Klimawandel

Bei der Erforschung von Anpassungsmaßnahmen an den Klimawandel spielen Informationen und Daten über die möglichen Veränderungen der verschiedenen Klimaparameter und deren Folgen für den regionalen Wasser- und Energiehaushalt eine wesentliche Rolle. Das Max-Planck-Institut für Meteorologie stellt über die Querschnittsaufgabe Klimawandel für sämtliche Teilprojekte in KLIMZUG-NORD Informationen zu Klimaänderungen für Norddeutschland bereit und berät zum Umgang mit regionalen Klimadaten und ihren Unsicherheiten. Im intensiven Dialog mit den Projektpartnern wird eine sinnvolle und konsistente Verwendung von Klimawissen abgestimmt. Es wird umfassend über die Möglichkeiten und Grenzen der regionalen Klimamodellierung informiert

What determines the sign of the evapotranspiration response to afforestation in European summer?

Uncertainties in the evapotranspiration response to afforestation constitute a major source of disagreement between model-based studies of the potential climate benefits of forests. Forests typically have higher evapotranspiration rates than grasslands in the tropics, but whether this is also the case in the midlatitudes is still debated. To explore this question and the underlying physical processes behind these varying evapotranspiration rates of forests and grasslands in more detail, a regional model study with idealized afforestation scenarios was performed for Europe. In the first experiment, Europe was maximally forested, and in the second one, all forests were turned into grassland. The results of this modeling study exhibit the same contradicting evapotranspiration characteristics of forests and grasslands as documented in observational studies, but by means of an additional sensitivity simulation in which the surface roughness of the forest was reduced to grassland, the mechanisms behind these varying evapotranspiration rates could be revealed. Due to the higher surface roughness of a forest, solar radiation is more efficiently transformed into turbulent sensible heat fluxes, leading to lower surface temperatures (top of vegetation) than in grassland. The saturation deficit between the vegetation and the atmosphere, which depends on the surface temperature, is consequently reduced over forests. This reduced saturation deficit counteracts the transpiration-facilitating characteristics of a forest (deeper roots, a higher leaf area index, LAI, and lower albedo values than grassland). If the impact of the reduced saturation deficit exceeds the effects of the transpiration-facilitating characteristics of a forest, evapotranspiration is reduced compared to grassland. If not, evapotranspiration rates of forests are higher. The interplay of these two counteracting factors depends on the latitude and the prevailing forest type in a region

Klimaprojektionen für das Modellgebiet Lüneburger Heide

Für das Modellgebiet der Lüneburger Heide werden zur Mitte des 21. Jahrhunderts für alle Jahreszeiten höhere Mitteltemperaturen projiziert. Zum Ende des 21. Jahrhunderts sind noch größere Temperaturzunahmen zu erwarten. Im Winter steigen die Temperaturen jeweils am stärksten, im Frühjahr am geringsten. Dabei nehmen im Winter die niedrigen Tagesmitteltemperaturen stärker zu als die höheren und Eis- und Frosttage treten deutlich seltener auf. Im Sommer können Tage mit extremen Temperaturen wie Hitzetage und Tropentage bzw. -nächte deutlich häufiger auftreten. Im Jahr nimmt die Anzahl der Tage mit Temperaturen höher als 5° C deutlich zu, was eine wichtige physiologische Schwelle für das Wachstum von Pflanzen ist. Im Verlauf des Jahrhunderts unterscheiden sich die für das B1 Szenario simulierten Temperaturen immer deutlicher von den Ergebnissen für die A1B und A2 Szenarien. Das bedeutet, wenn es gelingt, die Treibhausgasemissionen zu vermindern, deutlich geringere Klimaänderungen zu erwarten sind. Die projizierten Niederschläge nehmen 2036-2065 in allen Jahreszeiten für alle Szenarien leicht zu, mit Ausnahme abnehmender Niederschläge für das A1B Szenario im Sommer. Insgesamt sind die Veränderungen im Sommer sehr gering und zeigen keinen klaren Trend. Zum Ende des 21. Jahrhunderts dagegen zeigen die meisten Simulationen im Sommer eine Niederschlagsabnahme mit den stärksten Änderungen im A1B Szenario. In Winter und Herbst verstärkt sich die Niederschlagszunahme, sodass eine Umverteilung der Niederschläge im Jahresverlauf stattfindet mit insgesamt im Jahresmittel leicht steigenden Werten. Zudem zeigt sich im Sommer trotz abnehmender Niederschläge eine Zunahme der Intensität von starken Niederschlägen

Weather extremes over Europe under 1.5 °C and 2.0 °C global warming from HAPPI regional climate ensemble simulations

This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of > 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C

Klimaservice für die Klimafolgen- und Anpassungsforschung in der Metropolregion Hamburg

Vorstellung von Projektergebnissen aus KLIMZUG-NORD bezüglich jährliche und saisonale Temperatur- und Niederschlagsänderungen zur Mitte und Ende des 21. Jahrhunderts, sowie Ergebnisse aus dem Projekt Hamburg 2K. In Hamburg 2K wird analysiert, was eine Begrenzung auf eine Temperaturänderung von 2K für Hamburg bedeutet. Ausgewertet wurden Temperatur- und Niederschlagsänderungen sowie ausgewählte Indices

High-resolution land use and land cover dataset for regional climate modelling: Historical and future changes in Europe

Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS - "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC dataset Version 1.1 at 0.1&deg; resolution for Europe with annual LULC maps from 1950&ndash;2100 (Hoffmann et al., 2022b, a), which is tailored towards the use in state-of-the-art RCMs. The plant functional type distribution (PFT) for the year 2015 (i.e., LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method, cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by &nbsp;Reinhart et al. (2022b). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25&deg; resolution as input for CMIP6 experiments, to derive LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.</p

Machine learning models to predict myocardial infarctions from past climatic and environmental conditions

Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperatures (i.e. heat and cold) may increase the risk of MI. The relationship between health impacts and climate is complex and influenced by a multitude of climatic, environmental, sociodemographic and behavioural factors. Here, we present a machine learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany, between 1998 and 2015.Multivariable predictors include weather and climate, air pollution (PM10, NO, NO2, SO2 and O3), surrounding vegetation and demographic data. We tested the following ML regression algorithms: decision tree, random forest, multi-layer perceptron, gradient boosting and ridge regression. The models are able to predict the total annual number of MIs reasonably well (adjusted R2 = 0.62–0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approachprovides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes