A Generalized Density-Based Algorithm for the Spatiotemporal Tracking of Drought Events

Drought events evolve simultaneously in space and time; hence, a proper characterization of an event re-quires the tracking of its full spatiotemporal evolution. Here we present a generalized algorithm for the tracking of drought events based on a three-dimensional application of the DBSCAN (density-based spatial clustering of applications with noise) clustering approach. The need for a generalized and flexible algorithm is dictated by the absence of a unanimous consensus on the definition of a drought event, which often depends on the target of the study. The proposed methodology introduces a set of six parameters that control both the spatial and the temporal connectivity between cells under drought conditions, also accounting for the local intensity of the drought itself. The capability of the algorithm to adapt to different drought definitions is tested successfully over a study case in Australia in the period 2017-20 using a set of standardized precipitation index (SPI) data derived from the ERA5 precipitation reanalysis. Insights on the possible range of variability of the model parameters, as well as on their effects on the delineation of drought events, are provided for the case of mete-orological droughts in order to incentivize further applications of the methodology

When Will Current Climate Extremes Affecting Maize Production Become the Norm?

We estimate the effects of climate anomalies (heat stress and drought) on annual maize production, variability, and trend from the country level to the global scale using a statistical model. Moderate climate anomalies and extremes are diagnosed with two indicators of heat stress and drought computed over maize growing regions during the most relevant period of maize growth. The calibrated model linearly combines these two indicators into a single Combined Stress Index. The Combined Stress Index explains 50% of the observed global production variability in the period 1980?2010. We apply the model on an ensemble of high-resolution global climate model simulations. Global maize losses, due to extreme climate events with 10-year return times during the period 1980?2010, will become the new normal already at 1.5 °C global warming levels (approximately 2020s). At 2 °C warming (late 2030s), maize areas will be affected by heat stress and drought never experienced before, affecting many major and minor production regions.Fil: Zampieri, M.. European Commission Joint Research Centre; ItaliaFil: Ceglar, A.. European Commission Joint Research Centre; ItaliaFil: Dentener, F.. European Commission Joint Research Centre; ItaliaFil: Dosio, A.. European Commission Joint Research Centre; ItaliaFil: Naumann, Gustavo. European Commission Joint Research Centre; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaFil: van den Berg, M.. European Commission Joint Research Centre; ItaliaFil: Toreti, A.. European Commission Joint Research Centre; Itali

Exploiting the signal-to-noise ratio in multi-system predictions of boreal summer precipitation and temperature

Droughts and heatwaves are among the most impactful climate extremes. Their co-occurrence can have adverse consequences on natural and human systems. Early information on their possible occurrence on seasonal timescales is beneficial for many stakeholders. Seasonal climate forecasts have become openly available to the community, but a wider use is currently hindered by limited skill in certain regions and seasons. Here we show that a simple forecast metric from a multi-system ensemble, the signal-to-noise ratio, can help overcome some limitations. Forecasts of mean daily near-surface air temperature and precipitation in boreal summers with a high signal-to-noise ratio tend to coincide with observed larger deviations from the mean than summers with a low signal-to-noise ratio. The signal-to-noise ratio of the ensemble predictions may serve as a complementary measure of forecast reliability that could benefit users of climate predictions.</p

Projections of global changes in precipitation extremes from CMIP5 models

Precipitation extremes are expected to increase in a warming climate, thus it is essential to characterise their potential future changes. Here we evalu- ate eight high-resolution Global Climate Model simulations in the twenti- eth century and provide new evidence on projected global precipitation ex- tremes for the 21st century. A significant intensification of daily extremes for all seasons is projected for the mid and high latitudes of both hemispheres at the end of the present century. For the subtropics and tropics, the lack of reliable and consistent estimations found for both the historical and fu- ture simulations might be connected with model deficiencies in the repre- sentation of organised convective systems. Low inter-model variability and good agreement with high-resolution regional observations are found for the twentieth century winter over the Northern Hemisphere mid and high lat- itudes

Declining water resources in response to global warming and changes in atmospheric circulation patterns over southern Mediterranean France

Warming trends are responsible for an observed decrease of water discharge in southern France (northwestern Mediterranean). Ongoing climate change and the likely increase of water demand threaten the availability of water resources over the coming decades. Drought indices like the Reconnaissance Drought Index (RDI) are increasingly used in climate characterization studies, but little is known about the relationships between these indices, water resources, and the overall atmospheric circulation patterns. In this study, we investigate the relationships between the RDI, water discharge, and four atmospheric teleconnection patterns (TPs) for six coastal river basins in southern France, both for the historical period of the last 60 years and for a worst-case climatic scenario (RCP8.5) reaching the year 2100. We combine global and regional climate model (CGM and RCM, respectively) outputs with a set of observed climatic and hydrological data in order to investigate the past relationships between the RDI, water discharge, and TPs and to project their potential evolution in space and time. Results indicate that annual water discharge can be reduced by −49 % to −88 % by the end of the century under the extreme climate scenario conditions. Due to unequal links with TPs, the hydroclimatic evolution is unevenly distributed within the study area. Indeed a clustering analysis performed with the RDI time series detects two major climate clusters, separating the eastern and western part of the study region. The former indicates stronger relationships with the Atlantic TPs (e.g. the North Atlantic Oscillation (NAO) and the Scandinavian Oscillation (Scand) patterns), whereas the latter is more closely related to the Mediterranean TPs (Mediterranean Oscillation (MO) and Western Mediterranean Oscillation (WeMO)). The future climate simulations predict an antagonistic evolution in both clusters which are likely driven by decreasing trends of Scand and WeMO. The former provokes a general tendency of lower P in both clusters during spring, summer, and autumn, whereas the latter might partly compensate for this evolution by enhanced precipitation in the eastern cluster during autumn and winter. However, compared to observations, representation of the Mediterranean TPs WeMO and MO in the considered climate models is less satisfactory compared to the Atlantic TPs NAO and Scand, and further improvement of the model simulations therefore requires better representations of the Mediterranean TPs.</p

Surface Freshwater Limitation Explains Worst Rice Production Anomaly in India in 2002

India is the second-most populous country and the second-most important producer of rice of the world. Most Indian rice production depends on monsoon timing and dynamics. In 2002, the lowest monsoon precipitation of the last 130+ years was observed. It coincided with the worst rice production anomaly recorded by FAOSTAT from 1961 to 2014. In that year, freshwater limitation was blamed as responsible for the yield losses in the southeastern coastal regions. Given the important implication for local food security and international market stability, we here investigate the specific mechanisms behind the effects of this extreme meteorological drought on rice yield at the national and regional levels. To this purpose, we integrate output from the hydrological model, surface, and satellite observations for the different rice cropping cycles into state-of-the-art and novel climate indicators. In particular, we adopt the standardized precipitation evapotranspiration index (SPEI) as an indicator of drought due to the local surface water balance anomalies (i.e., precipitation and evapotranspiration). We propose a new indicator of the renewable surface freshwater availability due to non-local sources, i.e., the standardized river discharge index (SDI) based on the anomalies of modelled river discharge data. We compare these indicators to the soil moisture observations retrieved from satellites. We link all diagnostics to the recorded yields at the national and regional level, quantifying the long-term correlations and the best match of the 2002 anomaly. Our findings highlight the need for integrating non-local surface freshwater dynamics with local rainfall variability to determine the soil moisture conditions in rice fields for yields assessment, modeling, and forecasting

Changes in the annual cycle of heavy precipitation across the British Isles within the 21st century

We investigate future changes in the annual cycle of heavy daily precipitation events across the British Isles in the periods 2021–2060 and 2061–2100, relative to present day climate. Twelve combinations of regional and global climate models forced with the A1B scenario are used. The annual cycle is modelled as an inhomogeneous Poisson process with sinusoidal models for location and scale parameters of the generalized extreme value distribution. Although the peak times of the annual cycle vary considerably between projections for the 2061–2100 period, a robust shift towards later peak times is found for the south-east, while in the north-west there is evidence for a shift towards earlier peak times. In the remaining parts of the British Isles no changes in the peak times are projected. For 2021–2060 this signal is weak. The annual cycle’s relative amplitude shows no robust signal, where differences in projected changes are dominated by global climate model differences. The relative contribution of anthropogenic forcing and internal climate variability to changes in the relative amplitude cannot be identified with the available ensemble. The results might be relevant for the development of adequate risk-reduction strategies, for insurance companies and for the management and planning of water resource

Extreme and long-term drought in the La Plata Basin: event evolution and impact assessment until September 2022

The current drought conditions across the Parana-La Plata Basin (LPB) in Brazil-Argentina have been the worst since 1944. While this area is characterized by a rainy season with a peak from October to April, the hydrological year 2020-2021 was very deficient in rainfall, and the situation extended into the 2021-2022 hydrological year. Below-normal rainfall was dominant in south-eastern Brazil, northern Argentina, Paraguay, and Uruguay, suggesting a late onset and weaker South American Monsoon and the continuation of drier conditions since 2021. In fact, in 2021 Brazilian south and south-east regions faced their worst droughts in nine decades, raising the spectre of possible power rationing given the grid dependence on hydroelectric plants. The Paraná-La Plata Basin drought induced damages to agriculture and reduced crop production, including soybeans and maize, with effects on global crop markets. The drought situation continued in 2022 in the Pantanal region. Dry meteorological conditions are still present in the region at the end of September 2022 with below-average precipitation anomalies. Soil moisture anomaly and vegetation conditions are worst in the lower part of the La Plata Basin, in the southern regions. Conversely, upper and central part of the basin show partial and temporary recovery

Validation of the present day annual cycle in heavy precipitation over the British Islands simulated by 14 RCMs

The representation of the annual cycle of heavy daily precipitation events across the United Kingdom within 14 regional climate models (RCMs) and the European observation data set (E-OBS) over the 1961-2000 period is investigated. We model extreme precipitation as an inhomogeneous Poisson process with a non-stationary threshold and use a sinusoidal model for the location and scale parameter of the corresponding generalized extreme value distribution and a constant shape parameter. First we fit the statistical model to the UK Met Office 5 km gridded precipitation data set (UKMO). Second the statistical model is fitted to 14 reanalysis driven 25 km resolution RCMs from the ENSEMBLES project and to E-OBS. The resulting characteristics from the RCMs and from E-OBS are compared with those from UKMO. We study the peak time of the annual cycle of the monthly return levels, the relative amplitude of their annual cycle and the relative bias of their absolute values. We show that the performance of the RCMs depends strongly on the region. The RCMs show deficits in modeling the characteristics of the annual cycle, especially in modeling its relative amplitude and mainly in Eastern England. However the peak time of the annual cycle is adequately simulated by most RCMs. E-OBS exhibits considerable biases in the absolute values of all monthly return levels, but the relative amplitude and the phase of the annual cycle of heavy precipitation are well represented. Our results imply that studies which rely on the explicit annual cycle of simulated heavy precipitation should be carefully considered

European summer temperatures since Roman times

The spatial context is critical when assessing present-day climate anomalies, attributing them to potential forcings and making statements regarding frequency and severity in the long-term perspective. Recent initiatives have expanded the number of high-quality proxy-records and developed new reconstruction methods. These advances allow more rigorous regional past temperature reconstructions and the possibility of evaluating climate models on policy-relevant, spatio-temporal scales. We provide a new proxy-based, annually-resolved, spatial reconstruction of the European summer temperature fields back to 755 CE based on a Bayesian hierarchical modelling (BHM), together with estimates of the European mean temperature variation since 138 BCE based on Composite-plus-Scaling. Our reconstructions compare well with independent instrumental and proxy-based temperature estimates, but suggest a larger amplitude in summer temperature variability than previously reported. Temperature differences between the medieval period, the recent period and Little Ice Age are larger in reconstructions than simulations. This may indicate either inflated variability of the reconstructions, a lack of sensitivity to external forcing on sub-hemispheric scales in the climate models and/or an underestimation of internal variability on centennial and longer time scales including the representation of internal feedback mechanisms