Cooperative Programs for the Advancement of Earth system science

Abstract Atlantic hurricane seasons have a long history of causing significant financial impacts, with Harvey, Irma, Maria, Florence, and Michael combining to incur more than 345 billion USD in direct economic damage during 2017–2018. While Michael’s damage was primarily wind and storm surge-driven, Florence’s and Harvey’s damage was predominantly rainfall and inland flood-driven. Several revised scales have been proposed to replace the Saffir–Simpson Hurricane Wind Scale (SSHWS), which currently only categorizes the hurricane wind threat, while not explicitly handling the totality of storm impacts including storm surge and rainfall. However, most of these newly-proposed scales are not easily calculated in real-time, nor can they be reliably calculated historically. In particular, they depend on storm wind radii, which remain very uncertain. Herein, we analyze the relationship between normalized historical damage caused by continental United States (CONUS) landfalling hurricanes from 1900–2018 with both maximum sustained wind speed ( V max ) and minimum sea level pressure (MSLP). We show that MSLP is a more skillful predictor of normalized damage than V max , with a significantly higher rank correlation between normalized damage and MSLP ( r rank = 0.77) than between normalized damage and V max ( r rank = 0.66) for all CONUS landfalling hurricanes. MSLP has served as a much better predictor of hurricane damage in recent years than V max , with large hurricanes such as Ike (2008) and Sandy (2012) causing much more damage than anticipated from their SSHWS ranking. MSLP is also a more accurately-measured quantity than is V max , making it an ideal quantity for evaluating a hurricane’s potential damage.

Abstract Numerical simulations of three of the most severe historical tropical cyclones to affect the Delaware River Basin (DRB) are used to evaluate a new numerical approach that is a candidate model for the inland‐coastal compound flood forecast. This study includes simulating interactions of tides/surges, freshwater streamflows, winds, and atmospheric pressure for the DRB. One‐way coupling between the hydrologic (National Water Model [NWM]) and the ocean/wave (ADvanced CIRCulation model/WAVEWATCH III [ADCIRC/WW3]) models for the Delaware river‐estuarine system is developed. The links between the coastal processes and the NWM are provided by two different hydraulic and hydrodynamic models: (i) a well‐calibrated public‐domain 1D hydraulic solver model (Hydrologic Engineering Center's River Analysis System [HEC‐RAS]) and (ii) 1D/2D open‐sourced hydrodynamic model (D‐Flow Flexible Mesh [D‐Flow FM]). First, the modeling system is tested to confirm model verification and stability when the system is forced with only tidal forcing. Then, the relative performance of each modeling approach (NWM/ D‐Flow FM /ADCIRC/WW3 and NWM/ HEC‐RAS /ADCIRC/WW3) is evaluated using observational data from Hurricanes Isabel (2003), Irene (2011), and Sandy (2012). Furthermore, the sensitivity of water level prediction to the streamflows, different wind products, and bed roughness are examined. Results show that the D‐Flow FM is generally accurate for water levels: the water levels near the peak of the storms have a skill ranging from 0.79 to 0.91 with a negligible phase error. Simulations show that water level predictions depend on an accurate representation of the wind conditions and bottom roughness. The work shows that hydrodynamic predictions, especially upstream, are highly dependent on the streamflow discharges.

Abstract The polar region has been one of the fastest warming places on Earth in response to greenhouse gas (GHG) forcing. Two distinct processes contribute to the observed warming signal: (i) local warming in direct response to the GHG forcing and (ii) the effect of enhanced poleward heat transport from low latitudes. A series of aquaplanet experiments, which excludes the surface albedo feedback, is conducted to quantify the relative contributions of these two physical processes to the polar warming magnitude and degree of amplification relative to the global mean. The globe is divided into zonal bands with equal area in eight experiments. For each of these, an external heating is prescribed beneath the slab ocean layer in the respective forcing bands. The summation of the individual temperature responses to each local heating in these experiments is very similar to the response to a globally uniform heating. This allows the authors to decompose the polar warming and amplification signal into the effects of local and remote heating. Local polar heating that induces surface-trapped warming due to the large tropospheric static stability in this region accounts for about half of the polar surface warming. Cloud radiative effects act to enhance this local contribution. In contrast, remote nonpolar heating induces a robust polar warming pattern that features a midtropospheric peak, regardless of the meridional location of the forcing. Among all remote forcing experiments, the deep tropical forcing case contributes most to the polar-amplified surface warming pattern relative to the global mean, while the high-latitude forcing cases contribute most to enhancing the polar surface warming magnitude.

Abstract Statistical downscaling methods are extensively used to refine future climate change projections produced by physical models. Distributional methods, which are among the simplest to implement, are also among the most widely used, either by themselves or in conjunction with more complex approaches. Here, building off of earlier work we evaluate the performance of seven methods in this class that range widely in their degree of complexity. We employ daily maximum temperature over the Continental U.S. in a “Perfect Model” approach in which the output from a large‐scale dynamical model is used as a proxy for both observations and model output. Importantly, this experimental design allows one to estimate expected performance under a future high‐emissions climate‐change scenario. We examine skill over the full distribution as well in the tails, seasonal variations in skill, and the ability to reproduce the climate change signal. Viewed broadly, there generally are modest overall differences in performance across the majority of the methods. However, the choice of philosophical paradigms used to define the downscaling algorithms divides the seven methods into two classes, of better versus poorer overall performance. In particular, the bias‐correction plus change‐factor approach performs better overall than the bias‐correction only approach. Finally, we examine the performance of some special tail treatments that we introduced in earlier work which were based on extensions of a widely used existing scheme. We find that our tail treatments provide a further enhancement in downscaling extremes.

Abstract The active 2020 Atlantic hurricane season produced 30 named storms, 14 hurricanes, and 7 major hurricanes (category 3+ on the Saffir–Simpson hurricane wind scale). Though the season was active overall, the final two months (October–November) raised 2020 into the upper echelon of Atlantic hurricane activity for integrated metrics such as accumulated cyclone energy (ACE). This study focuses on October–November 2020, when 7 named storms, 6 hurricanes, and 5 major hurricanes formed and produced ACE of 74 × 10 4 kt 2 (1 kt ≈ 0.51 m s −1 ). Since 1950, October–November 2020 ranks tied for third for named storms, first for hurricanes and major hurricanes, and second for ACE. Six named storms also underwent rapid intensification (≥30 kt intensification in ≤24 h) in October–November 2020—the most on record. This manuscript includes a climatological analysis of October–November tropical cyclones (TCs) and their primary formation regions. In 2020, anomalously low wind shear in the western Caribbean and Gulf of Mexico, likely driven by a moderate-intensity La Niña event and anomalously high sea surface temperatures (SSTs) in the Caribbean, provided dynamic and thermodynamic conditions that were much more conducive than normal for late-season TC formation and rapid intensification. This study also highlights October–November 2020 landfalls, including Hurricanes Delta and Zeta in Louisiana and in Mexico and Hurricanes Eta and Iota in Nicaragua. The active late season in the Caribbean would have been anticipated by a statistical model using the July–September-averaged ENSO longitude index and Atlantic warm pool SSTs as predictors.

The cumulative distribution function transform (CDFt) downscaling method has been used widely to provide local‐scale information and bias correction to output from physical climate models. The CDFt approach is one from the category of statistical downscaling methods that operates via transformations between statistical distributions. Although numerous studies have demonstrated that such methods provide value overall, much less effort has focused on their performance with regard to values in the tails of distributions. We evaluate the performance of CDFt‐generated tail values based on four distinct approaches, two native to CDFt and two of our own creation, in the context of a “Perfect Model” setting in which global climate model output is used as a proxy for both observational and model data. We find that the native CDFt approaches can have sub‐optimal performance in the tails, particularly with regard to the maximum value. However, our alternative approaches provide substantial improvement.

Abstract Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.

Abstract We investigate relativistic electron precipitation events detected by Polar Environmental Satellites (POES) in low‐Earth orbit in close conjunction with Van Allen Probe A observations of electromagnetic ion cyclotron (EMIC) waves near the geomagnetic equator. We show that the occurrence rate of >0.7 MeV electron precipitation recorded by POES during those times strongly increases, reaching statistically significant levels when the minimum electron energy for cyclotron resonance with hydrogen or helium band EMIC waves at the equator decreases below ≃1.0–2.5 MeV, as expected from the quasi‐linear theory. Both hydrogen and helium band EMIC waves can be effective in precipitating MeV electrons. However, >0.7 MeV electron precipitation is more often observed (at statistically significant levels) when the minimum electron energy for cyclotron resonance with hydrogen band waves is low ( E min = 0.6–1.0 MeV), whereas it is more often observed when the minimum electron energy for cyclotron resonance with helium band waves is slightly larger ( E min = 1.0–2.5 MeV). This is indicative of the warm plasma effects for waves approaching the He + gyrofrequency. We further show that most precipitation events had energies > 0.7–1.0 MeV, consistent with the estimated minimum energy ( E min ∼ 0.6 − 2.5 MeV) of cyclotron resonance with the observed EMIC waves during the majority of these events. However, 4 out of the 12 detected precipitation events cannot be explained by electron quasi‐linear scattering by the observed EMIC waves, and 12 out of 20 theoretically expected precipitation events were not detected by POES, suggesting the possibility of nonlinear effects likely present near the magnetic equator, or warm plasma effects, and/or narrowly localized bursts of EMIC waves.

Abstract The damage potential of a hurricane is widely considered to depend more strongly on an integrated measure of the hurricane wind field, such as integrated kinetic energy (IKE), than a point‐based wind measure, such as maximum sustained wind speed ( V max ). Recent work has demonstrated that minimum sea level pressure (MSLP) is also an integrated measure of the wind field. This study investigates how well historical continental US hurricane damage is predicted by MSLP compared to both V max and IKE for continental United States hurricane landfalls for the period 1988–2021. We first show for the entire North Atlantic basin that MSLP is much better correlated with IKE ( r rank = 0.50) than V max ( r rank = 0.26). We then show that continental US hurricane normalized damage is better predicted by MSLP ( r rank = 0.83) than either V max ( r rank = 0.67) or IKE ( r rank = 0.65). For Georgia to Maine hurricane landfalls specifically, MSLP and IKE show similar levels of skill at predicting damage, whereas V max provides effectively no predictive power. Conclusions for IKE extend to power dissipation as well, as the two quantities are highly correlated because wind radii closely follow a Modified Rankine vortex. The physical relationship of MSLP to IKE and power dissipation is discussed. In addition to better representing damage, MSLP is also much easier to measure via aircraft or surface observations than either V max or IKE, and it is already routinely estimated operationally. We conclude that MSLP is an ideal metric for characterizing hurricane damage risk.

Abstract Precipitation into the Earth's atmosphere due to pitch angle scattering by plasma waves has been recognized as one of the major loss mechanisms for energetic electrons. In this study, we quantitatively evaluate their roles in precipitating electrons during a conjunction event with modulated electron precipitation observed at low altitudes by Electron Loss and Fields INvestigation and three types of whistler mode waves (hiss, plume hiss, and chorus) measured near the equator by Time History of Events and Macroscale Interactions during Substorms. Electron precipitation was observed from ∼50 keV to <1 MeV with a spatial modulation, suggested by a good correlation between L shell‐sorted precipitation fluxes and wave intensities. A quasi‐linear analysis supports the observed energy range of precipitation and the ratio of precipitating‐to‐trapped flux. Our findings reveal that the modulated energetic electron precipitation is driven by hiss, plume hiss, and chorus waves.

Abstract The 1933 Atlantic hurricane season was extremely active, with 20 named storms and 11 hurricanes including 6 major (category 3+; 1-min maximum sustained winds ≥96 kt) hurricanes occurring. The 1933 hurricane season also generated the most accumulated cyclone energy (an integrated metric that accounts for frequency, intensity, and duration) of any Atlantic hurricane season on record. A total of 8 hurricanes tracked through the Caribbean in 1933—the most on record. In addition, two category 3 hurricanes made landfall in the United States just 23 h apart: the Treasure Coast hurricane in southeast Florida followed by the Cuba–Brownsville hurricane in south Texas. This manuscript examines large-scale atmospheric and oceanic conditions that likely led to such an active hurricane season. Extremely weak vertical wind shear was prevalent over both the Caribbean and the tropical Atlantic throughout the peak months of the hurricane season, likely in part due to a weak-to-moderate La Niña event. These favorable dynamic conditions, combined with above-normal tropical Atlantic sea surface temperatures, created a very conducive environment for hurricane formation and intensification. The Madden–Julian oscillation was relatively active during the summer and fall of 1933, providing subseasonal conditions that were quite favorable for tropical cyclogenesis during mid- to late August and late September to early October. The current early June and August statistical models used by Colorado State University would have predicted a very active 1933 hurricane season. A better understanding of these extremely active historical Atlantic hurricane seasons may aid in anticipation of future hyperactive seasons.

Abstract The 2023 Atlantic hurricane season was above normal, producing 20 named storms, 7 hurricanes, 3 major hurricanes, and seasonal accumulated cyclone energy that exceeded the 1991–2020 average. Hurricane Idalia was the most damaging hurricane of the year, making landfall as a Category 3 hurricane in Florida, resulting in eight direct fatalities and 3.6 billion U.S. dollars in damage. The above-normal 2023 hurricane season occurred during a strong El Niño event. El Niño events tend to be associated with increased vertical wind shear across the Caribbean and tropical Atlantic, yet vertical wind shear during the peak hurricane season months of August–October was well below normal. The primary driver of the above-normal season was likely record warm tropical Atlantic sea surface temperatures (SSTs), which effectively counteracted some of the canonical impacts of El Niño. The extremely warm tropical Atlantic and Caribbean were associated with weaker-than-normal trade winds driven by an anomalously weak subtropical ridge, resulting in a positive wind–evaporation–SST feedback. We tested atmospheric circulation sensitivity to SSTs in both the tropical and subtropical Pacific and the Atlantic using the atmospheric component of the Community Earth System Model, version 2.3. We found that the extremely warm Atlantic was the primary driver of the reduced vertical wind shear relative to other moderate/strong El Niño events. The concentrated warmth in the eastern tropical Pacific in August–October may have contributed to increased levels of vertical wind shear than if the warming had been more evenly spread across the eastern and central tropical Pacific. Significance Statement The 2023 Atlantic hurricane season produced above-normal activity despite strong El Niño conditions. The season had 20 named storms, along with 7 hurricanes and 3 major hurricanes. Normally, El Niño decreases Atlantic hurricane activity due to increases in vertical wind shear. In 2023, vertical wind shear was below average, likely driven by the record warm tropical Atlantic and Caribbean Sea surface temperatures which led to tropical circulation patterns that were considerably different from the atmospheric flow typically observed during El Niño events. This manuscript also uses a state-of-the-art climate model to investigate the impacts of Atlantic and Pacific SST configurations on Atlantic vertical wind shear patterns.

Abstract In this study, we present simultaneous multi‐point observations of whistler‐mode waves detected by RBSP‐B, associated with conjugate electron precipitation observed through enhanced BARREL X‐rays at L ∼ 6 from noon to dusk. Both long period modulation at periods of several to tens of minutes and short period modulation at about tens of seconds are observed in X‐ray measurements. Similar periodicities are also observed for whistler‐mode wave amplitude. We show that the correlation coefficient between whistler‐mode waves and electron precipitation is high in several regions, including plumes and plasma trough. Ultra‐low‐frequency waves (8–30 mHz), which have been suggested to play a potential role in precipitating electrons by modulating whistler‐mode wave intensity or loss cone size, show a weak correlation with whistler‐mode wave amplitudes and the X‐ray counts during the conjunction. We further evaluate whistler‐mode wave driven electron precipitation using a physics‐based technique. The time evolution of the modeled electron precipitation is found to be remarkably consistent with the modulation in the BARREL X‐ray counts both in plumes and plasma trough. By taking advantage of the high‐resolution wave data and close conjunction, we provide strong evidence that whistler‐mode waves are not only directly responsible for the longer modulation (several to tens of minutes), but also the shorter modulation (tens of seconds) of the electron precipitation.

Abstract Statistical downscaling (SD) methods used to refine future climate change projections produced by physical models have been applied to a variety of variables. We evaluate four empirical distributional type SD methods as applied to daily precipitation, which because of its binary nature (wet vs. dry days) and tendency for a long right tail presents a special challenge. Using data over the Continental U.S. we use a ‘Perfect Model’ approach in which data from a large‐scale dynamical model is used as a proxy for both observations and model output. This experimental design allows for an assessment of expected performance of SD methods in a future high‐emissions climate‐change scenario. We find performance is tied much more to configuration options rather than choice of SD method. In particular, proper handling of dry days (i.e., those with zero precipitation) is crucial to success. Although SD skill in reproducing day‐to‐day variability is modest (~15–25%), about half that found for temperature in our earlier work, skill is much greater with regards to reproducing the statistical distribution of precipitation (~50–60%). This disparity is the result of the stochastic nature of precipitation as pointed out by other authors. Distributional skill in the tails is lower overall (~30–35%), although in some regions and seasons it is small to non‐existent. Even when SD skill in the tails is reasonably good, in some instances, particularly in the southeastern United States during summer, absolute daily errors at some gridpoints can be large (~20 mm or more), highlighting the challenges in projecting future extremes.

Assessing regional changes in tropical cyclones (TCs) and their future impacts are challenging given the short historical record and limited sample size of these extreme events. To address this, we use large climate model ensembles to increase sample size and explore historical variability and future changes in regional TC behavior. We demonstrate this approach on basin and sub-basin scales along Australia's East Coast. Applying a TC tracking algorithm to the large ensembles, we find that the large ensembles are skillful in detecting observed historical TC trends in the Southwest Pacific (SWP) basin. Furthermore, we show that projected TC activity in the SWP basin exposes larger land areas to extreme winds and high precipitation totals. This includes southern-most portions of the SWP basin, where future TC wind speeds regularly exceed current wind loading standards. Combined, our results point to rapidly increasing risks of damaging TC winds and major TC flooding, as well as a heightened risk of water ingress through wind-driven rain.

Abstract The access of solar energetic protons into the inner magnetosphere on September 7–8, 2017 is investigated by following reversed proton trajectories to compute the proton cutoff energy using the Dartmouth geomagnetic cutoff code (Kress et al., 2010, https://doi.org/10.1029/2009sw000488 ). The cutoff energies for protons coming from the west and east direction, the minimum and maximum cutoff energy respectively, are calculated every 5 min along the orbit of Van Allen Probes using TS07 and the Lyon‐Fedder‐Mobarry (LFM) MHD magnetic field model. The result shows that the cutoff energy increases significantly as the radial distance decreases, and that the cutoff energy decreases with the building up of the ring current during magnetic storms. Solar wind dynamic pressure also affects cutoff suppression (Kress et al., 2004, https://doi.org/10.1029/2003gl018599 ). The LFM‐RCM model shows stronger suppression of cutoff energy than TS07 during strong solar wind driving conditions. The simulation result is compared with proton flux measurements, showing consistent variation of the cutoff location during the September 7–8, 2017 geomagnetic storm.

Abstract The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.

As communities grapple with rising seas and more frequent flooding events, they need improved projections of future rise and flooding over multiple time horizons to assist in a multitude of planning efforts. There are currently a few different tools available that communities can use to plan including the Sea Level Report Card and products generated by a U.S. Federal interagency task force on sea level rise. These tools are a start, but it is recognized that they are not necessarily enough at present to give communities the types of information needed for decision support, which ranges from seasonal to decadal in nature, generally over relatively small geographic regions.

Methane is a potent greenhouse gas, an important energy source, and an important part of the global carbon cycle. The relative abundances of doubly substituted (“clumped”) methane isotopologues (13CH3D and 12CH2D2) offer important information on the sources and sinks of methane. However, the clumped isotope signatures of microbially produced methane from different methanogenic pathways lack a systematic investigation. In this study, we provide a data set encompassing isotopic signatures of hydrogenotrophic, methylotrophic, acetoclastic, and methoxydotrophic methanogenesis. We find that a statistical “combinatorial effect” generates significant differences in 12CH2D2 compositions between hydrogenotrophic methanogenesis and the other pathways, while variations in the fractionation factors of clumped isotopologues result in differences in 13CH3D compositions between the methylotrophic, acetoclastic, and methoxydotrophic pathways. The energy yield of methanogenesis and the energy conservation approaches implemented by different microbial strains may also influence the isotope values of methane. Further analysis suggests that previously observed isotopic signatures of methane in freshwater environments are potentially due to mixing between hydrogenotrophic and other methanogenesis pathways. This study provides new experimental constraints on the isotope signatures of different microbial methanogenic pathways and evidence of the mechanisms responsible for the observed differences. This enables a better understanding of the sources and sinks of methane in the environment.

The damage potential of a hurricane is widely considered to depend more strongly on an integrated measure of the hurricane wind field, such as Integrated Kinetic Energy (IKE), than a point-based wind measure, such as maximum sustained wind speed (V max ). Recent work has demonstrated that minimum sea level pressure (MSLP) is also an integrated measure of the wind field. This study investigates how well historical continental US hurricane damage is predicted by MSLP compared to both V max and IKE for continental United States hurricane landfalls for the period 1988–2020. We first show for the entire North Atlantic basin that MSLP is much better correlated with IKE ( r rank = 0.50) than V max ( r rank = 0.26). We then show that continental US hurricane normalized damage is better predicted by MSLP ( r rank = 0.81) than either V max ( r rank = 0.65) or IKE ( r rank = 0.68). For Georgia to Maine hurricane landfalls specifically, MSLP and IKE show similar levels of skill at predicting damage, whereas V max provides effectively no predictive power. Conclusions for IKE extend to power dissipation as well, as the two quantities are highly correlated because wind radii closely follow a Rankine vortex. The physical relationship of MSLP to IKE and power dissipation is discussed. In addition to better representing damage, MSLP is also much easier to measure via aircraft or surface observations than either V max or IKE, and it is already routinely estimated operationally. We conclude that MSLP is an ideal metric for characterizing hurricane damage risk.