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: One of the most prominent asymmetric features of the southern hemispheric (SH) circulation is the split jet over Australia and New Zealand in austral winter. Previous studies have developed indices to detect the degree to which the upper-level midlatitude westerlies are split and investigated the relationship between split events and the low-frequency teleconnection patterns, viz. the Antarctic Oscillation (AAO) and the El Niño–Southern Oscillation (ENSO). As the results were inconsistent, the relationship between the wintertime SH split jet and the climate variability indices remains unresolved and is the focus of this study. Until now, all split indices' definitions were based on the specific region where the split jet is recognizable. We consider the split jet as hemispheric rather than a regional feature and propose a new, hemispherical index that is based on the principal components (PCs) of the zonal wind field for the SH winter. A linear combination of PC2 and PC3 of the anomalous monthly (JAS) zonal wind is used to identify split-jet conditions. In a subsequent correlation analysis, our newly defined PC-based split index (PSI) indicates a strong coherence with the AAO. However, this significant relationship is unstable over the analysis period; during the 1980s, the AAO amplitude was higher than the PSI, and vice versa in the 1990s. It is probable that the PSI, as well as the AAO, underlie low-frequency variability on the decadal to centennial timescales, but the analyzed period is too short to draw these conclusions. A regression analysis with the Multivariate ENSO Index points to a nonlinear relationship between PSI and ENSO; i.e., split jets occur during both strong positive and negative phases of ENSO but rarely under “normal” conditions. The Pacific South American (PSA) patterns, defined as the second and third modes of the geopotential height variability at 500 hPa, correlate poorly with the PSI (rPSA−1 ≈ 0.2 and rPSA-2= 0.06), but significantly with the individual components (PCs) of the PSI, revealing an indirect influence on the SH split-jet variability. Our study suggests that the wintertime SH split jet is strongly associated with the AAO, while ENSO is to a lesser extent connected to the PSI. We conclude that a positive AAO phase, as well as both flavors of ENSO and the PSA-1 pattern produce favorable conditions for a SH split event.

Description: Paleoclimate data assimilation (DA) is a promising technique to systematically combine the information from climate model simulations and proxy records. Here, we investigate the assimilation of tree-ring-width (TRW) chronologies into an atmospheric global climate model using ensemble Kalman filter (EnKF) techniques and a process-based tree-growth forward model as an observation operator. Our results, within a perfect-model experiment setting, indicate that the "online DA" approach did not outperform the "off-line" one, despite its considerable additional implementation complexity. On the other hand, it was observed that the nonlinear response of tree growth to surface temperature and soil moisture does deteriorate the operation of the time-averaged EnKF methodology. Moreover, for the first time we show that this skill loss appears significantly sensitive to the structure of the growth rate function, used to represent the principle of limiting factors (PLF) within the forward model. In general, our experiments showed that the error reduction achieved by assimilating pseudo-TRW chronologies is modulated by the magnitude of the yearly internal variability in the model. This result might help the dendrochronology community to optimize their sampling efforts.

Description: The East Asian summer monsoon (EASM) is an important part of the global climate system and plays a vital role in the Asian climate. Its sub-seasonal-to-seasonal predictability is a long-standing issue within the monsoon scientist community. In this study, we analyse the seasonal (with six months lead time) prediction skill of the EASM rainfall and its associated general circulation in non-initialised and initialised simulations for the years 1979–2005 performed by six prediction systems (i.e., the BCC-CSM1-1, the CanCM4, the GFDL-CM2p1, the HadCM3, the MIROC5 and the MPI-ESM-LR) from the Coupled Model Intercomparison Project phase 5 (CMIP 5). We find that the simulation of the zonal wind is significantly improved in initialised simulations compared to non-initialized simulations. Based on the knowledge that zonal wind indices can be used as potential predictors for the EASM, we selected an EASM index based upon the zonal wind for further analysis. The assessment show that the GFDL-CM2p1 and the MIROC5 add prediction skill in simulating the EASM index with initialisation, the BCC-CSM1-1, the CanCM4, and the MPI-ESM-LR change the skill insignificantly, and the HadCM3 indicates a decreased skill score. The different response to the initialisation can be traced back to the ability of the models to capture the ENSO (El Niño-Southern Oscillation)-EASM coupled mode, particularly the Southern Oscillation-EASM coupled mode. In summary, we find that the GFDL-CM2p1 and the MIROC5 are capable to predict the EASM on a seasonal time-scale after initialisation.

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: We investigate the assimilation of Tree-Ring-Width (TRW) chronologies into an atmospheric global climate model using Ensemble Kalman Filter (EnKF) techniques and a process-based tree-growth forward model as observation operator. Our results, within a perfect-model experiment setting, indicate that the non-linear response of tree-growth to surface temperature and soil moisture does deteriorate the operation of the time-averaged (EnKF) methodology. Moreover, this skill loss appeared significantly sensitive to the structure of growth rate function, used to represent the Principle of Limiting Factors (PLF)s within the forward model. On the other hand, it was observed that the error reduction achieved by assimilating a particular pseudo-TRW chronology is modulated by the strength of the yearly internal variability of the model at the chronology site. This result might help the dendrochronology community to optimize their sampling efforts. In our experiments, the ''online'' (with cycling) paleao Data Assimilation (DA) approach did not outperform the ''offline'' (no-cycling) one, despite its considerable additional implementation complexity.

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: One of the most prominent asymmetric features of the southern hemispheric (SH) circulation is the split jet over Australia and New Zealand in Austral winter. Earlier studies developed indices to detect to which degree the upper-level westerlies are split and investigated the relationship between split events and the low-frequency teleconnection patterns Antarctic Oscillation (AAO) and El Niño/Southern Oscillation (ENSO). These different studies produced inconsistent results so that the relationships between the wintertime SH split jet and these climate variability indices remain unclear and are thus the scope of this study. Up to now, all split indices are based on a definition, which focuses on a specific region where the jet split is recognizable. We consider the split jet as hemispheric rather than a regional feature and propose a new, hemispherical defined index: Our index is based on the principal components (PC) for the SH wintertime. A linear combination of PC2 and PC3 of the anomalous monthly (JAS) zonal wind field of the SH is used to identify the split jet condition. In a subsequent correlation analysis, our newly defined index (PSI) indicates a strong coherence with the Antarctic Oscillation (AAO). However, the significant relationship is unstable over the analysis period: During the 1980s the AAO amplitude was higher than the PSI and vice versa in the 1990s. It is supposed that the PSI, as well as the AAO, underlie low-frequency variability on the decadal to centennial time scales, but the analysis period is too short to draw these conclusions. A regression analysis with the Multivariate ENSO index points to a nonlinear relation between PSI and ENSO, i.e. split jets occur during strong positive and negative phases of ENSO, but hardly under normal conditions. Our study suggests that the wintertime SH split jet is strongly associated with the AAO, while ENSO is to a lesser extent connected to the PSI. It is concluded that a positive AAO phase, as well as both flavors of ENSO, produce favorable conditions for an SH split event.

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.