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In recent decades, Europe has experienced more frequent flood and drought events. However, little is known about the long-term, spatiotemporal hydroloclimatic changes across Europe. We show the first climate field reconstruction spanning the entire European continent based on tree-ring stable isotopes. A pronounced seasonal consistency in climate response across Europe leads to a unique, well-verified spatial field reconstruction of European summer hydroclimate back to 1600. We find distinct phases of European hydroclimate variability as possible fingerprints of solar activity (coinciding with the Maunder Minimum and the end of the Little Ice Age), pronounced decadal variability and a long-term drying trend from the mid 20th century. The recent European summer conditions are highly unusual in a multi-century context and unprecedented for large parts of central and western Europe.

In recent decades, Europe has experienced more frequent flood and drought events. However, little is known about the long-term, spatiotemporal hydroclimatic changes across Europe. Here we present a climate field reconstruction spanning the entire European continent based on tree-ring stable isotopes. A pronounced seasonal consistency in climate response across Europe leads to a unique, well-verified spatial field reconstruction of European summer hydroclimate back to AD 1600. We find three distinct phases of European hydroclimate variability as possible fingerprints of solar activity (coinciding with the Maunder Minimum and the end of the Little Ice Age) and pronounced decadal variability superimposed by a long-term drying trend from the mid-20th century. We show that the recent European summer drought (2015–2018) is highly unusual in a multi-century context and unprecedented for large parts of central and western Europe. The reconstruction provides further evidence of European summer droughts potentially being influenced by anthropogenic warming and draws attention to regional differences.

Explosive volcanic eruptions affect surface climate, especially in monsoon regions, but responses vary in different regions and to volcanic aerosol injection (VAI) in different hemispheres. Here, we use six ensemble members from the last-millennium experiment of the Coupled Model Intercomparison Project Phase 5 to investigate the mechanisms of regional hydrological responses to different hemispheric VAIs in the Asian monsoon region (AMR). Northern hemispheric VAI (NHVAI) leads to an intensified aridity over the AMR after northern hemispheric VAI (NHVAI); spatially, a distinct inverse response pattern to the climatological conditions emerges, with an intensified aridity in the relatively wettest area (RWA) but a weakened aridity in the relatively driest area (RDA) of the AMR. Southern hemispheric VAI (SHVAI) shows a weakened aridity over the AMR, but the spatial response pattern is not that clear due to small aerosol magnitude. The mechanism of the hydrological impact relates to the indirect change of atmospheric circulation due to the direct radiative effect of volcanic aerosols. The decreased thermal contrast between the land and the ocean after NHVAI results in a weakened East Asian summer monsoon and South Asian summer monsoon. This changes the moisture transport and cloud formation in the monsoon and westerlies-dominated subregions. The subsequent radiative effect and physical feedbacks of local clouds lead to different hydrological effects in different areas. Results here indicate that future volcanic eruptions may temporarily alleviate the uneven distribution of precipitation in the AMR, which should be considered in the near-term climate predictions and future strategies of local adaptation to global warming. The local hydrological responses and mechanisms found here can also provide a reference for stratospheric aerosol engineering.

Today's climate science is being driven by IT more than ever. Earth system models on high-performance computers (HPC) are common tools for researching the past and projecting it into the future. In addition to that, statistical modelling is reborn thanks to modern computer architectures equipped with artificial intelligence (from ensemble to deep learning). Future advances in machine learning will also shape climate research through analysis tools, prediction techniques, signal and event classification, post-processing, Model Output Statistics (MOS), evaluation and verification, etc. This presentation will look at nowadays research about the future (part one) and the past (part two) of our climate system using AI/ML ideas and technologies in combination with numerical climate models - from two publications accordingly. A special focus will be on the importance of climate science, where the needs are, and how to choose the AI/ML hammer wisely:(1) FUTURE: Derived from machine (ensemble) learning and bagging, a new hybrid climate prediction technique called 'Ensemble Dispersion Filter' is developed. It exploits two important climate prediction paradigms: the ocean's heat capacity and the advantage of the ensemble mean. The Ensemble Dispersion Filter averages the ocean temperatures of the ensemble members every three months, uses this ensemble mean as a restart condition for each member, and further executes the prediction. The evaluation shows that the Ensemble Dispersion Filter results in a significant improvement in the predictive skill compared to the unfiltered reference system. Even in comparison with prediction systems of a larger ensemble size and higher resolution, the Ensemble Dispersion Filter system performs better. In particular, the prediction of the global average temperature of the forecast years 2 to 5 shows a significant skill improvement. Kadow, C., Illing, S., Kröner, I., Ulbrich, U., and Cubasch, U. (2017), Decadal climate predictions improved by ocean ensemble dispersion filtering, J. Adv. Model. Earth Syst., 9, 1138– 1149, doi:10.1002/2016MS000787. (2) PAST: Nowadays climate change research relies on climate information of the past. Historic climate records of temperature observations form global gridded datasets like HadCRUT4, which is investigated e.g. in the IPCC reports. However, record combining data-sets are sparse in the past. Even today they contain missing values. Here we show that artificial intelligence (AI) technology can be applied to reconstruct these missing climate values. We found that recently successful image inpainting technologies, using partial convolutions in a CUDA accelerated deep neural network, can be trained by 20CR reanalysis and CMIP5 experiments. The derived AI networks are capable to independently reconstruct artificially trimmed versions of 20CR and CMIP5 in grid space for every given month using the HadCRUT4 missing value mask. The evaluation reaches high temporal correlations and low errors for the global mean temperature. Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5

Freva – Free Evaluation System Framework for Earth system modeling is an efficient solution to handle evaluation systems of research projects, institutes or universities in the climate community. It is a scientific software framework for high performance computing that provides all its available features both in a shell and web environment. The main system design is equipped with the programming interface, history of evaluations, and a standardized model database. Plugin – a generic application programming interface allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language. History – the configuration sub-system stores every analysis performed with the evaluation system in a database. Databrowser – an implemented meta data system with its advanced but easy-to-handle search tool supports scientists and their plugins to retrieve the required information of the database. The combination of these three core components, increases the scientific outcome and enables transparency and reproducibility for research groups using Freva as their framework for evaluation of Earth system models.

Explosive volcanic eruptions influence near-surface temperature and precipitation especially in the monsoon regions, but the impact varies with different eruption seasons and latitudes. To study this variability, two groups of ensemble simulations are performed with volcanic eruptions in June and December at 0∘ representing an equatorial eruption (EQ) and at 30∘ N and 30∘ S representing Northern and Southern Hemisphere eruptions (NH and SH). Results show significant cooling especially in areas with enhanced volcanic aerosol content. Compared to the EQ eruption, stronger cooling emerges in the Northern Hemisphere after the NH eruption and in the Southern Hemisphere after the SH eruption. Stronger precipitation variations occur in the tropics than in the high latitudes. Summer and winter eruptions lead to similar hydrological impacts. The NH and the SH eruptions have reversed climate impacts, especially in the regions of the South Asian summer monsoon (SASM). After the NH eruption, direct radiative effects of volcanic aerosols induce changes in the interhemispheric and land–sea thermal contrasts, which move the intertropical convergence zone (ITCZ) southward and weaken the SASM. This reduces the moisture transport from the ocean and reduces cloud formation and precipitation in India. The subsequent radiative feedbacks due to regional cloud cover lead to warming in India. After the SH eruption, vice versa, a northward movement of the ITCZ and strengthening of the SASM, along with enhanced cloud formation, lead to enhanced precipitation and cooling in India. This emphasizes the sensitivity of regional climate impacts of volcanic eruptions to eruption latitude, which relates to the dynamical response of the climate system to radiative effects of volcanic aerosols and the subsequent regional physical feedbacks. Our results indicate the importance of considering dynamical and physical feedbacks to understand the mechanism behind regional climate responses to volcanic eruptions and may also shed light on the climate impact and potential mechanisms of stratospheric aerosol engineering.

Proxy‐model comparisons show large discrepancies in the impact of volcanic aerosols on the hydrology of the Asian monsoon region (AMR). This was mostly imputed to uncertainties arising from the use of a single model in previous studies. Here we compare two groups of CMIP5 multimodel ensemble mean (MMEM) with the tree‐ring‐based reconstruction Monsoon Asia Drought Atlas (MADA PDSI), to examine their reliability in reproducing the hydrological effects of the volcanic eruptions in 1300–1850 CE. Time series plots indicate that the MADA PDSI and the MMEMs agree on the significant drying effect of volcanic perturbation over the monsoon‐dominated subregion, while disparities exist over the westerlies‐dominated subregion. Comparisons of the spatial patterns suggest that the MADA PDSI and the MMEMs show better agreement 1 year after the volcanic eruption than in the eruption year and in subregions where more tree‐ring chronologies are available. The MADA PDSI and the CMIP5 MMEMs agree on the drying effect of volcanic eruptions in western‐East Asia, South Asian summer monsoon, and northern East Asian summer monsoon (EASM) regions. Model results suggest significant wetting effect in southern EASM and western‐South Asia, which agrees with the observed hydrological response to the 1991 Mount Pinatubo eruption. Analysis on model output from the Last Millennium Ensemble project shows similar hydrological responses. These results suggest that the CMIP5 MMEM is able to reproduce the impact of volcanic eruptions on the hydrology of the southern AMR., Key Points: Proxy and multimodel ensemble means agree (disagree) on post volcanic hydro‐responses over the Asian monsoon (westerlies)‐dominated subregion. Better agreement of spatial hydrological patterns is suggested 1 year after the eruption and in subregions with more tree‐ring data. Multimodel ensemble means can reproduce the hydrological response to volcanic perturbations in the southern Asian monsoon region.

The parameter uncertainty of a climate model represents the spectrum of the results obtained by perturbing its empirical and unconfined parameters used to represent sub-grid scale processes. In order to assess a model 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 performances are domain-dependent. In this study, the parameter space of the RCM COSMO-CLM is investigated for the CORDEX Central Asia domain, using a Perturbed Physics Ensemble (PPE) obtained by performing 1-year long 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 sub-regions characterized by different climate conditions, assessing by which degree it is possible to improve model performances 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 sub-set of model parameters present relevant changes in model performances for different parameter values. Importantly, for almost all parameter inputs, the model shows an opposite behavior 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 parameters perturbation for Central Asia is different than the one observed for Europe, suggesting that an RCM should be re-tuned, and its parameter uncertainty properly investigated, when setting up model-experiments to different domains of study.

Proxy-model comparisons show large discrepancies on volcanic aerosols' hydrological effects in the Asian monsoon region (AMR). This was mostly imputed to uncertainties of the single model used in p...

This paper investigates the applicability of the Vaganov–Shashkin–Lite (VSL) forward model for tree-ring-width chronologies as observation operator within a proxy data assimilation (DA) setting. Based on the principle of limiting factors, VSL combines temperature and moisture time series in a nonlinear fashion to obtain simulated TRW chronologies. When used as observation operator, this modelling approach implies three compounding, challenging features: (1) time averaging, (2) “switching recording” of 2 variables and (3) bounded response windows leading to “thresholded response”. We generate pseudo-TRW observations from a chaotic 2-scale dynamical system, used as a cartoon of the atmosphere-land system, and attempt to assimilate them via ensemble Kalman filtering techniques. Results within our simplified setting reveal that VSL’s nonlinearities may lead to considerable loss of assimilation skill, as compared to the utilization of a time-averaged (TA) linear observation operator. In order to understand this undesired effect, we embed VSL’s formulation into the framework of fuzzy logic (FL) theory, which thereby exposes multiple representations of the principle of limiting factors. DA experiments employing three alternative growth rate functions disclose a strong link between the lack of smoothness of the growth rate function and the loss of optimality in the estimate of the TA state. Accordingly, VSL’s performance as observation operator can be enhanced by resorting to smoother FL representations of the principle of limiting factors. This finding fosters new interpretations of tree-ring-growth limitation processes., Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe, 892

The Free Evaluation System Framework (Freva - freva.met.fu-berlin.de) is a software infrastructure for standardized data and tool solutions in Earth system science. Freva runs on high performance computers to handle customizable evaluation systems of research projects, institutes or universities. It combines different software technologies into one common hybrid infrastructure, including all features present in the shell and web environment. The database interface satisfies the international standards provided by the Earth System Grid Federation (ESGF). Freva indexes different data projects into one common search environment by storing the meta data information of the self-describing model, reanalysis and observational data sets in a database. This implemented meta data system with its advanced but easy-to-handle search tool supports users, developers and their plugins to retrieve the required information. A generic application programming interface (API) allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language used. Users of the evaluation techniques benefit from the common interface of the evaluation system without any need to understand the different scripting languages. Facilitation of the provision and usage of tools and climate data automatically increases the number of scientists working with the data sets and identifying discrepancies. The integrated webshell (shellinabox) adds a degree of freedom in the choice of the working environment and can be used as a gate to the research projects HPC. Plugins are able to integrate their e.g. post-processed results into the database of the user. This allows e.g. post-processing plugins to feed statistical analysis plugins, which fosters an active exchange between plugin developers of a research project. Additionally, the history and configuration sub-systemstores every analysis performed with the evaluation system in a database. Configurations and results of the toolscan be shared among scientists via shell or web system. Therefore, plugged-in tools benefit from transparency and reproducibility. Furthermore, if configurations match while starting an evaluation plugin, the system suggests touse results already produced by other users – saving CPU/h, I/O, disk space and time. The efficient interaction between different technologies improves the Earth system modeling science framed by Freva.New Features and aspects of further development and collaboration are discussed.

This research mainly evaluates the human bio-meteorological conditions and its changes in the Tibetan Plateau over the past 27 years under the simulation of regional climate models of HadGEM3-RA and RegCM from the CORDEX-East Asia experiments by using the Universal Thermal Climate Index (UTCI). Both models are able to reproduce the present climate realistically. As an extension, the human thermal comfort information about Tibetan Plateau in the next 27 years is projected under the scenarios of RCP4.5 and RCP8.5. The results show that UTCI in Tibetan Plateau covers four stress categories, namely strong cold stress, moderate cold stress, slight cold stress, no thermal stress, and cold stresses, is prevailing throughout the whole year. A small amount of no thermal stress category appears in the summer period and the human bio-meteorological condition is most stable at the same period, especially from July to September. According to the projections in the near future, with climate change taken into account, annual cumulative pleasant days will increase significantly while the cold stresses days will reduce. The distribution frequency of UTCI categories varies among regions showing clear altitude/latitude dependency. Lhasa, Xining, and Yushu will be the top three cities in terms of thermal favourability by analysing the results of both models. The policy of migration, urban planning, tourism authorities, travel agencies, resorts, and tourists in Tibetan Plateau could be beneficial from these results.

This study investigates the subseasonal intensity variation of South Asian high (SAH) and the dynamic linkage with precipitation induced diabatic heating during the summer of 1979–2005, based on the observation and 18 models from the Coupled Model Intercomparison Project (CMIP5). The SAH’s intensity variation with a period of approximately 10–36 days is identified both in the observation and 18 models, by performing an empirical orthogonal function (EOF) analysis on the standardized subseasonal anomalies in geopotential height at 100 hPa over the SAH’s main body region. With the observed strengthening of the SAH, an enhanced rainfall belt between 70°–120°E migrates northward from the equatorial region. The northward propagating rainfall belt occupies almost the whole Indian subcontinent, Indochina Peninsula and subtropical east Asian regions between 20°–40°N when the SAH reaches the strongest. The relative vorticity diagnosis reveals the dynamical linkage between the subseasonal SAH’s intensity variation and the precipitation anomalies over the Asian monsoon region: (1) The observed northward propagating rainfall band forms a horizontal gradient of diabatic heating, favoring an enhancement of the SAH over its southern part; (2) When the rainfall band approaches the regions of South and East Asia between 20°–40°N, the increased vertical gradient of diabatic heating in the upper level contributes significantly to the intensification of the SAH; (3) The horizontal advection process favors the westward expansion of the SAH. The observation-to-model comparisons indicate that the model’s capacity to reproduce the SAH’s intensity variation is highly associated with simulations of the anomalous rainfall band and its northward propagation. A realistic reproduction of precipitation anomalies is fundamental to simulating the dynamical processes related to diabatic heating, and thus the simulation of the SAH’s subseasonal intensity variation.

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.

The thermal comfort and its changes in the 31 provincial capital cities of mainland China in the past 30 years were comprehensively evaluated using the Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) indicators. The PET and UTCI values were highly correlated with each other and presented similar thermal comfort pattern, although their sensitivities might differ slightly. The results showed that these cities covered, respectively, 4–8 and 6–8 thermal comfort classes of the PET and UTCI scale. On the whole, the annual cumulative number of pleasant days was more than 160 days/year. In terms of seasonal variations in thermal comfort conditions, the 31 provincial capital cities in mainland China can be classified into 5 types, which are, respectively, characterized by pleasant summer and severe cold winter (type-I); pleasant spring, autumn, winter, and severe hot summer (type-II); pleasant spring and autumn, slightly pleasant summer, and cold winter (type-III); pleasant spring and autumn, hot stress summer, and slightly cold winter (type-IV); and pleasant spring, summer, autumn, and cool winter (type-V). Type-II cities are rare winter resorts, while type-I cities are natural summer resorts. Type-V cities are the year round pleasant resorts. In the past three decades, the cities in mainland China had experienced increasing pleasant duration in late winter and early spring and intensifying heat stress in summer. The reduction in annual cumulative number of cold stress days in higher latitude/altitude cities outweighed the increase in duration of heat stress in subtropical cities. These may provide some references for urban planning and administration in mainland China.

We present a climate modeling approach to reproduce the rainfall patterns over Iran due to the climatic forcings during the past 6000 years. The selected periods are simulated using a spatially high-resolved atmosphere General Circulation Model (GCM). Our results show that the winter rainfall patterns over Iran have changed due to the changes in solar insolation with a wetter condition starting around 3 ka BP and reaching its maximum during the Medieval Climate Anomaly ca. 1 ka BP. The rainfall variability can be explained by the changes in atmospheric conditions as a result of changing incoming solar irradiance based on the Milankovitch theory. A shift in the Earth's energy budget leads to the modulation of the West Asian Subtropical Westerly Jet (WASWJ). The investigations support the hypothesis that during the Holocene a northward shift in the WASWJ contributes to the less cyclonic activities over Iran. This brings less moisture into the region during the winter.

The North Atlantic is a crucial region for the prediction of weather and climate of North America and Europe and is the focus of this analysis. A skillful decadal prediction of the surface temperature was shown for several Earth system models, with the North Atlantic standing out as one region with higher predictive skill. This skill assessment concentrates on the rapid increase of the annual mean sea surface temperature of the North Atlantic subpolar gyre by about 1 K in the mid‑1990s and the adjacent years. This event-oriented analysis adds creditability to the decadal predictions and reveals the potential for improvements. The ability to simulate the observed sea surface temperature in the North Atlantic is quantified by using four versions of decadal predictions, which differ in model resolution, initialization technique, and the reanalysis data used in the assimilation run. While all four versions can reproduce the mid-1990s warming of the subpolar North Atlantic, the characteristics differ with lead time and version. The higher vertical resolution in the atmosphere and the higher horizontal resolution in the ocean improve the decadal prediction for longer lead times, and the anomaly initialization outperforms the full-field initialization for short lead times. The effect from the two different ocean reanalysis products on the predictive skill is strongest in the first two prediction years; a substantial cooling instead of the warming in the central North Atlantic reduces the skill score for the North Atlantic sea surface temperature in one version, whereas a too large interannual variability, compared with observations, lowers the skill score in the other version. The cooling patches are critical since the resulting gradients in sea surface temperature and their effect on atmospheric dynamics deviate from observations, and, moreover, hinder the skillful prediction of atmospheric variables.

To obtain an estimate of the average temperature of the northern hemisphere during the last 1200 years, proxy data have been merged with instrumental recordings. These instrumental measurements are, with a few exceptions, only available for the recent 150 years. In the city of Berlin the temperature has been recorded since as early as 1701. However, during the first 150 years the measurements were problematic as location, measurement procedure and instruments changed frequently and without proper documentation. From 1847 onwards observations became more reliable once the Royal Prussian Meteorological Institute had been established. For the last 100 years temperature and precipitation measurements have been performed in parallel at Berlin-Dahlem and Potsdam. The datasets recorded in the city of Berlin and in Berlin-Dahlem have been merged to obtain a record of more than 300 years. It indicates that the temperature of Berlin has risen by 1.04°C during the last 100 years after correcting for the urbanisation effect. In the same period, the total number of frost days has significantly decreased by almost 17 days, and the number of summer days has significantly increased by about 12 days. Annual mean precipitation has hardly changed (decrease less than 0.2 %) during the last century. However, rainfall has decreased by about 4 % in summer and increased by 3 % in winter. All precipitation changes are below the 95 % significance level. Model projections indicate that warming will continue which means that Berlin-Brandenburg will experience a temperature rise of about 3-3.5°C by the end of this century for the IPCC scenario A1B. For the same scenario precipitation is expected to increase by 10-20 % in winter and to decrease by 10-30 % in summer: The seasonal precipitation changes compensate each other resulting in an almost unchanged annual mean.

Residual mean circulation changes during the evolution of sudden stratospheric warming (SSW) are investigated by composite analyses of 76 major warming events identified in a present day simulation performed with a coupled ocean–troposphere–stratosphere model from 299 winters. Their dynamical signatures are compared with the 17 SSW events identified from 35 years of Era-Interim data. The main difference is that, relative frequency of simulated SSW events is smaller than that obtained from reanalysis. SSW events are classified as displacement or split events based on the geopotential field values at 10 hPa. The geopotential field values identify 10 and 3 split events in simulation and observation respectively. The model quite accurately simulates some of the dynamical features associated with the major SSW. Residual mean circulation induced by EP-flux divergence, sum of advection and residual forcing are stronger in split events than in displacement type SSW has been confirmed by both simulation and observation. Moreover, the contribution of EP-flux divergence or planetary wave forcing is larger than the contribution of other types of forcing.

Simulations using the ECHAM5/MPI-OM coupled atmosphere–ocean model both with and without the Tibetan Plateau are performed in order to study the large scale effects of orographic forcing on the behaviour of the Asian summer monsoon system. Our analysis emphasises the significant impact of plateau forcing on the atmosphere–ocean interactions. It is argued that, in addition to the orographic forcing of the Tibetan Plateau on the climate of Asia such as sensible heat pumping and thermal insulation, other significant direct processes exist, which link the Asian summer monsoon to the sea surface temperatures in the North Atlantic Ocean. The removal of the Tibetan Plateau modifies the wind-driven ocean circulations over the North Atlantic, which leads to a decrease in the surface heat advection over the North Atlantic Ocean and a decrease in the Atlantic Meridional Overturning Circulation. This, in turn, affects, via teleconnections, both the monsoon rainfall and the position of the intertropical convergence zone.

This study tackles one of the most debated ques- tions around the evolution of Central Asian climate: the "Puzzle" of moisture changes in Arid Central Asia (ACA) throughout the past millennium. A state-of-the-art Regional Climate Model (RCM) is subsequently employed to inves- tigate four different 31-year time slices of extreme dry and wet spells, chosen according to changes in the driving data, in order to analyse the spatio-temporal evolution of the moisture variability in two different climatological epochs: Medieval Climate Anomaly (MCA) and Little Ice Age (LIA). There is a clear regime behavior and bimodality in the westerly Jet phase space throughout the past millennium in ACA. The results indicate that the regime changes during LIA show a moist ACA and a dry East China. During the MCA, the Kazakhstan region shows a stronger response to the westerly jet equatorward shift than during the LIA. The out-of-phase pattern of moisture changes between India and ACA exists during both the LIA and the MCA. However, the pattern is more pronounced during the LIA.

Parameter and structure identifications are necessary in any modelling which aims to achieve a generalised model. Although ANFIS (Adaptive Network-based Fuzzy Inference System) employs well-known parameter-identification techniques, it needs to structure identification techniques for the determination of an optimum number of fuzzy rules and the selection of significant input variables from among the candidate input variables. In this study, a new structure identification scheme is developed and introduced, which is simultaneously capable of the selection of significant input variables and the determination of an optimum number of rules. This new structure identification was joined to ANFIS, and this joined modelling framework was applied to the simulation of virtual air-pollution monitoring stations in Berlin. In this study, 18 virtual particulate matter stations were simulated using the particulate matter data of some of the current stations. In other words, the particulate matter monitoring network of Berlin has been intensified. The evaluation of simulated virtual stations shows that, although the uncertainty of daily particulate matter measurement is about 10 percent, the simulated virtual stations can estimate the mean daily particulate matter with less than 10 percent of error. Mean absolute error and root mean square error of the simulations are less than 2.4 and 3.4µg/m3, respectively. The correlation coefficient of the simulation results was more than 0.94. In addition, the range of mean bias error is between −1.0 and 0.5µg/m3, and the range of factor of exceedance is between −14.8 and 10.8 percent. It means that the simulated virtual stations have a small bias. These results demonstrated the appropriate performance of the joined new structure identification scheme and ANFIS for development of a virtual air pollution monitoring network.

The objective of this study is the scale dependent evaluation of precipitation forecasts of the Lokal-Modell (LM) from the German Weather Service in relation to dynamical and cloud parameters. For this purpose the newly designed Dynamic State Index (DSI) is correlated with clouds and precipitation. The DSI quantitatively describes the deviation and relative distance from a stationary and adiabatic solution of the primitive equations. A case study and statistical analysis of clouds and precipitation demonstrates the availability of the DSI as a dynamical threshold parameter. This confirms the importance of imbalances of the atmospheric flow field, which dynamically induce the generation of rainfall.

Two PMIP3/CMIP5 climate model ensemble simulations of the past millennium have been analysed to identify the occurrence of Asian mega-droughts. The Palmer drought severity index (PDSI) is used as the key metric for the data comparison of hydro-climatological conditions. The model results are compared with the proxy data of the Monsoon Asia Drought Atlas (MADA). Our study shows that global circulation models (GCMs) are capable of capturing the majority of historically recorded Asian monsoon failures at the right time and with a comparable spatial distribution. The simulations indicate that El Niño-like events lead, in most cases, to these droughts. Both model simulations and proxy reconstructions point to fewer monsoon failures during the Little Ice Age. The results suggest an influential impact of volcanic forcing on the atmosphere–ocean interactions throughout the past millennium. During historic mega-droughts of the past millennium, the monsoon convection tends to assume a preferred regime described as a "break" event in Asian monsoon. This particular regime is coincident with a notable weakening in the Pacific trade winds and Somali Jet.

The spatial representativity of monitoring stations plays a major role for the reasonable estimation of air pollutants. The ranking of air pollution monitoring stations based upon their spatial representativity identifies the level of representativeness of the stations and is very useful for developing optimum monitoring networks. In this study, a new ranking method, named RTFI (Ranking Technique based upon Fuzzy Interpolation) is introduced. This ranking method is able to rank air pollution monitoring stations in the urban areas based upon their spatial representativity. Although spatial correlation techniques are often used in the ranking techniques in order to consider spatial representativity, in this ranking technique, the spatial representativity of a station is not limited to its surroundings and is measured independently of its location. RTFI was applied to airborne Particulate Matter (PM) at seven stations in Berlin, and ranked them according to their spatial representativity. The results showed that the Neukolln-Nanenstr station (MC 42) is the most spatially representative station among the studied stations.

A review of findings contained in the IPCC AR5 Synthesis Report, of particular relevance to the Polish audience, is offered. Polish perspectives on coal-climate nexus are discussed in a broader, universal, context. Positive examples of climate policies in other countries are provided. The title of this paper refers to a public conference organized in Warsaw by the Embassies of France, Germany, and the UK and the Polish Academy of Sciences.

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.

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 seasonal predictability is a long-standing issue within the monsoon scientist community. In this study, we analyse the seasonal (the leading time is at least 6 months) prediction skill of the EASM rainfall and its associated general circulation in non-initialised and initialised simulations for the years 1979–2005, which are 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 most prediction systems of simulated zonal wind over 850 and 200 hPa are significantly improved in the initialised simulations compared to non-initialised simulations. Based on the knowledge that zonal wind indices can be used as potential predictors for the EASM, we select an EASM index based upon the zonal wind over 850 hPa for further analysis. This assessment shows that the GFDL-CM2p1 and the MIROC5 added prediction skill in simulating the EASM index with initialisation, the BCC-CSM1-1, the CanCM4, and the MPI-ESM-LR changed the skill insignificantly, and the HadCM3 indicates a decreased skill score. The different responses to initialisation can be traced back to the ability of the models to capture the ENSO (El Niño–Southern Oscillation) and EASM coupled mode, particularly the Southern Oscillation–EASM coupled mode. As is known from observation studies, this mode links the oceanic circulation and the EASM rainfall. Overall, the GFDL-CM2p1 and the MIROC5 are capable of predicting the EASM on a seasonal timescale under the current initialisation strategy.

Several proxy data indicate an aridification of the East African climate during the Neogene, which might be influenced by the orographic changes of the East African Rift System (EARS) induced by tectonic forcing during the last 20 million years. To investigate the impact of the orography and especially of the rifts, the regional climate model CCLM is used, covering the EARS with Lake Victoria in the centre of the model domain. CCLM is driven by the ERA-Interim reanalysis and applied with a double-nesting method resulting in a very high spatial resolution of 7 km. The resolution clearly shows the shoulders and rifts of the western and eastern branch of the EARS and the Rwenzoris within the western branch. To analyse the orographic influence on climate, a new technique of modifying the orography is used in this sensitivity study. The shoulders of the branches are lowered and the rifts are elevated, resulting in a smoothed orography structure with less altitude difference between the shoulders and rifts. The changes in 2 m-temperature are very local and associated with the changes in the orography. The vertically integrated moisture transport is characterised by less vortices, and its zonal component is increased over the branches. The resulting amount of precipitation is mainly decreased west of the western branch and increased in the rift of the western branch. In the eastern branch, however, the changes in the amount of precipitation are not significant. The changes in the precipitation and temperature patterns lead to a shift of biomes towards a vegetation coverage characterised by more humid conditions in the northern part of the model domain and more arid conditions in the South. Thus, the aridification found in the proxy data can be attributed to the orographic changes of the rifts only in the northern model domain.

The Indian Monsoon Variability during the past Millennium has been simulated with the ECHAM5 model in two different time slices: Medieval Climate Anomaly and the Little Ice Age. The simulations are compared with new centennial-resolving paleo-reconstructions inferred from various well-dated multi-proxies from two core regions, the Himalaya and Central India. A qualitative moisture index is derived from the proxies and compared with simulated moisture anomalies. The reconstructed paleo-hydrological changes between the Little Ice Age and the Medieval Climate Anomaly depict a dipole pattern between Himalaya and Central India, which is also captured by the model. In the Medieval Climate Anomaly the model exhibits stronger (weaker) dipole signals during summer (winter) compared to Little Ice Age. In summer (winter) months of "Medieval Climate Anomaly minus Little Ice Age" the model simulates wetter conditions over eastern (western and central) Himalaya. Over Central India, a simulated weakening of Indian Summer Monsoon during warmer climate is coincident with reconstructed drying signal in the Lonar Lake record. Based on the model simulations, we can differentiate three physical mechanisms which can lead to the moisture anomalies: (i) the western and central Himalaya are influenced by extra-tropical Westerlies during winter, (ii) the eastern Himalaya is affected by summer variations of temperature gradient between Bay of Bengal and Indian subcontinent and by a zonal band of intensified Indian-East Asian monsoon link north of 25 degrees N, and (iii) Central India is dominated by summer sea surface temperature anomalies in the northern Arabian Sea which have an effect on the large-scale advection of moist air masses. The temperatures in the Arabian Sea are linked to the Ind Pacific Warm Pool, which modulates the Indian monsoon strength. (C) 2014 The Authors. Published by Elsevier B.V.

The surface climate response to 11-yr solar forcing during northern winter is first reestimated by applying a multiple linear regression (MLR) statistical model to Hadley Centre sea level pressure (SLP) and sea surface temperature (SST) data over the 1880–2009 period. In addition to a significant positive SLP response in the North Pacific found in previous studies, a positive SST response is obtained across the midlatitude North Pacific. Negative but insignificant SLP responses are obtained in the Arctic. The derived SLP response at zero lag therefore resembles a positive phase of the Arctic Oscillation (AO). Evaluation of the SLP and SST responses as a function of phase lag indicates that the response evolves from a negative AO-like mode a few years before solar maximum to a positive AO-like mode at and following solar maximum. For comparison, a similar MLR analysis is applied to model SLP and SST data from a series of simulations using an atmosphere–ocean general circulation model with a well-resolved stratosphere. The simulations differed only in the assumed solar cycle variation of stratospheric ozone. It is found that the simulation that assumed an ozone variation estimated from satellite data produces solar SLP and SST responses that are most consistent with the observational results, especially during a selected centennial period. In particular, a positive SLP response anomaly is obtained in the northeastern Pacific and a corresponding positive SST response anomaly extends across the midlatitude North Pacific. The model response versus phase lag also evolves from a mainly negative AO-like response before solar maximum to a mainly positive AO response at and following solar maximum.

A dense monitoring network is vital for the reliable assessment of PM10 in different parts of an urban area. In this study, a new idea is employed for the re-construction of the 20 shut-down PM10 monitoring stations of Berlin. It endeavours to find the non-linear relationship between the hourly PM10 concentration of both the still operating and the shut-down PM10 monitoring stations by using a fuzzy modelling technique, called modified active learning method (MALM). In addition, the simulations were performed by using not only raw PM10 databases but also log-transformed PM10 databases for skewness reduction. According to the results of hourly PM10 simulation (root mean square error about 13.0 μg/m3, correlation coefficient 0.88), the shut-down stations have been appropriately simulated and the idea of dense monitoring network development by the re-construction of the shut-down stations was realised. The results of simulations using raw and log-transformed databases showed that data transformation has no significant effect on the performance of MALM in the simulation of shut-down PM10 stations. By the combination of the 11 still operating stations and the 20 re-constructed stations, a dense monitoring network was generated for Berlin and was utilised for the calculation of the reliable monthly and mean annual PM10 concentration for five different PM10 zones in Berlin (the suburban-background, urban-background, urban-traffic, rural-background and suburban-traffic areas). The results showed that the mean annual concentration of PM10 at the five zones increased by about 13.0% in 2014 (26.3 μg/m3) in comparison with 2013 (23.3 μg/m3). Furthermore, the mean annual concentration of PM10 in the traffic lanes of the suburban (2013 25.0 μg/m3, 2014 26.9 μg/m3) and urban (2013 27.7 μg/m3, 2014 31.3 μg/m3) areas is about 14 and 20% higher than the PM10 concentration of suburban-background (2013 21.3 μg/m3, 2014 24.5 μg/m3) and urban-background (2013 23.0 μg/m3, 2014 26.1 μg/m3) areas, respectively.

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.

Sudden stratospheric warming (SSW) events are identified to investigate their influence on the equatorial tropospheric climate. Composite analysis of warming events from Era-Interim (1979–2013) record a cooling of the tropical lower stratosphere with corresponding changes in the mean meridional stratospheric circulation. A cooling of the upper troposphere induces enhanced convective activity near the equatorial region of the Southern Hemisphere and suppressed convective activity in the off-equatorial Northern Hemisphere. After selecting vortex splits, the see-saw pattern of convective activity in the troposphere grows prominent and robust.

The development of the East African Rift System (EARS) caused by tectonic forcing during the past 20 million years is believed to influence the regional climate in Africa. However, changes in the Earth's orbital parameters also have an influence on climate on these timescales. To analyze the influence of both tectonic and orbital forcing, we applied the non-hydrostatic regional climate model COSMO-CLM (COSMO model in Climate Mode, CCLM). The regional simulations are driven by different global simulations performed with the coupled ocean–atmosphere general circulation model ECHO-G. To analyze the impact of tectonic forcing a different topography representing a possible past stage of the development of the EARS is applied in the model. The results indicate that tectonic forcing has a strong impact on precipitation in Africa caused by circulation changes. To analyze the impact of orbital forcing, which means the impact of changing insolation patterns, we chose configurations of the Earth's orbit that result in significantly different patterns of insolation. One example is the orbital configuration of the last interglacial at 125,000 years before present, when the seasonality of insolation in the Northern Hemisphere was greatly enhanced, whereas in the Southern Hemisphere it was substantially weakened compared to preindustrial conditions. The simulation of this time slice shows a strong impact of orbital forcing on precipitation in large parts of Africa, caused by altered moisture transport. The application of a biome model helps in analyzing the impact of the two forcing factors on vegetation for a more direct comparison with proxy data.

We present the evaluation of temperature and precipitation forecasts obtained with the MiKlip decadal climate prediction system. These decadal hindcast experiments are verified with respect to the accuracy of the ensemble mean and the ensemble spread as a representative for the forecast uncertainty. The skill assessment follows the verification framework already used by the decadal prediction community, but enhanced with additional evaluation techniques like the logarithmic ensemble spread score. The core of the MiKlip system is the coupled Max Planck Institute Earth System Model. An ensemble of 10 members is initialized annually with ocean and atmosphere reanalyses of the European Centre for Medium-Range Weather Forecasts. For assessing the effect of the initialization, we compare these predictions to uninitialized climate projections with the same model system. Initialization improves the accuracy of temperature and precipitation forecasts in year 1, particularly in the Pacific region. The ensemble spread well represents the forecast uncertainty in lead year 1, except in the tropics. This estimate of prediction skill creates confidence in the respective 2014 forecasts, which depict less precipitation in the tropics and a warming almost everywhere. However, large cooling patterns appear in the Northern Hemisphere, the Pacific South America and the Southern Ocean. Forecasts for 2015 to 2022 show even warmer temperatures than for 2014, especially over the continents. The evaluation of lead years 2 to 9 for temperature shows skill globally with the exception of the eastern Pacific. The ensemble spread can again be used as an estimate of the forecast uncertainty in many regions: It improves over the tropics compared to lead year 1. Due to a reduction of the conditional bias, the decadal predictions of the initialized system gain skill in the accuracy compared to the uninitialized simulations in the lead years 2 to 9. Furthermore, we show that increasing the ensemble size improves the MiKlip decadal climate prediction system for all lead years.

In the early 1980s, Germany started a new era of modern Antarctic research. The Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) was founded and important research platforms such as the German permanent station in Antarctica, today called Neumayer III, and the research icebreaker Polarstern were installed. The research primarily focused on the Atlantic sector of the Southern Ocean. In parallel, the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) started a priority program ‘Antarctic Research’ (since 2003 called SPP-1158) to foster and intensify the cooperation between scientists from different German universities and the AWI as well as other institutes involved in polar research. Here, we review the main findings in meteorology and oceanography of the last decade, funded by the priority program. The paper presents field observations and modelling efforts, extending from the stratosphere to the deep ocean. The research spans a large range of temporal and spatial scales, including the interaction of both climate components. In particular, radiative processes, the interaction of the changing ozone layer with large-scale atmospheric circulations, and changes in the sea ice cover are discussed. Climate and weather forecast models provide an insight into the water cycle and the climate change signals associated with synoptic cyclones. Investigations of the atmospheric boundary layer focus on the interaction between atmosphere, sea ice and ocean in the vicinity of polynyas and leads. The chapters dedicated to polar oceanography review the interaction between the ocean and ice shelves with regard to the freshwater input and discuss the changes in water mass characteristics, ventilation and formation rates, crucial for the deepest limb of the global, climate-relevant meridional overturning circulation. They also highlight the associated storage of anthropogenic carbon as well as the cycling of carbon, nutrients and trace metals in the ocean with special emphasis on the Weddell Sea. DFG/SPP/1158

Results from 3 ½-yr experimental program of extended-range integrations of the ECMWF numerical weather prediction model are summarized. The topics discussed include: 1) The evolution of extended-range systematic errors and skill in forecasting large-scale weather regime transitions ; 2) The dependence of extended-range systematic error and skill on model horizontal resolution ; 3) Monthly mean forecast of tropical rainfall ; 4) Tropical/extratropical interaction, and the influence of tropical low-frequency variability on extratropical forecast skill ; 5) Ensemble forecasting, including the impact of ensemble averaging on forecast skill, and ensemble dispersion as a measure of forecast reliability ; and 6) Probabilistic forecasting using phase-space cluster analysis. Our results are broadly consistent with those from other major centers evaluating the feasibility of dynamical extended-range prediction. We believe that operational extende-range forecasting using the ECMWF model may be viable to day 20 – and possibly beyond – following further research on techniques for Monte Carlo forecasting, and when model systematic error in the tropics has been reduced significantly.

A set of 16 ensembles of time-lagged extended-range forecasts have been run at different times of year using the T63 version of the ECMWF operational model. Each ensemble was composed of 9 integrations from consecutive 6-hourly analyses. Theoretical properties of ensemble-mean skill and ensemble spread are studied using a simple model of error growth with a parametrization of the ECMWF model error. The impact of systematic error on the potential improvement in skill of the ensemble-mean forecast is discussed. The presence of model errors considerably reduces the gain from ensemble averaging. In practice, about a third of the ensemble-mean forecasts, at forecast days 11-20, were more skilful than both persistence and climate, and, in addition, were more skilful than the latest member of the ensemble. At days 21-30. only one of the ensemble-mean forecasts was similarly skilful. Whilst there is an overall hemispheric scale correlation between ensemble spread and skill, a substantial part of this reflects the impact of the annual cycle on both quantities. In the winter period, however, no clear spread/skill correlation was found. Within the winter period, there was considerable case-to-case variability in forecast skill. Of all the ensembles that of January 1986 was poorest, whereas that of February 1986 was one of the best. The different character of these two ensembles was shown by considering phase-space trajectories of the ensemble forecasts in the plane spanned by the two principal forecast EOFs of 500mb height. During the first 15 days, the trajectories of the January ensemble forecasts were consistent with each other, but not with the observed atmospheric trajectory (which was associated with the onset of blocking over Europe). During the last 15 days, as the January ensemble forecasts migrated from positive to negative PNA index, the trajectories dispersed quite strongly, becoming disordered. By contrast, the trajectories of the February forecasts remained mutually consistent and in agreement with the real atmosphere’s trajectory throughout most ot the forecast period.

This article details a correction to the article: Illing S, Kadow C, Oliver K, Cubasch U. MurCSS: A Tool for Standardized Evaluation of Decadal Hindcast Systems. Journal of Open Research Software. 2014; 2(1): e24. DOI: http://doi.org/10.5334/jors.bf

In April 2007, the German Weather Service (DWD) added the non-hydrostatic limited area model COSMO-DE to its model chain. COSMO-DE covers mainly Germany and bordering countries and has a horizontal resolution of 2.8 km (0.025°). An advantage of the COSMO-DE is that deep convection is expected to be resolved explicitly. In order to improve the initial state of COSMO-DE an assimilation of radar derived precipitation rates is applied by using "'Latent Heat Nudging'" (LHN). The aim of LHN is to adjust the model state so that the model will respond by producing a rain rate close to the observed value. In this study, the influence of LHN on diabatic processes is investigated by evaluation of the Dynamic State Index (DSI). The DSI is calculated on both isentropic (DSI θ ) and model levels (DSI θ ). To analyse the influence of LHN, model analyses with and without LHN are examined for summer 2009 (June to August). The influence of LHN is also examined in more detail in a case study of a heavy rainfall event on July 18, 2009 where a strong rainfall area was developed on the front side of a trough. Results, based on a statistical investigation and the case study, show that the model with LHN can reproduce the observed rainfall better than the runs without LHN. This is also suggested by a higher correlation between DSI and observed precipitation. The release of latent heat enhances the diabatic processes associated with the formation of additional potential vorticity anomalies resulting in stronger DSI-signals.

A global atmosphere–ocean model has been forced with topographic and orbital scenarios in order to evaluate the relative role of both factors for the past climate of East Africa. Forcing the model with a significantly reduced topography in Eastern and Southern Africa leads to a distinct increase in moisture transport from the Indian Ocean into the eastern part of the continent and increased precipitation in Eastern Africa. Simulations with step-wise reduced height show that this climate change occurs continuously with the change in topography, i.e., an abrupt change of local climatic features with a critical height is not found. Simulations of the last interglacial (at 125,000 years before present, i.e., the Eemian interglacial) and the last glacial inception (at 115,000 years before present) are used as examples for the role of orbital-induced changes in insolation. Here, changes in meridional temperature gradients lead to modifications in moisture transport of similar order of magnitude, but with different spatial and seasonal structure. For the Eemian interglacial, a distinct increase in summer moisture transport from the Atlantic deep into the continent at around 20°N is simulated.

The objective of this study is a scale-dependent analysis of precipitation forecasts of the German Weather Service’s (DWD) non-hydrostatic Lokal-Modell (LM, COSMO-DE) with regard to dynamical-statistical parameters and cloud properties. We propose a newly designed Dynamic State Index (DSI) to evaluate precipitation processes. The DSI is presented in the context of a case study in the synoptic scale and in a statistical approach. The DSI quantitatively describes the deviation from a stationary, adiabatic and reversible solution of the primitive equations. As demonstrated by the example of the winter storm “Kyrill”, the analysis of the vertical structure of the DSI gives a relation to the IPV-Thinking, introduced by Hoskins et al. (1985). Furthermore, the DSI-pattern features the characteristic filament-like structure of rainbands with embedded convective cells. In a next step the DSI is not only correlated with modelled precipitation but also with observed precipitation as well as cloud types. The absolute value of the DSI shows moderate correlations with hourly LM and high correlations with hourly COSMO-DE forecast data, based on 24 hour predictions. The statistical analysis of clouds with the index reveals a DSI-threshold, which is used to introduce a novel precipitation activity index of different cloud classes. In conclusion, the results highlight the importance of dynamical processes for the generation of rainfall. Zusammenfassung

Climate simulations for East Africa have been performed with the regional climate model CLM. Here we evaluate the quality of simulated precipitation by comparing it with observations. Different schemes for convection and cloud ice have been tested in order to determine the configuration with best representation of precipitation patterns for the region. The simulated seasonal behaviour of precipitation is reasonable in all configurations, whereas absolute values are strongly influenced by the selected schemes. Best agreement with observations is achieved with the Tiedtke mass-flux convection scheme in combination with a two-category cloud ice scheme, that considers cloud ice as an additional solid form of water. With the Kain-Fritsch convection scheme the model generally overestimates precipitation. Regional differences are discussed.