Rade de Villefranche-sur-Mer

Données satellitaires

Tendances de température

##                       Corrected Zc                        new P-value 
##              7.4251181901928537954              0.0000000000001126793 
##                               N/N*                         Original Z 
##             84.7254781944381534231             68.3455724331707727970 
##                        old P.value                                Tau 
##              0.0000000000000000000              0.3664793433848033688 
##                        Sen's slope                       old.variance 
##              0.0001230563042762353   410688326923.3333129882812500000 
##                       new.variance 
## 34795764887453.1640625000000000000

## 
## Call:
## lm(formula = ano_day ~ decimal_date(t), data = rdg)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0444 -0.5895 -0.0472  0.5621  4.2374 
## 
## Coefficients:
##                    Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     -89.2313906   1.2861764  -69.38 <0.0000000000000002 ***
## decimal_date(t)   0.0445490   0.0006421   69.38 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9639 on 15334 degrees of freedom
## Multiple R-squared:  0.2389, Adjusted R-squared:  0.2389 
## F-statistic:  4813 on 1 and 15334 DF,  p-value: < 0.00000000000000022
## 
## Call:
## lm(formula = ano_day ~ decimal_date(t), data = filter(rdg, year >= 
##     1995))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8135 -0.5914 -0.0719  0.5542  4.3922 
## 
## Coefficients:
##                    Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     -126.386137    2.216094  -57.03 <0.0000000000000002 ***
## decimal_date(t)    0.063017    0.001103   57.14 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9497 on 10586 degrees of freedom
## Multiple R-squared:  0.2357, Adjusted R-squared:  0.2357 
## F-statistic:  3265 on 1 and 10586 DF,  p-value: < 0.00000000000000022

MHWs

Table 1 - Résumé par année pour les données satellitaires
Année Nombre Durée moyenne (jours) Durée maximale Moyenne IM Moyenne des IMax IMax Moyenne des VarI Moyenne des IC Taux de croissance (°C/j) Taux de déclin (°C/j) Nombre de jours total Intensité cumulée totale
1982 3 6.666667 8 2.019300 2.425367 2.7486 0.2608667 13.32773 0.2310667 0.3373667 20 39.9832
1983 2 6.000000 7 1.206550 1.446850 1.9287 0.2182500 7.63180 0.3870000 0.2369500 12 15.2636
1984 1 6.000000 6 2.006300 2.263300 2.2633 0.2226000 12.03770 0.2731000 0.3629000 6 12.0377
1985 1 13.000000 13 1.776700 2.311500 2.3115 0.3036000 23.09700 0.0759000 0.3821000 13 23.0970
1986 1 13.000000 13 2.355200 2.907800 2.9078 0.3912000 30.61760 0.1426000 0.3623000 13 30.6176
1987 1 12.000000 12 1.985000 2.146800 2.1468 0.1860000 23.81970 0.0842000 0.1078000 12 23.8197
1988 0 NA NA NA NA NA NA NA NA NA NA NA
1989 1 8.000000 8 1.487000 1.787000 1.7870 0.2077000 11.89570 0.1686000 0.1576000 8 11.8957
1990 3 18.666667 39 1.421400 1.672700 2.3784 0.1872667 21.33513 0.0708000 0.2933000 56 64.0054
1991 2 7.000000 8 1.372000 1.801450 1.8115 0.2483000 9.71180 0.1659500 1.0342500 14 19.4236
1992 1 5.000000 5 1.803800 1.958600 1.9586 0.1227000 9.01920 0.3703000 0.1391000 5 9.0192
1993 0 NA NA NA NA NA NA NA NA NA NA NA
1994 2 9.000000 10 1.917500 2.135750 2.3375 0.1537500 17.48360 0.2283500 0.0966500 18 34.9672
1995 1 7.000000 7 1.940100 2.144600 2.1446 0.1496000 13.58060 0.2402000 0.0871000 7 13.5806
1996 0 NA NA NA NA NA NA NA NA NA NA NA
1997 0 NA NA NA NA NA NA NA NA NA NA NA
1998 0 NA NA NA NA NA NA NA NA NA NA NA
1999 2 6.000000 7 1.671750 1.891900 1.9616 0.1782000 10.13065 0.1779000 0.2049500 12 20.2613
2000 0 NA NA NA NA NA NA NA NA NA NA NA
2001 2 7.500000 8 1.153150 1.312400 1.7232 0.1140500 8.82875 0.1047500 0.2286500 15 17.6575
2002 2 10.000000 10 2.136650 2.693350 3.7438 0.3453500 21.36605 0.1899000 0.2927000 20 42.7321
2003 2 33.000000 39 3.135900 4.024200 4.6770 0.5784500 100.35220 0.1199500 0.2953000 66 200.7044
2004 0 NA NA NA NA NA NA NA NA NA NA NA
2005 3 9.000000 12 2.165667 2.555900 3.6284 0.2688333 20.69913 0.1371333 0.3206000 27 62.0974
2006 8 11.375000 25 1.864800 2.240975 4.0284 0.2566375 22.15974 0.2768250 0.1832250 91 177.2779
2007 4 14.250000 31 1.644150 2.146700 3.2684 0.3350000 27.23355 0.1935500 0.2740500 57 108.9342
2008 1 10.000000 10 2.775500 3.356900 3.3569 0.3336000 27.75470 0.2665000 0.2166000 10 27.7547
2009 3 23.000000 39 2.187533 3.161400 3.6789 0.4755667 45.39343 0.3077667 0.2911333 69 136.1803
2010 1 24.000000 24 3.027800 4.295200 4.2952 0.4991000 72.66830 0.2059000 0.1643000 24 72.6683
2011 8 12.125000 27 2.055063 2.611175 3.4178 0.4209125 23.37836 0.2155250 0.4202625 97 187.0269
2012 7 12.285714 25 1.893700 2.384357 4.0164 0.3455714 21.83336 0.2506571 0.2757571 86 152.8335
2013 4 8.000000 10 1.699275 2.000975 2.5124 0.1913750 13.42950 0.1024750 0.3813500 32 53.7180
2014 6 31.000000 63 1.904450 2.452800 4.4106 0.3340000 58.44450 0.3714167 0.1685833 186 350.6670
2015 8 15.000000 30 2.096975 2.878137 5.0584 0.3998250 31.55809 0.2740375 0.4361000 120 252.4647
2016 5 17.600000 49 1.750600 2.281280 3.4775 0.3072600 33.70274 0.1411400 0.2819200 88 168.5137
2017 7 14.714286 43 1.879486 2.614871 5.1752 0.4286000 29.39341 0.2231857 0.2704000 103 205.7539
2018 6 37.500000 120 2.123400 2.886633 4.3012 0.4511000 85.34360 0.1598500 0.1798167 225 512.0616
2019 8 22.500000 57 1.928600 2.501362 5.4016 0.3466000 42.79745 0.1813000 0.1590375 180 342.3796
2020 8 17.375000 35 1.696962 2.121412 3.1471 0.2411125 30.37866 0.1712125 0.1600875 139 243.0293
2021 6 12.833333 19 1.632267 2.076417 3.5870 0.2761667 23.43730 0.1618167 0.1592333 77 140.6238
2022 3 96.000000 133 2.009100 2.808267 4.6376 0.4047333 223.24343 0.0366333 0.0310667 288 669.7303
2023 8 23.375000 42 1.795012 2.392587 4.1183 0.3444250 49.73246 0.1369125 0.0705375 187 397.8597
2024 2 38.000000 40 1.309150 2.277800 3.2135 0.3993500 50.32425 0.0700000 0.1511000 76 100.6485
Table III - Tendances pour quelques métriques des MHWs
Mesures Theil-Sen (pente) T-S (p-val) Point de rupture Pettitt (p-val) Theil-Sen (avant) p-val (avant) Theil-Sen (après) p-val (après)
Nombre de jours de vague de chaleur par an 3.3928571 0.0000003 2004 0.0000077 -0.1250000 0.4199534 8.3636364 0.0009747
Nombre d’évènements par an 0.2000000 0.0000034 2004 0.0000162 0.0000000 0.4401483 0.4000000 0.0053619
Moyenne des IM (°C) 0.0452354 0.0000000 2002 0.0000006 0.0103043 0.3811873 0.0526577 0.0006577
Moyenne des IMax (°C) 0.0478887 0.0000004 2001 0.0000034 -0.0165967 0.2561450 0.0567108 0.0121987
IC annuelle (°C.j) 16.5327267 0.0000000 2002 0.0000006 3.7785299 0.3811873 19.2213311 0.0005175

Données in situ

Bouée EOL

Climatologie satellitaire

Pour calculer la climatologie, on utilise ici des données satellitaires (de 1982 à 2012), mais la détection d’évènements se fait sur les données issues de la bouée EOL uniquement. Note: si on choisit comme période de référence pour calculer la climatologie (toujours à partir des données satellitaires) la même période que pour les données EOL seules (2014-2024), on obtient sensiblement les mêmes résultats qu’en la calculant avec les données EOL. Pour ces dernières, on utilise une moyenne de toutes les valeurs de la journée. Le point utilisé comme référence est au large de Villefranche (c’est le centre du pixel de 0.25x0.25° qui contient la rade, situé à 43.625°N, 7.375°E)

Table IV - Résumé par année de quelques métriques pour les données EOL avec climatologie satellitaire
Année Nombre Durée moyenne (j) Moyenne IM (°C) Moyenne des IMax (°C) Moyenne des IC (°C.j) Nombre de jours total (j) Intensité cumulée totale (°C.j)
2014 4 38.25000 2.026850 2.727000 82.91030 153 331.6412
2015 9 13.55556 1.958767 2.426256 30.13709 122 271.2338
2016 8 15.50000 1.601550 1.924338 27.88276 124 223.0621
2017 10 12.90000 1.883910 2.506460 26.36788 129 263.6788
2018 4 61.25000 2.325975 3.158725 148.58378 245 594.3351
2019 3 86.00000 1.539933 2.769400 171.35583 258 514.0675
2020 6 20.33333 1.852250 2.348867 44.06693 122 264.4016
2021 8 26.25000 1.611600 1.967912 39.11744 210 312.9395
2022 2 81.50000 2.944800 3.934050 277.52310 163 555.0462
2023 6 35.66667 2.244033 3.050800 75.36787 214 452.2072

En utilisant les données EOL seules

Ici, la climatologie est calculée à partir des données EOL seules (à partir de 2014).

Table V - Résumé par année de quelques métriques pour les données EOL seules
Année Nombre Durée moyenne (j) Moyenne IM (°C) Moyenne des IMax (°C) Moyenne des IC (°C.j) Nombre de jours total (j) Intensité cumulée totale (°C.j)
2014 3 35.33333 1.19890 1.477033 40.86373 106 122.5912
2015 1 5.00000 2.25770 2.405800 11.28850 5 11.2885
2016 0 NA NA NA NA NA NA
2017 1 6.00000 2.20880 2.429700 13.25270 6 13.2527
2018 3 8.00000 1.41810 1.636633 11.89370 24 35.6811
2019 2 8.00000 2.04240 2.553750 17.11225 16 34.2245
2020 1 8.00000 0.45860 0.637200 3.66880 8 3.6688
2021 0 NA NA NA NA NA NA
2022 5 18.80000 1.99270 2.593520 38.77490 94 193.8745
2023 2 9.50000 1.81235 2.079950 16.27585 19 32.5517

CTD - SOMLIT

Evènements

Ces graphiques montrent, par profondeur (de 5 à 80m), l’évènement le plus important sur toute la période d’échantillonnage (de mai 1995 à avril 2023) d’après trois critères : celui qui contient le pic d’anomalie (l’intensité maximale) le plus élevé, le plus long, et celui dont l’intensité cumulée (au dessus du seuil, sur tout l’évènement) est la plus importante (en °C.j).

year count duration duration_max intensity_mean intensity_max intensity_max_max intensity_var intensity_cumulative rate_onset rate_decline total_days total_icum
1992 0 NA NA NA NA NA NA NA NA NA NA NA
1993 0 NA NA NA NA NA NA NA NA NA NA NA
1994 0 NA NA NA NA NA NA NA NA NA NA NA
1995 2 12.500000 16 1.3869500 1.784650 2.5758 0.2738000 18.929000 0.0871000 0.1516500 25 37.8580
1996 3 12.333333 17 2.2039333 3.570100 4.3704 0.8069667 26.452933 0.6161333 0.3740000 37 79.3588
1997 1 16.000000 16 1.4797000 2.232700 2.2327 0.4028000 23.675400 0.3037000 0.1085000 16 23.6754
1998 3 8.333333 10 0.9639000 1.180500 1.6592 0.1400667 7.422033 0.1280333 0.1655000 25 22.2661
1999 3 6.000000 6 1.8738667 2.282167 3.3829 0.2959667 11.243100 0.2552000 0.3613667 18 33.7293
2000 0 NA NA NA NA NA NA NA NA NA NA NA
2001 1 5.000000 5 1.5750000 2.170900 2.1709 0.4694000 7.875100 2.1709000 0.2969000 5 7.8751
2002 0 NA NA NA NA NA NA NA NA NA NA NA
2003 1 7.000000 7 1.2798000 1.639400 1.6394 0.3042000 8.958400 0.2072000 0.2475000 7 8.9584
2004 0 NA NA NA NA NA NA NA NA NA NA NA
2005 1 8.000000 8 1.8664000 2.496700 2.4967 0.4596000 14.931200 0.4281000 0.3464000 8 14.9312
2006 4 15.000000 38 1.8159500 2.053000 3.1736 0.1618500 22.702875 0.2191750 0.1705750 60 90.8115
2007 3 30.333333 71 0.9107333 1.167033 1.3359 0.1581667 26.038867 0.0657000 0.2341333 91 78.1166
2008 3 12.000000 16 1.9859667 2.882633 3.9374 0.5579000 23.123867 0.3190333 0.3571333 36 69.3716
2009 1 13.000000 13 2.4108000 3.576000 3.5760 0.6227000 31.339900 0.7990000 0.1980000 13 31.3399
2010 0 NA NA NA NA NA NA NA NA NA NA NA
2011 1 7.000000 7 1.1428000 1.191000 1.1910 0.0347000 7.999400 0.1025000 0.1664000 7 7.9994
2012 3 9.666667 11 1.4983667 2.038033 2.9144 0.3562000 14.827000 0.2345000 0.3452667 29 44.4810
2013 0 NA NA NA NA NA NA NA NA NA NA NA
2014 7 34.571429 136 1.2097429 1.779929 4.2326 0.2784000 55.218071 0.1160857 0.0722286 242 386.5265
2015 1 11.000000 11 2.6690000 3.373400 3.3734 0.4398000 29.359100 0.2151000 0.3407000 11 29.3591
2016 5 13.000000 25 1.2739600 1.484600 2.5744 0.1375400 16.049700 0.0807800 0.1034000 65 80.2485
2017 1 22.000000 22 0.5405000 0.654300 0.6543 0.0582000 11.890100 0.0118000 0.0255000 22 11.8901
2018 3 26.000000 49 2.3436667 3.506667 6.7605 0.6554333 47.946967 0.5036000 0.4738000 78 143.8409
2019 3 6.666667 9 1.5961333 1.810767 2.5292 0.1537000 11.588867 0.1560333 0.1814000 20 34.7666
2020 3 20.000000 36 1.0224333 1.360500 1.9367 0.2111333 21.214667 0.1033333 0.0916667 60 63.6440
2021 2 10.000000 11 0.9472000 1.225800 1.4512 0.1739500 9.297950 0.1926000 0.0848500 20 18.5959
2022 3 17.666667 36 1.3592333 1.651933 1.9599 0.2351667 21.075867 0.7792667 0.1519333 53 63.2276
2023 1 7.000000 7 0.9470000 1.126900 1.1269 0.1292000 6.628900 0.1295000 0.0801000 7 6.6289

Tendances de température

## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 147330, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             969 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 172444, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1001 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 122646, p-value = 0.000000000001957
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1001 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 98262, p-value = 0.0000000391
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1219 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 76434, p-value = 0.00004332
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1220 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 75240, p-value = 0.00006043
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1220 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 102480, p-value = 0.000000008244
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             611 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 124394, p-value = 0.0000000000008846
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             613 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 136254, p-value = 0.000000000000003011
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             613 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 176396, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             613 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 204196, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             790 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 226904, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             790 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 242160, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             790 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 260284, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             794 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 260260, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             794 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 240012, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             628
Table II - Tendances de températures sur les données satellitaires (0 m) et CTD
Profondeur (m) Mann-Kendall global (p-val) pente de Theil-Sen globale (°C/an) Test de Pettitt (p-val) Date de rupture probable (an) M-K pré-rupture (p-val) T-S pré-rupture (°C/an) M-K post-rupture (p-val) T-S post-rupture (°C/an)
0 0.0000000 0.0449463 0.0000000 2003 0.1199891 0.0116731 0.0000000 0.0645453
5 0.1205614 0.0182985 0.0000000 2014 0.1142450 -0.0127346 0.9092847 0.0073495
10 0.0349058 0.0234367 0.0000000 2014 0.7317975 -0.0031652 0.9863232 -0.0009794
15 0.1889768 0.0149327 0.0000000 2014 0.5271208 -0.0069491 0.2400788 0.0718663
20 0.4141574 0.0085188 0.0000000 2018 0.6798700 -0.0042948 0.4775946 -0.0679730
25 0.6658341 0.0034348 0.0000433 2018 0.4854060 -0.0055599 0.5139612 0.0465088
30 0.3214442 0.0070804 0.0000604 2018 0.7886515 0.0022075 0.2737767 0.0656808
35 0.0237262 0.0128976 0.0000000 2007 0.5393672 0.0103235 0.0545460 0.0202656
40 0.0002213 0.0146355 0.0000000 2007 0.4743317 0.0091436 0.0000000 0.0168127
45 0.0000000 0.0152467 0.0000000 2007 0.7965169 -0.0032829 0.0190862 0.0192232
50 0.0000011 0.0200254 0.0000000 2007 0.2620205 -0.0151005 0.0448443 0.0241822
55 0.0000052 0.0228366 0.0000000 2010 0.9667347 -0.0003634 0.7309746 0.0054197
60 0.0000008 0.0251920 0.0000000 2010 0.7041550 0.0033083 0.7361231 0.0052246
65 0.0000000 0.0266425 0.0000000 2010 0.4567128 0.0074335 0.6975092 0.0054632
70 0.0000000 0.0260191 0.0000000 2010 0.6375545 0.0049793 0.8758468 0.0020969
75 0.0000098 0.0229793 0.0000000 2010 0.6361859 0.0046526 0.4857258 -0.0083832
80 0.0000497 0.0214588 0.0000000 2007 0.2922477 -0.0117937 0.0793565 0.0140894

Heatmaps

T-MEDNet

Evènements

Ces graphiques montrent, par profondeur (de 5 à 40m), l’évènement le plus important sur toute la période d’échantillonnage (de juillet 2017 à décembre 2023) d’après trois critères : celui qui contient le pic d’anomalie (l’intensité maximale) le plus élevé, le plus long, et celui dont l’intensité cumulée (au dessus du seuil, sur tout l’évènement) est la plus importante (en °C.j).

Tendances de température

depth MK_pval slope S_p_val adj_R2
0 0.0000000 0.0449463 0.0000000 0.2356662
5 0.0000000 0.1221039 0.0000000 0.0300963
10 0.0000000 0.0974760 0.0000003 0.0156685
15 0.0000004 -0.0932293 0.0002490 0.0077784
20 0.0000000 0.0443820 0.0248297 0.0025346
25 0.0002817 0.0630441 0.0073930 0.0038752
30 0.2498132 0.0377052 0.0725585 0.0013978
35 0.5419588 0.0161625 0.3848496 -0.0001536
40 0.0000000 0.0158582 0.1063192 0.0010109

Heatmaps

Comparaisons

Comparaisons des datasets

Ici, on s’intéresse seulement à rapidement comparer certains jeu de données entre eux. Les données satellites et EOL d’abord, pour confirmer la similitude des données malgré le (relatif) éloignement spatial et ainsi pour justifier de la pertinence de l’utilisation des données satellites pour calculer une climatologie dont on se sert pour détecter des MHWs sur les données EOL. D’autre part, on compare aussi les données CTD et T-MEDNet pour chaque profondeur disponible pour T-MEDNet

Comparaisons des résultats

On compare les résultats obtenus après la détection d’évènements sur les données EOL seules et sur les données EOL+Satellite

Table VI - Tests de Wilcoxon appariés comparant les caractéristiques des MHWs détectées sur les données EOL, avec ou sans données satellites
Mesures Nombre d’évènements Durée moyenne (jours) Durée maximale Moyenne IM Moyenne des IMax Moyenne des IC Taux de croissance (°C/j) Taux de déclin (°C/j) Nombre de jours total Intensité cumulée totale
p-value 0.0184405 0.0078125 0.0207062 0.1484375 0.0078125 0.0078125 0.1953125 0.25 0.0078125 0.0078125

Bibliographie

Barkhordarian, Armineh, Jonas Bhend, and Hans Von Storch. 2012. “Consistency of Observed Near Surface Temperature Trends with Climate Change Projections over the Mediterranean Region.” Climate Dynamics 38 (9-10): 1695–1702. https://doi.org/10.1007/s00382-011-1060-y.
Benthuysen, Jessica A., Eric C. J. Oliver, Ming Feng, and Andrew G. Marshall. 2018. “Extreme Marine Warming Across Tropical Australia During Austral Summer 2015–2016.” Journal of Geophysical Research: Oceans 123 (2): 1301–26. https://doi.org/10.1002/2017JC013326.
Cramer, Wolfgang, Joël Guiot, Marianela Fader, Joaquim Garrabou, Jean-Pierre Gattuso, Ana Iglesias, Manfred A. Lange, et al. 2018. “Climate Change and Interconnected Risks to Sustainable Development in the Mediterranean.” Nature Climate Change 8 (11): 972–80. https://doi.org/10.1038/s41558-018-0299-2.
CSIRO, Alistair Hobday, Eric Oliver, Alex Sen Gupta, Jessica Benthuysen, Michael Burrows, Markus Donat, et al. 2018. “Categorizing and Naming Marine Heatwaves.” Oceanography 31 (2). https://doi.org/10.5670/oceanog.2018.205.
Darmaraki, Sofia, Samuel Somot, Florence Sevault, Pierre Nabat, William David Cabos Narvaez, Leone Cavicchia, Vladimir Djurdjevic, Laurent Li, Gianmaria Sannino, and Dmitry V. Sein. 2019. “Future Evolution of Marine Heatwaves in the Mediterranean Sea.” Climate Dynamics 53 (3-4): 1371–92. https://doi.org/10.1007/s00382-019-04661-z.
Garrabou, Joaquim, Daniel Gómez‐Gras, Alba Medrano, Carlo Cerrano, Massimo Ponti, Robert Schlegel, Nathaniel Bensoussan, et al. 2022. “Marine Heatwaves Drive Recurrent Mass Mortalities in the Mediterranean Sea.” Global Change Biology 28 (19): 5708–25. https://doi.org/10.1111/gcb.16301.
Genevier, Lily G. C., Tahira Jamil, Dionysios E. Raitsos, George Krokos, and Ibrahim Hoteit. 2019. “Marine Heatwaves Reveal Coral Reef Zones Susceptible to Bleaching in the Red Sea.” Global Change Biology 25 (7): 2338–51. https://doi.org/10.1111/gcb.14652.
Guinaldo, Thibault, Aurore Voldoire, Robin Waldman, Stéphane Saux Picart, and Hervé Roquet. 2023. “Response of the Sea Surface Temperature to Heatwaves During the France 2022 Meteorological Summer.” Ocean Science 19 (3): 629–47. https://doi.org/10.5194/os-19-629-2023.
Heron, Scott, Lyza Johnston, Gang Liu, Erick Geiger, Jeffrey Maynard, Jacqueline De La Cour, Steven Johnson, et al. 2016. “Validation of Reef-Scale Thermal Stress Satellite Products for Coral Bleaching Monitoring.” Remote Sensing 8 (1): 59. https://doi.org/10.3390/rs8010059.
Hobday, Alistair J., Lisa V. Alexander, Sarah E. Perkins, Dan A. Smale, Sandra C. Straub, Eric C. J. Oliver, Jessica A. Benthuysen, et al. 2016. “A Hierarchical Approach to Defining Marine Heatwaves.” Progress in Oceanography 141 (February): 227–38. https://doi.org/10.1016/j.pocean.2015.12.014.
Hobday, Alistair J., Eric C. J. Oliver, Alex Sen Gupta, Jessica A. Benthuysen, Michael T. Burrows, Markus G. Donat, Neil J. Holbrook, et al. 2018. “Categorizing and Naming MARINE HEATWAVES.” Oceanography 31 (2): 162–73. https://www.jstor.org/stable/26542662.
Holbrook, Neil J., Hillary A. Scannell, Alexander Sen Gupta, Jessica A. Benthuysen, Ming Feng, Eric C. J. Oliver, Lisa V. Alexander, et al. 2019. “A Global Assessment of Marine Heatwaves and Their Drivers.” Nature Communications 10 (1): 2624. https://doi.org/10.1038/s41467-019-10206-z.
Holbrook, Neil J., Alex Sen Gupta, Eric C. J. Oliver, Alistair J. Hobday, Jessica A. Benthuysen, Hillary A. Scannell, Dan A. Smale, and Thomas Wernberg. 2020. “Keeping Pace with Marine Heatwaves.” Nature Reviews Earth & Environment 1 (9): 482–93. https://doi.org/10.1038/s43017-020-0068-4.
Kapsenberg, Lydia, Samir Alliouane, Frédéric Gazeau, Laure Mousseau, and Jean-Pierre Gattuso. 2017. “Coastal Ocean Acidification and Increasing Total Alkalinity in the Northwestern Mediterranean Sea.” Ocean Science 13 (3): 411–26. https://doi.org/10.5194/os-13-411-2017.
Oliver, Eric C. J., Jessica A. Benthuysen, Nathaniel L. Bindoff, Alistair J. Hobday, Neil J. Holbrook, Craig N. Mundy, and Sarah E. Perkins-Kirkpatrick. 2017. “The Unprecedented 2015/16 Tasman Sea Marine Heatwave.” Nature Communications 8 (1): 16101. https://doi.org/10.1038/ncomms16101.
Oliver, Eric C. J., Jessica A. Benthuysen, Sofia Darmaraki, Markus G. Donat, Alistair J. Hobday, Neil J. Holbrook, Robert W. Schlegel, and Alex Sen Gupta. 2021. “Marine Heatwaves.” Annual Review of Marine Science 13 (1): 313–42. https://doi.org/10.1146/annurev-marine-032720-095144.
Oliver, Eric C. J., Markus G. Donat, Michael T. Burrows, Pippa J. Moore, Dan A. Smale, Lisa V. Alexander, Jessica A. Benthuysen, et al. 2018. “Longer and More Frequent Marine Heatwaves over the Past Century.” Nature Communications 9 (1): 1324. https://doi.org/10.1038/s41467-018-03732-9.
Oliver, Eric C. J., Véronique Lago, Alistair J. Hobday, Neil J. Holbrook, Scott D. Ling, and Craig N. Mundy. 2018. “Marine Heatwaves Off Eastern Tasmania: Trends, Interannual Variability, and Predictability.” Progress in Oceanography 161 (February): 116–30. https://doi.org/10.1016/j.pocean.2018.02.007.
Schlegel, Robert W., Eric C. J. Oliver, Alistair J. Hobday, and Albertus J. Smit. 2019. “Detecting Marine Heatwaves With Sub-Optimal Data.” Frontiers in Marine Science 6 (November): 737. https://doi.org/10.3389/fmars.2019.00737.
Sen Gupta, Alex, Mads Thomsen, Jessica A. Benthuysen, Alistair J. Hobday, Eric Oliver, Lisa V. Alexander, Michael T. Burrows, et al. 2020. “Drivers and Impacts of the Most Extreme Marine Heatwave Events.” Scientific Reports 10 (1): 19359. https://doi.org/10.1038/s41598-020-75445-3.
Smale, Dan A., Thomas Wernberg, Eric C. J. Oliver, Mads Thomsen, Ben P. Harvey, Sandra C. Straub, Michael T. Burrows, et al. 2019. “Marine Heatwaves Threaten Global Biodiversity and the Provision of Ecosystem Services.” Nature Climate Change 9 (4): 306–12. https://doi.org/10.1038/s41558-019-0412-1.
Smith, Kathryn E., Michael T. Burrows, Alistair J. Hobday, Nathan G. King, Pippa J. Moore, Alex Sen Gupta, Mads S. Thomsen, Thomas Wernberg, and Dan A. Smale. 2023. “Biological Impacts of Marine Heatwaves.” Annual Review of Marine Science 15 (1): 119–45. https://doi.org/10.1146/annurev-marine-032122-121437.
W. Schlegel, Robert, and Albertus J. Smit. 2018. “heatwaveR: A Central Algorithm for the Detection of Heatwaves and Cold-Spells.” Journal of Open Source Software 3 (27): 821. https://doi.org/10.21105/joss.00821.