Identification and interpretation of nonnormality in atmospheric time series

Nonnormal characteristics of geophysical time series are important determinants of extreme events and may provide insight into the underlying dynamics of a system. The structure of nonnormality in winter temperature is examined through the use of linear filtering of radiosonde temperature time series. Filtering either low or high frequencies generally suppresses what is otherwise statistically significant nonnormal variability in temperature. The structure of nonnormality is partly attributable to geometric relations between filtering and the appearance of skewness, kurtosis, and higher order moments in time series data, and partly attributable to the presence of nonnormal temperature variations at the highest resolved frequencies in the presence of atmospheric memory. A nonnormal autoregressive model and a multiplicative noise model are both consistent with the observed frequency structure of nonnormality. These results suggest that the generating mechanism for nonnormal variations does not necessarily act at the frequencies at which greatest nonnormality is observed.Earth and Planetary Science

New generation of climate models track recent unprecedented changes in Earth's radiation budget observed by CERES

We compare top‐of‐atmosphere (TOA) radiative fluxes observed by the Clouds and the Earth's Radiant Energy System (CERES) and simulated by seven general circulation models forced with observed sea‐surface temperature (SST) and sea‐ice boundary conditions. In response to increased SSTs along the equator and over the eastern Pacific (EP) following the so‐called global warming “hiatus” of the early 21st century, simulated TOA flux changes are remarkably similar to CERES. Both show outgoing shortwave and longwave TOA flux changes that largely cancel over the west and central tropical Pacific, and large reductions in shortwave flux for EP low‐cloud regions. A model's ability to represent changes in the relationship between global mean net TOA flux and surface temperature depends upon how well it represents shortwave flux changes in low‐cloud regions, with most showing too little sensitivity to EP SST changes, suggesting a “pattern effect” that may be too weak compared to observations

The Green's function model intercomparison project (GFMIP) protocol

The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the “pattern effect.” To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation

To Tune or not to Tune: Detecting Orbital Variability in Oligo-Miocene Climate Records

We address the problem of detecting quasi-periodic variability at orbital frequencies within pre-Pleistocene climate records using depth-derived and orbitally tuned chronologies. Many studies describing orbital variability in pre-Pleistocene sediment hosted isotope records employ climatic records that are set on orbitally tuned chronologies, without accounting for the bias in spectral power estimates introduced by orbital tuning. In this study we develop a method to quantify the effects of tuning upon spectral estimates and, in particular, to more properly determine the statistical significance of spectral peaks associated with orbital frequencies. We apply this method to two marine sediment δ18O records spanning the Oligo-Miocene, from ODP cores 1090 and 1218. We find that using linear age–depth relationships reveals statistically significant spectral peaks matching eccentricity in core 1090, and obliquity and precession in core 1218, where the last appears most significant. Tuning the chronologies to the orbital solutions of Laskar et al. (2004) increases the statistical significance of the precession peak, whereas the obliquity and eccentricity peaks become indistinguishable from those expected from tuning noise. This result can be understood in that tuning records with high signal to noise ratios tends to lead to more significant spectral peaks, whereas a linear age–depth relationship is better suited for detecting peaks when signal to noise ratios are low. We also demonstrate this concept using synthetic records.Earth and Planetary Science

Radiative feedbacks from stochastic variability in surface temperature and radiative imbalance

Estimates of radiative feedbacks obtained by regressing fluctuations in top-of-atmosphere (TOA) energy imbalance and surface temperature depend critically on assumptions about the nature of the stochastic forcing and on the sampling interval. Here we develop an energy-balance framework that allows us to model the different contributions of stochastic atmospheric and oceanic forcing on feed- back estimates. The contribution of different forcing components are parsed based on their impacts on the covariance structure of temperature and TOA energy fluxes, and the framework is validated in a hierarchy of climate model simulations that span a range of oceanic configurations and reproduce the key features seen in observations. We find that at least three distinct forcing sources, feedbacks, and time scales are needed to explain the full covariance structure. Atmospheric and oceanic forc- ings drive modes of variability with distinct relationships between near-surface air temperature and TOA radiation, and the net regression-based feedback estimate is found to be a weighted average of the distinct feedbacks associated with each mode. Moreover, the estimated feedback depends on whether surface temperature and TOA energy fluxes are sampled at monthly or annual timescales. The results suggest that regression-based feedback estimates reflect contributions from a combina- tion of stochastic forcings, and should not be interpreted as providing an estimate of the radiative feedback governing the climate response to greenhouse gas forcing

Strong remote control of future equatorial warming by off-equatorial forcing

The tropical climate response to GHG forcing is spatially non-uniform1,2,3. Even though enhanced equatorial and eastern Pacific warming is simulated by most climate models, the underlying mechanisms???including the relative roles of atmospheric and oceanic feedbacks???remain debated. Here, we use a climate model with idealized CO2-radiative forcing patterns to show that off-equatorial radiative forcing and corresponding coupled circulation/cloud adjustments are responsible for much of equatorial warming in response to global CO2 forcing. For equatorial forcing, the atmosphere responds by enhancing atmospheric heat export to the extra-tropics, an associated strengthening of the ascending Hadley circulation branch and strong negative equatorial cloud feedbacks. These processes together greatly dampen equatorial surface warming. Intensification of the oceanic subtropical cells and increased cold subsurface water upwelling in the eastern tropical Pacific provide an additional negative feedback for surface temperatures. In contrast, applying off-equatorial forcing, the atmosphere responds by exporting less heat from the tropics, Hadley circulation weakening and weaker negative equatorial cloud feedbacks, while the subtropical cells slow down in the ocean. Our results demonstrate a delicate balance in the coupled climate system between remote circulation adjustments and regional feedbacks that create the patterns of future climate change

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.ISSN:0027-8424ISSN:1091-649