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Ed Hawkins and Rowan Sutton

Abstract

A key aspect in designing an efficient decadal prediction system is ensuring that the uncertainty in the ocean initial conditions is sampled optimally. Here one strategy for addressing this issue is considered by investigating the growth of optimal perturbations in the third climate configuration of the Met Office Unified Model (HadCM3) global climate model (GCM). More specifically, climatically relevant singular vectors (CSVs)—the small perturbations of which grow most rapidly for a specific set of initial conditions—are estimated for decadal time scales in the Atlantic Ocean. It is found that reliable CSVs can be estimated by running a large ensemble of integrations of the GCM. Amplification of the optimal perturbations occurs for more than 10 yr, and possibly up to 40 yr. The identified regions for growing perturbations are found to be in the far North Atlantic, and these perturbations cause amplification through an anomalous meridional overturning circulation response. Additionally, this type of analysis potentially informs the design of future ocean observing systems by identifying the sensitive regions where small uncertainties in the ocean state can grow maximally. Although these CSVs are expensive to compute, ways in which the process could be made more efficient in the future are identified.

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Ed Hawkins and Rowan Sutton

Faced by the realities of a changing climate, decision makers in a wide variety of organizations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organizations that fund climate research, is what is the potential for climate science to deliver improvements—especially reductions in uncertainty—in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty, and scenario uncertainty. Using data from a suite of climate models, we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability.

Our findings have implications for managing adaptation to a changing climate. Because the costs of adaptation are very large, and greater uncertainty about future climate is likely to be associated with more expensive adaptation, reducing uncertainty in climate predictions is potentially of enormous economic value. We highlight the need for much more work to compare (a) the cost of various degrees of adaptation, given current levels of uncertainty and (b) the cost of new investments in climate science to reduce current levels of uncertainty. Our study also highlights the importance of targeting climate science investments on the most promising opportunities to reduce prediction uncertainty.

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Ed Hawkins and Rowan Sutton

Abstract

The decadal predictability of three-dimensional Atlantic Ocean anomalies is examined in a coupled global climate model [the third climate configuration of the Met Office Unified Model (HadCM3)] using a linear inverse modeling (LIM) approach. It is found that the evolution of temperature and salinity in the Atlantic, and the strength of the meridional overturning circulation (MOC), can be effectively described by a linear dynamical system forced by white noise. The forecasts produced using this linear model are more skillful than other reference forecasts for several decades. Furthermore, significant nonnormal amplification is found under several different norms. The regions from which this growth occurs are found to be fairly shallow and located in the far North Atlantic. Initially, anomalies in the Nordic seas impact the MOC and the anomalies then grow to fill the entire Atlantic basin, especially at depth, over one to three decades. It is found that the structure of the optimal initial condition for amplification is sensitive to the norm employed, but the initial growth seems to be dominated by MOC-related basin-scale changes, irrespective of the choice of norm. The consistent identification of the far North Atlantic as the most sensitive region for small perturbations suggests that additional observations in this region would be optimal for constraining decadal climate predictions.

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Ed Hawkins and Rowan Sutton

Abstract

Current state-of-the-art global climate models produce different values for Earth’s mean temperature. When comparing simulations with each other and with observations, it is standard practice to compare temperature anomalies with respect to a reference period. It is not always appreciated that the choice of reference period can affect conclusions, both about the skill of simulations of past climate and about the magnitude of expected future changes in climate. For example, observed global temperatures over the past decade are toward the lower end of the range of the phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations irrespective of what reference period is used, but exactly where they lie in the model distribution varies with the choice of reference period. Additionally, we demonstrate that projections of when particular temperature levels are reached, for example, 2 K above “preindustrial,” change by up to a decade depending on the choice of reference period. In this article, we discuss some of the key issues that arise when using anomalies relative to a reference period to generate climate projections. We highlight that there is no perfect choice of reference period. When evaluating models against observations, a long reference period should generally be used, but how long depends on the quality of the observations available. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) choice to use a 1986–2005 reference period for future global temperature projections was reasonable, but a case-by-case approach is needed for different purposes and when assessing projections of different climate variables. Finally, we recommend that any studies that involve the use of a reference period should explicitly examine the robustness of the conclusions to alternative choices.

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Steffen Tietsche, Ed Hawkins, and Jonathan J. Day

Abstract

Uncertainty of Arctic seasonal to interannual predictions arising from model errors and initial state uncertainty has been widely discussed in the literature, whereas the irreducible forecast uncertainty (IFU) arising from the chaoticity of the climate system has received less attention. However, IFU provides important insights into the mechanisms through which predictability is lost and hence can inform prioritization of model development and observations deployment. Here, the authors characterize how internal oceanic and surface atmospheric heat fluxes contribute to the IFU of Arctic sea ice and upper-ocean heat content in an Earth system model by analyzing a set of idealized ensemble prediction experiments. It is found that atmospheric and oceanic heat flux are often equally important for driving unpredictable Arctic-wide changes in sea ice and surface water temperatures and hence contribute equally to IFU. Atmospheric surface heat flux tends to dominate Arctic-wide changes for lead times of up to a year, whereas oceanic heat flux tends to dominate regionally and on interannual time scales. There is in general a strong negative covariance between surface heat flux and ocean vertical heat flux at depth, and anomalies of lateral ocean heat transport are wind driven, which suggests that the unpredictable oceanic heat flux variability is mainly forced by the atmosphere. These results are qualitatively robust across different initial states, but substantial variations in the amplitude of IFU exist. It is concluded that both atmospheric variability and the initial state of the upper ocean are key ingredients for predictions of Arctic surface climate on seasonal to interannual time scales.

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Ed Hawkins, Taran Fæhn, and Jan Fuglestvedt

Abstract

Graphical visualizations have the potential to engage diverse audiences in understanding the changes to our climate, especially when spread worldwide using both traditional and social media. The animated global temperature spiral was one of the first climate graphics to “go viral,” being viewed by millions of people online and by more than a billion people when it was used in the opening ceremony of the 2016 Rio Olympics. The idea, design, and communication aspects that led to the successes of this animated graphic are discussed, highlighting the benefits to scientists of engaging actively online and openly sharing their creative ideas.

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Caroline R. Holmes, Tim Woollings, Ed Hawkins, and Hylke de Vries

Abstract

Recent temperature extremes have highlighted the importance of assessing projected changes in the variability of temperature as well as the mean. A large fraction of present-day temperature variance is associated with thermal advection, as anomalous winds blow across the land–sea temperature contrast, for instance. Models project robust heterogeneity in the twenty-first-century warming pattern under greenhouse gas forcing, resulting in land–sea temperature contrasts increasing in summer and decreasing in winter and the pole-to-equator temperature gradient weakening in winter. In this study, future changes in monthly variability of near-surface temperature in the 17-member ensemble ESSENCE (Ensemble Simulations of Extreme Weather Events under Nonlinear Climate Change) are assessed. In winter, variability in midlatitudes decreases whereas in very high latitudes and the tropics it increases. In summer, variability increases over most land areas and in the tropics, with decreasing variability in high latitude oceans. Multiple regression analysis is used to determine the contributions to variability changes from changing temperature gradients and circulation patterns. Thermal advection is found to be of particular importance in the Northern Hemisphere winter midlatitudes, where the change in mean state temperature gradients alone could account for over half the projected changes. Changes in thermal advection are also found to be important in summer in Europe and coastal areas, although less so than in winter. Comparison with CMIP5 data shows that the midlatitude changes in variability are robust across large regions, particularly high northern latitudes in winter and middle northern latitudes in summer.

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Ed Hawkins, Buwen Dong, Jon Robson, Rowan Sutton, and Doug Smith

Abstract

Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts.

The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

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Stan Yip, Christopher A.T. Ferro, David B. Stephenson, and Ed Hawkins
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Christophe Cassou, Yochanan Kushnir, Ed Hawkins, Anna Pirani, Fred Kucharski, In-Sik Kang, and Nico Caltabiano

Abstract

The study of Decadal Climate Variability (DCV) and Predictability is the interdisciplinary endeavor to characterize, understand, attribute, simulate, and predict the slow, multiyear variations of climate at global (e.g., the recent slowdown of global mean temperature rise in the early 2000s) and regional (e.g., decadal modulation of hurricane activity in the Atlantic, ongoing drought in California or in the Sahel in the 1970s–80s, etc.) scales. This study remains very challenging despite decades of research, extensive progress in climate system modeling, and improvements in the availability and coverage of a wide variety of observations. Considerable obstacles in applying this knowledge to actual predictions remain.

This short article is a succint review paper about DCV and predictability. Based on listed issues and priorities, it also proposes a unifying theme referred to as “drivers of teleconnectivity” as a backbone to address and structure the core DCV research challenge. This framework goes beyond a preoccupation with changes in the global mean temperature and directly addresses the regional impacts of external (natural and anthropogenic) climate forcing and internal climate interactions; it thus explicitly deals with the societal needs for region-specific climate information. Such a framework also enables the integration of efforts in a large international research community toward advancing the observation, characterization, understanding, and prediction of DCV. Recommendations to make progress are provided as part of the contribution of the CLIVAR “DCVP Research Focus” group.

Open access