Microclimates can be accurately predicted across ecologically important remote ecosystems

ABSTRACTMicroclimate information is often crucial for understanding ecological patterns and processes, including under climate change, but is typically absent from ecological and biogeographic studies owing to difficulties in obtaining microclimate data. Recent advances in microclimate modelling, however, suggest that microclimate conditions can now be predicted anywhere at any time using hybrid physically- and empirically-based models. Here, for the first time, we test the utility of this approach across a remote, inaccessible, and climate change threatened polar island ecosystem at ecologically relevant scales. Microclimate predictions were generated at a 100 × 100 m grain (at a height of 4 cm) across the island, with models parameterised using either meteorological observations from the island’s weather station (AWS) or climate reanalysis data (CRA). AWS models had low error rates and were highly correlated with observed seasonal and daily temperatures (root mean squared error of predicted seasonal average Tmean ≤ 0.6 °C; Pearson’s correlation coefficient (r) for the daily Tmean ≥ 0.86). By comparison, CRA models had a slight warm bias in all seasons and a smaller diurnal range in the late summer period than in situ observations. Despite these differences, the modelled relationship between the percentage cover of the threatened endemic cushion plant Azorella macquariensis and microclimate varied little with the source of microclimate data (r = 0.97), suggesting that both model parameterisations capture similar patterns of spatial variation in microclimate conditions across the island ecosystem. Here, we have shown that the accurate prediction of microclimate conditions at ecologically relevant spatial and temporal scales is now possible using hybrid physically- and empirically-based models across even the most remote and climatically extreme environments. These advances will help add the microclimate dimension to ecological and biogeographic studies, which could be critical for delivering climate change-resilient conservation planning in climate-change exposed ecosystems.

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Seasonal diving and foraging behaviour of Eastern Canada-West Greenland bowhead whales

Marine Ecology Progress Series ◽

10.3354/meps13356 ◽

2020 ◽

Vol 643 ◽

pp. 197-217 ◽

Cited By ~ 4

Author(s):

SME Fortune ◽

SH Ferguson ◽

AW Trites ◽

B LeBlanc ◽

V LeMay ◽

...

Keyword(s):

Climate Change ◽

Late Summer ◽

Calanoid Copepods ◽

Eastern Canada ◽

West Greenland ◽

Bowhead Whales ◽

Seasonal Movement ◽

Dive Behaviour ◽

Calanus Glacialis ◽

Canada West

Climate change may affect the foraging success of bowhead whales Balaena mysticetus by altering the diversity and abundance of zooplankton species available as food. However, assessing climate-induced impacts first requires documenting feeding conditions under current environmental conditions. We collected seasonal movement and dive-behaviour data from 25 Eastern Canada-West Greenland bowheads instrumented with time-depth telemetry tags and used state-space models to examine whale movements and dive behaviours. Zooplankton samples were also collected in Cumberland Sound (CS) to determine species composition and biomass. We found that CS was used seasonally by 14 of the 25 tagged whales. Area-restricted movement was the dominant behaviour in CS, suggesting that the tagged whales allocated considerable time to feeding. Prey sampling data suggested that bowheads were exploiting energy-rich Arctic copepods such as Calanus glacialis and C. hyperboreus during summer. Dive behaviour changed seasonally in CS. Most notably, probable feeding dives were substantially shallower during spring and summer compared to fall and winter. These seasonal changes in dive depths likely reflect changes in the vertical distribution of calanoid copepods, which are known to suspend development and overwinter at depth during fall and winter when availability of their phytoplankton prey is presumed to be lower. Overall, CS appears to be an important year-round foraging habitat for bowheads, but is particularly important during the late summer and fall. Whether CS will remain a reliable feeding area for bowhead whales under climate change is not yet known.

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MaxEnt Modeling Based on CMIP6 Models to Project Potential Suitable Zones for Cunninghamia lanceolata in China

Forests ◽

10.3390/f12060752 ◽

2021 ◽

Vol 12 (6) ◽

pp. 752

Author(s):

Yichen Zhou ◽

Zengxin Zhang ◽

Bin Zhu ◽

Xuefei Cheng ◽

Liu Yang ◽

...

Keyword(s):

Climate Change ◽

Species Distribution ◽

Water Conservation ◽

Critical Role ◽

Cunninghamia Lanceolata ◽

Distribution Area ◽

Timber Species ◽

Diurnal Range ◽

The Future ◽

Suitable Area

Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% of the overall afforested area. Moreover, it plays a critical role in soil and water conservation; however, its suitability is subject to climate change. For this study, the appropriate distribution area of C. lanceolata was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed that (1) the minimum temperature of the coldest month (bio6), and the mean diurnal range (bio2) were the most important environmental variables that affected the distribution of C. lanceolata; (2) the currently suitable areas of C. lanceolata were primarily distributed along the southern coastal areas of China, of which 55% were moderately so, while only 18% were highly suitable; (3) the projected suitable area of C. lanceolata would likely expand based on the BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under different SSPs spanning 2041–2060. The increased area estimated for the future ranged from 0.18 to 0.29 million km2, where the total suitable area of C. lanceolata attained a maximum value of 2.50 million km2 under the SSP3-7.0 scenario, with a lowest value of 2.39 million km2 under the SSP5-8.5 scenario; (4) in combination with land use and farmland protection policies of China, it is estimated that more than 60% of suitable land area could be utilized for C. lanceolata planting from 2041–2060 under different SSP scenarios. Although climate change is having an increasing influence on species distribution, the deleterious impacts of anthropogenic activities cannot be ignored. In the future, further attention should be paid to the investigation of species distribution under the combined impacts of climate change and human activities.

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Quantifying the Effects of Long-Term Climate Change on Tropical Cyclone Rainfall Using a Cloud-Resolving Model: Examples of Two Landfall Typhoons in Taiwan

Journal of Climate ◽

10.1175/jcli-d-14-00044.1 ◽

2014 ◽

Vol 28 (1) ◽

pp. 66-85 ◽

Cited By ~ 18

Author(s):

Chung-Chieh Wang ◽

Bo-Xun Lin ◽

Cheng-Ta Chen ◽

Shih-How Lo

Keyword(s):

Climate Change ◽

Reanalysis Data ◽

Budget Analysis ◽

Sensitivity Tests ◽

Cloud Resolving Model ◽

Typhoon Rainfall ◽

Climate Background ◽

Rain Rates ◽

Long Term Trend

Abstract To quantify the effects of long-term climate change on typhoon rainfall near Taiwan, cloud-resolving simulations of Typhoon (TY) Sinlaku and TY Jangmi, both in September 2008, are performed and compared with sensitivity tests where these same typhoons are placed in the climate background of 1950–69, which is slightly cooler and drier compared to the modern climate of 1990–2009 computed using NCEP–NCAR reanalysis data. Using this strategy, largely consistent responses are found in the model although only two cases are studied. In control experiments, both modern-day typhoons yield more rainfall than their counterpart in the sensitivity test using past climate, by about 5%–6% at 200–500 km from the center for Sinlaku and roughly 4%–7% within 300 km of Jangmi, throughout much of the periods simulated. In both cases, the frequency of more-intense rainfall (20 to >50 mm h−1) also increases by about 5%–25% and the increase tends to be larger toward higher rain rates. Results from the water budget analysis, again quite consistent between the two cases, indicate that the increased rainfall from the typhoons in the modern climate is attributable to both a moister environment (by 2.5%–4%) as well as, on average, a more active secondary circulation of the storm. Thus, a changing climate may already have had a discernible impact on TC rainfall near Taiwan. While an overall increase in TC rainfall of roughly 5% may not seem large, it is certainly not insignificant considering that the long-term trend observed in the past 40–50 yr, whatever the causes might be, may continue for many decades in the foreseeable future.

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Future options in landscape ecology: development and research

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133309103888 ◽

2009 ◽

Vol 33 (1) ◽

pp. 31-48 ◽

Cited By ~ 17

Author(s):

Xiuzhen Li ◽

Ülo Mander

Keyword(s):

Climate Change ◽

Landscape Ecology ◽

Global Climate Change ◽

Landscape Metrics ◽

Global Climate ◽

Anthropogenic Factors ◽

Landscape Development ◽

Spatial And Temporal Scales ◽

Research Fields ◽

Interdisciplinary Field

The aim of this brief overview is to highlight some new and promising research fields in landscape ecology, which is essentially an interdisciplinary field of study. We also analyse the development of some classical branches of landscape ecology regarding pattern and process relationships at broad spatial and temporal scales, such as landscape metrics, the influence of anthropogenic factors and global climate change on landscape development, the fragmentation of ecosystems and disturbances of populations, and material and energy cycling in and between ecosystems.

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An Environmental Explanation for the Recent Increase in Tropical Cyclone Intensification

10.5194/egusphere-egu2020-18644 ◽

2020 ◽

Author(s):

Kieran Bhatia ◽

Alex Baker ◽

Gabriel Vecchi ◽

Hiroyuki Murakami ◽

James Kossin ◽

...

Keyword(s):

Climate Change ◽

Tropical Cyclone ◽

Reanalysis Data ◽

Environmental Data ◽

Physical Explanation ◽

Times Series ◽

Climate Change Response ◽

Synoptic Conditions ◽

Storm Evolution ◽

Change Response

Tropical cyclone (TC) rapid intensification events are responsible for intensity forecasts with the highest errors, and hurricanes that rapidly intensify cause a disproportionate amount of the fatalities and damage from TCs. According to a recent study by Bhatia et al. (2019), natural variability cannot account for the recent (1982-2009), observed increase in the highest TC intensification rates in the Atlantic Basin. These results agree well with the main conclusions of Bhatia et al. (2018), which demonstrated climate change could significantly increase TC intensification rates worldwide by the end of 21st century.Expanding on the work of Bhatia et al. (2018, 2019), TC intensification trends are analyzed for the period 1982-2017 using two observational datasets, the International Best-Track Archive for Climate Stewardship (IBTrACS) and the Advanced Dvorak Technique-HurricaneSatellite-B1 (ADT-HURSAT). The extended observational datasets confirm significant upward trends in intensifications metrics. To explore a physical explanation for the climate change response of TC intensification, we use ERA5 reanalysis data to calculate trends in the favorability of storm environments. When evaluating environmental data, we use 6-hour increments at specific annuli around already-formed storms in order to focus on synoptic conditions unique to storm evolution and not genesis. The robust trends in a 36-year times series and corresponding evolution of storm environments corroborates a climate change fingerprint on TC intensification.

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Pollen-based quantitative land-cover reconstruction for northern Asia covering the last 40 ka

10.5194/egusphere-egu2020-21976 ◽

2020 ◽

Author(s):

Xianyong Cao ◽

Fang Tian ◽

Furong Li ◽

Marie-José Gaillard ◽

Natalia Rudaya ◽

...

Keyword(s):

Climate Change ◽

Land Cover ◽

Vegetation Changes ◽

Northern Asia ◽

Spatial And Temporal Scales ◽

Conifer Tree ◽

Regional Estimates ◽

Model Time Series ◽

Detrended Canonical Correspondence Analysis ◽

Pollen Records

We collected the available relative pollen productivity estimates (PPEs) for 27 major pollen taxa from Eurasia and applied them to estimate plant abundances during the last 40 cal. ka BP (calibrated thousand years before present) using pollen counts from 203 fossil pollen records in northern Asia (north of 40&#176;N). These pollen records were organised into 42 site-groups, and regional mean plant abundances calculated using the REVEALS (Regional Estimates of Vegetation Abundance from Large Sites) model. Time-series clustering, constrained hierarchical clustering, and detrended canonical correspondence analysis were performed to investigate the regional pattern, time, and strength of vegetation changes, respectively. Reconstructed regional plant-functional type (PFT) components for each site-group are generally consistent with modern vegetation, in that vegetation changes within the regions are characterized by minor changes in the abundance of PFTs rather than by increase in new PFTs, particularly during the Holocene. We argue that pollen-based REVEALS estimates of plant abundances should be a more reliable reflection of the vegetation as pollen may overestimate the turnover, particularly when a high pollen producer invades areas dominated by low pollen producers. Comparisons with vegetation-independent climate records show that climate change is the primary factor driving land-cover changes at broad spatial and temporal scales. Vegetation changes in certain regions or periods, however, could not be explained by direct climate change, for example inland Siberia, where a sharp increase in evergreen conifer tree abundance occurred at ca. 7&#8211;8 cal. ka BP despite an unchanging climate, potentially reflecting their response to complex climate&#8211;permafrost&#8211;fire&#8211;vegetation interactions and thus a possible long-term-scale lagged climate response.

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Comparative Regression Analysis for Estimating Resonant Frequency of C-Like Patch Antennas

Mathematical Problems in Engineering ◽

10.1155/2021/6903925 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Umut Özkaya ◽

Enes Yiğit ◽

Levent Seyfi ◽

Şaban Öztürk ◽

Dilbag Singh

Keyword(s):

Comparative Analysis ◽

Resonant Frequency ◽

Mean Squared Error ◽

Error Rates ◽

Support Vector ◽

Patch Antennas ◽

Squared Error ◽

Median Error ◽

Regression Techniques ◽

Gaussian Regression

This study provides a comparative analysis of regression techniques to estimate the operating frequency of the C-like microstrip antenna. The performance of well-known regression techniques such as linear regression (LR), regression tree (RT), support vector regression (SVR), Gaussian regression (GR), and artificial neural network (ANN) is tested. For this purpose, 160 C-like microstrip antennas are simulated, of which 145 are used for training of regression techniques and 15 for testing. From the evaluated results, it is found that the pure quadratic Gaussian regression (PQGR) technique has the lowest error rates with 0.0109 mean absolute error (MAE), 0.0087 median error (ME), 0.0002 mean squared error (MSE), 0.0156 root mean squared error (RMSE), and 0.5981 average percentage error (APE). As can be seen in the comparative analysis, the PQGR method outperforms other regression methods on simulation and measurement data. Experimental analysis shows that the resonant frequency of the C-like patch antennas can be calculated very close to measurements.

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Ubiquity of human-induced changes in climate variability

10.5194/esd-2021-50 ◽

2021 ◽

Author(s):

Keith B. Rodgers ◽

Sun-Seon Lee ◽

Nan Rosenbloom ◽

Axel Timmermann ◽

Gokhan Danabasoglu ◽

...

Keyword(s):

Climate Change ◽

Climate Variability ◽

Climate Adaptation ◽

Southern Oscillation ◽

System Modeling ◽

Greenhouse Warming ◽

Earth System ◽

Spatial And Temporal Scales ◽

Wide Range ◽

Mean State

Abstract. While climate change mitigation targets necessarily concern maximum mean state change, understanding impacts and developing adaptation strategies will be largely contingent on how climate variability responds to increasing anthropogenic perturbations. Thus far Earth system modeling efforts have primarily focused on projected mean state changes and the sensitivity of specific modes of climate variability, such as the El Niño-Southern Oscillation. However, our knowledge of forced changes in the overall spectrum of climate variability and higher order statistics is relatively limited. Here we present a new 100-member large ensemble of climate change projections conducted with the Community Earth System Model version 2 to examine the sensitivity of internal climate fluctuations to greenhouse warming. Our unprecedented simulations reveal that changes in variability, considered broadly in terms of probability, distribution, amplitude, frequency, phasing, and patterns, are ubiquitous and span a wide range of physical and ecosystem variables across many spatial and temporal scales. Greenhouse warming will in particular alter variance spectra of Earth system variables that are characterized by non-Gaussian probability distributions, such as rainfall, primary production, or fire occurrence. Our modeling results have important implications for climate adaptation efforts, resource management, seasonal predictions, and for assessing potential stressors for terrestrial and marine ecosystems.

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High thermal plasticity and vulnerability in extreme environments at the warm distributional edge: the case of a tidepool shrimp

10.1101/2020.04.25.061424 ◽

2020 ◽

Author(s):

Eyal Amsalem ◽

Gil Rilov

Keyword(s):

Climate Change ◽

Prolonged Exposure ◽

Thermal Environment ◽

Extreme Environments ◽

Open Water ◽

Thermal Fluctuations ◽

Future Climate Change ◽

Regional Warming ◽

Thermal Plasticity ◽

Wide Range

1.AbstractClimate change threatens the resilience of species, especially at their warm distributional edge in extreme environments. However, not much is known about the thermal vulnerability of marine intertidal species at this edge. We investigated the thermal vulnerability of the tidepool shrimp, Palaemon elegans in the fast-warming southeastern Mediterranean, its warm distributional edge. Tidepool organisms experience strong and fast thermal fluctuations. This might make them more resilient to change, but also bring them closer to their thermal limits during extreme conditions. To test the shrimp’s resilience, we tested three hypotheses: (1) P. elegance in the southeast Mediterranean has higher critical thermal maximum (CTMax) than in cooler regions, (2) the shrimp possess seasonal acclimatization, but (3) long exposure to extreme summer temperatures might erode its thermal performance making it vulnerable to future climate change. We characterized the shrimp’s thermal environment and population dynamics, determined CTMax and tested diverse physiological performance attributes (respiration, digestion, activity, growth) under a wide range of temperatures during winter and summer. P. elegans has a wide optimum performance range between 20-30°C during summer and its CTMax is 38.1°C, higher than its Atlantic counterparts. However, its warming tolerance is only 0.3°C, indicating low capacity for dealing with further warming in pools compared to northeast Atlantic populations that have wider tolerance. Prolonged exposure to current mean summer values in open water (∼ 32°C) would also significantly reduce its performance and increase mortality. This suggests that its population viability may be reduced under continuous regional warming and intensification of extreme events.

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Social tipping processes for sustainability: An analytical framework

10.5194/egusphere-egu21-9161 ◽

2021 ◽

Author(s):

Ricarda Winkelmann ◽

Jonathan F. Donges ◽

E. Keith Smith ◽

Manjana Milkoreit ◽

Christina Eder ◽

...

Keyword(s):

Climate Change ◽

Analytical Framework ◽

Expert Elicitation ◽

Social Transformations ◽

Distinctive Features ◽

Spatial And Temporal Scales ◽

Development Goals ◽

Small Change ◽

Institutional Network

Societal transformations are necessary to address critical global challenges, such as mitigation of anthropogenic climate change and reaching UN sustainable development goals. Recently, social tipping processes have received increased attention, as they present a form of social change whereby a small change can shift a sensitive social system into a qualitatively different state due to strongly self-amplifying (mathematically positive) feedback mechanisms. Social tipping processes have been suggested as key drivers of sustainability transitions emerging in the fields of technological and energy systems, political mobilization, financial markets and sociocultural norms and behaviors.Drawing from expert elicitation and comprehensive literature review, we develop a framework to identify and characterize social tipping processes critical to facilitating rapid social transformations. We find that social tipping processes are distinguishable from those of already more widely studied climate and ecological tipping dynamics. In particular, we identify human agency, social-institutional network structures, different spatial and temporal scales and increased complexity as key distinctive features underlying social tipping processes. Building on these characteristics, we propose a formal definition for social tipping processes and filtering criteria for those processes that could be decisive for future trajectories to global sustainability in the Anthropocene. We illustrate this definition with the European political system as an example of potential social tipping processes, highlighting the potential role of the FridaysForFuture movement. Accordingly, this analytical framework for social tipping processes can be utilized to illuminate mechanisms for necessary transformative climate change mitigation policies and actions.&#160;

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