ALBERT

Description: The Standardized Precipitation Index (SPI) is a widely accepted drought index. Its calculation algorithm normalizes the index via a distribution function. Which distribution function to use is still disputed within the literature. This study illuminates that long-standing dispute and proposes a solution that ensures the normality of the index for all common accumulation periods in observations and simulations. We compare the normality of SPI time series derived with the gamma, Weibull, generalized gamma, and the exponentiated Weibull distribution. Our normality comparison is based on a complementary evaluation. Actual compared to theoretical occurrence probabilities of SPI categories evaluate the absolute performance of candidate distribution functions. Complementary, the Akaike information criterion evaluates candidate distribution functions relative to each other while analytically punishing complexity. SPI time series, spanning 1983–2013, are calculated from the Global Precipitation Climatology Project's monthly precipitation dataset, and seasonal precipitation hindcasts are from the Max Planck Institute Earth System Model. We evaluate these SPI time series over the global land area and for each continent individually during winter and summer. While focusing on regional performance disparities between observations and simulations that manifest in an accumulation period of 3 months, we additionally test the drawn conclusions for other common accumulation periods (1, 6, 9, and 12 months). Our results suggest that calculating SPI with the commonly used gamma distribution leads to deficiencies in the evaluation of ensemble simulations. Replacing it with the exponentiated Weibull distribution reduces the area of those regions where the index does not have any skill for precipitation obtained from ensemble simulations by more than one magnitude. The exponentiated Weibull distribution maximizes also the normality of SPI obtained from observational data and a single ensemble simulation. We demonstrate that calculating SPI with the exponentiated Weibull distribution delivers better results for each continent and every investigated accumulation period, irrespective of the heritage of the precipitation data. Therefore, we advocate the employment of the exponentiated Weibull distribution as the basis for SPI.

Description: We investigate changes in the seasonal cycle of the Atlantic Ocean meridional heat transport (OHT) in a climate projection experiment with the Max Planck Institute Earth System Model (MPI-ESM) performed for the Coupled Model Intercomparison Project Phase 5 (CMIP5). Specifically, we compare a Representative Concentration Pathway (RCP) RCP 8.5 climate change scenario, covering the simulation period from 2005 to 2300, to a historical simulation, covering the simulation period from 1850 to 2005. In RCP 8.5, the OHT declines by 30–50 % in comparison to the historical simulation in the North Atlantic by the end of the 23rd century. The decline in the OHT is accompanied by a change in the seasonal cycle of the total OHT and its components. We decompose the OHT into overturning and gyre component. For the OHT seasonal cycle, we find a northward shift of 5° and latitude-dependent shifts between 1 and 6 months that are mainly associated with changes in the meridional velocity field. We find that the changes in the OHT seasonal cycle predominantly result from changes in the wind-driven surface circulation, which projects onto the overturning component of the OHT in the tropical and subtropical North Atlantic. This leads in turn to latitude-dependent shifts between 1 and 6 months in the overturning component. In comparison to the historical simulation, in the subpolar North Atlantic, in RCP 8.5 we find a reduction of the North Atlantic Deep Water formation and changes in the gyre heat transport result in a strongly weakened seasonal cycle with a weakened amplitude by the end of the 23rd century.

Description: The multiple correlation and/or regression information that two competing forecast systems have on the same observations is decomposed into four components, adapting the method of multivariate information decomposition of Williams and Beer (2010), Wibral et al. (2015), and Lizier et al. (2018). Their concept is to divide source information about a target into total, (target) redundant or shared, and unique information from each source. It is applied here to the comparison of forecast systems using classic regression. Additionally, non-target redundant or shared information is newly defined that resumes the redundant information of the forecasts which is not observed. This provides views that go beyond classic correlation differences. These five terms share the same units and can be directly compared to put prediction results into perspective. The redundance terms in particular provide a new view. All components are given as maps of explained variance on the observations and for the non-target redundance on the models, respectively. Exerting this concept to lagged damped persistence is shown to be related to directed information entropy. To emphasize the benefit of the toolkit on all timescales, two analysis examples are provided. Firstly, two forecast systems of the German decadal prediction system of “Mittelfristige Klimaprognose”, namely the pre-operational version and a special version using ensemble Kalman filter for the ocean initialization, are compared. The analyses reveal the clear added value of the latter and provide an as yet unseen map of their non-target redundance. Secondly, 4 d lead forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are compared to a simple autoregressive and/or damped persistence model. The analysis of the information partition on this timescale shows that interannual changes in damped persistence, seen as target redundance changes between forecasts and damped persistence models, are balanced by associated changes in the added value of the dynamic forecasts in the extratropics but not in the tropics.

Description: We investigate the effect of a projected reduction in the Atlantic Ocean meridional heat transport (OHT) on changes in its seasonal cycle. We analyze a climate projection experiment with the Max-Planck Institute Earth System Model (MPI-ESM) performed for the Coupled Model Intercomparison Project phase 5 (CMIP5). In the RCP8.5 climate change scenario, the OHT declines in MPI-ESM in the North Atlantic by 30–50 % by the end of the 23rd century. The decline in the OHT is accompanied by a change in the seasonal cycle of the total OHT and its components. We decompose the OHT into overturning and gyre component. For the total OHT seasonal cycle, we find a northward shift of 5 degrees and latitude dependent temporal shifts of 1 to 6 months that are mainly associated with changes in the meridional velocity field. We find that the shift in the OHT seasonal cycle predominantly results from changes in the wind-driven surface circulation which projects onto the overturning component of the OHT in the tropical and subtropical North Atlantic. This leads to latitude dependent shifts of 1 to 6 months in the overturning component. In the subpolar North Atlantic, we find that the reduction of the North Atlantic Deep Water formation in RCP8.5 and changes in the gyre heat transport result in a strongly weakened seasonal cycle with a weakened seasonal amplitude by the end of the 23rd century and thus changes the OHT seasonal cycle in the SPG.

Description: Predictability on seasonal time scales over the North Atlantic–Europe region is assessed using a seasonal prediction system based on an initialized version of the Max Planck Institute Earth System Model (MPI-ESM). For this region, two of the dominant predictors on seasonal time scales are El Niño–Southern Oscillation (ENSO) and sudden stratospheric warming (SSW) events. Multiple studies have shown a potential for improved North Atlantic predictability for either predictor. Their respective influences are however difficult to disentangle, since the stratosphere is itself impacted by ENSO. Both El Niño and SSW events correspond to a negative signature of the North Atlantic Oscillation (NAO), which has a major influence on European weather. This study explores the impact on Europe by separating the stratospheric pathway of the El Niño teleconnection. In the seasonal prediction system, the evolution of El Niño events is well captured for lead times of up to 6 months, and stratospheric variability is reproduced with a realistic frequency of SSW events. The model reproduces the El Niño teleconnection through the stratosphere, involving a deepened Aleutian low connected to a warm anomaly in the northern winter stratosphere. The stratospheric anomaly signal then propagates downward into the troposphere through the winter season. Predictability of 500-hPa geopotential height over Europe at lead times of up to 4 months is shown to be increased only for El Niño events that exhibit SSW events, and it is shown that the characteristic negative NAO signal is only obtained for winters also containing major SSW events for both the model and the reanalysis data.

Description: Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.