Publications


Forced summer stationary waves: the opposing effects of direct radiative forcing and sea surface warming

Climate Dynamics (2019)

HS Baker, T Woollings, C Mbengue, MR Allen, CH O’Reilly, H Shiogama, S Sparrow

© 2019, The Author(s). We investigate the opposing effects of direct radiative forcing and sea surface warming on the atmospheric circulation using a hierarchy of models. In large ensembles of three general circulation models, direct CO 2 forcing produces a wavenumber 5 stationary wave over the Northern Hemisphere in summer. Sea surface warming produces a similar wave, but with the opposite sign. The waves are also present in the Coupled Model Intercomparison Project phase 5 ensemble with opposite signs due to direct CO 2 and sea surface warming. Analyses of tropical precipitation changes and equivalent potential temperature changes and the results from a simple barotropic model show that the wave is forced from the tropics. Key forcing locations are the Western Atlantic, Eastern Atlantic and in the Indian Ocean just off the east coast of Africa. The stationary wave has a significant impact on regional temperature anomalies in the Northern Hemisphere summer, explaining some of the direct effect that CO 2 concentration has on temperature extremes. Ultimately, the climate sensitivity and future changes in the land–sea temperature contrast will dictate the balance between the opposing effects on regional changes in mean and extreme temperature and precipitation under climate change.


Global reconstruction of historical ocean heat storage and transport.

Proceedings of the National Academy of Sciences of the United States of America 116 (2019) 1126-1131

L Zanna, S Khatiwala, JM Gregory, J Ison, P Heimbach

Most of the excess energy stored in the climate system due to anthropogenic greenhouse gas emissions has been taken up by the oceans, leading to thermal expansion and sea-level rise. The oceans thus have an important role in the Earth's energy imbalance. Observational constraints on future anthropogenic warming critically depend on accurate estimates of past ocean heat content (OHC) change. We present a reconstruction of OHC since 1871, with global coverage of the full ocean depth. Our estimates combine timeseries of observed sea surface temperatures with much longer historical coverage than those in the ocean interior together with a representation (a Green's function) of time-independent ocean transport processes. For 1955-2017, our estimates are comparable with direct estimates made by infilling the available 3D time-dependent ocean temperature observations. We find that the global ocean absorbed heat during this period at a rate of 0.30 ± 0.06 W/[Formula: see text] in the upper 2,000 m and 0.028 ± 0.026 W/[Formula: see text] below 2,000 m, with large decadal fluctuations. The total OHC change since 1871 is estimated at 436 ± 91 [Formula: see text] J, with an increase during 1921-1946 (145 ± 62 [Formula: see text] J) that is as large as during 1990-2015. By comparing with direct estimates, we also infer that, during 1955-2017, up to one-half of the Atlantic Ocean warming and thermosteric sea-level rise at low latitudes to midlatitudes emerged due to heat convergence from changes in ocean transport.


ENSO Bimodality and Extremes

Geophysical Research Letters (2019)

RR Rodrigues, A Subramanian, L Zanna, J Berner

© 2019. American Geophysical Union. All Rights Reserved. Tropical sea surface temperature (SST) and winds vary on a wide range of timescales and have a substantial impact on weather and climate across the globe. Here we study the variability of SST and zonal wind during El Niño-Southern Oscillation (ENSO) between 1982 and 2014. We focus on changes in extreme statistics using higher-order moments of SST and zonal winds. We find that ENSO characteristics exhibit bimodal distributions and fat tails with extreme warm and cold temperatures in 1982–1999, but not during 2000–2014. The changes in the distributions coincide with changes in the intensity of ENSO events and the phase of the Interdecadal Pacific Oscillation. We also find that the strongest Easterly Wind Bursts occur during extreme El Niños and not during La Niñas. Maps of SST kurtosis can serve as a diagnostic for the thermocline feedback mechanism responsible for the differences in ENSO diversity between the two periods.


Atmospheric Circulation of Tide-Locked Exoplanets

ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 51 (2019) 275-303

RT Pierrehumbert, M Hammond


The importance of stratospheric initial conditions for winter North Atlantic Oscillation predictability and implications for the signal-to-noise paradox

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 145 (2019) 131-146

CH O'Reilly, A Weisheimer, T Woollings, LJ Gray, D MacLeod


How confident are predictability estimates of the winter North Atlantic Oscillation?

Quarterly Journal of the Royal Meteorological Society (2019)

A Weisheimer, D Decremer, D MacLeod, C O'Reilly, TN Stockdale, S Johnson, TN Palmer

© 2018 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Atmospheric seasonal predictability in winter over the Euro-Atlantic region is studied with an emphasis on the signal-to-noise paradox of the North Atlantic Oscillation. Seasonal hindcasts of the ECMWF model for the recent period 1981–2009 show, in agreement with other studies, that correlation skill over Greenland and parts of the Arctic is higher than the signal-to-noise ratio implies. This leads to the paradoxical situation where the real world appears more predictable than the models suggest, with the forecast ensembles being overly dispersive (or underconfident). However, it is demonstrated that these conclusions are not supported by the diagnosed relationship between ensemble mean root-mean-square error (RMSE) and ensemble spread which indicates a slight under-dispersion (overconfidence). Furthermore, long atmospheric seasonal hindcasts suggest that over the 110-year period from 1900 to 2009 the ensemble system is well calibrated (neither over- nor under-dispersive). The observed skill changed drastically in the middle of the twentieth century and paradoxical regions during more recent hindcast periods were strongly under-dispersive during mid-century decades. Due to non-stationarities of the climate system in the form of decadal variability, relatively short hindcasts are not sufficiently representative of longer-term behaviour. In addition, small hindcast sample size can lead to skill estimates, in particular of correlation measures, that are not robust. It is shown that the relative uncertainty due to small hindcast sample size is often larger for correlation-based than for RMSE-based diagnostics. Correlation-based measures like the RPC are shown to be highly sensitive to the strength of the predictable signal, implying that disentangling of physical deficiencies in the models on the one hand, and the effects of sampling uncertainty on the other hand, is difficult. Given the current lack of a causal physical mechanism to unravel the puzzle, our hypotheses of non-stationarity and sampling uncertainty provide simple yet plausible explanations for the paradox.


Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization

Journal of Advances in Modeling Earth Systems (2019)

T Bolton, L Zanna

©2019. The Authors. Oceanographic observations are limited by sampling rates, while ocean models are limited by finite resolution and high viscosity and diffusion coefficients. Therefore, both data from observations and ocean models lack information at small and fast scales. Methods are needed to either extract information, extrapolate, or upscale existing oceanographic data sets, to account for or represent unresolved physical processes. Here we use machine learning to leverage observations and model data by predicting unresolved turbulent processes and subsurface flow fields. As a proof of concept, we train convolutional neural networks on degraded data from a high-resolution quasi-geostrophic ocean model. We demonstrate that convolutional neural networks successfully replicate the spatiotemporal variability of the subgrid eddy momentum forcing, are capable of generalizing to a range of dynamical behaviors, and can be forced to respect global momentum conservation. The training data of our convolutional neural networks can be subsampled to 10–20% of the original size without a significant decrease in accuracy. We also show that the subsurface flow field can be predicted using only information at the surface (e.g., using only satellite altimetry data). Our results indicate that data-driven approaches can be exploited to predict both subgrid and large-scale processes, while respecting physical principles, even when data are limited to a particular region or external forcing. Our in-depth study presents evidence for the successful design of ocean eddy parameterizations for implementation in coarse-resolution climate models.


Remote and local influences in forecasting Pacific SST: a linear inverse model and a multimodel ensemble study

Climate Dynamics (2018) 1-19

DF Dias, A Subramanian, L Zanna, AJ Miller

© 2018 Springer-Verlag GmbH Germany, part of Springer Nature A suite of statistical linear inverse models (LIMs) are used to understand the remote and local SST variability that influences SST predictions over the North Pacific region. Observed monthly SST anomalies in the Pacific are used to construct different regional LIMs for seasonal to decadal predictions. The seasonal forecast skills of the LIMs are compared to that from three operational forecast systems in the North American Multi-Model Ensemble (NMME), revealing that the LIM has better skill in the Northeastern Pacific than NMME models. The LIM is also found to have comparable forecast skill for SST in the Tropical Pacific with NMME models. This skill, however, is highly dependent on the initialization month, with forecasts initialized during the summer having better skill than those initialized during the winter. The data are also bandpass filtered into seasonal, interannual and decadal time scales to identify the relationships between time scales using the structure of the propagator matrix. Moreover, we investigate the influence of the tropics and extra-tropics in the predictability of the SST over the region. The Extratropical North Pacific seems to be a source of predictability for the tropics on seasonal to interannual time scales, while the tropics enhance the forecast skill for the decadal component. These results indicate the importance of temporal scale interactions in improving the predictions on decadal timescales. Hence, we show that LIMs are not only useful as benchmarks for estimates of statistical skill, but also to isolate contributions to the forecast skills from different timescales, spatial scales or even model components.


Climate impacts of cultured meat and beef cattle

Frontiers in Sustainable Food Systems Frontiers Media S.A. (2019)

J LYNCH, R PIERREHUMBERT


Eddy-mixing entropy and its maximization in forced-dissipative geostrophic turbulence

JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2018) ARTN 073206

TW David, L Zanna, DP Marshall


Diagnosing ENSO and Global Warming Tropical Precipitation Shifts Using Surface Relative Humidity and Temperature

Journal of Climate American Meteorological Society 31 (2018) 1413-1433

A Todd, M Collins, FH Lambert, R Chadwick


El Niño–Southern Oscillation complexity

Nature Springer Nature 559 (2018) 535-545

A Timmermann, S-I An, J-S Kug, F-F Jin, W Cai, A Capotondi, KM Cobb, M Lengaigne, MJ McPhaden, MF Stuecker, K Stein, AT Wittenberg, K-S Yun, T Bayr, H-C Chen, Y Chikamoto, B Dewitte, D Dommenget, P Grothe, E Guilyardi, Y-G Ham, M Hayashi, S Ineson, D Kang, S Kim, W Kim, J-Y Lee, T Li, J-J Luo, S McGregor, Y Planton, S Power, H Rashid, H-L Ren, A Santoso, K Takahashi, A Todd, G Wang, G Wang, R Xie, W-H Yang, S-W Yeh, J Yoon, E Zeller, X Zhang


Predicting the future is hard and other lessons from a population time series data science competition

ECOLOGICAL INFORMATICS 48 (2018) 1-11

GRW Humphries, C Che-Castaldo, PJ Bull, G Lipstein, A Ravia, B Carrion, T Bolton, A Ganguly, HJ Lynch


Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions

Quarterly Journal of the Royal Meteorological Society (2018)

L Zanna, JM Brankart, M Huber, S Leroux, T Penduff, PD Williams

© 2018 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. The ocean plays an important role in the climate system on time-scales of weeks to centuries. Despite improvements in ocean models, dynamical processes involving multiscale interactions remain poorly represented, leading to errors in forecasts. We present recent advances in understanding, quantifying, and representing physical and numerical sources of uncertainty in novel regional and global ocean ensembles at different horizontal resolutions. At coarse resolution, uncertainty in 21st century projections of the upper overturning cell in the Atlantic is mostly a result of buoyancy fluxes, while the uncertainty in projections of the bottom cell is driven equally by both wind and buoyancy flux uncertainty. In addition, freshwater and heat fluxes are the largest contributors to Atlantic Ocean heat content regional projections and their uncertainties, mostly as a result of uncertain ocean circulation projections. At both coarse and eddy-permitting resolutions, unresolved stochastic temperature and salinity fluctuations can lead to significant changes in large-scale density across the Gulf Stream front, therefore leading to major changes in large-scale transport. These perturbations can have an impact on the ensemble spread on monthly time-scales and subsequently interact nonlinearly with the dynamics of the flow, generating chaotic variability on multiannual time-scales. In the Gulf Stream region, the ratio of chaotic variability to atmospheric-forced variability in meridional heat transport is larger than 50% on time-scales shorter than 2 years, while between 40 and 48°S the ratio exceeds 50% on on time-scales up to 28 years. Based on these simulations, we show that air–sea interaction and ocean subgrid eddies remain an important source of error for simulating and predicting ocean circulation, sea level, and heat uptake on a range of spatial and temporal scales. We discuss how further refinement of these ensembles can help us assess the relative importance of oceanic versus atmospheric uncertainty in weather and climate.


The Signature of Oceanic Processes in Decadal Extratropical SST Anomalies

GEOPHYSICAL RESEARCH LETTERS 45 (2018) 7719-7730

CH O'Reilly, L Zanna


Seasonal to annual ocean forecasting skill and the role of model and observational uncertainty.

Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain) 144 (2018) 1947-1964

S Juricke, D MacLeod, A Weisheimer, L Zanna, TN Palmer

Accurate forecasts of the ocean state and the estimation of forecast uncertainties are crucial when it comes to providing skilful seasonal predictions. In this study we analyse the predictive skill and reliability of the ocean component in a seasonal forecasting system. Furthermore, we assess the effects of accounting for model and observational uncertainties. Ensemble forcasts are carried out with an updated version of the ECMWF seasonal forecasting model System 4, with a forecast length of ten months, initialized every May between 1981 and 2010. We find that, for essential quantities such as sea surface temperature and upper ocean 300 m heat content, the ocean forecasts are generally underdispersive and skilful beyond the first month mainly in the Tropics and parts of the North Atlantic. The reference reanalysis used for the forecast evaluation considerably affects diagnostics of forecast skill and reliability, throughout the entire ten-month forecasts but mostly during the first three months. Accounting for parametrization uncertainty by implementing stochastic parametrization perturbations has a positive impact on both reliability (from month 3 onwards) as well as forecast skill (from month 8 onwards). Skill improvements extend also to atmospheric variables such as 2 m temperature, mostly in the extratropical Pacific but also over the midlatitudes of the Americas. Hence, while model uncertainty impacts the skill of seasonal forecasts, observational uncertainty impacts our assessment of that skill. Future ocean model development should therefore aim not only to reduce model errors but to simultaneously assess and estimate uncertainties.


Skilful Seasonal Predictions of Summer European Rainfall

GEOPHYSICAL RESEARCH LETTERS 45 (2018) 3246-3254

N Dunstone, D Smith, A Scaife, L Hermanson, D Fereday, C O'Reilly, A Stirling, R Eade, M Gordon, C Maclachlan, T Woollings, K Sheen, S Belcher


Flow dependent ensemble spread in seasonal forecasts of the boreal winter extratropics

ATMOSPHERIC SCIENCE LETTERS 19 (2018) UNSP e815

D MacLeod, C O'Reilly, T Palmer, A Weisheimer


Interdecadal variability of the ENSO teleconnection to the wintertime North Pacific

CLIMATE DYNAMICS 51 (2018) 3333-3350

CH O'Reilly


Challenges and opportunities for improved understanding of regional climate dynamics

NATURE CLIMATE CHANGE 8 (2018) 101-108

M Collins, S Minobe, M Barreiro, S Bordoni, Y Kaspi, A Kuwano-Yoshida, N Keenlyside, E Manzini, CH O'Reilly, R Sutton, S-P Xie, O Zolina

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