Insights from a New High-Resolution Drought Atlas for the Caribbean Spanning 1950–2016
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1OCTOBER 2017 H E R R E R A A N D A U L T 7801 Insights from a New High-Resolution Drought Atlas for the Caribbean Spanning 1950–2016 DIMITRIS HERRERA AND TOBY AULT Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York (Manuscript received 23 November 2016, in final form 14 June 2017) ABSTRACT Climate change is expected to increase the severity and frequency of drought in the Caribbean. Un- derstanding drought variability and its trends is therefore critical for improving resiliency and adaptation capacity of this region, as well as for assessing the dynamics and predictability of regional hydroclimate across spatial and temporal scales. This work introduces a first of its kind high-resolution drought dataset for the Caribbean region from 1950 to 2016, using monthly estimates of the ‘‘self calibrating’’ Palmer drought severity index (scPDSI), with the physically based Penman–Monteith approximation for the potential evapotrans- piration. Statistically downscaled data products, including reanalysis, are employed to establish an historical baseline for characterizing drought from 1950 to the near present. Since 1950, the Caribbean has been affected by severe droughts in 1974–77, 1997/98, 2009/10, and 2013–16. Results indicate that the 2013–16 drought is the most severe event during the time interval analyzed in this work, which agrees with qualitative reports of many meteorological institutions across the Caribbean. Linear trends in the scPDSI show a significant drying 2 in the study area, averaging an scPDSI change of 20.09 decade 1 ( p , 0.05). However, this trend is not homogenous, and significant trends toward wetter conditions in portions of the study area were observed. Results further indicate a strong influence of both tropical Pacific and North Atlantic oceans in modulating drought variability across the study domain. Finally, this effort is the first step in building high-resolution drought products for the Caribbean to be updated regularly, with the purpose of drought monitoring and eventually seasonal drought prediction. 1. Introduction drought area since 1950 (Dai et al. 2004; Dai 2011, 2013; Dai and Zhao 2017; van der Schrier et al. 2013; Cook Droughts are among the deadliest and costliest natu- et al. 2015). These findings further correspond to in- ral phenomena, leading to food shortages and annual creasing global temperatures and hence evaporative losses of billions of dollars worldwide (e.g., Wilhite et al. demand, which in turn has been identified as a crucial 2007; Howitt et al. 2014). Although droughts do not driver in the observed trend (Dai 2013; Dai and Zhao unfold as rapidly as other meteorological hazards (e.g., 2017; van der Schrier et al. 2013; Cook et al. 2015; Zhao hurricanes or floods), their duration can put food secu- and Dai 2015). Furthermore, projections from phases 3 rity, water storage, and even energy production at risk. and 5 of the Coupled Model Intercomparison Project A drought is usually characterized by below-normal (CMIP3 and CMIP5) have suggested a substantial in- precipitation, and often associated with above-normal crease in global aridity by the end of the twenty-first temperatures that may span from several months to century as a consequence of rising concentrations of years and, in some cases, decades (Dai 2011; Cook et al. greenhouse gases (Dai 2013; IPCC 2014; Ault et al. 2014; 2016). From observations and model simulations, pre- Cook et al. 2015; Zhao and Dai 2015). Model simula- vious studies have shown an increase of the global tions also indicate that the greatest decline in pre- cipitation will occur in certain areas of the tropics and subtropics, where rainfall could be reduced by as much Denotes content that is immediately available upon publica- as 50% on average in regions like the Caribbean and tion as open access. Central America (IPCC 2014; Zhao and Dai 2015). In addition to these rainfall shortages, higher future tem- Corresponding author: Dimitris Herrera, [email protected] peratures could lead to even more severe droughts due DOI: 10.1175/JCLI-D-16-0838.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/24/21 11:18 PM UTC 7802 JOURNAL OF CLIMATE VOLUME 30 to an increased atmospheric demand of moisture (Dai Instrumental and historical records document the 2013; IPCC 2014; Cook et al. 2014, 2015; Zhao and Dai occurrence of multiyear droughts in the Caribbean 2015). Consequently, this would drive or worsen drought and Central America during the last 60 years (e.g., risk even if precipitation does not change appreciably Larsen 2000; Méndez and Magaña 2010; Peters 2015; from historical averages (Cook et al. 2015; Ault Blunden and Arndt 2016). These events have caused et al. 2016). water shortages in agriculture, energy generation, and Although many regional subtropical drying trends are municipal usage, affecting the economies of many robust across observational and model studies (IPCC countries in the region (Larsen 2000; Peters 2015; 2014; Ault et al. 2014; Cook et al. 2015; Zhao and Dai OCHA 2015; FAO 2016). Some of those dry intervals 2015), large uncertainties in this picture originate from have been linked to the warm phase of El Niño– differences in data and models used for assessing Southern Oscillation (ENSO; Peters 2015; Blunden drought. For example, Dai and Zhao (2017) calculated and Arndt 2016), including the 1997/98, 2009/10, and the Palmer drought severity index (PDSI)—a widely 2013–16 droughts. The recent drought between 2013 used indicator of agricultural drought—using different and 2016 has been referred to as the most severe event underlying climate data products. They found that his- in over 50–100 years in many countries of the Carib- torical trends and variances of the PDSI are sensitive to bean and Central America by some of their public differences between observational data products (espe- institutions (e.g., DRNA 2016; IMN 2016), although cially precipitation, net radiation, and wind speed) and this claim lacks firm quantitative backing because of the calibration period used to normalize the index, al- the paucity of hydroclimatic data in the region. While though the latter has a smaller contribution than the the effects of this event have not yet been fully underlying climate data. In terms of future projections quantified, agricultural losses of over $200 million of drought, both CMIP3 and CMIP5 are consistent in have been estimated in El Salvador and Guatemala showing increased global aridity in the twenty-first (OCHA 2015; FAO 2016). For reference, the gross century. Major differences among models are mostly domestic product of these two countries was $25.85 due to discrepancies in projected precipitation (Zhao and $63.79 billion, respectively, in 2015 according to and Dai 2015, 2017). However, uncertainties in future the World Bank. projections of drought still persist on simulated regional Although the Caribbean is likely to be affected by trends and variances, which could be due to the domi- drought and freshwater stress in the future, there is nant influence of the natural internal variability of currently no single study characterizing historical drought at regional scales (Dai 2013; Zhao and Dai droughts and their trends at a spatial resolution ap- 2015, 2017). propriate for the topography of the region. A few The Caribbean region is vulnerable to climate change studies have used data from weather stations, but they as a result of more severe and widespread droughts do not fully cover the entire region (e.g., Larsen 2000; observed and projected at the end of the twenty-first Giannini et al. 2000, 2001a,b; Taylor et al. 2002; Jury century (IPCC 2014; Stephenson et al. 2014, 2016). The et al. 2007; Peters 2015; Blunden and Arndt 2016). greatest decline in rainfall projected for the Caribbean Regional and global station datasets, such as the might occur during boreal summer (June–August; National Oceanic and Atmospheric Administration Rauscher et al. 2008; Campbell et al. 2011; Karmalkar (NOAA) Global Historical Climatology Network et al. 2011; IPCC 2014), a critical season for capturing (GHCN), are missing a considerable amount of data and storing water in many countries of the region. In in many sites in the Caribbean region and Central addition, the inherent insular nature of the Caribbean America, and many of the data that do exist are of islands makes them especially vulnerable to drought inconsistent quality (Blunden and Arndt 2016). Fur- because water cannot be collected, moved, or stored on thermore, existing gridded drought products (e.g., Dai large spatial scales (as it can be in the U.S. Southwest). et al. 2004; Dai 2011; Vicente-Serrano et al. 2010; Recent studies have also indicated that many of the Sheffield et al. 2012; van der Schrier et al. 2013)are small islands in the Caribbean Sea will face un- generated at spatial scales of 50–100 km. At these precedented freshwater and groundwater stresses be- scales, spatial variations in drought associated to the cause of climate change (e.g., Holding et al. 2016; complex topography of many islands in the Caribbean Karnauskas et al. 2016). The region is made even more Sea cannot be resolved. For example, a single grid cell vulnerable by its dense population and limited economic of 0.58, the highest resolution drought datasets cur- growth, most of which depends on tourism and a poorly rently available (Vicente-Serrano et al. 2010; van der developed agricultural sector (Sahay 2005; Martin and Schrier et al. 2013) covers more than twice the area of Schumacher 2011; IPCC 2014). Martinique (;1200 km2). Therefore, products like this Unauthenticated | Downloaded 09/24/21 11:18 PM UTC 1OCTOBER 2017 H E R R E R A A N D A U L T 7803 TABLE 1.