The backbone of the climate network

We propose a method to reconstruct and analyze a complex network from data generated by a spatio-temporal dynamical system, relying on the nonlinear mutual information of time series analysis and betweenness centrality of complex network theory. We show, that this approach reveals a rich internal structure in complex climate networks constructed from reanalysis and model surface air temperature data. Our novel method uncovers peculiar wave-like structures of high energy flow, that we relate to global surface ocean currents. This points to a major role of the oceanic surface circulation in coupling and stabilizing the global temperature field in the long term mean (140 years for the model run and 60 years for reanalysis data). We find that these results cannot be obtained using classical linear methods of multivariate data analysis, and have ensured their robustness by intensive significance testing.Comment: 6 pages, 5 figure

Network-based identification and characterization of teleconnections on different scales

Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole

Interactions Among Climate, Fire, And Vegetation In The Alaskan Boreal Forest

Thesis (Ph.D.) University of Alaska Fairbanks, 2006The boreal forest covers 12 million kM2 of the northern hemisphere and contains roughly 40% of the world's reactive soil carbon. The Northern high latitudes have experienced significant warming over the past century and there is a pressing need to characterize the response of the disturbance regime in the boreal forest to climatic change. The interior Alaskan boreal forest contains approximately 60 million burnable hectares and, relative to the other disturbance mechanisms that exist in Alaska, fire dominates at the landscape-scale. In order to assess the impact of forecast climate change on the structure and function of the Alaskan boreal forest, the interactions among climate, fire and vegetation need to be quantified. The results of this work demonstrate that monthly weather and teleconnection indices explain the majority of observed variability in annual area burned in Alaska from 1950-2003. Human impacts and fire-vegetation interactions likely account for a significant portion of the remaining variability. Analysis of stand age distributions indicate that anthropogenic disturbance in the early 1900's has left a distinct, yet localized impact. Additionally, we analyzed remotely sensed burn severity data to better understand interactions among fire, vegetation and topography. These results show a significant relationship between burn severity and vegetation type in flat landscapes but not in topographically complex landscapes, and collectively strengthen the argument that differential flammability of vegetation plays a significant role in fire-vegetation interactions. These results were used to calibrate a cellular automata model based on the current conceptual model of interactions among weather, fire and vegetation. The model generates spatially explicit maps of simulated stand ages at 1 km resolution across interior Alaska, and output was validated using observed stand age distributions. Analysis of simulation output suggests that significant temporal variability of both the mean and variance of the stand age distribution is an intrinsic property of the stand age distributions of the Alaskan boreal forest. As a consequence of this non-stationarity, we recommend that simulation based methods be used to analyze the impact of forecast climatic change on the structure and function of the Alaskan boreal forest. To assess the impact climate change has on the Alaskan boreal forest, interactions among climate, fire and vegetation were quantified. This work shows that climatic signals exert the dominant influence on area burned. These results inform a simulation model to assess the historical and future states of the Alaskan boreal forest

Spatio-temporal precipitation patterns: from teleconnections to improved long-term forecasts

The standardized precipitation index (SPI) is an important yet easy-to-calculate means to describe wet or dry conditions in very different climates. In this work, a new scheme for obtaining improved forecasts of this index is developed. The methodology is tested over Russia and West Africa, proving that it can be successfully applied to different forecasting models and world regions. For testing, we use two forecasting models: the semi-implicit semi-Lagrangian vorticity-divergence (SL–AV) model of the Hydrometeorological Centre of Russia and the Institute of Numerical Mathematics of the Russian Academy of Sciences for Russia and the Climate Forecast System Version 2 (CFSv2) of the National Center for Environmental Prediction (NCEP) for West Africa. Based on hindcast simulations of both models, we demonstrate relatively poor skills in obtaining direct zero to three month lead-time SPI forecasts in the regions of interest during summer season. In order to improve the accuracy of these forecasts, we utilize surface temperature, mean sea level pressure and 500 hPa geopotential height fields, obtained from the outputs of both models. The spatial patterns of crosscorrelations between previously obtained climatological fields and our target variable (SPI-1) are studied to identify informative co-variates, potentially affecting monthly scale precipitation variability. The cross-correlation structures between the different fields reveal relevant interdependencies between SPI-1, sea surface temperature, mean sea level pressure and 500 hPa geopotential height in different regions. Subsequently, we employ two different regression models based on statistical post-processing of regional climate model output. In the first model, we consider all combinations of pairs of the previously identified predictors in a set of linear regression equations, which generates an ensemble of individual SPI-1 forecasts. The second model is based on a multiple linear regression approach comprising the dependency between all predictor variables and the predictand (SPI-1) in a single equation. The resulting SPI-1 forecasts obtained from both regression models are subsequently analysed in both deterministic and probabilistic ways and checked by various verification metrics. We identify that the first proposed model provides a significant improvement in the SPI forecasting, pointing to the potential for its implementation in operational monthly precipitation forecasts

Teleconnection analysis of runoff and soil moisture over the Pearl River basin in Southern China

This study explores the teleconnection of two climatic patterns, namely the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), with hydrological processes over the Pearl River basin in southern China, particularly on a sub-basin-scale basis. The Variable Infiltration Capacity (VIC) model is used to simulate the daily hydrological processes over the basin for the study period 1952–2000, and then, using the simulation results, the time series of the monthly runoff and soil moisture anomalies for its ten sub-basins are aggregated. Wavelet analysis is performed to explore the variability properties of these time series at 49 timescales ranging from 2 months to 9 yr. Use of the wavelet coherence and rank correlation method reveals that the dominant variabilities of the time series of runoff and soil moisture are basically correlated with IOD. The influences of ENSO on the terrestrial hydrological processes are mainly found in the eastern sub-basins. The teleconnections between climatic patterns and hydrological variability also serve as a reference for inferences on the occurrence of extreme hydrological events (e.g., floods and droughts).published_or_final_versio

Machine learning tools for pattern recognition in polar climate science

This thesis explores the application of two novel machine learning approaches to the study of polar climate, with particular focus on Arctic sea ice. The first technique, complex networks, is based on an unsupervised learning approach which is able to exploit spatio-temporal patterns of variability within geospatial time series data sets. The second, Gaussian Process Regression (GPR), is a supervised learning Bayesian inference approach which establishes a principled framework for learning functional relationships between pairs of observation points, through updating prior uncertainty in the presence of new information. These methods are applied to a variety of problems facing the polar climate community at present, although each problem can be considered as an individual component of the wider problem relating to Arctic sea ice predictability. In the first instance, the complex networks methodology is combined with GPR in order to produce skilful seasonal forecasts of pan-Arctic and regional September sea ice extents, with up to 3 months lead time. De-trended forecast skills of 0.53, 0.62, and 0.81 are achieved at 3-, 2- and 1-month lead time respectively, as well as generally highest regional predictive skill ($> 0.30$) in the Pacific sectors of the Arctic, although the ability to skilfully predict many of these regions may be changing over time. Subsequently, the GPR approach is used to combine observations from CryoSat-2, Sentinel-3A and Sentinel-3B satellite radar altimeters, in order to produce daily pan-Arctic estimates of radar freeboard, as well as uncertainty, across the 2018--2019 winter season. The empirical Bayes numerical optimisation technique is also used to derive auxiliary properties relating to the radar freeboard, including its spatial and temporal (de-)correlation length scales, allowing daily pan-Arctic maps of these fields to be generated as well. The estimated daily freeboards are consistent to CryoSat-2 and Sentinel-3 to within $< 1$ mm (standard deviations $< 6$ cm) across the 2018--2019 season, and furthermore, cross-validation experiments show that prediction errors are generally $\leq 4$ mm across the same period. Finally, the complex networks approach is used to evaluate the presence of the winter Arctic Oscillation (AO) to summer sea ice teleconnection within 31 coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6). Two global metrics are used to compare patterns of variability between observations and models: the Adjusted Rand Index and a network distance metric. CMIP6 models generally over-estimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean, and under-estimate the variability over the north Africa and southern Europe, while they also under-estimate the importance of regions such as the Beaufort, East Siberian and Laptev seas in explaining pan-Arctic summer sea ice area variability. They also under-estimate the degree of covariance between the winter AO and summer sea ice in key regions such as the East Siberian Sea and Canada basin, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice

EXTREME PRECIPITATION INDICES IN VOJVODINA REGION (SERBIA)

The evolution of daily extreme precipitation from 1966 to 2013 in Vojvodina Region (Serbia) was investigated. We calculated trends of ten precipitation indices and tested their corresponding significances using the Student’s t-test for seven locations. The obtained results suggest that the climate of the northern and central parts of Vojvodina region becomes wetter in terms of precipitation magnitude and frequency, reflecting the characteristic of the central European regime, while the southernmost part of the region is drier, reflecting the characteristic of the Mediterranean regime. In addition, the results indicate an increase in the amount of precipitation in short time intervals. Positive annual trends are strongly influenced by the significant increase of autumn frequency and intensity of extreme precipitation. According to the correlation between extreme precipitation indices and atmospheric teleconnection patterns, it was found that the NAO has the strongest influence on precipitation intensity indices in spring and winter, while during winter it also affects the frequency of dry conditions. The EAWR pattern has a strong influence on the statistically significant positive autumn trends

Recent trends in wind speed across Saudi Arabia, 1978–2013: a break in the stilling

We analyse recent trends and variability of observed near-surface wind speed from 19 stations across Saudi Arabia (SA) for 1978–2013. The raw wind speed data set was subject to a robust homogenization protocol, and the stations were then classified under three categories: (1) coast, (2) inland and (3) mountain stations. The results reveal a statistically significant ( p < 0.05) reduction of wind speed of − 0.058 m s − 1 dec − 1 at annual scale across SA, with decreases in winter ( − 0.100 m s − 1 dec − 1 ) and spring ( − 0.066 m s − 1 dec − 1 ) also detected, being non-significant in summer and autumn. The coast, inland and mountain series showed similar magnitude and significance of the declining trends across all SA series, except for summer when a decoupled variability and opposite trends of wind speed between the coast and inland series (significant declines: − 0.101 m s − 1 dec − 1 and − 0.065 m s − 1 dec − 1 , respectively) and the high-elevation mountain series (significant increase: + 0.041 m s − 1 dec − 1 ) were observed. Even though wind speed declines dominated across much of the country throughout the year, only a small number of stations showed statistically significant negative trends in summer and autumn. Most interestingly, a break in the stilling was observed in the last 12-year (2002–2013) period ( + 0.057 m s − 1 dec − 1 ; not significant) compared to the significant slowdown detected in the previous 24-year (1978–2001) period ( − 0.089 m s − 1 dec − 1 ). This break in the slowdown of winds, even followed by a non-significant recovery trend, occurred in all seasons (and months) except for some winter months. Atmospheric circulation plays a key role in explaining the variability of winds, with the North Atlantic Oscillation positively affecting the annual wind speed, the Southern Oscillation displaying a significant negative relationship with winds in winter, spring and autumn, and the Eastern Atlantic negatively modulating winds in summer.C. A-M. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 703733 (STILLING project). This research was supported by the research projects: Swedish BECC, MERGE, VR (2014-5320)