A guided hybrid genetic algorithm for feature selection with expensive cost functions

We present a guided hybrid genetic algorithm for feature selection which is tailored to minimize the number of cost function evaluations. Guided variable elimination is used to make the stochastic backward search of the genetic algorithm much more efficient. Guiding means that a promising feature set is selected from a population and suggestions (for example by a trained Random Forest) are made which variable could be removed. It uses implicit diversity management and is able to return multiple optimal solutions if present, which might be important for interpreting the results. It uses a dynamic cost function that avoids prescribing an expected upper limit of performance or the number of features of the optimal solution. We illustrate the performance of the algorithm on artificial data, and show that the algorithm provides accurate results and is very efficient in minimizing the number of cost function evaluations.We present a guided hybrid genetic algorithm for feature selection which is tailored to minimize the number of cost function evaluations. Guided variable elimination is used to make the stochastic backward search of the genetic algorithm much more efficient. Guiding means that a promising feature set is selected from a population and suggestions (for example by a trained Random Forest) are made which variable could be removed. It uses implicit diversity management and is able to return multiple optimal solutions if present, which might be important for interpreting the results. It uses a dynamic cost function that avoids prescribing an expected upper limit of performance or the number of features of the optimal solution. We illustrate the performance of the algorithm on artificial data, and show that the algorithm provides accurate results and is very efficient in minimizing the number of cost function evaluations

The effect of univariate bias adjustment on multivariate hazard estimates

Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure.</p

A submonthly database for detecting changes in vegetation-atmosphere coupling

Land-atmosphere coupling and changes in coupling regimes are important for making precise future climate predictions and understanding vegetation-climate feedbacks. Here we introduce the Vegetation-Atmosphere Coupling (VAC) index which identifies regions and times of concurrent strong anomalies in temperature and photosynthetic activity. The different classes of the index determine whether a location is currently in an energy-limited or water-limited regime, and its high temporal resolution allows to investigate how these regimes change over time at the regional scale. We show that the VAC index helps to distinguish different evaporative regimes. It can therefore provide indirect information about the local soil moisture state. We further demonstrate how the index can be used to understand processes leading to and occurring during extreme climate events, using the 2010 heat wave in Russia and the 2010 Amazon drought as examples

Hotspots and drivers of compound marine heatwaves and low net primary production extremes

Extreme events can severely impact marine organisms and ecosystems. Of particular concern are multivariate compound events, namely when conditions are simultaneously extreme for multiple ocean ecosystem stressors. In 2013–2015 for example, an extensive marine heatwave (MHW), known as the Blob, co-occurred locally with extremely low net primary productivity (NPPX) and negatively impacted marine life in the northeast Pacific. Yet, little is known about the characteristics and drivers of such multivariate compound MHW–NPPX events. Using five different satellite-derived net primary productivity (NPP) estimates and large-ensemble-simulation output of two widely used and comprehensive Earth system models, the Geophysical Fluid Dynamics Laboratory (GFDL) ESM2M-LE and Community Earth System Model version 2 (CESM2-LE), we assess the present-day distribution of compound MHW–NPPX events and investigate their potential drivers on the global scale. The satellite-based estimates and both models reveal hotspots of frequent compound events in the center of the equatorial Pacific and in the subtropical Indian Ocean, where their occurrence is at least 3 times higher (more than 10 d yr−1) than if MHWs (temperature above the seasonally varying 90th-percentile threshold) and NPPX events (NPP below the seasonally varying 10th-percentile threshold) were to occur independently. However, the models show disparities in the northern high latitudes, where compound events are rare in the satellite-based estimates and GFDL ESM2M-LE (less than 3 d yr−1) but relatively frequent in CESM2-LE. In the Southern Ocean south of 60∘ S, low agreement between the observation-based estimates makes it difficult to determine which of the two models better simulates MHW–NPPX events. The frequency patterns can be explained by the drivers of compound events, which vary among the two models and phytoplankton types. In the low latitudes, MHWs are associated with enhanced nutrient limitation on phytoplankton growth, which results in frequent compound MHW–NPPX events in both models. In the high latitudes, NPPX events in GFDL ESM2M-LE are driven by enhanced light limitation, which rarely co-occurs with MHWs, resulting in rare compound events. In contrast, in CESM2-LE, NPPX events in the high latitudes are driven by reduced nutrient supply that often co-occurs with MHWs, moderates phytoplankton growth, and causes biomass to decrease. Compound MHW–NPPX events are associated with a relative shift towards larger phytoplankton in most regions, except in the eastern equatorial Pacific in both models, as well as in the northern high latitudes and between 35 and 50∘ S in CESM2-LE, where the models suggest a shift towards smaller phytoplankton, with potential repercussions on marine ecosystems. Overall, our analysis reveals that the likelihood of compound MHW–NPPX events is contingent on model representation of the factors limiting phytoplankton production. This identifies an important need for improved process understanding in Earth system models used for predicting and projecting compound MHW–NPPX events and their impacts.</p

Extreme events in gross primary production: a characterization across continents

Climate extremes can affect the functioning of terrestrial ecosystems, for instance via a reduction of the photosynthetic capacity or alterations of respiratory processes. Yet the dominant regional and seasonal effects of hydrometeorological extremes are still not well documented and in the focus of this paper. Specifically, we quantify and characterize the role of large spatiotemporal extreme events in gross primary production (GPP) as triggers of continental anomalies. We also investigate seasonal dynamics of extreme impacts on continental GPP anomalies. We find that the 50 largest positive extremes (i.e., statistically unusual increases in carbon uptake rates) and negative extremes (i.e., statistically unusual decreases in carbon uptake rates) on each continent can explain most of the continental variation in GPP, which is in line with previous results obtained at the global scale. We show that negative extremes are larger than positive ones and demonstrate that this asymmetry is particularly strong in South America and Europe. Our analysis indicates that the overall impacts and the spatial extents of GPP extremes are power-law distributed with exponents that vary little across continents. Moreover, we show that on all continents and for all data sets the spatial extents play a more important role for the overall impact of GPP extremes compared to the durations or maximal GPP. An analysis of possible causes across continents indicates that most negative extremes in GPP can be attributed clearly to water scarcity, whereas extreme temperatures play a secondary role. However, for Europe, South America and Oceania we also identify fire as an important driver. Our findings are consistent with remote sensing products. An independent validation against a literature survey on specific extreme events supports our results to a large extent

River flooding mechanisms and their changes in Europe revealed by explainable machine learning

Climate change may systematically impact hydrometeorological processes and their interactions, resulting in changes in flooding mechanisms. Identifying such changes is important for flood forecasting and projection. Currently, there is a lack of observational evidence regarding trends in flooding mechanisms in Europe, which requires reliable methods to disentangle emerging patterns from the complex interactions between flood drivers. Recently, numerous studies have demonstrated the skill of machine learning (ML) for predictions in hydrology, e.g., for predicting river discharge based on its relationship with meteorological drivers. The relationship, if explained properly, may provide us with new insights into hydrological processes. Here, by using a novel explainable ML framework, combined with cluster analysis, we identify three primary patterns that drive 53 968 annual maximum discharge events in around a thousand European catchments. The patterns can be associated with three catchment-wide river flooding mechanisms: recent precipitation, antecedent precipitation (i.e., excessive soil moisture), and snowmelt. The results indicate that over half of the studied catchments are controlled by a combination of the above mechanisms, especially recent precipitation in combination with excessive soil moisture, which is the dominant mechanism in one-third of the catchments. Over the past 70 years, significant changes in the dominant flooding mechanisms have been detected within a number of European catchments. Generally, the number of snowmelt-induced floods has decreased significantly, whereas floods driven by recent precipitation have increased. The detected changes in flooding mechanisms are consistent with the expected climate change responses, and we highlight the risks associated with the resulting impact on flooding seasonality and magnitude. Overall, the study offers a new perspective on understanding changes in weather and climate extreme events by using explainable ML and demonstrates the prospect of future scientific discoveries supported by artificial intelligence.</p

Varying soil moisture–atmosphere feedbacks explain divergent temperature extremes and precipitation projections in central Europe

The frequency and intensity of climate extremes is expected to increase in many regions due to anthropogenic climate change. In central Europe extreme temperatures are projected to change more strongly than global mean temperatures, and soil moisture–temperature feedbacks significantly contribute to this regional amplification. Because of their strong societal, ecological and economic impacts, robust projections of temperature extremes are needed. Unfortunately, in current model projections, temperature extremes in central Europe are prone to large uncertainties. In order to understand and potentially reduce the uncertainties of extreme temperature projections in Europe, we analyze global climate models from the CMIP5 (Coupled Model Intercomparison Project Phase 5) ensemble for the business-as-usual high-emission scenario (RCP8.5). We find a divergent behavior in long-term projections of summer precipitation until the end of the 21st century, resulting in a trimodal distribution of precipitation (wet, dry and very dry). All model groups show distinct characteristics for the summer latent heat flux, top soil moisture and temperatures on the hottest day of the year (TXx), whereas for net radiation and large-scale circulation no clear trimodal behavior is detectable. This suggests that different land–atmosphere coupling strengths may be able to explain the uncertainties in temperature extremes. Constraining the full model ensemble with observed present-day correlations between summer precipitation and TXx excludes most of the very dry and dry models. In particular, the very dry models tend to overestimate the negative coupling between precipitation and TXx, resulting in a warming that is too strong. This is particularly relevant for global warming levels above 2&thinsp;°C. For the first time, this analysis allows for the substantial reduction of uncertainties in the projected changes of TXx in global climate models. Our results suggest that long-term temperature changes in TXx in central Europe are about 20&thinsp;% lower than those projected by the multi-model median of the full ensemble. In addition, mean summer precipitation is found to be more likely to stay close to present-day levels. These results are highly relevant for improving estimates of regional climate-change impacts including heat stress, water supply and crop failure for central Europe.</p

Was the extreme Northern Hemisphere greening in 2015 predictable?

The year 2015 was, at the time, the warmest since 1880, and many regions in the Northern Hemisphere (NH) registered record breaking annual temperatures. Simultaneously, a remarkable and widespread growing season greening was observed over most of the NH in the record from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). While the response of vegetation to climate change (i.e. the long term trend) is assumed to be predictable, it is still unclear whether it is also possible to predict the interannual variability in vegetation activity. Here, we evaluate whether the unprecedented magnitude and extent of the greening observed in 2015 corresponds to an expected response to the 2015 climate anomaly, or to a change in the sensitivity of NH vegetation to climate. We decompose NDVI into the long-term and interannual variability components, and find that the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO) explain about half of NDVI interannual variability. This response is in addition to the long-term temperature and human-induced greening trend. We use a simple statistical approach to predict the NDVI anomaly in 2015, using the PDO and AMO states as predictors for interannual variability, and temperature and precipitation trends for the long-term component. We show that the 2015 anomaly can be predicted as an expected vegetation response to temperature and water-availability associated with the very strong state of the PDO in 2015. The link found between climate variability patterns and vegetation activity should contribute to increase the predictability of carbon-cycle processes at interannual time-scales, which may be relevant, for instance, for optimizing land-management strategies

Reviewing the Carbonation Resistance of Concrete

The paper reviews the studies on one of the important durability properties of concrete i.e. Carbonation. One of the main causes of deterioration of concrete is carbonation, which occurs when carbon dioxide (CO2) penetrates the concrete’s porous system to create an environment with lower pH around the reinforcement in which corrosion can proceed. Carbonation is a major cause of degradation of concrete structures leading to expensive maintenance and conservation operations. Herein, the importance, process and effect of various parameters such as water/cement ratio, water/binder ratio, curing conditions, concrete cover, super plasticizers, type of aggregates, grade of concrete, porosity, contaminants, compaction, gas permeability, supplementary cementitious materials (SCMs)/ admixtures on the carbonation of concrete has been reviewed. Various methods for estimating the carbonation depth are also reported briefl