Warm tropical oceans and ENSO flavours behind the late Holocene change in hydroclimates in northern South America

Bedoya, Juan Mauricio; Velez, Maria I.; Poveda, German

doi:https://doi.org/10.5194/egusphere-2022-1428

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https://doi.org/10.5194/egusphere-2022-1428

Preprints

09 Jan 2023

| 09 Jan 2023

Warm tropical oceans and ENSO flavours behind the late Holocene change in hydroclimates in northern South America

Juan Mauricio Bedoya, Maria I. Velez, and German Poveda

Abstract. At about 4,000 years ago the earth’s global climate underwent significant transformations resulting from changes in solar insolation. Manifestations of this change are relatively well known in higher latitudes, however, in the American tropics these are still not fully identified or understood. Recent paleo-environmental reconstructions based on paleolimnological and vegetational histories of two Colombian Andean sites suggest that between ~4,150 and 2,500 yr BP the Eastern Cordillera (EC) witness wetter anomalies, while the Western Cordillera (WC) suffered from drier anomalies between ~3,700 and 1,750 yr BP. Results from analyses of modern precipitation series from weather stations close to the study sites indicate that the long-term mean annual cycle of precipitation in both sites is out-of-phase and that precipitation anomalies on the western (eastern) site are negatively (positively) correlated with sea surface temperatures in the tropical Pacific (Tropical Atlantic). Hence that we propose that both oceans warmed up during the late Holocene, likely from a more active ENSO and ENSO flavours. With the current global rise in atmospheric temperature and the warming of tropical oceans, this study sheds light on possible anomalous effects on precipitation over the northern Andes.

Received: 09 Dec 2022 – Discussion started: 09 Jan 2023

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Juan Mauricio Bedoya, Maria I. Velez, and German Poveda

Status: final response (author comments only)

RC1:
'Comment on egusphere-2022-1428', Anonymous Referee #1, 07 Apr 2023
Title: Warm tropical oceans and ENSO flavours behind the late Holocene change in hydroclimates in northern South America
Author(s): Juan Mauricio Bedoya et al.
MS No.: egusphere-2022-1428
MS type: Research article

This manuscript aims to describe the ENSO-related climate variability during the late Holocene in both the western and eastern cordilleras of Colombia. This description is based on the present-day ENSO effects over precipitation and pollen records obtained from two sites: one located on the western branch of the Colombian Andes (Mistrató and Medellincito) and a kind of mirroring site on the eastern branch (Berlin and San Turbán). Present-day precipitation anomalies in the western (eastern) location are negatively (positively) correlated with the sea surface in Tropical Pacific (Tropical Atlantic). The authors propose that the late Holocene had an opposite warming structure in the tropical Atlantic and Pacific oceans by comparing it to today’s climate in these sites.
I have carefully reviewed the manuscript. While I appreciate the time and effort the authors put into their work and thank you for taking me into account to review this manuscript, I regret to inform you that I cannot recommend acceptance of this manuscript for publication in Climate of the Past at this time. My assessment is based on the major concerns I explain below.

Northern South America, specifically Colombia, is characterized by a large spatial variability in precipitation regimes and a very heterogeneous composition of moisture sources becoming rainfall. The interaction of local factors such as the stepped orography, regional circulation, global variability, and external forcing produce a complex spatio-temporal structure of regional rainfall variability. Despite the authors present the general context of climate variability in Colombia (Sec. 2), the line of argument related to the work done oversimplifies this complexity, reaching huge conclusions from some weak coincidences. This argumentative line is maintained throughout the manuscript as follows:
In lines 99-104, the authors imply that the conclusions derived from these couple of sites could be directly extended to all corresponding western/eastern Colombia. Why are these two places expected to be representative of western/eastern Colombia's climate variability? What does western/eastern Colombia refer to? What about altitude/latitude effects? A deeper discussion is needed.

In the same sense, Figure 1 shows the main mechanism of moisture transport but only referred to LLJs. I agree these structures are important mechanisms of moisture transport but only represent a portion of the entire atmospheric transport and these systems are seasonally active. In the framework of ENSO-related variability of Colombia rainfall, it is well known that regional circulation, length of deep convection, and the accumulated hydrological response (related to moisture recycling that accounts for more than 50% of total atmospheric moisture) are the main transport mechanisms underlying in the rainfall variability under ENSO stages.

In lines 299-326 the ms explains Figures 4 and 5 that show the correlation maps between precipitation trend from Mistratró and Berlin and global SST from two datasets. For Mistrató the correlation maps contain a lot of information, showing positive and negative correlated areas along the globe, not only TP and TNA. Even, the two datasets (HADISST and ERA5) have noticeably different spatial structures (nothing is said about it in the ms). For Berlin, the correlation maps look quite different. In both cases, the conclusion derived from maps is quite similar, the ms only refers to TP and TNA and immediately to ENSO (see lines 303-305 and 319-321). What does a globally connected precipitation mean? What does a lack of these connections mean? What about the role of regional, terrestrial, and recycling effects over precipitation?

Figure 6 introduces a new dataset (NCEP/NCAR Reanalysis I) and shows three Lagrangian trajectories at 700 mb for each site during a wet anomalous season in 2010. Is this a kind of example? How representative of an ensemble of anomalous behavior is it? Why this specific level? Why only three trajectories? Why this dataset? 6A looks to be not properly cropped at the north edge. Here again, a strong statement is derived from this very reduced picture of transport processes, see lines 333-335.

Spectral power analysis is quite interesting and shows a mix of time-scales of CP SST (Niño 3.4 index) and TA SST (TNA index) influencing precipitation in Mistrató and Berlin. However, the power spectrum intensity scale is different in each panel (Figures 8b, 8c, 9b, and 9c) and it must be unified in order to do a real comparison and avoid misleading. Also, the zero must be clearly shown. The time units must be also specified in these figures. In the text, the time scale signal of El Niño 3.4 influencing Mistrató is explained in months (lines 352-360), for Berlin is in years (lines 360-364). The TNA time scales influencing both Mistrató and Berlin are explained in years (lines 364-373). These couple of indices describe the interannual variability of tropical Pacific and tropical North Atlantic SST. Is there a mistake in the power time scale description? Despite the great variability and information displayed in power spectrum analysis (Figure 8 and 9), the take-home message is summarized in the text as follows: “At interdecadal timescales, an increase in SST in the Central Pacific is associated with negative rainfall anomalies at Mistrató and positive rainfall anomalies at Berlin / Positive anomalies in the TNA index are associated with negative rainfall anomalies at Mistrató and positive anomalies at Berlin”. In my opinion, this analysis deserves a deeper exploration including some dynamical explanations, for instance, what are the dynamical mechanisms underlying in the strengthening/weakening of rainfall in the sampling points due to the heating/cooling of SST in tropical Pacific and Atlantic oceans?

The first paragraph of the Discussion says precisely contrary to the ms presented in spectral analysis (lines 356 to 373). Please review, there may be a mistake in the use of parentheses for text simplification in this section or there is a mistake in the interpretation of power spectrum analysis. It could be useful to do a cross-spectra analysis between the El Niño 3.4 index and the TNA index.

The discussion (and conclusion) is based on the idea that today's climate in western/eastern Colombia is comprehensively explained by the SST anomalies in the tropical Pacific and Atlantic oceans and so, the late Holocene Colombia’s climate, opposite to today's configuration (wet in the high elevations of the EC and dry in the WC) was caused by the increase in SST in the TNA and TP. I see several problems with this proposal. Just to illustrate: First, today's climate variability in Colombia is more complex than the SST anomalies in the tropical Pacific and Atlantic oceans explain. Second, today's time scale variability explored in the ms is in the range of interannual variability and the paleo records expand several orders of magnitude. How are the different time scales (dis)aggregated? The authors do not provide any dynamical explanation suitable to integrate the climate dynamics in today's-interannual variability.

Finally, these issues could be addressed through further revisions and additional work for a new submission. I encourage you to consider my feedback and revise the manuscript accordingly.
Citation: https://doi.org/10.5194/egusphere-2022-1428-RC1
- AC1: 'Reply on RC1', Maria Velez, 21 Apr 2023
  
  We want to thank the Reviewer for very insinghful comments. We are currently addressing these in detail.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1428-AC1
- AC2:
  'Reply on RC1', Maria Velez, 12 Sep 2023
  Dear Reviewer # 1,
  Thank you very much for your time and effort to provide detailed comments on our manuscript. We have systematically addressed the following general comments (GC): GC1 the representativeness of the study sites, GC2 mechanisms of moisture transport, GC3 interpretation of SST's correlation maps, GC4 the Lagrangian-trajectories, GC5 power spectrum analysis, GC6 between the first paragraph of the Discussion and the spectral analysis, and GC7 the complexity of today's climate variability and the need for a comprehensive understanding of climate dynamics.
  Addressing GC7 has been challenging as it raises questions about the generalizability of our conclusions and suggests that our analysis may not fully explain today's climate variability in western/eastern Colombia. To address GC7, we include the CHIRPS dataset with more robust precipitation data and thus enhance our analyses. Additionally, we utilized Rotated Empirical Orthogonal Function (REOF) method to examine the coherence of precipitation patterns and their connections to large-scale climate drivers. While focusing on interannual variability, we recognize the presence of other timescales influencing climate dynamics. Our findings contribute valuable insights into precipitation variability, particularly in the context of ENSO and orographic effects. Despite the limitations of space, we offer a focused discussion on these relationships.
  In the supplementary file you will find all the new and revised figures.
  In the following lines we address in detail the GCs:
  GC1 (concerning the representativeness of the study sites): “In lines 99-104, the authors imply that the conclusions derived from these couple of sites could be directly extended to all corresponding western/eastern Colombia. Why are these two places expected to be representative of western/eastern Colombia's climate variability? What does western/eastern Colombia refer to? What about altitude/latitude effects? A deeper discussion is needed.”
  Response to GC1.Q1/. Why are these two places expected to be representative of western/eastern Colombia's climate variability? Thank you for this comment. We realized that we needed to add further clarification about the representativeness of the selected sites for the overall climate of Colombia. These two places are representative of the western/eastern Andean Cordilleras climate, given that overall seasonality in precipitation in both sites as well as for the entire country, is governed by the annual migration of the ITCZ (please see Figure 2 where we show that seasonality precipitation of both sites is governed by the dynamics of the ITZC). And in addition, we must consider other regional mechanisms that control annual variability of precipitation: in Medellincito (Mistrato) the effect of the Choco and the Caribbean low-level jets (LLJ), combined with the orographic lifting provided by the western slope of the western range of the Colombian Andes lead to the formation of Mesoscale Convective Systems that explain the existence of one of the rainiest regions on Earth that form a distinctive biogeographic region (the Choco rainforest), an entire region in the Pacific of Colombia. On the other hand, the Santurbán-Berlin site is under the influence of Trade winds, and the cross-equatorial southeasterly winds from the Amazon River Basin to the eastern Andes, and the Orinoco LLJ. This is, both Medellincito (Mistrato) and Santurban-Berlin (Santurban) are affected by the dominant systems that affect the Western and Eastern Andean Cordilleras. We would revise the Introduction and the section on the modern climate of Colombia to make sure that the representativeness of these two sites in the regional climate of WC and EC is clear throughout.
  
  GC1.Q2: What does western/eastern Colombia refer to? We would like to clarify that WC and EC refer to Western and Eastern Cordillera respectively not Eastern and Western Colombia. We would revise the paper to consistently rename Western Cordillera (WC) and Eastern Cordillera (EC) instead of western and eastern Colombia.
  We are confident that these two sites do represent the overall conditions of climates in the WC and EC. We would make sure that the representativeness of these two sites given the explanation above is clearer in the paper.
  Q2: What about altitude/latitude effects?
  
  Response: We agree that it is important to consider the influence of altitude and latitude on climate variability in Colombia. While our study focuses on the selected sites, we acknowledge that altitude and latitude effects can significantly influence local climate patterns. To address this, we made further analysis of regional climate variability in Colombia to include latitude and altitude and explored potential implications for broader regional climate dynamics. This was made using CHIRPS precipitation data and Rotated Empirical orthogonal Functions (REOF). These results provided a clearer picture of the regional climate variability of the study sites, and shed light to respond to GC7. This new analysis, that we would include, shows the connection of different latitude and altitude in the Colombian Andes () through spatiotemporal modes that are also connected with Mistrato-Medellincito (WC) and Santurban-Berlín (EC). Please note, this analysis also confirms the representativeness of these two sites of the regional climates. This analysis clearly shows how Mistrato-Medellincito belongs to the western pole and Santurban-Berlín to eastern pole, with a more mixed signal. Please see Figure 7 below which would be added to the manuscript.
  We would included some statements of the SVD/REOF using CHIRPS in the methods section:
  Finally, we have extracted time series of CHIRPS corresponding to the pixels of the two paleo “sites” Santurbán-Berlín and Medellincito and included them in the regional analysis in order to evaluate the representativeness of both sites.
  And in the results section (lines 368-370):
  “Also, for REOFs 2 and 3, Medellincito falls within one of these poles, while Santurbán-Berlín lies on the boundary between dipoles. This explained the mixed influence of different modes of variability on these sites.”
  GC2 (about including further mechanisms of moisture transport): “In the same sense, Figure 1 shows the main mechanism of moisture transport but only referred to LLJs. I agree these structures are important mechanisms of moisture transport but only represent a portion of the entire atmospheric transport and these systems are seasonally active. In the framework of ENSO-related variability of Colombia rainfall, it is well known that regional circulation, length of deep convection, and the accumulated hydrological response (related to moisture recycling that accounts for more than 50% of total atmospheric moisture) are the main transport mechanisms underlying in the rainfall variability under ENSO stages.”
  Response to GC2/. Thank you for your valuable comment and suggestion. We acknowledge that Figure 1 primarily focuses on the role of low-level jets (LLJs) as a significant mechanism of moisture transport in the Colombian Andes. However, we agree that LLJs represent only a portion of the entire atmospheric transport system, and their activity is seasonally influenced. In this sense, we would add a sentence (lines 157-160)
  Importantly, the main transport mechanisms underlying rainfall variability during different ENSO stages are well-known and include regional circulation, duration of deep convection, and accumulated hydrological response related to moisture recycling ( Cai et al., 2020; Arias et al., 2021; Escobar et al., 2022). In Colombia, previous studies have shown the importance of evapotranspiration fluxes on rainfall dynamics (Bedoya et al., 2019).
  GC3 (regarding the interpretation of correlation maps): “In lines 299-326 the ms explains Figures 4 and 5 that show the correlation maps between precipitation trend from Mistratró and Berlin and global SST from two datasets. For Mistrató the correlation maps contain a lot of information, showing positive and negative correlated areas along the globe, not only TP and TNA. Even, the two datasets (HADISST and ERA5) have noticeably different spatial structures (nothing is said about it in the ms). For Berlin, the correlation maps look quite different. In both cases, the conclusion derived from maps is quite similar, the ms only refers to TP and TNA and immediately to ENSO (see lines 303-305 and 319-321). What does a globally connected precipitation mean? What does a lack of these connections mean? What about the role of regional, terrestrial, and recycling effects over precipitation?”
  Response to GC3/. Thank you for bringing this up. We appreciate your observation and we have carefully revised and explored the correlation maps to provide a more thorough explanation of the results. The reviewer # 2 made a similar comment regarding the Interdecadal Pacific Oscillation (IPO) with the Mistrato site. We have made further analysis including correlations with the anomalies of SSTs (in addition to the trend signal through STL), to decompose the signal and to take into account both interdecadal and interannual variability and have a more robust insights into precipitation of our study sites. Results from this new analysis show a less intense and significative correlations but still the same overall connections prevailed.
  GC3.Q1: What does a globally connected precipitation mean? R/. Globally connected precipitation refers to the spatial and temporal coherence of precipitation patterns across different regions around the world. Precipitation in one region is globally connected when it significantly correlates with other distant regions (see Figure 4 of the ms). Such precipitation coherence on a global scale is mainly due to teleconnections and atmospheric rivers as climate drivers. A positive correlation between precipitation in Mistrató and SSTs across the globe indicates that changes in atmospheric conditions in those distant regions can influence precipitation patterns in Mistrató.
  Q2: What does a lack of these connections mean?
  
  Response: A lack of globally connected precipitation means that there is little correlation between precipitation patterns in different regions across the globe. In other words, changes in precipitation in one region have minimal influence on precipitation patterns in other distant regions, and vice versa. Lack of large-scale spatial connections suggests that local/regional factors play a more dominant role in determining precipitation variability. These factors can include local topography, land surface characteristics, atmospheric circulation patterns specific to the region, or localized weather systems. Precipitation patterns become more localized and driven by regional dynamics rather than being influenced by large-scale atmospheric teleconnections.
  Q3: What about the role of regional, terrestrial, and recycling effects over precipitation?
  
  Response: Indeed, after your comment we realize we need to expand further the role of the land-atmosphere interactions mediating between TNA and ENSO SSTs anomalies, we have explored the following papers (Poveda and Mesa, 1997; Builes et al., 2017, 2018; Casselman et al., 2021 among others) and we will include a section addressing this issue in the modern climate of Colombia and discussion sections.
  GC4 (raising questions about the Lagrangian-trajectories): “Figure 6 introduces a new dataset (NCEP/NCAR Reanalysis I) and shows three Lagrangian trajectories at 700 mb for each site during a wet anomalous season in 2010. Is this a kind of example? How representative of an ensemble of anomalous behavior is it? Why this specific level? Why only three trajectories? Why this dataset? 6A looks to be not properly cropped at the north edge. Here again, a strong statement is derived from this very reduced picture of transport processes, see lines 333-335.”
  Response: We appreciate the reviewer's suggestion to explore the ensemble of anomalous behavior and the choices made regarding the used dataset, level, and number of Lagrangian trajectories in Figure 6. It aims at providing a visual representation of the Lagrangian trajectories as it offers valuable insights into moisture origins during a significant La Niña-event (Arias et al., 2015; Bedoya et al., 2019; Cai et al., 2020). Of course, an in-depth analysis of the long-term mean moisture trajectories to the study regions far exceeds the scope of the present study.
  We have appropriately cited the NCAR/NCEP dataset in Table 2. Additionally, to address the reviewer's concern, we expanded our analyses to include pressure levels of 600, 700, 850, and 925 hPa. Moreover, we have considered crop issues in the northern region when remaking Figure 6 for the Berlin-Santurban site. This revised figure now combines subfigures A and B from the original submitted version into one figure.
  Incorporating these changes, we would change the text to reflect this in Results section, for example:
  “Results of the backward-Lagrangian tracking analysis of air parcels for the study sites during a humid period of the annual cycle enhanced by La Niña (October 2010) at four different pressure levels of the middle and low atmosphere (600, 700, 850, 925 hPa), are presented in Fig. 6. This analysis highlights the different origins of moist parcels and the underlying physical mechanisms governing rainfall between the sites. Parcels ending in Berlín originated in the TNA region and exhibited predominantly east-west trajectories, crossing the northernmost part of continental South America before entering Colombia through the northeast (Fig. 6). On the other hand, air parcels ending in Mistrató, originated in southeastern Pacific, and travelled from south to north and east to west, just before entering Colombia through the west (Fig. 6).
  Lagrangian trajectories presented here reflect atmospheric transport during one of the most notable La Niña-events (October 25, 2010), which was know for highly impacting Colombian’s climate (Arias et al., 2015; Bedoya et al., 2019; Cai et al., 2020) and for causing one of the wettest peaks of the annual cycle (Figure 2). While these trajectories offer valuable information about the origin of humid parcels, they solely capture a sample of the entire range of atmospheric transport pathways of humidity. The selection of the NCEP/NCAR RI dataset, the pressure levels, and the two trajectories, were based on considerations of data availability, the relevance of this level (700 mb) for capturing key atmospheric dynamics in the context of the heights of the Andes, and practical limitations in visual representation.
  Although the two sites are geographically close and interconnected by the influence of the three ranges of the Andes, it is also important to note that the origin of humid parcels and the physical mechanisms governing rainfall differ between the sites at seasonal and interannual timescales. The Lagrangian trajectories provide insights into the prevailing transport patterns during that specific humid period under La Niña conditions, but the complete picture of moisture transport and associated rainfall mechanisms involves additional factors such as regional circulation, altitude effects, and latitude variations (Poveda et al., 2014). “
  
  Figure 6. Backward Lagrangian air parcel-tracking at different pressure levels during La Niña of October 2010 (an anomalous wet year), showing the different moisture sources at Berlín and Mistrató. Trajectories provided by the NOAA Physical Sciences Laboratory, Boulder Colorado from their web site at https://psl.noaa.gov/
  
  GC5 (raising questions about the power spectrum analysis): “Spectral power analysis is quite interesting and shows a mix of time-scales of CP SST (Niño 3.4 index) and TA SST (TNA index) influencing precipitation in Mistrató and Berlin. However, the power spectrum intensity scale is different in each panel (Figures 8b, 8c, 9b, and 9c) and it must be unified in order to do a real comparison and avoid misleading. Also, the zero must be clearly shown. The time units must be also specified in these figures. In the text, the time scale signal of El Niño 3.4 influencing Mistrató is explained in months (lines 352-360), for Berlin is in years (lines 360-364). The TNA time scales influencing both Mistrató and Berlin are explained in years (lines 364-373). These couple of indices describe the interannual variability of tropical Pacific and tropical North Atlantic SST. Is there a mistake in the power time scale description? Despite the great variability and information displayed in power spectrum analysis (Figure 8 and 9), the take-home message is summarized in the text as follows: “At interdecadal timescales, an increase in SST in the Central Pacific is associated with negative rainfall anomalies at Mistrató and positive rainfall anomalies at Berlin / Positive anomalies in the TNA index are associated with negative rainfall anomalies at Mistrató and positive anomalies at Berlin”. In my opinion, this analysis deserves a deeper exploration including some dynamical explanations, for instance, what are the dynamical mechanisms underlying in the strengthening/weakening of rainfall in the sampling points due to the heating/cooling of SST in tropical Pacific and Atlantic oceans?”
  Response: Thank you very much for these comments. Wavelet spectral analyses were revised and parameters were fine-tuned. Although the time series of rainfall and macro-climatic indices (Niño 3.4 and TNA) are available at monthly resolutions, periodicities in the new spectral figures are presented in years (and their fractions) to avoid confusions in the interpretation of results. Color scales have been exactly unified between Figures 3a and 3b (wavelet transforms of raw series), between Figures 7a and 7b (wavelet transforms of standardized series), and closely between Figures 8b and 8c (cross power spectra between the Niño 3.4 index and both rainfall series), and less exactly between Figures 9b and 9c (cross power spectra between the TNA index and both rainfall series). So, we would adjust and revise all figures and sections related to spectral analyses and their interpretation, for example:
  Results of the wavelet analyses at interannual timescales are shown in Figure 7, using the standardized time series of rainfall at both study sites. As expected, the wavelet spectra at both sites (Figs. 7 and 7b) no longer exhibit significant, strong and well-defined peaks at semi-annual and annual timescales, but a broader signal at interannual and interdecadal timescales. The interdecadal signal is stronger than the interannual one at both sites. The cross-power spectrum (not shown) among both standardized rainfall series shows a rather weak coherence at interannual timescales (~ 3 years), but localized in time between 2008-2011, very likely in response of two strong La Niña events. Given that ENSO is the most important mechanism driving the hydroclimatology of these two Colombian regions, it is relevant to quantify the wavelet power spectrum of the Niño 3.4 index (Fig. 8a), one of the main indices of ENSO, and the cross-power spectra with the standardized rainfall series at Mistrató and Berlín (Figs. 8 b,c). As expected, the wavelet spectrum of the Niño 3.4 monthly series exhibits a broad-band global wavelet spectrum centered around 3-4 years, and some decadal timescales (~10-12 years) Interestingly, the global wavelet spectrum shows a sharp decrease around 7 years. The cross-spectra between El Niño 3.4 index and standardized precipitation at Mistrató shows an out-of-phase behavior at between 1995-1997 and 2008-2011 at ~3 years, at 5-6 years (1985-2005), and at longer timescales during the whole study period, albeit most within the insignificant cone of influence, which implies that an increase in SSTs in the Central Pacific is associated with negative rainfall anomalies at Mistrató at interannual timescales. Similar results albeit weaker are seen in the cross spectra between El Niño 3.4 index and the standardized series at Berlín (Fig. 8c), although some coherent in-phase behavior appear at interannual timescales (6-7 years), suggesting that positive interannual SST anomalies in the Central Pacific is associated with positive rainfall anomalies at Berlín.
  The TNA exhibits a strong interdecadal signal around 7-9 years (Fig. 9a), and an almost constant out- of-phase association with monthly standardized rainfall at Mistrató (Fig. 9b), mainly at interannual timescales (5-6 years), although a rather weak in-phase behavior at interdecadal scales (12-32 years, within the non-significant cone of influence), indicating that positive anomalies in the TNA index are associated with negative rainfall anomalies at Mistrató at interannual timescales. On the other hand, the cross-spectra between the TNA and standardized rainfall at Berlín (Fig. 9c) exhibit a coherent in-phase association at interannual timescale (7-9 years), which also confirms that positive anomalies in the TNA are associated with positive anomalies at Berlín at interannual (longer than ENSO) timescales.
  
  Q1: Is there a mistake in the power time scale description?
  
  Response: Yes, there was a mistake. We now have revised Figure 3 and included below and subsequently we would replace with the following text in the respective section: Results of the wavelet spectrum of monthly rainfall series at Mistrató and Berlín (Fig. 3 a-b) indicate a much sharper and more predominant semi-annual cycle at Mistrató (WC) and a weaker signal at the annual timescale, while in Berlín (EC), the annual cycle exhibits a stronger and broader signal than the semi-annual one. The cross-power spectra among both time series (Fig. 3c) shows an almost non-existent coherence among both rain gauges at semi-annual and annual timescales during the study period. These results indicate that, in spite of the bimodal character of the annual cycle of precipitation at both sites they respond to different processes and mechanisms, beyond the meridional oscillation of the ITCZ over northern South America at annual and semi-annual timescales.
  
  Figures 7-9 we redone as well, please supplementary material. The following figures are also provided in the Supplementary material atatched to this response:
  New Fig 7 Wavelets Mistrato-Berlín Stds_Fig7.png
  New Figure 8: nino34_crossps_mistratostd_Berlínsstd_Fig8.png
  New Figure 9: tna_crossps_mistratostd_Berlínstd.png
  Figure Cross Power Spectra Niño 3.4 – TNA:
  Cros_Power_Nino34_TNA.png
  
  Q2: What are the dynamical mechanisms underlying in the strengthening/weakening of rainfall in the sampling points due to the heating/cooling of SST in tropical Pacific and Atlantic oceans? R/.
  
  The dynamical mechanisms underlying the observed rainfall anomalies in the study sites due to SST anomalies in the surrounding oceans are extremely complex. In the following lines we explain in detail main mechanisms and we would be very happy to add some of this discussion to the manuscript both in Modern hydroclimates and in the Discussion sections.
  In Colombia, the ENSO signal propagates as a westerly wave, with stronger and earlier impacts over the WC (Medellincito) and weaker, delayed effects over the EC (Berlin). During El Niño events, critical mechanisms leading to reduced rainfall include the weakening of the Choco LLJ due to diminished SST gradients between Colombia and the cold Peruvian coast, the southwestward shift of the ITCZ's convection center, and fewer tropical easterly waves over the tropical North Atlantic (TNA) and the Caribbean Sea. Conversely, during La Niña, which increases rainfall, low SSTs in the Niño 3.4 region redirect moisture to Colombia from the Pacific, Caribbean Sea, and Atlantic. La Niña in Colombia is associated with more active moisture sources, including the Pacific (via a strongerChoco LLJ) and the Caribbean Sea (via a weakened Caribbean LLJ).
  More so given the two-way feedback mechanisms which have been identified between the ENSO in the tropical Pacific and the TNA, e.g. the TNA experiences nonlinear positive SST anomalies lagging the El Niño SST peak (December-January-February, DJF) by several months and being stronger during the March-April-May (MAM), seemingly phase-locked with the seasonal cycle (we can provide the references if desired). Diverse mechanisms contribute to explain such TNA anomalous warming, including wind–evaporation–SST feedbacks between 10N and the equator (Amaya et al. 2017; Xie and Philander 1994 ), which in turn inhibits the southward migration of the ITCZ during MAM (Caselmann et al., 2021).
  Ever since the study of Lau and Nath (1994) the existence of an “atmospheric bridge” has been proposed to links both oceanic regions, via anomalies in heat fluxes and changes in large-scale atmospheric circulation patterns in the tropics, the extratropics or a combination of tropical and extratropical pathways (we can provide the references if desired). A complementary explanation of a “land-atmosphere” bridge was put forward by Poveda and Mesa (1997) and further evidences of the existence of such land-atmosphere bridge have been provided by Ramos et al. (2017), Builes-Jaramillo et al. (2018) among others, which are consistent with the mechanisms outlined in Casselman et al. (2021) (see their figure 12) regarding a secondary Gill-type mechanism and precipitation over the Amazon river basin, and the existence of pressure gradients between the TNA and Amazonia. Also, diverse studies have shown that SST anomalies in the TNA impact on the tropical Pacific and ENSO dynamics (see for example Sasaki et al., 2014; Wang et al., 2017; Li et al., 2018; Jia et al., 2019; Ding et al., 2023; Zhao et al. 2023 among others). To add complexity, two mechanisms can cause the SST anomaly on the TNA: 1) Modified trade winds in the boreal winter (January to March, JFM), inducing a wind-evaporation-SST feedback, and 2) Adjustment of atmospheric stability, due to the propagation of a temperature anomaly by a Kelvin wave. And, on longer timescales, North Atlantic Oscillation (NAO) also mediates ENSO-TNA connections (see Cassou and Terray, 2001; Lee et al., 2008 to mention a few) and the Atlantic Multidecadal Oscillation (AMO) (Park and Li, 2019 ; Zhang et al., 2019 ; Rodriguex-Fonseca et al., 2022).
  Sea surface temperature anomalies in the TNA and rainfall in northern South America and Amazonia can be mediated by shifts in the ITCZ, increasing rainfall in MAM over northern South America (SA) including the Amazonia and the Caribbean Sea (see results of the SVD analysis between TNA SST and precipitation over northern SA in MAM shown below), and decreasing it over southern Amazonia and north-east Brazil (see Figure 8 of Jiménez et al., 2021). Regarding the former, the presence of anomalous atmospheric ascendance over the region and the nearby Atlantic contributes to explaining enhanced rainfall and discharge over northern SA. Precipitation over the Caribbean Sea can be influenced by the strength of the Caribbean low-level jet (CLLJ) (Poveda and Mesa, 1999; Muñoz et al. 2008; Casselman et al., 2021 among others). With regards to the latter region, low-level atmospheric circulation anomalies in the Amazon and north-east Brazil include weakened northeast trade winds into tropical South America, and atmospheric subsidence anomalies (Jiménez et al., 2021). In particular, the warming of the TNA SST during MAM (Figure S1 below) can increase rainfall in the Berlin-Santurbán site (Figure S2 below), by increasing the strength of the winds of the Orinoco low-level jet (Jimenez et al, 20##; Builes-Jaramillo et al., 202#), as clearly shown in Figure S4. The increase in precipitation can also be explained by an augmented vertically integrated water vapour flux between the ground and 300 hPa; and low-level winds over the region (see Figure 9 of Labat et al., 2012), and by the reinforcement of the Walker and Hadley cells between the Amazon basin and the eastern Pacific, and over the TNA.
  Another mechanism that might contribute to explain an increase in rainfall during the warming of the TNA is the increase of the Atlantic hurricane activity by influencing the Atlantic and Caribbean warm pool (Yao et al., 2020 ; Burn, 2021 ; Casselman et al., 2021), or by the greening of the Sahara and the consequent reduction of dust loadings, as reported during the mid-Holocene (Dandoy et al., 2021 ).
  Revised figures below are presented in the Supplementary material
  SVD analysis of TNA SSTs and Precipitation over northern South America during MAM (1980-2022). The first vector explains 47.06% of the variance between both fields.
  Left Singular Vector No. 1 (SSTs over TNA region)
  Figure S1. Left singular vector shown TNA SST during MAM
  Figure S2. Right Singular Vector No. 1 (Precipitation over northern South America)
  SVD Analysis between SSTs TNA vs Meridional surface Winds (10 m) during MAM (1980-2022). The first vector explains 54.3% of the variance between both fields.
  Left Singular Vector No. 1 (SSTs over TNA region)
  Figure S3. Left Singular Vector No. 1 (Meridional winds at 10 m)
  Figure S4. Right Singular Vector No. 1 (Meridional winds at 10 m)
  
  GC6 (regarding the consistency between the first paragraph of the Discussion and the spectral analysis): “The first paragraph of the Discussion says precisely contrary to the ms presented in spectral analysis (lines 356 to 373). Please review, there may be a mistake in the use of parentheses for text simplification in this section or there is a mistake in the interpretation of power spectrum analysis. It could be useful to do a cross-spectra analysis between the El Niño 3.4 index and the TNA index.”
  Response to GC6/. We thank the reviewer for pointing out the potential contradiction in the first paragraph of the Discussion. With the new revised figures and results, we would modify the manuscript to align it with the new results of power spectral analyses. The updated version now accurately reflects the association between ENSO frequency and the observed precipitation changes. We clarify this contradiction on the first paragraph of the Discussion:
  
  “Results from our analyses indicate that the SSTs and TCW in the TNA and TP exert significant effects on today’s precipitation in our study sites (Figs. 4 and 5). In Medellincito, decreases in precipitation corresponds to warming of both the TP and TNA, whereas Santurbán-Berlín experiences increased precipitation with warming in these regions. These findings are based on the in-situ measured precipitation data at both sites. Additionally, our results also confirm that monthly precipitation at both sites are out-of-phase, and that the main sources of moisture are the TNA for Santurbán-Berlin and the TP for Medellincito. Based on this, we propose that the increase in SSTs in the TNA and TP during the late Holocene contributed to the observed changes in precipitation patterns in Colombia. This supports the idea that interannual variability of moisture sources from the TP and TNA play a crucial role in modulating the moisture input to the Intertropical Convergence Zone (ITCZ) and subsequently influencing precipitation over the Colombian Andes (Fig. 7a-c). Therefore, our study underscores the complex interplay of climate drivers and mechanisms in shaping the precipitation patterns in the region.”
  
  GC7 (highlighting the complexity of today's climate variability and the need for a comprehensive understanding of climate dynamics): “The discussion (and conclusion) is based on the idea that today's climate in western/eastern Colombia is comprehensively explained by the SST anomalies in the tropical Pacific and Atlantic oceans and so, the late Holocene Colombia’s climate, opposite to today's configuration (wet in the high elevations of the EC and dry in the WC) was caused by the increase in SST in the TNA and TP. I see several problems with this proposal. Just to illustrate: First, today's climate variability in Colombia is more complex than the SST anomalies in the tropical Pacific and Atlantic oceans explain. Second, today's time scale variability explored in the ms is in the range of interannual variability and the paleo records expand several orders of magnitude. How are the different time scales (dis)aggregated? The authors do not provide any dynamical explanation suitable to integrate the climate dynamics in today's-interannual variability. GC7.Q1: First, today's climate variability in Colombia is more complex than the SST anomalies in the tropical Pacific and Atlantic oceans explain. Second, today's time scale variability explored in the ms is in the range of interannual variability and the paleo records expand several orders of magnitude. How are the different time scales (dis)aggregated?"
  
  Response to GC7/. Thank you for your observation. We had hoped that the complexity of today's climate variability and dynamics was presented in the original version of the submitted manuscript. In the subsection 'Modern hydroclimates of Colombia', we described the climatic controls and interactions that shape the present-day climate in the Colombian Andes discussing the major sources of atmospheric moisture, including the Caribbean Sea, TNA, Eastern Pacific, and the Amazon region. Additionally, we highlighted the role of the Colombian Andes, which divide the country into distinct hydroclimatic regions. Furthermore, we delved into the mechanisms that contribute to the temporal variability of climate and rainfall in the Colombian Andes. We emphasize the annual variability driven by the meridional oscillation of the Intertropical Convergence Zone (ITCZ) and its interactions with low-level jets (LLJs) and atmospheric rivers. We also explored the influence of climate drivers such as El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO), along with their teleconnections, on the climate system. We also discussed the interactions between LLJs, tropical easterlies, and the orography of the Colombian Andes play a crucial role in shaping local precipitation patterns. We have highlighted the importance of moisture blocking, synoptic disturbances, and the interplay between LLJs and the Andes in influencing rainfall distribution. Furthermore, we discuss the land-atmosphere interactions and the role of moisture recycling through evapotranspiration-precipitation fluxes and horizontal advection, with a specific focus on the significant contribution of the Magdalena River valley. We believe that our comprehensive presentation of Colombia’s climate dynamics, as outlined in Section 2, illustrates the complexity of today's climate variability and helps to provide a solid foundation for interpreting the results.
  Additionally, in response to General Comment 7, we have made comprehensive revisions to integrate climate dynamics in today's interannual variability in our study regions. To address this, we expanded our analysis to include additional factors and mechanisms influencing precipitation patterns. The newly introduced CHIRPS dataset provided high-resolution precipitation data (reported in the new version of table 2), enriching our assessment of local variability. The application of the Singular Value Decomposition/Rotated Empirical Orthogonal Functions (SVD/REOFs) method allowed us to identify dominant modes of variability and their spatial patterns, providing insights into driving mechanisms behind interannual variations. By integrating these methods and datasets, we have developed a dynamical explanation of climate dynamics in our study regions, significantly enhancing our understanding of interannual precipitation variations. These findings contribute to the scientific rigor of our paper and have implications for climate assessments.
  To incorporate the new dataset, techniques, and results, we would modify the data and methods section, for example:
  “Additional to the above, we have incorporated the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which is a high-resolution gridded dataset derived from a blend of satellite and station observations (Funk et al., 2015). This dataset offers improved spatial precision for the study regions (5 km x 5 km size pixel), covering the area between the Equator to 12N, and 78W to 70W. CHIRPS allows us to enhance the regional analysis of precipitation variability exploring the spatial-temporal patterns of precipitation through Singular Value Decomposition (SVD) and Rotated Empirical Orthogonal Functions (REOF) (Dommenget & Latif, 2002; Hannachi et al., 2007). SVD decompose the data of precipitation in the study regions into spatial patterns (U-matrix), singular values (S-matrix), and temporal variability (V-matrix), enabling a comprehensive understanding of its underlying structure. The REOF analysis further improve interpretability of the SVD patterns using varimax optimization (Hannachi et al., 2007). REOF provide insights into the spatial coherence of precipitation to assess the relationships between precipitation patterns and climate indices (Table 2). Finally, we have extracted time series of CHIRPS corresponding to the pixels of the two paleo “sites” Santurbán-Berlín and Medellincito and include them in the regional analysis in order to evaluate the representativeness of both sites.”
  And in the Results section:
  The interannual hydroclimate variability and its association with ENSO diversity in the Colombian Andes (0N-12N/78W-70W) highlights the significant influence of the orography on rainfall (Figure 7). Results of the REOF using CHIRPS reveal the spatial patterns and time series associated with the first three REOF-modes (Figs. 7a-d). In those spatial patterns, the elevation contour of 1000 m asl was indicated by a black line to distinguish the EC and WC regions where Santurbán-Berlin and Medellincito are located. These three modes, derived from the first k=3 principal SVD-modes, accounted for 48.5% of the square covariance fraction (SCF) of precipitation. Mainly shaped by the orography of the Colombian Andes, we identify distinct REOF patterns that exhibit strong dipole structures. By showing the coincidence of the 1000 m asl elevation in the eastern and western flank of the EC and WC illustrates that REOF-patterns are effectively shaped by the orography. This is explained by the pronounced modifications in moisture flow induced by the Cordilleras which significantly impact regional rainfall, as documented in previous studies (Lopez and Howell, 1957; Bedoya et al., 2019; Espinoza et al., 2020; Arias et al., 2021). Particularly, these patterns reveal southwest-northeast (SCF=18.5%, REOF-1), northwest-southeast (SCF=15.3%, REOF-2), and west-east dipoles (SCF=14.8%, REOF-3) (Figs. 7a,b,c). Also, for REOFs 2 and 3, Medellincito is located within one of these dipole poles, while Santurbán-Berlín lies on the boundary between the dipoles. This explained the mixed influence of different modes of variability on these sites.
  Additionally, regressions were conducted between the REOF-series and the standardized precipitation series obtained from the corresponding pixel locations of the Santurbán-Berlín and Medellincito sites (Figs. 7). The REOF-patterns are significantly correlated with the precipitation in pixels associated with Medellincito and Santurbán-Berlín (Figs. 7f-h). Specifically, Santurbán-Berlín displayed best correlation with the first REOF-pattern (r=0.64, p1%), while Medellincito exhibited a better connection with the third REOF-pattern (r=0.78, p<1%). The analysis also included a cross-correlation analysis between the REOF-series and indices of ENSO diversity, such as E and C, (Takahashi et al., 2011; Sulca et al., 2018; Cai et al., 2020) indicating strong teleconnections and coherent spatial patterns (Figs. 7i-k). These results suggest a clear influence of ENSO on the interannual variability of precipitation in the study region. The interannual variability of rainfall in this region is more intensely related with the EP than to CP (Fig. 7j,k) and the cross-correlation of first and third REOF shows also anti-phasing behaviour between them (see fig. 7).
  The Discussion section:
  Our comprehensive analysis utilizing REOF, and CHIRPS data has similarly yielded valuable insights into the interannual hydroclimate variability focused on ENSO in the Colombian Andes. The REOF patterns data revealed distinct dipole structures influenced significantly by the orography of this Andean region. The correlation between the REOF-series and precipitation patterns at our study sites, Santurbán-Berlín and Medellincito, further emphasized the regional influence of orography on rainfall distribution. Notably, we identified the dominant role of the EC and WC in shaping the moisture flow impacting regional rainfall. The pronounced modifications induced by these Cordilleras underscore their crucial contribution to the complexity of climate drivers in the area. Additionally, the significant relationships between the REOF-series and ENSO diversity indexes provide evidence of ENSO's influence on the interannual variability of precipitation in the region (Figure 7). Our findings highlight the importance of considering the interplay of climate drivers to understand the intricate hydroclimate dynamics in the Colombian Andes.
  And, finally, we would include a new paragraph in the conclusion section:
  The occurrence of out-of-phase precipitation anomalies in the WC and EC of the Colombian Andes during the late Holocene can potentially be attributed to the warming of the TP and TNA, possibly driven by an increase in ENSO and ENSO flavors events. Overall, this analysis enhances our understanding of the complex relationships between ENSO diversity, regional climate patterns, and the influence of the Cordilleras on hydroclimate dynamics in the Colombian Andes. These findings provide valuable insights into the differing responses of these locations to the diversity of ENSO events.
  This is also the new proposed Figure 7 in the supplementary material
  Response to GC7 (regarding paleo vs modern scales): “Second, today's time scale variability explored in the ms is in the range of interannual variability and the paleo records expand several orders of magnitude. How are the different time scales (dis)aggregated?" R/ the paleo sciences assume that what is deposited in the layers is an average of the phenomena occurring in centennial, millennial, etc., timescales. For this case, the paleo sciences would assume that the ENSO phenomena occurred more frequently and or more intensely in the studied period (Late Holocene). We are not claiming that our archives contain an interannual resolution, rather centennial and millennial. This is that over millennia, ENSO was more active or intense. This concurs with other authors’ findings.
  We sincerely thank Reviewer #1. From their comments and suggestions, we have:
  Run additional analysis and datasets to identify the main modes of variability in the study region and to make the case for representativeness of the study sites as representing regional climates of eastern and western cordillera more robust.
  
  Created a new figure (Figure 7) has been included, which presents the key findings from the application of the CHIRPS and REOF techniques.
  
  Included a new section in the discussion addressing the role of the tropospheric bridge that connects the TNA and ENSO regions through the Amazon (moisture recycling).
  
  Revised Table 2, now it is more complete as it presents the datasets used in this research.
  
  Revised the Lagrangian results adding more level pressure data and confirming that the moisture sources for both sites are consistently different.
  
  Redone the spectral analyses, made new figures, and revised the discussion. After all this, the fact that these two places have anti-phased signals remains unaltered.
  
  Revised the mechanisms connecting SSTs anomalies in the TP and TNA and would be very happy to expand the discussion to include it.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1428-AC2
RC2:
'Comment on egusphere-2022-1428', Gabriel M. Pontes, 14 Aug 2023
The study aims to understand the causes of precipitation changes the occurred in the Colombia Andes from the mid- to late Holocene based on the comparison to present-day drivers. Given the diverse climate drivers that affect Colombian precipitation, the authors focus on two sites with opposite responses. Mistrató that is more impacted by the Pacific Ocean and Berlín being affected by the Atlantic Ocean. I think the study is well-written and fills an important gap in the literature, where warmer conditions in the tropical North Atlantic and weaker ENSO variability in the early to mid-Holocene have been extensively described, their impact in northern South America has not, except for the Cariaco basin. However, the methodology is not the most appropriate and a higher degree of evidence is needed to support the author’s conclusions. As such, I recommend a major review before publication.

A better description of the results shown in Figure 3 is needed. The different spectrum between both localities is important to discuss and characterise their drivers.

Although the STL analysis is sometimes useful, I think that in this case it is leaving some important information aside. The correlation analysis in Figs. 4 and 5 uses the trend component obtained from the STL analysis. However, the trend component seems to be mostly capturing the low-frequency variability (Figs. S1 and S2), while there is variability in the residual of the analysis the is likely important, in particular for the Mistrató timeseries. For example, the residual has several peaks of higher magnitude than in the trend component, which could be related to ENSO events. As such, I think the correlation analysis in Fig 4 is highlighting more the relationship between Mistrató precipitation and the IPO than with ENSO. A very clear tripole SST spatial pattern arises in Pacific ocean in all panels. I recommend the authors to correlate SSTs with the anomaly time series instead of decomposing it into trend and residual.

The lagrangian tracking analysis shown in Fig. 6 is interesting to characterize the moisture source of both locations. However this analysis has to be performed on more appropriate periods for both Locations. Why the choice of October 2010 for both locations? Also, a better description of the dynamics for each location is missing. What changes in winds occur so that there is increased moisture in both locations? Lastly, the current figures are showing that La Niña events bring more moisture to both locations. I don’t this is the purpose of this analysis.

The wavelet analyses must be described more carefully. I found the description in L343-373 confusing. Given that both localities have different drives, they must be described separately and clearer.

The main conclusion of the study, which is that the “late Holocene change in Colombia was caused by the increase in SST in the TNA and TP” L415, is either wrong or needs further evidence. It is well-known that the TNA was warmer in the mid-Holocene due to insolation changes. Thus, it couldn’t have become warmer from the mid to late Holocene. I think the change from wet to dry conditions in Berlín was caused by a less warm TNA and, thus, the southward shift of the ITCZ weakened the easterlies trade winds, reducing moisture supply to Berlín. Futhermore, from my understanding from figures 4 and 6, an increase in the tropical Pacific SST would be related to dry conditions in Mistrató, which is the opposite of that in the late Holocene (fig 10). For instance, increased precipitation in Mistrató during the late Holocene can be related to increased ENSO variability, which also increases the number of La Niña events.

The discussion of the findings of this study must be put in context with the southward shift of the ITCZ from the mid to late Holocene. This is, how the present-day precipitation variability in Colombia is related to mean state changes in the past 4Ka. This might need further analysis. One clear misconclusion is at L443-447, Fig 10 shows increased precipitation at Mistrató in late Holocene and the auhtor’s are arguing that the ITCZ southward shift has reduced moisture to Mistrató. Just the same, Fig 10 indicates dryer conditions in the late Holocene in Berlín. An ITCZ southward shift would likely weaken the easterlies causing dryer conditions.
Citation: https://doi.org/10.5194/egusphere-2022-1428-RC2
- AC3:
  'Reply on RC2', Maria Velez, 12 Sep 2023
  We want to thank Dr. Pontes for his insightful comments and good directions. Below we answer to his comments. In addition, we also want to inform Dr. Pontes, that Reviewer 1 also suggested additional analyses and so we: have included the CHIRPS data set to increase robustness of the precipitation dataset, we used Rotated Empirical Orthogonal Function (REOF) to examine the coherence of precipitation patterns and their connections to large-scale climate drivers, and re-did all spectral analysis and figures 3, 7-9. While focusing on interannual variability, we recognize the presence of other timescales influencing climate dynamics.
  A better description of the results shown in Figure 3 is needed. The different spectrum between both localities is important to discuss and characterise their drivers.
  
  Response: We realized that Fig 3 could benefit from a deeper and clearer explanation. This was also raised by R1 and we refer Dr. Pontes to response to General Comment 5 (GC5) of Reviewer 1 where we explain in detail the results from the new figures.
  Although the STL analysis is sometimes useful, I think that in this case it is leaving some important information aside. The correlation analysis in Figs. 4 and 5 uses the trend component obtained from the STL analysis. However, the trend component seems to be mostly capturing the low-frequency variability (Figs. S1 and S2), while there is variability in the residual of the analysis the is likely important, in particular for the Mistrató timeseries. For example, the residual has several peaks of higher magnitude than in the trend component, which could be related to ENSO events. As such, I think the correlation analysis in Fig 4 is highlighting more the relationship between Mistrató precipitation and the IPO than with ENSO. A very clear tripole SST spatial pattern arises in Pacific ocean in all panels. I recommend the authors to correlate SSTs with the anomaly time series instead of decomposing it into trend and residual.
  
  Response: thank you for bringing this up. Indeed we can now see that the warming in the TP, reduction of precipitation in Mistrato can be due to IPO. These are significant correlations and so we: will make new correlations between SST and the Trend+Residual components, and will extend the discussion to incorporate IPO. We also would like to refer Dr. Pontes to our response to the General comment 1 raised by the other reviewer. In that response we explained the additional analyses we made. Below, we show the correlations between the SSts anomalies with the anomalies in monthly precipitation, for the two stations. From these figures similar patterjsn similar to the ones presented in the manuscript although of less intensity and stronger ENSO signal.
  The lagrangian tracking analysis shown in Fig. 6 is interesting to characterize the moisture source of both locations. However this analysis has to be performed on more appropriate periods for both Locations. Why the choice of October 2010 for both locations? Also, a better description of the dynamics for each location is missing. What changes in winds occur so that there is increased moisture in both locations? Lastly, the current figures are showing that La Niña events bring more moisture to both locations. I don’t this is the purpose of this analysis.
  
  Response: with this analysis we want to identify what are the main sources of moisture for the study sites in relation to the Atlantic and Pacific during an anomalously wet moth and year (Oct 2010, La Nina year). We agreed that the topic was poorly introduced. Reviewer one had a similar concern so we would like to refer Dr. Pontes to our response to the other reviewer addressing General Comment 4. Here we copied the response to R1 hoping it will satisfy Dr. Pontes:
  “We have appropriately cited the NCAR/NCEP dataset in Table 2. Additionally, to address the reviewer's concern, we expanded our analyses to include pressure levels of 600, 700, 850, and 925 hPa. Moreover, we have considered crop issues in the northern region when remaking Figure 6 for the Berlin-Santurban site. This revised figure now combines subfigures A and B from the original submitted version into one figure.
  Incorporating these changes, we have made the following revisions (new lines 360-385 588-623):
  
  Results of the backward-Lagrangian tracking analysis of air parcels for the study sites during a humid period of the annual cycle enhanced by La Niña (October 2010) at four different pressure levels of the middle and low atmosphere (600, 700, 850, 925 hPa), are presented in Fig. 6. This analysis highlights the different origins of moist parcels and the underlying physical mechanisms governing rainfall between the sites. Parcels ending in Berlín originated in the TNA region and exhibited predominantly east-west trajectories, crossing the northernmost part of continental South America before entering Colombia through the northeast (Fig. 6). On the other hand, air parcels ending in Mistrató, originated in southeastern Pacific, and travelled from south to north and east to west, just before entering Colombia through the west (Fig. 6).
  Lagrangian trajectories presented here reflect atmospheric transport during one of the most notable La Niña-events (October 25, 2010), which was know for highly impacting Colombian’s climate (Arias et al., 2015; Bedoya et al., 2019; Cai et al., 2020) and for causing one of the wettest peaks of the annual cycle (Figure 2). While these trajectories offer valuable information into the origin of humid parcels, they solely capture a sample of the entire range of atmospheric transport pathways of humidity. The selection of the NCEP/NCAR RI dataset, the pressure levels, and the two trajectories, were based on considerations of data availability, the relevance of this level (700 mb) for capturing key atmospheric dynamics in the context of the heights of the Andes, and practical limitations in visual representation.
  Although the two sites are geographically close and interconnected by the influence of the three ranges of the Andes, it is also important to note that the origin of humid parcels and the physical mechanisms governing rainfall differ between the sites at seasonal and interannual timescales. The Lagrangian trajectories provide insights into the prevailing transport patterns during that specific humid period under La Niña conditions, but the complete picture of moisture transport and associated rainfall mechanisms involves additional factors such as regional circulation, altitude effects, and latitude variations (Poveda et al., 2014). “
  The wavelet analyses must be described more carefully. I found the description in L343-373 confusing. Given that both localities have different drives, they must be described separately and clearer.
  
  Response: spectral analyses were revised and new figures produced. We refer Dr. Pontes to our responses to General Comment 5 by the other reviewer.
  The main conclusion of the study, which is that the “late Holocene change in Colombia was caused by the increase in SST in the TNA and TP” L415, is either wrong or needs further evidence. It is well-known that the TNA was warmer in the mid-Holocene due to insolation changes. Thus, it couldn’t have become warmer from the mid to late Holocene. I think the change from wet to dry conditions in Berlín was caused by a less warm TNA and, thus, the southward shift of the ITCZ weakened the easterlies trade winds, reducing moisture supply to Berlín. Futhermore, from my understanding from figures 4 and 6, an increase in the tropical Pacific SST would be related to dry conditions in Mistrató, which is the opposite of that in the late Holocene (fig 10). For instance, increased precipitation in Mistrató during the late Holocene can be related to increased ENSO variability, which also increases the number of La Niña events.
  
  Response: thank you for this observation. We are not stating the that TNA was permanently warmer in the Late Holocene relative to the mid Holocene; we are suggesting that ENSO occurred more frequently, and as seen today, after an El Nino event, there is a an anomalous increase in the SST of the TNA in the following spring; thus that we are saying that ENSO events and the subsequent changes in TNA occurred more frequently after ca. 4 kyr and probably until 2 kyr. Today, in Berlin, when the ITCZ moves south, the Easterlies enter and bring more precipitation, creating the rainy season. In addition to the ITCZ moving south in the Late Holocene, there must have been something else bringing more precipitation to Brelin.
  This part is not clear to us: ” Futhermore, from my understanding from figures 4 and 6, an increase in the tropical Pacific SST would be related to dry conditions in Mistrató, which is the opposite of that in the late Holocene (fig 10).” Figure 10 repsresents the paleo situation: dry in Medellincito (Mistrato) with a hiatus and wet in Berlin (peak in Aulacoseira).
  The discussion of the findings of this study must be put in context with the southward shift of the ITCZ from the mid to late Holocene. This is, how the present-day precipitation variability in Colombia is related to mean state changes in the past 4Ka. This might need further analysis. One clear misconclusion is at L443-447, Fig 10 shows increased precipitation at Mistrató in late Holocene and the auhtor’s are arguing that the ITCZ southward shift has reduced moisture to Mistrató. Just the same, Fig 10 indicates dryer conditions in the late Holocene in Berlín. An ITCZ southward shift would likely weaken the easterlies causing dryer conditions.
  
  Response: We agree with this statement: ……”this study must be put in context with the southward shift of the ITCZ from the mid to late Holocene”. We realized we need to deepen the discussion of the ITCZ moving south during the late Holocene and its consequences relating to precipitation in the study sites. However, the increase in SST in the TNA has to be considered as well: the effect of ENSO on the SST TNA is widely known, and in fact, it has been proposed by the Poveda group that the Amazon is a land bridge mechanism by which the what happens in the TP affects the TNA; given the correlations illustrated by the new analyses and the revision of the spectral analysis, we still think that the warming of the TP is involved in the antiphase signal
  
  Citation: https://doi.org/10.5194/egusphere-2022-1428-AC3

Juan Mauricio Bedoya, Maria I. Velez, and German Poveda

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Juan Mauricio Bedoya, Maria I. Velez, and German Poveda

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Month	HTML	PDF	XML	Total
Jan 2023	113	39	7	159
Feb 2023	62	15	0	77
Mar 2023	35	13	0	48
Apr 2023	51	15	4	70
May 2023	22	20	0	42
Jun 2023	26	11	1	38
Jul 2023	33	16	1	50
Aug 2023	44	13	2	59
Sep 2023	39	24	5	68
Oct 2023	16	9	1	26
Nov 2023	4	9	0	13
Dec 2023	11	8	1	20
Jan 2024	18	5	1	24
Feb 2024	18	12	0	30
Mar 2024	16	8	0	24
Apr 2024	7	5	2	14

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Latest update: 19 Apr 2024

Short summary

At 4 kyr Before Present there was a change in the overall climate of the Earth caused by orbital forcing. Recent paleoclimatic reconstructions from two sites in the Eastern and Western Andes of Colombia show that at ~4 kyr there was less (more) precipitation in the Western (Eastern) sites. Modern hydroclimate analyses indicate that the out of phase precipitation is caused by warming in the Tropical Pacific (Atlantic). We propose that this warming is due to ENSO flavours.


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