Predictability of weather and climate

Abstract:

The East Asian summer jet (EASJ) is an important component of the East Asian summer monsoon system and its variability is correlated with precipitation and surface temperature variations over this region. Whilst many studies have considered the interannual variability of the EASJ, less is known about variations on a decadal time-scale. This study investigates the relationship between decadal EASJ variability and sea surface temperatures (SSTs) and thus the potential predictability that SSTs may provide. Given the relatively short observational record, we make use of the long pre-industrial control simulations in the Coupled Model Intercomparison Project phase 6 (CMIP6) in addition to a large ensemble of atmosphere-only experiments, forced with random SST patterns. We then create an SST-based reconstruction of the dominant modes of EASJ variability in the CMIP6 models, finding a median EASJ–reconstruction correlation for the dominant mode of 0.43. Much of the skill in the reconstruction arises from variations in Pacific SSTs, however the tropical Atlantic also makes a significant contribution. These findings suggest the potential for multi-year predictions of the EASJ, provided that skilful SST forecasts are available.

Abstract:

In this study, Tropical Cyclones (TC) over the Western North Pacific (WNP) and North Atlantic (NA) basins are analysed in seasonal forecasting models from five European modelling centres. Most models are able to capture the observed seasonal cycle of TC frequencies over both basins; however, large differences for numbers and spatial track densities are found. In agreement with previous studies, TC numbers are often underestimated, which is likely related to coarse model resolutions. Besides shortcomings in TC characteristics, significant positive skill (deterministic and probabilistic) in predicting TC numbers and accumulated cyclone energy is found over both basins. Whereas the predictions of TC numbers over the WNP basin are mostly unreliable, most seasonal forecast provide reliable predictions for the NA basin. Besides positive skill over the entire NA basin, all seasonal forecasting models are skillful in predicting the interannual TC variability over a region covering the Caribbean and North American coastline, suggesting that the models carry useful information, e.g. for adaptation and mitigation purposes ahead of the upcoming TC season. However, skill in all forecast models over a smaller region centred along the Asian coastline is smaller compared to their skill in the entire WNP basin.

Abstract:

To study the forced variability of atmospheric circulation regimes, the use of model ensembles is often necessary for identifying statistically significant signals as the observed data constitute a small sample and are thus strongly affected by the noise associated with sampling uncertainty. However, the regime representation is itself affected by noise within the atmosphere, which can make it difficult to detect robust signals. To this end we employ a regularised k-means clustering algorithm to better identify the signal in a model ensemble. The approach allows for the identification of six regimes for the wintertime Euro-Atlantic sector and leads to more pronounced regime dynamics, compared to results without regularisation, both overall and on sub-seasonal and interannual timescales. We find that sub-seasonal variability in the regime occurrence rates is mainly explained by changes in the seasonal cycle of the mean climatology. On interannual timescales relations between the occurrence rates of the regimes and the El Ni˜no Southern Oscillation (ENSO) are identified. The use of six regimes captures a more detailed response of the circulation to ENSO compared to the common use of four regimes. Predictable signals in occurrence rate on interannual timescales are found for the two zonal flow regimes, namely a regime consisting of a negative geopotential height anomaly over the Norwegian Sea and Scandinavia, and the positive phase of the NAO. The signal strength for these regimes is comparable between observations and model, in contrast to that of the NAO-index where the signal strength in the observations is underestimated by a factor of two in the model. Our regime analysis suggests that this signal-to-noise problem for the NAO-index is primarily related to those atmospheric flow patterns associated with the negative NAO-index as we find poor predictability for the corresponding NAO− regime.

Abstract:

By utilizing Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomaly (TWSA) and remote sensing precipitation data, Flood Potential Index (FPI) has been widely used in large-scale flood monitoring. However, divergent post-processing dynamics of different GRACE solutions result in substantial uncertainties in GRACE TWSA and thus affecting predictive skills of FPI. To overcome this, this study develops an Integrated Flood Potential Index (IFPI) by linking the FPI derived from six GRACE products. The Gaussian copula is employed to establish the joint distribution of FPI from six spherical harmonic (SH) products and mass concentration blocks solutions. One of the most flood-prone regions, Yangtze River basin in China, is selected as a case study. We have identified and characterized the floods with different intensities using IFPI, which is evaluated against standardized discharge observations as well as the Total Storage Deficit Index (TSDI), Water Storage Deficit Index (WSDI) and Combined Climatologic Deviation Index (CCDI). Results show that the area under curve (AUC) values of IFPI for different levels of floods are generally greater than FPIs and their ensemble mean, implying the better predictive skill for the large-scale flood events. During the three severest floods in 2010, 2015, and 2016, IFPI captures the flood variability exhibited by TSDI, WSDI, and CCDI, as well as hydrological observations. This proposed approach might provide reference for flood monitoring and from multi-mission satellite data.