Temporal Climatology and Anomalous Weather Analysis


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Highlights

Help us improve our products. Sign up to take part. A Nature Research Journal. Although there are great concerns to what extent current and future climate change impacts biodiversity across different spatial and temporal scales, we still lack a clear information on different climate change metrics across fine spatial scales. We focus on seasonal climate variability and velocity and investigate changes in three time periods —, — and — using a novel statistical approach. Our results on climate variability showed the highest trends for the — time period.

For precipitation the strongest positive trends were most pronounced in the summer — and winter — The key results amplify the need for more local and regional scale studies to better understand species individualistic responses to recent climate change and allow for more accurate future projections and conservation strategies.

Introduction

The impact of current and future climate change on biodiversity and ecosystem services 1 , 2 , 3 is reflected in changes in species distribution including latitudinal, longitudinal and altitudinal changes , phenology, invasions, changes in populations, loss of genetic diversity and extinction 4 , 5. The threats to biodiversity produced by climate change are magnified by increased fragmentation of natural habitats and regional introduction of new species and diseases 6 , 7. Here, a regional focus is crucial as species-range reduction, population loss, species invasions and extinctions are spatially heterogeneous 10 , 11 , and these changes will affect regional and local ecosystem services and human wellbeing.

The lack of small spatial scale studies i. Such research is missing in many parts of the world due to a lack of long-term more than 50 years historical weather records and the difficulties of trend detection as climate data are often compromised by changes in observation techniques and changes in instruments or station relocations Without data homogenization 13 historical and archive records can show trends unrelated to climate variability which obscure our understanding of biological responses to recent climate change. In addition, magnitude and direction of temperature change are much easier to detect than precipitation change due to high spatial and temporal variability Despite these issues, the question of the attribution of biodiversity responses to climate change is one of the key unanswered questions in ecology 15 , Although attribution in climate-change research has made considerable progress, and although we are able to attribute certain specific weather events to anthropogenic climate change 17 , we are still not able to attribute individualistic species responses both terrestrial and aquatic species to climate variability, particularly at regional or local scales Achieving this would allow a more complete understanding of species sensitivity to climate change and provides the basis for the conservation of regionally and locally important species, such as key species or those crucial for ecosystem functioning and services It would also help to better understand the ecology and patterns of species invasions on local and regional scales and to identify if alien species have profited from recent climate change 19 , Here we use small scale climate trend analysis from Germany to provide a stepping stone in order to better understand abiotic responses to climate trends across small-spatial scales.

Beside trend analysis to estimate the magnitude and direction of climate change, the most commonly used metric to estimate the potential impact of climate change on biodiversity is climate change velocity Climate velocity has been defined as the speed at which species will need to migrate in order to stay in the same enveloped climatic condition Previous studies analysing climate change velocity have aimed to better understand species extinction risk 23 or the expected vector of past and future biological migrants 24 , It is known that some plant species only partly track trends in recent climate change 25 , and the velocity of how fast they should move has differed from the observed migration.

This could impact various trophic levels, for example change animal migration rates caused by the loss of nutrition or habitat An example, by Chivers et al. Such research is starting to explain responses to climate change at the species level, which is necessary to enable us to make realistic projections of species change in the current and the next century. Such studies will also help to more accurately identify species resilience to climate change associated with genetic variation, phenotypic plasticity, dispersal ability or interactions with other taxa.

As suggested by recent studies 30 , extensive regional and local climate change research is missing in many regions yet, and this is probably the only way to fully understand the response of ecological communities or individual species to climate drivers, including the identification of potential climate refugia 31 , Here we present the most detailed assessment that combines the analyses of seasonal climate change magnitude, direction and the recent velocity of climate change throughout the 20 th and 21 st century.

In this study we introduce three novel adaptations of the method for climate velocity calculation of Burrows et al. The benefit of these adaptations is an increased robustness in dealing with data distributions potentially skewed by extremes to which an average based approach is more sensitive. We analysed the seasonal climate change direction, magnitude and velocity across the whole of Germany, an area of km 2. All datasets were quality checked and homogenized by Deutscher Wetterdienst and, therefore, tests for homogeneity of variance and homogeneity adjustment were omitted 33 , As the winter season starts in December, the relevant ends of the periods were moved to February and February , respectively.

There are gaps in the data in the period between and for maximum and minimum temperatures, except for the minimum temperature in autumn.

All grid points affected by the missing data were excluded from all the analyses. As in Kosanic et al. This test is considered to be a robust non-parametric method for the detection of monotonic trends 36 , 37 , 38 , To be consistent with our approach to temporal trends, we calculated median instead of the mean long-term spatial gradients see Supplementary Information for more details. On the other side, as pointed out in 21 , 22 , 24 , the values can approach infinity in areas of small spatial gradients, such as flat terrain, and underestimate values in mountainous terrain where spatial gradients can be several orders of magnitude larger constraining near-zero values of spatial gradients is described in Supplementary Information 41 , 42 , Hence species in large topographically homogenous areas would have to move at high speed to keep pace with climate change, whereas in mountain regions the calculated migration rate might be lower than is actually required.

Temperature trends were highest for the time period — throughout the seasons. The results for Summer minimum temperature showed both positive and slightly negative trends in all three examined periods i. For the winter season, the most significant positive trends were detected for the Winter maximum temperature in the period — for most of Germany Fig. The season that follows next in showing the highest spatial coverage in number of analysed grid cells is spring with the highest positive trends for maximum temperature in — For Spring minimum temperature, there is a relatively small number of grid points with positive trends, and they are mainly concentrated in North-West Germany.

Autumn is the season with the least predominant trends in maximum and minimum temperature. For Autumn maximum temperature there were positive trends between — for grid points in South-West Germany.

Acknowledgments

The trends in precipitation are considerably weaker for all observed seasons when compared to temperature results. There are small, nearly all positive, trends for Winter precipitation in the — period evenly distributed across Germany. Similar results for Autumn precipitation show more spatially distributed mainly positive with some negative trends for century long data covering North-West and South-East Germany.

However, the trends for the period — showed higher positive magnitudes but lower spatial coverage. Summer and spring season showed the least change in precipitation across Germany, and mainly for the period — For further details of these results, see the Supplementary Information. To briefly recap, climate change velocities were calculated as ratios of Mann-Kendall temporal trends and absolute values of spatial gradients, so negative velocities correspond to a negative climatic trend and positive ones to a positive climatic trend.


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Since not many statistically significant temporal trends were detected in the period —, we focused on estimating climate velocities for the recent period from to As described in the Supplementary Information, we categorised values of climate change velocities for each variable and season by analysing their respective histograms.

Spring velocity for Tmax showed the highest values of 1. Climate change velocities for both minimum and maximum temperatures were higher in northern Germany and in topographically low elevation areas, whereas in mountain areas the velocities were lower. Furthermore, velocity showed negative values for summer and spring covering Majority of values for autumn and winter were positive The highest positive value of velocity for precipitation was detected for autumn 4 to 4.


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In our trend analysis of the local and regional scale of monthly temperature and precipitation, we analysed three periods —; —; — and detected positive and negative trends in climate variables for all four seasons. The spatial coverage and the magnitude of positive trends were highly uneven, but most pronounced in the latter third period — Our findings on climate change across Germany are thus consistent with previous research that shows an increase in seasonal temperatures across Europe since the mid th century Negative trends on the other hand were less spatially pronounced and were restricted for the first part of the 20 th century.

This is in contrast with findings of Matiu et al. They detected decreasing trends for spring and autumn and increasing trends for winter and summer. This illustrates the high spatial and temporal variability of temperature and precipitation, and highlights the necessity for local scale studies. NAO anomalies cause large shifts in regional winter temperatures and extreme weather conditions The strongest impact is visible through warmer than average winter temperatures during the positive mode of the NAO, and with an opposite effect during the negative mode of the NAO Here we found trends that are more spatially pronounced over the course of the whole 20 th century than for the first or the second half of the 20 st century.

This might be due to the fact that long-term time series have a higher statistical power, but also to the fact that time series could be affected by extreme values and variances within the dataset Winter and autumn precipitation trends were strongly dependent on the duration of the analysed time series and are more predominant and spatially heterogeneous for the — period than for — Although changes in precipitation are highly spatially and temporarily variable, and trends are less spatially homogenous, we found an increasing trend of Summer precipitation for northern Germany, which has been previously mainly detected for southern Europe 50 and for east-central Germany 51 , Previous studies detected an increasing Summer precipitation for some parts of northern Europe 53 , 54 , which is only to some extent consistent to our findings, as we detected positive trends in some parts of central and south west Germany.

Our analysis calculated climate change velocity since the s and estimates the rate of climate change that organisms will have to respond to Different results may be due to the different datasets and higher data resolution we used and to our approach in calculating the main constituents of climate velocities. Using the Mann-Kendall trend analysis instead of linear regression and medians instead of means of the spatial gradients decreases the sensitivity to a potentially skewed distribution of data. The highest velocity rates across Germany were calculated for lowland areas and the lowest rates in the mountain regions.

Similar results were found for California by Loarie et al. This is because the spatial gradient of temperature change is the highest on mountain slopes and no large movements up or down the slope are required, thus resulting in a large change in temperature but low velocity. The opposite is true for the lowland areas, where large movements are required across geographical areas requiring higher velocity 21 , 56 , Precipitation change velocity is also lowest in the mountain areas due to the orographic effect Although the limitation of this method is that it can underestimate constant climate conditions in the mountain areas, it has nevertheless been accepted as a baseline method and it could be complemented with other climate change velocity metrics i.

Nevertheless, climate change velocity results could, to some extent, depend on climatic variables that are used, spatial resolution of the data and to the parameter that defines analogue climates A commonly used method for limiting occurrences of near-zero spatial gradients in calculation of climate velocities is adding a small random noise to their values Here we use a bootstrap method 42 , 43 that, on the other hand, requires user input about the number of generated time series from which the uncertainty range is calculated and not the range itself.

The assumption about optimal calculation can be tested by adding more generated series to the analysis, hence making bootstrap more mathematically rigorous less arbitrary than the random noise generation. In any case, the choice of method for limiting near-zero spatial gradients can significantly affect only the extreme values of climate velocities, usually related to lowland areas, and therefore we recommend the use of a bootstrap method in calculation of near-zero spatial gradients. Data based studies like the one we present here can help to further explore the causal links and attribution of species shifts across multiple taxa to local and regional climate variability.

Species from multiple taxonomic groups have different climatic requirements and heterogeneous responses to climate change i. Some studies have used biotic velocity metrics developed based on climatic niche models 58 , 60 , and have analysed plant species vulnerability on a population level to local climate change velocity and their migration abilities i. Still, such studies use species distribution data from range maps which are scale dependent, and this could overestimate the presence of a particular species. In other words, they do not use species occurrence data 58 and, in order to better understand to what extent species have followed previous climate change velocity at the local and regional scales, we need studies based on occurrence data.

Such studies can serve as a guide to estimating the rate of future ecological migrations driven by climate velocity. This could lead towards more accurate future projections and to more effective policy and biodiversity management planning 12 , 57 , 64 , Bellard, C. Impacts of climate change on the future of biodiversity.


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Parmesan, C. Balvanera, P. Pacifici, M. Assessing species vulnerability to climate change. Nature Clim.

Heatwave in northern Europe, summer 2018

Primack, R. Insights into grass phenology from herbarium specimens. Palmer, G. Climate change, climatic variation and extreme biological responses.

Associated Data

Lewis, S. Defining the Anthropocene. Ummenhofer, C. Extreme weather and climate events with ecological relevance: a review. A globally coherent fingerprint of climate change impacts across natural systems. Nature , 37—42 Global exchange and accumulation of non-native plants. Baker, D. Neglected issues in using weather and climate information in ecology and biogeography. Costa, A. Greve, P. Global assessment of trends in wetting and drying over land. Overstretching attribution. Change 1 , 2—4 Plants and climate change: complexities and surprises.

Click on the cover image above to read some pages of this book! By breaking down atmospheric variables into temporal climatologies and anomalies, this book demonstrates that all weather extremes and climatic events are directly associated with the anomaly component of atmospheric motion.

We can use the anomaly-based synoptic chart and dynamical parameters to objectively describe these extremes and events. The conception and differences of weather, climate and general circulation tend to confuse us, because there are no clear physical definitions available for them.

Weather extremes such as heat waves, cold surges, freezing rains, heavy rains, severe drought, unusual storm tracks, and tornados are common on our planet's surface. Climatic events such as Arctic warming and declining sea ice have become hot topics in recent years. An approach based on breaking down total variables into temporal climatologies and anomalies can be used to identify general circulation, analyze climatic anomalies and forecast weather extremes. Accordingly, this book will appeal to students, teachers and forecasters in the field of weather and climate alike.

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Link Either by signing into your account or linking your membership details before your order is placed. Description Product Details Click on the cover image above to read some pages of this book! In Stock. Stage 1.

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Temporal Climatology and Anomalous Weather Analysis Temporal Climatology and Anomalous Weather Analysis
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