Recently we proposed a regression model for the number of hospital admissions for malaria in the Limpopo province of South Africa. We developed our model using the available weekly epidemiological reports from five districts in this province, in the period 2000-2020. We analyzed number of hospitalizations for malaria time series in relation to time series of temperature, rainfall and evaporation from bare soil ground or satellite data from the same geographical area and developed an algorithm that links combined changes in these three variables with the changes in number of malaria hospitalizations. We used wavelet spectral analysis to determine time lags in their cross-correlations.  

We used this model to provide projections for the Limpopo malaria cases for the next five years (2025-2029). Since there are no future projections available for evapotranspiration, we used three different methods to estimate future values of this variable in our model: 1) a combination of temperature and rainfall data, 2) use of total soil moisture content records and their projections, and 3) use of Hargreaves empirical formula. We will present and compare our results for all three cases.

Our calculations can be used for public health preparedness.  

How to cite: Blesic, S., Tosic, M., Aleksandrov, N., Kapwata, T., and Wright, C.: Modeling the number of hospital admissions for malaria in South Africa by using climate variables as disease drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10026, https://doi.org/10.5194/egusphere-egu24-10026, 2024.

The probability of incidence of compound events is increasing due to human-induced climate change: in particular, there is high confidence that concurrent heatwaves and droughts will become more frequent with increased global warming¹. Hereby, understanding the aggregated impact of multiple and synchronized compound hot and dry events at different spatial regions is a pressing issue, especially when it comes to predicting these extremes. In order to assess the evolution of these climate hazards, it is crucial to identify the synchronization structures of compound hot and dry events. To achieve this goal,  we highlight the hotspot regions where extremes are increasing and analyse the atmospheric precursors driving these anomalous conditions. Complex networks represent a promising tool in this perspective. In this work, we present an evolving network approach to assess the time evolution of synchronized compound hot and dry extremes due to global warming in continental Europe. Under this framework, we identify those regions where the frequency of these events has increased in the past 80 years and we describe their atmospheric drivers. Using ERA5 reanalysis data² and focusing on the extended summer seasons (from April to September) of the period 1941-2020, we construct an evolving network constituted by 51 consecutive layers. Each layer models the synchronization structure in space of compound hot and dry events for a certain time window. Once the evolving network is established, the 51 layers are analysed to highlight the main changes in the graph structure. In particular, by looking at different centrality and clustering metrics and their evolution, we identify hotspot regions, and consequently we describe the atmospheric conditions which drive the compound events at these key locations. Climate complex networks prove to be a powerful tool to reveal hidden features of climate processes; this approach indeed brings out key aspects concerning the spatial dynamics of hot and dry events, laying the foundations to build a forecasting method for these extremes.

References

1) S.I. Seneviratne, X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, et al. Weather and climate extreme events in a changing climate; climate change 2021: The physical science basis. contribution of working group i to the sixth assessment report of the intergovernmental panel on climate change, 2021.

2) H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J-N. Thépaut. Era5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2023.

How to cite: Giaquinto, D., Di Capua, G., Marzocchi, W., and Kurths, J.: An evolving network approach to assess compounding heat and dry extremes in Europe., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6251, https://doi.org/10.5194/egusphere-egu24-6251, 2024.

The quality of weather forecasts has improved considerably in recent decades as models can better represent the complexity of the Earth’s climate system, benefitting from assimilation of comprehensive Earth observation data and increased computational resources. Analysis of errors is an integral part of numerical weather prediction to produce better quality forecasts. The Earth’s climate, being a highly complex interacting system, often gives rise to significant statistical relationships between the states of the climate at distant geographical locations. Likewise, correlated errors in forecasting the state of the system can arise from predictable relationships between forecast errors at various regions resulting from an underlying systematic or random process. Estimation of error correlations is very important for producing quality forecasts and is a key issue for data assimilation. However, the size of the corresponding correlation matrix is larger than what is possible to represent on geographical maps in order to diagnose its full spatial variation.

In this work, we propose an approach based on complex network theory to quantitatively study the spatiotemporal coherent structures of medium-range forecast errors of different climate variables. We demonstrate that the spatial variation of the network measures computed from the error correlation matrix can provide insights into the origin of forecast errors in a climate variable by identifying spatially coherent patterns of regions having common sources of error. Notably, the network topology of forecast errors of a climate variable is significantly different from those of random networks corresponding to a deterministic phenomenon which the model fails to simulate adequately. This is especially important to reveal the spatial heterogeneity of the errors – for example, the forecast errors of outgoing long-wave radiation in tropical regions can be correlated across very long distances, indicating an underlying climate mechanism as the source of the error. Additionally, we highlight that these structures of forecast errors may not always be directly derivable from the spatiotemporal co-variability pattern of the corresponding climate variable, contrary to the expectations that the patterns should resemble each other. We further employ other common statistical tools such as, empirical orthogonal functions, to support these findings. Our results underline the potential of complex networks as a very promising diagnostic tool to gain better understanding of the spatial variation, origin, and propagation of forecast errors.

How to cite: Gupta, S., Banerjee, A., Marwan, N., Richardson, D., Magnusson, L., Kurths, J., and Pappenberger, F.: Spatially coherent structure of forecast errors – A complex network approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12314, https://doi.org/10.5194/egusphere-egu24-12314, 2024.

We apply potential forecasting [1,2] to the WISE database that contains water accounts of European river basins [3]. We identify basins under stress and discuss various scenarios of water use. The complexity of water inflows and abstractions introduces sources of uncertainty that require analysis of geophysical, climatic, agricultural and social factors of water use, and this data represents an important case study for development of multivariate data science techniques. We report our findings and projections of hydrological dynamics in European regions.

[1] Livina et al, Physica A 2013

[2] Billuroglu & Livina, Journal of Failure Analysis and Prevention 2022

[3] WISE database, European Environmental Agency, https://www.eea.europa.eu/en/datahub

How to cite: Livina, V. N.: Potential forecasting of water accounts of European river basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14571, https://doi.org/10.5194/egusphere-egu24-14571, 2024.

The El Niño Southern Oscillation (ENSO) is the most consequential driver of interannual global climate variability and can lead to extreme weather events like drought or flooding in various parts of the world. Current operational forecasts are hampered by the so-called spring predictability barrier (SPB), which makes forecasts before or during the boreal spring particularly challenging. 

In recent years, we developed several methods based on complex system science that can provide reliable El Niño forecasts well before the SPB, thus about doubling the pre-warning time. The first of these methods is based on a dynamical climate network (CN) consisting of nodes that are reanalysis grid points in the Pacific, and links between them, whose strength is characterized by the cross-correlations of the atmospheric surface temperatures at the grid points. In the calendar year before an El Niño event, the links between the eastern equatorial Pacific and the rest of the tropical Pacific tend to strengthen such that the average link strength exceeds a certain threshold. This property serves as a precursor to forecast the onset of El Niño events. In particular, the CN-based method has already provided 12 real-time forecasts, 11 of which turned out to be correct (p = 5.1*10^-3). Here, we discuss an improvement of the CN method as well as the combination with other El Niño forecasting methods. 

Approaches based on information entropy and the zonal temperature gradient in the western Pacific provide additional forecasts with about 1 year lead time for the magnitude and the type of an upcoming El Niño event, respectively. Combining the three methods provides not only more information about an upcoming El Niño, particularly about the risk exposure of a given geographical location, but concurring forecasts can support each other and lead to higher overall confidence in the forecast. This was the case, for instance, at the end of 2022, when the combined method correctly forecasted a moderate-to-strong El Niño of eastern Pacific type for 2023.  

How to cite: Ludescher, J., Bunde, A., and Schellnhuber, H. J.: Reliable El Niño forecasting before the spring predictability barrier, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13178, https://doi.org/10.5194/egusphere-egu24-13178, 2024.

Urban environments are especially sensitive to global warming due to their characteristic man-made surfaces and decreased vegetation cover. Elevated temperature in cities can facilitate the pole-wards expansion of arthropod disease vectors, including Phlebotomine sand flies (SFs). No study to date has yet been done to understand the effects of elevated urban temperatures on the distribution range shifts of SFs on continental scale. This study fills that gap and tests the role of urban heat island (UHI) in driving distribution range shifts of Phlebotomus perniciosus in Europe under two climatic scenarios. We find that P. perniciosus can occur more northly in summer due to UHI under both scenarios. Our study suggests that arthropod disease vectors can occur in cities where they are not expected due to UHI.

How to cite: Tak, V., Wang, D., and Matson, K.: The urban heat island effect aggravates the impact of climate change on the spatial distribution shifts of Phlebotomus perniciosus in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17798, https://doi.org/10.5194/egusphere-egu24-17798, 2024.

Landslides are particularly costly disasters, causing about 4,500 fatalities and US$20 billion in damages worldwide each year. In Western North America, where intense and frequent precipitation events interact with complex topography and steep slopes, precipitation-induced landslides (PILs) are a serious geological hazard. Recently, it has been revealed that the majority of PILs in the region are triggered by precipitation from atmospheric rivers (ARs), transient channels of intense water vapor flux in the troposphere. However, the synoptic conditions differentiating landslide-triggering and non-triggering ARs remain unknown. In this study, we explore opportunities for improved landslide forecasting in Western North America using catalogs of land-falling ARs and PILs, along with ERA5 climatological data, from 1996 to 2018. First, we employ event synchronization, a non-linear measure specially tailored for event series analysis, to identify landslide-triggering ARs. Based on the AR-strength scale, which ranks ARs in levels from 1 to 5, we further characterize landslide-triggering ARs in terms of intensity and persistence. Subsequently, we spatially resolve the conditional probability of PIL occurrence given the detection of AR-attributed precipitation in the antecedent week, revealing the contribution of each AR level. Lastly, using hourly estimates of integrated water vapour transport, geopotential height, and precipitation at 0.25° spatial resolution, we differentiate the spatio-temporal evolution of synoptic conditions preceding landslide-triggering and non-landslide triggering ARs. Our results constitute a first, fundamental, and necessary step toward AR-based landslide forecasts, contributing crucial insights to improve forecasting accuracy at the short and early medium-range (1–7 days).

How to cite: Vallejo-Bernal, S. M., Luna, L., Marwan, N., and Kurths, J.: Forecasting of Precipitation-Induced Landslides Using Atmospheric Rivers: Opportunities and Challenges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17215, https://doi.org/10.5194/egusphere-egu24-17215, 2024.

It is well recognized that climate memory is one the origins for climate predictability, but how to include the concept of climate memory into the climate prediction, is still an open question. Here in this work, we suggest the Fractional Integral Statistical Model (FISM), a generalized stochastic climate model, as a new way for this purpose. With FISM, one can extract the “forcing-induced direct component ε(t)” and the “memory-induced indirect component M(t)” from a given variable x(t). By predicting ε(t), one can further obtain the predicted x(t) using FISM. Different from traditional prediction approaches which normally focus on x(t), here this new strategy based on FISM clarifies the climate memory impacts. From this new perspective, we have quantified the climate memory induced predictability, and developed a temperature response model that can project the future warming trend. Compared to CMIP6 simulations, our approach projects lower global warming levels over the next few decades. A further examination indicates that many CMIP6 models overestimated the climate memory, which might contribute to the overestimated future warming trend.

How to cite: Yuan, N., Franzke, C., Nian, D., Fu, Z., Ying, K., Xiong, F., and Dong, W.: Fractional Integral Statistical Model: A new way for climate prediction and projection from the perspective of scaling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7007, https://doi.org/10.5194/egusphere-egu24-7007, 2024.

Climate change influences the transmission of vector-borne diseases by affecting the distribution and survival of disease vectors. Numerous diseases are transmitted by phlebotomine sand flies (SFs), including Leishmaniasis. Several major sand fly-borne diseases (SFBDs) are responsible for high global disease burdens and high socio-economic costs. In Europe, 22 known SF vector species are largely confined to the Mediterranean Basin, yet global warming is predicted to drive the spread of SFs to large areas of Europe in the 21^th century, an effect likely to be exacerbated by anthropogenic variables. However, the constraints to the geographic distributions of SFs are not well understood. This study aims to increase the understanding of the drivers of the spatial distributions of SFs. To achieve this, we use species distribution modelling (SDM) to assess the role of climate, land-use and socio-economic drivers in shaping the geographic distributions of all endemic SF vectors in Europe. With this knowledge, we predict future hotspots of SFs in Europe. Our predictions are spatially explicit, scenario-based, and informative for surveillance efforts.

How to cite: Wang, D., Hof, A., Matson, K., and van Langevelde, F.: Understanding and predicting the spread of Phlebotomine sand flies in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15381, https://doi.org/10.5194/egusphere-egu24-15381, 2024.