Transient response of the global mean warming rate and its spatial variation

The Earth has warmed over the past century. The warming rate (amount of warming over a given period) varies in time and space. Observations show a recent increase in global mean warming rate, which is initially maintained in model projections, but which diverges substantially in future depending on the emissions scenario followed. Scenarios that stabilize forcing lead to much lower warming rates, as the rate depends on the change in forcing, not the amount. Warming rates vary spatially across the planet, but most areas show a shift toward higher warming rates in recent decades. The areal distribution of warming rates is also changing shape to include a longer tail in recent decades. Some areas of the planet are already experiencing extreme warming rates of about 1 °C/decade. The fat tail in areal distribution of warming rates is pronounced in model runs when the forcing and global mean warming rate is increasing, and indicates a climate state more prone to regime transitions. The area-proportion of the Earth displaying warming/cooling trends is shown to be directly related to the global mean warming rate, especially for trends of length 15 years and longer. Since the global mean warming rate depends on the forcing rate, the proportion of warming/cooling trend areas in future also depends critically on the choice of future forcing scenario. Keywords: Climate variability, Climate projection, Transient response, Extreme warmin

Recent applications and potential of near-term (interannual to decadal) climate predictions

Following efforts from leading centres for climate forecasting, sustained routine operational near-term climate predictions (NTCP) are now produced that bridge the gap between seasonal forecasts and climate change projections offering the prospect of seamless climate services. Though NTCP is a new area of climate science and active research is taking place to increase understanding of the processes and mechanisms required to produce skillful predictions, this significant technical achievement combines advances in initialisation with ensemble prediction of future climate up to a decade ahead. With a growing NTCP database, the predictability of the evolving externally-forced and internally-generated components of the climate system can now be quantified. Decision-makers in key sectors of the economy can now begin to assess the utility of these products for informing climate risk and for planning adaptation and resilience strategies up to a decade into the future. Here, case studies are presented from finance and economics, water management, agriculture and fisheries management demonstrating the emerging utility and potential of operational NTCP to inform strategic planning across a broad range of applications in key sectors of the global economy

WMO Global Annual to Decadal Climate Update A Prediction for 2021-25

Under embargo until: 2022-10-01As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.publishedVersio

Recent applications and potential of near-term (interannual to decadal) climate predictions

Following efforts from leading centres for climate forecasting, sustained routine operational near-term climate predictions (NTCP) are now produced that bridge the gap between seasonal forecasts and climate change projections offering the prospect of seamless climate services. Though NTCP is a new area of climate science and active research is taking place to increase understanding of the processes and mechanisms required to produce skillful predictions, this significant technical achievement combines advances in initialisation with ensemble prediction of future climate up to a decade ahead. With a growing NTCP database, the predictability of the evolving externally-forced and internally-generated components of the climate system can now be quantified. Decision-makers in key sectors of the economy can now begin to assess the utility of these products for informing climate risk and for planning adaptation and resilience strategies up to a decade into the future. Here, case studies are presented from finance and economics, water management, agriculture and fisheries management demonstrating the emerging utility and potential of operational NTCP to inform strategic planning across a broad range of applications in key sectors of the global economy

Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses

Abstract We apply a Bayesian structure learning approach to study interactions between global climate modes, so illustrating its use as a framework for developing process‐based diagnostics with which to evaluate climate models. Homogeneous dynamic Bayesian network models are constructed for time series of empirical indices diagnosing the activity of major tropical, Northern and Southern Hemisphere modes of climate variability in the NCEP/NCAR and JRA‐55 reanalyses. The resulting probabilistic graphical models are comparable to Granger causal analyses that have recently been advocated. Reversible jump Markov Chain Monte Carlo is employed to provide a quantification of the uncertainty associated with the selection of a single network structure. In general, the models fitted from the NCEP/NCAR reanalysis and the JRA‐55 reanalysis are found to exhibit broad agreement in terms of associations for which there is high posterior confidence. Differences between the two reanalyses are found that involve modes for which known biases are present or that may be attributed to seasonal effects, as well as for features that, while present in point estimates, have low overall posterior mass. We argue that the ability to incorporate such measures of confidence in structural features is a significant advantage provided by the Bayesian approach, as point estimates alone may understate the relevant uncertainties and yield less informative measures of differences between products when network‐based approaches are used for model evaluation

On the stability and spatiotemporal variance distribution of salinity in the upper ocean

Despite recent advances in ocean observing arrays and satellite sensors, there remains great uncertainty in the large-scale spatial variations of upper ocean salinity on the interannual to decadal timescales. Consonant with both broad-scale surface warming and the amplification of the global hydrological cycle, observed global multidecadal salinity changes typically have focussed on the linear response to anthropogenic forcing but not on salinity variations due to changes in the static stability and or variability due to the intrinsic ocean or internal climate processes. Here, we examine the static stability and spatiotemporal variability of upper ocean salinity across a hierarchy of models and reanalyses. In particular, we partition the variance into time bands via application of singular spectral analysis, considering sea surface salinity (SSS), the Brunt Väisälä frequency (N2), and the ocean salinity stratification in terms of the stabilizing effect due to the haline part of N2 over the upper 500m. We identify regions of significant coherent SSS variability, either intrinsic to the ocean or in response to the interannually varying atmosphere. Based on consistency across models (CMIP5 and forced experiments) and reanalyses, we identify the stabilizing role of salinity in the tropics—typically associated with heavy precipitation and barrier layer formation, and the role of salinity in destabilizing upper ocean stratification in the subtropical regions where large-scale density compensation typically occurs

The Australian community climate and earth system simulator global and regional ensemble prediction scheme

We report on progress in the development of the Australian Community Climate and Earth Systems Simulator Global and Regional Ensemble numerical weather Prediction Scheme at the Australian Bureau of Meteorology. Based on the UK Met Office ensemble, AGREPS implements an Ensemble Transform Kalman Filter to generate independent initial perturbations as fast growing disturbances with structures and growth rates typical of the analysis errors. This method allows information about the fast growing errors to be incorporated into the initial perturbations for the forecast. An ensemble of model states is propagated, using the numerical weather prediction system and observing network at the Australian Bureau of Meteorology, from which covariances are constructed then localized and inflated to minimize the effect of small sample size. References Anderson, J. L., 2001 An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev. 129, 2884--2903 doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2 Bishop, C. H., Hodyss, D., 2007 Flow adaptive moderation of spurious ensemble correlation and its use in ensemble-based data assimilation. Q. J. R. Meteorol. Soc. 133, 2029--2044 doi:10.1002/qj.169 Buehner, M., 2005 Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting. Q. J. R. Meteorol. Soc. 131, 1013--1043 doi:10.1256/qj.04.n Charney, J. G., 1966 The feasibility of a global observation and analysis experiment. Bull. Amer. Meteor. Soc. 47, 200--220 Hamill, T. M., Whitaker, J. S., and Snyder, C., 2001 Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev. 129, 2776--2790 doi:10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2 Houtekamer, P. L., and Mitchell, H. L., 1998, Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev. 126, 796--811 doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2 Kasahara, A., 1972 Simulation experiments for meteorological observing systems for GARP. Bull. Amer. Meteor. Soc. 53, 252--264 O'Kane, T. J., and Frederiksen, J. S., 2008a,A comparison of statistical dynamical and ensemble prediction during blocking. J. Atmos. Sci. 65, 426--447 doi:10.1175/2007JAS2300.1 O'Kane, T. J., and Frederiksen, J. S., 2008b Comparison of statistical dynamical, square root and ensemble Kalman filters. entropy In Press Purser, J., 1996 Arrangement of ensemble in a simplex to produce given first and second moments, NCEP Internal Report. Available from the author at mailto:[email protected] Smagorinsky, J., 1969 Problems and promises of deterministic extended range forecasting. Bull. Amer. Meteor. Soc. 50, 286--311 Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker, J. S., 2003,Ensemble square root filters. Mon. Wea. Rev. 131, 1485--1490 doi:10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2 Toth, Z., and Kalnay, E., 1997,Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev. 125, 3297--3319 doi:10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2 Tracton, M. S., and Kalnay, E., 1993,Operational ensemble prediction at National Meteorological centre: Practical aspects. Weather and Forecasting 8, 379--398 doi:10.1175/1520-0434(1993)008<0379:OEPATN>2.0.CO;2 Wang, X., and Bishop, C. H., 2003,A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci. 60, 1140--1158 doi:10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2 Wang, X., Bishop, C. H., and Julier, S. J., 2004,Which is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble. Mon. Wea. Rev. 132, 1590--1605 doi:10.1175/1520-0493(2004)132<1590:WIBAEO>2.0.CO;2 Wei, M., Toth, Z., Wobus, R., Zhu, Y., Bishop, C. H. and Wang, X., 2006 {Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP.} Tellus, 58A, 2006, 28--44 doi:10.1111/j.1600-0870.2006.00159.

eSPA plus : Scalable entropy-optimal machine learning classification for small data problems

Classification problems in the small data regime (with small data statistic T and relatively large feature space dimension D) impose challenges for the common machine learning (ML) and deep learning (DL) tools. The standard learning methods from these areas tend to show a lack of robustness when applied to data sets with significantly fewer data points than dimensions and quickly reach the overfitting bound, thus leading to poor performance beyond the training set. To tackle this issue, we propose eSPA+, a significant extension of the recently formulated entropy-optimal scalable probabilistic approximation algorithm (eSPA). Specifically, we propose to change the order of the optimization steps and replace the most computationally expensive subproblem of eSPA with its closed-form solution. We prove that with these two enhancements, eSPA+ moves from the polynomial to the linear class of complexity scaling algorithms. On several small data learning benchmarks, we show that the eSPA+ algorithm achieves a many-fold speed-up with respect to eSPA and even better performance results when compared to a wide array of ML and DL tools. In particular, we benchmark eSPA+ against the standard eSPA and the main classes of common learning algorithms in the small data regime: various forms of support vector machines, random forests, and long short-term memory algorithms. In all the considered applications, the common learning methods and eSPA are markedly outperformed by eSPA+, which achieves significantly higher prediction accuracy with an orders-of-magnitude lower computational cost.Web of Science3451255122

Stochastic Climate Theory

In this chapter we review stochastic modelling methods in climate science. First we provide a conceptual framework for stochastic modelling of deterministic dynamical systems based on the Mori-Zwanzig formalism. The Mori-Zwanzig equations contain a Markov term, a memory term and a term suggestive of stochastic noise. Within this framework we express standard model reduction methods such as averaging and homogenization which eliminate the memory term. We further discuss ways to deal with the memory term and how the type of noise depends on the underlying deterministic chaotic system. Secondly, we review current approaches in stochastic data-driven models. We discuss how the drift and diffusion coefficients of models in the form of stochastic differential equations can be estimated from observational data. We pay attention to situations where the data stems from multi scale systems, a relevant topic in the context of data from the climate system. Furthermore, we discuss the use of discrete stochastic processes (Markov chains) for e.g. stochastic subgrid-scale modeling and other topics in climate science