Quantifying the Impact of Sea Surface Temperature Biases on Simulated Tropical Cyclones
Abstract
Sea surface temperature (SST) patterns both local to and remote from tropical cyclone (TC) development regions are important drivers of TC variability. Therefore, reliable simulations and skill predictions of TC activity depend on a realistic representation of tropical SST. However, severe SST biases are common to the current generation of global climate models, especially in the tropical Pacific and Atlantic. Nevertheless, how these SST biases influence simulated TC activity is still not well known. To investigate the impact of the SST biases on simulated TC activity, several suites of 16-member ensemble simulations from a tropical channel model were conducted. Simulation results show that the biases cause an overrepresentation (by 200%) and underrepresentation (by 60%) of seasonal TC activity in Eastern North Pacific (ENP) and Atlantic basin, respectively, while the impact on TCs in Western North Pacific is insubstantial. TCs in the ENP appear to be impacted by the joint effect of tropical Atlantic and Pacific SST biases, whereas Atlantic TC activity is mostly affected by the SST bias in the Northern Tropical Atlantic. Moreover, even though the spatial patterns and magnitudes are similar between the Atlantic and Pacific SST biases, the mechanisms of these biases' influence on simulated TC activity are different. The results of this study indicate the importance of considering SST bias effects on simulated TC activity in climate model studies and highlights key regions where reducing SST biases could potentially improve TC representation in climate models.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2016
- Bibcode:
- 2016AGUFM.A43I0363H
- Keywords:
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- 3315 Data assimilation;
- ATMOSPHERIC PROCESSESDE: 3360 Remote sensing;
- ATMOSPHERIC PROCESSESDE: 3372 Tropical cyclones;
- ATMOSPHERIC PROCESSESDE: 4313 Extreme events;
- NATURAL HAZARDS