Prospects for El Niño–Southern Oscillation (ENSO) predictability at long lead-times lie in the subsurface oceanic memory along the equatorial Pacific. Long considered a reliable precursor to ENSO, ...the oceanic heat content in springtime, often referred to as the recharge-discharge, is considered the most promising indicator of an ENSO event to come. In this study, we utilize January initialized hindcasts from the North American Multi-model Ensemble (NMME) over 1982–2010 to confront the hypothesis that the springtime recharge is a skillful predictor of ENSO the following winter. We find that the NMME ensemble mean predictions for the springtime recharge are highly skilled, even at a 10-months lead. Overall, as an independent predictor of ENSO, the springtime recharge-discharge tips the scale towards like-sign ENSO, but the spread of ENSO outcomes remains large. In both observations and the NMME predictions, recharged (discharged) states rarely evolve into La Niña (El Niño) events, yet an ENSO-neutral state is as likely to occur after a preconditioned state as is a like-sign ENSO event, particularly in observations. However, more often than in observations, the initialized predictions follow springtime recharged, neutral, and discharged states with El Niño, ENSO-neutral, and La Niña events, respectively, indicating that the NMME underestimates the uncertainty in nature. Predictions from initially recharged and discharged states also produce comparable signal-to-noise ratios in December ENSO predictions over the hindcast period. Therefore, in the realistic forecast setting considered, neither a recharged nor a discharged state produces a more predictable ENSO outcome, which is at odds with conclusions from recent predictability studies.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This study seeks to identify thermally driven sources of ENSO amplitude and uncertainty, as they are relatively unexplored compared to wind-driven sources. Pacific meridional modes are argued to be ...wind triggers for ENSO events. This study offers an alternative role for the South Pacific meridional mode (SPMM) in ENSO dynamics, not as an ENSO trigger, but as a coincident source of latent heat flux (LHF) forcing of ENSO SSTA that, if correctly (incorrectly) predicted, could reduce (increase) ENSO prediction errors. We utilize a coupled model simulation in which ENSO variability is perfectly periodic and each El Niño experiences identical wind stress forcing. Differences in El Niño amplitude are primarily thermally driven via the SPMM. When El Niño occurs coincidentally with positive phase SPMM, the positive SPMM LHF anomaly counteracts a fraction of the LHF damping of El Niño, allowing for a more intense El Niño. If the SPMM phase is instead negative, the SPMM LHF amplifies the LHF damping of El Niño, reducing the event’s amplitude. Therefore, SPMM LHF anomalies may either constructively or destructively interfere with coincident ENSO events, thus modulating the amplitude of ENSO. The ocean also plays a role, as the thermally forced SSTA is then advected westward by the mean zonal velocity, generating a warming or cooling in the ENSO SSTA tendency in addition to the wind-forced component. Results suggest that in addition to wind-driven sources, there exists a thermally driven piece to ENSO amplitude and uncertainty that is generally overlooked. Links between the SPMM and Pacific decadal variability are discussed.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Several mechanisms originating in the Northern (NH) and Southern Hemisphere (SH) are argued to have the ability to stochastically force ENSO events. In this study, the impact of these extratropical ...mechanisms on ENSO diversity and predictability are evaluated using linear regression methodologies from information theory and machine learning applied to observational data. Overfitting is often an issue when investigating different extratropical mechanisms, as they are highly correlated in both space and time. The statistical methods in this study are specifically designed to address this issue. Results show that at 1-year lead-times, the extratropics are related to development of Central Pacific (CP), but not Eastern Pacific (EP) ENSO events. In boreal winter, the SH extratropics contribute to the predictability of CP ENSO, much further in advance than previous studies have indicated. The dominant NH predictor of CP ENSO from one winter to the next is identified as a sea surface temperature dipole in the Western North Pacific. Finally, separation of CP ENSO into its extratropical and tropical related components demonstrates that CP ENSO events with strong forcing from the extratropics start one season earlier than events primarily forced from the Tropics and thus have the potential for longer lead predictability, up to 1-year in advance of a CP ENSO event.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
4.
THE SUBSEASONAL EXPERIMENT (SubX) Pegion, Kathy; Kirtman, Ben P.; Becker, Emily ...
Bulletin of the American Meteorological Society,
10/2019, Volume:
100, Issue:
10
Journal Article
Peer reviewed
Open access
The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have ...produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The present study compares the local simultaneous correlation between rainfall–evaporation and sea surface temperature (SST)–SST tendency among observations, coupled general circulation model (CGCM) ...simulations, and stand-alone atmospheric general circulation model (AGCM) simulations. The purpose is to demonstrate to what extent the model simulations can reproduce the observed air–sea relationship. While the model-simulated correlation agrees with the observations in the tropical eastern Pacific, large discrepancies are found in the subtropics, midlatitudes, and tropical Indo-western Pacific Ocean regions. In tropical Indo-western Pacific Ocean regions and the midlatitudes where the atmosphere contributes to the observed SST changes, the specified SST simulations produce excessive SST forcing, whereas the CGCM captures the atmospheric feedback on the SST, but with somewhat of an overestimation. In the subtropics, both the AGCM and CGCM produce unrealistic positive rainfall–SST correlations. In the tropical westerncentral Pacific and the North Indian Ocean, the CGCM-simulated evaporation–SST correlation is opposite to that observed because of an excessive dependence of the sea–air humidity difference on the SST.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The impact of coupled air–sea feedbacks on the simulation of tropical intraseasonal variability is investigated in this study using the National Centers for Environmental Prediction Climate Forecast ...System. The simulation of tropical intraseasonal variability in a freely coupled simulation is compared with two simulations of the atmospheric component of the model. In one experiment, the uncoupled model is forced with the daily sea surface temperature (SST) from the coupled run. In the other, the uncoupled model is forced with climatological SST from the coupled run. Results indicate that the overall intraseasonal variability of precipitation is reduced in the coupled simulation compared to the uncoupled simulation forced by daily SST. Additionally, air–sea coupling is responsible for differences in the simulation of the tropical intraseasonal oscillation between the coupled and uncoupled models, specifically in terms of organization and propagation in the western Pacific. The differences between the coupled and uncoupled simulations are due to the fact that the relationships between precipitation and SST and latent heat flux and SST are much stronger in the coupled model than in the uncoupled model. Additionally, these relationships are delayed by about 5 days in the uncoupled model compared to the coupled model. As demonstrated by the uncoupled simulation forced with climatological SST, some of the intraseasonal oscillation can be simulated by internal atmospheric dynamics. However, the intraseasonally varying SST appears to be important to the amplitude and propagation of the oscillation beyond the Maritime Continent.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study investigates whether air–sea interactions contribute to differences in the predictability of the boreal winter tropical intraseasonal oscillation (TISO) using the NCEP operational climate ...model. A series of coupled and uncoupled, “perfect” model predictability experiments are performed for 10 strong model intraseasonal events. The uncoupled experiments are forced by prescribed SST containing different types of variability. These experiments are specifically designed to be directly comparable to actual forecasts. Predictability estimates are calculated using three metrics, including one that does not require the use of time filtering. The estimates are compared between these experiments to determine the impact of coupled air–sea interactions on the predictability of the tropical intraseasonal oscillation and the sensitivity of the potential predictability estimates to the different SST forcings.
Results from all three metrics are surprisingly similar. They indicate that predictability estimates are longest for precipitation and outgoing longwave radiation (OLR) when the ensemble mean from the coupled model is used. Most importantly, the experiments that contain intraseasonally varying SST consistently predict the control events better than those that do not for precipitation, OLR, 200-hPa zonal wind, and 850-hPa zonal wind after the first 10 days. The uncoupled model is able to predict the TISO with similar skill to that of the coupled model, provided that an SST forecast that includes these intraseasonal variations is used to force the model. This indicates that the intraseasonally varying SSTs are a key factor for increased predictability and presumably better prediction of the TISO.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The present study documents local rainfall‐SST relationship on subseasonal time scales in the tropical Indo‐western Pacific region based on satellite observations and the Climate Forecast System ...(CFS). It is found that the intensity of subseasonal air‐sea coupling strongly depends on the season with most prominent interaction in the summer hemisphere. The time lag between rainfall and SST displays pronounced spatial variations and asymmetric feature. In observations, the regions of the atmosphere forcing the SST tend to shift to subtropics compared to those of the SST forcing the atmosphere. In the CFS, the regions of the atmosphere forcing the SST tend to be collocated with those of the SST forcing the atmosphere. The CFS captures reasonably well the seasonality in the intensity of air‐sea coupling, but probably with an overestimation of the coupling strength. The SST response to the atmosphere takes longer time in the CFS compared to observations.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Coastal flooding operational forecasting in the US is limited to short-range temporal scales (3–7 days), which limits the response time for emergency preparation and planning. The sub-seasonal ...prediction project (SubX), which produces weather forecasts with a lead time of up to four weeks, provides an opportunity to assess the potential for creating probabilistic flood forecasts with longer lead times. Using the ADCIRC hydrodynamic model for coastal storm surge, two major hurricanes, Isabel (2003) and Katrina (2005), were used as case studies to test coastal flood predictions induced by wind and pressure fields generated from five global weather models within SubX. The storm surges simulations are forced by Sea Level Pressure (SLP) and 10 m winds fields from SubX models for a lead-time of up to 30 days before storm landfall. The subseasonal surge forecasts are evaluated temporally and spatially at 1–4 weeks lead-time against the NOAA tide gages observations and a verification dataset derived by forcing the storm surge model with wind and pressure fields from the NCEP-Reanalysis. The results are evaluated in terms of lead-time and forecast skill metrics. The storm surge forecast skill is measured using the mean square error skill score (MSESS) relative to the verification dataset and an approximate of the climatology. A skill score greater than 0.55 is considered here useful for flood forecasting. The multi-model ensemble (MME) mean surge forecasts driven by several members of SubX models demonstrate skill greater than 0.55 up to a 4-day and 10-day lead for Katrina and Isabel, respectively. A sharper decrease in MSESS was noted from week 1 to week 3 lead-times for Katrina, in comparison to Isabel. Some ensemble members forecasted hurricanes and storm surges as early as 3–4 weeks lead-time. However, due to the offsets developed in the timing and magnitude of the peak at these lead-times, and based on a sample size of only two events, it is hard to establish the significance of these longer lead-time results. While a follow up study involving flood reforecasts over the entire SubX reforecast period (1999–2015) is needed to support more robust statistics of the forecast skill, our case studies demonstrate the feasibility of probabilistic flood forecasting at subseasonal timescales using the SubX models.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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