Are climate models that allow better approximations of local meteorology better for the assessment of hydrological impacts? A statistical analysis of droughts

This work studies the benefit of using more reliable local climate scenarios to analyse hydrological impacts. It assumes that more reliable local scenarios are defined with the statistically corrected Regional Climate Model (RCM) simulations when they provide better approximations to the historical basic and drought statistics. The paper analyses if the best solutions in terms of their approximation to the local meteorology also provide the best hydrological assessments. A classification of the corrected RCM simulations attending to both approximations is performed. It has been applied in the Cenajo Basin (southeast Spain), where we demonstrate that the best approximations of the historical meteorological statistics provide also the best approximations of the hydrology ones. The selected RCMs were used to generate future (2071–2100) local scenarios under the RCP 8.5 emission scenario. The two selected RCMs predict significant changes of mean precipitation (−31.6 and −44.0 %) and mean temperature (+26.0 and +32.2 %). They also predict higher frequency (from 5 events in the historical period to 20 and 22 in the future), length (4.8 to 7.4 and 10.5 months), magnitude (2.53 to 6.56 and 9.62 SPI) and intensity (0.48 to 1.00 and 0.94 SPI) of extreme meteorological droughts.

Numerical experiments on sensitivity of local meteorology vs. land-cover changes in the Arctic through seamless Enviro-HIRLAM modelling

<p>In the recent decade, the Arctic as a whole is subject to amplified warming and well documented changes in the Arctic ecosystems, and especially, these changes are became more and more pronounced over territories of the Russian Arctic.</p><p>In this research, to study atmosphere-land-sea surfaces interactions, and in particular, heat-moisture exchange/ regime between these surfaces and for better understanding and forecasting of local meteorology in the Arctic, the seamless modelling approach was tested and applied. The Enviro-HIRLAM (Environment HIgh Resolution Limited Area Model) is an online integrated meteorology – atmospheric composition multi-scales and -processes modeling system. This model was adapted for a region of interest located in the Russian Arctic covering the inland, seashore and adjacent seas territories with the Yamal Peninsula in the center of the domain. Two short-term periods during summer (in July) and winter (in January) were chosen.</p><p>The performed model runs include changes in vegetation and land-cover as well as taking into account direct  and indirect aerosol effects (for summer), which is needed to estimate interactions and feedbacks between meteorology – atmospheric composition – land cover changes. In this study, the model was run in a downscaling chain with 5 and 1+ km horizontal resolutions. The meteorological and aerosols/ gases initial and boundary conditions required were extracted from ECMWF. The model output includes both 3D meteorology and atmospheric composition (with focus on aerosols in this study) in the surface, boundary layer and free troposphere.</p><p>The analysis of variabilities on a diurnal cycle (for key selected meteorological parameters such as air temperature, relative humidity, wind characteristics, boundary layer height, latent and sensible heat fluxes) due to changes in vegetation and land-cover was performed for selected warm and cold periods and will be presented.</p>

The Climatology of Lower Tropospheric Temperature Inversions in China from Radiosonde Measurements: Roles of Black Carbon, Local Meteorology, and Large-Scale Subsidence

The variability of the lower tropospheric temperature inversion (TI) across China remains poorly understood. Using seven years’ worth of high-resolution radiosonde measurements at 120 sites, we compile the climatology of lower tropospheric TI in terms of frequency, intensity, and depth during the period from 2011 to 2017. The TI generally exhibits strong seasonal and geographic dependencies. Particularly, the TI frequency is found to be high in winter and low in summer, likely due to the strong aerosol radiative effect in winter. The frequency of the surface-based inversion (SBI) exhibits a “west low, east high” pattern at 0800 Beijing time (BJT), which then switches to “west high, east low” at 2000 BJT. Both the summertime SBI and elevated inversion (EI) reach a peak at 0800 BJT and a trough at 1400 BJT. Interestingly, the maximum wintertime EI frequency occurs over Southeast China (SEC) rather than over the North China Plain (NCP), likely attributable to the combination of the heating effect of black carbon (BC) originating from the NCP, along with the strong subsidence and trade inversion in SEC. Correlation analyses between local meteorology and TI indicate that larger lower tropospheric stability (LTS) favors more frequent and stronger TIs, whereas the stronger EI under smaller LTS conditions (unstable atmosphere) is more associated with subsidence rather than BC. Overall, the spatial pattern of the lower tropospheric TI and its variability in China are mainly controlled by three factors: local meteorology, large-scale subsidence, and BC-induced heating. These findings help shed some light on the magnitude, spatial distribution, and underlying mechanisms of the lower tropospheric TI variation in China.

Mesoscale resolving high-resolution simulation of wind farms in COSMO-CLM 5

<p>The rapid development of offshore wind farms has raised concerns about the local environment and ecosystem. Wind farms influence the local meteorology by extracting kinetic energy from the wind field and by generating a large wake. The North Sea is one of the main regions of the world where the growth of offshore wind farms is rapidly increasing. In this study, we analyze the impact of large-scale offshore wind farms in the North Sea on local meteorology using regional climate model COSMO-CLM. For this purpose, the parametrization for wind turbine driven by Fitch et al. (2012) and Blahak et al. (2010), previously implemented in COSMO-CLM v 4.8 at KU-Leuven (Chatterjee et al. 2016), has been implemented in the latest version 5 of COSMO-CLM. Here we present the first results of COSMO-CLM long-term simulations with and without wind farms using mesoscale resolving high-resolution horizontal atmospheric grid spacing (~ 2 km).</p>