Geophysical Fluid Dynamics Laboratory Research Articles

A flexible tropical cyclone vortex initialization technique for GFDL SHiELD

Tropical cyclone (TC) intensity forecasting poses challenges due to complex dynamical processes and data inadequacies during model initialization. This paper describes efforts to improve TC intensity prediction in the Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model by implementing a Vortex Initialization (VI) technique. The GFDL SHiELD model, relying on the Global Forecast System (GFS) analysis for initialization, faces deficiencies in initial TC structure and intensity. The VI method involves adjusting the TC vortex inherited from the GFS analysis and merging it back into the environment at the observed location, enhancing the analyzed representation of storm structure. We made modifications to the VI package implemented in the operational Hurricane Analysis and Forecast System, including handling initial condition data, reducing input domain size, and improving storm intensity enhancement. Experiments using the T-SHiELD configuration demonstrate that using VI significantly improves the representation of initial TC intensity and size, enhancing TC predictions, particularly in storm intensity and outer wind forecasts within the first 48 h.

An Assessment of Convergence of Climate Reanalyses from Two Coupled Data Assimilation Systems with Identical High-Efficiently Filtering

Abstract Coupled data assimilation (CDA) uses coupled model dynamics and physics to extract observational information from measured data in multiple Earth system domains to reconstruct historical states of the Earth system, forming a reanalysis of climate variability. Due to imperfect numerical schemes in modeling dynamics and physics, models are usually biased from the real world. Such model bias is a critical obstacle in the reconstruction of historical variability by combining model and observations, and, to some degree, causes divergence of CDA results because of individual model behavior in each system. Here, based on a multitimescale high-efficiency filtering algorithm which includes a deep ocean bias relaxing scheme, we first develop a high-efficiency online CDA system with the Community Earth System Model (CESM-MSHea-CDA). Then, together with the other previously-established CDA system (CM2-MSHea-CDA) within the Coupled Model version 2.1 model that is developed by Geophysical Fluid Dynamics Laboratory, we conduct climate reanalysis for the past four decades (1978–2018). Evaluations show that due to improved representation for multiscale background statistics and effective deep ocean model bias relaxing, both CDA systems produce convergent estimation of variability for major climate signals such as variability of basin-scale ocean heat content, ENSO, PDO, etc. Particularly, both CDA systems generate similar time-mean of global and Atlantic meridional overturning circulations that converge to the geostrophic velocity estimate from climatological temperature and salinity data. The CDA-estimated mass transport at typical measurement sections is mostly consistent with the observations.

The Land Component LM4.1 of the GFDL Earth System Model ESM4.1: Model Description and Characteristics of Land Surface Climate and Carbon Cycling in the Historical Simulation

AbstractWe describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Land Model version 4.1 (LM4.1), which builds on component and coupled model developments over 2013–2019 for the coupled carbon‐chemistry‐climate Earth System Model Version 4.1 (ESM4.1) simulation as part of the sixth phase of the Coupled Model Intercomparison Project. Analysis of ESM4.1/LM4.1 is focused on biophysical and biogeochemical processes and interactions with climate. Key features include advanced vegetation dynamics and multi‐layer canopy energy and moisture exchanges, daily fire, land use representation, and dynamic atmospheric dust coupling. We compare LM4.1 performance in the GFDL Earth System Model (ESM) configuration ESM4.1 to the previous generation component LM3.0 in the ESM2G configuration. ESM4.1/LM4.1 provides significant improvement in the treatment of ecological processes from GFDL's previous generation models. However, ESM4.1/LM4.1 likely overestimates the influence of land use and land cover change on vegetation characteristics, particularly on pasturelands, as it overestimates the competitiveness of grasses versus trees in the tropics, and as a result, underestimates present‐day biomass and carbon uptake in comparison to observations.

The GFDL Variable‐Resolution Global Chemistry‐Climate Model for Research at the Nexus of US Climate and Air Quality Extremes

AbstractWe present a variable‐resolution global chemistry‐climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban‐to‐rural chemical regimes, mesoscale convective systems, and land‐surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi‐decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25‐km uniform‐resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal‐to‐diurnal cycles and extremes, notably reducing the central US dry‐and‐warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process‐level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land‐biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate.

Contributions of Tropical Cyclones and Atmospheric Rivers to Extreme Precipitation Trends Over the Northeast US

AbstractThe Northeast United States (NEUS) has faced the most rapidly increasing occurrences of extreme precipitation within the US in the past few decades. Understanding the physics leading to long‐term trends in regional extreme precipitation is essential but the progress is limited partially by the horizontal resolution of climate models. The latest fully coupled 25‐km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) simulations provide a good opportunity to study changes in regional extreme precipitation and the relevant physical processes. Here, we focus on the contributions of changes in synoptic‐scale events, including atmospheric rivers (AR) and tropical cyclone (TC)‐related events, to the trend of extreme precipitation in the fall season over the Northeast US in both the recent past and future projections using the 25‐km GFDL‐SPEAR. In observations, increasing extreme precipitation over the NEUS since the 1990s is mainly linked to TC‐related events, especially those undergoing extratropical transitions. In the future, both AR‐related and TC‐related extreme precipitation over the NEUS are projected to increase, even though the numbers of TCs in the North Atlantic are projected to decrease in the SPEAR simulations using the SSP5‐8.5 projection of future radiative forcing. Factors such as enhancing TC intensity, strengthening TC‐related precipitation, and/or westward shift in Atlantic TC tracks may offset the influence of declining Atlantic TC numbers in the model projections, leading to more frequent TC‐related extreme precipitation over the NEUS.

Climate Model Simulations of the Effects of Orbital Parameters on Glacier Equilibrium Line Altitude

AbstractThe effects of obliquity and precession on conditions favorable for Northern Hemisphere glaciation are explored using an energy balance and mass balance model of equilibrium line altitude (ELA), the height on a glacier where accumulation and ablation are in balance annually. Climate forcing for the ELA model is obtained from idealized single‐forcing orbital simulations with two atm‐ocean general circulation models, Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 and National Center for Atmospheric Research (NCAR) Community Earth System Model version 1.2. Over Scandinavia and Baffin Island, the respective locations in which the Scandinavian and Laurentide ice sheets are thought to have originated, low obliquity and perihelion at the boreal winter solstice are associated with lower ELA values, as would be expected from the orbital theory of the ice ages. Linear reconstructions of ELA variations over the past 800 kyr indicate that precession dominated ELA variations in Scandinavia and Baffin Island in the GFDL model, and in Scandinavia in the NCAR model. Obliquity and precession played equal roles in Baffin Island in the NCAR model. A decomposition of the ELA responses finds that the effects of ablation on ELA are much larger than the effects of precipitation. Overall, the findings of this study point to precession being a more important factor in glacial inception than obliquity, which contrasts with previous findings in which obliquity had a slightly larger effect on positive degree days (PDDs), a simple metric for ablation. This is likely due to differences in seasonality of melt from the ELA model and PDDs.

The Precipitation Response to Warming and CO2 Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models

AbstractGlobal storm‐resolving models (GSRMs) that can explicitly resolve some of deep convection are now being integrated for climate timescales. GSRMs are able to simulate more realistic precipitation distributions relative to traditional Coupled Model Intercomparison Project 6 (CMIP6) models. In this study, we present results from two‐year‐long integrations of a GSRM developed at Geophysical Fluid Dynamics Laboratory, eXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), for the response of precipitation to sea surface temperature warming and an isolated increase in CO2 and compare it to CMIP6 models. At leading order, X‐SHiELD's response is within the range of the CMIP6 models. However, a close examination of the precipitation distribution response reveals that X‐SHiELD has a different response at lower percentiles and the response of the extreme events are at the lower end of the range of CMIP6 models. A regional decomposition reveals that the difference is most pronounced for midlatitude land, where X‐SHiELD shows a lower increase at intermediate percentiles and drying at lower percentiles.

Projections and patterns of heat-related mortality impacts from climate change in Southeast Asia

This study aims to investigate the impact of climate change on heat-related mortality in Southeast Asia in the future. The ensemble mean from five General Circulation Models (GCMs) including the Flexible Global Ocean-Atmosphere-Land System Model: Grid-Point Version 3 (FGOALS-g3), Max Planck Institute Earth System Model Version 1.2 (MPI-ESM1-2-LR), EC-Earth3, The Meteorological Research Institute Earth System Model Version 2.0 (MRI-ESM2-0), and Geophysical Fluid Dynamics Laboratory Earth System Model Version 4 (GFDL-ESM4) was used to project severe temperatures and heat indices in Southeast Asia under the Coupled Model Intercomparison Projects Phase 6 (CMIP6). This data was used to correlate with mortality data from the Global Burden of Disease database to quantify heat-related mortality in the region. The ensemble mean results show a reasonable level of accuracy in capturing temperature patterns in the Southeast Asian region with an R2 of 0.96, root mean square error (RMSE) of 0.84 and a standard deviation of residual (SDR) of 0.02. When compared to the baseline (1990–2019), temperature extreme indices are rising across all climatic scenarios, with a substantial increase in the SSP3–7.0 and SSP5–8.5 scenarios, ranging from 10% to 50% over the regions, with the heat index predicted to peak in the middle of the century. The two low-emission scenarios, SSP1-2.6 and SSP2-4.5, on the other hand, anticipate more moderate increases, indicating a potentially less severe impact on the region. As a result, under high-emission scenarios, there is expected to be a significant increase in heat-related mortality across Southeast Asia. The expected impact is estimated to affect between 200 and 300 people per 100,000 people from 2030 to 2079, accordingly. Our results highlight the critical need to address health-related impacts of climate change in this region.

Response of the Current Climate to Land‐Ocean Contrasts in Parameterized Cumulus Entrainment

AbstractCumulus entrainment substantially regulates the earth's climate but remains poorly constrained in global climate models. Recent studies have shown that cumulus bulk entrainment (or dilution) is particularly sensitive to continentality, with the entrainment rate in simulated maritime cumuli nearly double that of continental cumuli. This study examines the impacts of such land–ocean entrainment contrasts on the current climate using 21‐year simulations with the Geophysical Fluid Dynamics Laboratory's High‐Resolution Atmospheric Model (HIRAM). In response to a 25% reduction in the HIRAM entrainment parameter c0 over land, precipitation over tropical land regions increases by up to 40%. Along with directly facilitating enhanced convective precipitation, this c0 reduction induces an increase in soil moisture, which may contribute to a further enhancement of convective precipitation over land. A 25% c0 reduction over the oceans leads to more widespread modifications of convection patterns, with the strongest signal in the tropical Pacific. Deep convection shifts upstream (eastward) there, inducing enhanced large‐scale ascent over the central Pacific with compensating subsidence and reduced humidity and precipitation over the western Pacific (WP). Land–ocean variations in c0 project onto the Pacific Walker circulation, with the 25% land reduction strengthening it by 4% and the 25% ocean reduction weakening it by 14%. These changes are driven by variations in convective and large‐scale stratiform heating over the Pacific. While reduced c0 over land enhances diabatic heating in the Maritime Continent to strengthen the Walker circulation, reduced c0 over the oceans decreases diabatic heating in the WP to weaken the Walker circulation.

Monsoon Planet: Bimodal Rainfall Distribution due to Barrier Structure in Pressure Fields

Abstract Monsoon systems transport water and energy across the globe, making them a central component of the global circulation system. Changes in different forcing parameters have the potential to fundamentally change the monsoon characteristics as indicated in various paleoclimatic records. Here, we use the Atmosphere Model developed at the Geophysical Fluid Dynamics Laboratory (GFDL-AM2) and couple it with a slab ocean in order to analyze the monsoon’s sensitivity to changes in different parameters on a planet with idealized topography (varying land position, slab depth, atmospheric CO2 concentration, solar radiation, sulfate aerosol concentration, and surface albedo). This Monsoon Planet concept of an aquaplanet with a broad zonal land stripe allows us to reduce the influence of topography and to access the relevant meridional monsoon dynamics. In simulations with monsoon dynamics, a bimodal rainfall distribution develops during the monsoon months with one maximum over the tropical ocean and the other one over land. The intensity and extent of the monsoon depend on the relative height of a local maximum in the surface pressure field that is acting as a barrier and is determining the coastward moisture transport. Changes in the barrier height occur during the course of one year but can also be induced when varying different parameters in the sensitivity analysis (e.g., the increase of atmospheric CO2 reduces the barrier height, resulting in an increase of rainfall, while aerosols have the opposing effect). This bimodal rainfall structure separated by a pressure barrier is also present in reanalysis data of the West African monsoon. Significance Statement Monsoon rainfall directly impacts the livelihood of millions of individuals in the tropics. Because monsoons transport energy and water around the globe, their influence reaches far beyond the tropics, and changes in their dynamics affect the climate both locally and globally. The individual monsoon systems are subject to various forcing factors that determine the monsoon characteristics in partly opposing ways. Here, we implement a model setup of the Monsoon Planet to study the monsoon’s sensitivity to various forcings in a simplified design, allowing us to gain new insights into monsoon dynamics. We find that a local maximum in the pressure field is acting as a barrier for moisture transport and thus determines the monsoon characteristics over land.

Poleward intensification of midlatitude extreme winds under warmer climate

Our study investigates the global impact of midlatitude cyclones on extreme wind speed events in both hemispheres under a warmer climate. Using the latest version of the high-resolution ≈ 50 km grid-spacing atmospheric climate model AM4, developed by the Geophysical Fluid Dynamics Laboratory, we conducted simulations covering the 71-years period 1949–2019 for both the present-day climate and an idealised future global warming climate scenario with a homogeneous Sea Surface Temperature (SST) increase by 2 K. Our findings reveal that extreme near-surface wind speeds increase by up to 3% K−1 towards the poles while decrease by a similar amount in the lower midlatitudes. When considering only extreme wind speed events objectively attributed to midlatitude cyclones, we observe a migration by the same amount towards higher latitudes both in percentage per degree SST warming and absolute value. The total number of midlatitude cyclones decreases by roughly 4%, but the proportion of cyclone-associated extreme wind speed events increases by 10% in a warmer climate. Finally, Northwestern Europe, the British Isles, and the West Coast of North America are identified as hot spots with the greatest socio-economic impacts from increased cyclone-associated extreme winds.

Effective Radiative Forcing of the Internally Mixed Sulfate and Black Carbon Aerosol in the GFDL AM4 Model: The Role Played by Other Aerosol Species

AbstractWe compute the effective radiative forcing (ERF) of the internally mixed sulfate‐black carbon (SBC) aerosol species in the Geophysical Fluid Dynamics Laboratory's (GFDL) Atmospheric Model version 4 (AM4) model using five different formulations. The formulations differ in how they account for the presence of other aerosol species. The global mean ERF of SBC in the GFDL AM4 model ranges from −0.51 ± 0.1 to −1.06 ± 0.1 W m−2. The three most realistic configurations of the five, in which the emissions of other aerosol species vary between 1850 and 2010 states, depict a tighter distribution of ERF (−0.51, −0.55, and −0.57 ± 0.1). The two outlier configurations completely exclude one or more other aerosol species, which is slightly unrealistic but included for completeness. The former three configurations, however, result in substantially different ERFs over the regional hot spots of aerosols, e.g., over the land‐mass of East China; the choice of the emission conditions for organic carbon (i.e., present‐day or preindustrial) affects the ERF of SBC by ∼37%. The component of ERF related to aerosol‐cloud interactions (ACI) gets principally affected by the presence of other aerosol species. The higher the emissions of other aerosol species, the lesser is the ERF of SBC associated with ACI. This finding suggests that for ERF estimates, the choice of the emission level/concentrations of the other aerosol species significantly affects the estimates of SBC, especially over the aerosol hot spots.

Atmospheric River Sequences as Indicators of Hydrologic Hazard in Historical Reanalysis and GFDL SPEAR Future Climate Projections

AbstractWhen multiple atmospheric rivers (ARs) occur in rapid succession, the combined effect on the hydrologic system can lead to more flooding and damage than would be expected from the individual events. This temporally compounding risk is a source of growing concern for water managers in California. We present a novel moving average‐based definition of AR “sequences” that identifies the time periods of elevated hydrologic hazard that occur during and after consecutive AR events. This marks the first quantitative evaluation of when temporal compounding is contributing to AR flood risk. We also assess projected changes in sequence frequency, intensity, and duration in California using the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and EArth system Research (GFDL SPEAR) global coupled model. Sequence frequency increases over time and is fairly uniform across the state under both intermediate (SSP2–4.5) and very high (SSP5–8.5) emissions scenarios, with the largest changes occurring by the end of the century (+0.72 sequences/year in SSP2–4.5, +1.13 sequences/year in SSP5–8.5). Sequence intensity and duration both see increases in the medians and extreme values of their respective distributions relative to the historical baselines. In particular, “super‐sequence” events longer than 60 days are projected to occur 2–3x more frequently and to emerge in places that have never seen them in the historical record. In a world where California precipitation is becoming more variable, our definition of sequences will help identify when and where hydrologic impacts will be most extreme, which can in turn support better management of the state's highly variable water resources and inform future flood mitigation strategies.

On the Resolution Sensitivity of Equatorial Precipitation in a GFDL Global Atmospheric Model

AbstractWe performed a series of aquaplanet simulations at the horizontal resolution from 50 to 6 km with identical parameterization settings using the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4 implemented with the two‐moment Morrison‐Gettelman cloud microphysics with prognostic precipitation (GFDL AM4‐MG2). At the finer resolution, the global mean resolved‐scale precipitation increases and that from cumulus parameterization decreases. The model also simulates less/thinner clouds over the low latitudes and more/thicker clouds over the high latitudes as the resolution increases. The precipitation over the deep tropics is investigated in detail. We find little resolution sensitivity in the daily mean precipitation extremes. Changes of the equatorial resolved precipitation with resolution cannot be fully explained by the resolution dependence in the vertical velocity amplitude. We report a robust sensitivity in the convective organization over the deep tropics to the model resolution. In simulations of finer resolution, the localized convection is suppressed, and the organized convective system associated with large‐scale circulations becomes more prominent.

Estimating Daily Temperatures over Andhra Pradesh, India, Using Artificial Neural Networks

In the recent past, Andhra Pradesh (AP) has experienced increasing trends in surface air mean temperature (SAT at a height of 2 m) because of climate change. In this paper, we attempt to estimate the SAT using the GFDL-ESM2G (Geophysical Fluid Dynamics Laboratory Earth System Model version 2G), available from the Coupled Model Intercomparison Project Phase-5 (CMIP5). This model has a mismatch with the India Meteorological Department (IMD)’s observations during April and May, which are the most heat-prone months in the state. Hence, in addition to the SAT from the model, the present paper considers other parameters, such as mean sea level pressure, surface winds, surface relative humidity, and surface solar radiation downwards, that have influenced the SAT. Since all five meteorological parameters from the GFDL-ESM2G model influence the IMD’s SAT, an artificial neural network (ANN) technique has been used to predict the SAT using the above five meteorological parameters as predictors (input) and the IMD’s SAT as the predictand (output). The model was developed using 1981–2020 data with different time lags, and results were tested for 2021 and 2022 in addition to the random testing conducted for 1981–2020. The statistical parameters between the IMD observations and the ANN estimations using GFDL-ESM2G predictions as input confirm that the SAT can be estimated accurately as described in the analysis section. The analysis conducted for different regions of AP reveals that the diurnal variations of SAT in the IMD observations and the ANN predictions over three regions (North, Central, and South AP) and overall AP compare well, with root mean square error varying between 0.97 °C and 1.33 °C. Thus, the SAT predictions provided in the GFDL-ESM2G model simulations could be improved statistically by using the ANN technique over the AP region.

Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments

AbstractData assimilation is often viewed as a framework for correcting short‐term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short‐term corrections, or analysis increments, can closely mirror the systematic bias patterns of the dynamical model. In this study, we use convolutional neural networks (CNNs) to learn a mapping from model state variables to analysis increments, in order to showcase the feasibility of a data‐driven model parameterization which can predict state‐dependent model errors. We undertake this problem using an ice‐ocean data assimilation system within the Seamless system for Prediction and EArth system Research (SPEAR) model, developed at the Geophysical Fluid Dynamics Laboratory, which assimilates satellite observations of sea ice concentration every 5 days between 1982 and 2017. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are states and tendencies of sea ice concentration, sea‐surface temperature, ice velocities, ice thickness, net shortwave radiation, ice‐surface skin temperature, sea‐surface salinity, as well as a land‐sea mask. We find the CNN is able to make skillful predictions of the increments in both the Arctic and Antarctic and across all seasons, with skill that consistently exceeds that of a climatological increment prediction. This suggests that the CNN could be used to reduce sea ice biases in free‐running SPEAR simulations, either as a sea ice parameterization or an online bias correction tool for numerical sea ice forecasts.

Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model

AbstractAntarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice.

A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields

AbstractThough tropical cyclone (TC) models have been routinely evaluated against track and intensity observations, little work has been performed to validate modeled TC wind fields over land. In this paper, we present a simple framework for evaluating simulated low‐level inland winds with in‐situ observations and existing TC structure theory. The Automated Surface Observing Systems, Florida Coastal Monitoring Program, and best track data are used to generate a theory‐predicted wind profile that reasonably represents the observed radial distribution of TC wind speeds. We quantitatively and qualitatively evaluated the modeled inland TC wind fields, and described the model performance with a set of simple indicators. The framework was used to examine the performance of a high‐resolution two‐way nested Geophysical Fluid Dynamics Laboratory model on recent U.S. landfalling TCs. Results demonstrate the capacity of using this framework to assess the modeled TC low‐level wind field in the absence of dense inland observations.

Optimal Precursors for Central Pacific El Niño Events in GFDL CM2p1

Abstract Compared to the canonical eastern Pacific El Niño, the understanding and ability to predict the central Pacific (CP)-type event still need further improvement. In this study, the principal component analysis–based particle swarm optimization algorithm (PPSO) is applied in Geophysical Fluid Dynamics Laboratory Climate Model version 2p1 (GFDL CM2p1) to obtain the optimal precursors (OPRs) for CP El Niño events, based on the conditional nonlinear optimal perturbation (CNOP) method. For this, three normal years with neither El Niño nor La Niña events, i.e., three cases, are chosen as the reference states. The obtained OPRs for these cases exhibit a consistent positive sea surface temperature (SST) perturbation distribution in the subtropical North Pacific (20°–40°N, 175°E–140°W), which is further proven to be crucial for the evolution of CP El Niño based on the Northern and Southern Hemisphere significance test results. Mechanically, these positive SST perturbations are enhanced and reach the equatorial Pacific via wind–evaporation–SST (WES) feedback to evolve into a CP El Niño at the end of the year. The nonlinear approach is adopted to investigate the predictability of CP El Niño events and can shed some light on future studies.

On the Role of Sea Surface Temperature in the Clustering of Global Tropical Cyclone Formation

Abstract This study examines the potential impacts of large-scale atmospheric circulations that are forced by sea surface temperatures (SST) on global tropical cyclone (TC) formation. Using the Geophysical Fluid Dynamics Laboratory (GFDL) global atmosphere and land surface model, version 4 (AM4), under different SST distributions, it is found that the east–west clustering of global TC formation is mainly governed by large-scale circulations in response to given SSTs, instead of direct ocean surface fluxes associated with zonal SST anomalies. Our zonally homogeneous SST simulations in the presence of realistic surface coverage show that TC clusters still emerge as a result of the breakdown of zonal circulations related to land–sea distribution, which produce specific “hotspots” for global TC formation. Sensitivity experiments with different climate warming scenarios and model physics confirm the persistence of these TC clusters in the absence of all zonal SST variations. These robust results offer new insights into the effects of large-scale circulation and terrain forcing on TC clusters beyond the traditional view of direct SST impacts, which are based on the direct alignment of the warmest SST regions and TC clusters. In addition, our experiments also capture internal variability of the global TC frequency, with an average fluctuation of 6–8 TCs at several dominant frequencies of ∼3, 6, and 9 years, even in the absence of all SST interannual variability and ocean coupling. This finding reveals an intrinsic “noise” level of the global TC frequency that one has to take into account when examining the past and future trends in TC activity and their related significance or detectability. Significance Statement In this study, the clustering of global tropical cyclone (TC) formation is investigated, using global simulations under different idealized sea surface temperature (SST) distributions. Our results show that it is the response of the large-scale tropical circulations to SST anomalies that is mostly responsible for the clustering of global TC formation rather than surface flux differences. It is also found that the tropical atmosphere contains inherent fluctuations in the global TC frequency of 6–8 TCs every 3–9 years, even in the absence of all SST interannual and zonal variability. These results offer new insight into the role of tropical dynamics in governing TC climatology and suggest possible mechanisms underlying the clustering of global TC formation under different climate conditions.