1. Introduction
The approximately 50 million human inhabitants of Central America1 live between the warm waters of the eastern tropical Pacific Ocean, Caribbean Sea, and Gulf of Mexico. The majority of Central America is less than 400 km wide from coast to coast; in some countries, such as Costa Rica and Panama, it is much narrower (Fig. 1, top). It is also interesting to examine the spatial distribution of population density in Central America; a greater portion of the Mexican population lives in the interior and on the Gulf coast, while throughout Central America southeast of the Yucatan Peninsula, population density heavily favors the Pacific coast (Fig. 1, middle). Clearly, the mountainous topography plays a role in shaping the distribution of population in a region such as Central America where outward expansion is not possible. Central America is also in very close proximity to a major center of precipitation. The eastern Pacific intertropical convergence zone (EP ITCZ), located over the east Pacific warm pool (EPWP; Fig. 1, bottom), is the rainiest place on earth in boreal summer. On average, up to 50 cm of rain falls on the EPWP between May and July, which rivals the Indo-Pacific warm pool and Indian monsoon (Fig. 2). In terms of a first-order spatial scale, the EP ITCZ is part of a tropics-wide belt of heavy precipitation, and therefore rainfall over Central America can be considered within that context.
An improved understanding of, and ability to predict, interannual variations in rainfall over Central America would be potentially useful in agricultural, land use, and disaster management sectors. For example, recent studies by Hong et al. (2006, 2007a,b) and Nadim et al. (2006) have focused on identifying regions that are particularly susceptible to rain-induced landslides. The Pacific side of Central America has been identified by both groups as one of the major global landslide hotspots. At present, the EPWP/Central America region is also home to one of the largest rainfall biases in global climate models. For example, simulations of the boreal summertime climate by the Community Climate System Model, version 3 (CCSM3) overestimate precipitation rates by nearly 30 cm month−1 or 100% in a region that is precisely collocated with the EPWP and Central America (Collier et al. 2004). The only other boreal summertime rainfall bias comparable in magnitude and spatial scale was over the north-central Indian Ocean.
The EPWP represents the warmest water in the eastern Pacific Ocean, and it is located directly adjacent to Central America (Fig. 1, bottom). In simplistic terms, rainfall requires the following two ingredients: water vapor and a lifting mechanism. What role does the EPWP play in the atmospheric circulations that are relevant to rainfall in Central America? Specifically, are interannual variations in the mean surface temperature of the EPWP related to the interannual variations in rainfall over Central America? In Karnauskas and Busalacchi (2009), the physical mechanisms governing the interannual variability of SST in the EPWP were diagnosed; shortwave heating was identified as the primary driver of the interannual SST tendency. The high correlation between the EPWP and ENSO is explained by the fact that equatorial SST anomalies modify the distribution of atmospheric vertical motions, and therefore cloud cover and shortwave heating. For example, during an ENSO warm event, rising motions over the warm equatorial SSTs overturn as subsidence to the north over the EPWP, resulting in clear skies and enhanced shortwave heating. However, the interactions between equatorial SSTs, the EPWP, and the ITCZ in generating interannual variations in rainfall over Central America are not yet understood.
The literature regarding the interannual variability of rainfall in Central America can be summed up in a small handful of key studies. Hastenrath (1976) averaged stations from southern Mexico, northern South America, and the Caribbean basin (to 60°W), and analyzed composite maps of observed SLP, wind speed, and SST associated with extreme wet and dry events in that region. While Hastenrath (1976) had precious little data available from the tropical Pacific, he did make some interesting observations that highlighted the potential importance of processes based in the tropical Pacific. The focus of Hastenrath (1976) was divided between the Atlantic and Pacific, but with regard to the eastern tropical Pacific, Hastenrath identified the makeup of wet and dry events in the Central America–Caribbean region as summarized in Table 1. That wet (dry) events corresponded to a poleward (equatorward)-displaced ITCZ, and that summertime poleward (equatorward) displacements of the ITCZ corresponded to cold (warm) equatorial Pacific SST, makes sense on first principles. However, that wintertime poleward (equatorward) displacements of the ITCZ corresponded to warm (cold) equatorial Pacific SST does not seem to fit within first principles, given the apparent mismatch between SST and convection that would be implied by such a configuration. Hastenrath left this unresolved, perhaps in part due to the lack of observations from the eastern tropical Pacific from which he constructed composite maps. Also, the magnitudes of the warm and cold equatorial Pacific SSTs were only on the order of ±0.5°C. All composite departures were less pronounced for wet events. Still focusing on the Central America–Caribbean region, Hastenrath (1978) used principal component analysis to identify leading modes of sea level pressure (SLP) and SST in the Atlantic and eastern Pacific, and compare with rainfall variability in the Central America–Caribbean region and elsewhere in the tropics. The results seem to suggest greater complexity in the relationship between ENSO and rainfall in the Central America–Caribbean region. Nonetheless, Hastenrath demonstrated that tropics-wide mass exchanges play an important role in tropical climate anomalies. Mechanisms linking SST anomalies, the ITCZ, and rainfall in Central America–Caribbean were also left unresolved. Once again, however, given that Hastenrath’s analyses were performed entirely before the satellite era, the foundation he laid for further analysis is remarkable.
Using a very similar domain (Central America, northern South America, and the Caribbean out to 60°W), Giannini et al. (2000) returned to the question of the role of the Atlantic and Pacific basins in the seasonal and interannual variability of rainfall in that region. With regard to the Pacific, Giannini et al. (2000) found that dry conditions tend to prevail in the rainy season preceding the mature phase of an El Niño event, and wet conditions in the following rainy season. The mechanism proposed for the latter is that, during the spring following the El Niño event, the Caribbean Sea remains warm, driving anomalous convection over the Caribbean basin. This mechanism is physically viable because the rainfall index covers such a broad region, primarily encompassing the Caribbean. Similar to Giannini et al. (2000), Wang and Enfield (2003) showed that an anomalously large tropical Western Hemisphere warm pool (WHWP; defined as the combined warm waters of the EPWP, Caribbean Sea, Gulf of Mexico, and western tropical North Atlantic Ocean) forces anomalous Hadley and Walker circulations, which includes strong ascent over the WHWP. Whether these mechanisms explain the response of Central American rainfall to distinct SST–circulation forcing from the eastern tropical Pacific is unclear. It is also suggested that the importance of the eastern tropical Pacific in the interannual variability of rainfall in Central America (strictly speaking) may have been underestimated, because most prior work has only considered Central America as a part of a much larger region, and has thus presumed a large degree of homogeneity across the entire Caribbean and even the western Atlantic.
Peña and Douglas (2002) took a slightly different approach. Focusing on the Pacific coast of southern Central America, the authors found that wet spells were characterized by weakened Caribbean trades and stronger cross-equatorial flow over the eastern Pacific toward Central America, and the opposite for dry spells. The authors also noted that composite OLR maps of wet and dry spells reveal convective cloudiness anomalies extending from Central America westward, well into the eastern Pacific. This perspective paints a very different picture of Central American precipitation than that of the previous studies. It should also be noted that cross- equatorial flow toward Central America implies flow over the EPWP. The authors did note interannual variations in the number of wet and dry spells, but pursuing this observation was neither within the scope of this paper nor feasible given the length of the dataset. Nonetheless, the first question posed in the concluding remarks of Peña and Douglas (2002) was the following: what are the processes modulating the lower-frequency variations in wet and dry spells? Within that context, this paper aims to elucidate the specific role of SST in the EPWP in such lower-frequency variations.
The datasets and methodology are described in the following section (2). Section 3a describes the temporal and spatial scales of interannual variability of precipitation in Central America. An examination of the mechanisms linking SST in the eastern tropical Pacific Ocean to Central American rainfall is given in section 3b. The implications for predictability are discussed in section 4. Section 5 concludes the paper with a summary of the major findings and proposed future work.
2. Data and methodology
a. Datasets
Given Central America’s complex and narrow geography, horizontal resolution is of paramount importance because the southern stretch of Central America itself is nearly “subgrid scale” from the perspective of global reanalyses, climate models, and some satellite-based analyses (Costa Rica and Panama, in particular). The Willmott and Matsuura (2001; hereafter WM01) precipitation dataset has a horizontal resolution of 0.5° and covers 1950–99, and is used in the present study. The WM01 dataset is a spatial interpolation of the Global Historical Climatology Network version 2 (GHCN v.2; Peterson and Vose 1997) and the Legates and Willmott (1990) station observations of monthly mean precipitation. Complete details of the WM01 land precipitation dataset are available (online at http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html). In many analyses herein, WM01 land precipitation is complemented by the satellite-based Climate Prediction Center (CPC) Merged Analysis of Precipitation [CMAP; with 2.5° horizontal resolution, 1979–present; see Xie and Arkin (1997)], particularly for neighboring ocean regions. Sea surface temperatures from the National Oceanic and Atmospheric Administration (NOAA) optimum interpolation, version 2 (OI v.2; Reynolds et al. 2002) and Extended Reconstructed SST, version 2 (ERSST v.2; Smith and Reynolds 2004) datasets, and atmospheric fields from the National Centers for Environmental Predication–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996) are also used. All of the above-mentioned datasets were obtained freely from the NOAA/Earth System Research Laboratory (ESRL) Web site (http://www.cdc.noaa.gov/PublicData/).
b. Spatial and temporal considerations
Shown in Fig. 3 is the monthly climatology of precipitation in Central America from WM01 (all land between 6° and 22°N). The relative minimum in precipitation, which occurs during the middle of the summer [a.k.a., the “midsummer drought” (“MSD”); see Magaña et al. (1999)], is evident even in such a broad region. The rainy season for Central America will be defined as May through November. This is a fairly liberal definition, but necessarily so because (a) early onsets and late-progressing rainy seasons can be accounted for, and (b) the climatology show in Fig. 3 is for a wide range of latitude; the seasonal cycle at different latitudes may include more or less precipitation toward the limits of our definition. Furthermore, in many analyses, the rainy season will be split between an early rainy season (May–July) and a late rainy season (September–November).
There is considerable spatial variability in the distribution of rainfall over Central America. Shown in Fig. 4a is the observed climatological mean precipitation averaged over the full rainy season. In southern Mexico, rainfall tends to favor the Gulf coast (as does population), while throughout the rest of Central America, rainfall is found on both sides of the isthmus and/or the interior. Figure 4b shows the difference between rainfall in the late and early rainy seasons. Compared to the early rainy season, rainfall in the late season increases over parts of the Gulf and Caribbean coasts and decreases over much of the Pacific coast, but increases along the Pacific side of Nicaragua, Costa Rica, and Panama. CMAP data confirm the observed spatial seasonality (Fig. 4c), which foretells its potential utility in understanding the broader (ocean) context of land precipitation variability in Central America. Some of the discrepancies between Figs. 4b,c are likely explained by the substantially reduced spatial resolution of the CMAP satellite observations (2.5°) relative to the WM01 in situ data (0.5°).
It is clear that insightful analysis of rainfall variability over Central America will require a regional perspective. To further characterize the spatial variability of rainfall in Central America, an empirical orthogonal functions (EOF) analysis was performed on the monthly WM01 land precipitation (seasonal cycle removed), which is shown in Fig. 5. The first EOF is a rainfall monopole with higher loading from southern Mexico through Nicaragua. The first EOF, which explains 25.5% of the overall variance of monthly precipitation, essentially describes a state of either wet or dry conditions throughout Central America. The second EOF, explaining 12.1% of the variance of precipitation, is a rainfall dipole. When wet conditions prevail northwest of the Isthmus of Tehuantepec, the opposite conditions prevail from the Yucatan Peninsula through Panama. To provide a qualitative sense of validation of the leading WM01 EOFs, shown in Fig. 6 are regression maps of CMAP precipitation onto the principal component time series corresponding to the leading WM01 EOFs. The regression maps suggest that the EOF analysis of WM01 land precipitation does harmonize with the variability of the larger-scale precipitation field as inferred from satellite observations. The subsequent WM01 EOFs (3 and beyond), which explain substantially smaller fractions of the total variance, bear no resemblance to regressions of CMAP data onto their principal components.
A section of the WM01 data grid, superimposed upon the long-term mean rainy season precipitation field, and the boundaries of the five precipitation indices that will be analyzed are shown in Fig. 7. These will be referred to as P#, where # is the number indicated on the map in Fig. 7. Respectively, P1 and P2 are effectively the Pacific and Gulf coasts of southern Mexico, P3 and P4 are effectively the Pacific and Caribbean coasts of Central America from Nicaragua through the Yucatan Peninsula, and P5 is effectively Panama and Costa Rica, which are too narrow in comparison to the resolution of WM01 precipitation to be split into Pacific and Caribbean sides. Analysis of the covariance between rainfall anomalies in each region to those in neighboring regions confirms that it is necessary to analyze each region separately, and to analyze early and late rainy seasons separately. The covariance analysis is summarized in Table 2. Regions of Central America are more independent from neighbors along a coast than from neighbors across the isthmus. Finally, rainfall in the early and late rainy season appears highly independent of one another in each region. As an interesting corollary, a seasonal prediction of rainfall in Central America based on persistence would be rivaled by a coin flip!
3. Results
a. Observed interannual variability
Shown in Fig. 8 are time series of the early rainy season rainfall (departure from the mean) for the rainfall indices P1–P5. There are considerable interannual variations in each of the indices. Standard deviations, as a percentage of the mean, range from 11% to 27%. It is also of interest to understand the spatial scale associated with such rainfall departures. This is where CMAP is most useful, because WM01 precipitation is only available over land. Using a threshold of ±10 cm, wet and dry composites were constructed around each rainfall index. Because visual inspection revealed that the wet and dry composites are approximately symmetric (not shown), shown in Fig. 9 are the differences between the wet and dry composites. This also serves as an internal consistency check of the representativeness of each index to rainfall in that region; each index nicely captured land precipitation patterns strongly affecting that region, which in some cases were unique to that region. The CMAP contours show the larger-scale precipitation concurrent with the land precipitation, especially the EP ITCZ and western Caribbean rainfall. Diagnostics of mechanisms behind these rainfall departures are reserved for the following sections, but the EP ITCZ has the strongest signal in the P3 and P4 composites (i.e., the Pacific and Caribbean sides of Central America south of the Yucatan Peninsula). The CMAP precipitation anomalies found over the northeastern tropical Pacific are comparable in magnitude to those over Central America.
Shown in Fig. 10 is the same rainfall time series depiction as that in Fig. 8, but for the late rainy season. As expected from the covariance analysis between early versus late rainy seasons at individual regions, the variability differs from the early rainy season time series. Again, however, the interannual variability ranges from 11% to 24% of the annual mean. Composite wet–dry precipitation maps corresponding to the indices in Fig. 10 are shown in Fig. 11. The difference in the composites of late rainy season precipitation versus those of the early rainy season is primarily the spatial extent and involvement of larger-scale precipitation patterns to the west of the EPWP. In the early rainy season composites, CMAP revealed the ITCZ to be a relevant feature for primarily P3 and P4 rainfall departures. In the late rainy season composites, heavy involvement of the ITCZ is evident in each composite. It is also interesting to note the similarity between the spatial patterns of precipitation anomalies during the late rainy season (Fig. 11) with those related to intraseasonal oscillations, shown to be associated with strong westerly wind anomalies over the EPWP (e.g., Barlow and Salstein 2006; Maloney and Esbensen 2007), which could further contribute to the strength and location of the EP ITCZ by increasing surface latent heat fluxes over the EPWP. Indeed, the precipitation patterns shown for the late rainy season in P1 through P4 (but not P5) are associated with westerly surface wind anomalies over the EPWP (Fig. 11, top-left panel), highly consistent with the pattern shown by Maloney and Esbensen (2007, Fig. 2) and therefore suggests that the interannual modulation of intraseasonal oscillations may play an important role in late rainy season rainfall in Central America. Furthermore, the interannual modulation of the strength of intraseasonal oscillations by SST is an additional way by which the EPWP could influence the interannual variability of Central American rainfall.
Cleary there is a role for the EP ITCZ in the interannual variability of Central American rainfall. What role does the EPWP play in those processes? To begin disentangling the role of the eastern tropical Pacific Ocean, shown in Fig. 12 are composite seasonal cycles for each rainfall index, stratified by the phase of ENSO. An ENSO warm (cold) event was defined as a case when the Niño-1 + Niño-2 indexes exceeded positive (negative) 0.5 standard deviations. Regions P1–P3 indicate substantial and similar responses to ENSO, which are primarily manifest during the rainy season preceding the mature ENSO phase. During the rainy season preceding an ENSO warm (cold) event, monthly rainfall is significantly reduced (enhanced). These differences in the seasonal cycle composites are consistent with Giannini et al. (2000) and Hastenrath (1976).
Other major differences in the seasonal cycle composites stratified by ENSO phase are primarily manifest as changes in the MSD in July–August. Substantial changes in the MSD are observed in P3 corresponding to ENSO events. The composite differences in rainfall at the height of the early or late rainy season at P3 are small; however, the differences in July and August are large. Thus, during the rainy season preceding the mature phase, ENSO warm events tend to enhance the MSD), which is consistent with the results of Curtis (2002), and ENSO cold events appear to reduce that aspect of the rainfall climatology.
The composite evolution of the Niño-1 + Niño-2 index (Fig. 12, lower-left panel) indicates that SST in the eastern equatorial Pacific is anomalously warm or cold throughout the entire rainy season preceding the mature ENSO phase. However, in the calendar year following the mature ENSO phase, the SST anomaly in the eastern equatorial Pacific decays more rapidly than it grew, hence crossing the zero line by the middle of the rainy season. Therefore, during the late rainy season following the mature ENSO phase, although precipitation changes are noted, the equatorial SST anomaly is no longer present in the composite mean. One may thus expect that mechanisms linking SST in the eastern tropical Pacific with the rainy season in Central American be different before versus after mature ENSO phases. Similarly, how long an equatorial SST anomaly persists into the following calendar year may determine the way in which the eastern tropical Pacific Ocean influences that rainy season. Does this explain why composite mean rainfall differences following the mature ENSO phase tend to be smaller than those in the rainy season preceding the mature ENSO phase?
b. On the relationship between SST in the EPWP and Central American rainfall
Given that SST in the EPWP is driven by ENSO (Karnauskas and Busalacchi 2009), it is a reasonable contention that the role of the EPWP in Central American rainfall is primarily as an instrument in the way ENSO events unfold in the northeastern tropical Pacific. It will be shown in this section that the EPWP does play a major role in Central American rainfall anomalies during rainy seasons following the mature ENSO phase. It is hypothesized that, depending on when the equatorial SSTs relinquish control over the ITCZ, the EPWP modulates the strength of the ITCZ, which is a major contributor to rainfall over Central America.
To examine the interplay between SST, vertical motion, and precipitation, and where the EPWP fits into that interplay, we analyze the unfolding of the 1997/98 El Niño event. The 1997/98 El Niño was chosen because it was quite strong and also fell within the period for which CMAP precipitation observations are available. SST is zonally averaged across 100°–85°W, and from 5°S northward to the latitude at which the Pacific Ocean is absent within that longitudinal band. Vertical motion (−ω850) and CMAP precipitation are analyzed in the same manner, but extend northward throughout Central America. The reanalysis level of 850 hPa was chosen for diagnosing vertical motions because that was found to optimally characterize the vertical motion anomalies relevant to ENSO over the equatorial and northeastern tropical Pacific Ocean in Karnauskas and Busalacchi (2009). The bounds of the zonal averaging were chosen so that they encompass the equatorial belt, the EPWP, and Central America, while minimizing contamination from the Atlantic sector.
The results are presented in Fig. 13. The gray region to the north represents latitudes at which the Pacific Ocean is absent between 100° and 85°W. For reference, the climatological mean (1950–99) SST and vertical motion is shown in the top-left panel. From the climatology panel, it can be seen that the ITCZ (maximum ascent) remains over the warmer waters to the north of the equator throughout the year (i.e., over the EPWP), except during February and March when the ITCZ weakens and migrates southward in response to the seasonal warming (cooling) of equatorial (EPWP) SST. Note the out-of-phase relationship between the climatological SST in the eastern equatorial Pacific and that in the EPWP (e.g., the eastern equatorial Pacific is coldest during September, which is aligned with the EPWP warm season). Turning to the progression of SST and vertical motion throughout 1997/98 (Fig. 13, top-right panel), important differences are noted during the rainy season preceding the mature phase. During the 1997 rainy season, the developing El Niño causes the ITCZ to be weaker and displaced southward toward the equator. Hence, Central America tends to receive less rainfall during rainy seasons preceding El Niño events, consistent with previous studies. At the same time, SST in the EPWP is warmer than normal. If the EPWP were playing a major role in the rainy season preceding the mature ENSO phase, it would be forcing an anomalous thermally direct circulation and thus increase the overlying tropospheric ascent. As previously mentioned, during this time the EPWP and vertical motions over Central America are simply being driven by the same, overwhelming remote influence (i.e., anomalously warm equatorial SST). However, based on the same argument—that vertical motions in the tropics tend to follow the warmer SSTs—it is likely that the ITCZ would be shifted even farther southward toward the equator were the EPWP is not anomalously warm during that time. In this sense, it could be speculated that the EPWP serves to buffer the otherwise higher-amplitude effect of ENSO on the vertical motions over Central America during rainy seasons preceding mature ENSO phases. However, regional atmospheric modeling with specified SSTs would be useful to confirm this.
During the rainy season following the mature phase of the 1997/98 El Niño, a very different story emerges. After July 1998, as the equatorial SST returns to normal and the ITCZ finally moves northward toward its nominal boreal summertime position, the EPWP is still anomalously warm (SST ≥ 29°C). Hence, a rapid enhancement of the ITCZ is triggered. This is manifest in the vertical motion field as a strong gradient of −ω850 along the temporal axis coinciding with the warmest SSTs between 5°N and land. The core of the ITCZ is 22% stronger than normal in the late rainy season of 1998. Throughout this process, the EPWP is expending energy through enhanced latent heat flux, and thus cooler SSTs emerge at the same point on the temporal axis as when the ITCZ ceases to intensify. This mechanism follows that proposed by Neelin and Held (1987), whereby warmer SSTs result in increased surface heat fluxes, which control low-level convergence via the effect of the surface heat fluxes on the supply of moist static energy to the troposphere, and the observational analyses of Zhang (1993). Further, the response of the ITCZ was forced locally by the EPWP without contribution from a cold equatorial SST anomaly (Fig. 13; cf. equatorial SST in July–December 1998 versus the climatology).
During boreal summer 1998, the meridional gradient of vertical motion on the south edge of the ITCZ is sharpened, and on the north edge it is more diffuse. As can be seen in the gray region north of the SST data, this translates into a northward propagation and intensification of the ITCZ well into Central America north of the EPWP. Finally, the intensified ITCZ lasts beyond December 1998. The implication for precipitation anomalies in the late rainy season is obvious: just after the ITCZ undergoes a rapid and northward intensification, precipitation between 5°–25°N latitudes is up to 75% higher than normal (Fig. 13, bottom-right panel). To illustrate the spatial distribution of the land precipitation anomaly at the height of the late rainy season in 1998, shown in Fig. 14 is the WM01 precipitation anomaly and NCEP −ω850 anomaly for October 1998. The precipitation anomaly is up to 50 cm along the Pacific Coast of Central America, which is strongest in the P3 rainfall region. If the precipitation anomaly over Central America in October 1998 were due to an influence from the Caribbean, and not the SST-enhanced EP ITCZ, one would expect to observe the precipitation anomalies hugging the Caribbean coast of Central America, or at the very least see similar rainfall or ascent anomalies elsewhere in the Caribbean. On the contrary, no rainfall anomalies greater than 10 cm (minimum contour interval) are observed anywhere else in the Caribbean basin (and WM01 data exist for most of the Caribbean, including Cuba and Hispaniola). The spatial pattern of the precipitation anomaly is also consistent with the enhanced ITCZ uniquely located over the EPWP.
The specific role of the EPWP in the interannual variations of rainfall in Central America is as a modulator of the strength of the ITCZ, which is most important in rainy seasons following mature ENSO phases. As shown in the case of the 1997/98 El Niño, the ITCZ intensified and resulted in a large rainfall anomaly somewhat later in the year (the late rainy season). It could then be hypothesized that ENSO events that do not persist as long into the rainy season might produce rainfall anomalies by the same mechanism earlier in the year.
Although no two ENSO events are exactly the same, one important commonality is evident in the fact that the variance of SST anomalies in the eastern equatorial Pacific is maximum in December. However, there is a large amount of variability in the persistence of the anomaly beyond December. This fact is illustrated in Fig. 15. Each point in the scatterplot represents one ENSO event; where the point falls along the x axis represents its peak strength, and where the point falls along the y axis represents its persistence. For clarity, only ENSO events in which the December SSTA exceeded ±0.5 standard deviations are shown. For example, points falling within the dashed box in the upper-right quadrant represent El Niño events that persisted (maintained an SSTA greater than 0.5 standard deviations into the following June). In contrast, points falling within lower-right quadrant represent El Niño events in which, by the following June, the SSTA was of the opposite sign by at least 0.5 standard deviations (i.e., an antipersisting El Niño). Points that do not fall within any of the four dashed boxes are ENSO events that simply decayed. What is particularly interesting is that, of the 26 ENSO events in the period of 1950–99, the distribution based on this persistence characteristic is spread fairly evenly. As will be shown, this is precisely the characteristic of the ENSO event that determines the timing and strength of the impact of the EPWP on the interannual precipitation anomaly during the following rainy season.
Composite seasonal cycles of precipitation before and after the ENSO events represented in Fig. 15 (i.e., stratified by warm or cold and persisting or antipersisting) were constructed for each of the Central American precipitation indices P1–P5. The results, compared with the climatological mean (1950–99) seasonal cycles, are shown in Fig. 16. During the rainy seasons following mature ENSO phases, the evolution as stratified by persisting or antipersisting ENSO events is as expected from the SST-enhanced EP ITCZ mechanism. Persisting El Niños tend to result in reduced precipitation in the early rainy season, and increased rainfall in the late rainy season. Removing the two largest persisting El Niño events or the two weakest persisting El Niño events does not change the main results. The fact that the large anomaly in the late rainy season is not evident in P2 is actually consistent with the SST-enhanced EP ITCZ mechanism; P2 is by far the most remote region to the EPWP. The mechanism is the same, but the timing depends on the persistence characteristic of the ENSO event. Because the persistence of the El Niño event dictates how much time the EPWP receives anomalous shortwave radiation, the longer the El Niño event persists, the warmer the EPWP will be when the ITCZ eventually does migrate northward. This concept is analogous to a slingshot, where the elastic band is the ITCZ, the projectile is rainfall, the hand pulling the band is ENSO, and the target is Central America. The longer the hand (ENSO) pulls back on the band (the ITCZ), the greater the tension in the band (the warmth of the EPWP) will become. If the hand releases sooner, the projectile will arrive at the target sooner, but with less force (forcing a small anomaly early in the rainy season). If the hand pulls back longer before releasing, the projectile will arrive at the target later, but with greater force (forcing a large anomaly late in the rainy season).
4. Implications for predictability
The results of the preceding section would seem to suggest that improvement of the prediction of rainfall over Central America will depend on improvements in the ability to predict the termination of an ENSO event. If, in December, one knows the magnitude of the SST anomaly in the eastern equatorial Pacific Ocean, then one presumably knows the approximate maximum magnitude of the SST anomaly associated with that ENSO event. If one has a forecast of how far into the following calendar year that anomaly will persist, however, then one could conceivably make a useful prediction of the rainfall anomaly in the upcoming year, including its timing. In a place like Central America, where the seasonal cycle of rainfall has a marked bimodal distribution, the difference between a rainfall anomaly in August versus that in October has important implications. To know that the MSD will be instead be a wet spell could help guide the agricultural sector, which hedges its investments annually based on the MSD. Similarly, to know that the MSD will be especially dry, but shortly thereafter will be a considerable wet spell, could be very useful to those involved in both agriculture and disaster management, becauase Central America is especially prone to landslides.
As evident in the sensitive time-dependent relationships between equatorial SST, the overlying atmosphere, EPWP SST, and, once again, the overlying atmosphere, a simple index of SST would be of little use. Put very generally, rainfall depends on the following two factors: water vapor availability and a lifting mechanism. One of the many possible lifting mechanisms is ascent associated with the ITCZ. The results of the present paper describe a mechanism that can modulate the strength of that lifting mechanism.
Regarding the applicability of the results of the present paper to the problem of predictability (and climate monitoring and diagnostic analysis), the following is offered, which refers to Fig. 13:
(a) to predict rainfall at 1, one must predict ω at 1;
(b) to predict ω at 1, one must predict ∂ω/∂t at 2;
(c) to predict ∂ω/∂t at 2, one must predict SST at 2;
(d) to predict SST at 2, one must predict ∂SST/∂t at 3;
(e) to predict ∂SST/∂t at 3, one must predict SSTA at 4;
(f) to predict SSTA at 4, one must use SST at 5 as part of the set of initial conditions to be applied to a statistical or numerical model.
Statements a–c follow directly from the results of the present paper. As a prediction plan, the above statements are highly simplified and, for a perfect prediction, must assume that the SST-enhanced ITCZ is the only mechanism that can cause interannual variations in Central American rainfall. In practice, of course, several other factors require attention. However, it was established that the SST-enhanced ITCZ mechanism is dominant on interannual time scales, and serves to connect ENSO with Central American rainfall.
From the preceding discussion of predictability, two important inferences regarding seasonal and interannual prediction of Central American rainfall can be extracted by this simple prediction plan. For interannual prediction of Central American rainfall, predictive skill relies heavily on the skill of ENSO forecasts, especially amplitude and persistence into the following rainy season (follows statement f ). Interannual predictions, including those for analogous regional tropical climates, also depend on the quality of the representation of regional tropical atmospheric circulation cells of only a few degrees in the meridional direction (following statements a–e). For example, if the representation of the atmospheric mechanism connecting equatorial SST with SST in the EPWP is deficient, or if SST in the EPWP is not coupled correctly with the overlying vertical motion field, then the model and thus prediction cannot account for one of the dominant mechanisms influencing the interannual variability of Central American rainfall.
Finally, seasonal prediction of Central American rainfall depends critically on climate monitoring capabilities in the eastern tropical Pacific region, particularly north of the equator. Assuming a “seasonal” prediction means it is late winter or early spring, and one wishes to make a prediction for the upcoming rainy season, then one absolutely requires high-quality observations of SST and other relevant variables that serve to couple the lower troposphere to the tropical sea surface. Provided high-quality observations of SST in the equatorial and northeastern tropical Pacific, potential predictability several months in advance can be demonstrated without the use of a GCM. Based on the notion that the timing between SST anomalies on the equator and in the EPWP is the critical factor in determining the timing and strength of the SST-enhanced ITCZ, a simple index can be defined as the difference between standardized SST anomalies in the EPWP and the Niño-1 + Niño-2 regions. The rationale is that the ΔSSTA index will be positive if the EPWP is anomalously warm, but can be overruled if the eastern equatorial Pacific is also anomalously warm. In other words, the most important variable is SST in the EPWP at the time when the equatorial SST anomaly is returning to zero, which is precisely what ΔSSTA captures.
Shown in Fig. 17 are the seasonal P3 rainfall index and the ΔSSTA index (shifted 4 months forward). At 4-months lag, as is shown in Fig. 17, the correlation between P3 rainfall and ΔSSTA is 0.35, significant at the 98% confidence level. This is substantially higher than the correlation between P3 and the EPWP alone (0.14), or the anticorrelation between P3 and Niño-1 + Niño-2 alone (−0.11). The correlations between ΔSSTA and the other regions of Central America are as would be expected if they were physically based; P4 (immediately adjacent to P3 and thus under the influence of the EP ITCZ) exhibits a very similar correlation (0.34), P1 and P5 (to the north and south of P3 but also bordering the Pacific Ocean) have slightly lower correlations (0.20 and 0.27, respectively), while P2 (the farthest region from the EPWP and borders the Gulf of Mexico) has no correlation (0.01). The ΔSSTA index therefore provides unique information, including the anomalous SST in the EPWP, particularly at the time when the equatorial SST anomaly retreats and allows the ITCZ to migrate northward over the EPWP and thus Central America. Combined with coupled model forecasts of the evolving SST field and regional atmospheric response (i.e., the ITCZ), seasonal predictions of Central American rainfall could benefit from (a) explicit consideration of the SST-enhanced EP ITCZ mechanism, and (b) improved and sustained in situ observations of SST in the eastern equatorial and northeastern tropical Pacific Ocean. An in-depth, practical analysis of the potential predictability of Central American rainfall from information such as that provided by a ΔSSTA index, in tandem with other considerations (e.g., the state of the Atlantic sector), is a major area of opportunity for future work.
The Tropical Atmosphere Ocean (TAO) array (McPhaden et al. 1998) in the eastern tropical Pacific Ocean extends to 95°W, which is probably far enough east to be useful in the prediction of Central American rainfall from the perspective described in the present paper. However, observations north of 8°N are extremely limited, including shortwave radiation (spanning 2000–03). SST observations at 95°N, 8°W have since resumed, but it would be worthwhile to extend observations to 10° and 12°N, including shortwave radiation, especially given the demonstrated usefulness of such observations, which existed during the East Pacific Investigation of Climate field program (EPIC2001; Raymond et al. 2004). Advances in high-resolution satellite remote sensing, such as microwave SST observations from Tropical Rainfall Measuring Mission (TRMM) and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E), and passive estimates from sensors like Moderate Resolution Imaging Spectroradiometer (MODIS), can make up for some deficiencies of in situ observing platforms, although a substantial amount of care must be taken when using microwave SST observations in a region such as the EPWP resulting from rain contamination and satellite overpass frequency, which are issues common to all regions in the tropics (e.g., Maloney et al. 2008).
5. Summary and conclusions
Almost all of the previous studies on the interannual variability of rainfall in Central America have focused on the broader Caribbean region. One of the first outcomes of the present paper was a strong sense of heterogeneity within Central America; let alone what that implies about the Caribbean region as a whole. This observation, analyses of composite precipitation fields over and near Central America, and the work of Peña and Douglas (2002), which focused on the Pacific coast of Central America, motivated a hypothesis that the interannual variability of rainfall in Central America might be explained mechanistically differently than that of the Caribbean. The present study complements the works of Hastenrath (1976), Giannini et al. (2000), Peña and Douglas, and Wang and Enfield (2003) by providing a more comprehensive understanding of the role of the eastern tropical Pacific Ocean in the interannual variability of Central American rainfall, including the as yet unknown role of the EPWP. It was shown that the EPWP does play a major role in rainy seasons following mature ENSO phases, as was previously suspected based on the notion that the EPWP is thermodynamically part of the much broader Western Hemisphere warm pool (e.g., Wang and Enfield 2003), although the SST enhancement of the EP ITCZ was not brought to focus until the present study.
As shown in Karnauskas and Busalacchi (2009), the EPWP tends to lag ENSO and thus propagate SST anomalies nearby to Central America forward in time. Depending on when the equatorial SST anomaly retreats and allows the ITCZ to resume its seasonal march northward, the EPWP acts to trigger a rapid enhancement of the ITCZ as is evident by the vertical motion field. The ITCZ enhancement occurs over the warmest SSTs in the EPWP, and eventually leads to a cooler EPWP. The longer equatorial SSTs force the ITCZ southward, the longer the EPWP is subject to anomalous shortwave radiation, and the greater the intensification of the ITCZ will be once it migrates northward over the EPWP. The critical aspect of this mechanism is the relative timing of SST anomalies between the equator and the EPWP. The notion of Central America remaining dry as long as the anomalously warm equatorial SSTs are locking the ITCZ equatorward is consistent with the mechanism proposed by Eltahir and Gong (1996) for West African rainfall variability, except in that case there is no later intensification of the ITCZ by a coastal warm pool, which would serve to reverse the rainfall anomaly later in the year.
One of the most intriguing implications of this paper is that one of the major ways in which ENSO exerts its influence on Central American rainfall is through the EPWP. ENSO influences the EPWP through the atmosphere, and the EPWP in turn influences Central American rainfall through the atmosphere. One opportunity thus motivated is the focused use of SST observations and coupled modeling to improve seasonal predictions of Central American rainfall. This paper provides a new understanding of the important role of the EPWP in the Central American region which, in tandem with mechanisms identified by other investigators, could result in a more complete picture of an important regional climate and overall improved predictions of what is critical to the well being of its inhabitants: rainfall.
Acknowledgments
The authors wish to thank Drs. Ernesto Hugo Berbery, Wayne Higgins, Sumant Nigam, and Raghu Murtugudde for helpful input to the work comprising this manuscript. This research was supported by the National Oceanic and Atmospheric Administration (NOAA) Pan American Climate Studies (PACS) program through Grant NA17EC1483.
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(top) Elevation (meters above sea level) from the NOAA/National Geophysical Data Center (NGDC) Global Land One-Kilometer Base Elevation (GLOBE) dataset (Hastings and Dunbar 1999). (middle) Population density in 2000 (persons per square kilometer) from the Columbia University Gridded Population of the World, version 3 (GPW v.3) dataset. (bottom) Sea surface temperature (°C) averaged from December 1981 through April 2008 from the NOAA OI v.2 dataset (Reynolds et al. 2002). In the bottom panel, SST is contoured every 1°C, red indicates SST warmer than 28°C, and the black box represents the boundaries of the EPWP used throughout the main text.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
(top) Elevation (meters above sea level) from the NOAA/National Geophysical Data Center (NGDC) Global Land One-Kilometer Base Elevation (GLOBE) dataset (Hastings and Dunbar 1999). (middle) Population density in 2000 (persons per square kilometer) from the Columbia University Gridded Population of the World, version 3 (GPW v.3) dataset. (bottom) Sea surface temperature (°C) averaged from December 1981 through April 2008 from the NOAA OI v.2 dataset (Reynolds et al. 2002). In the bottom panel, SST is contoured every 1°C, red indicates SST warmer than 28°C, and the black box represents the boundaries of the EPWP used throughout the main text.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
(top) Elevation (meters above sea level) from the NOAA/National Geophysical Data Center (NGDC) Global Land One-Kilometer Base Elevation (GLOBE) dataset (Hastings and Dunbar 1999). (middle) Population density in 2000 (persons per square kilometer) from the Columbia University Gridded Population of the World, version 3 (GPW v.3) dataset. (bottom) Sea surface temperature (°C) averaged from December 1981 through April 2008 from the NOAA OI v.2 dataset (Reynolds et al. 2002). In the bottom panel, SST is contoured every 1°C, red indicates SST warmer than 28°C, and the black box represents the boundaries of the EPWP used throughout the main text.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Climatological mean global precipitation for early boreal summer (May–July; cm) over the period 1979–2006 from CMAP (Xie and Arkin 1997).
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Climatological mean global precipitation for early boreal summer (May–July; cm) over the period 1979–2006 from CMAP (Xie and Arkin 1997).
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Climatological mean global precipitation for early boreal summer (May–July; cm) over the period 1979–2006 from CMAP (Xie and Arkin 1997).
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Monthly climatology of precipitation (cm) in Central America (area average of all land precipitation within 6°–22°N, 105°–80°W) from WM01 precipitation (1950–99). White bars are not included in “rainy season” calculations. The two separate groups of gray bars delineate the early from the late rainy season in Central America.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Monthly climatology of precipitation (cm) in Central America (area average of all land precipitation within 6°–22°N, 105°–80°W) from WM01 precipitation (1950–99). White bars are not included in “rainy season” calculations. The two separate groups of gray bars delineate the early from the late rainy season in Central America.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Monthly climatology of precipitation (cm) in Central America (area average of all land precipitation within 6°–22°N, 105°–80°W) from WM01 precipitation (1950–99). White bars are not included in “rainy season” calculations. The two separate groups of gray bars delineate the early from the late rainy season in Central America.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
(a) Climatological mean rainy season (May–November) precipitation in Central America (cm) from WM01 precipitation (1950–99; (b) mean precipitation difference (cm) between the late (September–November) and early rainy season (May–July) from WM01 precipitation, and (c) same as in (b), but for CMAP precipitation (1979–2006).
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
(a) Climatological mean rainy season (May–November) precipitation in Central America (cm) from WM01 precipitation (1950–99; (b) mean precipitation difference (cm) between the late (September–November) and early rainy season (May–July) from WM01 precipitation, and (c) same as in (b), but for CMAP precipitation (1979–2006).
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
(a) Climatological mean rainy season (May–November) precipitation in Central America (cm) from WM01 precipitation (1950–99; (b) mean precipitation difference (cm) between the late (September–November) and early rainy season (May–July) from WM01 precipitation, and (c) same as in (b), but for CMAP precipitation (1979–2006).
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
The leading two empirical orthogonal functions of monthly precipitation (seasonal cycle removed) from WM01 precipitation (1950–99), using the domain shown.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
The leading two empirical orthogonal functions of monthly precipitation (seasonal cycle removed) from WM01 precipitation (1950–99), using the domain shown.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
The leading two empirical orthogonal functions of monthly precipitation (seasonal cycle removed) from WM01 precipitation (1950–99), using the domain shown.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Linear regression of monthly CMAP precipitation anomaly (cm month−1) onto the (a) first and (b) second principal components of WM01 precipitation (seasonal cycle removed), 1979–99.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Linear regression of monthly CMAP precipitation anomaly (cm month−1) onto the (a) first and (b) second principal components of WM01 precipitation (seasonal cycle removed), 1979–99.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Linear regression of monthly CMAP precipitation anomaly (cm month−1) onto the (a) first and (b) second principal components of WM01 precipitation (seasonal cycle removed), 1979–99.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Map of Central America, the mean rainy season (May–November) precipitation (cm) from WM01 precipitation (1950–99), the 0.5° × 0.5° grid on which the WM01 precipitation dataset is arranged, and the five precipitation index regions discussed in the main text.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Map of Central America, the mean rainy season (May–November) precipitation (cm) from WM01 precipitation (1950–99), the 0.5° × 0.5° grid on which the WM01 precipitation dataset is arranged, and the five precipitation index regions discussed in the main text.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Map of Central America, the mean rainy season (May–November) precipitation (cm) from WM01 precipitation (1950–99), the 0.5° × 0.5° grid on which the WM01 precipitation dataset is arranged, and the five precipitation index regions discussed in the main text.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Time series of early rainy season precipitation (departure from mean; cm) for indices P1 through P5 from WM01 precipitation (1950–99). The time series mean is displayed on each panel. Black lines at ±10 cm indicate the threshold for the composites shown in Figs. 9 and 11 and discussed in the main text.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Time series of early rainy season precipitation (departure from mean; cm) for indices P1 through P5 from WM01 precipitation (1950–99). The time series mean is displayed on each panel. Black lines at ±10 cm indicate the threshold for the composites shown in Figs. 9 and 11 and discussed in the main text.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Time series of early rainy season precipitation (departure from mean; cm) for indices P1 through P5 from WM01 precipitation (1950–99). The time series mean is displayed on each panel. Black lines at ±10 cm indicate the threshold for the composites shown in Figs. 9 and 11 and discussed in the main text.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Composite early rainy season precipitation anomaly differences (cm; wet − dry) in the Inter-Americas region from WM01 precipitation (1950–99; shading) and CMAP precipitation (1979–99; contours) for indices P1 through P5.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Composite early rainy season precipitation anomaly differences (cm; wet − dry) in the Inter-Americas region from WM01 precipitation (1950–99; shading) and CMAP precipitation (1979–99; contours) for indices P1 through P5.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Composite early rainy season precipitation anomaly differences (cm; wet − dry) in the Inter-Americas region from WM01 precipitation (1950–99; shading) and CMAP precipitation (1979–99; contours) for indices P1 through P5.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Same as in Fig. 8, but for the late rainy season.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Same as in Fig. 8, but for the late rainy season.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Same as in Fig. 8, but for the late rainy season.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Same as in Fig. 9, but for the late rainy season. In addition, superimposed onto P1 are the composite late rainy season surface wind vectors from the NCEP–NCAR reanalysis (m s−1; wet − dry). The composite wind vector field shown is the average of the composites from P1, P2, P3, and P4, but is simply shown on P1 for reference.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Same as in Fig. 9, but for the late rainy season. In addition, superimposed onto P1 are the composite late rainy season surface wind vectors from the NCEP–NCAR reanalysis (m s−1; wet − dry). The composite wind vector field shown is the average of the composites from P1, P2, P3, and P4, but is simply shown on P1 for reference.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Same as in Fig. 9, but for the late rainy season. In addition, superimposed onto P1 are the composite late rainy season surface wind vectors from the NCEP–NCAR reanalysis (m s−1; wet − dry). The composite wind vector field shown is the average of the composites from P1, P2, P3, and P4, but is simply shown on P1 for reference.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Composite seasonal cycles of monthly WM01 (1950–99) precipitation (cm month−1) in Central American index regions P1–P5 for climatology (dashed line), El Niño (filled boxes), and La Niña (open boxes). The calendar years before and after the mature phase of the composite ENSO event are shown. An El Niño (La Niña) event was defined as a case where the Niño-1 + Niño-2 index was in excess of positive (negative) 0.5 standard deviations. The composite evolutions of Niño-1 + Niño-2 are shown in the lower-left corner.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Composite seasonal cycles of monthly WM01 (1950–99) precipitation (cm month−1) in Central American index regions P1–P5 for climatology (dashed line), El Niño (filled boxes), and La Niña (open boxes). The calendar years before and after the mature phase of the composite ENSO event are shown. An El Niño (La Niña) event was defined as a case where the Niño-1 + Niño-2 index was in excess of positive (negative) 0.5 standard deviations. The composite evolutions of Niño-1 + Niño-2 are shown in the lower-left corner.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Composite seasonal cycles of monthly WM01 (1950–99) precipitation (cm month−1) in Central American index regions P1–P5 for climatology (dashed line), El Niño (filled boxes), and La Niña (open boxes). The calendar years before and after the mature phase of the composite ENSO event are shown. An El Niño (La Niña) event was defined as a case where the Niño-1 + Niño-2 index was in excess of positive (negative) 0.5 standard deviations. The composite evolutions of Niño-1 + Niño-2 are shown in the lower-left corner.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
(top) Time–latitude plot of SST (°C; shaded; NOAA ERSST v.2) and −ω850 [Pa s−1; contour interval 0.01 Pa s−1 beginning at 0.01 Pa s−1 (negative contours omitted); NCEP–NCAR reanalysis] for the (left) 1950–99 climatology and (right) 1997/98 period zonally averaged between 100° and 85°W. (bottom) Time–latitude plot of CMAP precipitation anomaly (cm month−1; shaded) and climatology (cm month−1; contour interval 10 cm month−1 beginning at 10 cm month−1) averaged between 100° and 85°W. White annotated labels are referred to in section 4.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
(top) Time–latitude plot of SST (°C; shaded; NOAA ERSST v.2) and −ω850 [Pa s−1; contour interval 0.01 Pa s−1 beginning at 0.01 Pa s−1 (negative contours omitted); NCEP–NCAR reanalysis] for the (left) 1950–99 climatology and (right) 1997/98 period zonally averaged between 100° and 85°W. (bottom) Time–latitude plot of CMAP precipitation anomaly (cm month−1; shaded) and climatology (cm month−1; contour interval 10 cm month−1 beginning at 10 cm month−1) averaged between 100° and 85°W. White annotated labels are referred to in section 4.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
(top) Time–latitude plot of SST (°C; shaded; NOAA ERSST v.2) and −ω850 [Pa s−1; contour interval 0.01 Pa s−1 beginning at 0.01 Pa s−1 (negative contours omitted); NCEP–NCAR reanalysis] for the (left) 1950–99 climatology and (right) 1997/98 period zonally averaged between 100° and 85°W. (bottom) Time–latitude plot of CMAP precipitation anomaly (cm month−1; shaded) and climatology (cm month−1; contour interval 10 cm month−1 beginning at 10 cm month−1) averaged between 100° and 85°W. White annotated labels are referred to in section 4.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
WM01 precipitation anomaly (cm; shades) and −ω850 anomaly (Pa s−1; contour interval 0.01 Pa s−1 beginning at 0.03 Pa s−1; NCEP–NCAR reanalysis) for October 1998.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
WM01 precipitation anomaly (cm; shades) and −ω850 anomaly (Pa s−1; contour interval 0.01 Pa s−1 beginning at 0.03 Pa s−1; NCEP–NCAR reanalysis) for October 1998.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
WM01 precipitation anomaly (cm; shades) and −ω850 anomaly (Pa s−1; contour interval 0.01 Pa s−1 beginning at 0.03 Pa s−1; NCEP–NCAR reanalysis) for October 1998.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Scatterplot of Niño-1 + Niño-2 SSTA (standardized anomalies) in December (x axis) and in the following June (y axis) for all cases during the period 1950–99 where the December Niño-1 + Niño-2 SSTA exceeded ±0.5 standard deviation.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Scatterplot of Niño-1 + Niño-2 SSTA (standardized anomalies) in December (x axis) and in the following June (y axis) for all cases during the period 1950–99 where the December Niño-1 + Niño-2 SSTA exceeded ±0.5 standard deviation.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Scatterplot of Niño-1 + Niño-2 SSTA (standardized anomalies) in December (x axis) and in the following June (y axis) for all cases during the period 1950–99 where the December Niño-1 + Niño-2 SSTA exceeded ±0.5 standard deviation.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Composite seasonal cycles of monthly WM01 (1950–99) precipitation (cm month−1) in Central American index regions P1–P5 for climatology (dashed line), persisting El Niño events (filled boxes), and antipersisting El Niño events (open boxes). Only the calendar year following the mature phase of the ENSO event is shown. The composite evolutions of Niño-1 + Niño-2 are shown in the lower-left corner.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Composite seasonal cycles of monthly WM01 (1950–99) precipitation (cm month−1) in Central American index regions P1–P5 for climatology (dashed line), persisting El Niño events (filled boxes), and antipersisting El Niño events (open boxes). Only the calendar year following the mature phase of the ENSO event is shown. The composite evolutions of Niño-1 + Niño-2 are shown in the lower-left corner.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Composite seasonal cycles of monthly WM01 (1950–99) precipitation (cm month−1) in Central American index regions P1–P5 for climatology (dashed line), persisting El Niño events (filled boxes), and antipersisting El Niño events (open boxes). Only the calendar year following the mature phase of the ENSO event is shown. The composite evolutions of Niño-1 + Niño-2 are shown in the lower-left corner.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Time series of standardized P3 precipitation anomalies averaged over the rainy season (May–November; dashed line), and standardized ΔSSTA averaged over a period of equal length beginning 4 months earlier (January–July; solid line). ΔSSTA is defined as the difference between standardized SSTA in the EPWP and Niño-1 + Niño-2 regions. The correlation is 0.35 (significant at the 98% confidence level). The same calculation using standardized P1, P2, P4, and P5 precipitation anomalies yields correlations 0.20, 0.01, 0.34, and 0.27, respectively.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Time series of standardized P3 precipitation anomalies averaged over the rainy season (May–November; dashed line), and standardized ΔSSTA averaged over a period of equal length beginning 4 months earlier (January–July; solid line). ΔSSTA is defined as the difference between standardized SSTA in the EPWP and Niño-1 + Niño-2 regions. The correlation is 0.35 (significant at the 98% confidence level). The same calculation using standardized P1, P2, P4, and P5 precipitation anomalies yields correlations 0.20, 0.01, 0.34, and 0.27, respectively.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Time series of standardized P3 precipitation anomalies averaged over the rainy season (May–November; dashed line), and standardized ΔSSTA averaged over a period of equal length beginning 4 months earlier (January–July; solid line). ΔSSTA is defined as the difference between standardized SSTA in the EPWP and Niño-1 + Niño-2 regions. The correlation is 0.35 (significant at the 98% confidence level). The same calculation using standardized P1, P2, P4, and P5 precipitation anomalies yields correlations 0.20, 0.01, 0.34, and 0.27, respectively.
Citation: Journal of Climate 22, 10; 10.1175/2008JCLI2468.1
Summary of the characteristics of equatorial Pacific climate associated with anomalously wet and dry rainy seasons in Central America and the Caribbean identified by Hastenrath (1976).
The percent of the variance of total rainy season (May–November) precipitation in each region of Central America that is explained by that of a neighboring region. If two regions are not geographical neighbors or are the same region, the cell is left blank. The far right column indicates the percent of the variance of early rainy season (May–July) precipitation in each region that is explained by that of the late rainy season (September–November) in the same region.
Throughout the present paper, “Central America” refers to geopolitical Central America (Belize, Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, and Panama) and southern Mexico (all states east of the Isthmus of Tehuantepec).
