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.
Holocene temperature proxy records are commonly used in quantitative synthesis and model-data comparisons. However, comparing correlations between time series from records collected in proximity to one another with the expected correlations based on climate model simulations indicates either regional or noisy climate signals in Holocene temperature proxy records. In this study, we evaluate the consistency of spatial correlations present in Holocene proxy records with those found in data from the Last Glacial Maximum (LGM). Specifically, we predict correlations expected in LGM proxy records if the only difference to Holocene correlations would be due to more time uncertainty and more climate variability in the LGM. We compare this simple prediction to the actual correlation structure in the LGM proxy records. We found that time series data of ice-core stable isotope records and planktonic foraminifera Mg/Ca ratios were consistent between the Holocene and LGM periods, while time series of Uk'37 proxy records were not as we found no correlation between nearby LGM records. Our results support the finding of highly regional or noisy marine proxy records in the compilation analysed here and suggest the need for further studies on the role of climate proxies and the processes of climate signal recording and preservation.
<p>Today's climate science is being driven by IT more than ever. Earth system models on high-performance computers (HPC) are common tools for researching the past and projecting it into the future. In addition to that, statistical modelling is reborn thanks to modern computer architectures equipped with artificial intelligence (from ensemble to deep learning). Future advances in machine learning will also shape climate research through analysis tools, prediction techniques, signal and event classification, post-processing, Model Output Statistics (MOS), evaluation and verification, etc. This presentation will look at nowadays research about the future (part one) and the past (part two) of our climate system using AI/ML ideas and technologies in combination with numerical climate models - from two publications accordingly. A special focus will be on the importance of climate science, where the needs are, and how to choose the AI/ML hammer wisely:</p><p>(1) FUTURE: Derived from machine (ensemble) learning and bagging, a new hybrid climate prediction technique called 'Ensemble Dispersion Filter' is developed. It exploits two important climate prediction paradigms: the ocean's heat capacity and the advantage of the ensemble mean. The Ensemble Dispersion Filter averages the ocean temperatures of the ensemble members every three months, uses this ensemble mean as a restart condition for each member, and further executes the prediction. The evaluation &#160;shows that the Ensemble Dispersion Filter results in a significant improvement in the predictive skill compared to the unfiltered reference system. Even in comparison with prediction systems of a larger ensemble size and higher resolution, the Ensemble Dispersion Filter system performs better. In particular, the prediction of the global average temperature of the forecast years 2 to 5 shows a significant skill improvement. </p><p><span>Kadow, C.</span>, <span>Illing, S.</span>, <span>Kr&#246;ner, I.</span>, <span>Ulbrich, U.</span>, and <span>Cubasch, U.</span> (<span>2017</span>), <span>Decadal climate predictions improved by ocean ensemble dispersion filtering</span>, <em>J. Adv. Model. Earth Syst.</em>, <span>9</span>, <span>1138</span>&#8211; <span>1149</span>, doi:10.1002/2016MS000787.&#160;</p><p>(2) PAST: Nowadays climate change research relies on climate information of the past. Historic climate records of temperature observations form global gridded datasets like HadCRUT4, which is investigated e.g. in the IPCC reports. However, record combining data-sets are sparse in the past. Even today they contain missing values. Here we show that artificial intelligence (AI) technology can be applied to reconstruct these missing climate values. We found that recently successful image inpainting technologies, using partial convolutions in a CUDA accelerated deep neural network, can be trained by 20CR reanalysis and CMIP5 experiments. The derived AI networks are capable to independently reconstruct artificially trimmed versions of 20CR and CMIP5 in grid space for every given month using the HadCRUT4 missing value mask. The evaluation reaches high temporal correlations and low errors for the global mean temperature. </p><p>Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. <em>Nat. Geosci.</em> <strong>13, </strong>408&#8211;413 (2020). https://doi.org/10.1038/s41561-020-0582-5</p>
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