skip to main content


Title: Did the Climate Forecast System Anticipate the 2015 Caribbean Drought?
Abstract In groundwater-limited settings, such as Puerto Rico and other Caribbean islands, societal, ecological, and agricultural water needs depend on regular rainfall. Though long-range numerical weather predication models explicitly predict precipitation, such quantitative precipitation forecasts (QPF) critically failed to detect the historic 2015 Caribbean drought. Consequently, this work examines the feasibility of developing a drought early warning tool using the Gálvez–Davison index (GDI), a tropical convective potential index, derived from the Climate Forecast System, version 2 (CFSv2). Drought forecasts are focused on Puerto Rico’s early rainfall season (ERS; April–July), which is susceptible to intrusions of strongly stable Saharan air and represents the largest source of hydroclimatic variability for the island. A fully coupled atmosphere–ocean–land model, the CFSv2 can plausibly detect the transatlantic advection of low-GDI Saharan air with multimonth lead times. The mean ERS GDI is calculated from semidaily CFSv2 forecasts beginning 1 January of each year between 2012 and 2018 and monitored as the initialization approaches 1 April. The CFSv2 demonstrates a broad region of statistically significant correlations with observed GDI across the eastern Caribbean up to 30 days prior to the ERS. During 2015, the CFSv2 forecast a low-GDI tongue extending across the Atlantic toward the Caribbean with 60–90 days lead time and placed Puerto Rico’s 2015 ERS beneath the 15th percentile of all 1982–2018 ERS forecasts with up to 30 days lead time. A preliminary GDI-based QPF tool tested herein is a statistically significant improvement over climatology for the driest years.  more » « less
Award ID(s):
1831952
NSF-PAR ID:
10207110
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Journal of Hydrometeorology
Volume:
21
Issue:
6
ISSN:
1525-755X
Page Range / eLocation ID:
1245 to 1258
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    This study aims to determine the impacts of tropical island processes on local convective storms. An analysis of rain events on the island of Puerto Rico between 1 June 2015 and 31 July 2016 showed that local island‐enhanced western storms accounted for 89 of 322 storms. This period is of particular importance for the Caribbean as 2015 was one of the driest years on record. While large‐scale influences such as the El Niño–Southern Oscillation, the North Atlantic Oscillation, African easterly waves, and Saharan dust transport modulate moisture conditions in the region, correlations between precipitation and El Niño–Southern Oscillation (−0.14), North Atlantic Oscillation (−0.42), and Saharan dust (0.1) for 1980–2016 ranged from weak to moderate. Local data for the island of Puerto Rico from weather stations, the Convection, Aerosol, and Synoptic‐Effects in the Tropics field campaign, and the North American Mesoscale model support the initiation or enhancement of convective rain events due to local island processes. In particular, analysis of surface wind speed/direction, convective available potential energy, lifted index, and the bulk Richardson number substantiate local instability due to surface heating, orographic uplift, and sea breeze trade‐wind convergence. These convective forcings along with available precipitable water in excess of 50 mm ultimately led to intense storms despite severe rainfall‐mitigating dust episodes for which aerosol optical thickness exceeded 0.4. These results may have major implications for considering the impacts of local air‐sea‐land interactions on rainfall over other tropical islands.

     
    more » « less
  2. Abstract

    Long‐range aerosol transport is an important physical mechanism for ecological, biological, and hydrological elements of the earth system. Regarding the latter, regional climate models have no way of assimilating future aerosol concentrations, so dust aerosol emissions must be parameterized using local landscape and meteorological conditions. The purpose of this study is to evaluate the accuracy of different dust emission settings within the Weather Research and Forecasting model coupled with chemistry (WRF‐Chem) to facilitate future dynamical downscaling work. This study performs nine WRF‐Chem hindcasts, each utilizing a different dust emission configuration, from 1 March to 31 May 2015, coinciding with a Saharan air layer (SAL) dust outbreak during the 2015 Caribbean drought. WRF‐Chem aerosol optical depth (AOD) and Gálvez‐Davison Index (GDI), a convective forecasting parameter, are validated against analogous MODIS, AERONET, and ERA5 products. In aggregate, the GOCART dust emission scheme with Air Force Weather Agency modifications (GOCART‐AFWA) achieved the best balance between AOD and GDI accuracy when employing the default tuning constant (1.00). As the schemes emitted dust more aggressively, WRF‐Chem produced warming at 500 hPa, reducing GDI over the central and eastern Atlantic near the modeled dust trajectory. Though AOD was generally too low over the southwest Atlantic, the eastern Caribbean occupies a transition zone between negative and positive AOD biases where this field was hindcast with relative accuracy. Meanwhile, areas with positive AOD biases were associated with negative GDI biases (and vice versa) indicating the covariability between SAL dust loadings and thermodynamic conditions in the tropical north Atlantic.

     
    more » « less
  3. Abstract

    This study examines the different characteristics of ensemble spread (ESP) between drought and flood years of Indian summer monsoon (ISM) during June to September mean using the Climate Forecast System version 2 (CFSv2). We have analyzed a set of 20‐member ensemble seasonal reforecasts for 1958–2015 using CFSv2 initialized in April. The ESP of ISM Rainfall (ISMR) is negatively (positively) correlated with ISMR (NINO3.4) index, indicating that ESP in drought ISMR years seems to be larger than that in flood years. The mean value of ESP for drought ISMR years is larger than flood years. The spatial structure of ISMR composite anomalies during drought years shows better agreement with observed rainfall anomalies in comparison to flood years. As a result, smaller ESP in flood years may suggest that ensemble prediction of ISMR in flood years tends to be overconfident and less reliable due to underestimate of forecast uncertainty, compared to drought years.

     
    more » « less
  4. null (Ed.)
    Abstract In this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can be used to predict extreme fire events. An assessment of the classified BUI ensemble members at the subseasonal scale shows that persistently occurring BUI forecasts exceeding 150 in the cumulative drought season can be used as an indicator that extreme fire events will occur during the upcoming season. This study demonstrates the ability of QM-corrected CFSv2 forecasts to predict the potential fire season in advance. This information could, therefore, assist fire managers in resource allocation and disaster response preparedness. 
    more » « less
  5. Abstract

    Between October 2018 ‐ May 2019, sea surface temperature conditions in the central‐eastern tropical Pacific indicated a mild El Niño event. In May 2019, the global El Niño Southern Oscillation (ENSO) forecast consensus was that these generally weak warm patterns will persist at least until the end of the northern hemisphere summer. El Niño and its impact on local climatic conditions in southern coastal Ecuador influence the inter‐annual transmission of dengue fever in the region. In this study, we use an ENSO model to issue forecasts of El Niño for the year 2019, which are then used to predict local climate variables, precipitation and minimum temperature, in the city of Machala, Ecuador. All these forecasts are incorporated in a dengue transmission model, specifically developed and tested for this area, to produce out‐of‐sample predictions of dengue risk. Predictions are issued at the beginning of January 2019 for the whole year, thus providing the longest forecast lead time of 12 months. Preliminary results indicate that the mild and ongoing El Niño event did not provide the optimum climate conditions for dengue transmission, with the model predicting a very low probability of a dengue outbreak during the typical peak season in Machala in 2019. This is contrary to 2016, when a large El Niño event resulted in excess rainfall and warmer temperatures in the region, and a dengue outbreak occurred 3 months earlier than expected. This event was successfully predicted using a similar prediction framework to the one applied here. With the present study, we continue our efforts to build and test a climate service tool to issue early warnings of dengue outbreaks in the region.

     
    more » « less