Excitation of equatorial Kelvin and Yanai waves by tropical cyclones in an ocean general circulation model

Tropical cyclones (TCs) actively contribute to the dynamics of Earth's coupled climate system. They influence oceanic mixing rates, upper-ocean heat content, and air–sea fluxes, with implications for atmosphere and ocean dynamics on multiple spatial and temporal scales. Using an ocean general circulation model with modified surface wind forcing, we explore how TC winds can excite equatorial ocean waves in the tropical Pacific. We highlight a situation where three successive TCs in the western North Pacific region, corresponding to events in 2003, excite a combination of Kelvin and Yanai waves in the equatorial Pacific. The resultant thermocline adjustment significantly modifies the thermal structure of the upper equatorial Pacific and leads to eastward zonal heat transport. Observations of upper-ocean temperature by the Tropical Atmosphere Ocean (TAO) buoy array and sea-level height anomalies using altimetry reveal wave passage during the same time period with similar properties to the modeled wave, although our idealized model methodology disallows precise identification of the TC forcing with the observed waves. Results indicate that direct oceanographic forcing by TCs may be important for understanding the spectrum of equatorial ocean waves, thus remotely influencing tropical mixing and surface energy budgets. Because equatorial Kelvin waves are closely linked to interannual variability in the tropical Pacific, these findings also suggest TC wind forcing may influence the timing and amplitude of El Niño events

Extreme sea levels at different global warming levels

The Paris agreement focused global climate mitigation policy on limiting global warming to 1.5 or 2 °C above pre-industrial levels. Consequently, projections of hazards and risk are increasingly framed in terms of global warming levels rather than emission scenarios. Here, we use a multimethod approach to describe changes in extreme sea levels driven by changes in mean sea level associated with a wide range of global warming levels, from 1.5 to 5 °C, and for a large number of locations, providing uniform coverage over most of the world’s coastlines. We estimate that by 2100 ~50% of the 7,000+ locations considered will experience the present-day 100-yr extreme-sea-level event at least once a year, even under 1.5 °C of warming, and often well before the end of the century. The tropics appear more sensitive than the Northern high latitudes, where some locations do not see this frequency change even for the highest global warming levels

Future climate emulations using quantile regressions on large ensembles

The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows the efficacy of the method to be tested with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model.</p

Ocean barrier layers’ effect on tropical cyclone intensification

Improving a tropical cyclone’s forecast and mitigating its destructive potential requires knowledge of various environmental factors that influence the cyclone’s path and intensity. Herein, using a combination of observations and model simulations, we systematically demonstrate that tropical cyclone intensification is significantly affected by salinity-induced barrier layers, which are “quasi-permanent” features in the upper tropical oceans. When tropical cyclones pass over regions with barrier layers, the increased stratification and stability within the layer reduce storm-induced vertical mixing and sea surface temperature cooling. This causes an increase in enthalpy flux from the ocean to the atmosphere and, consequently, an intensification of tropical cyclones. On average, the tropical cyclone intensification rate is nearly 50% higher over regions with barrier layers, compared to regions without. Our finding, which underscores the importance of observing not only the upper-ocean thermal structure but also the salinity structure in deep tropical barrier layer regions, may be a key to more skillful predictions of tropical cyclone intensities through improved ocean state estimates and simulations of barrier layer processes. As the hydrological cycle responds to global warming, any associated changes in the barrier layer distribution must be considered in projecting future tropical cyclone activity

Patterns and mechanisms of early Pliocene warmth

About five to four million years ago, in the early Pliocene epoch, Earth had a warm, temperate climate. The gradual cooling that followed led to the establishment of modern temperature patterns, possibly in response to a decrease in atmospheric CO2 concentration, of the order of 100 parts per million, towards preindustrial values. Here we synthesize the available geochemical proxy records of sea surface temperature and show that, compared with that of today, the early Pliocene climate had substantially lower meridional and zonal temperature gradients but similar maximum ocean temperatures. Using an Earth system model, we show that none of the mechanisms currently proposed to explain Pliocene warmth can simultaneously reproduce all three crucial features. We suggest that a combination of several dynamical feedbacks underestimated in the models at present, such as those related to ocean mixing and cloud albedo, may have been responsible for these climate conditions

Descent toward the icehouse: Eocene sea surface cooling inferred from GDGT distributions

The TEX86 proxy, based on the distribution of marine isoprenoidal glycerol dialkyl glycerol tetraether lipids (GDGTs), is increasingly used to reconstruct sea surface temperature (SST) during the Eocene epoch (56.0–33.9 Ma). Here we compile published TEX86 records, critically reevaluate them in light of new understandings in TEX86 palaeothermometry, and supplement them with new data in order to evaluate long-term temperature trends in the Eocene. We investigate the effect of archaea other than marine Thaumarchaeota upon TEX86 values using the branched-to-isoprenoid tetraether index (BIT), the abundance of GDGT-0 relative to crenarchaeol (%GDGT-0), and the Methane Index (MI). We also introduce a new ratio, % GDGTRS, which may help identify Red Sea-type GDGT distributions in the geological record. Using the offset between TEX86H and TEX86L(ΔH-L) and the ratio between GDGT-2 and GDGT-3 ([2]/[3]), we evaluate different TEX86 calibrations and present the first integrated SST compilation for the Eocene (55 to 34 Ma). Although the available data are still sparse some geographic trends can now be resolved. In the high latitudes (>55°), there was substantial cooling during the Eocene (~6°C). Our compiled record also indicates tropical cooling of ~2.5°C during the same interval. Using an ensemble of climate model simulations that span the Eocene, our results indicate that only a small percentage (~10%) of the reconstructed temperature change can be ascribed to ocean gateway reorganization or paleogeographic change. Collectively, this indicates that atmospheric carbon dioxide (pCO2) was the likely driver of surface water cooling during the descent toward the icehouse

Global representation of tropical cyclone-induced short-term ocean thermal changes using Argo data

Argo floats are used to examine tropical cyclone (TC) induced ocean thermal changes on the global scale by comparing temperature profiles before and after TC passage. We present a footprint method that analyzes cross-track thermal responses along all storm tracks during the period 2004–2012. We combine the results into composite representations of the vertical structure of the average thermal response for two different categories: tropical storms/tropical depressions (TS/TD) and hurricanes. The two footprint composites are functions of three variables: cross-track distance, water depth and time relative to TC passage. We find that this footprint strategy captures the major features of the upper-ocean thermal response to TCs on timescales up to 20 days when compared against previous case study results using in situ measurements. On the global scale, TCs are responsible for 1.87 PW (11.05 W m^&minus;2) of heat transfer annually from the global ocean to the atmosphere during storm passage (0–3 days). Of this total, 1.05 ± 0.20 PW (4.80 ± 0.85 W m^&minus;2) is caused by TS/TD and 0.82 ± 0.21 PW (6.25 ± 1.5 W m^&minus;2) is caused by hurricanes. Our findings indicate that ocean heat loss by TCs may be a substantial missing piece of the global ocean heat budget. Changes in ocean heat content (OHC) after storm passage are estimated by analyzing the temperature anomalies during wake recovery following storm events (4–20 days after storm passage) relative to pre-storm conditions. Results indicate the global ocean experiences a 0.75 ± 0.25 PW (5.98 ± 2.1 W m^&minus;2) heat gain annually for hurricanes. In contrast, under TS/TD conditions, the ocean experiences 0.41 ± 0.21 PW (1.90 ± 0.96 W m^&minus;2) ocean heat loss, suggesting the overall oceanic thermal response is particularly sensitive to the intensity of the event. The ocean heat uptake caused by all storms during the restorative stage is 0.34 PW

Historical and future learning about climate sensitivity

Equilibrium climate sensitivity measures the long-term response of surface temperature to changes in atmospheric CO2. The range of climate sensitivities in the IPCC AR5 Report is unchanged from that published almost 30 years earlier in the Charney Report. We conduct perfect-model experiments using an energy balance model to study the rate at which uncertainties might be reduced by observation of global temperature and ocean heat uptake. We find that a climate sensitivity of 1.5 <°C can be statistically distinguished from 3 °C by 2030; 3 °C from 4.5 °C by 2040; and 4.5 °C from 6 °C by 2065. Learning rates are slowest in the scenarios of greatest concern (high sensitivities), due to a longer ocean response time, which may have bearing on wait-and-see vs. precautionary mitigation policies. Learning rates are optimistic in presuming the availability of whole- ocean heat data, but pessimistic by using simple aggregated metrics and model physics