Browse
Abstract
Soil moisture–precipitation (SM–PPT) feedbacks at the mesoscale represent a major challenge for numerical weather prediction, especially for subtropical regions that exhibit large variability in surface SM. How does surface heterogeneity, specifically mesoscale gradients in SM and land surface temperature (LST), affect convective initiation (CI) over South America? Using satellite data, we track nascent, daytime convective clouds and quantify the underlying antecedent (morning) surface heterogeneity. We find that convection initiates preferentially on the dry side of strong SM/LST boundaries with spatial scales of tens of kilometers. The strongest alongwind gradients in LST anomalies at 30-km length scale underlying the CI location occur during weak background low-level wind (<2.5 m s−1), high convective available potential energy (>1500 J kg−1), and low convective inhibition (<250 J kg−1) over sparse vegetation. At 100-km scale, strong gradients occur at the CI location during convectively unfavorable conditions and strong background flow. The location of PPT is strongly sensitive to the strength of the background flow. The wind profile during weak background flow inhibits propagation of convection away from the dry regions leading to negative SM–PPT feedback whereas strong background flow is related to longer life cycle and rainfall hundreds of kilometers away from the CI location. Thus, the sign of the SM–PPT feedback is dependent on the background flow. This work presents the first observational evidence that CI over subtropical South America is associated with dry soil patches on the order of tens of kilometers. Convection-permitting numerical weather prediction models need to be examined for accurately capturing the effect of SM heterogeneity in initiating convection over such semiarid regions.
Abstract
Soil moisture–precipitation (SM–PPT) feedbacks at the mesoscale represent a major challenge for numerical weather prediction, especially for subtropical regions that exhibit large variability in surface SM. How does surface heterogeneity, specifically mesoscale gradients in SM and land surface temperature (LST), affect convective initiation (CI) over South America? Using satellite data, we track nascent, daytime convective clouds and quantify the underlying antecedent (morning) surface heterogeneity. We find that convection initiates preferentially on the dry side of strong SM/LST boundaries with spatial scales of tens of kilometers. The strongest alongwind gradients in LST anomalies at 30-km length scale underlying the CI location occur during weak background low-level wind (<2.5 m s−1), high convective available potential energy (>1500 J kg−1), and low convective inhibition (<250 J kg−1) over sparse vegetation. At 100-km scale, strong gradients occur at the CI location during convectively unfavorable conditions and strong background flow. The location of PPT is strongly sensitive to the strength of the background flow. The wind profile during weak background flow inhibits propagation of convection away from the dry regions leading to negative SM–PPT feedback whereas strong background flow is related to longer life cycle and rainfall hundreds of kilometers away from the CI location. Thus, the sign of the SM–PPT feedback is dependent on the background flow. This work presents the first observational evidence that CI over subtropical South America is associated with dry soil patches on the order of tens of kilometers. Convection-permitting numerical weather prediction models need to be examined for accurately capturing the effect of SM heterogeneity in initiating convection over such semiarid regions.
Abstract
Although seasonal climate forecasts have major socioeconomic impacts, current forecast products, especially those for precipitation, are not yet reliable for forecasters and decision-makers. Here we developed a novel statistical–dynamical hybrid model for precipitation by applying weather regimes (WRs) and Gaussian mixture models (WR-GMM) to the National Oceanic and Atmospheric Administration’s Climate Forecast System, version 2 (CFSv2), precipitation forecasts across the continental United States. Instead of directly forecasting precipitation, WR-GMM uses observed precipitation from synoptic patterns similar to the future CFSv2 forecast. Traditionally K-means has been used to classify daily synoptic patterns into individual WRs, but the new GMM approach allows multiple WRs to be represented for the same day. The novel WR-GMM forecast model is trained on daily Climate Forecast System Reanalysis (CFSR) geopotential height and observed precipitation data during the 1981–2010 period and is verified for 2011–22. Overall, the WR-GMM method outperforms the CFSv2 ensemble forecast precipitation in terms of root-mean-square error and for Pearson correlation coefficient for lead months 1–4. Previous studies have used global climate models to forecast WRs in the Pacific Ocean and Mediterranean Sea regions, usually with an emphasis on winter months, but the WR-GMM model is the first of its kind that promises great untapped potential to improve precipitation forecasts produced by CFSv2 across the continental United States.
Abstract
Although seasonal climate forecasts have major socioeconomic impacts, current forecast products, especially those for precipitation, are not yet reliable for forecasters and decision-makers. Here we developed a novel statistical–dynamical hybrid model for precipitation by applying weather regimes (WRs) and Gaussian mixture models (WR-GMM) to the National Oceanic and Atmospheric Administration’s Climate Forecast System, version 2 (CFSv2), precipitation forecasts across the continental United States. Instead of directly forecasting precipitation, WR-GMM uses observed precipitation from synoptic patterns similar to the future CFSv2 forecast. Traditionally K-means has been used to classify daily synoptic patterns into individual WRs, but the new GMM approach allows multiple WRs to be represented for the same day. The novel WR-GMM forecast model is trained on daily Climate Forecast System Reanalysis (CFSR) geopotential height and observed precipitation data during the 1981–2010 period and is verified for 2011–22. Overall, the WR-GMM method outperforms the CFSv2 ensemble forecast precipitation in terms of root-mean-square error and for Pearson correlation coefficient for lead months 1–4. Previous studies have used global climate models to forecast WRs in the Pacific Ocean and Mediterranean Sea regions, usually with an emphasis on winter months, but the WR-GMM model is the first of its kind that promises great untapped potential to improve precipitation forecasts produced by CFSv2 across the continental United States.
Abstract
Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental United States across a set of widely used QPE products. We examine five datasets over an 18-yr period (2002–19). The products include three satellite-based products, CPC morphing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Tropical Rainfall Measuring Mission–Multisatellite Precipitation Analysis (TRMM-TMPA); the ground-radar- and rain-gauge-based NCEP Stage IV; and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans, Louisiana, and Houston, Texas, metropolitan areas. Along the East Coast, TCs can contribute up to 20% of the warm season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM, and ERA5. Satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty in QPEs is further reflected in the TC flooding potential measured by the extreme rainfall multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced by the satellite and reanalysis QPEs compared to that using NCEP Stage IV.
Significance Statement
Tropical cyclones (TCs) can produce extreme rainfall and widespread flooding. Improvements in our preparedness, mitigation, and adaptation efforts rest on a better understanding of TC rainfall and its impact. Assessment of various rainfall products and uncertainties is urgently needed for decision making and other applications. Different quantitative precipitation estimate (QPE) datasets have been developed over the past decades. This study uses 18 years of data to better understand how key properties of TC rainfall, including its contribution to extreme events and flooding potential, are represented across five widely used QPE products. Our analysis shows that satellite-based, and to a lesser extent, reanalysis-based QPEs underestimate TC rainfall properties both in terms of climatology and extreme events compared to ground-radar-gauge-based QPE. The uncertainty increases in the coastal region and is reflected in the estimated TC flooding potential.
Abstract
Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental United States across a set of widely used QPE products. We examine five datasets over an 18-yr period (2002–19). The products include three satellite-based products, CPC morphing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Tropical Rainfall Measuring Mission–Multisatellite Precipitation Analysis (TRMM-TMPA); the ground-radar- and rain-gauge-based NCEP Stage IV; and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans, Louisiana, and Houston, Texas, metropolitan areas. Along the East Coast, TCs can contribute up to 20% of the warm season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM, and ERA5. Satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty in QPEs is further reflected in the TC flooding potential measured by the extreme rainfall multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced by the satellite and reanalysis QPEs compared to that using NCEP Stage IV.
Significance Statement
Tropical cyclones (TCs) can produce extreme rainfall and widespread flooding. Improvements in our preparedness, mitigation, and adaptation efforts rest on a better understanding of TC rainfall and its impact. Assessment of various rainfall products and uncertainties is urgently needed for decision making and other applications. Different quantitative precipitation estimate (QPE) datasets have been developed over the past decades. This study uses 18 years of data to better understand how key properties of TC rainfall, including its contribution to extreme events and flooding potential, are represented across five widely used QPE products. Our analysis shows that satellite-based, and to a lesser extent, reanalysis-based QPEs underestimate TC rainfall properties both in terms of climatology and extreme events compared to ground-radar-gauge-based QPE. The uncertainty increases in the coastal region and is reflected in the estimated TC flooding potential.
Abstract
Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: i) the Demer catchment dominated by agriculture, and ii) the Ourthe catchment dominated by mixed forests. We present results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and Leaf Area Index (LAI). The DA experiments covered the period January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture-runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments.
Abstract
Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: i) the Demer catchment dominated by agriculture, and ii) the Ourthe catchment dominated by mixed forests. We present results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and Leaf Area Index (LAI). The DA experiments covered the period January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture-runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments.
Abstract
In this study, the FLEXible PARTicle Dispersion Model (FLEXPART) is applied to analyze the moisture sources of Northeast China precipitation from March 1979 to February 2018. The results show that there is mainly one particle aggregation channel in winter, namely the Eastern Europe–Siberia–Lake Baikal–Northeast Asia channel (the western channel). Compared with winter, there are two extra channels in summer, namely the Indochina Peninsula–South China Sea–East China channel (the southern channel) and the Philippine Sea–Ryukyu Islands channel (the southeastern channel). From the long-term mean, Siberia–Mongolia–Xinjiang (SMX) is the most dominant moisture source of Northeast China precipitation in all seasons. As for the moisture contribution rate of each source region to Northeast China precipitation, there is a seesaw interannual relationship between SMX and other source regions. The moisture from the Central–East China is critical to the interdecadal shift of Northeast China summer precipitation. This interdecadal shift is related to the moisture transport from low latitudes to Northeast China, which is modulated by the positive phase of the Pacific Decadal Oscillation and the negative phase of the Atlantic Multidecadal Oscillation.
Abstract
In this study, the FLEXible PARTicle Dispersion Model (FLEXPART) is applied to analyze the moisture sources of Northeast China precipitation from March 1979 to February 2018. The results show that there is mainly one particle aggregation channel in winter, namely the Eastern Europe–Siberia–Lake Baikal–Northeast Asia channel (the western channel). Compared with winter, there are two extra channels in summer, namely the Indochina Peninsula–South China Sea–East China channel (the southern channel) and the Philippine Sea–Ryukyu Islands channel (the southeastern channel). From the long-term mean, Siberia–Mongolia–Xinjiang (SMX) is the most dominant moisture source of Northeast China precipitation in all seasons. As for the moisture contribution rate of each source region to Northeast China precipitation, there is a seesaw interannual relationship between SMX and other source regions. The moisture from the Central–East China is critical to the interdecadal shift of Northeast China summer precipitation. This interdecadal shift is related to the moisture transport from low latitudes to Northeast China, which is modulated by the positive phase of the Pacific Decadal Oscillation and the negative phase of the Atlantic Multidecadal Oscillation.
Abstract
This article explores the application of thermodynamic perturbations to a historical mid-latitude, winter-time, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14km variable-resolution simulations completed with the Department of Energy’s global Energy Exascale Earth System Model (E3SM). We show the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under pre-industrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks around +1 to +2K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential non-linearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB.
Abstract
This article explores the application of thermodynamic perturbations to a historical mid-latitude, winter-time, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14km variable-resolution simulations completed with the Department of Energy’s global Energy Exascale Earth System Model (E3SM). We show the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under pre-industrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks around +1 to +2K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential non-linearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB.
Abstract
High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean-atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high-resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in-situ oceanic rain rate observations collected by Passive Aquatic Listeners (PAL) in eleven different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods, and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristic or combinations thereof leads to the most improvement or consistent improvement when applying RainFARM to IMERG.
Abstract
High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean-atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high-resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in-situ oceanic rain rate observations collected by Passive Aquatic Listeners (PAL) in eleven different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods, and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristic or combinations thereof leads to the most improvement or consistent improvement when applying RainFARM to IMERG.
Abstract
Three satellite precipitation datasets—CMORPH, PERSIANN-CDR, and GPCP—from the NOAA/Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007–18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products’ (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling–Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance—smaller bias, higher correlation, and a better KGE score—than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH’s performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 0°C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products.
Abstract
Three satellite precipitation datasets—CMORPH, PERSIANN-CDR, and GPCP—from the NOAA/Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007–18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products’ (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling–Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance—smaller bias, higher correlation, and a better KGE score—than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH’s performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 0°C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products.
Abstract
Despite the intensifying interest in flash drought both within the U.S. and globally, moist tropical landscapes have largely escaped the attention of the flash drought community. Because these ecozones are acclimatized to receiving regular, near-daily precipitation, they are especially vulnerable to rapid-drying events. This is particularly true within the Caribbean basin where numerous small islands lack the surface and groundwater resources to cope with swiftly developing drought conditions. This study fills the tropical flash drought gap by examining the pervasiveness of flash drought across the pan-Caribbean region using a recently proposed criterion based on the Evaporative Demand Drought Index (EDDI). The EDDI identifies 46 instances of widespread flash drought “outbreaks” in which significant fractions of the pan-Caribbean encounter rapid drying over 15 days and then maintain this condition for another 15 days. Moreover, a self-organizing maps (SOM) classification reveals a tendency for flash drought to assume recurring typologies concentrated in either the Central American, South American, or Greater Antilles coastlines, though a simultaneous, Caribbean-wide drought is never observed within the 40-year (1981-2020) period examined. Further, three of the six flash drought typologies identified by the SOM initiate most often during Phase 2 of the Madden-Julian Oscillation. Collectively, these findings motivate the need to more critically examine the transferability of flash drought definitions into the global tropics, particularly for small water-vulnerable islands where even island-wide flash droughts may only occupy a few pixels in most reanalysis datasets.
Abstract
Despite the intensifying interest in flash drought both within the U.S. and globally, moist tropical landscapes have largely escaped the attention of the flash drought community. Because these ecozones are acclimatized to receiving regular, near-daily precipitation, they are especially vulnerable to rapid-drying events. This is particularly true within the Caribbean basin where numerous small islands lack the surface and groundwater resources to cope with swiftly developing drought conditions. This study fills the tropical flash drought gap by examining the pervasiveness of flash drought across the pan-Caribbean region using a recently proposed criterion based on the Evaporative Demand Drought Index (EDDI). The EDDI identifies 46 instances of widespread flash drought “outbreaks” in which significant fractions of the pan-Caribbean encounter rapid drying over 15 days and then maintain this condition for another 15 days. Moreover, a self-organizing maps (SOM) classification reveals a tendency for flash drought to assume recurring typologies concentrated in either the Central American, South American, or Greater Antilles coastlines, though a simultaneous, Caribbean-wide drought is never observed within the 40-year (1981-2020) period examined. Further, three of the six flash drought typologies identified by the SOM initiate most often during Phase 2 of the Madden-Julian Oscillation. Collectively, these findings motivate the need to more critically examine the transferability of flash drought definitions into the global tropics, particularly for small water-vulnerable islands where even island-wide flash droughts may only occupy a few pixels in most reanalysis datasets.
