scholarly journals Forest wildflowers bloom earlier as Europe warms – but not everywhere equally

2021 ◽  
Author(s):  
Franziska M. Willems ◽  
J. F. Scheepens ◽  
Oliver Bossdorf
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Spatial Scales ◽  
Flowering Phenology ◽  
Spatial Modelling ◽  
Forest Understory ◽  
Herbarium Specimens ◽  
Ecological Communities ◽  
Improved Model

AbstractSome of the most striking biological responses to climate change are the observed shifts in the timing of life-history events of many organisms. Plants, in particular, often flower earlier in response to climate warming, and herbarium specimens are excellent witnesses of such long-term changes. However, in large-scale analyses the magnitude of phenological shifts may vary geographically, and the data are often clustered, and it is thus necessary to account for spatial correlation to avoid geographical biases and pseudoreplication. Here, we analysed herbarium specimens of 20 spring-flowering forest understory herbs to estimate how their flowering phenology shifted across Europe during the last century. Our analyses show that on average these forest wildflowers now bloom over six days earlier than at the beginning of the last century. These changes were strongly associated with warmer spring temperatures. Plants flowered on average of 3.6 days earlier per 1°C warming. However, in some parts of Europe plants flowered earlier or later than expected. This means, there was significant residual spatial variation in flowering time across Europe, even after accounting for the effects of temperature, precipitation, elevation and year. Including this spatial autocorrelation into our statistical models significantly improved model fit and reduced bias in coefficient estimates. Our study indicates that forest wildflowers in Europe strongly advanced their phenology in response to climate change during the last century, with potential severe consequences for their associated ecological communities. It also demonstrates the power of combining herbarium data with spatial modelling when testing for long-term phenology trends across large spatial scales.

scholarly journals Repeated surveying over 6 years reveals that fine-scale habitat variables are key to tropical mountain ant assemblage composition and functional diversity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mulalo M. Muluvhahothe ◽  
Grant S. Joseph ◽  
Colleen L. Seymour ◽  
Thinandavha C. Munyai ◽  
Stefan H. Foord
Keyword(s):  
Climate Change ◽  
Species Composition ◽  
High Altitude ◽  
Functional Diversity ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Abiotic Factors ◽  
Composition Analysis ◽  
Fine Scale

AbstractHigh-altitude-adapted ectotherms can escape competition from dominant species by tolerating low temperatures at cooler elevations, but climate change is eroding such advantages. Studies evaluating broad-scale impacts of global change for high-altitude organisms often overlook the mitigating role of biotic factors. Yet, at fine spatial-scales, vegetation-associated microclimates provide refuges from climatic extremes. Using one of the largest standardised data sets collected to date, we tested how ant species composition and functional diversity (i.e., the range and value of species traits found within assemblages) respond to large-scale abiotic factors (altitude, aspect), and fine-scale factors (vegetation, soil structure) along an elevational gradient in tropical Africa. Altitude emerged as the principal factor explaining species composition. Analysis of nestedness and turnover components of beta diversity indicated that ant assemblages are specific to each elevation, so species are not filtered out but replaced with new species as elevation increases. Similarity of assemblages over time (assessed using beta decay) did not change significantly at low and mid elevations but declined at the highest elevations. Assemblages also differed between northern and southern mountain aspects, although at highest elevations, composition was restricted to a set of species found on both aspects. Functional diversity was not explained by large scale variables like elevation, but by factors associated with elevation that operate at fine scales (i.e., temperature and habitat structure). Our findings highlight the significance of fine-scale variables in predicting organisms’ responses to changing temperature, offering management possibilities that might dilute climate change impacts, and caution when predicting assemblage responses using climate models, alone.

scholarly journals Variability of Cloudiness over Mountain Terrain in the Western United States

2017 ◽  
Vol 18 (5) ◽  
pp. 1227-1245 ◽  
Author(s):  
Edwin Sumargo ◽  
Daniel R. Cayan
Keyword(s):  
United States ◽  
Large Scale ◽  
Spatial Scales ◽  
High Elevation ◽  
Empirical Orthogonal Functions ◽  
Western United States ◽  
Spatial And Temporal Variability ◽  
Regional Patterns ◽  
Cloud Albedo

Abstract This study investigates the spatial and temporal variability of cloudiness across mountain zones in the western United States. Daily average cloud albedo is derived from a 19-yr series (1996–2014) of half-hourly Geostationary Operational Environmental Satellite (GOES) images. During springtime when incident radiation is active in driving snowmelt–runoff processes, the magnitude of daily cloud variations can exceed 50% of long-term averages. Even when aggregated over 3-month periods, cloud albedo varies by ±10% of long-term averages in many locations. Rotated empirical orthogonal functions (REOFs) of daily cloud albedo anomalies over high-elevation regions of the western conterminous United States identify distinct regional patterns, wherein the first five REOFs account for ~67% of the total variance. REOF1 is centered over Northern California and Oregon and is pronounced between November and March. REOF2 is centered over the interior northwest and is accentuated between March and July. Each of the REOF/rotated principal components (RPC) modes associates with anomalous large-scale atmospheric circulation patterns and one or more large-scale teleconnection indices (Arctic Oscillation, Niño-3.4, and Pacific–North American), which helps to explain why anomalous cloudiness patterns take on regional spatial scales and contain substantial variability over seasonal time scales.

scholarly journals What can we learn from long-term groundwater data to improve climate change impact studies?

2011 ◽  
Vol 8 (4) ◽  
pp. 7621-7655 ◽  
Author(s):  
S. Stoll ◽  
H. J. Hendricks Franssen ◽  
R. Barthel ◽  
W. Kinzelbach
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Climate Change Impact ◽  
Large Scale ◽  
Climate Models ◽  
Groundwater Resources ◽  
Groundwater Dynamics ◽  
Impact Studies ◽  
Change Impact

Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

scholarly journals Comparison of generalized non-data-driven lake and reservoir routing models for global-scale hydrologic forecasting of reservoir outflow at diurnal time steps

2020 ◽  
Vol 24 (5) ◽  
pp. 2711-2729 ◽  
Author(s):  
Joseph L. Gutenson ◽  
Ahmad A. Tavakoly ◽  
Mark D. Wahl ◽  
Michael L. Follum
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Army Corps ◽  
Time Step ◽  
Large Spatial Scale ◽  
Reservoir Routing ◽  
Routing Methods ◽  
Improved Model

Abstract. Large-scale hydrologic forecasts should account for attenuation through lakes and reservoirs when flow regulation is present. Globally generalized methods for approximating outflow are required but must contend with operational complexity and a dearth of information on dam characteristics at global spatial scales. There is currently no consensus on the best approach for approximating reservoir release rates in large spatial scale hydrologic forecasting, particularly at diurnal time steps. This research compares two parsimonious reservoir routing methods at daily steps: Döll et al. (2003) and Hanasaki et al. (2006). These reservoir routing methods have been previously implemented in large-scale hydrologic modeling applications and have been typically evaluated seasonally. These routing methods are compared across 60 reservoirs operated by the U.S. Army Corps of Engineers. The authors vary empirical coefficients for both reservoir routing methods as part of a sensitivity analysis. The method proposed by Döll et al. (2003) outperformed that presented by Hanasaki et al. (2006) at a daily time step and improved model skill over most run-of-the-river conditions. The temporal resolution of the model influences model performances. The optimal model coefficients varied across the reservoirs in this study and model performance fluctuates between wet years and dry years, and for different configurations such as dams in series. Overall, the method proposed by Döll et al. (2003) could enhance large-scale hydrologic forecasting, but can be subject to instability under certain conditions.

scholarly journals Long-term data reveal unimodal responses of ground beetle abundance to precipitation and land use but no changes in taxonomic and functional diversity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Petr Zajicek ◽  
Ellen A. R. Welti ◽  
Nathan J. Baker ◽  
Kathrin Januschke ◽  
Oliver Brauner ◽  
...  
Keyword(s):  
Land Use ◽  
Functional Diversity ◽  
Spatial Scales ◽  
Ground Beetle ◽  
Temporal Trends ◽  
Ground Beetles ◽  
Community Change ◽  
Ecological Communities ◽  
Activity Density

AbstractWhile much of global biodiversity is undoubtedly under threat, the responses of ecological communities to changing climate, land use intensification, and long-term changes in both taxonomic and functional diversity over time, has still not been fully explored for many taxonomic groups, especially invertebrates. We compiled time series of ground beetles covering the past two decades from 40 sites located in five regions across Germany. We calculated site-based trends for 21 community metrics representing taxonomic and functional diversity of ground beetles, activity density (a proxy for abundance), and activity densities of functional groups. We assessed both overall and regional temporal trends and the influence of the global change drivers of temperature, precipitation, and land use on ground beetle communities. While we did not detect overall temporal changes in ground beetle taxonomic and functional diversity, taxonomic turnover changed within two regions, illustrating that community change at the local scale does not always correspond to patterns at broader spatial scales. Additionally, ground beetle activity density had a unimodal response to both annual precipitation and land use. Limited temporal change in ground beetle communities may indicate a shifting baseline, where community degradation was reached prior to the start of our observation in 1999. In addition, nonlinear responses of animal communities to environmental change present a challenge when quantifying temporal trends.

scholarly journals Space-based geoengineering: Challenges and requirements

2009 ◽  
Vol 224 (3) ◽  
pp. 571-580 ◽  
Author(s):  
C R McInnes
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Large Scale ◽  
Climate Model ◽  
Carbon Dioxide Concentration ◽  
Complex Solution ◽  
Lagrange Point ◽  
Simple Climate Model ◽  
Earth’S Climate

The prospect of engineering the Earth's climate (geoengineering) raises a multitude of issues associated with climatology, engineering on macroscopic scales, and indeed the ethics of such ventures. Depending on personal views, such large-scale engineering is either an obvious necessity for the deep future, or yet another example of human conceit. In this article a simple climate model will be used to estimate requirements for engineering the Earth's climate, principally using space-based geoengineering. Active cooling of the climate to mitigate anthropogenic climate change due to a doubling of the carbon dioxide concentration in the Earth's atmosphere is considered. This representative scenario will allow the scale of the engineering challenge to be determined. It will be argued that simple occulting discs at the interior Lagrange point may represent a less complex solution than concepts for highly engineered refracting discs proposed recently. While engineering on macroscopic scales can appear formidable, emerging capabilities may allow such ventures to be seriously considered in the long term. This article is not an exhaustive review of geoengineering, but aims to provide a foretaste of the future opportunities, challenges, and requirements for space-based geoengineering ventures.

scholarly journals Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand

2016 ◽  
Vol 48 (3) ◽  
pp. 867-882 ◽  
Author(s):  
M. S. Babel ◽  
T. A. J. G. Sirisena ◽  
N. Singhrattna
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Large Scale ◽  
Resource Planning ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Rainfall Forecasting ◽  
Artificial Neural ◽  
Atmospheric Variables

Understanding long-term seasonal or annual or inter-annual rainfall variability and its relationship with large-scale atmospheric variables (LSAVs) is important for water resource planning and management. In this study, rainfall forecasting models using the artificial neural network technique were developed to forecast seasonal rainfall in May–June–July (MJJ), August–September–October (ASO), November–December–January (NDJ), and February–March–April (FMA) and to determine the effects of climate change on seasonal rainfall. LSAVs, temperature, pressure, wind, precipitable water, and relative humidity at different lead times were identified as the significant predictors. To determine the impacts of climate change the predictors obtained from two general circulation models, CSIRO Mk3.6 and MPI-ESM-MR, were used with quantile mapping bias correction. Our results show that the models with the best performance for FMA and MJJ seasons are able to forecast rainfall one month in advance for these seasons and the best models for ASO and NDJ seasons are able do so two months in advance. Under the RCP4.5 scenario, a decreasing trend of MJJ rainfall and an increasing trend of ASO rainfall can be observed from 2011 to 2040. For the dry season, while NDJ rainfall decreases, FMA rainfall increases for the same period of time.

scholarly journals Evaluation of a unique approach to high-resolution climate modeling using the Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1

2019 ◽  
Vol 12 (8) ◽  
pp. 3725-3743 ◽  
Author(s):  
Allison C. Michaelis ◽  
Gary M. Lackmann ◽  
Walter A. Robinson
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Sea Ice ◽  
Northern Hemisphere ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Coupled Model ◽  
General Circulation Models

Abstract. We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations. The present-day simulations provide a reasonable reproduction of large-scale atmospheric features in the Northern Hemisphere such as the wintertime midlatitude storm tracks, upper-tropospheric jets, and maritime sea-level pressure features as well as annual precipitation patterns across the tropics. The simulations also adequately represent tropical cyclone (TC) characteristics such as strength, spatial distribution, and seasonal cycles for most Northern Hemisphere basins. These results demonstrate the applicability of these model simulations for future studies examining climate change effects on various Northern Hemisphere phenomena, and, more generally, the utility of MPAS-A for studying climate change at spatial scales generally unachievable in GCMs.

scholarly journals An Integrated Procedure to Determine a Reference Station Network for Evaluating and Adjusting Urban Bias in Surface Air Temperature Data

2015 ◽  
Vol 54 (6) ◽  
pp. 1248-1266 ◽  
Author(s):  
Guoyu Ren ◽  
Jiao Li ◽  
Yuyu Ren ◽  
Ziying Chu ◽  
Aiying Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Historical Data ◽  
Spatial Scales ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Reference Station ◽  
Reference Network ◽  
Station Network

AbstractTrends in surface air temperature (SAT) are a critical indicator for climate change at varied spatial scales. Because of urbanization effects, however, the current SAT records of many urban stations can hardly meet the demands of the studies. Evaluation and adjustment of the urbanization effects on the SAT trends are needed, which requires an objective selection of reference (rural) stations. Based on the station history information from all meteorological stations with long-term records in mainland China, an integrated procedure for determining the reference SAT stations has been developed and is applied in forming a network of reference SAT stations. Historical data from the network are used to assess the urbanization effects on the long-term SAT trends of the stations of the national Reference Climate Network and Basic Meteorological Network (RCN+BMN or national stations), which had been used most frequently in studies of regional climate change throughout the country. This paper describes in detail the integrated procedure and the assessment results of urbanization effects on the SAT trends of the national stations applying the data from the reference station network determined using the procedure. The results showed a highly significant urbanization effect of 0.074°C (10 yr)−1 and urbanization contribution of 24.9% for the national stations of mainland China during the time period 1961–2004, which compared well to results that were reported in previous studies by the authors using the predecessor of the present reference network and the reference stations selected but when applying other methods. The authors are thus confident that the SAT data from the updated China reference station network as reported in this paper best represented the baseline SAT trends nationwide and could be used for evaluating and adjusting the urban biases in the historical data series of the SAT from different observational networks.

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