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  • 1
    Publication Date: 2022-05-01
    Description: An accurate representation of the Indian Ocean Dipole (IOD) is crucial for the reliable projection of Indian summer monsoon rainfall, making it necessary to improve the understanding of the response of the IOD in the warming climate. For the first time, a high-resolution regional earth system model (RESM) over the CORDEX-SA domain is used to investigate the IOD characteristics. The model performance is evaluated in simulating the IOD and associated mechanism. RESM shows a good resemblance in simulating IOD phases (positive and negative). However, the systematic discrepancy is observed in magnitude. Additionally, RESM well represented the positive IOD's inter-event variability. For example, the stronger event dominated by significant cold anomalies over Sumatra with enhanced westward-extended while a moderate event shows weak cooling confined to the region of Sumatra. Additionally, RESM shows potential to distinguish the ENSO and non-ENSO years with more remarkable skill in representing the spatial pattern of SST over IOD region during non-ESNO years than ENSO years. The RESM realistically simulated the IOD amplitude with greater skill than CMIP5/6 models reported in the earlier studies, indicating reliability towards the projection of the Indian summer monsoon. The weaker IOD-ENSO relationship is caused by producing the more significant number of IOD during non-ENSO years. Despite this reliable fidelity, IOD's slightly earlier peak is driven by the early establishment of low-level equatorial easterly wind. This study provided valuable insight into the IOD's different phases, responsible forcings, and limitations of the RESM in accounting for the role of internal climate variability that can be useful for further improvement in the model physics.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 2
    Publication Date: 2022-05-01
    Description: An effort is made to implement a regional earth system model (RESM); ROM, over CORDEX-South Asia (SA). The added value of RESM is assessed for mean precipitation, its variability (intraseasonal to interannual), extremes, and associated processes. In this regard, ROM’s fields are compared with the respective fields of its standalone version (REMO), the models belonging coupled model intercomparison project (CMIP5 and CMIP6), and regional climate models of CORDEX-CORE simulations. RESM shows substantial improvement for most of the Indian monsoon’s aspects; however, the magnitude of the value addition varies spatiotemporally and also with different aspects.. The improved representation of intraseasonal variability (active-break spell’s duration and intensity) and Interannual variability attributed to improved mean seasonal precipitation. Additionally, correct representation of sea surface temperature, Indian Ocean Dipole, and its underlying dynamics also contribute to improving the mean precipitation. The notable improvement is seen especially over the south-eastern regions of the Bay of Bengal (BoB) and South-Central India, where increasing (decreasing) low-pressure systems over Central India (BoB) are noticed as a consequence of air-sea coupling, leading to enhanced (reduced) precipitation over Central India (BoB), reducing dry (wet) bias found in REMO and the other models. Despite substantial improvements, RESM has a systematic wet bias in the mean precipitation associated with a warm bias over the western coast of the Arabian Sea. An overestimation of very high extreme precipitation due to the enhanced contribution of low-pressure systems indicates the model’s limitations, suggesting the need for further tuning of the RESM.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 3
    Publication Date: 2022-05-01
    Description: A new high-resolution Regional Earth System Model, namely ROM, has been implemented over CORDEX-SA towards examining the impact of air–sea coupling on the Indian summer monsoon characteristics. ROM's simulated mean ISM rainfall and associated dynamical and thermodynamical processes, including the representation of northward and eastward propagating convention bands, are closer to observation than its standalone atmospheric model component (REMO), highlighting the advantage of air–sea coupling. However, the value addition of air–sea coupling varies spatially with more significant improvements over regions with large biases. Bay of Bengal and the eastern equatorial Indian Ocean are the most prominent region where the highest added value is observed with a significant reduction up to 50–500% precipitation bias. Most of the changes in precipitation over the ocean are associated with convective precipitation (CP) due to the suppression of convective activity caused by the negative feedback due to the inclusion of air–sea coupling. However, CP and large-scale precipitation (LP) improvements show east–west asymmetry over the Indian land region. The substantial LP bias reduction is noticed over the wet bias region of western central India due to its suppression, while enhanced CP over eastern central India contributed to the reduction of dry bias. An insignificant change is noticed over Tibetan Plateau, northern India, and Indo Gangetic plains. The weakening of moisture-laden low-level Somalia Jets causes the diminishing of moisture supply from the Arabian Sea (AS) towards Indian land regions resulting in suppressed precipitation, reducing wet bias, especially over western central India. The anomalous high kinetic energy over AS, wind shear, and tropospheric temperature gradient in REMO compared to observation is substantially reduced in the ROM, facilitating the favourable condition for suppressing moisture feeding and hence the wet bias over west-central India in ROM. The warmer midlatitude in ROM than REMO over eastern central India strengthens the convection, enhancing precipitation results in reducing the dry bias. Despite substantially improved ROM’performance, it still exhibits some systematic biases (wet/dry) partially associated with the persistent warm/cold SST bias and land–atmosphere interaction.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 4
    Publication Date: 2022-09-23
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 5
    Publication Date: 2022-05-01
    Description: We investigate the effect of variable marine biogeochemical light absorption on Indian Ocean sea surface temperature (SST) and how this affects the South Asian climate. In twin experiments with a regional Earth system model, we found that the average SST is lower over most of the domain when variable marine biogeochemical light absorption is taken into account, compared to the reference experiment with a constant light attenuation coefficient equal to 0.06 m−1. The most significant deviations (more than 1 ∘C) in SST are observed in the monsoon season. A considerable cooling of subsurface layers occurs, and the thermocline shifts upward in the experiment with the activated biogeochemical impact. Also, the phytoplankton primary production becomes higher, especially during periods of winter and summer phytoplankton blooms. The effect of altered SST variability on climate was investigated by coupling the ocean models to a regional atmosphere model. We find the largest effects on the amount of precipitation, particularly during the monsoon season. In the Arabian Sea, the reduction of the transport of humidity across the Equator leads to a reduction of the large-scale precipitation in the eastern part of the basin, reinforcing the reduction of the convective precipitation. In the Bay of Bengal, it increases the large-scale precipitation, countering convective precipitation decline. Thus, the key impacts of including the full biogeochemical coupling with corresponding light attenuation, which in turn depends on variable chlorophyll a concentration, include the enhanced phytoplankton primary production, a shallower thermocline, and decreased SST and water temperature in subsurface layers, with cascading effects upon the model ocean physics which further translates into altered atmosphere dynamics.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 6
    Publication Date: 2022-02-16
    Description: One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 7
    Publication Date: 2022-01-28
    Description: Climate change and urbanization are significantly magnifying flood hazard, leading to a greater vulnerability of urban concentrations. This paper investigates the impact of climate change on urban flooding using future projected rainfall data and a calibrated hydraulic model. Two urban watersheds in Delhi, India (the Qudesia Nallah catchment and the Jahangirpuri drain catchment) are considered to evaluate the climate change impact on urban flooding. Regional climate models (RCMs) are used to project future precipitation, which is then utilized by the hydraulic model to evaluate the impact on flooding. Climate data from three RCMs extracted from the Coordinated Regional Climate Downscaling Experiment (CORDEX) are used to study the impact of climate change for historical (1990–2016) and future scenario (Representative Concentration Pathway (RCP) 4.5, 2021–2100). The rainfall projections are fed as 2-, 5-, 10-, and 20-year return periods to a calibrated hydrodynamic Storm Water Management Model (SWMM). The results show that the flooded nodes vary between 2–6 and 12–43, respectively, in the Qudesia Nallah catchment and the Jahangirpuri drain catchment under present conditions but increase from 11 to 51 and 42 to 91, respectively, for future climate conditions. The results suggest that the risk of occurrence of flooding, duration, and frequency in the two study areas will increase in the future when compared to those under the present conditions. The results also indicate that the damage induced by the 20-year return period rainfall at the present time will likely be caused just by the 2-year return period in the future. This is due to the greater likelihood of rainfall extremes in the region. The potential flooding sites identified in this study will provide the urban municipalities with substantive information to perform ameliorative strategies.
    Type: info:eu-repo/semantics/article
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  • 8
    Publication Date: 2022-04-07
    Description: The higher frequency and intensity of sustained heat events have increased the demand for cooling energy across the globe. Current estimates of summer‐time energy demand are primarily based on Cooling Degree Days (CDD), representing the number of degrees a day's average temperature exceeds a predetermined comfort zone temperature. Through a comprehensive analysis of the historical energy demand data across the USA, we show that the commonly used CDD estimates fall significantly short (±25%) of capturing regional thermal comfort levels. Moreover, given the increasingly compelling evidence that air temperature alone is not sufficient for characterizing human thermal comfort, we extend the widely used CDD calculation to heat index, which accounts for both air temperature and humidity. Our results indicate significant mis‐estimation of regional thermal comfort when humidity is ignored. Our findings have significant implications for the security, sustainability, and resilience of the grid under climate change.
    Description: Plain Language Summary: Hotter summer days and more frequent and intense heatwaves are causing a sharp rise in demand for air conditioning across the globe. Accurate estimation of demand for space cooling is an integral component of resilient planning, operation, and management of the grid. One widely used metric for characterizing this demand is the Cooling Degree Days (CDD), which is calculated by measuring the difference between the mean daily temperature and a pre‐defined base temperature that represents a “comfort zone.” In this study, we analyze historical data on climate and energy demand and find that the most frequently used base temperature of 65°F in CDD calculations leads to mis‐characterizing comfort zones across different geographic areas in the United States. This can cause significant under‐ or over‐estimations of cooling energy demand. Moreover, we extend the temperature‐based CDD calculations to also account for the role of humidity and demonstrate the cost of ignoring humidity in CDD calculations under present and future climate conditions.
    Description: Key Points: Analysis of electricity demand shows that the widely used Cooling Degree Days (CDD) estimates fall short of capturing regional thermal comfort zones. Estimates of air conditioning penetration and affordability based on traditional calculation of CDD can lead to significant misestimation. Extending CDD calculations to include humidity improves the characterization of climate‐demand nexus under present and future climate.
    Description: National Science Foundation (NSF) http://dx.doi.org/10.13039/100000001
    Keywords: ddc:333.79
    Language: English
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  • 9
    Publication Date: 2022-04-05
    Description: Human alteration of nutrient cycles has caused persistent and widespread degradation of water quality around the globe. In many regions, including Western Europe, elevated nitrate (NO3−) concentration in surface waters contributes to eutrophication and noncompliance with environmental legislation. Discharge, NO3− concentrations and the vulnerability of the aquatic ecosystems to eutrophication often exhibit a distinct seasonality. Understanding spatial patterns and long‐term trends in this seasonality is crucial to improve water quality management. Here, we hypothesized that NO3− concentrations during high‐flow periods would respond faster to changes in nutrient inputs than low‐flow concentrations because of greater connectivity of shallow diffuse NO3− sources with the river network. To test this hypothesis, we compiled long‐term NO3− and discharge time series from 290 Western European catchments. To characterize the long‐term trajectories of seasonal NO3− concentration, we propose a novel hysteresis approach comparing low‐ and high‐flow NO3− concentration in the context of multi‐decadal N input changes. We found synchronous winter maxima of NO3− and discharge in 84% of the study catchments. However, contrary to our hypothesis, there were surprisingly diverse long‐term trajectories of seasonal NO3− concentration. Both clockwise (faster high‐flow NO3− response) and counterclockwise hysteresis (faster low‐flow NO3− response) occurred in similar proportions, potentially due to a high complexity in the underlying processes. Spatial variability of seasonality in NO3− concentration across the catchments was more pronounced and better predictable than its long‐term variability. This work demonstrates the value of seasonal and inter‐annual hydrochemical analysis and provides new tools for water quality monitoring and management.
    Description: Plain Language Summary: Nitrogen is an essential element of all living organisms and has thus often been used excessively as fertilizer to secure food production. However, surface waters can suffer from elevated nutrients inputs, causing toxic algal blooms and impairing drinking water quality, especially during summer low flows. To manage water quality, it is crucial to understand these seasonal variations of nitrogen and discharge and the underlying processes. We used data from 290 catchments in France and Germany to characterize average seasonality patterns and their long‐term evolution across the variety of landscapes and human influences. This allowed classifying catchment behavior and linking them to controls. As expected, both nitrogen and discharge peak during winter in most catchments (84%). However, there are well explainable deviations, for example, in mountainous regions. The long‐term evolution of seasonality was more diverse than expected suggesting a complex interplay of various processes with the long input history from fertilization and wastewater being part of the controls. We found that the differences among catchments were greater than the long‐term changes of seasonality within most catchments. By identifying catchment typologies, our study increases the understanding of nitrate seasonality patterns across a large extent and thus supports ecological water quality management.
    Description: Key Points: Spatial patterns of nitrate and discharge seasonality are linked to topography and hydroclimate with winter maxima dominating for both. After decreasing nutrient inputs, cases with decreases in river nitrate preceding during low‐ and high‐flow seasons occurred equally often. Spatial variability of nitrate seasonality is greater and more predictable from catchment characteristics than its long‐term variability.
    Description: Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659
    Description: Helmholtz Association http://dx.doi.org/10.13039/501100009318
    Description: US National Science Foundation (NSF)
    Keywords: ddc:551
    Language: English
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  • 10
    Publication Date: 2022-05-25
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jacox, M. G., Alexander, M. A., Siedlecki, S., Chen, K., Kwon, Y., Brodie, S., Ortiz, I., Tommasi, D., Widlansky, M. J., Barrie, D., Capotondi, A., Cheng, W., Di Lorenzo, E., Edwards, C., Fiechter, J., Fratantoni, P., Hazen, E. L., Hermann, A. J., Kumar, A., Miller, A. J., Pirhalla, D., Buil, M. P., Ray, S., Sheridan, S. C., Subramanian, A., Thompson, P., Thorne, L., Annamalai, H., Aydin, K., Bograd, S. J., Griffis, R. B., Kearney, K., Kim, H., Mariotti, A., Merrifield, M., & Rykaczewski, R. Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Progress in Oceanography, 183, (2020): 102307, doi:10.1016/j.pocean.2020.102307.
    Description: Marine ecosystem forecasting is an area of active research and rapid development. Promise has been shown for skillful prediction of physical, biogeochemical, and ecological variables on a range of timescales, suggesting potential for forecasts to aid in the management of living marine resources and coastal communities. However, the mechanisms underlying forecast skill in marine ecosystems are often poorly understood, and many forecasts, especially for biological variables, rely on empirical statistical relationships developed from historical observations. Here, we review statistical and dynamical marine ecosystem forecasting methods and highlight examples of their application along U.S. coastlines for seasonal-to-interannual (1–24 month) prediction of properties ranging from coastal sea level to marine top predator distributions. We then describe known mechanisms governing marine ecosystem predictability and how they have been used in forecasts to date. These mechanisms include physical atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing, and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on forecasting techniques and mechanisms underlying marine ecosystem predictability, we aim to facilitate forecast development and uptake by (i) identifying methods and processes that can be exploited for development of skillful regional forecasts, (ii) informing priorities for forecast development and verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.
    Description: This study was supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) program through grants NA17OAR4310108, NA17OAR4310112, NA17OAR4310111, NA17OAR4310110, NA17OAR4310109, NA17OAR4310104, NA17OAR4310106, and NA17OAR4310113. This paper is a product of the NOAA/MAPP Marine Prediction Task Force.
    Keywords: Prediction ; Predictability ; Forecast ; Ecological forecast ; Mechanism ; Seasonal ; Interannual ; Large marine ecosystem
    Repository Name: Woods Hole Open Access Server
    Type: Article
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