On skillful decadal predictions of the subpolar North Atlantic

The North Atlantic is a crucial region for the prediction of weather and climate of North America and Europe and is the focus of this analysis. A skillful decadal prediction of the surface temperature was shown for several Earth system models, with the North Atlantic standing out as one region with higher predictive skill. This skill assessment concentrates on the rapid increase of the annual mean sea surface temperature of the North Atlantic subpolar gyre by about 1 K in the mid‑1990s and the adjacent years. This event-oriented analysis adds creditability to the decadal predictions and reveals the potential for improvements. The ability to simulate the observed sea surface temperature in the North Atlantic is quantified by using four versions of decadal predictions, which differ in model resolution, initialization technique, and the reanalysis data used in the assimilation run. While all four versions can reproduce the mid-1990s warming of the subpolar North Atlantic, the characteristics differ with lead time and version. The higher vertical resolution in the atmosphere and the higher horizontal resolution in the ocean improve the decadal prediction for longer lead times, and the anomaly initialization outperforms the full-field initialization for short lead times. The effect from the two different ocean reanalysis products on the predictive skill is strongest in the first two prediction years; a substantial cooling instead of the warming in the central North Atlantic reduces the skill score for the North Atlantic sea surface temperature in one version, whereas a too large interannual variability, compared with observations, lowers the skill score in the other version. The cooling patches are critical since the resulting gradients in sea surface temperature and their effect on atmospheric dynamics deviate from observations, and, moreover, hinder the skillful prediction of atmospheric variables

Mermin-Wagner fluctuations in 2D amorphous solids

In a recent comment, M. Kosterlitz described how the discrepancy about the lack of broken translational symmetry in two dimensions - doubting the existence of 2D crystals - and the first computer simulations foretelling 2D crystals at least in tiny systems, motivated him and D. Thouless to investigate melting and suprafluidity in two dimensions [Jour. of Phys. Cond. Matt. \textbf{28}, 481001 (2016)]. The lack of broken symmetries proposed by D. Mermin and H. Wagner is caused by long wavelength density fluctuations. Those fluctuations do not only have structural impact but additionally a dynamical one: They cause the Lindemann criterion to fail in 2D and the mean squared displacement not to be limited. Comparing experimental data from 3D and 2D amorphous solids with 2D crystals we disentangle Mermin-Wagner fluctuations from glassy structural relaxations. Furthermore we can demonstrate with computer simulations the logarithmic increase of displacements predicted by Mermin and Wagner: periodicity is not a requirement for Mermin-Wagner fluctuations which conserve the homogeneity of space on long scales.Comment: 7 pages, 4 figure

Initialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods

Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system “Mittelfristige Klimaprognose” (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter, the filtered anomaly initialization, and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter corrects each ensemble member with the ensemble mean during model integration. And the bred vectors perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the ensemble Kalman filter and filtered anomaly initialization show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the bred vectors, the ensemble dispersion filter, and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability and with respect to the temporal evolution at the 26° N latitude

Decadal climate predictions improved by ocean ensemble dispersion filtering

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean

Improvement in the decadal prediction skill of the North Atlantic extratropical winter circulation through increased model resolution

In this study the latest version of the MiKlip decadal hindcast system is analyzed, and the effect of an increased horizontal and vertical resolution on the prediction skill of the extratropical winter circulation is assessed. Four different metrics – the storm track, blocking, cyclone and windstorm frequencies – are analyzed in the North Atlantic and European region. The model bias and the deterministic decadal hindcast skill are evaluated in ensembles of five members in a lower-resolution version (LR, atm: T63L47, ocean: 1.5∘ L40) and a higher-resolution version (HR, atm: T127L95, ocean: 0.4∘ L40) of the MiKlip system based on the Max Planck Institute Earth System model (MPI-ESM). The skill is assessed for the lead winters 2–5 in terms of the anomaly correlation of the quantities' winter averages using initializations between 1978 and 2012. The deterministic predictions are considered skillful if the anomaly correlation is positive and statistically significant. While the LR version shows common shortcomings of lower-resolution climate models, e.g., a storm track that is too zonal and southward displaced as well as a negative bias of blocking frequencies over the eastern North Atlantic and Europe, the HR version counteracts these biases. Cyclones, i.e., their frequencies and characteristics like strength and lifetime, are particularly better represented in HR. As a result, a chain of significantly improved decadal prediction skill between all four metrics is found with the increase in the spatial resolution. While the skill of the storm track is significantly improved primarily over the main source region of synoptic activity – the North Atlantic Current – the other extratropical quantities experience a significant improvement primarily downstream thereof, i.e., in regions where the synoptic systems typically intensify. Thus, the skill of the cyclone frequencies is significantly improved over the central North Atlantic and northern Europe, the skill of the blocking frequencies is significantly improved over the Mediterranean, Scandinavia and eastern Europe, and the skill of the windstorms is significantly improved over Newfoundland and central Europe. Not only is the skill improved with the increase in resolution, but the HR system itself also exhibits significant skill over large areas of the North Atlantic and European sector for all four circulation metrics. These results are particularly promising regarding the high socioeconomic impact of European winter windstorms and blocking situations

Bias and Drift of the Medium-Range Decadal Climate Prediction System (MiKlip) validated by European Radiosonde Data

Quality controlled and homogenized radiosonde observations have been used to validate decadal hindcasts of the MPI-Earth-System-Model for Europe (excl. some Eastern European countries). Simulated temperatures have a cold bias of 1 to 4 K, increasing with height throughout the free troposphere over Europe. This implies that the simulated troposphere is less stable than observed by the radiosondes over Europe. Simulated relative humidity is 10 to 40 % higher than observed. Part of the humidity bias, 10 to 25 % relative humidity, is due to the simulated lower temperature, but the remainder indicates that modelled water vapour pressure is too high in the free troposphere above Europe. After full-field initialization with oceanic state, the atmospheric temperature bias changes over the first couple of years, with a relaxation time of 5 years near the surface (850 hPa) and less than 1 year near the tropopause (200 hPa). Anomaly correlations, mean-square error and logarithmic ensemble spread score indicate small improvements in hindcasted tropospheric temperatures over Europe when going from ocean anomaly initialisation to ocean anomaly initialisation plus full field atmospheric initialisation, and then to full field ocean initialisation plus full field atmospheric initialisation. In the stratosphere, these changes have little effect. For humidity, correlations and skill scores are much poorer, and little can be said about changes over Europe due to different initializations

Initialization and ensemble generation for decadal climate predictions: A comparison of different methods

Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system "Mittelfristige Klimaprognose" (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter (EnKF), the filtered anomaly initialization (FAI) and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter (EDF) corrects each ensemble member with the ensemble mean during model integration. And the bred vectors (BV) perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the EnKF and FAI show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the BV, the EDF and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability, and with respect to the temporal evolution at the 26° N latitude

MurCSS

<p><strong>MurCSS</strong><br>=====<br>A Tool for Standardized Evaluation of Decadal Hindcast Systems</p> <p>The tool calculates the Mean Squared Error Skill Score (MSESS) its decomposition (Correlation + Conditional Bi<br>as) and the Continuous Ranked Probability Skill Score (CRPSS) as proposed by Goddard et al. [2013].<br>The MSESS of both models and the MSESS "between" the two models (model versions) are calculated for different leadtimes.<br>The CRPSS is calculated for both models defined by the input parameters.</p> <p>The main documentation can be found here /doc/build/index.html and here https://www-miklip.dkrz.de/about/murcss</p> <p>For install instructions please visit the GitHub repository:</p> <p>https://github.com/illing2005/murcss</p

Earth system model results by the MPI-ESM-LR of the MiKlip Decadal climate prediction experiment improved by ocean ensemble dispersion filtering, links to NetCDF files

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two timescales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state towards the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering towards the ensemble mean