Predictability of weather and climate

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.

Abstract:

The question of how humans have influenced individual extreme weather events is both scientifically and socially important. However, deficiencies in climate models’ representations of key mechanisms within the process chains that drive weather reduce our confidence in estimates of the human influence on extreme events. We propose that using forecast models that successfully predicted the event in question could increase the robustness of such estimates. Using a successful forecast means we can be confident that the model is able to faithfully represent the characteristics of the specific extreme event. We use this forecast-based methodology to estimate the direct radiative impact of increased CO2 concentrations (one component, but not the entirety, of human influence) on the European heatwave of February 2019.

Abstract:

Summer 2010 saw two simultaneous extremes linked by an atmospheric wave train: a record-breaking heatwave in Russia and severe floods in Pakistan. Here, we study this wave event using a large ensemble climate model experiment. First, we show that the circulation in 2010 reflected a recurrent wave train connecting the heatwave and flooding events. Second, we show that the occurrence of the wave train is favored by three drivers: (1) 2010 sea surface temperature anomalies increase the probability of this wave train by a factor 2-to-4 relative to the model’s climatology, (2) early-summer soil moisture deficit in Russia not only increases the probability of local heatwaves, but also enhances rainfall extremes over Pakistan by forcing an atmospheric wave response, and (3) high-latitude land warming favors wave-train occurrence and therefore rainfall and heat extremes. These findings highlight the complexity and synergistic interactions between different drivers, reconciling some seemingly contradictory results from previous studies.