The rightward tendency (in northern hemisphere) of enhanced phytoplankton bloom often observed in the wake of a tropical cyclone has commonly been attributed to the rightward bias of mixing due to stronger wind and wind‐current resonance. We demonstrated using a high‐resolution biophysical model that vertical mixing alone resulted only in weak asymmetry after the passage of the storm. The enhanced bloom was caused instead by decreased turbulence due to restratification by submesoscale recirculation cells preferentially produced on the right side, rightward shift of cool isotherms, and spin‐up of a subsurface jet. We showed using a two‐time scale asymptotic expansion that these slower‐evolving features were forced by resonance Reynolds stresses of the energetic and rapidly oscillating near‐inertial internal waves.
Abstract. It is widely recognised that the variation of average surface chlorophyll a concentration (Chl) in the South China Sea (SCS) is closely related to wind forcing, especially during the intense winter monsoon. In this study, we demonstrate that after removal of the seasonal cycles, the variation of Chl showed strong asymmetric responses to wind speed under El Niño or La Niña conditions. The analysis was based on a time-series of Chl in the study area (115–117° E, 17–19° N) around the SEATS (South-East Asian Time-series Study) station located in the central northern SCS from September 1997 to the end of 2011, which was constructed by merging the SeaWiFS data (1997–2006) and MODIS data (2003–2011). The merged daily data were validated by shipboard observations at the SEATS station. The non-seasonal variations of monthly mean Chl, wind speed, sea surface height (SSH) and sea surface temperature (SST) were examined against the multivariate ENSO index (MEI). The analysis reveals strongly asymmetric correlations of Chl and SST with positive MEI (El Niño) or negative MEI (La Niña). Under El Niño conditions, both showed significant correlations with MEI or wind speed; under La Niña conditions, both showed weak or insignificant correlations. The contrast was more pronounced for Chl than for SST. The subdued responses of Chl to wind forcing under La Niña conditions were attributable to a deepened thermocline, for which wind driven nutrient pumping is less efficient. A deeper thermocline, which was observed during the 1999–2000 La Niña event and inferred by positive SSH anomalies during other La Niña events, was probably caused by reduced SCS throughflow under La Niña conditions. Intrusion of the nutrient-depleted Kuroshio water in the surface layer as observed during the 1999–2000 La Niña could be partially responsible for the suppressed Chl response.
It is widely recognized that the variation of average surface chlorophyll a concentration (Chl) in the South China Sea (SCS) is closely related to wind forcing, especially during the intense winter monsoon. In this study we demonstrate that, after removal of the seasonal cycles, the variation of Chl showed strong asymmetric responses to wind speed under El Niño or La Niña conditions. The analysis was based on a time-series of Chl in the study area (115–117° E, 17–19° N) around the SEATS (South-East Asian Time-series Study) station located in the central northern SCS from September 1997 to the end of 2011, which was constructed by merging the SeaWiFS data (1997–2006) and MODIS data (2003–2011). The merged daily data were validated by shipboard observations at the SEATS station. The non-seasonal variations of monthly mean Chl, wind speed, sea surface height (SSH) and sea surface temperature (SST) were examined against the multivariate ENSO index (MEI). The analysis reveals strongly asymmetric correlations of Chl and SST with positive MEI (El Niño) or negative MEI (La Niña). Under El Niño conditions, both showed significant correlations with MEI or wind speed; under La Niña conditions, both showed weak or insignificant correlations. The contrast was more pronounced for Chl than for SST. The subdued responses of Chl to wind forcing under La Niña conditions were probably attributed to a deepened thermocline, for which wind driven nutrient pumping is less efficient. A deeper thermocline, which was observed during the 1999–2000 La Niña event and inferred by positive SSH anomalies during other La Niña events, was probably caused by reduced SCS throughflow under La Niña conditions. Intrusion of the nutrient-depleted Kuroshio water in the surface layer as observed during the 1999–2000 La Niña could be partially responsible for the suppressed Chl response
A significant poleward shift of tropical cyclones (TCs or typhoons) and TC‐induced storm surge in the western North Pacific has occurred in recent decades. Here we use 64 year rainfall observations around Taiwan to provide an independent evidence of the shift. We show that, due to the island's unique location relative to typhoon tracks, TC‐induced rainfall trends are significantly rising west and north of the island but are insignificant east and southeast, caused by a preference in recent decades for TCs to veer more poleward. Analyses of large‐scale fields indicate that the TCs' poleward shift is caused by the weakening of the steering flow and western North Pacific subtropical high, which in turn is due to tropic‐subtropical Indo‐Pacific warming and a weakened monsoon, consistent with the expansion of the tropics due to climate change.
Land managers rely on prescribed burning and naturally ignited wildfires for ecosystem management, and must balance trade-offs of air quality, carbon storage, and ecosystem health. A current challenge for land managers when using fire for ecosystem management is managing smoke production. Smoke emissions are a potential human health hazard due to the production of fine particulate matter (PM2.5), carbon monoxide (CO), and ozone (O3) precursors. In addition, smoke emissions can impact transportation safety and contribute to regional haze issues. Quantifying wildland fire emissions is a critical step for evaluating the impact of smoke on human health and welfare, and is also required for air quality modeling efforts and greenhouse gas reporting. Smoke emissions modeling is a complex process that requires the combination of multiple sources of data, the application of scientific knowledge from divergent scientific disciplines, and the linking of various scientific models in a logical, progressive sequence. Typically, estimates of fire size, available fuel loading (biomass available to burn), and fuel consumption (biomass consumed) are needed to calculate the quantities of pollutants produced by a fire. Here we examine the 2006 Tripod Fire Complex as a case study for comparing alternative data sets and combinations of scientific models available for calculating fire emissions. Specifically, we use five fire size information sources, seven fuel loading maps, and two consumption models (Consume 4.0 and FOFEM 5.7) that also include sets of emissions factors. We find that the choice of fuel loading is the most critical step in the modeling pathway, with different fuel loading maps varying by 108 %, while fire size and fuel consumption show smaller variations (36 % and 23 %, respectively). Moreover, we find that modeled fuel loading maps likely underestimate the amount of fuel burned during wildfires as field assessments of total woody fuel loading were consistently higher than modeled fuel loadings in all cases. The PM2.5 emissions estimates from Consume and FOFEM vary by 37 %. In addition, comparisons with available observational data demonstrate the value of using local data sets where possible.
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