Refine
Year of publication
Document Type
- Article (39)
- Book (1)
- Conference Proceeding (1)
- Preprint (1)
- Review (1)
Has Fulltext
- yes (43)
Is part of the Bibliography
- no (43)
Keywords
- COSMO-CLM (5)
- precipitation (4)
- Clausius–Clapeyron scaling (3)
- convective storms (3)
- tracking (3)
- Europe (2)
- Odra (2)
- convection-permitting simulation (2)
- homogeneity (2)
- transfer entropy (2)
In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44∘ (∼ 50 km), 0.22∘ (∼ 25 km), and 0.11∘ (∼ 12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM–RCM modeling chain.
In the last decade, the Climate Limited-area Modeling (CLM) Community has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM community model, ERA-Interim reanalysis and eight Global Climate Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44◦(∼50 km), 0.22◦ (∼25 km) and 0.11◦ (∼12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modelling communities is needed to increase the reliability of the GCM-RCM modelling chain.
Im Rahmen einer Zusammenarbeit zwischen der Thüringer Landesanstalt für Umwelt und Geologie und der Goethe-Universität Frankfurt fand in Kooperation mit dem Deutschen Wetterdienst (DWD) eine umfassende Studie zum konvektiven Unwetterpotential über Thüringen statt. Unwetterereignisse, die durch konvektive Prozesse in der Atmosphäre verursacht werden, besitzen ein nicht unerhebliches Schadenspotential, obwohl sie oftmals eine räumlich eng begrenzte Ausdehnung aufweisen. Aufgrund ihrer Charakteristik ist sowohl die Vorhersage solcher Ereignisse, als auch eine vollständige, systematische Erfassung für eine detaillierte Auswertung längerer Zeitreihen noch immer eine Herausforderung. Zusätzliches Interesse besteht in der Abschätzung der durch den Klimawandel abhängigen Entwicklung des zukünftigen Gefährdungspotentials konvektiver Unwetter. Für eine gezielte Untersuchung des Themenkomplexes ist eine Vielzahl unterschiedlicher Daten und Methoden verwendet worden. Mit Hilfe von Fernerkundungsdatensätzen wird ein räumlich differenziertes Gefährdungspotential über Thüringen nachgewiesen. Bedingt durch das Relief ist das Auftreten von Konvektion am häufigsten und intensivsten über dem südlichen Thüringer Wald und dessen Ostrand zu beobachten, während Nordthüringen eine deutlich geringere Aktivität solcher Unwetterereignisse aufweist. Eine Abschätzung mittels globaler Klimamodelle und daraus abgeleiteten Wetterlagen zeigt unter Berücksichtigung des RCP8.5 Klimaszenarios für die nahe Zukunft (2016-2045) eine Zunahme des Gefährdungspotentials durch konvektive Unwetter. Aufgrund des Anstiegs feuchter Wetterlagen (49 % auf 82 %) erhöht sich die Zunahme der Gefährdung für den Zeitraum 2071-2100 noch deutlicher. Im Vergleich zu diesem statistischen Ansatz nimmt die projizierte Gefährdung durch extreme Ereignisse erheblich zu (Faktor 6), wenn die Ergebnisse expliziter Simulationen konvektiver Ereignisse mit einem regionalen Klimamodell (mit horizontaler Gitterdistanz von 1 km) und eine Zunahme der Tage mit konvektiven Extremereignissen berücksichtigt werden. Ein Anstieg der Gefährdung durch konvektive Unwetter in der Zukunft ist wahrscheinlich. Eine Quantifizierung bleibt jedoch unsicher.
Convective shower characteristics simulated with the convection-permitting climate model COSMO-CLM
(2019)
This paper evaluates convective precipitation as simulated by the convection-permitting climate model (CPM) Consortium for Small-Scale Modeling in climate mode (COSMO-CLM) (with 2.8 km grid-spacing) over Germany in the period 2001–2015. Characteristics of simulated convective precipitation objects like lifetime, area, mean intensity, and total precipitation are compared to characteristics observed by weather radar. For this purpose, a tracking algorithm was applied to simulated and observed precipitation with 5-min temporal resolution. The total amount of convective precipitation is well simulated, with a small overestimation of 2%. However, the simulation underestimates convective activity, represented by the number of convective objects, by 33%. This underestimation is especially pronounced in the lowlands of Northern Germany, whereas the simulation matches observations well in the mountainous areas of Southern Germany. The underestimation of activity is compensated by an overestimation of the simulated lifetime of convective objects. The observed mean intensity, maximum intensity, and area of precipitation objects increase with their lifetime showing the spectrum of convective storms ranging from short-living single-cell storms to long-living organized convection like supercells or squall lines. The CPM is capable of reproducing the lifetime dependence of these characteristics but shows a weaker increase in mean intensity with lifetime resulting in an especially pronounced underestimation (up to 25%) of mean precipitation intensity of long-living, extreme events. This limitation of the CPM is not identifiable by classical evaluation techniques using rain gauges. The simulation can reproduce the general increase of the highest percentiles of cell area, total precipitation, and mean intensity with temperature but fails to reproduce the increase of lifetime. The scaling rates of mean intensity and total precipitation resemble observed rates only in parts of the temperature range. The results suggest that the evaluation of coarse-grained (e.g., hourly) precipitation fields is insufficient for revealing challenges in convection-permitting simulations.
Convection-permitting models (CPMs) have proven their usefulness in representing precipitation on a sub-daily scale. However, investigations on sub-hourly scales are still lacking, even though these are the scales for which showers exhibit the most variability. A Lagrangian approach is implemented here to evaluate the representation of showers in a CPM, using the limited-area climate model COSMO-CLM. This approach consists of tracking 5‑min precipitation fields to retrieve different features of showers (e.g., temporal pattern, horizontal speed, lifetime). In total, 312 cases are simulated at a resolution of 0.01 ° over Central Germany, and among these cases, 78 are evaluated against a radar dataset. The model is able to represent most observed features for different types of convective cells. In addition, the CPM reproduced well the observed relationship between the precipitation characteristics and temperature indicating that the COSMO-CLM model is sophisticated enough to represent the climatological features of showers.
Observed weather and projected climate change suggest an increase in the transmission of vector-borne diseases (VBDs) in the Hindu Kush Himalayan (HKH) region. In this study, we systematically explore the literature for empiric associations between the climate variables and specific VBDs and their vectors in the HKH region. We conducted a systematic synthesis of the published literature on climate variables, VBDs and vectors in the HKH region until the 8th of December 2020. The majority of studies show significant positive associations of VBDs with climatic factors, such as temperature, precipitation, relative humidity, etc. This systematic review allowed us to identify the most significant variables to be considered for evidence-based trend estimates of the effects of climate change on VBDs and their vectors in the HKH region. This evidence-based trend was set into the context of climate change as well as the observed expansion of VBDs and disease vectors in the HKH region. The geographic range of VBDs expanded into previously considered non-endemic areas of highlands (mountains) in the HKH region. Based on scarce, but clear evidence of a positive relationship of most climate variables and VBDs and the observed climatic changes, we strongly recommend an expansion of vector control and surveillance programmes in areas of the HKH region that were previously considered to be non-endemic.
Seasonal forecasting systems still have difficulties predicting temperature over continental regions, while their performance is better over some maritime regions. On the other hand, the land surface is a substantial source of (sub-)seasonal predictability. A crucial land surface component in focus here is the snow cover, which stores water and modulates the surface radiation balance. This paper’s goal is to attribute snow cover seasonal forecasting biases and lack of skill to either initialization or parameterization errors. For this purpose, we compare the snow representation in five seasonal forecasting systems (from DWD, ECMWF, Météo-France, CMCC, and ECCC) and their performances in predicting snow and 2-m temperature over a Siberian region against ERA5 reanalysis and station data. Although all systems use similar atmospheric and land initialization approaches and data, their snow and temperature biases differ in sign and amplitude. Too-large initial snow biases persist over the forecast period, delaying and prolonging the melting phase. The simplest snow scheme (used in DWD’s system) shows too-early and fast melting in spring. However, systems including multi-layer snow schemes (Météo-France and CMCC) do not necessarily perform better. Both initialization and parameterization are causes of snow biases, but, depending on the system, one can be more dominant.
Lightning climate change projections show large uncertainties caused by limited empirical knowledge and strong assumptions inherent to coarse-grid climate modeling. This study addresses the latter issue by implementing and applying the lightning potential index parameterization (LPI) into a fine-grid convection-permitting regional climate model (CPM). This setup takes advantage of the explicit representation of deep convection in CPMs and allows for process-oriented LPI inputs such as vertical velocity within convective cells and coexistence of microphysical hydrometeor types, which are known to contribute to charge separation mechanisms. The LPI output is compared to output from a simpler flash rate parameterization, namely the CAPE × PREC parameterization, applied in a non-CPM on a coarser grid. The LPI’s implementation into the regional climate model COSMO-CLM successfully reproduces the observed lightning climatology, including its latitudinal gradient, its daily and hourly probability distributions, and its diurnal and annual cycles. Besides, the simulated temperature dependence of lightning reflects the observed dependency. The LPI outperforms the CAPE × PREC parameterization in all applied diagnostics. Based on this satisfactory evaluation, we used the LPI to a climate change projection under the RCP8.5 scenario. For the domain under investigation centered over Germany, the LPI projects a decrease of 4.8% in flash rate by the end of the century, in opposition to a projected increase of 17.4% as projected using the CAPE × PREC parameterization. The future decrease of LPI occurs mostly during the summer afternoons and is related to (i) a change in convection occurrence and (ii) changes in the microphysical mixing. The two parameterizations differ because of different convection occurrences in the CPM and non-CPM and because of changes in the microphysical mixing, which is only represented in the LPI lightning parameterization.
Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limited-area ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS). The observational references in the evaluation are (a) analyzed rain gauge data by ordinary Kriging and (b) ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty) or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km2) of Switzerland with a mean rain gauge distance as good as 7 km: the one- to three-day precipitation forecasts have skill decreasing with forecast lead time but the one- and two-day forecast performances differ not significantly.
Driven by globalization, urbanization and climate change, the distribution range of invasive vector species has expanded to previously colder ecoregions. To reduce health-threatening impacts on humans, insect vectors are extensively studied. Population genomics can reveal the genomic basis of adaptation and help to identify emerging trends of vector expansion. By applying whole genome analyses and genotype-environment associations to populations of the main dengue vector Aedes aegypti, sampled along an altitudinal gradient in Nepal (200–1300 m), we identify putatively adaptive traits and describe the species' genomic footprint of climate adaptation to colder ecoregions. We found two differentiated clusters with significantly different allele frequencies in genes associated to climate adaptation between the highland population (1300 m) and all other lowland populations (≤800 m). We revealed nonsynonymous mutations in 13 of the candidate genes associated to either altitude, precipitation or cold tolerance and identified an isolation-by-environment differentiation pattern. Other than the expected gradual differentiation along the altitudinal gradient, our results reveal a distinct genomic differentiation of the highland population. Local high-altitude adaptation could be one explanation of the population's phenotypic cold tolerance. Carrying alleles relevant for survival under colder climate increases the likelihood of this highland population to a worldwide expansion into other colder ecoregions.