45 research outputs found
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Seasonal forecasts of North Atlantic tropical cyclone activity in the North American Multi-Model Ensemble
The North American Multi-Model Ensemble (NMME)-Phase II models are evaluated in terms of their retrospective seasonal forecast skill of the North Atlantic (NA) tropical cyclone (TC) activity, with a focus on TC frequency. The TC identification and tracking algorithm is modified to accommodate model data at daily resolution. It is also applied to three reanalysis products at the spatial and temporal resolution of the NMME-Phase II ensemble to allow for a more objective estimation of forecast skill. When used with the reanalysis data, the TC tracking generates realistic climatological distributions of the NA TC formation and tracks, and represents the interannual variability of the NA TC frequency quite well. Forecasts with the multi-model ensemble (MME) when initialized in April and later tend to have skill in predicting the NA seasonal TC counts (and TC days). At longer leads, the skill is low or marginal, although one of the models produces skillful forecasts when initialized as early as January and February. At short lead times, while demonstrating the highest skill levels the MME also tends to significantly outperform the individual models and attain skill comparable to the reanalysis. In addition, the short-lead MME forecasts are quite reliable. At regional scales, the skill is rather limited and mostly present in the western tropical NA and the Caribbean Sea. It is found that the overall MME forecast skill is limited by poor representation of the low-frequency variability in the predicted TC frequency, and large fluctuations in skill on decadal time scales. Addressing these deficiencies is thought to increase the value of the NMME ensemble in providing operational guidance
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The Second Phase of the Global LandâAtmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill
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North Atlantic climate far more predictable than models imply
This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this recordData availability:
The datasets analysed in this study are available from the CMIP data archives: https://esgf-node.llnl.gov/projects/cmip5/ and https://esgf-node.llnl.gov/projects/cmip6/. NCAR data are available from http://www.cesm.ucar.edu/projects/community-projects/DPLE/.Code availability:
The code used in this study is available from the corresponding author on reasonable request.Note that the title of the author accepted manuscript is different from the title of the final published versionQuantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain. This leads to low confidence in regional projections, especially for precipitation, over the coming decades. The chaotic nature of the climate system may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.Met Office Hadley Centre Climate ProgrammeEuropean Union Horizon 202