scholarly journals Fine-scale variation in projected climate change presents opportunities for biodiversity conservation in Europe

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomáš Hlásny ◽  
Martin Mokroš ◽  
Laura Dobor ◽  
Katarína Merganičová ◽  
Martin Lukac
Keyword(s):  
Climate Change ◽  
Biodiversity Conservation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Scale ◽  
Biodiversity Loss ◽  
Future Climate ◽  
Conservation Priorities ◽  
Continental Scale ◽  
Climate Stability

AbstractClimate change is a major threat to global biodiversity, although projected changes show remarkable geographical and temporal variability. Understanding this variability allows for the identification of regions where the present-day conservation objectives may be at risk or where opportunities for biodiversity conservation emerge. We use a multi-model ensemble of regional climate models to identify areas with significantly high and low climate stability persistent throughout the twenty-first century in Europe. We then confront our predictions with the land coverage of three prominent biodiversity conservation initiatives at two scales. The continental-scale assessment shows that areas with the least stable future climate in Europe are likely to occur at low and high latitudes, with the Iberian Peninsula and the Boreal zones identified as prominent areas of low climatic stability. A follow-up regional scale investigation shows that robust climatic refugia exist even within the highly exposed southern and northern macro-regions. About 23–31% of assessed biodiversity conservation sites in Europe coincide with areas of high future climate stability, we contend that these sites should be prioritised in the formulation of future conservation priorities as the stability of future climate is one of the key factors determining their conservation prospects. Although such focus on climate refugia cannot halt the ongoing biodiversity loss, along with measures such as resilience-based stewardship, it may improve the effectiveness of biodiversity conservation under climate change.

scholarly journals Fine-scale variation in projected climate change presents opportunities for biodiversity conservation in Europe

2021 ◽  
Author(s):  
Tomáš Hlásny ◽  
Martin Mokroš ◽  
Laura Dobor ◽  
Katarína Merganičová ◽  
Martin Lukac
Keyword(s):  
Climate Change ◽  
Biodiversity Conservation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Scale ◽  
Regional Climate Models ◽  
Biodiversity Loss ◽  
Future Climate ◽  
Conservation Priorities ◽  
Continental Scale

Abstract Climate change is a major threat to global biodiversity, although projected changes show remarkable geographical and temporal variability. Understanding this variability allows for the identification of regions where the present-day conservation objectives may be at risk or where opportunities for biodiversity conservation emerge. We use a multi-model ensemble of regional climate models to identify areas with significantly high and low climate stability persistent throughout the 21st century in Europe. We then confront our predictions with the land coverage of three prominent biodiversity conservation initiatives at two scales. The continental-scale assessment shows that the most instable future climate in Europe is likely to occur at low and high latitudes, with the Iberian Peninsula and the Boreal zones identified as prominent areas of climatic instability. A follow-up regional scale investigation shows that robust climatic refugia exist even within the highly exposed southern and northern macro-regions. About 23–31 % of assessed biodiversity conservation sites in Europe coincide with these identified climatic refugia, we contend that these sites should be prioritised in the formulation of future conservation priorities as the stability of future climate is one of key factors determining their conservation prospects. Although such focus on climate refugia cannot halt the ongoing biodiversity loss, along with measures such as the resilience-based stewardship, it may improve the effectiveness of biodiversity conservation under climate change.

scholarly journals Regional climate change projections from NA-CORDEX and their relation to climate sensitivity

2020 ◽  
Vol 162 (2) ◽  
pp. 645-665
Author(s):  
Melissa S. Bukovsky ◽  
Linda O. Mearns
Keyword(s):  
Climate Change ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Full Range ◽  
Regional Scale ◽  
Global Climate Models ◽  
Continental Scale

Abstract The climate sensitivity of global climate models (GCMs) strongly influences projected climate change due to increased atmospheric carbon dioxide. Reasonably, the climate sensitivity of a GCM may be expected to affect dynamically downscaled projections. However, there has been little examination of the effect of the climate sensitivity of GCMs on regional climate model (RCM) ensembles. Therefore, we present projections of temperature and precipitation from the ensemble of projections produced as a part of the North American branch of the international Coordinated Regional Downscaling Experiment (NA-CORDEX) in the context of their relationship to the climate sensitivity of their parent GCMs. NA-CORDEX simulations were produced at 50-km and 25-km resolutions with multiple RCMs which downscaled multiple GCMs that spanned nearly the full range of climate sensitivity available in the CMIP5 archive. We show that climate sensitivity is a very important source of spread in the NA-CORDEX ensemble, particularly for temperature. Temperature projections correlate with driving GCM climate sensitivity annually and seasonally across North America not only at a continental scale but also at a local-to-regional scale. Importantly, the spread in temperature projections would be reduced if only low, mid, or high climate sensitivity simulations were considered, or if only the ensemble mean were considered. Precipitation projections correlate with climate sensitivity, but only at a continental scale during the cold season, due to the increasing influence of other processes at finer scales. Additionally, it is shown that the RCMs do alter the projection space sampled by their driving GCMs.

Realised added value in dynamical downscaling of Australian climate change

2021 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Jason P. Evans ◽  
Alejandro Di Luca ◽  
Michael R. Grose ◽  
Vanessa Round ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Future Climate ◽  
Added Value ◽  
Climate Projections ◽  
Eastern Australia ◽  
Australian Alps ◽  
Future Climate Projections

<p>Coarse resolution global climate models (GCM) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, ‘realised added value’, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.</p>

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

scholarly journals Species Favourability Shift in Europe due to Climate Change: A Case Study for Fagus sylvatica L. and Picea abies (L.) Karst. Based on an Ensemble of Climate Models

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Wolfgang Falk ◽  
Nils Hempelmann
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Climate Models ◽  
Species Distribution Model ◽  
Regional Climate ◽  
European Beech ◽  
Regional Climate Models ◽  
Distribution Model ◽  
Continental Scale ◽  
The North

Climate is the main environmental driver determining the spatial distribution of most tree species at the continental scale. We investigated the distribution change of European beech and Norway spruce due to climate change. We applied a species distribution model (SDM), driven by an ensemble of 21 regional climate models in order to study the shift of the favourability distribution of these species. SDMs were parameterized for 1971–2000, as well as 2021–2050 and 2071–2100 using the SRES scenario A1B and three physiological meaningful climate variables. Growing degree sum and precipitation sum were calculated for the growing season on a basis of daily data. Results show a general north-eastern and altitudinal shift in climatological favourability for both species, although the shift is more marked for spruce. The gain of new favourable sites in the north or in the Alps is stronger for beech compared to spruce. Uncertainty is expressed as the variance of the averaged maps and with a density function. Uncertainty in species distribution increases over time. This study demonstrates the importance of data ensembles and shows how to deal with different outcomes in order to improve impact studies by showing uncertainty of the resulting maps.

scholarly journals Development of Statistically Downscaled Regional Climate Model based on Representative Concentration Pathways for Ipoh, Subang and KLIA Sepang in Peninsular Malaysia

2021 ◽  
Vol 945 (1) ◽  
pp. 012022
Author(s):  
Chin Kah Seng ◽  
Tan Kok Weng ◽  
Akihiko Nakayama
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Simulated Data ◽  
Peninsular Malaysia ◽  
Future Climate ◽  
Monthly Average ◽  
Maximum Temperatures

Abstract Climate change is one of the challenging global issues that our world is facing and it is intensely debated on the international agenda. It is a fact that climate change has brought about many disastrous events on a global scale which affect our livelihoods. Climate models are commonly used by researchers to study the magnitude of the changing climate and to simulate future climate projections. Most climate models are developed based on various interactions among the Earth’s climate components such as the land surface, oceans, atmosphere and sea-ice. In this study, the second-generation Canadian Earth System Model (CanESM2) was statistically downscaled to develop a regional climate model (RCM) based on three representative concentration pathways (RCPs): RCP2.6, RCP4.5 and RCP8.5. The RCM will be used to simulate the average minimum and maximum temperatures and average precipitation for Ipoh, Subang and KLIA Sepang in Peninsular Malaysia for the years 2006 to 2100. The simulated data were bias corrected using the historical observation data of monthly average minimum and maximum temperatures and monthly average rainfall retrieved from the Malaysian Meteorological Department (MMD). The different trends of the simulated data for all the three locations based on the RCP2.6, RCP4.5 and RCP8.5 were evaluated for future climate projection.

scholarly journals Projections for the Köppen-Geiger climate classification under future climate change for the Iberian Peninsula

2021 ◽  
Author(s):  
Cristina Andrade ◽  
Joana Contente
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Classification ◽  
Dry Climates ◽  
Projected Changes

<p>Projections of the Köppen-Geiger climate classification under future climate change for the Iberian Peninsula (IP) are investigated by using a seven-ensemble mean of regional climate models (RCMs) attained from EURO-CORDEX. Maps with predicted future scenarios for temperature, precipitation and Köppen-Geiger classification are analyzed under RCP4.5 and RCP8.5 in Iberia. Widespread statistically significant shifts in temperature, precipitation and climate regimes are projected between 2041 and 2070, with higher expression under RCP8.5. An overall increase of temperatures and a decrease of precipitation in the south-southeast is predicted. Of the two climate types dry (B) and temperate (C), the dominant one was C in 86% of the Iberian territory for 1961-1990, predicted to decrease by 8.0% towards 2041-2070 under RCP4.5 (9.1% under RCP8.5). The hot-summer Mediterranean climate (CSa) will progressively replaces CSb (warm-summer) type towards north in the northwestern half of Iberia until 2070. This shift, depicted by the SSIM index, is noticeable in Portugal with a projected establishment of the CSa climate by 2041-2070. A predicted retreat of humid subtropical (Cfa) and temperate oceanic (Cfb) areas in the northeast towards Pyrenees region is noteworthy, alongside an increase of desert (BW) and semi-desert (BS) climates (7.8% and 9%) that progressively sets in the southeast (between Granada and Valencia). Climate types BSh and BWh (hot semi-desert and hot-desert, respectively), non-existent in 1961-1990 period, are projected to represent 2.8% of territory in 2041-2070 under RCP4.5 (5% under RCP8.5). The statistically significant projected changes hint at the disappearance of some vegetation species in certain regions of Iberia, with an expected increase of steppe, bush, grassland and wasteland vegetation cover, typical of dry climates in the southeast.</p><p><strong>Funding:</strong> This research was funded by National Funds by FCT - Portuguese Foundation for Science and Technology, under the project <strong>UIDB/04033/2020.</strong></p>

scholarly journals New Insights in Regional Climate Change: Coupled Land Albedo Change Estimation in Greenland from 1981 to 2017

2020 ◽  
Vol 12 (5) ◽  
pp. 756
Author(s):  
Fei Peng ◽  
Haoran Zhou ◽  
Gong Chen ◽  
Qi Li ◽  
Yongxing Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Spatiotemporal Variation ◽  
Future Climate Change ◽  
Anthropogenic Factors ◽  
The Past

Land albedo is an essential variable in land surface energy balance and climate change. Within regional land, albedo has been altered in Greenland as ice melts and runoff increases in response to global warming against the period of the pre-industrial revolution. The assessment of spatiotemporal variation in albedo is a prerequisite for accurate prediction of ice sheet loss and future climate change, as well as crucial prior knowledge for improving current climate models. In our study, we employed the satellite data product from the global land surface satellite (GLASS) project to obtain the spatiotemporal variation of albedo from 1981 to 2017 using the non-parameter-based M-K (Mann-Kendall) method. It was found that the albedo generally showed a decreasing trend in the past 37 years (−0.013 ± 0.001 decade−1, p < 0.01); in particular, the albedo showed a significant increasing trend in the middle part of the study area but a decreasing trend in the coastal area. The interannual and seasonal variations of albedo showed strong spatial-temporal heterogeneity. Additionally, based on natural and anthropogenic factors, in order to further reveal the potential effects of spatiotemporal variation of albedo on the regional climate, we coupled climate model data with observed data documented by satellite and adopted a conceptual experiment for detections and attributions analysis. Our results showed that both the greenhouse gas forcing and aerosol forcing induced by anthropogenic activities in the past 37 decades were likely to be the main contributors (46.1%) to the decrease of albedo in Greenland. Here, we indicated that overall, Greenland might exhibit a local warming effect based on our study. Albedo–ice melting feedback is strongly associated with local temperature changes in Greenland. Therefore, this study provides a potential pathway to understanding climate change on a regional scale based on the coupled dataset.

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