowest for CCLM at ± 2 K. Most WRF configurations and CCAM simulated minimum temperatures more accurately than maximum temperatures, with biases in the range of ± 1.5 K. RCMs overestimated precipitation, especially over Australia’s populous eastern seaboard. Strong negative correlations between mean monthly biases in precipitation and maximum temperature suggest that the maximum temperature cold bias is linked to precipitation overestimation. This analysis shows that the CORDEX Australasia ensemble is a valuable dataset for future impact studies, but improving the representation of land surface processes, and subsequently of surface temperatures, will improve RCM performance. The varying RCM capabilities identified here serve as a foundation for the development of future regional climate projections and impact assessments for Australia.
To accurately calculate the impact of renewables on power production in complex electric power grids, high-resolution and ideally seamless data within the planetary boundary layer are required. ...Therefore, the quality of different regional reanalyses and hindcasts is evaluated with respect to the representation of the planetery boundary layer and related sub-daily processes. On the one hand, high resolution regional reanalysis from the UERRA (UE-SMHI, UE-UKMO) and a similar project (COSMO-REA6) are considered. On the other hand, two hindcasts based on the COSMO-REA6 configuration are included in this study, i.e. a simulation with perfect boundaries and a simulation additionally utilizing spectral nudging. The focus of the evaluation is on measurements at four flux towers that are not part of any assimilation procedure. In this paper, we will show that the model's quality depends on both the complete model system - assimilation method, resolution and physical parameterization - as well as on the performance measure. The daily cycle is best depicted by the hindcasts and even COSMO-REA6 hardly introduces spurious variability. UE-SMHI (3D-Var) suffers from spin-up in particular visible at the elevated levels, whereas the spin-up is damped in UE-UKMO (4D-Var). Investigation of atmospheric stability reveals that diurnal variation of stratification is for the most part well reproduced, but strong deficits were found for all COSMO simulations in reproducing strong stratification and corresponding wind speed gradients. Moreover, an overestimation of superadiabatic lapse rates and corresponding overly weak turbulent mixing is found for UE-UKMO. Furthermore, a combination of ramp statistics and contingency tables is utilized to detect a clear advantage of sophisticated assimilation systems over hindcasts. The evaluation framework presented underpins the importance of ramp statistics and vertical measurement profiles, especially with respect to assessing long-term simulations.
An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like ...polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties).
However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales.
Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.
In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional ...climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44∘ (∼ 50 km), 0.22∘ (∼ 25 km), and 0.11∘ (∼ 12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM–RCM modeling chain.
The analysis of possible regional climate changes over Europe as simulated by 10 regional climate models within the context of PRUDENCE requires a careful investigation of possible systematic biases ...in the models. The purpose of this paper is to identify how the main model systematic biases vary across the different models. Two fundamental aspects of model validation are addressed here: the ability to simulate (1) the long-term (30 or 40 years) mean climate and (2) the inter-annual variability. The analysis concentrates on near-surface air temperature and precipitation over land and focuses mainly on winter and summer. In general, there is a warm bias with respect to the CRU data set in these extreme seasons and a tendency to cold biases in the transition seasons. In winter the typical spread (standard deviation) between the models is 1 K. During summer there is generally a better agreement between observed and simulated values of inter-annual variability although there is a relatively clear signal that the modeled temperature variability is larger than suggested by observations, while precipitation variability is closer to observations. The areas with warm (cold) bias in winter generally exhibit wet (dry) biases, whereas the relationship is the reverse during summer (though much less clear, coupling warm (cold) biases with dry (wet) ones). When comparing the RCMs with their driving GCM, they generally reproduce the large-scale circulation of the GCM though in some cases there are substantial differences between regional biases in surface temperature and precipitation.
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