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 46 (2019) 4883-4893

RR Rodrigues, A Subramanian, L Zanna, J Berner


Investigating the predictability of North Atlantic sea surface height

Climate Dynamics (2019)

R Fraser, M Palmer, C Roberts, C Wilson, D Copsey, L Zanna

© 2019, The Author(s). Interannual sea surface height (SSH) forecasts are subject to several sources of uncertainty. Methods relying on statistical forecasts have proven useful in assessing predictability and associated uncertainty due to both initial conditions and boundary conditions. In this study, the interannual predictability of SSH dynamics in the North Atlantic is investigated using the output from a 150 year long control simulation based on HadGEM3, a coupled climate model at eddy-permitting resolution. Linear inverse modeling (LIM) is used to create a statistical model for the evolution of monthly-mean SSH anomalies. The forecasts based on the LIM model demonstrate skill on interannanual timescales O(1–2 years). Forecast skill is found to be largest in both the subtropical and subpolar gyres, with decreased skill in the Gulf Stream extension region. The SSH initial conditions involving a tripolar anomaly off Cape Hatteras lead to a maximum growth in SSH about 20 months later. At this time, there is a meridional shift in the 0 m-SSH contour on the order of 0.5 ∘–1.5 ∘-latitude, coupled with a change in SSH along the US East Coast. To complement the LIM-based study, interannual SSH predictability is also quantified using the system’s average predictability time (APT). The APT analysis extracted large-scale SSH patterns which displayed predictability on timescales longer than 2 years. These patterns are responsible for changes in SSH on the order of 10 cm along the US East Coast, driven by variations in Ekman velocity. Our results shed light on the timescales of SSH predictability in the North Atlantic. In addition, the diagnosed optimal initial conditions and predictable patterns could improve interannual forecasts of the Gulf Stream’s characteristics and coastal SSH.


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.


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


Lagrangian ocean analysis: Fundamentals and practices

OCEAN MODELLING 121 (2018) 49-75

E van Sebille, SM Griffies, R Abernathey, TP Adams, P Berloff, A Biastoch, B Blanke, EP Chassignet, Y Cheng, CJ Cotter, E Deleersnijder, K Doos, HF Drake, S Drijfhout, SF Gary, AW Heemink, J Kjellsson, IM Koszalka, M Lange, C Lique, GA MacGilchrist, R Marsh, CGM Adame, R McAdam, F Nencioli, CB Paris, MD Piggott, JA Polton, S Ruehs, SHAM Shah, MD Thomas, J Wang, PJ Wolfram, L Zanna, JD Zika


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 Science and Business Media LLC 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.


Recent multivariate changes in the North Atlantic climate system, with a focus on 2005-2016

INTERNATIONAL JOURNAL OF CLIMATOLOGY 38 (2018) 5050-5076

J Robson, RT Sutton, A Archibald, F Cooper, M Christensen, LJ Gray, NP Holliday, C Macintosh, M McMillan, B Moat, M Russo, R Tilling, K Carslaw, D Desbruyeres, O Embury, DL Feltham, DP Grosvenor, S Josey, B King, A Lewis, GD McCarthy, C Merchant, AL New, CH O'Reilly, SM Osprey, K Read, A Scaife, A Shepherd, B Sinha, D Smeed, D Smith, A Ridout, T Woollings, M Yang


The Impact of Tropical Precipitation on Summertime Euro-Atlantic Circulation via a Circumglobal Wave Train

JOURNAL OF CLIMATE 31 (2018) 6481-6504

CH O'Reilly, T Woollings, L Zanna, A Weisheimer


Southern Ocean carbon-wind stress feedback

Climate Dynamics (2018) 1-15

B Bronselaer, L Zanna, DR Munday, J Lowe

© 2017 The Author(s) The Southern Ocean is the largest sink of anthropogenic carbon in the present-day climate. Here, Southern Ocean (Formula presented.) and its dependence on wind forcing are investigated using an equilibrium mixed layer carbon budget. This budget is used to derive an expression for Southern Ocean (Formula presented.) sensitivity to wind stress. Southern Ocean (Formula presented.) is found to vary as the square root of area-mean wind stress, arising from the dominance of vertical mixing over other processes such as lateral Ekman transport. The expression for p\hbox {CO}_{2} is validated using idealised coarse-resolution ocean numerical experiments. Additionally, we show that increased (decreased) stratification through surface warming reduces (increases) the sensitivity of the Southern Ocean (Formula presented.) to wind stress. The scaling is then used to estimate the wind-stress induced changes of atmospheric (Formula presented.) in CMIP5 models using only a handful of parameters. The scaling is further used to model the anthropogenic carbon sink, showing a long-term reversal of the Southern Ocean sink for large wind stress strength.


The relationship between a deformation-based eddy parameterization and the LANS-α turbulence model

Ocean Modelling 126 (2018) 56-62

SD Bachman, JA Anstey, L Zanna

© 2018 Elsevier Ltd A recent class of ocean eddy parameterizations proposed by Porta Mana and Zanna (2014) and Anstey and Zanna (2017) modeled the large-scale flow as a non-Newtonian fluid whose subgridscale eddy stress is a nonlinear function of the deformation. This idea, while largely new to ocean modeling, has a history in turbulence modeling dating at least back to Rivlin (1957). The new class of parameterizations results in equations that resemble the Lagrangian-averaged Navier–Stokes-α model (LANS-α e.g., Holm et al., 1998a). In this note we employ basic tensor mathematics to highlight the similarities between these turbulence models using component-free notation. We extend the Anstey and Zanna (2017) parameterization, which was originally presented in 2D, to 3D, and derive variants of this closure that arise when the full non-Newtonian stress tensor is used. Despite the mathematical similarities between the non-Newtonian and LANS-α models which might provide insight into numerical implementation, the input and dissipation of kinetic energy between these two turbulent models differ.


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

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