EARLY SPRING FLOWERING IN NOVA SCOTIA: AN EXTREME SPRING IS REFLECTED IN ADVANCED FLOWERING

Twenty species of herbaceous plants and four non-amentiferous shrubs were found in flower in March-April in Nova Scotia during the spring of 2012. Plants were observed primarily in Kings and Antigonish Counties, with several observations from Inverness County. The precocious flowering is attributed to an abnormally warm late winter and spring (February-March) in which climate normals for monthly average temperature were exceeded by a minimum of 1.2°C in February (Tracadie) to a maximum of 8.5°C in March (Kentville). Flowering was an average of 17 days earlier than herbarium records in the largest regional herbaria (ACAD, NSAC). Proportional contribution to the early flowering guild was greater for exotic species which featured weedy families not represented in the native group. These observations of spring climate conditions and flowering phenology are consistent with regional climate change associated with milder and earlier springs.Key Words: climate change, Nova Scotia, phenology, spring flowering, exotic range expansion.

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LATE BLOOMING OF PLANTS FROM NORTHERN NOVA SCOTIA: RESPONSES TO A MILD FALL AND WINTER

Proceedings of the Nova Scotian Institute of Science (NSIS) ◽

10.15273/pnsis.v46i2.4058 ◽

2011 ◽

Vol 46 (2) ◽

Author(s):

David J. Garbary ◽

Jonathan Ferrier ◽

Barry R. Taylor

Keyword(s):

Climate Change ◽

Nova Scotia ◽

Woody Plants ◽

Regional Climate ◽

Growing Season ◽

Flowering Phenology ◽

Flowering Date ◽

Cornus Sericea ◽

Allium Schoenoprasum ◽

Herbaceous Dicots

Over 1400 flowering records of 135 species were recorded from over 125visits to more than 20 sites in Antigonish County, Nova Scotia from November2005 to January 2006, when the growing season is normally over. The speciesidentified were primarily herbaceous dicots; however, there were four speciesof woody plants (Cornus sericea, Spiraea latifolia, Symphoricarpos albusand Salix sp.) and one monocot (Allium schoenoprasum). The number ofspecies flowering declined linearly as fall progressed, as did the amountof flowering for each species. Nevertheless, over 40 species were still inflower in early December, and over 20 species flowered in January. Thefinal flowering date was 21 January, when ten species were found. Thiswork builds on a previous study in 2001, when 93 species were recordedin flower during November-December. In addition to the 30% increase inrecorded species in 2005, almost 50% of the species found in 2005 werenot recorded in 2001. This study provides an expanded baseline againstwhich changes in flowering phenology can be evaluated with respect tosubsequent regional climate change.Key Words: Antigonish, flowering, Nova Scotia, phenology, climate change

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Ensemble-based analysis of regional climate change effects on the cabbage stem weevil (Ceutorhynchus pallidactylus (Mrsh.)) in winter oilseed rape (Brassica napus L.)

The Journal of Agricultural Science ◽

10.1017/s0021859611000529 ◽

2011 ◽

Vol 150 (2) ◽

pp. 191-202 ◽

Cited By ~ 19

Author(s):

J. JUNK ◽

M. EICKERMANN ◽

K. GÖRGEN ◽

M. BEYER ◽

L. HOFFMANN

Keyword(s):

Climate Change ◽

Oilseed Rape ◽

Stem Elongation ◽

Regional Climate ◽

Regional Climate Change ◽

Future Climate ◽

Brassica Napus L ◽

Climate Conditions ◽

Ceutorhynchus Pallidactylus ◽

The Impact

SUMMARYThe impact of projected regional climate change on the migration of cabbage stem weevil (Ceutorhynchus pallidactylus) to oilseed rape crops in the Grand Duchy of Luxembourg is evaluated for past and future time spans. Several threshold-based statistical models for the emergence and the main migration of C. pallidactylus were chosen from the literature and combined with selected regional climate change projections of the EU ENSEMBLES project. Additionally, a simple degree-day based model was used to assess the plant development under expected climate change conditions. An earlier onset as well as a prolongation of the possible emergence times and the main migration periods was detected. The onset of stem elongation of oilseed rape was predicted to occur 3·0 days earlier per decade, while emergence of C. pallidactylus was expected to occur between 3·0 and 3·3 days earlier per decade. The main migration period of the weevil to the field may start 2·0 days earlier per decade under future climate conditions. Additionally, the time span of possible migration is prolonged for about 30 days under projected future climate conditions.

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GPS Time Series as Proxies for Regional Climate Change

Proceedings of the 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018) ◽

10.33012/2018.16088 ◽

2018 ◽

Author(s):

Shimon Wdowinski

Keyword(s):

Climate Change ◽

Time Series ◽

Regional Climate ◽

Regional Climate Change ◽

Gps Time Series

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Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models

npj Climate and Atmospheric Science ◽

10.1038/s41612-020-00159-2 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Zhili Wang ◽

Lei Lin ◽

Yangyang Xu ◽

Huizheng Che ◽

Xiaoye Zhang ◽

...

Keyword(s):

Climate Change ◽

Radiative Forcing ◽

Impact Analysis ◽

Climate Model ◽

Regional Climate ◽

Eastern China ◽

Coupled Model ◽

Regional Climate Change ◽

Anthropogenic Aerosol ◽

Wide Range

AbstractAnthropogenic aerosol (AA) forcing has been shown as a critical driver of climate change over Asia since the mid-20th century. Here we show that almost all Coupled Model Intercomparison Project Phase 6 (CMIP6) models fail to capture the observed dipole pattern of aerosol optical depth (AOD) trends over Asia during 2006–2014, last decade of CMIP6 historical simulation, due to an opposite trend over eastern China compared with observations. The incorrect AOD trend over China is attributed to problematic AA emissions adopted by CMIP6. There are obvious differences in simulated regional aerosol radiative forcing and temperature responses over Asia when using two different emissions inventories (one adopted by CMIP6; the other from Peking university, a more trustworthy inventory) to driving a global aerosol-climate model separately. We further show that some widely adopted CMIP6 pathways (after 2015) also significantly underestimate the more recent decline in AA emissions over China. These flaws may bring about errors to the CMIP6-based regional climate attribution over Asia for the last two decades and projection for the next few decades, previously anticipated to inform a wide range of impact analysis.

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Regional Climate Change Competitiveness—Modelling Approach

Energies ◽

10.3390/en14123704 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3704

Author(s):

Agnieszka Karman ◽

Andrzej Miszczuk ◽

Urszula Bronisz

Keyword(s):

Climate Change ◽

Regional Climate ◽

Regional Climate Change ◽

Measurement Scale ◽

Practical Application ◽

Adaptation To Climate Change ◽

Regional Competitiveness ◽

The Face ◽

Competitiveness Index ◽

Modelling Approach

The article deals with the competitiveness of regions in the face of climate change. The aim was to present the concept of measuring the Regional Climate Change Competitiveness Index. We used a comparative and logical analysis of the concept of regional competitiveness and heuristic conceptual methods to construct the index and measurement scale. The structure of the index includes six broad sub-indexes: Basic, Natural, Efficiency, Innovation, Sectoral, Social, and 89 indicators. A practical application of the model was presented for the Mazowieckie province in Poland. This allowed the region’s performance in the context of climate change to be presented, and regional weaknesses in the process of adaptation to climate change to be identified. The conclusions of the research confirm the possibility of applying the Regional Climate Change Competitiveness Index in the economic analysis and strategic planning. The presented model constitutes one of the earliest tools for the evaluation of climate change competitiveness at a regional level.

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Configuration and intercomparison of deep learning neural models for statistical downscaling

Geoscientific Model Development ◽

10.5194/gmd-13-2109-2020 ◽

2020 ◽

Vol 13 (4) ◽

pp. 2109-2124 ◽

Cited By ~ 1

Author(s):

Jorge Baño-Medina ◽

Rodrigo Manzanas ◽

José Manuel Gutiérrez

Keyword(s):

Climate Change ◽

Deep Learning ◽

Statistical Downscaling ◽

Linear Models ◽

Regional Climate ◽

Added Value ◽

Climate Conditions ◽

Continental Scale ◽

Spatial Features ◽

Learning Techniques

Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatiotemporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes a proper assessment of the (possible) added value offered by these techniques difficult. As a result, these models are usually seen as black boxes, generating distrust among the climate community, particularly in climate change applications. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied to downscale temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their potential application in climate change studies. To do this, we use a warm test period as a surrogate for possible future climate conditions. Our results show that, while the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones in the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g., continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as Coordinated Regional Climate Downscaling Experiment (CORDEX).

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