Isabelle Gouirand
email: isabelle.gouirand@cavehill.uwi.edu

Vincent Moron
email: moron@cerege.fr

Bernd Sing
email: bernd.sing@cavehill.uwi.edu

Seasonal atmospheric transitions in the Caribbean basin and Central America

The dates of the transition between winter 1 and summer (W2S) and between summer and winter 2 (S2W) regional-scale atmospheric regimes have been 3 defined using daily weather types above and around the 4 Caribbean basin from 1979 to 2017. The uncertainties 5 due to either the use of two different reanalyses (i.e., 6 NCEP-DOE and ERA-Interim) or the parametrization 7 used for the definition of the transition dates have typi-8 cally a small impact on the interannual variability of the 9 seasonal transitions. When both reanalyses are consid-10 ered together, the average W2S transition date occurs, 11 on average, on May 13 (with a standard deviation of 9 12 days) while the S2W transition date occurs, on average, 13 on October 26 (with a standard deviation of 12 days). 14 The atmospheric characteristics associated with both 15 transitions reveal asymmetries in the annual cycle. The 16 W2S transition is rather abrupt and near-synchronous 17 to a rather sharp increase of rainfall, propagating from 18 Central America to the NE of the Caribbean basin,

and a weakening of the Caribbean Low Level Jet. The W2S transition is also not preceded by any significant sea surface temperature (SST) anomalies either in the tropical North Atlantic or the Eastern Pacific. On the other hand, the S2W transition is overall smoother, and anomalously warm (cold) SST over the Caribbean Sea and Gulf of Mexico (Eastern Pacific) during the boreal summer are usually related to a delayed transition (and vice versa). The interannual variations of S2W and W2S transitions are mostly independent to each other. The potential and real-time predictability of the W2S transition is explored using a subseasonal-to-seasonal prediction ensemble (11 runs from 1998 to 2017) from the ECMWF model. Its skill is close to zero with a lead time longer than 15-20 days, confirming the weak impact of the antecedent SST upon the W2S transition. The skill suddenly increases from late April, 2-3 weeks only before the mean W2S transition date. It suggests that some atmospheric forcing, operating from synoptic to intra-seasonal time scale, plays a role, but it seems barely related to any occurrence, or sequence, of specific weather types.

Keywords weather type, annual cycle and predictability 1 Introduction

As for much of the tropics, the seasonality of the Caribbean basin rainfall broadly follows the seasonal march of the sun with the main rainy season in boreal summer. The rainy season is then synchronous to the seasonal northward shift of the inter-tropical convergence zone [START_REF] Gu | Interannual rainfall variability in the tropical Atlantic region[END_REF][START_REF] Angeles | Origins of the Car-49 ibbean rainfall bimodal behavior[END_REF]Gamble and Curtis, 2008) and of the North Atlantic High, the weakening of the trade winds, the weakening of the Car- However, despite the increased number of research papers on the Caribbean rainfall regime, little focus is placed on the analysis of the transition between seasons, namely from "winter" to "summer" and from "summer" to "winter", and on the characteristics of these transitions. At a first guess, "summer" and "winter" may broadly refer to the wet (i.e., April-May to October-November) and dry regional-scale seasons, respectively, since the Caribbean basin is not associated with alternating low level winds as in "classical" monsoon climates. The triggers of these shifts are still unclear, although the annual cycle of the incoming solar radiation is potentially playing an important role. One way to analyze these transitions consists in estimating a regional-scale average date at which the atmospheric system shifts from the dry to the wet (and vice versa) local-scale season, as it has been already done for the onset of the monsoon in India [START_REF] Moron | Interannual variability of Indian summer monsoon rainfall onset date at local scale[END_REF], in Indonesia [START_REF] Marteau | Spatial coherence of monsoon onset over western and central Sahel (1950-2000)[END_REF], in West Africa [START_REF] Marteau | Spatial coherence of monsoon onset over western and central Sahel (1950-2000)[END_REF], and in South America (Kousky, 1988), [START_REF] Marengo | Onset and end of the rainy season in the Brazilian Amazon basin[END_REF], (Gonzalez et al, 2002), (Liebmann et al, 2007),(da Silva and de Carvalho, 2007). Kousky (1988) also suggested that the onset of the rainy season over the Caribbean basin cannot be determined from the Outgoing Longwave Radiation (OLR) due to a lack of a clear seasonal change between the dry and wet seasons. Nevertheless, [START_REF] Mapes | Strides, steps and stumbles in the march of the seasons[END_REF] performing wavelet analysis on daily OLR dated the onset of the Caribbean rainfall approximately between May 1st and May 10th propagating northeastward from the Southwest of the Caribbean region to Florida (Fig. 6, [START_REF] Mapes | Strides, steps and stumbles in the march of the seasons[END_REF]). [START_REF] Mapes | Strides, steps and stumbles in the march of the seasons[END_REF] also highlighted that the beginning of rainfall over the Caribbean presents some similarities with a classical monsoonal climate, despite the lack of alternating nearsurface winds. Moreover, [START_REF] Moron | Weather types across the Caribbean basin and their relationship with rainfall and sea surface temperature[END_REF] suggested that there is an abrupt transition between the winter and summer weather types in early May in good agreement with [START_REF] Magana | The mid-44 summer drought over Mexico and Central Amer-45 ica[END_REF]. This points to the possibility to determine a regional-scale average date of the transition between winter to summer (hereafter, W2S) and summer to winter (hereafter, S2W) atmospheric conditions over the Caribbean Region and Central America. convection across the northern part of South America and Central America and have a larger frequency of occurrence from May to October, while the five other WTs (i.e., WTs 1,2,3 and 7,8 referred to as "winter" WTs hereafter) show increased subsidence around 12 • N-28 • N and are more frequent from November to April, hence showing a strong phase locking with the annual cycle of the Caribbean rainfall. The WTs, based on unfiltered daily data, filter out small-scale features and emphasize recurrent and regional-scale atmospheric patterns, including the more or less gradual transitions related to the annual cycle. This offers the opportunity to estimate a regional-scale date of the W2S and S2W transitions instead of using the noisier rainfall which makes it difficult to identify a signal on this scale. Moreover, the estimate of a transition date will allow to determine the characteristics of the atmospheric and/or oceanic conditions during the W2S transition in boreal spring and the S2W transition in boreal fall over the Caribbean basin and Central America.

The objectives of this paper consist in ; (i) determining the date of the regional-scale shift between the seasons based on the transition of the WTs from winter to summer (and vice versa) atmospheric regimes ; (ii) estimating the sensitivity of the dates of the regional- and after the regional-scale W2S and S2W transitions over this region. The CHIRPS data set developed by the University of Santa Barbara provides land precipita-51 tion with a 0.25 • resolution for the period 1981 to 2017 52 (Funk et al, 2014) while PERSIAN-CDR provides land 53 and sea precipitation with a 0.25 • spatial resolution for 54 the period 1983 to 2017 [START_REF] Ashouri | PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies[END_REF]. Both 55 data sets were used to gain an estimate of the inde-56 pendence of the results obtained from the data used to 57 perform the analysis. Two indices representing spatial-58 averaged rainfall, one over the Caribbean basin (10 • N-59 25 • N, 90 • W-50 • W) (hereafter, CarRC for CHIRPS and 60 CarRP for PERSIAN-CDR) and the other one over 61 the Amazon region (1 • S-5 • S, 75 • W-50 • W) (hereafter, 62 AmaR), have been calculated from these data sets to 63 monitor the regional change of rainfall associated with 64 the transitions.

65

The daily interpolated outgoing longwave radiation 66 is extracted from the 2.5 • data set by (Liebmann and 67 Smith, 1996). The precipitable water is used to estimate the 85 amount of water available in the atmospheric column 86 prior and after the W2S and S2W transitions in spring 87 and fall. A spatially averaged index has then been cre-88 ated to represent the regional average of precipitable 89 water (hereafter, CarPW) in the atmosphere over the 90 Caribbean basin between 10

• N-25 • N, 90 • W-50 • W. 91
The dew point for the Caribbean basin was computed 92 from pressure, temperature and specific humidity at 2 93 meters from Clausius -Clapeyron law. Then, the fol-94 lowing equation Td = T -((100 -RH)/5) is used to 95 compute the dew point. [START_REF] May | Operational processing of satellite sea surface temperature retrievals at the Naval Oceanographic Office[END_REF]Reynolds et al, 2002). It is used to determined the average state of the sea sur- The WTs determined by [START_REF] Moron | Weather types across the Caribbean basin and their relationship with rainfall and sea surface temperature[END_REF] are based on the k-means dynamical clustering of the standardized unfiltered (i.e., the annual cycle is kept) anomalies of the 925hPa wind fields that provides a good description of low-level circulation and interpolated daily OLR. In this study the WTs from Moron, [START_REF] Moron | Weather types across the Caribbean basin and their relationship with rainfall and sea surface temperature[END_REF] have been updated till 2017 and re-computed using two different data sets: the NCEP-DOE and the ERA-Interim reanalyses (concatenated with the same interpolated OLR dataset) to evaluate the sensitivity of the cluster analysis to the reanalyses used to determine the WTs. A total of 92% of the days are clustered in the same WTs. Most of the discrepancies between both reanalyses appear around the 110th and 130th day of the year and around the 300th day, hence just before the early rainy season and at the end of the late rainy season (not show), respectively. The sensitivity of the estimation of the transition dates due to both reanalyses is evaluated below.

ECWMF S2S runs

The W2S transition and S2W transition dates have been estimated from the daily sequence of WTs (Moron, [START_REF] Moron | Weather types across the Caribbean basin and their relationship with rainfall and sea surface temperature[END_REF]. The protocol is fairly similar to the one used to define an "agronomical" onset of rainfall [START_REF] Bombardi | Detection, variability, and predictability of monsoon onset and withdrawal dates: A review[END_REF]. In this definition, the first (or last for the withdrawal) sequence of consecutive wet days receiving a significant amount of rainfall without a too long dry spell thenafter (or before for the withdrawal). We adapted this algorithm by replacing the daily rainfall by the daily 8 WTs coded into a binary vector either as "summer" (i.e., the WTs 4, 5, 6) or "winter" (i.e., the WTs 1,2,3,7,8) ones. The W2S transition date is defined as the initial sequence of consecutive "summer" WTs without a too long sequence of "winter" WTs thenafter while the S2W transition date is defined symmetrically as the last sequence of consecutive "summer" WTs without a too long sequence of "winter" WTs before.

Two sensitivities are evaluated in this study: (i) the sensitivity to the parametrization (i.e., to the length of the spell of winter and summer WTs), and (ii) the sensitivity of the date of transition to the reanalyses (NCEP-DOE vs. ERA-Interim). First, the length of the spell of consecutive days of seasonal WTs, followed (or not) by a spell of opposed WTs is always subjective. Therefore a first probabilistic choice has been based on the combinations having a similar (40-60%) conditional probability to randomly occur (assuming independence and equal probability of summer and winter WTs to occur), thus excluding combinations with a too low probability. The parameters include the following combinations for W2S: a sequence of S (ranging from 3 to 10) consecutive days of summer WTs not followed by W consecutive days with winter WTs during a spell of P days following the last summer WTs of the initial sequence. Here, W equals 2 when P is between 4 and 5, 3 when P is between 8 and 12 and lastly 4 when P is between 17 and 27. This gave a total of 144 different combinations (i.e., 16 when W=2; 40 when W=3 and 88 when W=4). The criteria for the S2W transition are symmetrically reversed.

Secondly, the sensitivity of the transition date to the reanalyses has been estimated for four different "scenarios" based on the reanalysis used to determine the WTs: The criteria used to analyze the predictability of W2S transition dates (in Section 4) from the eleven ECMWF runs are reduced to seven requests out of the 144 described above. The seven requests used to determine the date of the shift between winter to summer WTs correspond the following criteria (with "S" representing the number of consecutive summer WTs, "W" the number of consecutive winter WTs and "P" the number of day following the last summer WTs in which we are looking for the "W" number of winter WTs): The results indicate that a regional-scale W2S tran-58 sition occurs around May 14, 13, 18 and 13, on av-59 erage over the 39 years and the 144 requests for the 60 NCEP, ERA, NCEP+ERA and NCEP+ERA1 scenar-61 ios. NCEP+ERA leads, as expected, to the latest av-62 erage date since it is the most conservative choice by 63 excluding the days classified differently in NCEP-DOE 64 and ERA-Interim (amongst the 8 WTs). The intra-65 scenario variability (i.e., between the 144 requests of 66 each scenario defined in section 3.5) is rather weak as 67 suggested by the error bars of the NCEP+ERA1 sce-68 nario (Fig. 1a). The average correlations between the 69 144 requests equal 0.74, 0.77, 0.68 and 0.72 (signifi-70 cant according to a Monte Carlo test at 99% level) for 71 NCEP, ERA, NCEP+ERA and NCEP+ERA1 scenario 72 respectively. The average intra-scenario standard de-73 viation (sd) amongst the 144 requests varies between 74 4.0 (NCEP), 3.2 (ERA), 5.7 (NCEP+ERA) and 4.0 75 (NCEP+ERA1) days, while the average interannual sd 76 equals to 10.4 (NCEP), 9.2 (ERA), 11.4 (NCEP+ERA) 77 and 9.3 (NCEP+ERA1) days. The percentage of com-78 mon variance amongst the 144 requests for each sce-79 nario is also high with 74% (NCEP), 78% (ERA), 68% 80 (NCEP+ERA) and 72% (NCEP+ERA1), respectively. 81 Therefore, in each of the four scenarios, the interannual 82 variability is larger than the intra-scenario spread be-83 tween the 144 requests. These results thus indicate a 84 weak sensitivity of the W2S transition date to the cri-85 teria selected for each request (i.e., criteria S, W and P 86 presented in section 2.5).

(i

87

The results for the S2W transition indicate an av-88 erage date occurring around October 26 (NCEP), 26 89 (ERA), 22 (NCEP+ERA) and 26 (NCEP+ERA1). The 90 average intra-scenario sd varies between 5 to 6 days, 91 while the interannual average sd of each scenario equals 92 11.4 (NCEP), 12.0 (ERA), 13.1 (NCEP+ERA) and 93 11.6 (NCEP+ERA1) days. The variability amongst the 94 144 requests of NCEP+ERA1 scenario is small (see 95 Fig. 1b), and the percentage of common variance be-96 tween the 144 requests of each scenario equals to 66% 97 (NCEP), 64% (ERA), 66% (NCEP+ERA) and 65% 98 (NCEP+ERA1). This indicates that there is a small 99 sensitivity of the S2W transition date to its parame-100 trization. The S2W dates of regional-scale transition 101 are neither sensitive to the requests nor to the reanal-102 ysis, as it is observed for the W2S transition. Thus, on average, the regional-scale atmospheric Venezuela is defined as region 7 (dark blue in Fig. 2).

45

Note that regions 3, 4 and 5 are similar to the clusfocused their study on the northern Caribbean Islands.

48

Four regions (2, 3, 4 and 8) present the classical an- to October with two maxima of rainfall separated by a reduced amount of rainfall usually associated to the mid-summer drought (Curtis and Gamble, 2008). These regions differ slightly from each other by the timing of the increase of rainfall and the date of the two rainfall maxima. Indeed, the rainfall starts first in regions 8 and 7, then in regions 4 and 3 following an almost northward progression. The sharp increase of rainfall in region 8 occurred before the average regional-scale W2S transition date. Regions 1, 2, 6 and 7 do not show a clear bi-modality of rainfall during the wet season. Region 7 presents a bell shape distribution of rainfall suggesting that the dry and wet season follow the annual solar cycle, while region 5 shows a progressive increase of rainfall from April to November with a maximum occurring in late September-October, possibly related to the tropical storm activity. Region 6 has two maxima of rainfall, one in July and the other in November, but the maxima of region 6 are delayed compared to the usual bi-modality of the rainy season associated with the Caribbean region. The delayed maximum in June seems to coincide with the maximum intensity of the CLLJ (Muñoz et al, 2008). Fig. 2 also points out that the mid-summer drought is not marked over the northern part of South America and the Eastern Antilles, the Caribbean side of Central America and Florida but is clear over the Pacific side of Central America and the north-western part of the Greater Antilles. The results obtained with this classification are relatively similar to the one from Martinez et a (2019) that did not include data over the northern part of South America. The main difference resides in the bundling of the southern part of Florida with the NW Caribbean while this classification separates them. This classification also highlights the difference in the mean annual cycle between the Pacific and Caribbean side of Central America suggesting that several factors are influencing the rainfall in these regions such as the contrast ocean-land.

The average W2S date of transition obtain from the WTs thus corresponds to an an almost synchronous and abrupt increase of rainfall over the whole Caribbean and Central America region, especially for the regions forming a SW-NE band stretched from region 3 to region 4 (Fig. 2). The W2S transition date occurs after the increase in rainfall over Columbia and Panama and before the increase of rainfall in Florida where the increase starts approximately 20 days after the rest of the Caribbean, in agreement with [START_REF] Mapes | Strides, steps and stumbles in the march of the seasons[END_REF]. This abrupt regional-scale increase detected even in a climatological time series averaging more than three decades suggests a strong solar forcing and is fully consistent with the rather reduced sd of the W2S transition at interannual time scale. Note that the Leeward Islands (re-1975 1980 1985 1990 1995 2000 2005 The climatology of the daily OLR, the daily WTs and the daily average of Caribbean rainfall have been plotted against the climatological W2S and S2W transition dates (Fig. 3). The OLR time-latitude diagram (Fig. 3), indicates a pulse of convection at 15 • N-20 • N shortly after the average date of the W2S transition, while the S2W transition seems to be smoother. Fig. 3a also highlights the northward migration of the convection over the northern part of South America. Nevertheless, the main deep convection center (i.e., the intertropical convergence zone) at these longitudes (80 • W-60 • W) never crosses 10 • N. This emphasizes the importance of the land-sea contrast in keeping the convection locked inland over South America and suggests different mechanisms in action between South America and the Caribbean Sea. The cooler Caribbean sea surface temperature associated with coastal upwelling along the northern coast of South America combined with the mean anticyclonic circulation could indeed inhibit the further northward shift of the ITCZ while the vegetation over South America may contribute to the low level moisture and fuel, at least partly, the deep convection. The climatology of the SST superimposed with the climatological OLR (Fig. 3a) indicates a gradual increase of SST from 26 • C to 27 • C over the Caribbean Sea around the W2S (S2W) transition date. This suggests that the SST are not likely to trigger alone neither the W2S nor the S2W transition, although SST warmer than 26 • C [START_REF] Zhang | Large-scale variability of atmospheric deep convection in relation to sea surface temperature in the tropics[END_REF] could still be a sine qua non condition for deep convection to occur at the W2S transition time. In order to further investigate the fingerprints of the 26 regional-scale W2S and S2W transitions on rainfall, 27 weekly amounts of rainfall have been calculated start-28 ing 4 weeks before the yearly W2S and S2W transition 29 dates and up to two weeks thereafter. Note that the 30 weeks prior to the transition dates are identified with 31 (-1) ending the day before the transition day, while the 32 weeks after are identified with (+1) starting with the 33 transition day. First, the weekly average amount of rain-34 fall for the fourth week (week4(-1)) before the W2S and 35 S2W transition dates has been calculated (Fig. 4a,g). 36

Then, the difference between week3(-1) and week4(-1) 37 is computed and so on till week2(+1) (Fig. 4b-f,h-l).

38

On average, the Caribbean region is receiving less 39 than 5 mm/day of rainfall a month before the W2S 40 transition date, with the exception of Panama and the 41 Costa Rica (4-6mm/day) located in the exit area of the 42 CLLJ and part of Colombia affected by the CJ (compare Fig. 4a). At week3(-1), the rainfall increases by 1mm/day with a SW-NE pattern from south of Central America towards Hispaniola (Fig. 4b). From week3(-1) to week2(-1), the weak change in rainfall is mainly located over the east of South America and Central America (Fig. 4c). One week before the W2S transition (Fig. 4d), rainfall increases by 2mm/day mainly over Costa Rica and Panama while the amount of rainfall over the Caribbean islands remains the same as before.

Then, between week1(+1) and week1(-1), corresponding to the difference in rainfall after vs. prior to the W2S transition date, the daily rainfall shows a more than 3mm increase over the whole Caribbean basin and Central America, with a maximum increase over the Pacific side of Nicaragua and Salvador and stretching toward the Greater Antilles (Fig. 4e). During week2(+1), the rainfall still increases over the northern part of Central America but remains rather constant over the Caribbean Islands (Fig. 4f). The increase in rainfall occurs earlier over the southern part of Central America (Costa Rica, Panama and Nicaragua) compared to the rest of the Caribbean basin in agreement with Fig. 2.

These results support the abrupt shift in rainfall and the rather simultaneous change in the regime of rainfall over the whole Caribbean basin and Central America as well as the accuracy of the WT-transition W2S date [START_REF] Moron | Weather types across the Caribbean basin and their relationship with rainfall and sea surface temperature[END_REF] to, in fact, represent the dry-to-wet transition in the Caribbean regime of rainfall.

Four weeks before the S2W transition, the Caribbean basin and Central America are receiving more than 6mm/day of rainfall over the Greater Antilles and 12mm/day over Central America (Fig. 4g). The rela-tively dry conditions observed over the southern Carib-35 bean Sea may be firstly related to the regional-scale an-36 ticyclonic conditions under the Azores/Bermuda high, 37 and cool SST due to the upwelling offshore of South 38 America may reinforce the dry conditions there. The 39 local-scale forced instability related to the island to-40 pography and sea/mountain breeze systems may be 41 able to break up the trade inversion and increase rain-42 fall over most of the islands. In Central America, in-43 tense rainfall is also probably due to the close loca-44 tion of the Pacific and South America ITCZ. From 45 week4(-1) until week1(-1), the amount of rainfall re-46 mains broadly similar (compare Fig. 4h-j). A decrease 47 in daily rainfall is then observed between week1(-1) and 48 week1(+1) (Fig. 4k). The western side of Cuba and the 49 northern part of Central America observe a decrease of 50 more than 2mm/day, while the eastern Caribbean only 51 shows a decrease lower than 1mm/day in agreement 52 with (Fig. 2k). At the same time, the rainfall amount 53 increases over the Caribbean side of Central America 54 suggesting a symmetric pattern similar to the one ob-55 served with a strong CLLJ. The decrease in rainfall thus 56 persists two weeks after the transition date over the 57 western part of the Caribbean Sea and Central America 58 (Fig. 4(l)). Although the S2W shift is overall smoother 59 over the Caribbean compare to the more abrupt W2S 60 transition, the S2W transition date still represents a 61 regional-scale change from wet-to-dry conditions over 62 the Caribbean basin and Central America. This suggests an increased rate in the moisture supply over the domain around the W2S transition date and a potential minimum level of precipitable water required to trigger regional-scale rainfall. The decrease in the amount of daily rainfall over the Amazon starts 10 to 20 days before the W2S transition date but does not seems to be a potential indicator of a shift in the rainfall regime over the Caribbean basin (correlation between W2S and Amazon rainfall at the time of the transition is around 0.3, see Fig. 6).

None of the SST indices shows a rapid warming around the W2S transition date (Fig. 5d). Their gradual warming is likely associated with the seasonal increase of the amount of incoming solar radiation. Nevertheless, CARS, PAC and GMEX show temperature above 26.5 • C at the time of the W2S transition, a condition favorable to deep convection, while the EEP temperature cools down to 26 • C leading to a 1 • C gradient between EEP and the CARS (Fig. 5d). In summary, the changes in SST around the W2S transition date are broadly gradual and may not be the main (or unique) trigger of the abrupt change in the rainfall, except perhaps for a threshold effect related to SST crossing the 26.5 • C-27 • C temperature possibly combined with a certain amount of precipitable water around 36kg/m 2 . Prior to the W2S transition date, neither the CLLJ or the CJ are showing a significant decrease or increase. However, the intensity of the CLLJ decreases rapidly from the W2S transition date up to ten days after when it reaches its minimum annual value (Figs. 5e,f). On the other hand, the CJ veers to become eastward at the time of the W2S transition date suggesting a shift in the atmospheric circulation at that time. All the elements required for deep convection are thus present at the date of W2S transition (i.e., an SST greater than 27 • C, precipitable water amount more than 37kg/m 2 , and a weak CLLJ). However, none of the variables exhibits the abrupt change noticed in local-scale rainfall around the W2S transition date (Fig. 3). At that time, all of the variables show an average annual value suggesting that the atmospheric and oceanic states reach a pseudo-balance (i.e., an average state of the system) with all the necessary conditions for convection being present).

At the date of the S2W transition (Fig. 5g-i The rainfall amount and precipitable water are still decreasing after the S2W transition date. It is possible that the Atlantic subtropical high has started its southward shift thus potentially inhibiting the deep convection despite ocean temperature warmer than 27 • C (Fig. 5j). The CLLJ strengthens from 5m/s to 7m/s around the S2W transition date, along with the increase of rainfall over the Caribbean side of Central America (Fig. 4k), and then further strengthens toward a maximum forty days after the S2W transition (Fig. 5k).

The CJ still blows eastward but weakens after the S2W transition date (Fig. 5l). All these features indicate a clear change, even if it is smoother than around the W2S transition, in the atmospheric circulation at the time of the S2W transition, while oceanic changes are rather limited. These results suggest that despite the 54 warm surface ocean, others factors could slowly inhibit 55 the rainfall, such as the increase of the CLLJ (and its 56 related shear) potentially linked to a increase of pres-57 sure gradient between Azores/Bermuda high and low 58 pressure located over South America.

59

The correlations between the 31-day moving average 60 indices and the interannual variations of the W2S tran-61 sition dates are significant for the CarRC, CarRP and 62 CarPW indices at the time of or just before it (Fig. 6). 63 Fig. 6 also shows significant, albeit weak, correlations 64 (i.e., -0.3) between the W2S transition date and the 65 CLLJ and CJ speed around the time of the transition. 66 Therefore an earlier (later) transition is associated, as 67 expected, with a higher (lower) amount of rainfall and 68 precipitable water and, with a weaker (stronger) CLLJ 69 and a westward (eastward) anomaly of the CJ. The cor-70 relations between the W2S transition date with the an-71 tecedent SST indices are not significant, suggesting that 72 the interannual variations of the SST are weakly influ-73 encing the date of transition in boreal spring.

74

The correlations between S2W transition and the 75 rainfall and precipitable water suggest that when the 76 transition occurs earlier (resp., later), the amount of 77 rainfall and precipitable water are logically lower (resp., 78 higher) than normal (here, r=0.6). Fig. 6 also indi-79 cates that an earlier (resp., later) S2W shift tends to be 80 preceded and accompanied by a cooler (resp. warmer) 81 CARS and GMEX and a warmer (resp., cooler) EEP 82 even if the correlations are mostly around 0.3-0.4, sug-83 gesting a weak to moderate seasonal predictability of 84 S2W transitions associated with the boundary forcing 85 of regional-scale SST. A westward (eastward) and 86 stronger (weaker) CJ and a stronger CLLJ are also as-87 sociated to an early (resp.,late) S2W transition with 88 significant correlation at 0.3 at the time of the transi-89 tion. It seems that the CJ anomaly related to the S2W 90 transition is rather persistent during the whole rainy 91 season. 7a shows the skill (i.e., the correlation between the 94 average W2S transition date in ECMWF and the ob-95 served one in the NCEP+ERA1 scenario using the same 96 parameterization) vs. the initialization date. The skill 97 is close to zero until April 19, then suddenly increases 98 to 0.5-0.6 around April 24-25 and then stays at a con-99 stant level around 0.7 until May 10 close to the mean 100 observed average W2S transition, for the best combina-101 tions (i.e., requests #1 and #7). The lack of any signifi-102 cant skill more than 3 weeks before the mean observed 103 W2S transition date for all requests is fully consistent 104 with the lack of sustained significant relationships be- The issue raised by Fig. 7 is the nature of the atmo-16 spheric process(es) involved in the skill's increase after mid-April. This is explored in Fig. 8. We extracted the 76 cases (out of a total 220 forecasts of transition date) where the average W2S transition date is accurately predicted on April 26 (with a tolerance of ±2 days). Then, the sequences of predicted daily WTs before the corresponding average W2S transition dates are keyed to the simulated average W2S transition dates (Fig. 8a). This is compared with the climatological CDF, that is the full set of daily sequence of WTs from April 26 to the simulated (right or wrong) average W2S transition date (Fig. 8b). Both panels are rather noisy with the 8 WTs usually observed before the average W2S transition dates. Some differences are visible between the successful prediction and the climatological CDF, as for example the positive frequency of WT 3 (which is a "dry" WT over the middle Caribbean basin [START_REF] Moron | Weather types across the Caribbean basin and their relationship with rainfall and sea surface temperature[END_REF] in the 5 to 1 day before the summer-to-winter winter-to-summer Fig. 6 Correlation between the 31-day moving average indices and the average W2S and S2W transition date for the 1982-2017 period. W2S (S2W) located on the left (right) panel. Top panels: W2S (S2W) vs. CarRC (blue), AmaR (pink) and CarPW (cyan); middle panels: W2S (S2W) vs. SST indices for CARS (green), PAC (cyan), EEP (pink), TNA (blue) and GMEX (orange); bottom panel: W2S (S2W) vs. 925hPa zonal wind CLLJ (pink) and CJ (blue). The dots mark significant correlation at 90% level according to Monte Carlo test. The vertical line represents the average date of transition and the vertical dotted lines represent ±1 sd.

successful average W2S transition date, but no obvious 1 WT sequence is observed. It suggests that successful In other words, the transition between wintertime (i.e., 5 WT 1-3 and 7-8) and summertime (i.e., WT 4-6) at-6 mospheric mode, revealed by the W2S transition date, 7 is barely associated with a specific transition amongst 8 the WTs.

9

The temporal modulation of the intensity of the predictability is further explored on Fig. 9. The predictability of the regional-scale atmosphere is analyzed from two complementary points of view. First a "deterministic" error (Fig. 9a) is estimated through the root mean square error (RMSE) of zonal and meridional components of 925hPa winds between the predictions and the observations (= initializations) from the 13 starting dates (from March 29, approximately 1.5 months before the observed average W2S transition The W2S and S2W regional-scale transition dates are 31 identified from the same approach than the one used 32 to determine the onset of rainfall based on cumula-33 tive amount of rainfall over a certain number of days 34 or pentad. The cumulative daily amount of rainfall is 35 substituted by a sequence of summer and winter WTs. 36 Additionally, conditional probability, considering equi-37 probable combinations having a chance of 40-60% to 38 occur, have been used to select the requests, thus re-39 ducing the subjectivity of the selection. The compari-40 son of the results obtained from the four scenarios using 41 Amazon region to the threshold of rainfall used to iden-13 tify it. In consequence, the WTs approach appears as 14 an alternative method to determine a seasonal transi-15 tion at regional-scale, especially when no abrupt change 16 in rainfall amount exists, as it is usually observed in 17 stricto-sensu monsoon climate.

18

The results show that the average date of the W2S 19 and S2W regional-scale seasonal transitions can indeed 20 be estimated from the switch between wintertime and 1 summertime regional-scale WTs (and vice versa). The 2 W2S transition date is near-synchronous with a rapid ending of the late rainy season seems to be associated with a warmer (resp., colder) EEP added to an anomalously cold (resp. warm) TNA, CARS and GMEX.

The fluctuations of both the CJ and CLLJ seem to also play a role in the S2W transition. Several studies have shown a relationships between the CLLJ and the Caribbean rainfall, a stronger CLLJ being associated with lower rainfall except on the Caribbean side of Costa Rica (Wang and Lee, 2007). The relationships between CLLJ and rainfall were mainly studied in regard to the mid-summer drought but not with respect to the W2S and S2W transitions. Note that at the time of the W2S transition, the CLLJ weakens (Fig. 5), then slightly strengthens in July at the time of the mid-summer drought (Curtis and Gamble, 2008;[START_REF] Angeles | Origins of the Car-49 ibbean rainfall bimodal behavior[END_REF][START_REF] Cook | Hydrodynamics of the Caribbean low-level jet and its relationship to precipitation[END_REF], weakens again in August-September before reaching its maximum around and after the S2W transition [START_REF] Cook | Hydrodynamics of the Caribbean low-level jet and its relationship to precipitation[END_REF].

In consequence, the mean characteristics around both the W2S and the S2W transition dates reveal a major asymmetry in the annual cycle around the Caribbean basin. The regional-scale W2S date is locked with an abrupt increase in the daily amount of rainfall starting from the southern part of Central America before propagating further north and east a few days later in agreement with [START_REF] Mapes | Strides, steps and stumbles in the march of the seasons[END_REF] and [START_REF] Moron | Interannual and intra-annual variability of rainfall in Haiti (1905-2005)[END_REF], while the S2W date is far smoother. The rapid increase of the amount of rainfall during the W2S transition could be dependent on a tipping point (or a combination of) in any of the oceanic or atmospheric variables such as the SST threshold for the convection, a needed amount of precipitable water in the atmosphere, a weaker CLLJ or a combo of several oceanic and atmospheric factors. The W2S transition date occurs when the general atmospheric circulation is close to its annual average suggesting that, in boreal spring, the climate seems to be in "conditional stability". Therefore, any atmospheric processes such as easterly waves, troughs, cold surge or the Madden Julian oscillation could destabilize the system and potentially trigger the W2S transition and thus the start of the regional-scale summer (and main rainy) season. The S2W transition in boreal fall presents a slightly larger interannual variability and the shift is not synchronous to a regional-scale decrease of rainfall everywhere over the whole Caribbean basin and Central America. Indeed, the southern part of the Caribbean basin does not show a sharp decrease in rainfall at the time of the S2W transition while the northern part of the basin shows a decrease in the daily amount of rainfall synchronous with it.

Additionally, the analysis of the predictability of the W2S transition dates have been investigated using ret-rospective forecast of an 11-run ensemble of ECWMF In conclusion, an approach using WTs could be used for South America monsoon (LISAM). Atmosph Sci

1 ibbean

 1 Low Level Jet (CLLJ) as well as the warming of but none have shown a change in the length of the rainy season.

  scale shift to the criteria (i.e sequence of summer WT and winter WT during a certain number of days) used to determine the transition date of the WTs and to the use of two different sets of reanalyses (i.e., NCEP-DOE and ERA-Interim) ; (iii) analyzing the atmospheric and oceanic conditions associated with the transition periods ; (iv) evaluating the representativeness of the regional-scale date in subregional-scale rainfall and ; (v) assessing the potential and real-time predictability of the W2S transition. The paper is structured as follows: the data and methodologies used in the analysis are detailed in Section 2. Section 3 includes the results related to the second, third and fourth objectives, while Section 4 assesses the seasonal and interannual predictability of the W2S transition. Section 5 closes the paper with the disbeen extracted from the CHIRPS and PERSIAN-CDR data set over the Caribbean basin and Central America (5 • N-30 • N, 50 • W-100 • W) to determine the change in precipitation prior

68 2 . 2

 22 Atmospheric data 69 All atmospheric data are extracted from the second 70 version of the NCEP reanalyses (i.e., NCEP-DOE, see 71 Kanamitsu et al (2002)) for the period 1979-2017, 72 within the region 8.75 • N-31.25 • N, 98.75 • W-56.25 • W 73 at an horizontal resolution of 2.5 • . Zonal and merid-74 ional wind data at 925hPa are also extracted from ERA-75 Interim to update the time series of WTs from Moron, 76 Gouirand, and Taylor (2015) (see section 2.5). The Car-77 ibbean Low Level Jet index (CLLJ, compare Amador 78 (2018)) was calculated by averaging the 925hPa zonal 79 wind over the area 10 • N-17.5 • N, 80 • W-65 • W, and the 80 Choco Jet (CJ, see Poveda and Mesa (2000)) was calcu-81 lated by averaging the 925hPa zonal wind over the area 82 5 • S-7 • N, 85 • W-75 • W, to determine their influence in 83 the spring and fall transitions.

  84

1

  An ensemble of 11 runs covering the period 1998-2017 2 have been extracted from the S2S database at the In-3 ternational Research Institute for Climate and Society 4 (https://iridl.ldeo.columbia.edu/SOURCES/ 5 .ECMWF/.S2S/). The ECMWF forecasting system is re-6 ferred to as Var-EPS-monthly, which is a merged 47 7 days (from day 0 to a lead time of 46 days) ensemble 8 system updated twice (each Mondays and Thursdays) 9 a week. We use two slightly different model configura-10 tions (CY43R23 for the starting dates before June 6 and CY45R1 thereafter) due to the fact that 20 years 12 of retrospective forecasts are performed each time that 13 a new version of the model is available (Moron and 14 Robertson, 2020). The EPS integration uses an atmo-15 spheric model at Tco639 resolution (about 16km) up to 16 day 15 and Tco319 (about 32km) after day 15. 17 The WTs for the ECMWF runs are computed on 18 the ensemble of the 11 runs starting from March 29 19 to May 10, i.e., 13 different starting dates leading to 20 a full set of 13 times 47 days times 11 runs times 20 21 years (thus, in total 134 420) days. The 925hPa zonal 22 and meridional wind components have been extracted 23 and linearly interpolated onto the 2.5 • grid of ERA and 24 NCEP. The Euclidean distances have been computed 25 between the standardized anomalies (over the whole 26 available time period, so keeping the annual cycle as in 27 the reanalyses) of the zonal and meridional ECMWF 28 925hPa winds (OLR is not used at this step since it is 29 not available in this S2S ensemble) and the centroids of 30 the 8 WTs for 925hPa winds obtained from days consis-31 tent between both reanalyses. The smallest Euclidean 32 distance attributes then each day of the 11 runs of the 33 S2S ensemble to one of the 8 WTs.

34 2 . 4

 24 Sea surface temperatures 35 The daily sea surface temperatures have been extracted 36 from the NOAA OISST v2 high resolution data set. 37 The OISST is available from 1981 to 2017 with a spa-38 tial resolution of 0.25 • and has been selected for the 39 region extending from 15 • S to 45 • N and from 100 • W 40 to 10 • E

42

  face temperature prior and following the date of the 43 W2S and S2W regional-scale transitions. Five SST in-44 dices are computed: (i) the TNA index corresponding to the average of SST over the region 9 • N-18 • N and 46 80 • W-60 • W, (ii) the tropical East Pacific index (PAC) 47 corresponding to the average of SST over the region 48 7 • N-16 • N) and 110 • W-85 • W, (iii) the EEP index cor-49 responding to the average of SST over the region 5 • S-50 5 • N and 110 • W-85 • W, (iv) the Gulf of Mexico index (GMEX) corresponding to the average of SST over the region 18 • N-25 • N and 95 • W-80 • W, and (v) the Caribbean Sea index (CARS) corresponding to the average of SST over the region 9 • N-18 • N and 85 • W-65 • W. 2.5 The definition of W2S and S2W transition dates from the weather types

( 1 )

 1 In the "NCEP" scenario, the dates of transition are determined from the WT time series from NCEP-DOE only ; (2) In the "ERA" scenario, the dates of transition are determined from the WT time series from ERA-Interim only ; (3) In the "NCEP+ERA" scenario, a new daily WT time series corresponds to the WTs common to both NCEP-DOE and ERA-Interim based on the 8 WTs. The days having different WTs (amongst the 8 WTs) between both reanalyses are coded as missing value. The transition dates are then calculated based from this new daily WT time series ; (4) In the "NCEP+ERA1" scenario, the eight WTs are first coded into summer and winter categories. Then a new daily WTs timeseries is created based on the common daily WTs in both reanalyses, while the inconsistent days are coded as missing value. The average of the 144 requests from the NCEP+ERA1 scenario have been calculated and this average is then used as reference from section 3.2.

1

  shift from the wet to the dry season, the W2S transition, 2 occurs on May 13 and the S2W transition occurs on Oc-3 tober 26 implying that the duration of the wet season is 4 165-166 days. The correlations between the interannual 5 W2S and S2W transition dates are weak and not signif-6 icant (-0.14 between W2S and S2W and 0.13 between 7 S2W and W2S seven months after, respectively) for the 8 period 1979-2017. This suggests that these transitions 9 are statistically independent from each other. This re-10 sult also suggests that the spring and fall shifts may 11 be influenced by different factors such as the SST over 12 the Tropical North Atlantic or over the Pacific as it has 13been observed for the rainfall amount received during 14 the early and late seasons[START_REF] Taylor | Influence of the tropical Atlantic versus the tropical Pacific on Caribbean rainfall[END_REF][START_REF] Ashby | Statistical models for predicting rainfall in the Caribbean[END_REF].

163. 2

 2 Regional-scale transition date vs. sub-regional 17 rainfall 18 A cluster analysis has been performed on the standard-19 ized mean annual cycle of rainfall (i.e., climatological 20 daily mean amounts are standardized to zero mean and 21 unit standard deviation) using the CHIRPS data set 22 (Funk et al, 2014). It aims to cluster the annual cycle of 23 rainfall over the Caribbean region independently on the 24 total annual amount of rainfall. Eight annual regimes 25 have been retained. The annual cycle of each area has 26 then been compared with the regional-scale W2S and 27 S2W dates of transition previously defined (Fig. 2). 28 The eight regions highlighted by the classification 29 are referred as follow: in the north, regions 1 and 2 30 correspond to South and North Florida, respectively 31 (orange and yellow in Fig. 2). Region 3 includes the 32 western and central part of Cuba, the northern Ba-33 hamas islands, Belize and the Pacific side of Central 34 America (blue in Fig. 2), while region 4 aggregates the 35 eastern part of Cuba, the southern Bahamas islands, 36Jamaica, Hispaniola and Puerto Rico (brown in Fig.2).37The lesser Antilles, Aruba, Bonaire and Curacao islands 38 and the north of Honduras belong to region 5 (light 39 blue) while region 6 (green in Fig. 2) associates the 40 Caribbean side of Costa Rica, Honduras, the eastern part of Venezuela and the southern Windward Islands. 42 In the south, Colombia, Panama and the Pacific side of 43 Costa Rica constitute region 8 (red) and central part of

44

 44

  49 nual cycle of the Caribbean region, i.e., a dry season 50 from November to April and a wet season from May

51

 51

Fig. 1

 1 Fig. 1 (a) Day of the W2S transition on the average of the 144 requests for the NCEP+ERA1 scenario (black line with error bars corresponding to ±1 sd), for NCEP+ERA scenario (blue line with triangle), ERA scenario (orange line with triangle) and NCEP scenario (pink line with square); (b) same as (a) but for the S2W transition.

Fig. 2

 2 Fig.2Clustering of the mean annual cycle of daily rainfall. The daily rainfall are extracted from CHIRPS data set and the climatological daily mean on the whole period 1981-2017 is then standardized to zero mean and unit variance. The clustering is done on the leading 25 principal components explaining 75% of the total variance. The vertical dashed line refers to the mean S2W and W2S transition dates from the NCEP+ERA1 scenario(May 13 and October 26)

Fig. 3

 3 Fig. 3 (a) Daily evolution of the OLR (shadings in W/m 2 ) and SST (contours in degres C) daily mean climatology for each latitude from 10 • N to 30 • N averaged between 80 • W-50 • W. (b) WTs from the 1st January 1981 (innermost circle) to the 31st of December 2017 (outermost circle). On the circle 0 represents all the January 1st and each complete circle represent one year; (c) same as (b) but for the daily rainfall spatially averaged over the Caribbean basin and Central America (5 • N-30 • N, 50 • W-100 • W, in mm). The radial lines on the circles indicated the W2S and S2W average transition dates.

Fig. 4 2 Fig7

 42 Fig.4Two left (right) panels correspond to the W2S (S2W) transition: (a),(g) average of rainfall (mm) over the Caribbean basin in the fourth week (week4(-1)) before the W2S (S2W) transition; (b),(h) difference between the amount of rainfall received on the third week (week3(-1)) before the transition minus the amount of rain in week4(-1); (c),(i) difference between the average amount of rainfall received on the second week (week2(-1)) before the transition minus the amount received during week3(-1); (d),(j) difference between the average amount of rainfall received on the first week (week1(-1)) before the transition minus the amount received during week2(-1); (e),(k) difference between the average amount of rainfall received on the first week (week1(+1)) after the transition minus the amount in week1(-1); and (f),(l) difference between the average amount of rainfall received on the second week (week2(+1)) after the transition minus week1(+1). Only the significant differences are shown based on a Monte Carlo test at level 95%. Both rainfall and rainfall differences are in mm/day.

  ), CarRC decreases more than 6mm/day to 4mm/day, and CarPR decreases 5mm/day to less than 4mm/day. CarPW also decreases, namely from 42kg/m 2 to less than 40kg/m 2 nine days after the S2W transition. On the other hand, SST indices are still warmer than 27-28 • C after the S2W transition date. Unlike the W2S transition, the SST indices still all have values above their annual average at the time of the S2W transition.

  Fig.7ashows the skill (i.e., the correlation between the 94 average W2S transition date in ECMWF and the ob-95 served one in the NCEP+ERA1 scenario using the same 96 parameterization) vs. the initialization date. The skill 97 is close to zero until April 19, then suddenly increases 98 to 0.5-0.6 around April 24-25 and then stays at a con-99 stant level around 0.7 until May 10 close to the mean 100 observed average W2S transition, for the best combina-101 tions (i.e., requests #1 and #7). The lack of any signifi-102 cant skill more than 3 weeks before the mean observed 103 W2S transition date for all requests is fully consistent 104

Fig. 5

 5 Fig. 5 Average of the indices 60 days before and after the transition date denoted by zero on the x-axis. Indices for the W2S transition (a)-(f) and the S2W transition (g)-(l). (a),(g) rainfall CarRC (blue line), CarRP (green line) (10 • N-25 • N, 90 • W-50 • W), and (b),(h) AmaR (1 • S-5 • S, 75 • W-50 • W) in mm/day; (c),(i) precipitable water (CarPW) (10 • N-25 • N, 80 • W-60 • W) in kg/m 2 ; (d),(j) SST over PAC (red line) (7 • N-16 • N,110 • W-85 • W), EEP (yellow line) (5 • S-5 • N, 110 • W-80 • W), TNA (blue line) (9 • N-18 • N, 80 • W-60 • W), GMEX (purple line) (18 • N-25 • N, 95 • W-80 • W), and CarS (green line) (9 • N-18 • N, 85 • W-60 • W), in degree Celsius; (e),(k) CLLJ (10 • N-17.5 • N, 65 • W-80 • W); and (f),(l) CJ (5 • S-7 • N, 85 • W-75 • W) in m/s. The zero (vertical line) indicate the date of transition and the dotted lines represent ±1 sd.

1

  tween the observed transition dates and the SST indices 2 shown in Fig.6. This is also fully consistent with the 3 weak variance explained by the logistic hindcast model 4 of WT occurrence with SST indices at the start of the 5 rainy season in Moron, Gouirand, and Taylor (2015). 6 On the contrary, the sudden increase of skill concen-7 trated in one week, between April 19 and April 26, sug-8 gests the main implication of an atmospheric process, 9 possibly superimposed on the mean annual cycle. The 10 lower three panels of Fig. 7 show the observed and fore-11 cast transition date using the parametrization #7 for 12 three starting dates, one (April 19) without any skill 13(Fig.7b), and two others one (Fig.7c) and two weeks 14 (Fig.7d) thereafter.

  15

2

  transition is not related to either a single atmospheric 3 sequence or a single or few dominant sustained WTs.

  4

Fig. 7

 7 Fig.7(a) Skill of WT-transition in a 11-run ensemble of the ECMWF model vs. the initialization date. The x-axis shows the initialization dates and the different curves shows seven parametrizations ofWT-transition (detailed in text). The observed WT-transition date occurring before the initialization dates are not used to compute the correlations between observed and the ensemble mean WT-transition. (b)-(d) Time series of observed transition dates based on the average of the 144 requests from NCEP+ERA1 (green circle), based on request #7 in the ECWMF (black circle) and the ECMWF (box plot with red line as median, upper and lower limit and upper and lower quartiles, red crosses as outliers) WT-transition for 3 initialization dates (April 19, April 26 and May 3). The WT-transition is defined using request #7 which uses the following criterion: 5 days of summer WTs not followed by 2 days of winter WTs in following 4 days after the last summer WT. The dashed horizontal line is the initialisation date and the full horizontal line is the mean of observed WT-transition.

Fig. 8 5

 8 Fig.8(a) CDF of the ECMWF WT-transition occurring before the WT-transition (= 0 in the abscissa) for the 76 run-years when the observed WT-onset is accurately predicted (with a tolerance of ±2 days) from April 26. The WT-transition is defined using the following parametrization (5 days of summer WTs not followed by 2 days of winter WTs in following 4 days after the last summer WT). (b) Same as (a) except for the whole set of 220 run-year.

Fig. 9

 9 Fig.9(a) Deterministic error (= RMSE) between predicted fields and initial state for successive initialization dates indicated in the abscissa and lead time shown as colored curves. The RMSE is computed on U and V components at 925hPa of each run and each year and then averaged across the 11 runs and the 20 years; (b) inter-run error amongst the 11 runs for successive initialization dates indicated in the abscissa and 5 lead time shown as colored curves. It is computed as the standard deviations amongst the 11 runs averaged across the years.

3

  regional-scale increase in the daily amount of rainfall 4 around early May, while the S2W transition is overall 5 smoother around late October and less clearly keyed 6 to an abrupt decrease of local-scale rainfall, especially 7 over the south and east of the domain. Therefore, the 8 average W2S transition date (less clearly for the S2W 9 transition date) constitutes an indirect indicator of the 10 average shift from dry-to-wet (wet-to-dry) season over 11 the Caribbean basin and Central America. The average W2S date, over the 1979-2017 period, occurs on 13 May 13 plus or minus 9 days, in agreement with Mapes "summertime" atmospheric regime (from a WT point 18 of view) or regional-scale wet season lasts 165 days on 19 average and no change in the length of the wet season 20 has been noticed since 1979. The absence of significant 21 correlation between the W2S and S2W and between 22 S2W(0) and W2S(+1) transition dates suggests that 23 the seasonal shifts are independent and influenced by 24 different forcings. This is in agreement with Taylor et 25 al (2002) and Taylor et al (2011) who have shown that 26 the beginning of the rainy season is more influenced by 27 the Atlantic Ocean while the end of the rainy season 28 is more affected by the SST over the East Equatorial 29 Pacific. 30 Moreover, the analysis of the SST indices also high-31 lights a main difference between both W2S and S2W 32 transitions. Indeed, there are no significant correlations 33 between antecedent variations of SST over the tropical 34 Atlantic and Eastern Pacific and the W2S transition 35 (and also the near-zero predictability beyond a 15-20 36 day lead time in the S2S exercise), while the interan-37 nual variations of S2W is preceded by significant, albeit 38 weak, SSTA in both basins. The first result is some-39 what in disagreement with other studies showing the 40 influence of the tropical North Atlantic on the early

1

  S2S model. The skill of the model is nil up to April 19th 2 and increases rapidly after this date. This supports a 3 null-to-weak forcing of the sea surface temperature, but 4 also suggests a main influence of atmospheric processes, 5 possibly superimposed on the mean annual cycle of so-6 lar radiation. Moreover, the analysis of the sequence of 7 the WTs associated with successfully predicted average 8 W2S date indicates that the transition is barely related 9 to a specific sequence of the WTs in agreement with 10 Moron, Gouirand, and Taylor (2015) showing that the 11 WTs type were occurring randomly, i.e., with no prefer-12 ential sequences. Furthermore, the approach, consisting 13 in looking at (i) a "deterministic" error based on the 14 RMSE of zonal and meridional components of 925hPa 15 between the predictions and the initializations from the 16 starting dates from early April, and (ii) at the error 17 corresponding to the spread between the 11 run across 18 the same starting dates and lead time, shows that the 19 predictability of the regional-scale atmosphere increases 20 beyond the synoptic time scale when getting closer to 21 the end of April and the beginning of May. This sup-22 ports the idea that multiple atmospheric processes are 23 forcing the W2S transition thus limiting its predictabil-24 ity to 2-3 weeks at most, before the transition.

  25

26

  to indirectly determine a regional-scale seasonal shift in 27 rainfall in regions where the change in rainfall amount is 28 not consequent enough to be accurately captured by the 29 agro-meteorological method. The method used in this 30 study could also be applied to estimate the start and 31 end of the mid-summer spell and to analyse the factors 32 involved in the reduced amount of rainfall observed in 33 the Caribbean basin and Central America in July. Curtis S (2013) Daily precipitation distributions over the intra-Americas sea and their interannual variability. Atmósfera 26:243-259. DOI: 10.1016/S0187-6236(13)71074-9 Curtis S, Gamble DW (2008) Regional variations of the Caribbean mid-summer drought. Theor Appl Climatol 94:25-34. DOI: 10.1007/s00704-007-0342-0 Diday E, Simon JC (1976) Clustering analysis. In: Fu KS (ed) Digital Pattern Recognition. Communication and Cybernetics, vol 10. Springer, Berlin, pp 47/1520-0442(1993)006<1665:TMM>2.0.CO;2 Dunion JP (2011) Rewritting the climatology 1 of the tropical North Atlantic and Carib-2 bean sea atmosphere. J Clim 24:893-908.

  Mayer DA (1997) Tropical Atlantic sea sur-14 face temperature variability and its relation to El 15 Niño-Southern Oscillation. J Geophys Res 102:929-16 945. DOI: 10.1029/96JC03296 17 Funk CC, Peterson PJ, Landsfeld MF, Pedreros DH, 18 Verdin JP, Rowland JD, Romero BE, Husak GJ, 19 Michaelsen JC, Verdin AP (2014) A quasi-global 20 precipitation time series for drought monitoring. 21 U.S. Geological Survey Data Series 832, 4 pages.

  , model and prospect. Progress in Phys-25 ical Geography: Earth and Environment 32:265-276.

1 31

 1 FJ (2017) Revisiting the ENSO tele-29 connection to the tropical North Atlantic. J Clim 30 30:6945-6957. DOI: 10.1175/JCLI-D-16-0641.Ghil M, Robertson AW (2002) "Waves" vs. "parti-32 cles" in the atmosphere's phase space: A pathway 33 to long-range forecasting? Proc Natl Acad Sci U.S.A 34 99:2493-2500. DOI: 10.1073/pnas.012580899 35 Giannini A, Chiang JCH, Cane MA, Kushnir Y, Sea-36 ger R (2001) The ENSO teleconnection to the trop-37 ical Atlantic ocean: Contributions of the remote 38 and local SSTs to rainfall variability in the tropical 39 Americas. J Clim 14:4530-4544. DOI: 10.1175/1520-seasonal predictability of mon-5 soon onset over Indonesia. J Clim 22:840-850.

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  Moron V, Robertson AW, Qian J-H (2010) Local versus 8 regional-scale characteristics of monsoon onset and 9 post-onset rainfall over Indonesia. Clim Dyn 34:281-10 299. DOI: 10.1007/s00382-009-0547-2 11 Muñoz E, Busalacchi AJ, Nigam S, Ruiz-Barradas 12 A (2008), Winter and summer structure of the 13 Caribbean low-level jet,. J Clim 21:1260-1276.

  Douglas MW (2002) Characteristics of 16 wet and dry spells over the Pacific side of Cen-17 tral America during the rainy season. Mon 18 Wea Rev 130:3054-3073. DOI: 10.1175/1520-19 0493(2002)130<3054:COWADS>2.0.CO;2 20 Poveda G, Mesa OJ (2000) On the existence of 21 Lloró (the rainiest locality on Earth): En-22 hanced ocean-land-atmosphere interaction by 23 low-level jet. Geophys Res Lett 27:1675-1678.

  Durán-Quesada AM (2015) A climatology of 37 low level wind regimes over Central America using a 38 weather type classification approach. Front Earth Sci 39 3, 15 pages. DOI: 10.3389/feart.2015.00015 40 da Silva AE, de Carvalho LMV (2007) Large-scale index 41

ters 1, 2 and 4 identified by Jury et al (2007) who only

et al (2004) that detected an onset date of the rain-

fall around May 1st and May 10th. The S2W date oc-16 curs, on October 26 plus or minus 12 days. Thus, the Caribbean rainfall [START_REF] Chen | Investigating the link between early season Caribbean rainfall and the El Niño + 1 year[END_REF]Giannini 42 et al, 2000;Gouirand et al, 2016;[START_REF] Taylor | Influence of the tropical Atlantic versus the tropical Pacific on Caribbean rainfall[END_REF].