@article{LibandaPaeth2023,
  author    = {Libanda, Brigadier and Paeth, Heiko},
  title     = {Modelling wind speed across Zambia: Implications for wind energy},
  series = {International Journal of Climatology},
  volume    = {43},
  journal   = {International Journal of Climatology},
  number    = {2},
  doi       = {10.1002/joc.7826},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312134},
  pages     = {772 -- 786},
  year      = {2023},
  abstract  = {Wind energy is a key option in global dialogues about climate change mitigation. Here, we combined observations from surface wind stations, reanalysis datasets, and state-of-the-art regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX Africa) to study the current and future wind energy potential in Zambia. We found that winds are dominated by southeasterlies and are rarely strong with an average speed of 2.8 m·s\(^{-1}\). When we converted the observed surface wind speed to a turbine hub height of 100 m, we found a ~38\% increase in mean wind speed for the period 1981-2000. Further, both simulated and observed wind speed data show statistically significant increments across much of the country. The only areas that divert from this upward trend of wind speeds are the low land terrains of the Eastern Province bordering Malawi. Examining projections of wind power density (WPD), we found that although wind speed is increasing, it is still generally too weak to support large-scale wind power generation. We found a meagre projected annual average WPD of 46.6 W·m\(^{-2}\). The highest WPDs of ~80 W·m\(^{-2}\) are projected in the northern and central parts of the country while the lowest are to be expected along the Luangwa valley in agreement with wind speed simulations. On average, Zambia is expected to experience minor WPD increments of 0.004 W·m\(^{-2}\) per year from 2031 to 2050. We conclude that small-scale wind turbines that accommodate cut-in wind speeds of 3.8 m·s\(^{-1}\) are the most suitable for power generation in Zambia. Further, given the limitations of small wind turbines, they are best suited for rural and suburban areas of the country where obstructions are few, thus making them ideal for complementing the government of the Republic of Zambia's rural electrification efforts.},
  language  = {en}
}
@article{SteiningerAbelZiegleretal.2023,
  author    = {Steininger, Michael and Abel, Daniel and Ziegler, Katrin and Krause, Anna and Paeth, Heiko and Hotho, Andreas},
  title     = {ConvMOS: climate model output statistics with deep learning},
  series = {Data Mining and Knowledge Discovery},
  volume    = {37},
  journal   = {Data Mining and Knowledge Discovery},
  number    = {1},
  issn      = {1384-5810},
  doi       = {10.1007/s10618-022-00877-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324213},
  pages     = {136-166},
  year      = {2023},
  abstract  = {Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.},
  subject      = {Klima},
  language  = {en}
}
@article{WeismannMoeckelPaethetal.2023,
  author    = {Weismann, Dirk and M{\"o}ckel, Martin and Paeth, Heiko and Slagman, Anna},
  title     = {Modelling variations of emergency attendances using data on community mobility, climate and air pollution},
  series = {Scientific Reports},
  volume    = {13},
  journal   = {Scientific Reports},
  doi       = {10.1038/s41598-023-47857-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357578},
  year      = {2023},
  abstract  = {Air pollution is associated with morbidity and mortality worldwide. We investigated the impact of improved air quality during the economic lockdown during the SARS-Cov2 pandemic on emergency room (ER) admissions in Germany. Weekly aggregated clinical data from 33 hospitals were collected in 2019 and 2020. Hourly concentrations of nitrogen and sulfur dioxide (NO2, SO2), carbon and nitrogen monoxide (CO, NO), ozone (O3) and particulate matter (PM10, PM2.5) measured by ground stations and meteorological data (ERA5) were selected from a 30 km radius around the corresponding ED. Mobility was assessed using aggregated cell phone data. A linear stepwise multiple regression model was used to predict ER admissions. The average weekly emergency numbers vary from 200 to over 1600 cases (total n = 2,216,217). The mean maximum decrease in caseload was 5 standard deviations. With the enforcement of the shutdown in March, the mobility index dropped by almost 40\%. Of all air pollutants, NO2 has the strongest correlation with ER visits when averaged across all departments. Using a linear stepwise multiple regression model, 63\% of the variation in ER visits is explained by the mobility index, but still 6\% of the variation is explained by air quality and climate change.},
  language  = {en}
}
@article{IbebuchiSchoenbeinAdakudluetal.2022,
  author    = {Ibebuchi, Chibuike Chiedozie and Sch{\"o}nbein, Daniel and Adakudlu, Muralidhar and Xoplaki, Elena and Paeth, Heiko},
  title     = {Comparison of three techniques to adjust daily precipitation biases from regional climate models over Germany},
  series = {Water},
  volume    = {14},
  journal   = {Water},
  number    = {4},
  issn      = {2073-4441},
  doi       = {10.3390/w14040600},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262064},
  year      = {2022},
  abstract  = {This study compares the performance of three bias correction (BC) techniques in adjusting simulated precipitation estimates over Germany. The BC techniques are the multivariate quantile delta mapping (MQDM) where the grids are used as variables to incorporate the spatial dependency structure of precipitation in the bias correction; empirical quantile mapping (EQM) and, the linear scaling (LS) approach. Several metrics that include first to fourth moments and extremes characterized by the frequency of heavy wet days and return periods during boreal summer were applied to score the performance of the BC techniques. Our results indicate a strong dependency of the relative performances of the BC techniques on the choice of the regional climate model (RCM), the region, the season, and the metrics of interest. Hence, each BC technique has relative strengths and weaknesses. The LS approach performs well in adjusting the first moment but tends to fall short for higher moments and extreme precipitation during boreal summer. Depending on the season, the region and the RCM considered, there is a trade-off between the relative performances of the EQM and the MQDM in adjusting the simulated precipitation biases. However, the MQDM performs well across all considered metrics. Overall, the MQDM outperforms the EQM in improving the higher moments and in capturing the observed return level of extreme summer precipitation, averaged over Germany.},
  language  = {en}
}
@article{IbebuchiSchoenbeinPaeth2022,
  author    = {Ibebuchi, Chibuike Chiedozie and Sch{\"o}nbein, Daniel and Paeth, Heiko},
  title     = {On the added value of statistical post-processing of regional climate models to identify homogeneous patterns of summer rainfall anomalies in Germany},
  series = {Climate Dynamics},
  volume    = {59},
  journal   = {Climate Dynamics},
  number    = {9-10},
  issn      = {0930-7575},
  doi       = {10.1007/s00382-022-06258-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324122},
  pages     = {2769-2783},
  year      = {2022},
  abstract  = {A fuzzy classification scheme that results in physically interpretable meteorological patterns associated with rainfall generation is applied to classify homogeneous regions of boreal summer rainfall anomalies in Germany. Four leading homogeneous regions are classified, representing the western, southeastern, eastern, and northern/northwestern parts of Germany with some overlap in the central parts of Germany. Variations of the sea level pressure gradient across Europe, e.g., between the continental and maritime regions, is the major phenomenon that triggers the time development of the rainfall regions by modulating wind patterns and moisture advection. Two regional climate models (REMO and CCLM4) were used to investigate the capability of climate models to reproduce the observed summer rainfall regions. Both regional climate models (RCMs) were once driven by the ERA-Interim reanalysis and once by the MPI-ESM general circulation model (GCM). Overall, the RCMs exhibit good performance in terms of the regionalization of summer rainfall in Germany; though the goodness-of-match with the rainfall regions/patterns from observational data is low in some cases and the REMO model driven by MPI-ESM fails to reproduce the western homogeneous rainfall region. Under future climate change, virtually the same leading modes of summer rainfall occur, suggesting that the basic synoptic processes associated with the regional patterns remain the same over Germany. We have also assessed the added value of bias-correcting the MPI-ESM driven RCMs using a simple linear scaling approach. The bias correction does not significantly alter the identification of homogeneous rainfall regions and, hence, does not improve their goodness-of-match compared to the observed patterns, except for the one case where the original RCM output completely fails to reproduce the observed pattern. While the linear scaling method improves the basic statistics of precipitation, it does not improve the simulated meteorological patterns represented by the precipitation regimes.},
  language  = {en}
}
@article{IbebuchiPaeth2021,
  author    = {Ibebuchi, Chibuike Chiedozie and Paeth, Heiko},
  title     = {The Imprint of the Southern Annular Mode on Black Carbon AOD in the Western Cape Province},
  series = {Atmosphere},
  volume    = {12},
  journal   = {Atmosphere},
  number    = {10},
  issn      = {2073-4433},
  doi       = {10.3390/atmos12101287},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-248387},
  year      = {2021},
  abstract  = {This study examines the relationship between variations of the Southern Annular Mode (SAM) and black carbon (BC) at 550 nm aerosol optical depth (AOD) in the Western Cape province (WC). Variations of the positive (negative) phase of the SAM are found to be related to regional circulation types (CTs) in southern Africa, associated with suppressed (enhanced) westerly wind over the WC through the southward (northward) migration of Southern Hemisphere mid-latitude cyclones. The CTs related to positive (negative) SAM anomalies induce stable (unstable) atmospheric conditions over the southwestern regions of the WC, especially during the austral winter and autumn seasons. Through the control of CTs, positive (negative) SAM phases tend to contribute to the build-up (dispersion and dilution) of BC in the study region because they imply dry (wet) conditions which favor the build-up (washing out) of pollutant particles in the atmosphere. Indeed, recent years with an above-average frequency of CTs related to positive (negative) SAM anomalies are associated with a high (low) BC AOD over southwesternmost Africa.},
  language  = {en}
}
@article{ZieglerPollingerBoelletal.2020,
  author    = {Ziegler, Katrin and Pollinger, Felix and B{\"o}ll, Susanne and Paeth, Heiko},
  title     = {Statistical modeling of phenology in Bavaria based on past and future meteorological information},
  series = {Theoretical and Applied Climatology},
  volume    = {140},
  journal   = {Theoretical and Applied Climatology},
  issn      = {0177-798X},
  doi       = {10.1007/s00704-020-03178-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-232717},
  pages     = {1467-1481},
  year      = {2020},
  abstract  = {Plant phenology is well known to be affected by meteorology. Observed changes in the occurrence of phenological phases arecommonly considered some of the most obvious effects of climate change. However, current climate models lack a representationof vegetation suitable for studying future changes in phenology itself. This study presents a statistical-dynamical modelingapproach for Bavaria in southern Germany, using over 13,000 paired samples of phenological and meteorological data foranalyses and climate change scenarios provided by a state-of-the-art regional climate model (RCM). Anomalies of severalmeteorological variables were used as predictors and phenological anomalies of the flowering date of the test plantForsythiasuspensaas predictand. Several cross-validated prediction models using various numbers and differently constructed predictorswere developed, compared, and evaluated via bootstrapping. As our approach needs a small set of meteorological observationsper phenological station, it allows for reliable parameter estimation and an easy transfer to other regions. The most robust andsuccessful model comprises predictors based on mean temperature, precipitation, wind velocity, and snow depth. Its averagecoefficient of determination and root mean square error (RMSE) per station are 60\% and ± 8.6 days, respectively. However, theprediction error strongly differs among stations. When transferred to other indicator plants, this method achieves a comparablelevel of predictive accuracy. Its application to two climate change scenarios reveals distinct changes for various plants andregions. The flowering date is simulated to occur between 5 and 25 days earlier at the end of the twenty-first century comparedto the phenology of the reference period (1961-1990).},
  language  = {en}
}
@article{PaethPollinger2019,
  author    = {Paeth, Heiko and Pollinger, Felix},
  title     = {Changes in mean flow and atmospheric wave activity in the North Atlantic sector},
  series = {Quarterly Journal of the Royal Meteorological Society},
  volume    = {145},
  journal   = {Quarterly Journal of the Royal Meteorological Society},
  number    = {725},
  doi       = {10.1002/qj.3660},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-208079},
  pages     = {3801-3818},
  year      = {2019},
  abstract  = {In recent years, the midlatitudes are characterized by more intense heatwaves in summer and sometimes severe cold spells in winter that might emanate from changes in atmospheric circulation, including synoptic-scale and planetary wave activity in the midlatitudes. In this study, we investigate the heat and momentum exchange between the mean flow and atmospheric waves in the North Atlantic sector and adjacent continents by means of the physically consistent Eliassen-Palm flux diagnostics applied to reanalysis and forced climate model data. In the long-term mean, momentum is transferred from the mean flow to atmospheric waves in the northwest Atlantic region, where cyclogenesis prevails. Further downstream over Europe, eddy fluxes return momentum to the mean flow, sustaining the jet stream against friction. A global climate model is able to reproduce this pattern with high accuracy. Atmospheric variability related to atmospheric wave activity is much more expressed at the intraseasonal rather than the interannual time-scale. Over the last 40 years, reanalyses reveal a northward shift of the jet stream and a weakening of intraseasonal weather variability related to synoptic-scale and planetary wave activity. This pertains to the winter and summer seasons, especially over central Europe, and correlates with changes in the North Atlantic Oscillation as well as regional temperature and precipitation. A very similar phenomenon is found in a climate model simulation with business-as-usual scenario, suggesting an anthropogenic trigger in the weakening of intraseasonal weather variability in the midlatitudes.},
  language  = {en}
}
@article{HaggMayrMannigetal.2018,
  author    = {Hagg, Wilfried and Mayr, Elisabeth and Mannig, Birgit and Reyers, Mark and Schubert, David and Pinto, Joaquim G. and Peters, Juliane and Pieczonka, Tino and Juen, Martin and Bolch, Tobias and Paeth, Heiko and Mayer, Christoph},
  title     = {Future climate change and its impact on runoff generation from the debris-covered Inylchek glaciers, Central Tian Shan, Kyrgyzstan},
  series = {Water},
  volume    = {10},
  journal   = {Water},
  number    = {11},
  issn      = {2073-4441},
  doi       = {10.3390/w10111513},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197592},
  pages     = {1513},
  year      = {2018},
  abstract  = {The heavily debris-covered Inylchek glaciers in the central Tian Shan are the largest glacier system in the Tarim catchment. It is assumed that almost 50\% of the discharge of Tarim River are provided by glaciers. For this reason, climatic changes, and thus changes in glacier mass balance and glacier discharge are of high impact for the whole region. In this study, a conceptual hydrological model able to incorporate discharge from debris-covered glacier areas is presented. To simulate glacier melt and subsequent runoff in the past (1970/1971-1999/2000) and future (2070/2071-2099/2100), meteorological input data were generated based on ECHAM5/MPI-OM1 global climate model projections. The hydrological model HBV-LMU was calibrated by an automatic calibration algorithm using runoff and snow cover information as objective functions. Manual fine-tuning was performed to avoid unrealistic results for glacier mass balance. The simulations show that annual runoff sums will increase significantly under future climate conditions. A sensitivity analysis revealed that total runoff does not decrease until the glacier area is reduced by 43\%. Ice melt is the major runoff source in the recent past, and its contribution will even increase in the coming decades. Seasonal changes reveal a trend towards enhanced melt in spring, but a change from a glacial-nival to a nival-pluvial runoff regime will not be reached until the end of this century.},
  language  = {en}
}
@article{PaethPaxianSeinetal.2017,
  author    = {Paeth, Heiko and Paxian, Andreas and Sein, Dimitry V. and Jacob, Daniela and Panitz, Hans-J{\"u}rgen and Warscher, Michael and Fink, Andreas H. and Kunstmann, Harald and Breil, Marcus and Engel, Thomas and Krause, Andreas and Toedter, Julian and Ahrens, Bodo},
  title     = {Decadal and multi-year predictability of the West African monsoon and the role of dynamical downscaling},
  series = {Meteorologische Zeitschrift},
  volume    = {26},
  journal   = {Meteorologische Zeitschrift},
  number    = {4},
  doi       = {10.1127/metz/2017/0811},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172018},
  pages     = {363-377},
  year      = {2017},
  abstract  = {West African summer monsoon precipitation is characterized by distinct decadal variability. Due to its welldocumented link to oceanic boundary conditions in various ocean basins it represents a paradigm for decadal predictability. In this study, we reappraise this hypothesis for several sub-regions of sub-Saharan West Africa using the new German contribution to the coupled model intercomparison project phase 5 (CMIP5) near-term prediction system. In addition, we assume that dynamical downscaling of the global decadal predictions leads to an enhanced predictive skill because enhanced resolution improves the atmospheric response to oceanic forcing and landsurface feedbacks. Based on three regional climate models, a heterogeneous picture is drawn: none of the regional climate models outperforms the global decadal predictions or all other regional climate models in every region nor decade. However, for every test case at least one regional climate model was identified which outperforms the global predictions. The highest predictive skill is found in the western and central Sahel Zone with correlation coefficients and mean-square skill scores exceeding 0.9 and 0.8, respectively.},
  language  = {en}
}