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Abstract
Cloud feedback plays a key role in the future climate projection. Using global nonhydrostatic model (GNHM) simulation data for a present-day [control (CTL)] and a warmer [global warming (GW)] experiment, the authors estimate the contribution of tropical cyclones (TCs) to ice water paths (IWP) and liquid water paths (LWP) associated with TCs and their changes between CTL and GW experiments. They use GNHM with a 14-km horizontal mesh for explicitly calculating cloud microphysics without cumulus parameterization. This dataset shows that the cyclogenesis under GW conditions reduces to approximately 70% of that under CTL conditions, as shown in a previous study, and the tropical averaged IWP (LWP) is reduced by approximately 2.76% (0.86%). Horizontal distributions of IWP and LWP changes seem to be closely related to TC track changes. To isolate the contributions of IWP/LWP associated with TCs, the authors first examine the radial distributions of IWP/LWP from the TC center at their mature stages and find that they generally increase for more intense TCs. As the intense TC in GW increases, the IWP and LWP around the TC center in GW becomes larger than that in CTL. The authors next define the TC area as the region within 500 km from the TC center at its mature stages. They find that the TC’s contribution to the total tropical IWP (LWP) is 4.93% (3.00%) in CTL and 5.84% (3.69%) in GW. Although this indicates that the TC’s contributions to the tropical IWP/LWP are small, IWP/LWP changes in each basin behave in a manner similar to those of the cyclogenesis and track changes under GW.
Abstract
Cloud feedback plays a key role in the future climate projection. Using global nonhydrostatic model (GNHM) simulation data for a present-day [control (CTL)] and a warmer [global warming (GW)] experiment, the authors estimate the contribution of tropical cyclones (TCs) to ice water paths (IWP) and liquid water paths (LWP) associated with TCs and their changes between CTL and GW experiments. They use GNHM with a 14-km horizontal mesh for explicitly calculating cloud microphysics without cumulus parameterization. This dataset shows that the cyclogenesis under GW conditions reduces to approximately 70% of that under CTL conditions, as shown in a previous study, and the tropical averaged IWP (LWP) is reduced by approximately 2.76% (0.86%). Horizontal distributions of IWP and LWP changes seem to be closely related to TC track changes. To isolate the contributions of IWP/LWP associated with TCs, the authors first examine the radial distributions of IWP/LWP from the TC center at their mature stages and find that they generally increase for more intense TCs. As the intense TC in GW increases, the IWP and LWP around the TC center in GW becomes larger than that in CTL. The authors next define the TC area as the region within 500 km from the TC center at its mature stages. They find that the TC’s contribution to the total tropical IWP (LWP) is 4.93% (3.00%) in CTL and 5.84% (3.69%) in GW. Although this indicates that the TC’s contributions to the tropical IWP/LWP are small, IWP/LWP changes in each basin behave in a manner similar to those of the cyclogenesis and track changes under GW.
Abstract
Of broad scientific and public interest is the reliability of global climate models (GCMs) to simulate future regional and local tropical cyclone (TC) occurrences. Atmospheric GCMs are now able to generate vortices resembling actual TCs, but questions remain about their fidelity to observed TCs. Here the authors demonstrate a spatial lattice approach for comparing actual with simulated TC occurrences regionally using observed TCs from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset and GCM-generated TCs from the Geophysical Fluid Dynamics Laboratory (GFDL) High Resolution Atmospheric Model (HiRAM) and Florida State University (FSU) Center for Ocean–Atmospheric Prediction Studies (COAPS) model over the common period 1982–2008. Results show that the spatial distribution of TCs generated by the GFDL model compares well with observations globally, although there are areas of over- and underprediction, particularly in parts of the Pacific Ocean. Difference maps using the spatial lattice highlight these discrepancies. Additionally, comparisons focusing on the North Atlantic Ocean basin are made. Results confirm a large area of overprediction by the FSU COAPS model in the south-central portion of the basin. Relevant to projections of future U.S. hurricane activity is the fact that both models underpredict TC activity in the Gulf of Mexico.
Abstract
Of broad scientific and public interest is the reliability of global climate models (GCMs) to simulate future regional and local tropical cyclone (TC) occurrences. Atmospheric GCMs are now able to generate vortices resembling actual TCs, but questions remain about their fidelity to observed TCs. Here the authors demonstrate a spatial lattice approach for comparing actual with simulated TC occurrences regionally using observed TCs from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset and GCM-generated TCs from the Geophysical Fluid Dynamics Laboratory (GFDL) High Resolution Atmospheric Model (HiRAM) and Florida State University (FSU) Center for Ocean–Atmospheric Prediction Studies (COAPS) model over the common period 1982–2008. Results show that the spatial distribution of TCs generated by the GFDL model compares well with observations globally, although there are areas of over- and underprediction, particularly in parts of the Pacific Ocean. Difference maps using the spatial lattice highlight these discrepancies. Additionally, comparisons focusing on the North Atlantic Ocean basin are made. Results confirm a large area of overprediction by the FSU COAPS model in the south-central portion of the basin. Relevant to projections of future U.S. hurricane activity is the fact that both models underpredict TC activity in the Gulf of Mexico.
Abstract
A statistical model for the intensity of the strongest hurricanes has been developed and a new methodology introduced for estimating the sensitivity of the strongest hurricanes to changes in sea surface temperature. Here, the authors use this methodology on observed hurricanes and hurricanes generated from two global climate models (GCMs). Hurricanes over the North Atlantic Ocean during the period 1981–2010 show a sensitivity of 7.9 ± 1.19 m s−1 K−1 (standard error; SE) when over seas warmer than 25°C. In contrast, hurricanes over the same region and period generated from the GFDL High Resolution Atmospheric Model (HiRAM) show a significantly lower sensitivity with the highest at 1.8 ± 0.42 m s−1 K−1 (SE). Similar weaker sensitivity is found using hurricanes generated from the Florida State University Center for Ocean–Atmospheric Prediction Studies (FSU-COAPS) model with the highest at 2.9 ± 2.64 m s−1 K−1 (SE). A statistical refinement of HiRAM-generated hurricane intensities heightens the sensitivity to a maximum of 6.9 ± 3.33 m s−1 K−1 (SE), but the increase is offset by additional uncertainty associated with the refinement. Results suggest that the caution that should be exercised when interpreting GCM scenarios of future hurricane intensity stems from the low sensitivity of limiting GCM-generated hurricane intensity to ocean temperature.
Abstract
A statistical model for the intensity of the strongest hurricanes has been developed and a new methodology introduced for estimating the sensitivity of the strongest hurricanes to changes in sea surface temperature. Here, the authors use this methodology on observed hurricanes and hurricanes generated from two global climate models (GCMs). Hurricanes over the North Atlantic Ocean during the period 1981–2010 show a sensitivity of 7.9 ± 1.19 m s−1 K−1 (standard error; SE) when over seas warmer than 25°C. In contrast, hurricanes over the same region and period generated from the GFDL High Resolution Atmospheric Model (HiRAM) show a significantly lower sensitivity with the highest at 1.8 ± 0.42 m s−1 K−1 (SE). Similar weaker sensitivity is found using hurricanes generated from the Florida State University Center for Ocean–Atmospheric Prediction Studies (FSU-COAPS) model with the highest at 2.9 ± 2.64 m s−1 K−1 (SE). A statistical refinement of HiRAM-generated hurricane intensities heightens the sensitivity to a maximum of 6.9 ± 3.33 m s−1 K−1 (SE), but the increase is offset by additional uncertainty associated with the refinement. Results suggest that the caution that should be exercised when interpreting GCM scenarios of future hurricane intensity stems from the low sensitivity of limiting GCM-generated hurricane intensity to ocean temperature.
