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Ruud Sperna Weiland, Karin van der Wiel, Frank Selten, and Dim Coumou

Abstract

Persistent hot-dry or cold-wet summer weather can have significant impacts on agriculture, health and the environment. For North-Western Europe, these weather regimes are typically linked to, respectively, blocked or zonal jetstream states. The fundamental dynamics underlying these circulation states are still poorly understood. Edward Lorenz postulated that summer circulation may be either fully or almost intransitive, implying that part of the phase space (capturing circulation variability) cannot be reached within one specific summer. If true, this would have major implications for the predictability of summer weather and our understanding of the drivers of interannual variability of summer weather. Here, we test the two Lorenz hypotheses (i.e. fully or almost intransitive) for European summer circulation, capitalising on a newly-available, very large ensemble (2000 years) of present-day climate data in the fully-coupled global climate model EC-Earth. Using Self-Organising Maps, we quantify the phase space of summer circulation and the trajectories through phase space in unprecedented detail. We show that, based on Markov assumptions, the summer circulation is strongly dependent on its initial state in early summer with the atmospheric memory ranging from 28 days up to ~45 days. The memory is particularly long if the initial state is either a blocked or a zonal flow state. Furthermore, we identify two groups of summers which are characterised by distinctly different trajectories through phase space, and which prefer either a blocked or zonal circulation state, respectively. These results suggest that intransitivity is indeed a fundamental property of the atmosphere and an important driver of interannual variability.

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Ryuichiro Inoue and Satoshi Osafune

Abstract

A part of near-inertial wind energies dissipates locally below the surface mixed layer. Here, their role in the climate system is studied by adopting near-inertial near-field wind-mixing parameterization to a coarse-forward ocean general circulation model. After confirming a problem of the parameterization in the equatorial region, we investigate effects of near-field wind mixing due to storm track activities in the North Pacific. We found that, in the center of the Pacific Decadal Oscillation (PDO) around 170°W in the mid latitude, near-field wind mixing transfers the PDO signal into deeper layers. Since the results suggest that near-field wind mixing is important in the climate system, we also compared the parameterization with velocity observations by a float in the North Pacific. The float observed abrupt and local propagation of near-inertial internal waves and shear instabilities in the main thermocline along the Kuroshio Extension for 460 km. Vertical diffusivities inferred from the parameterization do not reproduce the enhanced diffusivities in the deeper layer inferred from the float. Wave-ray tracing indicates that wave trapping near the Kuroshio front is responsible for the elevated diffusivities. Therefore, enhanced mixing due to trapping should be included in the parameterization.

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Yaru Guo, Yuanlong Li, Fan Wang, and Yuntao Wei

Abstract

Ningaloo Niño – the interannually occurring warming episode in the southeast Indian Ocean (SEIO) – has strong signatures in ocean temperature and circulation and exerts profound impacts on regional climate and marine biosystems. Analysis of observational data and eddy-resolving regional ocean model simulations reveals that the Ningaloo Niño/Niña can also induce pronounced variability in ocean salinity, causing large-scale sea surface salinity (SSS) freshening of 0.15–0.20 psu in the SEIO during its warm phase. Model experiments are performed to understand the underlying processes. This SSS freshening is mutually caused by the increased local precipitation (~68%) and enhanced fresh-water transport of the Indonesian Throughflow (ITF; ~28%) during Ningaloo Niño events. The effects of other processes, such as local winds and evaporation, are secondary (~18%). The ITF enhances the southward fresh-water advection near the eastern boundary, which is critical in causing the strong freshening (> 0.20 psu) near the Western Australian coast. Owing to the strong modulation effect of the ITF, SSS near the coast bears a higher correlation with the El Niño-Southern Oscillation (0.57, 0.77, and 0.70 with Niño-3, Niño-4, and Niño-3.4 indices, respectively) than sea surface temperature (-0.27, -0.42, and -0.35) during 1993-2016. Yet, an idealized model experiment with artificial damping for salinity anomaly indicates that ocean salinity has limited impact on ocean near-surface stratification and thus minimal feedback effect on the warming of Ningaloo Niño.

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D. J. Webb, P. Spence, R. M. Holmes, and M. H. England

Abstract

The Atlantic meridional overturning circulation (AMOC) plays a key role in determining the distribution of heat and nutrients in the global ocean. Climate models suggest that Southern Ocean winds will strengthen and shift poleward in the future, which could have implications for future AMOC trends. Using a coupled global-ocean sea-ice model at 1/4°horizontal resolution, we study the response of the North Atlantic overturning to two anomalous Southern Ocean wind-forcing (τ +15%), and a poleward intensification(τ4°S+15%). In both scenarios a strengthening in the North Atlantic overturning develops within a decade, with a much stronger response in the τ4°S+15% case. In τ4°S+15%, we find that the primary link between the North Atlantic response and the Southern Ocean forcing is via the propagation of baroclinic waves. In fact, due to the rapid northward propagation of these waves, changes in the AMOC in the τ4°S+15% case appear to originate in the North Atlantic and propagate southward, whereas in the τ +15% case AMOC anomalies propagate northward from the Southern Ocean. We find the difference to be predominately caused by the sign of the baroclinic waves propagating from the forcing region into the North Atlantic; downwelling in the τ +15% case, versus upwelling in the τ4°S+15% case. In the τ4°S+15% case, upwelling waves propagating into the NADW formation regions along shelf-slope topography bringing dense water to the surface. This reduces vertical density gradients leading to deeper wintertime convective overturn of surface waters, and an intensification of the AMOC.

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Matthew Watts, Wieslaw Maslowski, Younjoo J. Lee, Jaclyn Clement Kinney, and Robert Osinski

Abstract

The Arctic sea ice response to a warming climate is assessed in a subset of models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice-edge error, Brier score, and Spatial Probability Score. We find that the CMIP6 multi-model mean captures the mean annual cycle and 1979-2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic sub-regions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.

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Kenta Kurosawa and Jonathan Poterjoy

Abstract

The ensemble Kalman Filter (EnKF) and the 4D variational method (4DVar) are the most commonly used filters and smoothers in atmospheric science. These methods typically approximate prior densities using a Gaussian and solve a linear system of equations for the posterior mean and covariance. Therefore, strongly nonlinear model dynamics and measurement operators can lead to bias in posterior estimates. To improve the performance in nonlinear regimes, minimization of the 4DVar cost function typically follows multiple sets of iterations, known as an “outer loop”, which helps reduce bias caused by linear assumptions. Alternatively, “iterative ensemble methods” follow a similar strategy of periodically re-linearizing model and measurement operators. These methods come with different, possibly more appropriate, assumptions for drawing samples from the posterior density, but have seen little attention in numerical weather prediction (NWP) communities. Lastly, particle filters (PFs) present a purely Bayesian filtering approach for state estimation, which avoids many of the assumptions made by the above methods. Several strategies for applying localized PFs for NWP have been proposed very recently. The current study investigates intrinsic limitations of current data assimilation methodology for applications that require nonlinear measurement operators. In doing so, it targets a specific problem that is relevant to the assimilation of remotely-sensed measurements, such as radar reflectivity and all-sky radiances, which pose challenges for Gaussian-based data assimilation systems. This comparison includes multiple data assimilation approaches designed recently for nonlinear/non-Gaussian applications, as well as those currently used for NWP.

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Robert Spirig, Christian Feigenwinter, Markus Kalberer, Eberhard Parlow, and Roland Vogt

Abstract

Dolueg is a two-component framework to dynamically display time series. It serves as outreach to other researchers and the local public, educational resource and quality control tool. The first component is a set of Python functions. These create different types of visualisation with meta information about the data in the zoomable, modern SVG format. The second component is a simple but highly customizable website, that groups these figures according to the displayed data. We provide the code in two separate repositories on GitHub for interested parties including more detailed instructions for the installation.

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Isla R. Simpson, Karen A. McKinnon, Frances V. Davenport, Martin Tingley, Flavio Lehner, Abdullah Al Fahad, and Di Chen

Abstract

An ‘emergent constraint’ (EC) is a statistical relationship, across a model ensemble, between a measurable aspect of the present day climate (the predictor) and an aspect of future projected climate change (the predictand). If such a relationship is robust and understood, it may provide constrained projections for the real world. Here, Coupled Model Intercomparison Project 6 (CMIP6) models are used to revisit several ECs that were proposed in prior model intercomparisons with two aims: (1) to assess whether these ECs survive the partial out-of-sample test of CMIP6 and (2) to more rigorously quantify the constrained projected change than previous studies. To achieve the latter, methods are proposed whereby uncertainties can be appropriately accounted for, including the influence of internal variability, uncertainty on the linear relationship, and the uncertainty associated with model structural differences, aside from those described by the EC. Both least squares regression and a Bayesian Hierarchical Model are used. Three ECs are assessed: (a) the relationship between Southern Hemisphere jet latitude and projected jet shift, which is found to be a robust and quantitatively useful constraint on future projections; (b) the relationship between stationary wave amplitude in the Pacific-North American sector and meridional wind changes over North America (with extensions to hydroclimate), which is found to be robust but improvements in the predictor in CMIP6 result in it no longer substantially constrains projected change in either circulation or hydroclimate; and (c) the relationship between ENSO teleconnections to California and California precipitation change, which does not appear to be robust when using historical ENSO teleconnections as the predictor.

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Jonathan M. Garner, William C. Iwasko, Tyler D. Jewel, Richard L. Thompson, and Bryan T. Smith

Abstract

A dataset maintained by the Storm Prediction Center (SPC) of 6300 tornado events from 2009–2015, consisting of radar-identified convective modes and near-storm environmental information obtained from Rapid Update Cycle and Rapid Refresh model analysis grids, has been augmented with additional radar information related to the low-level mesocyclones associated with tornado longevity, path-length, and width. All EF2–EF5 tornadoes, in addition to randomly selected EF0–EF1 tornadoes, were extracted from the SPC dataset, which yielded 1268 events for inclusion in the current study. Analysis of that data revealed similar values of the effective-layer significant tornado parameter for the longest-lived (60+ min) tornadic circulations, longest-tracked (≥ 68 km) tornadoes, and widest tornadoes (≥ 1.2 km). However, the widest tornadoes occurring west of –94° longitude were associated with larger mean-layer convective available potential energy, storm-top divergence, and low-level rotational velocity. Furthermore, wide tornadoes occurred when low-level winds were out of the southeast resulting in large low-level hodograph curvature and near-surface horizontal vorticity that was more purely streamwise compared to long-lived and long-tracked events. On the other hand, tornado path-length and longevity were maximized with eastward migrating synoptic-scale cyclones associated with strong southwesterly wind profiles through much of the troposphere, fast storm motions, large values of bulk wind difference and storm-relative helicity, and lower buoyancy.

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Yunxia Zhao, Hamid Norouzi, Marzi Azarderakhsh, and Amir AghaKouchak

Abstract

Most previous studies of extreme temperatures have primarily focused on atmospheric temperatures. Using 18 years of the latest version of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, we globally investigate the spatial patterns of hot and cold extremes as well as diurnal temperature range (DTR). We show that the world’s highest LST of 80.8 °C, observed in the Lut Desert in Iran and the Sonoran Desert in Mexico, is over ten degrees above the previous global record of 70.7 °C observed in 2005. The coldest place on Earth is Antarctica with the record low temperature of -110.9 °C. The world’s maximum DTR of 81.8 °C is observed in a desert environment in China. We see strong latitudinal patterns in hot and cold extremes as well as DTR. Biomes worldwide are faced with different levels of temperature extremes and DTR: we observe the highest zonal average maximum LST of 61.1 ± 5.3 °C in the deserts and xeric shrublands; the lowest zonal average minimum LST of -66.6 ± 14.8 °C in the Tundra; and the highest zonal average maximum DTR of 43.5 ± 9.9 °C in the montane grasslands and shrublands. This global exploration of extreme LST and DTR across different biomes sheds light on the type of extremes different ecosystems are faced with.

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