Description: We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year‐to‐year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores. We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter‐basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi‐model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter‐annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins. These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20% of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter‐basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real‐time forecasts.

Description: Near-term climate predictions — which operate on annual to decadal timescales — offer benefits for climate adaptation and resilience, and are thus important for society. Although skilful near-term predictions are now possible, particularly when coupled models are initialized from the current climate state (most importantly from the ocean), several scientific challenges remain, including gaps in understanding and modelling the underlying physical mechanisms. This Perspective discusses how these challenges can be overcome, outlining concrete steps towards the provision of operational near-term climate predictions. Progress in this endeavour will bridge the gap between current seasonal forecasts and century-scale climate change projections, allowing a seamless climate service delivery chain to be established.

Description: 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.

Description: A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI-ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low-level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two-layer model.

Description: Many nations responded to the corona virus disease-2019 (COVID-19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020–2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate. Key Points: - Lockdown restrictions during COVID-19 have reduced emissions of aerosols and greenhouse gases - 12 CMIP6 Earth system models have performed coordinated experiments to assess the impact of this on climate - Aerosol amounts are reduced over southern and eastern Asia but there is no detectable change in annually averaged temperature or precipitation

Description: In this thesis a dynamical forecast approach is considered to evaluate the potential seasonal predictability in the European-Atlantic region with emphasis on the mean winter climate. Two state-of-the-art seasonal forecast systems are used, namely the Seasonal Forecast System 2 from the European Centre for Medium Range Weather Forecast (ECMWF) and a multi-model system developed within the joint European project DEMETER (Development of a European Multi-Model Ensemble Prediction System for Seasonal to Interannual Prediction). The predictions are verified with the ERA-40 re-analysis data. Seasonal forecasts are probabilistic in nature and hence require verification techniques based on probabilistic skill measures. Here a multi-category skill score, namely the ranked probability skill score (RPSS) is applied. The RPSS is sensitive to the shape and the shift of the predicted probability density distribution. However, the RPSS shows a negative bias for ensemble systems with small ensemble sizes. It is shown that the negative bias can be attributed to a discretization and squaring error in the quadratic norm of the RPSS. In the following two strategies are explored to tackle this flaw. First, it is shown that the RPSSl=i based on the absolute rather than the squared norm is unbiased. Nevertheless, it is not strictly proper in a statistical sense. Second, an unbiased and strictly proper skill score can be defined based on the quadratic norm, along with the reference forecast reduced to sub-samples of the same size as the forecast ensemble size. This is denoted as the de-biased ranked probability skill score (RPSSd). Based on a hypothetical set up comparable to the ECMWF hindcast system (40 members, 15 hindcast years) the RPSSd is used to show, that statistically significant skill scores can only be found for climate anomalies with a signal-to-noise ratio larger than -0.3. Furthermore, the seasonal predictability is evaluated using a forecast approach (FA) based on 2m mean temperature predictions on grid-point scale for the years 1987-2001. The ECMWF Seasonal Forecast System 2 provides a marked improvement in skill relative to climatological forecasts over the North-Atlantic Ocean with maximum values of up to 30 %. Over Europe no significantly positive skill scores are found. The DEMETER multi-model has higher forecast skills than individual models. Moreover, the potential predictability is investigated applying a perfect model approach (PMA). Such approach assumes that the climate system is fully represented by the model physics. The potential winter predictability over the European continent amounts to approximately -10%. The 3r part of the thesis examines the potential seasonal predictability is examined via the leading mode of the European winter climate variability, namely the North Atlantic Oscillation (NAO). The PMA shows that the mean winter NAO and the NAO temperature related impact is potentially predictable for lead time 1 month, but with a gain in skill of only -8 % compared to climatology. Using the FA, the results are quite different. For the period 1959-2001, the NAO skill score is not statistically significant, while the skill score is surprisingly large (16 % to 27 % relative to the observed climatology) for the period 1987-2001. For this period a weak relation between the strength of the NAO amplitude and the skill score of the NAO is found. This contrasts with ENSO variability where the amplitude dependent forecast skill is strong. Finally, the seasonal forecasts are examined from the end user's perspective. A so-called "Klimagram" is introduced to assess seasonal climate forecasts for particular cities or regions. A first analysis reveals that the forecast skills can be improved in a relative sense, looking at spatial and temporal averaged quantities. Overall, this study suggests a positive potential seasonal predictability in the European-Atlantic domain in winter. However, the potential benefit is rather small and constitutes a fraction only, compared to currently possible results in the tropics.

Description: Author(s): Devin Kachan, Kei W. Müller, Wolfgang A. Wall, and Alex J. Levine Fluctuation-induced interactions are an important organizing principle in a variety of soft matter systems. We investigate the role of fluctuation-based or thermal Casimir interactions between cross linkers in a semiflexible network. One finds that, by integrating out the polymer degrees of freedom,… [Phys. Rev. E 94, 032505] Published Mon Sep 19, 2016