Loading in 2 Seconds...
Loading in 2 Seconds...
Impact of African Dust on Clouds and Precipitation in a Caribbean Tropical Montane Cloud Forest (PRADACS). Olga L. Mayol-Bracero Institute for Tropical Ecosystem Studies University of Puerto Rico, Río Piedras Campus Kickoff Meeting University of Puerto Rico – Río Piedras Campus
Olga L. Mayol-Bracero
Institute for Tropical Ecosystem Studies
University of Puerto Rico, Río Piedras Campus
University of Puerto Rico – Río Piedras Campus
Interactions among aerosols, clouds, and precipitation are thought to shape the behavior of the climate system (ACPC).
Aerosols – leading source of uncertainty in the climate forcing
Clouds – largest source of uncertainty in estimates of equilibrium climate sensitivity
Precipitation – most poorly quantified yet essential climate variable.
ACP interactions are key aspects for tropical climate research, yet lack of data has limited our understanding of processes occurring in these pristine regions of the world, especially for the Caribbean.
We propose to meet this need by performing field measurements to provide a comprehensive data set on clouds, precipitation, and on cloud-influencing aerosols, particularly from African dust (AD), in a TMCF in the Caribbean.
We envision that our findings will apply not only to the TMCF and PR, but also to other tropical islands and coastal tropical regions that are highly vulnerable to climate change.
ACPC Science Plan and Implementation Strategy
TMCFs that are tightly coupled to the atmospheric hydrologic cycle.
TMCFs are believed to be the most sensitive and vulnerable of the world’s ecosystems to climate change (Lugo and Scatena, 1992, Loope and Gambelluca, 1998).
Relative to lowland tropical forests, TMCFs have persistent cloud or fog cover, reduced solar isolation, low temperatures, low nutrient availability, saturated soils, impeded root respiration, high winds, high humidity, and lower productivity (e.g., Tanner et al. 1992; Scatena, 1995).
Hotspots of biodiversity associated with high levels of endemism (Brown and Kapelle, 2001).
Atmospheric warming is raising the altitude of cloud cover that provides TMCF species with moisture via predictable and prolonged immersion in clouds or rainfall (Pounds et al. 1999), shifting habitats upslope and forcing TMCF species into an increasingly smaller area and even to extinction. (Monteverde, Costa Rica, Pounds et al. 1999, 2006).
TMCFs’ biological diversity and extreme sensitivity to climate change point to their use as an advanced warning system for detecting climate change (Lugo and Scatena, 1992; Benzing, 1998).
Therefore… TMCFs an interesting ecosystem to see the effects AD might have on cloud formation and precipitation.
Dust particles impact visibility, health, ecosystems, and influence the Earth’s radiative balance by scattering solar radiation in the atmosphere and by affecting cloud formation, and thus, cloud albedo.
Cloud radiative properties and rain formation in warm clouds both depend on cloud droplet size distribution (CDSD).
CDSD is determined by aerosol CCN activation spectra and cloud base updraft.
AD is important because it not only changes the number of CCN, but also may provide giant CCN (GCCN) (Teller and Levin 2006) and, thus, has a significant effect on raindrop size and rainfall intensity.
The impact of aerosols on rainfall, cloud lifecycle and cover and eventual energy budget is a primary reason to investigate processes controlling the aerosol CCN activation spectra.
LRTAD affects ecosystems, human health, weather, and climate.
LRTAD particles alter the size distributions and chemical composition of Caribbean aerosols, as well as their water uptake properties (Mayol-Bracero et al. 2006b; Morales-García et al. 2007).
LRTAD has also been linked to a large-scale reduction in precipitation in the Caribbean (Prospero and Lamb, 2003; Angeles et al. 2007) and South Florida (DeMott et al. 2003; Sassen et al. 2003; Toon, 2003).
How aging (chemical and physical properties) impacts cloud properties and climate as the dust particles travel from, for example, Africa to the Caribbean region?
Is African Dust (AD) a major source of tropical hurricane suppression in the Northern Tropical Atlantic due to factors such as cooling of sea surface temperatures in the hurricane main developing region (Lau and Kim 2007) and the Saharan Air Layer (Dunion and Velden, 2004 )?
Caribbean AD events coincide with the midyear dry-wet cycle (June-October) (Griffin et al. 2001a, b; Larsen, 2000). Therefore, does intensification of the frequency and intensity of AD events could magnify dry periods and summer drought?
LRTAD – long-range transported African dust
January 2006 – RICO-PRACS
Enhancement due to anthropogenic pollution - Jan 20-21 (black line).
Enhancement in the accumulation and coarse modes, most likely due to dust particles - Jan 14-16 (blue line).
PCASP = Passive Cavity Aerosol Spectrometer Probe
Mayol-Bracero et al., in progress
Cl/Na = 1.45
SO4/Na = 0.77
Ca/Na = 0.077
nss-sulfate = 74 ng m-3
Cl/Na = 1.63
SO4/Na = 0.70
Ca/Na = 0.17
nss-sulfate = 128 ng m-3
Cl/Na = 1.11 (sea-spray acidification)
SO4/Na = 2.03
Ca/Na = 0.08
nss-sulfate = 193 ng m-3
Marine Aerosol (Warneck, 1988)
Cl/Na = 1.590 SO4/Na = 0.885 Ca/Na = 0.058
nss-sulfate in remote/clean areas is about 200 ng m-3.
Mayol-Bracero et al., in progress
Located in the subtropics, 18’15 N, 66’30 W
Heavily influenced by the trade winds, by hurricane storm tracks, and by long-range transported African dust (LRTAD) (also volcanic ash)
Key convergence zone between 2 continental landmasses, thus, plays a role in hemispheric climate
Highly undersampled/understudied region
Highly vulnerable to the Earth’s climate change
Presence of tropical montane cloud forests
Two ideal sampling locations: the natural reserve of Cabezas de San Juan, Fajardo (CSJ) and El Yunque National Forest (East Peak , cloud forest - 1051 m)
Large university research center (UPR-RP) within ~60 min drive.
How the physico-chemical properties of long-range transported African dust (LRTAD) aerosols influence Caribbean cloud properties and precipitation levels in a unique Puerto Rican tropical montane cloud forest (TMCF)?
H1: Cloud properties in the TMCF are different during intense LRTAD periods
H2: LRTAD has unique chemical and physical properties which influence cloud properties and precipitation processes
CSJ is part of the AERONET, has support from NOAA ESRL, and is a GAW contributing station.
View looking upwind to CSJ, pointed to by the arrow.
Darrel Baumgardner (UNAM) and Stephan Borrmann (MPIC)
Cloud water collector
Kim Prather and her family (2009)
CN counter, CCN counter, SMPS, aethalometer, nephelometer, CIMEL sunphotometer, PSAP, OPC, APS, APSD, disdrometer
LWC, CVI, visibility sensor, FM-100, webcam system
Thermal/optical analysis (TC, OC, EC)
Total Organic Carbon and Total Nitrogen Analyzer (TOC, DOC, TN)
Ion Chromatography (Na+, NH4+, Ca2+, K+, Mg2+, Cl-, NO3- SO42-, acetate, formate, oxalate, and MSA)
ICP (P, K, Mn, Fe, Ca, Mg, Na, Al)
pH, cloud and rain water volumes
Aerosols – Stacked-filter units, low-pressure impactors
Cloud water - Caltech Active Strand Cloud Water Collector (CASCC, mini CASCC)
Rainwater - Bottle and funnel
Daily Aerosol Optical Thickness Satellite Images from NOAA / NESDIS
Air Mass Backward Trajectories
NOAA ARL HYSPLIT model (HYbrid Single-Particle Lagrangian Integrated Trajectory)
Measurements and Analyses (see attachments)
Measurements: Cloud properties (e.g., droplet concentration (Ndrops), drop size distribution, cloud drop effective radius (Reff), cloud albedo, liquid water content (LWC), cloud base height, composition). These properties are influenced by the characteristics of the aerosol they form upon, thus we would expect cloud properties to be different during LRTAD events.
AVHRR and MODIS satellite images together with HYSPLIT back trajectories and aerosol properties will be used to classify samples according to the presence or absence of LRTAD. We will use this classification to compare cloud properties, wet deposition, and precipitation for LRTAD periods with non-LRTAD periods.
Satellite data from CALIPSO, CloudSat, MISR and TRMMM
Cloud properties combined with cloud occurrence and frequency in the presence and absence of LRTAD will also give an indication of the impact LRTAD is having on climate. These results will allow us to begin understanding the relative effects of LRTAD on cloud formation and precipitation.
First, we will identify the chemical, physical, and optical properties of LRTAD and differentiate this dust from local aerosol sources. (LRTAD vs non-LRTAD)
ATOFMS, AMS, MOUDI, DLPI, SFUs, CN, CCN, aethalometer, Neph, PSAP, sunphotometer, hygroscopicity,…
Second, we will determine how those unique LRTAD properties affect cloud properties and precipitation processes, and how those properties might explain the observed differences in cloud or rain properties for LRTAD periods found in H1.
use of statistical correlations of residual particles of cloud droplets and interstitial particles with various cloud parameters
Aerosols, clouds, and rainwater samples
See attached Work Plan and Tables
Test, calibration, and diagnosis year that will allow to begin developing procedures for data analysis and archiving of combined data sets without distractions of a full blown intensive field campaign.
Sampling logistics (e.g., shipping, transport, power, and installations).
Running things side by side to find out the right sampling times, if we need other measurements,...
Train students and technician
Short intensive field period (end of July 2010) to test and fine tune instruments and methods
ATOFMS, nephelometers with the humidification system, new SMPS, OPC, CPC (see attached table of instruments to be deployed).
In summary, Y1 will be a test run that will allow us to run a much stronger large intensive field period in Y2, ensuring the success of the intensive field phase and of the project.
The intensive field phase of the project will take place in Y2.
July-August (dust period) and Oct-Nov (non-dust period) sampling periods (need to fine tune specific dates). This Oct-Nov period could be either in Y2 or at the beginning of Y3. This needs to be decided today.
See attached table of instruments to be deployed.
Starting in Y2, we will start the search for additional funds to support the completion of the data analyses, as well as to be able to use the data already generated to support the modeling component.
Data analysis, presentations and publications.
Symposium and workshop
Andy Heymsfield (ICE-T – C-130)
Dan Czizco (DOE G-1)
Jason Dunion (NOAA)
Bjoern Stevens (HALO)
Teen’s University – intermediate and high school students (S. Borrmann)
PISCAS program – university students
Birch Aquarium (Scripps) – general public
PI: O. L. Mayol-Bracero - UPR-RP ITES
CoPIs: E. Andrews - UC Boulder and NOAA ESRL, K. Prather - UCSD and Scripps
C. Valle, P. Vallejo – UPR-RP Grad students (ITES and Chemistry)
J. Collett - CSU, Colorado
D. Rosenfeld -Hebrew University, Israel
G. Frank - Lund University, Sweden
S. Borrmann -Max Planck Institute for Chemistry – Germany
W. Eugster – ETH, Switzerland
P. Formenti – LISA University, France
S. Raizada – Arecibo Observatory, PR
R. Morales – UPR-RP
I. Matos – NWS San Juan, PR
P. Diaz – USGS, PR