BORA IN REGIONAL CLIMATE MODELS: IMPACT OF MODEL RESOLUTION ON SIMULATIONS OF GAP WIND AND WAVE BREAKING

Bora, a mesoscale wind system on the eastern Adriatic coast, profoundly impacts the local weather conditions. During easterly inflow, wave breaking generates heavy downslope winds in the lee of the Dinaric Alps. Additionally, gap winds emerge in canyons like the Vratnik Pass near Senj and enhance the Bora to a jet-like flow. The representation of these processes in numerical models is highly dependent on the surface description and therefore on model grid spacing. This study evaluates two simulations with the regional climate model COSMO-CLM with grid spacing of 0.025° and 0.11° regarding Bora winds. Strong Bora events are discussed in detail using observations between December 1999 and November 2000. The model results show that a 0.025° high-resolution simulation can well reproduce both phenomena gap wind and wave breaking. The 0.11° simulation resolves gap winds surprisingly well but misses wave breaking events

BORA IN REGIONAL CLIMATE MODELS: IMPACT OF MODEL RESOLUTION ON SIMULATIONS OF GAP WIND AND WAVE BREAKING

Bora, a mesoscale wind system on the eastern Adriatic coast, profoundly impacts the local weather conditions. During easterly inflow, wave breaking generates heavy downslope winds in the lee of the Dinaric Alps. Additionally, gap winds emerge in canyons like the Vratnik Pass near Senj and enhance the Bora to a jet-like flow. The representation of these processes in numerical models is highly dependent on the surface description and therefore on model grid spacing. This study evaluates two simulations with the regional climate model COSMO-CLM with grid spacing of 0.025° and 0.11° regarding Bora winds. Strong Bora events are discussed in detail using observations between December 1999 and November 2000. The model results show that a 0.025° high-resolution simulation can well reproduce both phenomena gap wind and wave breaking. The 0.11° simulation resolves gap winds surprisingly well but misses wave breaking events

Bestimmung des atmosphärischen Konvektionspotentials über Thüringen

Im Rahmen einer Zusammenarbeit zwischen der Thüringer Landesanstalt für Umwelt und Geologie (TLUG) und der Goethe-Universität Frankfurt (GUF) 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

Local impact analysis of climate change on precipitation extremes : are high-resolution climate models needed for realistic simulations?

This study explores whether climate models with higher spatial resolutions provide higher accuracy for precipitation simulations and/or different climate change signals. The outputs from two convection-permitting climate models (ALARO and CCLM) with a spatial resolution of 3-4 km are compared with those from the coarse-scale driving models or reanalysis data for simulating/projecting daily and sub-daily precipitation quantiles. Validation of historical design precipitation statistics derived from intensityduration-frequency (IDF) curves shows a better match of the convection-permitting model results with the observations-based IDF statistics compared to the driving GCMs and reanalysis data. This is the case for simulation of local subdaily precipitation extremes during the summer season, while the convection-permitting models do not appear to bring added value to simulation of daily precipitation extremes. Results moreover indicate that one has to be careful in assuming spatial-scale independency of climate change signals for the delta change downscaling method, as high-resolution models may show larger changes in extreme precipitation. These larger changes appear to be dependent on the timescale, since such intensification is not observed for daily timescales for both the ALARO and CCLM models

The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: Evaluation of precipitation

Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of ∼ 3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution (∼ 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from ∼ −40% at 12 km to ∼ −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales.Fil: Ban, Nikolina. Universidad de Innsbruck; AustriaFil: Caillaud, Cécile. Université de Toulouse; FranciaFil: Coppola, Erika. The Abdus Salam. International Centre for Theoretical Physics; Italia. The Abdus Salam; ItaliaFil: Pichelli, Emanuela. The Abdus Salam; Italia. The Abdus Salam. International Centre for Theoretical Physics; ItaliaFil: Sobolowski, Stefan. Norwegian Research Centre; NoruegaFil: Adinolfi, Marianna. Fondazione Centro Euro-Mediterraneo sui cambiamenti climatici; ItaliaFil: Ahrens, Bodo. Goethe Universitat Frankfurt; AlemaniaFil: Alias, Antoinette. Université de Toulouse; FranciaFil: Anders, Ivonne. German Climate Computing Center; AlemaniaFil: Bastin, Sophie. Universite Paris-Saclay;Fil: Belušić, Danijel. Swedish Meteorological and Hydrological Institute; SuizaFil: Berthou, Ségolène. Met Office Hadley Centre; Reino UnidoFil: Brisson, Erwan. Université de Toulouse; FranciaFil: Cardoso, Rita M.. Universidade Nova de Lisboa; PortugalFil: Chan, Steven C.. University of Newcastle; Reino UnidoFil: Christensen, Ole Bøssing. Danish Meteorological Institute; DinamarcaFil: Fernández, Jesús. Universidad de Cantabria; EspañaFil: Fita Borrell, Lluís. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; ArgentinaFil: Frisius, Thomas. Helmholtz Gemeinschaft; AlemaniaFil: Gaparac, Goran. Croatia Control Ltd.; CroaciaFil: Giorgi, Filippo. The Abdus Salam. International Centre for Theoretical Physics; Italia. The Abdus Salam; ItaliaFil: Goergen, Klaus. Centre for High-Performance Scientific Computing in Terrestrial Systems; Alemania. Helmholtz Gemeinschaft. Forschungszentrum Jülich; AlemaniaFil: Haugen, Jan Erik. Norwegian Meteorological Institute; NoruegaFil: Hodnebrog, Øivind. Center for International Climate and Environmental Research-Oslo; NoruegaFil: Kartsios, Stergios. Aristotle University Of Thessaloniki; GreciaFil: Katragkou, Eleni. Aristotle University Of Thessaloniki; GreciaFil: Kendon, Elizabeth J.. Met Office Hadley Centre; Reino UnidoFil: Keuler, Klaus. Brandenburg University of Technology Cottbus-Senftenberg; AlemaniaFil: Lavin Gullon, Alvaro. Universidad de Cantabria; EspañaFil: Lenderink, Geert. Royal Netherlands Meteorological Institute; Países Bajo

Towards high-resolution climate projections for Belgium: Combining convection permitting models with statistical downscaling techniques

Precipitation has a large impact on the society resulting in a high demand for precipitation projections for the future. Apart from average precipitation projections, extreme precipitation projections are also crucial as hazardous weather associated with extreme precipitation can cause great damages. However, performing projections for intense precipitation is difficult as the climate models currently used to perform these projections have rather poor performance for extremes. Due to the typical grid size of current climate models (i.e., up to 12 km), convective processes are represented by parametrizations resulting in deficiencies, notably underestimations of hourly precipitation intensity and misrepresentations of the diurnal cycle of precipitation. Models with a grid fine enough to partly represent dynamically convective processes (at least as fine as 4 km) show clear improvements. Until recently such models, also referred to as convection permitting scale (CPS) models, were used only in a weather forecasting framework due to their high computational cost. However, the increase in computational resources currently allows their integration over yearly or decadal time-scales. The question arises whether climate projections could benefit from CPS simulations and if the computational cost of such CPS- simulation based projections can be lowered. To assess the need for climate projections benefiting from CPS simulations, a first step is to ensure that CPS simulations show an added value compared to coarser simulations. This verification was performed with eleven-year COSMO-CLM simulations at different resolutions (25 km, 7 km and 2.8 km). It was found that the main biases occurring at non-CPS are corrected at CPS. The representation of the diurnal cycle of precipitation is improved, especially in the afternoon when convective activities reach a maximum. In addition, the representation of hourly precipitation is more realistic in the CPS simulation compared to the non-CPS ones. Finally, the spatial representation of both precipitation and temperature is more accurate in the finest resolution simulation. The need for CPS simulations in climate projections is also depending on the existence of significant differences between the CPS and non-CPS simulations. If CPS simulations show identical climate sensitivity than non-CPS simulations then a simple bias correction of the non-CPS simulation could provide similar performances and much lower computational cost than CPS models. To verify this, two additional simulations were performed at CPS using the EC-Earth model as lateral boundary conditions. No significant changes were found for precipitation’s spatial variability. However, the climate sensitivity for daily precipitation intensity quantiles greater than 20 mm/day was found to be 25% higher in the CPS simulations compared to the non-CPS ones. Such results show the need to include CPS simulations in climate projections to improve the range of uncertainty notably concerning the highest precipitation quantiles. Although climate projections could benefit from the use of CPS simulations, the current computational cost of CPS simulations does not allow the production of simulations ensembles at CPS. In this thesis, three approaches are developed to lower the computational cost of CPS simulations on the one hand and of climate projections on the other hand. These approaches consist of (1) the selection of a CPS model configuration that combines low computational cost and realistic representation of convective precipitation, (2) the quantification of the uncertainty related to the use of shorter integration periods compared to what is commonly done in current climate projections and (3) the development of a physically-based statistical framework that could be later used as basis for complimentary alternative computationally cheap statistical downscaling models (SDMs). (1) Three different options are investigated that are likely to reduce the computational costs required to perform CPS simulations, namely switching off the parametrization of graupel, changing the domain size or using different nesting strategies. It was found that, among these three options, only the use of an efficient nesting strategy has the potential for reducing the computational costs (up to 25% lower than the reference simulation in our study) without deteriorating the representation of convective precipitation. (2) Decreasing the length of the time-period for which the climate model is integrated can also help to decrease the computational cost of climate projections. However, a reduction of the integration period results in an increase of the uncertainty related to the climate variability. For precipitation averages in Westdorpe (The Netherlands) the uncertainty of 11% over a 30-year integration period increases to 18% for a reduced 11-year period. Depending on the expected difference between two simulations one could adjust the integration period of these simulations and lower their computational cost. (3) Another approach to lower the computational cost of climate projections based on CPS simulations is to use a combination of computationally cheap SDMs together with CPS models. The circulation type (CT) framework was developed as a possible basis for developing physically based SDMs. It was found that by using the CT only, a large part of the precipitation variability is explained for winter and spring. However for convectively active seasons and for short time-scales (e.g., days), additional predictors are needed. First investigations show that consistent relations between temperature and precipitation are found within individual CTs bringing confidence that the framework of CTs is a good basis to develop robust SDMs with statistical relationships that hold for different climates.status: publishe

Convective rain cell characteristics and scaling in climate projections for Germany

Extreme convective precipitation is expected to increase with global warming. However, the rate of increase and the understanding of contributing processes remain highly uncertain. We investigated characteristics of convective rain cells like area, intensity, and lifetime as simulated by a convection‐permitting climate model in the area of Germany under historical (1976–2005) and future (end‐of‐century, RCP8.5 scenario) conditions. To this end, a tracking algorithm was applied to 5‐min precipitation output. While the number of convective cells is virtually similar under historical and future conditions, there are more intense and larger cells in the future. This yields an increase in hourly precipitation extremes, although mean precipitation decreases. The relative change in the frequency distributions of area, intensity, and precipitation sum per cell is highest for the most extreme percentiles, suggesting that extreme events intensify the most. Furthermore, we investigated the temperature and moisture scaling of cell characteristics. The temperature scaling drops off at high temperatures, with a shift in drop‐off towards higher temperatures in the future, allowing for higher peak values. In contrast, dew point temperature scaling shows consistent rates across the whole dew point range. Cell characteristics scale at varying rates, either below (mean intensity), at about (maximum intensity and area), or above (precipitation sum) the Clausius–Clapeyron rate. Thus, the widely investigated extreme precipitation scaling at fixed locations is a complex product of the scaling of different cell characteristics. The dew point scaling rates and absolute values of the scaling curves in historical and future conditions are closest for the highest percentiles. Therefore, near‐surface humidity provides a good predictor for the upper limit of for example, maximum intensity and total precipitation of individual convective cells. However, the frequency distribution of the number of cells depending on dew point temperature changes in the future, preventing statistical inference of extreme precipitation from near‐surface humidity.We investigated characteristics of convective rain cells under historical and future conditions in convection‐permitting climate simulations using a tracking algorithm. There are more intense and larger cells in the future yielding an increase in hourly precipitation extremes. The temperature scaling curves of cell characteristics shift towards higher peak values at higher temperatures in the future. In contrast, cell characteristics scale consistently with dew point temperature. Therefore, near‐surface humidity provides a good predictor for the upper limit of for example, maximum intensity, and total precipitation of convective cells

Convective shower characteristics simulated with the convection-permitting climate model COSMO-CLM

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

Modelling strategies for performing convection-permitting climate simulations

The computational cost still remains a limiting factor for performing convection-permitting climate simulations. Choosing a model set-up with the lowest computational cost without deteriorating the model performances is, therefore, of relevance before starting any decadal simulations at convection-permitting scale (CPS). In this study three different strategies that aim at reducing this computational cost are evaluated. These strategies are (1) excluding graupel in the microphysical scheme, (2) reducing the nesting steps to downscale from ERA-Interim scale to CPS and (3) reducing the domain size. To test these strategies, the COSMO-CLM regional model was integrated over a four-month summer period for Belgium and evaluated using both radar and rain-gauges precipitation data. It was found that excluding the graupel parametrization at CPS induces a dry bias, but that excluding the graupel parametrization in the parent nest of the CPS simulation does not impact daily accumulated precipitation. In addition, it was also found that the best downscaling strategy is to use two nesting steps, in our case 25 km and 2.8 km. The 7 km nest was found to be redundant. Finally, it was found that a minimum distance of ∼ 150 km between the evaluation domain and the lateral boundary is needed for daily precipitation to converge towards observed values. This indicates that the domain size must be large enough for the model to spin-up convective precipitation and in our case a domain size of 180 × 180 grid-points was found to be necessary. Our recommendations for CPS simulations at lowest computational cost are therefore (1) to include graupel parametrization at CPS but not in the parent nest, (2) to use two nesting steps to downscale from ERA-Interim to CPS and (3) to use a domain size large enough to allow for 150 km spatial spin-up.status: publishe