Sonja Siljak-Yakovlev

Perla Farhat

PEPY BAREKA Nicolas Valentin

Georgia Kamari

New estimates of nuclear DNA amount for 25 taxa from Kefallinia island

Keywords: genome size, 2C DNA value, endemic taxa, chromosome number, ploidy level, conservation, Ionian Islands, Greece

The nuclear DNA amount has been assessed, by flow cytometry, for 25 plant taxa indigenous on Kefallinia Island. Genome size for all these taxa are here reported for the first time. Of these 25 taxa, 18 are Greek and Adriatic/Ionian endemics and 7 are more widely distributed, but of particular interest otherwise. The 2C DNA values ranged from 0.58 pg for Briza minor to 49.53 pg for Paeonia mascula subsp. russoi. In addition, two ploidy levels (2x & 4x) were confirmed for Centaurea subciliaris subsp. subciliaris.

Introduction

The nuclear DNA amount (2C-value), or genome size, is a fundamental biological character of living organisms and it is frequently correlated with other biotic and abiotic characters [START_REF] Leitch | Evolution of DNA amounts across land plants (Embryophyta)[END_REF][START_REF] Pustahija | Small genomes dominate in plants growing on serpentine soils in West Balkans, an exhaustive study of 8 habitats covering 308 taxa[END_REF]. Information on C-value can be profitably used in numerous branches of plant science, including ecology and phytogeography (GRIME & MOWFORTH 1982, VEKEMANS & al. 1996, PRICE & JOHN-STON 1996, PUSTAHIJA & al. 2013), systematics and evolution [START_REF] Cerbah | Evolutionary DNA variation in the genus Hypochoeris[END_REF], 2001, NIKETIC & al. 2013[START_REF] Lepers-Andrzejewski | Diversity and dynamics of plant genome size: an example of polysomaty from a cytogenetic study of Tahitian vanilla (Vanilla x tahitensis, Orchidaceae)[END_REF], HAJRUDINOVIC & al. 2015) in biotechnology, and agronomy [START_REF] Fyad-Lameche | Chromosome number, genome size, seed storage protein profile and competence for direct somatic embryo formation in Algerian annual Medicago species[END_REF][START_REF] Srisuwan | Change in nuclear DNA content and pollen size during polyploidisation of the sweet potato Ipomoea batatas (Convolvulaceae) complex[END_REF] and also in evaluation of biodiversity [START_REF] Bennett | Nuclear DNA amounts in angiosperms and their modern uses -807 new estimates[END_REF], SILJAK-YAKOVLEV & al. 2010, BOU DAGHER KARATT & al. 2013, SILJAK-YAKOVLEV & al. 2017).

Flow cytometry is the widely accepted method of choice in plant genome size estimation [START_REF] Marie | A cytometric exercise in plant DNA histograms with 2C values for 70 species[END_REF][START_REF] Kamaté | Nuclear DNA content and base composition in 28 taxa of Musa[END_REF], DOLEŽEL & al. 2007[START_REF] Siljak-Yakovlev | Genome size variation and polyploidy in the resurrection plant genus Ramonda: Cytogeography of living fossils[END_REF], SILJAK-YAKOVLEV & al. 2010, BAREKA & al. 2012[START_REF] Pellicer | The Application of flow cytometry for estimating genome size and ploidy level in plants[END_REF][START_REF] Bourge | Flow cytometry as tool in plant sciences, with emphasis on genome size and ploidy level assessment[END_REF]). This method is rapid, precise, involves easy sample preparation, and is accurate in detecting small differences in DNA content (BEN-MILOUD-MAHIEDDINE & al. 2011[START_REF] Karrat-Souissi | Cytogeography of 28 populations of three ploidy levels of Cenchrus ciliaris L. in Tunisia[END_REF][START_REF] Pellicer | The Application of flow cytometry for estimating genome size and ploidy level in plants[END_REF].

However, despite its importance, genome size has been estimated for only 3.1% of all angiosperms and 41% of gymnosperms (PELLICER & al. 2018). Evidently, there is still a wide demand for improved coverage of DNA estimates for higher plants, especially angiosperms.

The Ionian region is characterised by a high plant diversity. Kefallinia island, in particular, has attracted the interest of many botanists, and has been well studied from the floristic and phytogeographical points of view [START_REF] Heldreich | Flore de l'île de Céphalonie[END_REF], PHITOS & DAM-BOLDT 1985[START_REF] Karagianni | Diachirisi ke prostasia ton endimikon, apilumenon kai spanion fiton tou Ethnikou Drimou Enou Kefalonias[END_REF], EFTHIMIATOU-KATSOUNI 2012).

A large part of Kefallinia is mountainous, peaking at an elevation of 1,628 m in Mt. Aenos, a mountain that hosts an Abies cephalonica Loudon forest that is unique for an island. The ecological, scientific and historical importance of that forest (which is the locus classicus of Abies cephalonica) resulted in having declared the area as a National Park.

Until now, ca. 1,100 taxa of indigenous vascular plants are known from Kefallinia island, and ca. 60 of them are Ionian and Greek endemics.Many taxa have been studied karyologically [START_REF] Phitos | Beitrӓge zur Flora Ionica[END_REF][START_REF] Erben | Cytotaxonomische Untersuchungen an südosteuropäischen Viola-Arten der Section Melanium[END_REF], KAPASA & al. 2001, SAMAROPOULOU & al. 2013a& b, BAREKA & al. 2018), but little is known about their genome size. We have measured for the first time the DNA content of 25 taxa, most of them Greek endemics or, else, of a particular taxonomic, phytogeographic or karyological interest.

The aim of this paper is to determine the genome size and ploidy level of several species growing in a geographical region with high plant diversity. Our data will be included in the genome size database of the Balkans flora (SILJAK-YAKOVLEV & al. 2010).

To check for possible pre-existing data and confirm the claimed novelty of our reports, we consulted six genome size and chromosome number databases, all of them accessed on November 25, 2018: Kew plant DNA C-values database (http://data.kew.org/cvalues), GSAD -genome size in the Asteraceae database (http://www.etnobiofic.cat/gsad), FLOWer -a plant DNA flow cytometry database (http://botany.natur.cuni.cz/flower/index.php), and Index to chromosome numbers in Asteraceae (http://www.lib.kobe-u.ac.jp/infolib/meta_pub/engG0000003asteraceae), the Chromosome Counts Database (CCDB) [START_REF] Rice | The Chromosome Counts Database (CCDB) -a community resource of plant chromosome numbers[END_REF] and PhytoKaryon, the chromosome database for the Greek plants (http://www.phytokaryon.gr).

Material and methods

Collection of material

Material (leaves) of 25 wild taxa, were collected in Kefallinia (Ionian Islands, from 13 localities, mostly from Aenos National Park (Table 1, Fig. 1). The material was immediately onsite dried and conserved in silica gel until used. Locality (Fig. 1) and collection data are detailed in Table 1.

Flow cytometric analyses

The total nuclear DNA amount was assessed by flow cytometry according to [START_REF] Marie | A cytometric exercise in plant DNA histograms with 2C values for 70 species[END_REF] using silica dried leaves of samples and fresh leaves of one of five internal standards, in order to cover the range of DNA content: Solanum lycopersicum L. 'Montfavet 63-5' (2C = 1.99 pg, [START_REF] Lepers-Andrzejewski | Diversity and dynamics of plant genome size: an example of polysomaty from a cytogenetic study of Tahitian vanilla (Vanilla x tahitensis, Orchidaceae)[END_REF], Petunia hybrida Vilm. 'PxPc6' and Hordeum vulgare L. 'Sultan' (2C = 2.85 pg and 9.81 pg, [START_REF] Marie | A cytometric exercise in plant DNA histograms with 2C values for 70 species[END_REF][START_REF] Marie | A cytometric exercise in plant DNA histograms with 2C values for 70 species[END_REF]GARNATJE &al. 2004 respectively).

The leaves (Approx. 30 mg) of both internal standard and target species were simultaneously chopped using a razor blade in a plastic Petri dish with 1 ml of Gif nuclei-isolation buffer [START_REF] Bourge | Flow cytometry as tool in plant sciences, with emphasis on genome size and ploidy level assessment[END_REF]): (45 mM MgCl2, 30 mM sodium citrate, 60 mM MOPS (4-morpholine propane sulphonate, pH 7), and 1% (w/v) polyvinylpyrrolidone 10,000, pH 7.2) containing 0.1% (w/v) Triton X-100, supplemented with 5 mM sodium metabisulphite and RNAse (2.5 U/ml). The nuclei suspension was filtered through 30 μm nylon mesh. The nuclei were stained with 100 μg/ml propidium iodide (PI), a specific DNA intercalating fluorochrome dye, and kept 5 min at 4°C DNA content of about 3,000 stained nuclei was determined for each sample using the cytometer CytoFLEX S (Beckman Coulter-Life Science United States. Excitation 561 nm, 26 mW; emission through a 610/20 nm band-pass filter. The samples comprised from one to three individuals, measured separately. To check the reproducibility of values, two independent measurements were performed for each individual.

CytExpert software was used for histogram analyses. The total 2C DNA value (DNA contents of the diploid (2n) sets of chromosomes (irrespective of ploidy level), was calculated using the linear relationship between the fluorescent signals from stained nuclei of the species and the internal standard. The total nuclear DNA content was calculated according to the following formula:

2C DNA content/nucleus = [Sample 2C peak mean × Standard 2C DNA pg] Standard 2C peak mean
The symbol C corresponds to the holoploid nuclear genome size (the whole chromosome complement with chromosome number n; 1C and 2C being, respectively, the DNA content of the haploid (n) and diploid (2n) chromosome sets, irrespective of ploidy level [START_REF] Greilhuber | The origin, evolution, and proposed stabilization of the terms 'genome size' and 'C-value' to describe nuclear DNA contents[END_REF].

The mean 2C-value as well as the standard deviation were calculated for measurements of samples comprising more than one individual. The monoploid genome size (1Cx) is the DNA content of a monoploid genome, with chromosome base number x, calculated by dividing the 2C value by ploidy level [START_REF] Greilhuber | The origin, evolution, and proposed stabilization of the terms 'genome size' and 'C-value' to describe nuclear DNA contents[END_REF].

Results and discussion

The nuclear DNA amounts for our 25 taxa (Figs 2345678) are presented in Table 2, and some flow cytometric histograms in Fig. 9. In our selection, 25 species of 19 genera from 12 families are represented (Table 2). Only 5 of our taxa are included in the Greek Red Data Book (PHITOS & al. 2009) according to IUCN (2019) criteria, but 5 more are legally protected in Greece (Table 2). Eighteen of the taxa are endemic, as follows: 3 are endemic to Kefallinia island: Scutellaria rupestris subsp. cephalonica (Fig. 5B), Silene cephallenia subsp. cephallenia (Fig. 6A) and Viola cephalonica (Fig. 8A). All have been included in the Red Data Book of Rare and Threatened Plants of Greece (PHITOS & al. 2009), the first as Endangered (EN) and the other two as Critically Endangered (CR), due to their extremely restricted area of occupancy and the limited number of individuals.

5 are endemic to the Ionian area: Campanula garganica subsp. cephallenica (Fig. 2B) is characterized as Vulnerable (Vu) in the Red Data Book (PHITOS & al. 2009). It is found on four of the Ionian Ιslands (Ζakynthos, Kefallinia, Ithaki, Lefkada). In Kefallinia it lies within the core of the National Park of Mt. Ainos, the protection of which is governed by strict regulations (PHITOS & al. 2015).

Centaurea subciliaris subsp. subciliaris (Fig. 2D) is an endemic subspecies that grows on the islands of Kefallinia and Lefkada, while in mainland Greece (on Mt. Boumistos), subsp. acarnanica Matthäs occurs [START_REF] Phitos | Beitrӓge zur Flora Ionica[END_REF][START_REF] Erben | Cytotaxonomische Untersuchungen an südosteuropäischen Viola-Arten der Section Melanium[END_REF][START_REF] Matthäs | Zur Cytotaxonomie von Centaurea subciliaris Boiss. & Heldr. (sektio Phalolepis (Cass.) DC.) und verwandter Sippen im europäischen Mediterrangebiet. I[END_REF], SAMAROPOULOU & al. 2013a, BAREKA & al. 2018).

Cerastium illyricum is a polymorphic Greek endemic, divided into three subspecies. The typical one (Fig. 2E) grows on the islands of Zakynthos, Kefallinia, and Kerkyra; subsp. crinitum in the Akarnanika Mts of mainland Greece; and subsp. brachiatum in Peloponnisos, on Kefallinia and Zakynthos.

Stachys ionica (Fig. 7A) is endemic to the Ionian Ιslands of Ζakynthos, Kefallinia, Ithaki, Lefkada and some of the neighbouring islets.

Thymus holosericeus (Fig. 7C) has an intense aroma and is occasionally used as a medicinal and aromatic plant. 9 are endemic to Greece (none of them is included in the Red Data Book, PHITOS & al. 2009; but 4 are protected by the Greek law (Presidential Decree 67/81).

Two, Astragalus sempervirens subsp. cephalonicus and Erysimum cephalonicum (Fig. 4A) have their locus classicus in Kefallinia island.

Four, Delphinium hellenicum (Fig. 3B), Dianthus fruticosus subsp. occidentalis (Fig. 3C), Scaligeria moreana (Fig. 5C) and Stachys parolinii (Fig. 7B), are legally protected in Greece (Presidential Decree 67/81).

The other three, Cymbalaria microcalyx subsp. minor (Fig. 2F), Petrorhagia graminea (Fig. 5A) and Silene ionica (Fig. 6B1 & 6B2), grow on the Ionian Islands and also in neighboring areas of mainland Greece.

1 is endemic to the Adriatic-Ionian area: Crepis neglecta subsp. corymbosa, which also extends to Sicily. 7 taxa have a wider distribution: 3 are Mediterranean elements: Ajuga orientalis, Crepis rubra (Fig. 3A), and Paeonia mascula subsp. russoi (Fig. 4B), an attractive taxon, that is included as Near Threatened (NT) in the Greek Red Data Book of PHITOS & al. (2009), 1 it is E Mediterranean: Crepis fraasii subsp. fraasii, 1 occurs at the Balkan Peninsula plus Italy: Campanula versicolor (Fig. 2C), 1 is European-SW Asian: Viola odorata (Fig. 8B), 1 has a cosmopolitan distribution: Briza minor.

The 2C DNA values encompass a large range of values, from 0.58 pg for Briza minor to 49.53 pg for Paeonia mascula subsp. russoi. The values of nuclear DNA content were reported for the first time for 22 taxa. For the three remaining taxa, deviating values were obtained. We already determined the genome size of Crepis neglecta subsp. corymbosa from the same geographical region and had found 2C = 2.43 pg [START_REF] Enke | Phylogeny and karyotype evolution in Crepis section Neglectoides[END_REF], while in this study we report 2C = 2.37 pg, which is a very similar value. Previously [START_REF] Wallace | Nuclear DNA content of three Crepis species[END_REF] reported 2C = 3.6 pg for C. neglecta s. l., but the technique used for that estimate was Feulgen densitometry. For Crepis rubra (Fig. 3A) our value (2C = 6.49 pg) was larger than those (5.7 pg) of BROWN & JONES (1975: personal communication, cited in BENNETT & SMITH 1976). For Paeonia mascula subsp. russoi we determined 2C = 49.53 pg, whereas MULRY & HANSON (1999, personal communication in BENNETT & al. 2000) had reported 35.6 pg for P. mascula subsp. mascula and BOU DAGHER-KARRAT (2013), 58.5 pg for P. mascula s. l. This important difference in genome size could be explained by different techniques having been used for determining the 2C DNA amount or, more likely, by taxonomic differences between the plants studied.

Thanks to our genome size assessment, two ploidy levels (2x & 4x) were confirmed for Centaurea subciliaris subsp. subciliaris, as had already been reported based on actual chromosome counts [START_REF] Phitos | Beitrӓge zur Flora Ionica[END_REF][START_REF] Erben | Cytotaxonomische Untersuchungen an südosteuropäischen Viola-Arten der Section Melanium[END_REF][START_REF] Matthäs | Zur Cytotaxonomie von Centaurea subciliaris Boiss. & Heldr. (sektio Phalolepis (Cass.) DC.) und verwandter Sippen im europäischen Mediterrangebiet. I[END_REF], SAMAROPOULOU & al. 2013a, BAREKA & al. 2018).

According to the categories established by [START_REF] Leitch | Nuclear DNA amounts in angiosperms -progress, problems and prospects[END_REF], the 2C DNA values reported here are very small to rather small. Ten taxa possess a very small genome size (2C < 2.8 pg), 14 taxa belong to the category of small genome size (2.8 ≤ 2C < 7), in one taxon (Delphinium hellenicum) it is medium-sized (7 ≤ 2C < 28), and one (Paeonia mascula subsp. russoi) has a large genome size (28 ≤ 2C ≤ 75). In our sample of taxa, diploids dominate: only three tetraploids were detected (Table 2). This is the first work to focus particularly on the nuclear DNA content of members of the Greek flora. We hope it is just a beginning, and that this important biological character will increasingly be taken into account in taxonomic, evolutionary and biodiversity studies.

Fig. 1 .

 1 Fig. 1. Map of Kefallinia island with the collection localities of the studied 25 taxa.

Fig. 2 .

 2 Fig. 2. Studied taxa from Kefallinia island: A, Ajuga orientalis; B, Campanula garganica subsp. cephallenica; C, Campanula versicolor; D, Centaurea subciliaris subsp. subciliaris; E, Cerastium illyricum subsp. illyricum; F, Cymbalaria microcalyx subsp. minor.

Fig. 3 .

 3 Fig. 3. Studied taxa from Kefallinia island: A, Crepis rubra; B, Delphinium hellenicum; C, Dianthus fruticosus subsp. occidentalis.

Fig. 4 .

 4 Fig. 4. Studied taxa from Kefallinia island: A, Erysimum cephalonicum; B, Paeonia mascula subsp. russoi.

Fig. 5 .

 5 Fig. 5. Studied taxa from Kefallinia island: A, Petrorhagia graminea; B, Scutellaria rupestris subsp. cephalonica; C, Scaligeria moreana.

Fig. 6 .

 6 Fig. 6. Studied taxa from Kefallinia island: A, Silene cephallenia subsp. cephallenia; B1 & 2, Silene ionica.

Fig. 7 .

 7 Fig. 7. Studied taxa from Kefallinia island: A, Stachys ionica; B, Stachys parolinii; C, Thymus holosericeus.

Fig. 9 .

 9 Fig. 9. Flow cytometric histograms for genome size estimation. Species (A-F) are co-chopped with different standards, as specified into the left of each boxes: (A-B) with Lycopersicon esculentum, (C-D) with Petunia hybrida, (E-F) with Hordeum vulgare. After setting up a gating strategy, initial gates (not show) helped identification of nuclei by discarding debris and doublets, results are then shown as histograms with fluorescence intensity (DNA content) on x-axis (log scale) and number of events (count) on y-axis. Highlights in blue correspond to sample peaks and ones in red to standards peaks, used to measure fluorescence geometric mean to calculate Cvalue.

Table 1 .

 1 Geographic origin and collection data of 25 studied taxa from Kefallinia island.

	Loc. no	Locality	Geographic data Latitude Longi-tude Altitude	Collection data & collectors
		Kefallinia: Aenos National Park: at			21 Jul 2018,
	1	the lowlands of Mt. Aenos, Omala area, above the village Epanochori,	38° 09′ 43.3´´ N 20° 35′ 45.1´´ E	470 m	leg.: G. Kamari, S. Moshopoulou
		in gravels			& D. Spanou
	2	Kefallinia: Aenos National Park: Mt. Roudi, between the places Agrapidies and the small church Agios Eleftherios, at the road, on calcareous rocky places	38° 11′ 33.7´´ N 20° 35′ 36.8´´ E	650-850 m	21 Jul 2018, leg.: G. Kamari, S. Moshopoulou & D. Spanou
		Kefallinia: Aenos National Park:			21 Jul 2018,
	3	Mt. Aenos, at the summit named "Chionistra", at the pale area for the	38° 08′ 24.5´´ N 20° 39′ 32.8´´ E	1610 m	leg.: G. Kamari, S. Moshopoulou
		protection of Viola cephalonica			& D. Spanou
	4	Kefallinia: Aenos National Park: Mt. Aenos, at the place named "Thea", on calcareous rocky places	38° 08′ 16.1´´ N 20° 39′ 46.0´´ E	ca. 1600 m	21 Jul 2018, leg.: G. Kamari, S. Moshopoulou & D. Spanou
		Kefallinia: Aenos National Park:			21 Jul 2018,
	5	Mt. Aenos, at the place named "Parko Anapsichis", on calcareous	38° 09′ 09.1´´ N 20° 38′ 18.7´´ E	1320 m	leg.: G. Kamari, S. Moshopoulou
		petrosis places			& D. Spanou
	6	Kefallinia: Aenos National Park: Mt. Aenos, at the NW main enter of the Park	38° 09′ 35.0´´ N 20° 37′ 20.2´´ E	1070 m	21 Jul 2018, leg.: G. Kamari, S. Moshopoulou & D. Spanou
		Kefallinia: Across to the town Ar-			22 Jul 2018,
	7	gostoli: At the place named Agia Barbara, at the road to the town	38° 10′ 52.4´´ N 20° 30′ 13.9´´ E	ca. 60 m	leg.:G. Kamari, V. Karagianni
		Sami, on calcareous rocks			& D. Spanou
		Kefallinia: Aenos National Park:			22 Jul 2018,
	8	Mt. Aenos, between the places named "Thea" and "Fteri", on	38° 08′ 15.0´´ N 20° 40′ 02.8´´ E	1560 m	leg.: G. Kamari, V. Karagianni
		rocky limestone places			& D. Spanou
		Kefallinia: Aenos National Park:			22 Jul 2018,
	9	Mt. Aenos, close to the main SE en-ter of the Park, above the village	38° 07′ 24.0´´ N 20° 42′ 15.2´´ E	ca. 1100 m	leg.: G. Kamari, V. Karagianni
		Arginia, on calcareous rocks			& D. Spanou
		Kefallinia: Aenos National Park:
	10	Mt. Roudi, at the north-east slopes, in clearing petrosis places of Abies	38° 11′ 50.4´´ N 20° 36′ 45.4´´ E	ca. 750 m
		cephalonica forest

Table 2 .

 2 Origin, Chorology, IUCN status, Nuclear DNA amount (2C), monoploid genome size (1Cx), chromosome number and relevant cytological references of 25 studied taxa from Kefallinia island.

		References		SAMAROPOULOU & al. (2015)		CARTIER (1979)		CCDB-Database	(RICE & al. 2015)		DAMBOLDT (1965)		DAMBOLDT & PHITOS (1971)	MATTHÄS (1976), PHITOS &	DAMBOLDT (1971 & 1985),	BAREKA & al. (2018)	PHITOS & DAMBOLDT	(1971 & 1985),	SAMAROPOULOU & al. (2013a),	BAREKA & al. (2018)	DAMBOLDT & PHITOS (1971)	ANAGNOSTOPOULOS & KAMARI	(1992)
	2n	(x)	32+ 0-4Β	(4x)	16	(2x)	10	(2x)	34	(4x)	34	(4x)	18+0-2B	(2x)	36+1-4B	(4x)	34	(4x)	12	(2x)
		Standard		Petunia		Petunia		Petunia		Petunia		Solanum		Petunia		Petunia	Petunia	Petunia
	1Cx	DNA	in Mbp	575		1120		284		1721		1677		1027		985	1912	3095
	2C DNA	in pg *	(+ SD)	2.35	(±0.04)	2.29	(±0.06)	0.58	3.52	(±0.29)	3.43	(±0.1)	2.10	(±0.01)	4.03	(±0.07)	3.91	(±0.07)	6.33	(±0.1)
	IUCN	status										VU
		Chorology		Me		E-Gr		Co		E-IoI		Balkans	& Italy		E-IoI		E-IoI	E-IoI	EM
	Pop.	no	10		9		6		4		7		3		1	3	11
		Taxon (Family)		Ajuga orientalis L.	(Lamiaceae)	Astragalus sempervirens	subsp. cephalonicus (C. Presl)	Asch. & Graebn. (Fabaceae)	Briza minor L. (Poaceae)	Campanula garganica	subsp. cephallenica (Feer)	Hayek (Campanulaceae)	Campanula versicolor Sm. (Cam-	panulaceae)	Centaurea subciliaris	subsp. subciliaris Boiss. &	Heldr. (Compositae)	''	Cerastium illyricum Ard.	subsp. Illyricum	(Caryophyllaceae)	Crepis fraasii Sch.Bip.	subsp. fraasii	(Compositae)

Table 2 (

 2 contin.). Origin, Chorology, IUCN status, Nuclear DNA amount (2C), monoploid genome size (1Cx), chromosome number and relevant cytological references of 25 studied taxa from Kefallinia island.

		References		KAMARI (1976),	ENKE & al. (2015)
	2n	(x)	8	(2x)
		Standard			Petunia
	1Cx	DNA	in Mbp		1159
	2C DNA	in pg *	(+ SD)		2.37	6.49	(±0.17
	IUCN	status
		Chorology			E-Ad/IoI	Me
	Pop.	no		6	6
		Taxon (Family)		Crepis neglecta	subsp. corymbosa (Ten.) Nym.	(Compositae)	Crepis rubra L. (Compositae)

Jul 2018, leg.: G. Kamari, V. Karagianni & D. Spanou 11 Kefallinia: Aenos National Park: Mt. Roudi, at the north slopes with Abies cephalonica forest, at shady places 38° 11′ 48.1´´ N 20° 36′ 10.5´´ E ca. 650 m 22 Jul 2018, leg.: G. Kamari, V. Karagianni & D. Spanou

Jul 2018, leg.: G. Kamari, V.Karagianni & D. Spanou

BOTANIKA CHRONIKA 22, 2019

Acknowledgements

We would like to thank the staff of the Management Body of Mt. Aenos National Park: Vasiliki Karagianni MSc, Sofia Moshopoulou MSc & Dionisia Spanou, for their company and help during field work; and Dr. Mihail Xanthakis for the production of the map. Most of the plant photographs are from the photo archive of Aenos National Park, which we sincerely thank. We also thank Dr. Mickael Bourge (Platform of Cytometry, Institute of Integrative Biology of the

Cell (I2BC), Gif-sur-Yvette) for his very useful assistance in cytometry; and the Laboratoire d'Ecologie, Systématique et Evolution (UMR CNRS 8079) for financial support of genome size measurement.


Ségolène Berthou

Malcolm J Roberts

Benoı ˆt Vannière

Nikolina Ban

Danijel Belušić

Cécile Caillaud

Thomas Crocker

Hylke De Vries

Andreas Dobler

Dan Harris

Elizabeth J Kendon

Oskar Landgren

Colin Manning

Orcid Ids

Convection in future winter storms over Northern Europe

Keywords: convection-permitting models, precipitation, winter storms, extratropical cyclones, climate change, convection

Precipitation within extratropical cyclones is very likely to increase towards the end of the century in a business-as-usual scenario. We investigate hourly precipitation changes in end-of-century winter storms with the first km-scale model ensemble covering northwest Europe and the Baltic region. This is an ensemble that explicitly represents convection (convection permitting models (CPMs)). Models agree that future winter storms will bring 10%-50% more precipitation, with the same level of light precipitation but more moderate and heavy precipitation, together with less frequent frozen precipitation. The warm sector precipitation rates will get closer (up to similar) to those in present-day autumn storms, along with higher convective available potential energy and convective inhibition, suggesting more convection embedded in storms. To the first order, mean hourly precipitation changes in winter storms are driven by temperature increase (with little relative humidity changes) and storm dynamical intensity (more uncertain), both captured by regional climate models (RCMs). The CPMs agree with this, and in addition, most CPMs show more increase in intense precipitation in the warm sector of storms compared to their parent RCM.

Introduction

Climate change is likely to bring milder and wetter winters to Northern Europe (Doblas-Reyes et al 2021). Given the current vulnerability of northern European societies to extensive winter flooding [START_REF] Muchan | The winter storms of 2013/2014 in the UK: hydrological responses and impacts[END_REF], we need to better understand how the nature of winter precipitation will change. Precipitation is expected to intensify in a warming climate, in the first order due to increased water holding capacity of the atmosphere [START_REF] Fischer | Observed heavy precipitation increase confirms theory and early models[END_REF]. In regions and seasons not limited in atmospheric moisture supply, this will translate into an increase in mean precipitation, like in Northern Europe in winter [START_REF] Poujol | Physical processes driving intensification of future precipitation in the mid-to high latitudes[END_REF]. Global climate models project more precipitation per storm [START_REF] Hawcroft | Significantly increased extreme precipitation expected in Europe and North America from extratropical cyclones[END_REF], Catto et al 2019) but dynamical changes in storm tracks, storm numbers and storm intensity are less certain [START_REF] Zappa | The response of North Atlantic cyclones to climate change in CMIP5 models[END_REF][START_REF] Fereday | Atmospheric dynamics is the largest source of uncertainty in future winter European rainfall[END_REF], Catto et al 2019). The most robust dynamical changes over Europe in winter are an eastward extension of North Atlantic jet stream, which means an increase in storm numbers over the UK and Baltic regions [START_REF] Harvey | The response of the Northern Hemisphere storm tracks and jet streams to climate change in the CMIP3, CMIP5 and CMIP6 climate models[END_REF][START_REF] Albern | Tropical cloud-radiative changes contribute to robust climate change-induced jet exit strengthening over Europe during boreal winter[END_REF] and an increase in dynamically extreme storms [START_REF] Priestley | Future changes in the extratropical storm tracks and cyclone intensity, wind speed and structure[END_REF].

With the growing availability of ensembles of km-scale models at climate scales [START_REF] Ban | The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation[END_REF][START_REF] Pichelli | The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: historical and future simulations of precipitation[END_REF], we can examine finer-scale hourly © 2022 Crown copyright, Met Office & The Authors precipitation processes in future winter (FW) storms. Indeed, case studies show that many precipitation processes in mid-latitude cyclones are improved at km-scale modelling: shallow convection in the cold sector/dry intrusion overlaying a warmer surface [START_REF] Grossman | Air-sea interaction during an extreme cold air outbreak from the Eastern Coast of the United States Mon[END_REF], slantwise convection releasing conditional symmetric instability associated within fronts [START_REF] Lean | The effects of changing resolution on mesoscale modelling of line convection and slantwise circulations in FASTEX IOP16 Q[END_REF][START_REF] Glinton | Modulation of precipitation by conditional symmetric instability release[END_REF][START_REF] Chen | Sensitivities of slantwise convection dynamics to model grid spacing under an idealized framework Q[END_REF], better representation of frontal structures, dry intrusion overrunning the warm sector [START_REF] Browning | The dry intrusion perspective of extra-tropical cyclone development[END_REF], embedded convection in warm conveyor belt and cold fronts [START_REF] Neiman | The life cycle of an extratropical marine cyclone. Part II: mesoscale structure and diagnostics Mon[END_REF][START_REF] Rasp | Convective and slantwise trajectory ascent in convection-permitting simulations of midlatitude cyclones Mon[END_REF][START_REF] Oertel | Convective activity in an extratropical cyclone and its warm conveyor belt-a case-study combining observations and a convection-permitting model simulation Q[END_REF].

In this study, we investigate how the nature of hourly precipitation and its rate may change in FW storms in a business-as-usual RCP8.5 end-ofcentury scenario. A change in the nature of precipitation within storms will have strong local impacts through its modulation of surface runoff and will also affect storm predictability through modification of the upper-level potential vorticity field by convection [START_REF] Oertel | Potential vorticity structure of embedded convection in a warm conveyor belt and its relevance for large-scale dynamics[END_REF].

After showing that the sub-sampling of models that we use is representative of the uncertainty range of a larger ensemble, we show how precipitation rates change in different sectors of the storms. We then investigate reasons behind the diversity of storm precipitation projections between models and finally examine changes in convective properties in the model with the highest level of warming.

Data and methods

Data

We use five pairs of convection permitting models (CPMs) at 2-3 km resolution, double nested in 12 km regional climate models (convection-parameterised) (RCMs) and general circulation models (GCMs) (table 1). The model evaluation papers are listed in table 1. CPMs were shown to better represent hourly precipitation characteristics. KNMI and HCLIMcom use the same CPM (but different RCM and GCM), while CNRM also uses a similar CPM. ETHZ uses a pseudo-global warming (PGW) approach: the present-day RCM is driven by ERA-interim reanalysis and the future RCM by ERA-interim plus a 3D Max Planck Institut-Earth System Model-Low Resolution (MPI-ESM-LR)-derived thermodynamical climate change signal which includes slow circulation changes [START_REF] Brogli | Causes of future Mediterranean precipitation decline depend on the season[END_REF][START_REF] Hentgen | Clouds in convection-resolving climate simulations over Europe[END_REF]. By design, the number of extratropical cyclones entering the domain is the same between the present and future ETHZ runs. MOHC does not use an intermediate nest since it is driven by a 25 km GCM. We use the 25 km GCM as reference (and call it RCM for simplicity in the figures). KNMI downscales different members of their GCM ensemble for the presentday and future time slices to be able to get a reasonably unbiased response with respect to the ensemble mean response in a number of parameters (De Vries, personal communication).

We compare a ten-year historic time-period with a ten-year end-of-century time period. ETHZ and MOHC simulations do not share the exact same dates as they were run before a common protocol was agreed in the European Climate Prediction project (EUCP). Both the GCM and time period difference mean that ETHZ has the smallest warming signal and MOHC the strongest (see section 3.1). The future scenario is RCP8.5.

Figure 1 shows the diversity of CPM domains covering Northern Europe. The reason for this choice was to keep the computing costs reasonable while covering most of Europe within the EUCP project. CPM data is regridded to the RCM grid for a fair comparison, except in section 3.4, where the CPM is analysed on its native grid.

Methods

Tracking algorithm

We track the storms using a widely used feature tracking package called TempestExtremes (Zarzycki andUllrich 2017, Ullrich et al 2021). Candidates are identified as local minima in the sea level pressure (SLP). Candidates with SLP increasing by 150 Pa over a 5 • radius around them are selected (closed contour criteria). Individual candidates are then stitched together into tracks based on further criteria (e.g. minimum lifetime, maximum distance between each point, minimum total distance). The same parameters were used for all RCMs (supplementary material).

Selection of tracks

Tracks are selected within subregions centred on the CPM domain (boxes in figure 1). The regions are slightly different between CPMs, but always remain within the region of mean precipitation increase in all CPMs. We show that the processes at play are consistent between models and regions. Sensitivity tests to the exact region definition showed little impact on the results (not shown). The driving model is masked to match the CPM domain for a fair comparison.

Once the track is identified in the RCM, we find the associated minimum SLP in the CPM within a 3 • radius, commonly used in inter-tracking comparisons [START_REF] Neu | IMILAST: a community effort to intercompare extratropical cyclone detection and tracking algorithms Bull[END_REF]. For all models, the CPM SLP minima stays within 2 • of the RCM one for at least 75% of the storm frames. The CNRM CPM is closest to its parent RCM and MOHC CPM is furthest away, potentially because of the large domain and GCM/CPM configuration differences [START_REF] Berthou | Pan-European climate at convection-permitting scale: a model intercomparison study[END_REF].

Storm size and within-storm variables

Once the storm is selected, SLP, precipitation and wet bulb potential temperature (WBPT) are used within a 10 • radius of the SLP minima, which is large enough to encompass some of the trailing front and shallow convection within the dry intrusion [START_REF] Pfahl | On the relationship between extratropical cyclone precipitation and intensity[END_REF]. We also removed all the data points within the Mediterranean region to focus on Central and Northern Europe (up to 63 N), where models agree on the sign of precipitation change. We used WBPT at 850 hPa (θ w ) to separate storm sectors as it incorporates both temperature and humidity fields. θ w is preferred over θ as its inclusion of temperature and humidity, which change in the same direction across fronts, allows a better separation of storm sectors [START_REF] Hewson | The application of wet-bulb potential temperature to air mass analysis. III rainfall in depressions Q[END_REF]. It is also a good indicator of frozen precipitation when it is below 273 K [START_REF] Knox | Two simple and accurate approximations for wet-bulb temperature in moist conditions, with forecasting applications[END_REF]. θ w at 850 hPa is directly output by the MOHC and CNRM models. For the other models, we calculated it using temperature and specific or relative humidity (RH). Following [START_REF] Knox | Two simple and accurate approximations for wet-bulb temperature in moist conditions, with forecasting applications[END_REF], we developed a simple but accurate formula for the 850 hPa θ w (supplementary material and figure S1).

Storm intensity

We define the storm intensity as the difference between the SLP minimum at the storm centre and the average SLP on a 2 • radius around it to avoid variations in the climatological mean, following [START_REF] Pfahl | On the relationship between extratropical cyclone precipitation and intensity[END_REF].

Definition of storm sectors

Figure 2 illustrates a few storm samples. We regrid the CPMs onto the RCM grid via conservative remapping, which means a 12 km grid for most models except MOHC, for which the GCM has a 25 km resolution. Following the simplest way of defining frontal zones provided in [START_REF] Thomas | What are the best thermodynamic quantity and function to define a front in gridded model output?[END_REF], we use the moderate baroclinic zone definition of [START_REF] Sanders | A proposed method of surface map analysis Mon[END_REF] to define fronts as zones where the norm of θ w gradients is larger than 4 K/110 km on the 25 km MOHC grid. This value is too low for the 12 km RCM grids (too many non-frontal gradients included) so we use 5 K/110 km for them. Sensitivity analysis on the thresholds used to define the fronts do not change the main results as long as the threshold is not too low, which would select small scale frontal structures outside the main ones. We then take the median of θ w values in this zone to separate a cold sector and a warm sector. As illustrated in figure 2, precipitation in the warm sector is associated with the warm conveyor belt ascent, the cold front and part of the warm front.

In this definition of the cold sector, precipitation is mostly coming from shallow convection in the dry intrusion and precipitation ahead of the warm front, in the cloud head. Finally, we also define a central sector (within a 2 • radius of the storm centre), where convection is also likely to happen. These definitions are computationally simple and require few variables to provide a first order understanding of which parts of the storms contribute most to precipitation increase.

Results

Winter mean precipitation changes

Figure 1 shows mean precipitation increases by 15%-40% over Northern Europe at the end of the century. MOHC model shows the largest changes, and the CPM amplifies the change by a further 10%-20%. In contrast, the other CPMs have mean changes closer to their parent RCM. Figure S2 shows that this ensemble is representative of the range of precipitation changes in traditional convection-parameterised ensembles over the largest box shown in figure 1. Figure S3 further highlights that mean precipitation increase comes from the top half of the precipitation intensity distribution. Models agree on this and there is little land sea contrast in this change. However, the magnitude is different between models, from a 30% increase in the top half of the distribution in most models to a doubling in MOHC models. The further increase in the MOHC CPM also comes from the top half of the distribution. With the same model on a smaller domain, [START_REF] Kendon | winter precipitation increase in new convectionpermitting scenarios[END_REF] showed that about half of the larger increase in precipitation was linked with better ability for shower advection over land in the CPM compared to the RCM. We further investigate this from the perspective of precipitation within storms to understand where the remaining differences come from, as well as whether similar amplification happens in other models. From figure S3, ETHZ CPM is very close to its RCM and CNRM, KNMI and HCLIMcom CPM models have a shifted distribution towards higher intensities, which translates into larger changes in the tail of the distribution.

Precipitation increases dominated by the warm sector

Interestingly, the increases in the top half of the distribution seen in the precipitation climatology (figure S3) are also found for in-storm precipitation (figure 3). This emphasizes that the dominant mechanism for increased precipitation in winter is more precipitation per storm, consistent with Catto et al (2019). For the MOHC models, the larger increases in the CPM (plain line) compared to the RCM (dashed line) shown in the climatology (figure S3) are also found inside storms (figure 3), indicating that the processes generating RCM/CPM differences happen within storms.

By breaking down the precipitation distribution by sectors (defined in section 2.2.5), we show that about two thirds of precipitation comes from the warm sector of storms in present-day winter storms, and most of the intense precipitation happens there. Intense precipitation also happens within the storm centre, but it is a smaller contributor. Frontal zones as defined here are a small contributor, mostly because of their small area.

Looking at future changes, models agree that the warm sector is the main contributor to future changes for the top half of the precipitation distribution. In all models, the increased precipitation contribution from the cold sector peaks at lower precipitation rates (1.5-2 mm h -1 ) compared to the warm sector. Compared to other models, the MOHC models show a larger contribution to precipitation change from the cold sector, likely linked to increases in the size of the cold sector of storms in these models, as discussed later in section 3.4.

There is also moderate agreement among models that the largest difference between CPMs and RCMs in terms of future changes in precipitation is found for the heaviest precipitation in the warm sector of storms. CNRM, KNMI, MOHC and HCLI-Mcom CPMs all show significant larger increases in the warm sector for rates above 4 mm h -1 . Indeed, at these high rates, the dashed red line representing the RCM is often below the red-shaded range of CPM changes in figure 3. Except in the MOHC model, these larger CPM increases at high rates have a small impact on mean increase.

Larger increases in precipitation in the MOHC CPM compared to the RCM come equally from the cold and warm sectors. The larger increase in cold sector precipitation in the CPM is consistent with the findings of [START_REF] Kendon | winter precipitation increase in new convectionpermitting scenarios[END_REF], who diagnosed shallow convection and showed it propagates into land in the CPM, and not the RCM. They quantified that this explained 60% of the larger increase in the CPM over land. Figure 3 shows that the rest of the CPM rainfall increase comes from the warm sector. We will explore this in section 3.4.

Thermodynamical changes dominate the precipitation increase

We now investigate the sources of the spread in projections of mean hourly precipitation per storm between RCMs and the difference in projected changes for moderate to heavy precipitation in CPMs compared to RCMs. Because the warm sector is a dominant contributor to mean precipitation and precipitation changes, we use median θ w in the warm sector to scale the spatial mean and 99th percentile of hourly precipitation in storms in figures 4 and S4.

The relationship between median θ w in the warm sector and hourly mean precipitation is robust, with Pearson correlation coefficient (R) above 0.5 for most models in present-day winter and FW (figure 4). Note that this relationship is less robust if using the whole storm median θ w , consistent with the warm sector being the largest contributor to in-storm total precipitation and changes (figure 3). These high correlations and the fact that the present-day and future slopes are similar suggest that the firstorder driver of increased precipitation per storm is the increase in temperature in the warm sector. The slope ranges between 8% K -1 and 14.2% K -1 in present-day winters and 9.7%-13.6% K -1 in FWs, depending on models and regions. This is above the Clausius-Clapeyron 6%-7% rate (thin black lines in figure 4). This is potentially explained by the fact that storm precipitation also depends on storm dynamical intensity (R between 0.2 and 0.4 (not shown)), in line with [START_REF] Pfahl | On the relationship between extratropical cyclone precipitation and intensity[END_REF], and storm intensity is lower at lower θ w in the warm sector (R between 0.1 and 0.2). To illustrate mean precipitation dependency on dynamical intensity, we added dots to highlight the strongest storms (minimum SLP depression <-5 hPa) in intensification phase (decreasing minimum SLP trend, Catto and Raveh-Rubin ( 2019)). They are all above the fitted line, confirming that for a given θ w range, intense storms reach larger precipitation rates.

The models with an increasing proportion of these dynamically strong storms in the future (MOHC, HCLIMcom and CNRM, table 2) tend to have a steeper mean precipitation increase per degree of θ w in the future (although other factors may play a role in the similar present and future trends in MOHC). In the KNMI model, storms become less dynamically intense (table 2 and figure S5(a)), and the slope is less steep in the future. This illustrates model disagreement in how mean storm dynamical intensity will change (Catto et al 2019), and consequently how it will modulate thermodynamical precipitation increase.

Figure 4 also shows the fitted line for present-day autumn (PDA) storms in grey, to explore the hypothesis that the relationship between precipitation and θ w in FW storms may be similar to PDA storms. However, the correlation coefficient and slope for PDA storms are much lower for all models. Their intensity is not the main explanation (table 2), although figure S6(a) suggests the median PDA storm is less intense. The largest differences between PDA and FW storms is the median RH in the warm sector which is 2%-6% higher in FW storms than in PDA storms (figure S6(b)). To highlight this, we added crosses for storms with RH below 84% in figure 4. Most of these crosses are below the fitted line in present-day winter and FW: for a given θ w , storms with lower RH have less precipitation. The proportion of storms with low RH changes from 0% to 15.5% in present winter and FW to 4%-20% in PDA (table 2). PDA storms are therefore limited by lower RH at higher temperature. In contrast, RH in the warm sector of winter storms remains, in future, significantly higher than in PDA storm (figure S6(b)). This explains why FW storms could become as or more rainy than PDA storms while remaining 1 to 2 K cooler (figures S6(c) and (d)).

Figure S4 shows that the 99th percentile of precipitation in PDA storms (ranging from 1 to 9 mm h -1 ) does reach a similar slope and correlation coefficient as FW storms, suggesting that lower RH in PDA is reducing mean precipitation by reducing lower precipitation rates.

We now investigate the difference in projected changes for moderate to heavy precipitation in CPMs compared to RCMs (for all CPMs apart from ETHZ, figure 3). Figure S4 shows that the slopes between the 99th percentile of precipitation and median θ w is similar between CPMs and RCMs (when R > 0.4), but CPM values are shifted higher. This shift is especially large in PDA storms and FW storms, suggesting that fine-scale processes at larger θ w values contribute to heavier precipitation in CPMs. Frequency (right axis) of low precipitation rates (0.1-1 mm h -1 ) scaled by 1/15, moderate rates (1-5 mm h -1 ) scaled by 1/10, intense rates (5-10 mm h -1 ) and heavy rates (>10 mm h -1 ), CAPE (>250 J kg) and CIN (<-100 J kg -1 ) scaled by 1/10, total column water (TCW >20 mm) scaled by 1/30, binned by θw values and normalised by the number of points in all storms on the native grid of the MOHC CPM. Grey vertical line is median θw, black vertical line is the freezing point.

The MOHC models shows a peculiar behaviour in two ways: the CPM shows more intense 99th values only in the future and the cold sector contributes significantly to future changes (figure 3). We will investigate these in the next section.

Understanding MOHC larger increases in storm precipitation

In figure 5, individual grid-points in storms are pooled and binned by θ w values. Intense precipitation (>5 mm h -1 ) in storms occurs at θ w above 280 K and is accompanied by moderate convective available potential energy (CAPE) (>250 J kg -1 ). In present-day winter storms, these are infrequent, but they become much more frequent in FW storms. When comparing FW storms with PDA storms, the range of θ w spanned by all storms is the same: 272-292 K. The proportion of grid-points with intense precipitation is similar between FW storms and PDA storms. The frequency of moderate CAPE, convective inhibition (CIN) and high total column water increase in FW storms, favouring more intense convection [START_REF] Rasmussen K L, Prein A F, Rasmussen R M | Changes in the convective population and thermodynamic environments in convection-permitting regional climate simulations over the United States[END_REF] in the warm sector of storms. Nevertheless, it remains lower than PDA storms. As shown in figure S5(b) and discussed above, RH in the warm sector remains high in FW storms, higher than PDA storms (figure S6b). Because CIN is closely related to low-level RH (Chen et al 2020), higher winter RH may explain why CIN values are not as large as PDA storms. Subsequently, CAPE may be released before it can build up to higher values.

In other models, figure S7 shows that θ w change is less large than in the MOHC model: FW storms in those models are only half-way between presentday winter and autumn ones, and intense precipitation increases in the warm sector are significant but less large than in the MOHC model.

Regarding the larger increase in cold sector precipitation in MOHC models compared to other models, figure 5 shows a skewed θ w distribution towards lower temperatures compared to the present-day distribution. Figure S5(e) confirms that the cold sector size increases in the future in the MOHC model. A possible explanation is the change in storm types in this model, with more frequent occurrences of Shapiro-Keyser storms (Manning et al 2022). The CPM-RCM difference in rain frequency over land due to the lack of shower advection over land in the RCM shown by [START_REF] Kendon | winter precipitation increase in new convectionpermitting scenarios[END_REF], already known in present-day weather situations, is enhanced in the future because of the cold-sector size increase in the MOHC model. This process, which can be detected in a differential change in wet-hour frequency between land and sea, with further increases in the CPM over land, is only at play in the MOHC model (figure S8).

Finally, we note that the frequency of conditions for frozen precipitation (θ w < 273 K) decreases by at least two thirds in all models (figures 5 and S7).

Conclusions and discussion

We investigated the changes in hourly precipitation in FW storms (extratropical cyclones) in a multi-model regional ensemble at km-scale resolution together with its driving 12-25 km ensemble. The main difference between the two sets of models is that km-scale models allow an explicit representation of convective precipitation (CPMs).

We showed that our small ensemble of five model pairs give a representative sample of end-of-century FW precipitation projections for Northern Europe as provided by more traditional models (CMIP5, CMIP6, CORDEX) in a strong warming scenario (RCP8.5). Changes in storm numbers depend on the global driving model and on inter-decadal variability, and therefore are not investigated here: we examine hourly precipitation within 10 • of low-pressure centres.

Our main results are that mean precipitation will increase in FW storms, due to increased moderate and intense precipitation. This increase mostly comes from the warm sector of storms. Differences between models are linked with differences in their warming levels in the warm sector of storms and differences in changes in dynamical strength of storms. The latter is less certain, with little model agreement (Catto et al 2019), also potentially because dynamical strength is more prone to inter-decadal variability. Our small ensemble design is too limited to address this.

Because WBPT ranges in FW storms may reach similar values to PDA ones, we compare both sets of storms. Hourly mean precipitation in FW storms reaches larger values than PDA storms because FW storms have higher RH in their warm sector, and more light precipitation. Intense (>5 mm h -1 ) and heavy (>10 mm h -1 ) precipitation within winter storms increase in frequency, and get closer to (or even up to for one model) PDA storms. The frequency of favourable conditions for frozen precipitation also gets closer to PDA storms, with a decrease by at least two thirds in frequency.

The warm sector increase in precipitation is likely linked with more convection, as shown by CAPE and CIN increases in the MOHC model, which is coherent with other CPMs also showing a greater increase in intense precipitation rates (>4 mm h -1 ) than RCMs in this sector. A particular feature of the MOHC CPM is a larger increase in precipitation in the cold sector, likely linked with a deficiency in the RCM to advect showers over land [START_REF] Kendon | winter precipitation increase in new convectionpermitting scenarios[END_REF]. This may be because this model shows the largest increase in cold sector size in future storms or it may be a particular feature of the convection scheme used in the MOHC RCM.

This study shows that the magnitude of the larger increase in mean precipitation in the MOHC CPM is not found in other RCM/CPM pairs. We therefore recommend consideration of multi-model ensembles when possible. We showed that CPMs are useful tools to better understand changes in intense rain rates and fine-scale processes in winter storms. Nevertheless, levels of warming within the warm sector of storms and dynamical strength of storms, well captured by RCMS, are the largest factors explaining mean hourly precipitation changes in storms.

Figure 1 .

 1 Figure 1. Percentage change in mean winter precipitation at the end of the century compared to the reference period. Left: convection-parameterised models (RCMs), right: convection-permitting models (CPMs). The box shows where storm tracks are selected: (named 'Baltic' for HCLIMcom, 'North Sea' for CNRM and KNMI and 'North Sea & Baltic' for ETHZ and MOHC). The greyed-out Mediterranean zone is removed from the analysis.

Figure 2 .

 2 Figure 2. Examples of storms selected for each model: two different storms (left/right) in the CPM (top) or RCM (bottom). Storm sector definitions are explained in the legend, frontal zones are defined as areas with the norm of wet bulb potential temperature (WBPT) gradients stronger than 5 K/110 km (or 4 km/110 km for MOHC model on a 25 km grid).

Figure 3 .

 3 Figure 3. Winter precipitation contribution to the mean within storms (frequency × value of bin, using exponential bins defined in Berthou et al 2019). This way, the area under the curve represents mean precipitation (thin lines show present-day contribution) or mean precipitation change (thick lines show future changes, number (%) indicates mean change). Contribution from all points in storms (black), points in warm sector (red), cold sector (blue), central sector (purple) and fronts (grey)-defined in figure 2. Plain line is for CPM, dashed line for RCM. Shading shows the 2.5-97.5 percentile range of CPM precipitation changes using a 1000 bootstrap resampling on storms.

Figure 4 .

 4 Figure 4. Joint density of mean hourly precipitation per storm and warm sector wet bulb potential temperature (median) in present storms (blue) and future (red). Individual variable density shown at the top (WBPT) and right (mean precipitation). Each panel shows a different model pair. Plain line shows best fit for the CPM, dotted line for the RCM. Grey line is for present-day autumn storms (underlying density not shown). Slope in % K -1 and Pearson correlation coefficient are given in insets. Dots represent intense storms; crosses are storms with low relative humidity in their warm sector. Thin grey lines represent Clausius-Clapeyron (CC) 7% K -1 scaling and thin red lines show 2×CC scaling.

Figure 5 .

 5 Figure5. Density of 850 hPa WBPT (black line with grey shading, left axis). Frequency (right axis) of low precipitation rates (0.1-1 mm h -1 ) scaled by 1/15, moderate rates (1-5 mm h -1 ) scaled by 1/10, intense rates (5-10 mm h -1 ) and heavy rates (>10 mm h -1 ), CAPE (>250 J kg) and CIN (<-100 J kg -1 ) scaled by 1/10, total column water (TCW >20 mm) scaled by 1/30, binned by θw values and normalised by the number of points in all storms on the native grid of the MOHC CPM. Grey vertical line is median θw, black vertical line is the freezing point.

Table 1 .

 1 List of simulations. Acronym definition: HARMONIE-Climate community (HCLIMcom), Swedish Meteorological and Hydrological Institute (SMHI), Finnish Meteorological Institute (FMI), Danish Meteorological Institute (DMI), Norwegian Meteorological Institute (Met Norway), National Centre of Meteorological Research (CNRM), Royal Netherlands Meteorological Institute (KNMI), Swiss Federal Institute of Technology in Zürich (ETHZ), Met Office Hadley Centre (MOHC). See table S1 referencing the RCM deep convection parameterizations.

	Name	Institute	GCM	RCM	CPM	Time periods
	HCLIMcom SMHI/FMI/DMI/	EC-EARTH2.2-	HCLIM38-ALADIN	HCLIM38-AROME	1996-2005
		Met Norway	r12i1p1	12 km	3 km
			(Hazeleger	(Belušić et al 2020)	(Médus et al 2022)	2090-2099
			et al 2012)
	CNRM	Météo France	CNRM-CM5	CNRM-ALADIN63 12 km CNRM-AROME41t1	1996-2005
		CNRM			2.5km
			(Voldoire et al 2013) (Nabat et al 2020)	(Caillaud et al 2021)	2090-2099
	KNMI	KNMI	EC-EARTH2.3	RACMO2.2 12 km (van	HCLIM38-AROME 2.5
			(Hazeleger	Meijgaardet al 2008, De	km (Belušić et al 2020)
			et al 2012)	Vries et al 2022)
			member 14			1996-2005
			member 4			2090-2099
	ETHZ	ETHZ	ERA-interim	COSMO-CLM 12 km	COSMO-CLM 2.2 km 1999-2008
			ERA-interim+MPI-		(Leutwyler et al 2017)	2079-2089
			ESM-LR r1 Pseudo
			Global Warming
	MOHC	MOHC	HadGEM3-GC3.1-	None	HadREM3-RA-	1999-2008
			N512 25 km		UM10.1 2.2km
					(Chan et al 2020)	2095-2105

Table 2 .

 2 Proportion of intense storms and storms with low median relative humidity in their warm sector.

		Intense storm	Intense storm	Intense storm	Low RH	Low RH	Low RH
	Model name	DJF present	DJF future	SON present	DJF present	DJF future	SON present
	MOHC	4.8%	8.2%	5.1%	0.8%	3%	12.8%
	ETHZ	6.3%	6.3%	7.0%	9.4%	3.7%	19.2%
	HCLIMcom	2.9%	6.0%	2.7%	1.6%	0%	4.1%
	CNRM	2.5%	6.5%	3.6%	5.0%	3.2%	7.7%
	KNMI	9.7%	2.7%	8.0%	13.2%	15.5%	20.7%

Acknowledgments

This study received support from the European Union's Horizon 2020 EUCP project (Grant No. GA776613). NB UIBK and ETHZ acknowledge PRACE for awarding us the access to Piz Daint at Swiss National Supercomputing Center (CSCS, Switzerland) and the COSMO, CLM and C2SM communities for developing and maintaining COSMO in climate mode. CM is supported by the UKRI NERC funded project STORMY-WEATHER (Grant No. NE/V004166/1). DB was partially funded by the Formas project EDUCAS (Grant No. 2019-00829). S B, M J R and E K gratefully acknowledge support from the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (Grant No. GA01101).

Data availability statement

The data that support the findings of this study are openly available at the following URL/DOI: https:// doi.org/10.5281/zenodo.6883357.