ALBERT

Description: The biophysical effects of reforestation and afforestation (herein jointly called re/afforestation) on the diurnal temperature cycle in European summer are investigated by analyzing a regional climate model (RCM) ensemble, established within the Land Use and Climate Across Scales Flagship Pilot Study (LUCAS FPS). With this RCM ensemble, two idealized experiments are performed for Europe, one with a continent with maximized forest cover, and one in which all forests are turned into grassland. First, an in-depth analysis of one ensemble member (“CCLM-VEG3D”) is carried out, to reveal the complex process chain caused by such land use changes (LUCs). From these findings, the whole ensemble is analyzed and principal biophysical effects of re/afforestation are derived. Results show that the diurnal temperature range is reduced at the surface (top of the vegetation) with re/afforestation. Most RCMs simulate colder surface temperatures Tsurf during the day and warmer Tsurf during the night. Thus, for the first time, the principal temperature interrelations found in observation-based studies in the midlatitudes could be reproduced within a model intercomparison study. On the contrary, the diurnal temperature range in the lowest atmospheric model level (Tair) is increased with re/afforestation. This opposing temperature response is mainly caused by the higher surface roughness of forest, enhancing the turbulent heat exchange. Furthermore, these opposing temperature responses demonstrate that the use of the diagnostic 2-m temperature (weighted interpolation between Tsurf and Tair) has a limited potential to assess the effects of re/afforestation. Thus, studies about the biophysical impacts of LUCs should investigate the whole near-surface temperature profile.

Description: A 10 × 10 km irrigated biomass plantation was simulated in an arid region of Israel to simulate diurnal energy balances during the summer of 2012 (JJA). The goal is to examine daytime horizontal flux gradients between plantation and desert. Simulations were carried out within the coupled WRF-NOAH atmosphere/land surface model. MODIS land surface data was adjusted by prescribing tailored land surface and soil/plant parameters, and by adding a controllable sub-surface irrigation scheme to NOAH. Two model cases studies were compared – Impact and Control. Impact simulates the irrigated plantation. Control simulates the existing land surface, where the predominant land surface is bare desert soil. Central to the study is parameter validation against land surface observations from a desert site and from a 400 ha Simmondsia chinensis (jojoba) plantation. Control was validated with desert observations, and Impact with Jojoba observations. Model evapotranspiration was validated with two Penman–Monteith estimates based on the observations. Control simulates daytime desert conditions with a maximum deviation for surface 2 m air temperatures (T2) of 0.2 °C, vapour pressure deficit (VPD) of 0.25 hPa, wind speed (U) of 0.5 m s−1, surface radiation (Rn) of 25 W m−2, soil heat flux (G) of 30 W m−2 and 5 cm soil temperatures (ST5) of 1.5 °C. Impact simulates irrigated vegetation conditions with a maximum deviation for T2 of 1–1.5 °C, VPD of 0.5 hPa, U of 0.5 m s−1, Rn of 50 W m−5, G of 40 W m−2 and ST5 of 2 °C. Latent heat curves in Impact correspond closely with Penman–Monteith estimates, and magnitudes of 160 W m−2 over the plantation are usual. Sensible heat fluxes, are around 450 W m−2 and are at least 100–110 W m−2 higher than the surrounding desert. This surplus is driven by reduced albedo and high surface resistance, and demonstrates that high evaporation rates may not occur over Jojoba if irrigation is optimized. Furthermore, increased daytime T2 over plantations highlight the need for hourly as well as daily mean statistics. Daily mean statistics alone may imply an overall cooling effect due to surplus nocturnal cooling, when in fact a daytime warming effect is observed.

Description: Despite considerable progress in recent years, output of both global and regional circulation models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem, bias correction (BC; i.e. the correction of model output towards observations in a post-processing step) has now become a standard procedure in climate change impact studies. In this paper we argue that BC is currently often used in an invalid way: it is added to the GCM/RCM model chain without sufficient proof that the consistency of the latter (i.e. the agreement between model dynamics/model output and our judgement) as well as the generality of its applicability increases. BC methods often impair the advantages of circulation models by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Currently used BC methods largely neglect feedback mechanisms, and it is unclear whether they are time-invariant under climate change conditions. Applying BC increases agreement of climate model output with observations in hindcasts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this hides rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers. We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of circulation models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future global and regional circulation model simulations is the increase in model resolution to the convection-permitting scale in combination with ensemble predictions based on sophisticated approaches for ensemble perturbation. With this article, we advocate communicating the entire uncertainty range associated with climate change predictions openly and hope to stimulate a lively discussion on bias correction among the atmospheric and hydrological community and end users of climate change impact studies.

Description: Despite considerable progress in recent years, output of both Global and Regional Circulation Models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem bias correction (BC), i.e. the correction of model output towards observations in a post processing step for its subsequent application in climate change impact studies has now become a standard procedure. In this paper we argue that bias correction, which has a considerable influence on the results of impact studies, is not a valid procedure in the way it is currently used: it impairs the advantages of Circulation Models which are based on established physical laws by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Bias correction largely neglects feedback mechanisms and it is unclear whether bias correction methods are time-invariant under climate change conditions. Applying bias correction increases agreement of Climate Model output with observations in hind casts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this masks rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers. We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of Circulation Models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future Global and Regional Circulation Model simulations is the increase in model resolution to the convection-permitting scale in combination with ensemble predictions based on sophisticated approaches for ensemble perturbation. With this article, we advocate communicating the entire uncertainty range associated with climate change predictions openly and hope to stimulate a lively discussion on bias correction among the atmospheric and hydrological community and end users of climate change impact studies.

Description: Streamflow depends on the soil moisture of a river catchment and can be measured with relatively high accuracy. The soil moisture in the root zone influences the latent heat flux and, hence, the quantity and spatial distribution of atmospheric water vapour and precipitation. As numerical weather forecast and climate models require a proper soil moisture initialization for their land surface models, we enhanced an Ensemble Kalman Filter to assimilate streamflow time series into the multi-layer land surface model TERRA-ML of the regional weather forecast model COSMO. The impact of streamflow assimilation was studied by an observing system simulation experiment in the Enz River catchment (located at the downwind side of the northern Black Forest in Germany). The results demonstrate a clear improvement of the soil moisture field in the catchment. We illustrate the potential of streamflow data assimilation for weather forecasting and discuss its spatial and temporal requirements for a corresponding, automated river gauging network.

Description: A large irrigated biomass plantation was simulated in an arid region of Israel within the WRF-NOAH coupled atmospheric/land surface model in order to assess land surface atmosphere feedbacks. Simulations were carried out for the 2012 summer season (JJA). The irrigated plantations were simulated by prescribing tailored land surface and soil/plant parameters, and by implementing a newly devised, controllable sub-surface irrigation scheme within NOAH. Two model cases studies were considered and compared – Impact and Control. Impact simulates a hypothetical 10 km × 10 km irrigated plantation. Control represents a baseline and uses the existing land surface data, where the predominant land surface type in the area is bare desert soil. Central to the study is model validation against observations collected for the study over the same period. Surface meteorological and soil observations were made at a desert site and from a 400 ha Simmondsia chinensis (Jojoba) plantation. Control was validated with data from the desert, and Impact from the Jojoba. Finally, estimations were made of the energy balance, applying two Penman–Monteith based methods along with observed meteorological data. These estimations were compared with simulated energy fluxes. Control simulates the daytime desert surface 2 m air temperatures (T2) with less than 0.2 °C deviation and the vapour pressure deficit (VPD) to within 0.25 hPa. Desert wind speed (U) is simulated to within 0.5 m s−1 and the net surface radiation (Rn) to 25 W m−2. Soil heat flux (G) is not so accurately simulated by Control (up to 30 W m−2 deviation) and 5 cm soil temperatures (ST5) are simulated to within 1.5 °C. Impact simulates daytime T2 over irrigated vegetation to within 1–1.5 °C, the VPD to 0.5 hPa, Rn to 50 W m−2 and ST5 to within 2 °C. Simulated Impact G deviates up to 40 W m−2, highlighting a need for re-parameterisation or better soil classification, but the overall contribution to the energy balance is small (5–6%). During the night, significant T2 and ST5 cold biases of 2–4 °C are present. Diurnal latent heat values from WRF Impact correspond closely with Penman–Monteith estimation curves, and latent heat magnitudes of 160 W m−2 over the plantation are usual. Simulated plantation sensible heat fluxes are high (450 W m−2) – around 100–110 W m−2 higher than over the surrounding desert. The high relative HFX over the vegetation, driven by high Rn and high surface resistances, indicate that low Bowen ratios should not necessarily be assumed when irrigated plantations are implemented in, and optimized for arid regions. Furthermore, the high plantation T2 magnitudes highlight the importance of considering diurnal dynamics, which drive the evolution of boundary layers, rather than only on daily mean statistics which often indicate an irrigation cooling effect.

Description: Streamflow depends on the soil moisture of a river catchment and can be measured with relatively high accuracy. The soil moisture in the root zone influences the latent heat flux and hence the quantity and spatial distribution of atmospheric water vapour and precipitation. As numerical weather forecast and climate models require a proper soil moisture initialization for their land surface models, we enhanced an Ensemble Kalman Filter to assimilate streamflow timeseries into the multi-layer land surface model TERRA-ML of the regional weather forecast model COSMO. The impact of streamflow assimilation was studied by an observing system simulation experiment in the Enz River catchment (located at the downwind side of the northern Black Forest in Germany). The results demonstrate a clear improvement of the soil moisture field in the catchment. We illustrate the potential of streamflow data assimilation for weather forecasting and discuss its spatial and temporal requirements for a corresponding, automated river gauging network.

Description: EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatio-temporal patterns of the European climate for the period 1989–2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS dataset as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight sub-domains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies non-negligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and south-eastern Europe reflect common model biases. For seasonal mean quantities averaged over large European sub-domains, no clear benefit of an increased spatial resolution (12 km vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.