ALBERT

Description: Due to its extension, geography and the presence of several underdeveloped or developing economies, the Central Asia domain of the Coordinated Regional Climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, regional climate models (RCMs) play a fundamental role. In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for the present day, testing the effects of singular changes in the model physical configuration and their mutual interaction with the simulation of monthly and seasonal values of three variables that are important for impact studies: near-surface temperature, precipitation and diurnal temperature range. The final goal of this study is two-fold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration. Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo, taking into consideration the ratio of forest fractions, and the soil conductivity, taking into account the ratio of liquid water and ice in the soil, allows one to achieve the best improvements in model performance in terms of climatological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The analyses also show that improvements in model performance are not achievable for all domain subregions and variables, and they are the result of a compensation effect in the different cases. The proposed better performing configuration in terms of mean climate leads to similar positive improvements when considering different observational data sets and boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability. This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area.

Description: Paleo-proxy observations have been recently used to constrain the climate models through data assimilation (DA). However, both DA and climate models are computationally very expensive. Moreover, in paleo-DA, the assimilation period is usually too long for a dynamical model to follow the previous analysis state and the chaotic behavior of the model becomes dominant. The majority of the recent paleoclimate studies using DA have performed low or intermediate resolution global simulations along with an off-line DA approach. In an off-line DA, the re-initialisation cycle is completely removed after the assimilation step. In this paper, we design a computationally affordable DA to assimilate yearly pseudo and real observations into an ensemble of COSMO-CLM high resolution regional climate model (RCM) simulations over Europe, where the ensemble members slightly differ in boundary and initial conditions. Within a perfect model experiment, the performance of the applied DA scheme is evaluated with respect to its sensitivity to the noise levels of pseudo-observations. It was observed that the injected bias in the pseudo-observations does linearly impact the DA skill. Such experiments can serve as a tool for selection of proxy records, which can potentially reduce the background error when they are assimilated in the model. Additionally, the sensibility of the COSMO-CLM to the boundary conditions is addressed. The geographical regions, where the model exhibits high internal variability are identified. Two sets of experiments are conducted by averaging the observations over summer and winter. The dependency of the DA skill to different seasons is investigated. Furthermore, the effect of the spurious correlations within the observation space is studied and the optimal correlation length, within which the observations are assumed to be correlated, is detected. Finally, the real yearly-averaged observations are assimilated into the RCM and the performance is evaluated against a gridded observation dataset. We conclude that the DA approach is a promising tool for creating high resolution yearly analysis quantities. The affordable DA method can be applied to efficiently improve the climate field reconstruction efforts by combining high resolution paleo-climate simulations and the available proxy observations.

Description: The improvement in resolution of climate models is always been mentioned as one of the most important factors when investigating past climatic conditions especially in order to evaluate and compare the results against proxy data. In this paper we present for the first time a set of high resolution simulations for different time slices of mid-to-late Holocene performed over Europe using a Regional Climate Model. Through a validation against a new pollen-based climate reconstructions dataset, covering almost all of Europe, we test the model performances for paleoclimatic applications and investigate the response of temperature to variations in the seasonal cycle of insolation, with the aim of clarifying earlier debated uncertainties, giving physically plausible interpretations of both the pollen data and the model results. The results reinforce previous findings showing that summertime temperatures were driven mainly by changes in insolation and that the model is too sensitive to such changes over Southern Europe, resulting in drier and warmer conditions. In winter, instead, the model does not reproduce correctly the same amplitude of changes, even if it captures the main pattern of the pollen dataset over most of the domain for the time periods under investigation. Through the analysis of variations in atmospheric circulation we suggest that, even though in some areas the discrepancies between the two datasets are most likely due to high pollen uncertanties, in general the model seems to underestimate the changes in the amplitude of the North Atlantic Oscillation, overestimating the contribution of secondary modes of variability

Description: The improvement in resolution of climate models has always been mentioned as one of the most important factors when investigating past climatic conditions, especially in order to evaluate and compare the results against proxy data. Despite this, only a few studies have tried to directly estimate the possible advantages of highly resolved simulations for the study of past climate change. Motivated by such considerations, in this paper we present a set of high-resolution simulations for different time slices of the mid-to-late Holocene performed over Europe using the state-of-the-art regional climate model COSMO-CLM. After proposing and testing a model configuration suitable for paleoclimate applications, the aforementioned mid-to-late Holocene simulations are compared against a new pollen-based climate reconstruction data set, covering almost all of Europe, with two main objectives: testing the advantages of high-resolution simulations for paleoclimatic applications, and investigating the response of temperature to variations in the seasonal cycle of insolation during the mid-to-late Holocene. With the aim of giving physically plausible interpretations of the mismatches between model and reconstructions, possible uncertainties of the pollen-based reconstructions are taken into consideration. Focusing our analysis on near-surface temperature, we can demonstrate that concrete advantages arise in the use of highly resolved data for the comparison against proxy-reconstructions and the investigation of past climate change. Additionally, our results reinforce previous findings showing that summertime temperatures during the mid-to-late Holocene were driven mainly by changes in insolation and that the model is too sensitive to such changes over Southern Europe, resulting in drier and warmer conditions. However, in winter, the model does not correctly reproduce the same amplitude of changes evident in the reconstructions, even if it captures the main pattern of the pollen data set over most of the domain for the time periods under investigation. Through the analysis of variations in atmospheric circulation we suggest that, even though the wintertime discrepancies between the two data sets in some areas are most likely due to high pollen uncertainties, in general the model seems to underestimate the changes in the amplitude of the North Atlantic Oscillation, overestimating the contribution of secondary modes of variability.

Description: In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

Description: In this paper we describe the PALM model system 6.0. PALM is a Fortran based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model by components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

Description: Data assimilation (DA) methods have been used recently to constrain the climate model forecasts by paleo-proxy records. Both DA and climate models are computationally very expensive. Moreover, in paleo-DA, the time step of consequence for observations is usually too long for a dynamical model to follow the previous analysis state and the chaotic behavior of the model becomes dominant. The majority of recent paleoclimate studies using DA have performed low- or intermediate-resolution global simulations along with an “off-line” DA approach. In an off-line DA, the re-initialization cycle is completely removed after the assimilation step. In this paper, we design a computationally affordable DA to assimilate yearly pseudo-observations and real observations into an ensemble of COSMO-CLM high-resolution regional climate model (RCM) simulations over Europe, for which the ensemble members slightly differ in boundary and initial conditions. Within a perfect model experiment, the performance of the applied DA scheme is evaluated with respect to its sensitivity to the noise levels of pseudo-observations. It was observed that the injected bias in the pseudo-observations linearly impacts the DA skill. Such experiments can serve as a tool for the selection of proxy records, which can potentially reduce the state estimation error when they are assimilated. Additionally, the sensitivity of COSMO-CLM to the boundary conditions is addressed. The geographical regions where the model exhibits high internal variability are identified. Two sets of experiments are conducted by averaging the observations over summer and winter. Furthermore, the effect of the spurious correlations within the observation space is studied and a optimal correlation radius, within which the observations are assumed to be correlated, is detected. Finally, the pollen-based reconstructed quantities at the mid-Holocene are assimilated into the RCM and the performance is evaluated against a test dataset. We conclude that the DA approach is a promising tool for creating high-resolution yearly analysis quantities. The affordable DA method can be applied to efficiently improve climate field reconstruction efforts by combining high-resolution paleoclimate simulations and the available proxy records.

Description: Due to its extension, geography and the presence of several under-developed or developing economies, the Central Asia domain of the Coordinated Regional climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, Regional Climate Models (RCMs) play a fundamental role. In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for present-days, testing the effects of a set of singular physical parameterizations and their mutual interaction on the simulation of monthly and seasonal values of three variables that are important for impact studies: 2-meter temperature, precipitation and diurnal temperature range. The final goal of this study is two-fold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration. Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo taking into consideration the ratio of forest fractions and the soil conductivity taking into account the ratio of liquid water and ice in the soil, allows to achieve the best improvements in model performance in terms of climatological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The results for the mean climate appear to be independent of the observational dataset used for evaluation and of the boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability. This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area.

Description: The parameter uncertainty of a climate model represents the spectrum of the results obtained by perturbing its empirical and unconfined parameters used to represent subgrid-scale processes. In order to assess a model's reliability and to better understand its limitations and sensitivity to different physical processes, the spread of model parameters needs to be carefully investigated. This is particularly true for regional climate models (RCMs), whose performance is domain dependent. In this study, the parameter space of the Consortium for Small-scale Modeling CLimate Mode (COSMO-CLM) RCM is investigated for the Central Asia Coordinated Regional Climate Downscaling Experiment (CORDEX) domain, using a perturbed physics ensemble (PPE) obtained by performing 1-year simulations with different parameter values. The main goal is to characterize the parameter uncertainty of the model and to determine the most sensitive parameters for the region. Moreover, the presented experiments are used to study the effect of several parameters on the simulation of selected variables for subregions characterized by different climate conditions, assessing by which degree it is possible to improve model performance by properly selecting parameter inputs in each case. Finally, the paper explores the model parameter sensitivity over different domains, tackling the question of transferability of an RCM model setup to different regions of study. Results show that only a subset of model parameters present relevant changes in model performance for different parameter values. Importantly, for almost all parameter inputs, the model shows an opposite behaviour among different clusters and regions. This indicates that conducting a calibration of the model against observations to determine optimal parameter values for the Central Asia domain is particularly challenging: in this case, the use of objective calibration methods is highly necessary. Finally, the sensitivity of the model to parameter perturbation for Central Asia is different than the one observed for Europe, suggesting that an RCM should be retuned, and its parameter uncertainty properly investigated, when setting up model experiments for different domains of study.