A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?

Predictability estimates of ensemble prediction systems are uncertain due to limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic, and thus allows for quantifying uncertainty in predictability measures such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles forecasts. The framework is used to address important questions concerning the skill of winter hindcasts of the North Atlantic Oscillation for 1992-2011 issued by the Met Office GloSea5 climate prediction system. Although there is much uncertainty in the correlation between ensemble mean and observations, there is strong evidence of skill: the 95% credible interval of the correlation coefficient of [0.19,0.68] does not overlap zero. There is also strong evidence that the forecasts are not exchangeable with the observations: With over 99% certainty, the signal-to-noise ratio of the forecasts is smaller than the signal-to-noise ratio of the observations, which suggests that raw forecasts should not be taken as representative scenarios of the observations. Forecast recalibration is thus required, which can be coherently addressed within the proposed framework.Comment: 36 pages, 10 figure

Do CMIP5 models reproduce observed low-frequency North Atlantic jet variability?

The magnitude of observed multi‐decadal variations in the North Atlantic Oscillation (NAO) is at the upper end of the range simulated by climate models and a clear explanation for this remains elusive. Recent research shows that observed multi‐decadal NAO variability is more strongly associated with North Atlantic (NA) jet strength than latitude, thus motivating a comprehensive comparison of NA jet and NAO variability across the CMIP5 models. Our results show that the observed peak in multi‐decadal jet strength variability is even more unusual than NAO variability when compared to the model‐simulated range across 133 historical CMIP5 simulations. Some CMIP5 models appear capable of reproducing the observed low‐frequency peak in jet strength, but there are too few simulations of each model to clearly identify which. It is also found that an observed strong multi‐decadal correlation between jet strength and NAO since the mid‐19th century may be specific to this period

Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion

Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems.Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems

Preindustrial control simulations with HadGEM3-GC3.1 for CMIP6

Pre‐industrial control simulations with the HadGEM3‐GC3.1 climate model are presented at two resolutions. These are N216ORCA025, which has a horizontal resolution of 60km in the atmosphere and 0.25° in the ocean, and N96ORCA1, which has a horizontal resolution of 130km in the atmosphere and 1° in the ocean. The aim of this study is to document the climate variability in these simulations, make comparisons against present‐day observations (albeit under different forcing), and discuss differences arising due to resolution. In terms of interannual variability in the leading modes of climate variability the two resolutions behave generally very similarly. Notable differences are in the westward extent of El‐Niño and the pattern of Atlantic multidecadal variability, in which N216ORCA025 compares more favourably to observations, and in the Antarctic Circumpolar Current, which is far too weak in N216ORCA025. In the North Atlantic region, N216ORCA025 has a stronger and deeper AMOC, which compares well against observations, and reduced biases in temperature and salinity in the North Atlantic subpolar gyre (NA SPG). These simulations are being provided to the sixth Coupled Model Intercomparison Project (CMIP6) and provide a baseline against which further forced experiments may be assessed

Predicted chance that global warming will temporarily exceed 1.5 °C

The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5 °C above preindustrial levels. However, natural internal variability may exacerbate anthropogenic warming to produce temporary excursions above 1.5 °C. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5 °C above preindustrial levels in the coming 5 years. For the period 2017 to 2021 we predict a 38% and 10% chance, respectively, of monthly or yearly temperatures exceeding 1.5 °C, with virtually no chance of the 5‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events

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

Earth's energy imbalance since 1960 in observations and CMIP5 models

Observational analyses of running 5 year ocean heat content trends (Ht) and net downward top of atmosphere radiation (N) are significantly correlated (r?~?0.6) from 1960 to 1999, but a spike in Ht in the early 2000s is likely spurious since it is inconsistent with estimates of N from both satellite observations and climate model simulations. Variations in N between 1960 and 2000 were dominated by volcanic eruptions and are well simulated by the ensemble mean of coupled models from the Fifth Coupled Model Intercomparison Project (CMIP5). We find an observation-based reduction in N of ??0.31?±?0.21?W?m?2 between 1999 and 2005 that potentially contributed to the recent warming slowdown, but the relative roles of external forcing and internal variability remain unclear. While present-day anomalies of N in the CMIP5 ensemble mean and observations agree, this may be due to a cancelation of errors in outgoing longwave and absorbed solar radiation