Total Organic Carbon and the Contribution From Speciated Organics in Cloud Water: Airborne Data Analysis From the CAMP2Ex Field Campaign

This work focuses on total organic carbon (TOC) and contributing species in cloud water over Southeast Asia using a rare airborne dataset collected during NASA’s Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex), in which a wide variety of maritime clouds were studied, including cumulus congestus, altocumulus, altostratus, and cumulus. Knowledge of TOC masses and their contributing species is needed for improved modeling of cloud processing of organics and to understand how aerosols and gases impact and are impacted by clouds. This work relies on 159 samples collected with an axial cyclone cloudwater collector at altitudes of 0.2–6.8 km that had sufficient volume for both TOC and speciated organic composition analysis. Species included monocarboxylic acids (glycolate, acetate, formate, and pyruvate), dicarboxylic acids (glutarate, adipate, succinate, maleate, and oxalate), methanesulfonic acid (MSA), and dimethylamine (DMA). TOC values range between 0.018 and 13.66 ppm C with a mean of 0.902 ppm C. The highest TOC values are observed below 2 km with a general reduction aloft. An exception is samples impacted by biomass burning for which TOC remains enhanced at altitudes as high as 6.5 km (7.048 ppm C). Estimated total organic matter derived from TOC contributes a mean of 30.7 % to total measured mass (inorganics + organics). Speciated organics contribute (on a carbon mass basis) an average of 30.0 % to TOC in the study region and account for an average of 10.3 % to total measured mass. The order of the average contribution of species to TOC, in decreasing contribution of carbon mass, is as follows (±1 standard deviation): acetate (14.7 ± 20.5 %), formate (5.4 ± 9.3 %), oxalate (2.8 ± 4.3 %), DMA (1.7 ± 6.3 %), succinate (1.6 ± 2.4 %), pyruvate (1.3 ± 4.5 %), glycolate (1.3 ± 3.7 %), adipate (1.0 ± 3.6 %), MSA (0.1 ± 0.1 %), glutarate (0.1 ± 0.2 %), and maleate (\u3c 0.1 ± 0.1 %). Approximately 70 % of TOC remains unaccounted for, highlighting the complex nature of organics in the study region; in samples collected in biomass burning plumes, up to 95.6 % of TOC mass is unaccounted for based on the species detected. Consistent with other regions, monocarboxylic acids dominate the speciated organic mass (∼ 75 %) and are about 4 times more abundant than dicarboxylic acids. Samples are categorized into four cases based on backtrajectory history, revealing source-independent similarity between the bulk contributions of monocarboxylic and dicarboxylic acids to TOC (16.03 %–23.66 % and 3.70 %–8.75 %, respectively). Furthermore, acetate, formate, succinate, glutarate, pyruvate, oxalate, and MSA are especially enhanced during biomass burning periods, which is attributed to peat emissions transported from Sumatra and Borneo. Lastly, dust (Ca2+) and sea salt (Na+/Cl−) tracers exhibit strong correlations with speciated organics, supporting how coarse aerosol surfaces interact with these water-soluble organics

Potential Influence of Sea Surface Temperature Representation in Climate Model Simulations Over CORDEX-SEA Domain

Regional climate simulations from the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment – Southeast Asia (SEA) indicated model biases in temperature and rainfall over SEA. Given the influence of sea surface temperature (SST) variability on SEA climate, this study examines SST representation in climate models to investigate its potential contribution to the resulting model biases over the Philippines. Observed SST over SEA is first characterized by its spatial patterns and temporal variability. An analysis of the SST representation over SEA and its potential influence on modelled climate over the Philippines in Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) is then conducted, followed by an assessment of the potential influence of SST representation in CMIP5 GCMs on downscaled regional climate output. Our results show that GCMs with well represented SSTs (i.e., low bias, well captured variability, and pattern) can produce climate simulations well over the Philippines. Whether or not the GCMs with poor SST representation can perform well is inconclusive. During boreal winter (summer), climate variables with high (low) spatial correlation with model SST get poor (better) spatial correlation with observed climate. Over west of the Philippines, where model SST seasonal variability is captured well, models also adequately simulate climate variables. Results suggest that the negative temperature biases, and positive precipitation and wind speed biases, in both GCMs and downscaled simulations, are associated with negative model SST biases. These findings give a better understanding on how SST potentially influences modelled climatology over the Philippines