Tropical Mean Sea Surface Temperature Research Articles

Constraining Regional Hydrological Sensitivity Over Tropical Oceans

AbstractRegional hydrological sensitivity (i.e., precipitation change per degree local surface warming) contributes substantially to the uncertainty in future precipitation projections over tropical oceans. Here, we investigate the sensitivity of relative precipitation (P*, precipitation divided by the basin average precipitation) to local sea surface temperature (SST) change by dissecting it into three components, namely the sensitivity of P* to relative SST (SSTrel, SST minus the tropical mean SST) changes, the sensitivity of P* to surface convergence changes, and the sensitivity of surface convergence to SST gradient changes. We show that the relationships between P* and SSTrel, and between P*, surface convergence, and SST gradients are largely constant during climate change. This allows us to constrain regional hydrological sensitivity based on present‐day SST‐precipitation relationships. The sensitivity of surface convergence to SST gradient changes is a main source of uncertainty in regional hydrological sensitivity and is likely underestimated in GCMs.

A Relative Sea Surface Temperature Index for Classifying ENSO Events in a Changing Climate

Abstract El Niño–Southern Oscillation (ENSO) is often characterized through the use of sea surface temperature (SST) departures from their climatological values, as in the Niño-3.4 index. However, this approach is problematic in a changing climate when the climatology itself is varying. To address this issue, van Oldenborgh et al. proposed a relative Niño-3.4 SST index, which subtracts the tropical mean SST anomaly from the Niño-3.4 index and multiplies by a scaling factor. We extend their work by providing a simplified calculation procedure for the scaling factor, and confirm that the relative index demonstrates reduced sensitivity to climate change and multidecadal variability. In particular, we show in three observational SST datasets that the relative index provides a more consistent classification of historical El Niño and La Niña oceanic conditions that is more robust across climatological periods compared to the nonrelative index. Forecast skill of the relative Niño-3.4 index in the North American Multimodel Ensemble (NMME) and ACCESS-S2 is slightly reduced for targets during the first half of the year because subtracting the tropical mean removes a source of additional skill. For targets in the second half of the year, the relative and nonrelative indices are equally skillful. Observed ENSO teleconnections in 200-hPa geopotential height and precipitation during key seasons are sharper and explain more variability over Australia and the contiguous United States when computed with the relative index. Overall, the relative Niño-3.4 index provides a more robust option for real-time monitoring and forecasting ENSO in a changing climate. Significance Statement The goal of this study is to further explore a relative sea surface temperature index for monitoring and prediction of El Niño–Southern Oscillation. Sea surface temperature indices are typically computed as a difference from a 30-yr climatological average, and El Niño and La Niña events occur when values exceed a certain threshold. This method is suitable when the climate is stationary. However, because of climate change and other lower-frequency variations, historical El Niño and La Niña events are reclassified depending on which climatological period is selected. A relative index is investigated to ameliorate this problem.

Significant Increase in Sea Surface Temperature at the Genesis of Tropical Mesoscale Convective Systems

AbstractAn event‐based assessment of the sea surface temperature (SST) threshold at the genesis of tropical mesoscale convective systems (MCSs) is performed in this study. We show that this threshold (SSTG) has undergone a significant warming trend at a rate of ∼0.2°C per decade. The SSTG shows a remarkable correspondence with the tropical mean SST and upper‐tropospheric temperature on interannual and longer timescales. Using a high‐resolution global climate model that permits realistic simulations of tropical MCSs, we find that the observed features of SSTG are well simulated. Both observation and model simulations demonstrate that the upward tendency in SSTG primarily results from the environmental SST warming over MCS genesis regions rather than the changes in MCS genesis location. A continuous increase in SSTG is projected in a warming simulation, but the relationship between SSTG and upper‐tropospheric temperature remains unchanged, suggesting that the tropical tropospheric temperature generally follows a moist‐adiabatic adjustment.

Estimation of maximum seasonal tropical cyclone damage in the Atlantic using climate models

There are several different estimates of the observed cyclone damage potential of tropical cyclones based on observations of size, intensity and track. For the analysis of climate model data, previous work identified an index, the cyclone damage potential climate index (CDPClimate), based on relative sea surface temperature (SST) and tropical cyclone steering flow to estimate the damage potential in climate models. Using millennia-long climate models, CDPClimate is estimated for the North Atlantic basin and compared against values from reanalyses and the observed damage potential. The peak in SSTs in the cyclone main development region with respect to the tropical mean SSTs is smaller in these models than reanalyses, resulting in smaller variations in CDPClimate. Although the year 1995 had the highest observed cyclone damage potential, the year 2010 is a maximum for CDPClimate in the reanalysis data. The models exceed this 2010 value in less than 1% of model years. Using a model with 100 ensemble members, the variability in CDPClimate is examined further. The interannual variability of the ensemble mean results has a very high correlation (R = 0.95) with reanalyses. The high decadal variability is evident and interannual variability is found to have increased during the 30 years after 1981 relative to those prior. The 2010 ensemble mean value is exceeded in other years by individual ensemble members 1.1% of the time. The results from this study suggest that although it is possible to exceed the observed CDP, this is rare in the current climate. However, this study does not consider changes as we move to future climates.

U.S. Tropical Cyclone Activity in the 2030s Based on Projected Changes in Tropical Sea Surface Temperature

AbstractWe use a statistical tropical cyclone (TC) model, the North Atlantic Stochastic Hurricane Model (NASHM), in combination with sea surface temperature (SST) projections from climate models, to estimate regional changes in U.S. TC activity into the 2030s. NASHM is trained on historical variations in TC characteristics with two SST indices: global–tropical mean SST and the difference between tropical North Atlantic Ocean (NA) SST and the rest of the global tropics, often referred to as “relative SST.” Testing confirms the model’s ability to reproduce historical U.S. TC activity as well as to make skillful predictions. When NASHM is driven by SST projections into the 2030s, overall NA annual TC counts increase, and the fractional increase is the greatest at the highest wind intensities. However, an eastward anomaly in mean TC tracks and an eastward shift in TC formation region result in a geographically varied signal in U.S. coastal activity. Florida’s Gulf Coast is projected to see significant increases in TC activity relative to the long-term historical mean, and these increases are fractionally greatest at the highest intensities. By contrast, the northwestern U.S. Gulf Coast and the U.S. East Coast will see little change.

A Basic Effect of Cloud Radiative Effects on Tropical Sea Surface Temperature Variability

AbstractCloud radiative effects (CREs) are known to play a central role in governing the long-term mean distribution of sea surface temperatures (SSTs). Very recent work suggests that CREs may also play a role in governing the variability of SSTs in the context of El Niño–Southern Oscillation. Here, the authors exploit numerical simulations in the Max Planck Institute Earth System Model with two different representations of CREs to demonstrate that coupling between CREs and the atmospheric circulation has a much more general and widespread effect on tropical climate than that indicated in previous work. The results reveal that coupling between CREs and the atmospheric circulation leads to robust increases in SST variability on time scales longer than a month throughout the tropical oceans. Remarkably, cloud–circulation coupling leads to more than a doubling of the amplitude of decadal-scale variability in tropical-mean SSTs. It is argued that the increases in tropical SST variance derive primarily from the coupling between SSTs and shortwave CREs: Coupling increases the memory in shortwave CREs on hourly and daily time scales and thus reddens the spectrum of shortwave CREs and increases their variance on time scales spanning weeks to decades. Coupling between SSTs and CREs does not noticeably affect the variance of SSTs in the extratropics, where the effects from variability in CREs on the surface energy budget are much smaller than the effects from the turbulent heat fluxes. The results indicate a basic but critical role of CREs in climate variability throughout the tropics.

Variability and Predictability of North Atlantic Hurricane Frequency in a Large Ensemble of High-Resolution Atmospheric Simulations

AbstractVariability of North Atlantic annual hurricane frequency during 1951–2010 is studied using a 100-member ensemble of climate simulations by a 60-km atmospheric general circulation model that is forced by observed sea surface temperatures (SSTs). The ensemble mean results well capture the interannual-to-decadal variability of hurricane frequency in best track data since 1970, and suggest that the current best track data might underestimate hurricane frequency prior to 1966 when satellite measurements were unavailable. A genesis potential index (GPI) averaged over the main development region (MDR) accounts for more than 80% of the SST-forced variations in hurricane frequency, with potential intensity and vertical wind shear being the dominant factors. In line with previous studies, the difference between MDR SST and tropical mean SST is a useful predictor; a 1°C increase in this SST difference produces 7.05 ± 1.39 more hurricanes. The hurricane frequency also exhibits strong internal variability that is systematically larger in the model than observations. The seasonal-mean environment is highly correlated among ensemble members and contributes to less than 10% of the ensemble spread in hurricane frequency. The strong internal variability is suggested to originate from weather to intraseasonal variability and nonlinearity. In practice, a 20-member ensemble is sufficient to capture the SST-forced variability.

Multi-model ensemble forecasting of North Atlantic tropical cyclone activity

North Atlantic tropical cyclones (TCs) and hurricanes are responsible for a large number of fatalities and economic damage. Skillful seasonal predictions of the North Atlantic TC activity can provide basic information critical to our improved preparedness. This study focuses on the development of statistical–dynamical seasonal forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs. These models use only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational records because they reflect the importance of both local and non-local effects on the genesis and development of TCs in the North Atlantic basin. A set of retrospective forecasts of SSTs by six experimental seasonal-to-interannual prediction systems from the North American Multi-Model Ensemble are used as covariates. The retrospective forecasts are performed over the period 1982–2015. The skill of these statistical–dynamical models is quantified for different quantities (basin-wide number of tropical storms and hurricanes, power dissipation index and accumulated cyclone energy) for forecasts initialized as early as November of the year prior to the season to forecast. The results of this work show that it is possible to obtain skillful retrospective forecasts of North Atlantic TC activity with a long lead time. Moreover, probabilistic forecasts of North Atlantic TC activity for the 2016 season are provided.

The impact of eastern equatorial Pacific convection on the diversity of boreal winter El Niño teleconnection patterns

It is widely recognized that no two El Nino episodes are the same; hence the predictable variations of the climate impacts associated with El Nino remain an open problem. Through an analysis of observational data and of large ensembles from six climate models forced by the observed time-varying sea surface temperatures (SSTs), this study raises the argument that the most fundamental predictable variations of boreal wintertime El Nino teleconnection patterns relate to the distinction between convective (EPC) and non-convective eastern Pacific (EPN) events. This distinction is a consequence of the nonlinear relationship between deep convection and eastern Pacific SSTs, and the transition to a convective eastern Pacific has a predictable relationship with local and tropical mean SSTs. Notable differences (EPC minus EPN) between the teleconnection patterns include positive precipitation differences over southern North America and northern Europe, positive temperature differences over northeast North America, and negative temperature differences over the Arctic. These differences are stronger and more statistically significant than the more common partitioning between eastern Pacific and central Pacific El Nino. Most of the seasonal mean composite anomalies associated with EPN El Nino are not statistically significant owing to the weak SST forcing and small sample sizes; however, the EPN teleconnection is more robust on subseasonal timescales following periods when the EPN pattern of tropical convection is active. These findings suggest that the differences between EPC and EPN climate impacts are physically robust and potentially useful for intraseasonal forecasts for lead times of up to a few weeks.

Tropical cyclone rainfall area controlled by relative sea surface temperature.

Tropical cyclone rainfall rates have been projected to increase in a warmer climate. The area coverage of tropical cyclones influences their impact on human lives, yet little is known about how tropical cyclone rainfall area will change in the future. Here, using satellite data and global atmospheric model simulations, we show that tropical cyclone rainfall area is controlled primarily by its environmental sea surface temperature (SST) relative to the tropical mean SST (that is, the relative SST), while rainfall rate increases with increasing absolute SST. Our result is consistent with previous numerical simulations that indicated tight relationships between tropical cyclone size and mid-tropospheric relative humidity. Global statistics of tropical cyclone rainfall area are not expected to change markedly under a warmer climate provided that SST change is relatively uniform, implying that increases in total rainfall will be confined to similar size domains with higher rainfall rates.

Mechanisms of Forced Tropical Meridional Energy Flux Change

Abstract Anthropogenically forced changes to the mean and spatial pattern of sea surface temperatures (SSTs) alter tropical atmospheric meridional energy transport throughout the seasonal cycle—in total, its partitioning between the Hadley cells and eddies and, for the Hadley cells, the relative roles of the mass flux and the gross moist stability (GMS). The authors investigate this behavior using an atmospheric general circulation model forced with SST anomalies caused by either historical greenhouse gas or aerosol forcing, dividing the SST anomalies into two components: the tropical mean SST anomaly applied uniformly and the full SST anomalies minus the tropical mean. For greenhouse gases, the polar-amplified SST spatial pattern partially negates enhanced eddy poleward energy transport driven by mean warming. Both SST components weaken winter Hadley cell circulation and alter GMS. The Northern Hemisphere–focused aerosol cooling induces northward energy flux anomalies in the deep tropics, which manifest partially via strengthened northern and weakened southern Hadley cell overturning. Aerosol-induced GMS changes also contribute to the northward energy fluxes. A simple thermodynamic scaling qualitatively captures these changes, although it performs less well for the greenhouse gas simulations. The scaling provides an explanation for the tight correlation demonstrated in previous studies between shifts in the intertropical convergence zone and cross-equatorial energy fluxes.

Understanding the Anthropogenically Forced Change of Equatorial Pacific Trade Winds in Coupled Climate Models*

Abstract Understanding the change of equatorial Pacific trade winds is pivotal for understanding the global mean temperature change and the El Niño–Southern Oscillation (ENSO) property change. The weakening of the Walker circulation due to anthropogenic greenhouse gas (GHG) forcing was suggested as one of the most robust phenomena in current climate models by examining zonal sea level pressure gradient over the tropical Pacific. This study explores another component of the Walker circulation change focusing on equatorial Pacific trade wind change. Model sensitivity experiments demonstrate that the direct/fast response due to GHG forcing is to increase the trade winds, especially over the equatorial central-western Pacific (ECWP) (5°S–5°N, 140°E–150°W), while the indirect/slow response associated with sea surface temperature (SST) warming weakens the trade winds. Further, analysis of the results from 19 models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and the Parallel Ocean Program (POP)–Ocean Atmosphere Sea Ice Soil (OASIS)–ECHAM model (POEM) shows that the projected weakening of the trades is robust only in the equatorial eastern Pacific (EEP) ( 5°S–5°N, 150°–80°W), but highly uncertain over the ECWP with 9 out of 19 CMIP5 models producing intensified trades. The prominent and robust weakening of EEP trades is suggested to be mainly driven by a top-down mechanism: the mean vertical advection of more upper-tropospheric warming downward to generate a cyclonic circulation anomaly in the southeast tropical Pacific. In the ECWP, the large intermodel spread is primarily linked to model diversity in simulating the relative warming of the equatorial Pacific versus the tropical mean sea surface temperature. The possible root causes of the uncertainty for the trade wind change are also discussed.

Multiyear Predictions of North Atlantic Hurricane Frequency: Promise and Limitations

AbstractRetrospective predictions of multiyear North Atlantic Ocean hurricane frequency are explored by applying a hybrid statistical–dynamical forecast system to initialized and noninitialized multiyear forecasts of tropical Atlantic and tropical-mean sea surface temperatures (SSTs) from two global climate model forecast systems. By accounting for impacts of initialization and radiative forcing, retrospective predictions of 5- and 9-yr mean tropical Atlantic hurricane frequency show significant correlations relative to a null hypothesis of zero correlation. The retrospective correlations are increased in a two-model average forecast and by using a lagged-ensemble approach, with the two-model ensemble decadal forecasts of hurricane frequency over 1961–2011 yielding correlation coefficients that approach 0.9. These encouraging retrospective multiyear hurricane predictions, however, should be interpreted with care: although initialized forecasts have higher nominal skill than uninitialized ones, the relatively short record and large autocorrelation of the time series limits confidence in distinguishing between the skill caused by external forcing and that added by initialization. The nominal increase in correlation in the initialized forecasts relative to the uninitialized experiments is caused by improved representation of the multiyear tropical Atlantic SST anomalies. The skill in the initialized forecasts comes in large part from the persistence of a mid-1990s shift by the initialized forecasts, rather than from predicting its evolution. Predicting shifts like that observed in 1994/95 remains a critical issue for the success of multiyear forecasts of Atlantic hurricane frequency. The retrospective forecasts highlight the possibility that changes in observing system impact forecast performance.

Multiseason Lead Forecast of the North Atlantic Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE)

Abstract By considering the intensity, duration, and frequency of tropical cyclones, the power dissipation index (PDI) and accumulated cyclone energy (ACE) are concise metrics routinely used to assess tropical storm activity. This study focuses on the development of a hybrid statistical–dynamical seasonal forecasting system for the North Atlantic Ocean’s PDI and ACE over the period 1982–2011. The statistical model uses only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational record, reflecting the role of both local and nonlocal effects on the genesis and development of tropical cyclones in the North Atlantic basin. SSTs are predicted using a 10-member ensemble of the Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1 (GFDL CM2.1), an experimental dynamical seasonal-to-interannual prediction system. To assess prediction skill, a set of retrospective predictions is initialized for each month from November to April, over the years 1981–2011. The skill assessment indicates that it is possible to make skillful predictions of ACE and PDI starting from November of the previous year: skillful predictions of the seasonally integrated North Atlantic tropical cyclone activity for the coming season could be made even while the current one is still under way. Probabilistic predictions for the 2012 North Atlantic tropical cyclone season are presented.

TC-Permitting GCM Simulations of Hurricane Frequency Response to Sea Surface Temperature Anomalies Projected for the Late-Twenty-First Century

Abstract A tropical cyclone–permitting global atmospheric model is used to explore the hurricane frequency response to sea surface temperature (SST) anomalies generated by coupled models for the late-twenty-first century. Results are presented for SST anomalies averaged over 18 models as well as from 8 individual models. For each basin, there exists large intermodel spread in the magnitude and even the sign of the frequency response among the different SST projections. These sizable variations in response are explored to understand features of SST distributions that are important for the basin-wide hurricane responses. In the North Atlantic, the eastern Pacific, and the southern Indian basins, most (72%–86%) of the intermodel variance in storm frequency response can be explained by a simple relative SST index defined as a basin’s storm development region SST minus the tropical mean SST. The explained variance is significantly lower in the South Pacific (48%) and much lower in the western Pacific basin (27%). Several atmospheric parameters are utilized to probe changes in tropical atmospheric circulation and thermodynamical properties relevant to storm genesis in the model. While all present strong correlation to storm response in some basins, a parameter-measuring tropospheric convective mass flux stands out as skillful in explaining the simulated differences for all basins. Globally, in addition to a modest reduction of total storm frequency, the simulations exhibit a small, but robust eastward and poleward migration of genesis frequency in both the North Pacific and the North Atlantic Oceans. This eastward migration of storms can also be explained by changes in convection.

North Atlantic Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE): Statistical Modeling and Sensitivity to Sea Surface Temperature Changes

Abstract This study focuses on the statistical modeling of the power dissipation index (PDI) and accumulated cyclone energy (ACE) for the North Atlantic basin over the period 1949–2008, which are metrics routinely used to assess tropical storm activity, and their sensitivity to sea surface temperature (SST) changes. To describe the variability exhibited by the data, four different statistical distributions are considered (gamma, Gumbel, lognormal, and Weibull), and tropical Atlantic and tropical mean SSTs are used as predictors. Model selection, both in terms of significant covariates and their functional relation to the parameters of the statistical distribution, is performed using two penalty criteria. Two different SST datasets are considered [the Met Office’s Global Sea Ice and Sea Surface Temperature dataset (HadISSTv1) and NOAA’s extended reconstructed SST dataset (ERSSTv3b)] to examine the sensitivity of the results to the input data. The statistical models presented in this study are able to well describe the variability in the observations according to several goodness-of-fit diagnostics. Both tropical Atlantic and tropical mean SSTs are significant predictors, independently of the SST input data, penalty criterion, and tropical storm activity metric. The application of these models to centennial reconstructions and seasonal forecasting is illustrated. The sensitivity of North Atlantic tropical cyclone frequency, duration, and intensity is examined for both uniform and nonuniform SST changes. Under uniform SST warming, these results indicate that there is a modest sensitivity of intensity, and a decrease in tropical storm and hurricane frequencies. On the other hand, increases in tropical Atlantic SST relative to the tropical mean SST suggest an increase in the intensity and frequency of North Atlantic tropical storms and hurricanes.

U.S. Landfalling and North Atlantic Hurricanes: Statistical Modeling of Their Frequencies and Ratios

Abstract Time series of U.S. landfalling and North Atlantic hurricane counts and their ratios over the period 1878–2008 are modeled using tropical Atlantic sea surface temperature (SST), tropical mean SST, the North Atlantic Oscillation (NAO), and the Southern Oscillation index (SOI). Two SST input datasets are employed to examine the uncertainties in the reconstructed SST data on the modeling results. Because of the likely undercount of recorded hurricanes in the earliest part of the record, both the uncorrected hurricane dataset (HURDAT) and a time series with a recently proposed undercount correction are considered. Modeling of the count data is performed using a conditional Poisson regression model, in which the rate of occurrence can depend linearly or nonlinearly on the climate indexes. Model selection is performed following a stepwise approach and using two penalty criteria. These results do not allow one to identify a single “best” model because of the different model configurations (different SST data, corrected versus uncorrected datasets, and penalty criteria). Despite the lack of an objectively identified unique final model, the authors recommend a set of models in which the parameter of the Poisson distribution depends linearly on tropical Atlantic and tropical mean SSTs. Modeling of the fractions of North Atlantic hurricanes making U.S. landfall is performed using a binomial regression model. Similar to the count data, it is not possible to identify a single best model, but different model configurations are obtained depending on the SST data, undercount correction, and penalty criteria These results suggest that these fractions are controlled by local (related to the NAO) and remote (SOI and tropical mean SST) effects.

Estimating Annual Numbers of Atlantic Hurricanes Missing from the HURDAT Database (1878–1965) Using Ship Track Density

Abstract This study assesses the impact of imperfect sampling in the presatellite era (between 1878 and 1965) on North Atlantic hurricane activity measures and on the long-term trends in those measures. The results indicate that a substantial upward adjustment of hurricane counts may be needed prior to 1965 to account for likely “missed” hurricanes due to sparse density of reporting ship traffic. After adjusting for the estimate of missed hurricanes in the basin, the long-term (1878–2008) trend in hurricane counts changes from significantly positive to no significant change (with a nominally negative trend). The adjusted hurricane count record is more strongly connected to the difference between main development region (MDR) sea surface temperature (SST) and tropical-mean SST than with MDR SST. These results do not support the hypothesis that the warming of the tropical North Atlantic due to anthropogenic greenhouse gas emissions has caused Atlantic hurricane frequency to increase.

Changes in the sea surface temperature threshold for tropical convection

Deep convection in the tropics is observed generally above a threshold for sea surface temperatures of about 26–28 °C. An analysis of satellite observations of tropical rainfall shows that the threshold has varied in the past 30 years in parallel with tropical mean sea surface temperatures.

Retrospective Forecasts of the Hurricane Season Using a Global Atmospheric Model Assuming Persistence of SST Anomalies

Abstract Retrospective predictions of seasonal hurricane activity in the Atlantic and east Pacific are generated using an atmospheric model with 50-km horizontal resolution by simply persisting sea surface temperature (SST) anomalies from June through the hurricane season. Using an ensemble of 5 realizations for each year between 1982 and 2008, the correlations of the model mean predictions with observations of basin-wide hurricane frequency are 0.69 in the North Atlantic and 0.58 in the east Pacific. In the North Atlantic, a significant part of the degradation in skill as compared to a model forced with observed SSTs during the hurricane season (correlation of 0.78) can be explained by the change from June through the hurricane season in one parameter, the difference between the SST in the main development region and the tropical mean SST. In fact, simple linear regression models with this one predictor perform nearly as well as the full dynamical model for basin-wide hurricane frequency in both the east Pacific and the North Atlantic. The implication is that the quality of seasonal forecasts based on a coupled atmosphere–ocean model will depend in large part on the model’s ability to predict the evolution of this difference between main development region SST and tropical mean SST.