This study examines the role of the air–sea coupled process in the seasonal predictability of Asia–Pacific summer monsoon rainfall by comparing seasonal predictions from two carefully designed model ...experiments: tier 1 (fully coupled model) and tier 2 (AGCM with prescribed SSTs). In these experiments, an identical AGCM is used in both tier 1 and tier 2 predictions; the daily mean SSTs from tier 1 coupled predictions are prescribed as a boundary condition in tier 2 predictions. Both predictions start in April from 1982 to 2009, with four ensemble members for each case. The model used is the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model for seasonal-to-interannual prediction at the National Centers for Environmental Prediction (NCEP). Comparisons indicate that tier 2 predictions produce not only higher rainfall biases but also unrealistically high rainfall variations in the tropical western North Pacific (TWNP) and some coastal regions as well. While the prediction skill in terms of anomaly correlations does not present a significant difference between the two types of predictions, the root-mean-square errors (RMSEs) are clearly larger over the above-mentioned regions in the tier 2 prediction. The reduced RMSE skills in the tier 2 predictions are due to the lack of a coupling process in AGCM-alone simulations, which, particularly, results in an unrealistic SST–rainfall relationship over the TWNP region. It is suggested that for a prediction of summer monsoon rainfall over the Asia–Pacific region, it is necessary to use a coupled atmosphere–ocean (tier 1) prediction system.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In recent decades, unprecedented extreme summer heat waves have occurred in Europe, and they have exhibited an increasing trend since 1970s. Although previous studies have suggested that these recent ...hot European summers could have been instigated by the underlying surface thermal conditions, the possible influence of shrinking Arctic sea ice and Eurasian snow cover on heat waves are not well understood. Herein, we present evidence obtained via observational analyses and numerical experiments indicating that the interdecadal increase in European heat waves is closely linked to the reductions in Arctic sea ice concentration (ASIC) and Eurasian snow cover fraction (EASC) across mid–high latitudes via the excitation of the anomalous Eurasian wave train. The combined effects of declined ASIC and EASC, accompanied by the drier soil and the stronger heat flux, tend to weaken the poleward temperature gradient at mid–high latitudes and affect the midlatitude jet stream and transient eddy activities. These dynamic and thermodynamic circulations increase the likelihood of more persistent European blocking events that favor frequent and strengthened heat waves. Further projection analysis of simulations from 13 CMIP5 climate models suggests that Europe may experience more hot summers as the ASIC and EASC continue to decline over the next century.
As an update on the current NOAA/NCEP operational ocean reanalysis systems, a new system named GLobal Ocean Reanalysis (GLORe) is recently built up based on the JEDI‐SOCA 3DVar scheme. In this study, ...the quality of GLORe is assessed in initializing ENSO predictions using the NOAA Unified Forecast System (UFS). In details, initialized by GLORe, 9‐month ensemble hindcasts are conducted from each May/November during 1982–2021. The ENSO prediction skill is compared to the current NOAA operational system CFSv2, suggesting that UFS initialized with GLORe has an improved skill in ENSO predictions. By conducting another set of hindcasts with UFS and the same initializations as CFSv2, it is found that the skill improvement is largely attributed to the ocean initialization with GLORe, but with some contributions from model improvements as well. The effect of ocean initializations is further confirmed by the superiority of GLORe over CFSR as validated against an objective analysis.
Plain Language Summary
The operational climate monitoring and outlooks heavily rely on ocean data assimilation systems. At NOAA/NCEP, there are two such systems running in real time—GODAS and CFSR. As two highly related systems, GODAS and CFSR share some common weaknesses, and are lagging to meet the latest operational requirements. In this study, a new ocean reanalysis system (GLORe) is built based on the latest scientific advances. As a first evaluation about GLORe, this work focuses on its performance in initializing dynamical ENSO predictions, a critical component of climate outlooks. In particular, we complete a set of 9‐month ensemble hindcasts initialized with GLORe, by using the NOAA Unified Forecast System as the forecast model. The hindcasts are compared to the present NOAA operational system CFSv2 that is initialized with CFSR, and significant skill enhancements in ENSO predictions are seen in our hindcasts. Further experiments and diagnostics suggest that the skill improvement is mostly attributed to the GLORe initialization, but with some contributions from model improvements as well. As the configurations for our hindcasts are highly relevant to a future operational Seasonal Forecast System (SFS) at NOAA, the hindcasts reported in this paper will serve as an important benchmark for its developments.
Key Points
A newly developed ocean reanalysis (GLORe) is introduced and evaluated for its quality in initializing ENSO predictions
UFS initialized with GLORe presents a better performance in ENSO predictions than the current operational system at NOAA
The hindcasts will be a benchmark for the Seasonal Forecast System (SFS) development at NOAA
In this work, the evolution and prediction of the persistent and remarkable warm sea surface temperature anomaly (SSTA) in the northeastern Pacific during October 2013–June 2016 are examined. Based ...on experiments with an atmospheric model, the possible contribution of SSTAs in different ocean basins to the atmospheric circulation anomalies is identified. Further, through verifying the real-time forecasts, current capabilities in predicting such an extreme warm event with a state-of-the-art coupled general circulation model are assessed.
During the long-lasting warm event, there were two warm maxima in the area-averaged SSTA around January 2014 and July 2015, respectively. The warm anomaly originated at the oceanic surface and propagated downward and reached about 300 m. Model experiments forced by observed SST suggest that the long persistence of the atmospheric anomalies in the northeastern Pacific as a whole may be partially explained by SST forcing, particularly in the tropical Pacific Ocean associated with a persistent warm SSTA in 2014/15 and an extremely strong El Niño in 2015/16, via its influence on atmospheric circulation over the North Pacific. Nevertheless, it was a challenge to predict the evolution of this warm event, especially for its growth. That is consistent with the fact that the SSTAs in extratropical oceans are largely a consequence of unpredictable atmospheric variability.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The Unified Forecast System (UFS) is the next generation modeling infrastructure under development for NOAA's operational numerical weather/climate predictions. This study is the first attempt with ...UFS for seasonal predictions application. In particular, 9‐month hindcasts are performed starting from every month during 2007–2020. The UFS performance in predicting Arctic sea ice was compared to hindcasts by (a) the current operational Climate Forecast System version 2 (CFSv2) and (b) an experimental sea ice prediction system (CFSm5) at NOAA. Evaluations indicate that UFS demonstrates consistently improved skills than both CFSv2 and CFSm5 in seasonal sea ice predictions, together with more realistic climatological sea ice distributions. Diagnostics suggest that the better climatological sea ice distributions in UFS is related to its atmospheric states simulated with FV3, the atmospheric component of UFS, and reinforced by associated ocean circulations. In addition, applications of the multi‐model ensemble strategy do not present skill improvements over the UFS forecasts alone.
Plain Language Summary
Arctic sea‐ice cover illustrates significant year‐to‐year fluctuations superimposed on the long‐term decline trend. Seasonal forecasts of the ice interannual fluctuations could play an important role in providing planning information for shipping industries, improving management of ocean and coastal resources in the Arctic, and better serving Northern communities. In this study, we evaluated the capability of the Unified Forecast System (UFS) in predicting the Arctic sea ice coverage. The UFS is the next generation modeling infrastructure under development for NOAA's operational numerical weather/climate predictions, which has not been tried with seasonal predictions application before. Evaluations suggested that UFS demonstrates consistently improved skills in seasonal sea ice predictions than two systems currently in operations at NOAA, together with more realistic climatological sea ice distributions. Our further diagnostics suggest that the improvement is mainly related to the atmospheric states simulated with UFS, but is also reinforced by associated ocean circulations.
Key Points
This work is the first attempt with the Unified Forecast System (UFS) for seasonal predictions
UFS presents better performance in seasonal prediction of Arctic sea ice than current operational systems at NOAA
Better sea ice predictions with UFS are mainly related to its better representation of atmospheric states
Seasonal prediction skill of SSTs from coupled models has considerable spatial variations. In the tropics, SST prediction skill in the tropical Pacific clearly exceeds prediction skill over the ...Atlantic and Indian Oceans. Such skill variations can be due to spatial variations in observing system used for forecast initializations or systematic errors in the seasonal prediction systems, or they could be a consequence of inherent properties of the coupled ocean–atmosphere system leaving a fingerprint on the spatial structure of SST predictability. Out of various alternatives, the spatial variability in SST prediction skill is argued to be a consequence of inherent characteristics of climate system. This inference is supported based on the following analyses. SST prediction skill is higher over the regions where coupled air–sea interactions (or Bjerknes feedback) are inferred to be stronger. Coupled air–sea interactions, and the longer time scales associated with them, imprint longer memory and thereby support higher SST prediction skill. The spatial variability of SST prediction skill is also consistent with differences in the ocean–atmosphere interaction regimes that distinguish between whether ocean drives the atmosphere or atmosphere drives the ocean. Regions of high SST prediction skill generally coincide with regions where ocean forces the atmosphere. Such regimes correspond to regions where oceanic variability is on longer time scales compared to regions where atmosphere forces the ocean. Such regional differences in the spatial characteristics of ocean–atmosphere interactions, in turn, also govern the spatial variations in SST skill, making spatial variations in skill an intrinsic property of the climate system and not an artifact of the observing system or model biases.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In contrast to the temporal evolution of forecast ensemble mean (signal) and spread (noise) in an ensemble of seasonal forecasts, the spatial patterns of signal and noise components for sea surface ...temperature (SST) predictions have not been analyzed. In this work, we examine the leading patterns of signal and noise components of SST forecasts by the National Centers for Environmental Prediction Climate Forecast System version 2. It is noted that the leading empirical orthogonal function pattern of SST is similar between the signal and the noise with maximum loading in the central and eastern tropical Pacific associated with El Niño–Southern Oscillation (ENSO) variability. The similarity implies that while some members of the forecasts predict a stronger (weaker) ENSO than others, the dominant pattern of SST anomalies from all members still resembles the ENSO SST pattern. This reflects the notion that for each forecast ensemble member, the evolution of ENSO is governed by the similar air–sea coupled interactions, the strength of which, however, differs due to unpredictable noise. On the other hand, the leading mode of the signal and the noise are found temporally independent. Thus, it is concluded that although the largest variability in the signal and the noise is spatially collocated, their temporal evolution is independent.
The observed Madden–Julian oscillation (MJO) tends to propagate eastward across the Maritime Continent from the eastern equatorial Indian Ocean to the western Pacific. However, numerical simulations ...present different levels of fidelity in representing the propagation, especially for the tropical convection associated with the MJO. This study conducts a series of coupled simulations using the NCEP CFSv2 to explore the impacts of SST feedback and convection parameterization on the propagation simulations. First, two simulations differing in the model horizontal resolutions are conducted. The MJO propagation in these two simulations is found generally insensitive to the resolution change. Further, based on the CFSv2 with a lower resolution, two additional experiments are performed with model SSTs nudged to climatologies with different time scales representing different air–sea coupling strength. It is demonstrated that weakening the air–sea coupling strength significantly degrades the MJO propagation simulation, suggesting the critical role of SST feedback in maintaining MJO propagation. Last, the sensitivity to convection parameterization is explored by comparing two simulations with different convection parameterization schemes. Analyses of these simulations indicate that including air–sea coupling alone in a dynamical model does not result in realistic maintenance of the MJO eastward propagation without the development of favorable SST conditions in the western Pacific. In both observations and one simulation with realistic MJO propagations, the preconditioning of SSTs is strongly affected by surface latent heat fluxes that are modulated by surface wind anomalies in both zonal and meridional directions. The diagnostics highlight the critical contribution from meridional winds in wind speed variations, which has been neglected in most MJO studies.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study focuses on improving prediction of the two types of ENSO by combining multi-model ensemble (MME) with a statistical error correction method that is based on a stepwise pattern projection ...and applied to all models before doing the MME. We evaluate such a combinational approach using five dynamical model datasets from the North American Multi-model Ensemble (NMME) project for the period of 1982–2010. The prediction skills of the proposed MME show an improvement over most tropical Pacific regions. With regard to the two ENSO types, improvements in prediction skills of the proposed MME are particularly evident for the Niño indices for short lead time. The differences between the Eastern Pacific and Central Pacific ENSO types are more pronounced in the corrected forecasts compared with the uncorrected ones. The zonal center position of sea surface temperature anomalies for the corrected MME is closer to the observed than that for the uncorrected MME. The results indicate that reducing prediction errors of each model member by a good correction method before applying the MME method can provide an effective way for empirically improving forecasts of the two ENSO types.
We use a version of the NOAA Climate Forecast System with enhanced (up to 1‐m) ocean model vertical resolution to investigate the mean diurnal cycles of upper ocean temperature and currents. The ...model sea surface temperature diurnal cycle agrees well with a global observational analysis. The simulated time‐depth profiles of temperature and current also match closely observations from densely instrumented moorings in the tropical Pacific. Our analyses provide new insights into subsurface ocean diurnal cycles. Significant temperature diurnal range occurs, with seasonal modulation, at depths greater than 10 m across broad areas of the subtropical and midlatitude oceans. Significant current diurnal cycles are evident below 30 m across parts of the tropics, including in areas where deep‐cycle turbulence has been observed.
Plain Language Summary
We used computer model simulations of Earth's atmosphere and ocean to understand how ocean temperatures and currents vary by time of day. The model has 12 levels in the top 20 m of the ocean—greater than normal for this kind of simulation. This allows realistic simulated diurnal variations of sea surface temperature (compared to global observations), and realistic changes in temperature and current at and below the surface (compared to mooring observations). These results give us confidence in the global simulations of currents, which provide new insights into diurnal variations of surface ocean velocity and turbulence below the surface.
Key Points
A 1‐m vertical resolution ocean model accurately simulates global patterns of sea surface temperature mean diurnal cycle
The model gives new insights into modes of subsurface ocean temperature and current diurnal variation
Global maps of current diurnal cycle extend understanding past that from relatively sparse observations
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