scholarly journals Synergistic impacts of global warming and thermohaline circulation collapse on amphibians

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Julián A. Velasco ◽  
Francisco Estrada ◽  
Oscar Calderón-Bustamante ◽  
Didier Swingedouw ◽  
Carolina Ureta ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Thermohaline Circulation ◽  
Climate Model ◽  
Global Climate ◽  
Extinction Risk ◽  
Meridional Overturning Circulation ◽  
Mitigation Strategies ◽  
Amphibian Species ◽  
Climate Conditions

AbstractImpacts on ecosystems and biodiversity are a prominent area of research in climate change. However, little is known about the effects of abrupt climate change and climate catastrophes on them. The probability of occurrence of such events is largely unknown but the associated risks could be large enough to influence global climate policy. Amphibians are indicators of ecosystems’ health and particularly sensitive to novel climate conditions. Using state-of-the-art climate model simulations, we present a global assessment of the effects of unabated global warming and a collapse of the Atlantic meridional overturning circulation (AMOC) on the distribution of 2509 amphibian species across six biogeographical realms and extinction risk categories. Global warming impacts are severe and strongly enhanced by additional and substantial AMOC weakening, showing tipping point behavior for many amphibian species. Further declines in climatically suitable areas are projected across multiple clades, and biogeographical regions. Species loss in regional assemblages is extensive across regions, with Neotropical, Nearctic and Palearctic regions being most affected. Results underline the need to expand existing knowledge about the consequences of climate catastrophes on human and natural systems to properly assess the risks of unabated warming and the benefits of active mitigation strategies.

Diatoms in Saline Lakes Paleoclimate and Paleoecology Interpretations

2007 ◽  
Vol 13 ◽  
pp. 149-168 ◽  
Author(s):  
Erik J. Ekdahl
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Global Climate Change ◽  
Water Conservation ◽  
Environmental Changes ◽  
Global Climate ◽  
Light Availability ◽  
Ionic Concentration ◽  
Mitigation Strategies ◽  
Environmental Response

Average global temperatures are predicted to rise over the next century and changes in precipitation, humidity, and drought frequency will likely accompany this global warming. Understanding associated changes in continental precipitation and temperature patterns in response to global change is an important component of long-range environmental planning. For example, agricultural management plans that account for decreased precipitation over time will be less susceptible to the effects of drought through implementation of water conservation techniques.A detailed understanding of environmental response to past climate change is key to understanding environmental changes associated with global climate change. To this end, diatoms are sensitive to a variety of limnologic parameters, including nutrient concentration, light availability, and the ionic concentration and composition of the waters that they live in (e.g. salinity). Diatoms from numerous environments have been used to reconstruct paleosalinity levels, which in turn have been used as a proxy records for regional and local paleoprecipitation. Long-term records of salinity or paleoprecipitation are valuable in reconstructing Quaternary paleoclimate, and are important in terms of developing mitigation strategies for future global climate change. High-resolution paleoclimate records are also important in groundtruthing global climate simulations, especially in regions where the consequences of global warming may be severe.

scholarly journals Assessing the impact of global warming on worldwide open field tomato cultivation through CSIRO-Mk3·0 global climate model

2016 ◽  
Vol 155 (3) ◽  
pp. 407-420 ◽  
Author(s):  
R. S. SILVA ◽  
L. KUMAR ◽  
F. SHABANI ◽  
M. C. PICANÇO
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Open Field ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Current Model ◽  
Global Climate Models ◽  
The Impact

SUMMARYTomato (Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

scholarly journals Downscaling a global climate model to simulate climate change over the US and the implication on regional and urban air quality

2013 ◽  
Vol 6 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
M. Trail ◽  
A. P. Tsimpidi ◽  
P. Liu ◽  
K. Tsigaridis ◽  
Y. Hu ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Conditions ◽  
Southeastern Us ◽  
The Us ◽  
Ozone Levels

Abstract. Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with the Weather Research and Forecasting (WRF) model to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the contiguous United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF regional climate model (RCM) to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12 km by 12 km resolution, as well as the effect of evolving climate conditions on the air quality at major US cities. The high-resolution simulations produce somewhat different results than the coarse-resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the US during fall (western US, Texas, northeastern, and southeastern US), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (Northeast). Changes in regional climate that would enhance ozone levels are increased temperatures and stagnation along with decreased precipitation and ventilation. We also find that daily peak temperatures tend to increase in most major cities in the US, which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

scholarly journals Downscaling a global climate model to simulate climate change impacts on US regional and urban air quality

2013 ◽  
Vol 6 (2) ◽  
pp. 2517-2549 ◽  
Author(s):  
M. Trail ◽  
A. P. Tsimpidi ◽  
P. Liu ◽  
K. Tsigaridis ◽  
Y. Hu ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Climate Conditions ◽  
Southeastern Us ◽  
The Us

Abstract. Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with WRF to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the continental United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF RCM to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12 km by 12 km resolution, as well as the effect of evolving climate conditions on the air quality at major US cities. The high resolution simulations produce somewhat different results than the coarse resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the US during fall (Western US, Texas, Northeastern, and Southeastern US), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (northeast). We also find that daily peak temperatures tend to increase in most major cities in the US which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

scholarly journals Changes in tropical cyclones under stabilized 1.5 °C and 2.0 °C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols

2017 ◽  
Author(s):  
Michael F. Wehner ◽  
Kevin A. Reed ◽  
Burlen Loring ◽  
Dáithí Stone ◽  
Harinarayan Krishnan
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Tropical Cyclones ◽  
Scientific Community ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Atmospheric Model ◽  
Tropical Storms ◽  
Aerosol Forcing

Abstract. The United Nations Framework Convention on Climate Change (UNFCCC) invited the scientific community to explore the impacts of a world where anthropogenic global warming is stabilized at only 1.5 °C above preindustrial average temperatures. We present a projection of future tropical cyclone statistics for both 1.5 °C and 2.0 °C stabilized warming scenarios by direct numerical simulation using a high resolution global climate model. As in similar projections at higher warming levels, we find that even at these low warming levels the most intense tropical cyclones becomes more frequent and more intense, while simultaneously the frequency of weaker tropical storms is decreased. We also conclude that in the 1.5 °C stabilization, the effect of aerosol forcing changes complicates the interpretation of greenhouse gas forcing changes.

scholarly journals Thermal niches of planktonic foraminifera are static throughout glacial–interglacial climate change

2021 ◽  
Vol 118 (18) ◽  
pp. e2017105118
Author(s):  
Gwen S. Antell ◽  
Isabel S. Fenton ◽  
Paul J. Valdes ◽  
Erin E. Saupe
Keyword(s):  
Climate Change ◽  
Environmental Conditions ◽  
Climate Model ◽  
Global Climate ◽  
Extinction Risk ◽  
Global Climate Model ◽  
Planktonic Foraminifera ◽  
Mean Annual Temperature ◽  
Suitable Habitat ◽  
Evolutionary Relatedness

Abiotic niche lability reduces extinction risk by allowing species to adapt to changing environmental conditions in situ. In contrast, species with static niches must keep pace with the velocity of climate change as they track suitable habitat. The rate and frequency of niche lability have been studied on human timescales (months to decades) and geological timescales (millions of years), but lability on intermediate timescales (millennia) remains largely uninvestigated. Here, we quantified abiotic niche lability at 8-ka resolution across the last 700 ka of glacial–interglacial climate fluctuations, using the exceptionally well-known fossil record of planktonic foraminifera coupled with Atmosphere–Ocean Global Climate Model reconstructions of paleoclimate. We tracked foraminiferal niches through time along the univariate axis of mean annual temperature, measured both at the sea surface and at species’ depth habitats. Species’ temperature preferences were uncoupled from the global temperature regime, undermining a hypothesis of local adaptation to changing environmental conditions. Furthermore, intraspecific niches were equally similar through time, regardless of climate change magnitude on short timescales (8 ka) and across contrasts of glacial and interglacial extremes. Evolutionary trait models fitted to time series of occupied temperature values supported widespread niche stasis above randomly wandering or directional change. Ecotype explained little variation in species-level differences in niche lability after accounting for evolutionary relatedness. Together, these results suggest that warming and ocean acidification over the next hundreds to thousands of years could redistribute and reduce populations of foraminifera and other calcifying plankton, which are primary components of marine food webs and biogeochemical cycles.

Going with the trend: forecasting seasonal climate conditions under climate change

2021 ◽  
Author(s):  
Yawen Shao ◽  
Quan J. Wang ◽  
Andrew Schepen ◽  
Dongryeol Ryu
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Daily Maximum ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Conditions ◽  
Seasonal Climate

AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast post-processing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical post-processing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast post-processing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting post-processed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast post-processing is expected to boost user confidence in seasonal climate forecasts.

Future storylines of the 2012 soybean failure event

2021 ◽  
Author(s):  
Henrique Moreno Dumont Goulart ◽  
Bart van den Hurk ◽  
Karin van der Wiel
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Random Forest ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Climatic Conditions ◽  
Soybean Production ◽  
Weather Events ◽  
Failure Event

<p>Weather events are a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, the most common type of failure-inducing weather events are compounded. For these cases, explaining which conditions triggered a failure event is a complex task, as the links connecting climate and crop yield can be multiple and non-linear. On top of that, the climate change is likely to perturb the interface between climate and agriculture, possibly altering the occurrences or the drivers of crop failures, or generating new types of extreme impacts. In this context, the goal of this study is to demonstrate how global warming can affect the climate-crop connection. For that, we use a storyline approach and focus on an observed failure event, the extreme low soybean production during the 2012 season in hotspots regions, such as the Midwest US, Brazil and Argentina. The scale of this event drove the global soybean prices to the highest values ever recorded. We set out to quantify the change in occurrence of similar events in a warmer scenario. The storylines allow for event attribution, where a given impact can be examined and its causes disentangled. Here, four hotspots of soybean production are examined to contemplate the local consequences of climate change. The study is divided in two parts. We first link climatic features with soybean yields. For each hotspot region, a random forest classifier model is used to establish which meteorological variables are most important and how they are correlated with low soybean yields. With the model trained, we identify the climatic conditions that lead to the 2012 event. Second, we explore the influence of global warming on crop failures. Three large ensembles of simulated weather are obtained from the EC-Earth global climate model, one relating to the present-day period (including the 2012 event) and two relating to future periods with different levels of future warming . We apply the random forest model to these data, and obtain failure statistics for both present and future conditions, isolating the influence of climate change on the soybean failure.  </p>

scholarly journals Reassessing Statistical Downscaling Techniques for Their Robust Application under Climate Change Conditions

2013 ◽  
Vol 26 (1) ◽  
pp. 171-188 ◽  
Author(s):  
J. M. Gutiérrez ◽  
D. San-Martín ◽  
S. Brands ◽  
R. Manzanas ◽  
S. Herrera
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Lower Troposphere ◽  
Temperature Inversion ◽  
Max Planck ◽  
Near Surface ◽  
Climate Conditions

Abstract The performance of statistical downscaling (SD) techniques is critically reassessed with respect to their robust applicability in climate change studies. To this end, in addition to standard accuracy measures and distributional similarity scores, the authors estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods. This validation framework is applied to intercompare the performances of 12 different SD methods (from the analog, weather typing, and regression families) for downscaling minimum and maximum temperatures in Spain. First, a calibration of these methods is performed in terms of both geographical domains and predictor sets; the results are highly dependent on the latter, with optimum predictor sets including near-surface temperature data (in particular 2-m temperature), which appropriately discriminate cold episodes related to temperature inversion in the lower troposphere. Although regression methods perform best in terms of correlation, analog and weather generator approaches are more appropriate for reproducing the observed distributions, especially in case of wintertime minimum temperature. However, the latter two families significantly underestimate the temperature anomalies of the warm periods considered in this work. This underestimation is found to be critical when considering the warming signal in the late twenty-first century as given by a global climate model [the ECHAM5–Max Planck Institute (MPI) model]. In this case, the different downscaling methods provide warming values with differences in the range of 1°C, in agreement with the robustness significance values. Therefore, the proposed test is a promising technique for detecting lack of robustness in statistical downscaling methods applied in climate change studies.

scholarly journals AMOC-emulator M-AMOC1.0 for uncertainty assessment of future projections

2016 ◽  
Author(s):  
Pepijn Bakker ◽  
Andreas Schmittner
Keyword(s):  
Global Warming ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Uncertainty Assessment ◽  
Global Climate Models ◽  
Meridional Overturning Circulation

Abstract. State-of-the-science global climate models show that global warming is likely to weaken the Atlantic Meridional Overturning Circulation (AMOC). While such models are arguably the best tools to perform AMOC projections, they do not allow a comprehensive uncertainty assessment because of limited computational resources. Here we present an AMOC-emulator, a box model with a number of free parameters that can be tuned to mimic the sensitivity of the AMOC to climate change of a specific global climate model. The AMOC-emulator (M-AMOC1.0) is applied to simulations of global warming and melting of the Greenland Ice Sheet, performed with an intermediate complexity model. Predictive power of the AMOC-emulator is shown by comparison with a number of additional warming and Greenland Ice Sheet melt scenario that have not been used in the tuning of the AMOC-emulator, highlighting the potential of the AMOC-emulator to assess the uncertainty in AMOC projections.

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