Regional Variations in Potential Plant Habitat Changes in Response to Multiple Global Warming Scenarios*

2015 ◽  
Vol 28 (7) ◽  
pp. 2884-2899 ◽  
Author(s):  
Chang-Eui Park ◽  
Su-Jong Jeong ◽  
Chang-Hoi Ho ◽  
Jinwon Kim

Abstract This study examines the impacts of global warming on the timing of plant habitat changes in the twenty-first century using climate scenarios from multiple global climate models (GCMs). The plant habitat changes are predicted by driving the bioclimate rule in a dynamic global vegetation model using the climate projections from 16 coupled GCMs. The timing of plant habitat changes is estimated by the first occurrence of specified fractional changes (10%, 20%, and 30%). All future projections are categorized into three groups by the magnitude of the projected global-mean land surface temperature changes: low (<2.5 K), medium (2.5–3.5 K), and high (>3.5 K) warming. During the course of the twenty-first century, dominant plant habitat changes are projected in ecologically transitional (i.e., from tropical to temperate and temperate to boreal) regions. The timing of plant habitat changes varies substantially according to regions. In the low-warming group, habitat changes of 10% in southern Africa occur in 2028, earlier than in the Americas by more than 70 yr. Differences in the timing between regions increase with the increase in warming and fractional threshold. In the subtropics, fast plant habitat changes are projected for the Asia and Africa regions, where countries of relatively small gross domestic product (GDP) per capita are concentrated. Ecosystems in these regions will be more vulnerable to global warming, because countries of low economic power lack the capability to deal with the warming-induced habitat changes. Thus, it is important to establish international collaboration via which developed countries provide assistance to mitigate the impacts of global warming.

2014 ◽  
Vol 27 (17) ◽  
pp. 6526-6550 ◽  
Author(s):  
Michael Notaro ◽  
David Lorenz ◽  
Christopher Hoving ◽  
Michael Schummer

Abstract Statistically downscaled climate projections from nine global climate models (GCMs) are used to force a snow accumulation and ablation model (SNOW-17) across the central-eastern North American Landscape Conservation Cooperatives (LCCs) to develop high-resolution projections of snowfall, snow depth, and winter severity index (WSI) by the middle and late twenty-first century. Here, projections of a cumulative WSI (CWSI) known to influence autumn–winter waterfowl migration are used to demonstrate the utility of SNOW-17 results. The application of statistically downscaled climate data and a snow model leads to a better representation of lake processes in the Great Lakes basin, topographic effects in the Appalachian Mountains, and spatial patterns of climatological snowfall, compared to the original GCMs. Annual mean snowfall is simulated to decline across the region, particularly in early winter (December–January), leading to a delay in the mean onset of the snow season. Because of a warming-induced acceleration of snowmelt, the percentage loss in snow depth exceeds that of snowfall. Across the Plains and Prairie Potholes LCC and the Upper Midwest and Great Lakes LCC, daily snowfall events are projected to become less common but more intense. The greatest reductions in the number of days per year with a present snowpack are expected close to the historical position of the −5°C isotherm in December–March, around 44°N. The CWSI is projected to decline substantially during December–January, leading to increased likelihood of delays in timing and intensity of autumn–winter waterfowl migrations.


Author(s):  
William R. Thompson ◽  
Leila Zakhirova

This chapter introduces the issue of how systemic leadership and energy are intertwined. One compound question is: How did we shift from a primarily agrarian economy to a primarily industrial economy, and how did this shift shape world politics? We develop an interactive model of the significant factors involved in this change, not all of which necessarily had an equal impact in each single case. A second set of questions involve the linkages between the systemic leadership that emerged from these historical processes and the global warming crisis of the twenty-first century. How is systemic leadership linked to the crisis in the first place? What is systemic leadership’s likely role in responding to the crisis?


2019 ◽  
Vol 172 ◽  
pp. 69-87 ◽  
Author(s):  
Gil Lemos ◽  
Alvaro Semedo ◽  
Mikhail Dobrynin ◽  
Arno Behrens ◽  
Joanna Staneva ◽  
...  

2021 ◽  
pp. 1-48
Author(s):  
Daniel F. Schmidt ◽  
Kevin M. Grise

AbstractClimate change during the twenty-first century has the potential to substantially alter geographic patterns of precipitation. However, regional precipitation changes can be very difficult to project, and in some regions, global climate models do not even agree on the sign of the precipitation trend. Since some of this uncertainty is due to internal variability rather than model bias, models cannot be used to narrow the possibilities to a single outcome, but they can usefully quantify the range of plausible outcomes and identify the combination of dynamical drivers that would be likely to produce each.This study uses a storylines approach—a type of regression-based analysis—to identify some of the key dynamical drivers that explain the variance in 21st century U.S. winter precipitation trends across CMIP6 models under the SSP3-7.0 emissions scenario. This analysis shows that the spread in precipitation trends is not primarily driven by differences in modeled climate sensitivity. Key drivers include global-mean surface temperature, but also tropical upper-troposphere temperature, the El Niño-Southern Oscillation (ENSO), the Pacific-North America (PNA) pattern, and the East Pacific (EP) dipole (a dipole pattern in geopotential heights over North America’s Pacific coast). Combinations of these drivers can reinforce or cancel to produce various high- or low-impact scenarios for winter precipitation trends in various regions of the United States. For example, the most extreme winter precipitation trends in the southwestern U.S. result from opposite trends in ENSO and EP, whereas the wettest winter precipitation trends in the midwestern U.S. result from a combination of strong global warming and a negative PNA trend.


2019 ◽  
Author(s):  
Øivind Hodnebrog ◽  
Gunnar Myhre ◽  
Bjørn H. Samset ◽  
Kari Alterskjær ◽  
Timothy Andrews ◽  
...  

Abstract. The relationship between changes in integrated water vapour (IWV) and precipitation can be characterized by quantifying changes in atmospheric water vapour lifetime. Precipitation isotope ratios correlate with this lifetime, a relationship that helps understand dynamical processes and may lead to improved climate projections. We investigate how water vapour and its lifetime respond to different drivers of climate change, such as greenhouse gases and aerosols. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulphate have been perturbed separately. A lifetime increase from 8 to 10 days is projected between 1986–2005 and 2081–2100, under a business-as-usual pathway. By disentangling contributions from individual climate drivers, we present a physical understanding of how global warming slows down the hydrological cycle, due to longer lifetime, but still amplifies the cycle due to stronger precipitation/evaporation fluxes. The feedback response of IWV to surface temperature change differs somewhat between drivers. Fast responses amplify these differences and lead to net changes in IWV per degree surface warming ranging from 6.4±0.9 %/K for sulphate to 9.8±2 %/K for BC. While BC is the driver with the strongest increase in IWV per degree surface warming, it is also the only driver with a reduction in precipitation per degree surface warming. Consequently, increases in BC aerosol concentrations yield the strongest slowdown of the hydrological cycle among the climate drivers studied, with a change in water vapour lifetime per degree surface warming of 1.1±0.4 days/K, compared to less than 0.5 days/K for the other climate drivers (CO2, methane, solar irradiance, sulphate).


2014 ◽  
Vol 27 (23) ◽  
pp. 8793-8808 ◽  
Author(s):  
Paul J. Northrop ◽  
Richard E. Chandler

Abstract A simple statistical model is used to partition uncertainty from different sources, in projections of future climate from multimodel ensembles. Three major sources of uncertainty are considered: the choice of climate model, the choice of emissions scenario, and the internal variability of the modeled climate system. The relative contributions of these sources are quantified for mid- and late-twenty-first-century climate projections, using data from 23 coupled atmosphere–ocean general circulation models obtained from phase 3 of the Coupled Model Intercomparison Project (CMIP3). Similar investigations have been carried out recently by other authors but within a statistical framework for which the unbalanced nature of the data and the small number (three) of scenarios involved are potentially problematic. Here, a Bayesian analysis is used to overcome these difficulties. Global and regional analyses of surface air temperature and precipitation are performed. It is found that the relative contributions to uncertainty depend on the climate variable considered, as well as the region and time horizon. As expected, the uncertainty due to the choice of emissions scenario becomes more important toward the end of the twenty-first century. However, for midcentury temperature, model internal variability makes a large contribution in high-latitude regions. For midcentury precipitation, model internal variability is even more important and this persists in some regions into the late century. Implications for the design of climate model experiments are discussed.


2017 ◽  
Vol 17 (8) ◽  
pp. 2421-2432 ◽  
Author(s):  
Edwin P. Maurer ◽  
Nicholas Roby ◽  
Iris T. Stewart-Frey ◽  
Christopher M. Bacon

2020 ◽  
Vol 13 (12) ◽  
Author(s):  
Mansour Almazroui

Abstract The present study analyzes the Survivability for a Fit Human Threshold (SFHT) maximum temperature during the summer (June–August) over the six Middle Eastern countries known as the Gulf Cooperation Council (GCC) in the twenty-first century. An ensemble of three dynamically downscaled global climate models available from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the Representative Concentration Pathways (RCPs) RCP4.5 and RCP8.5 emission scenarios is used to analyze the future climate (2006–2099) over the region. The ground-truth air temperature for ten major cities across the GCC countries is utilized for model evaluation and to estimate the model-simulated temperature biases. Both positive and negative biases found during the present climate (1976–2005) are used to adjust the future temperature changes. These adjustments show that the summer maximum temperature is likely to increase continuously for most cities in the GCC countries at the rate of about 0.2 °C (0.6 °C) per decade under RCP4.5 (RCP8.5) for the future period (2020–2099), which is significant at the 99% confidence level. For RCP8.5, the adjusted summer maximum temperature may exceed the SFHT limit of 42 °C in five capital cities of the GCC states and four major cities of Saudi Arabia. The projections based on adjusted values indicate that the average summer maximum temperature should not exceed 52 °C in any city investigated by the end of the twenty-first century. The daily maximum temperature is projected to exceed 55 °C in some cities in the GCC region by the end of the twenty-first century under a business-as-usual scenario that seems to be unrealistic if the biases are not taken into account. It is highly recommended that the GCC states should coordinate their efforts to respond appropriately to these projections using large ensembles of multimodel simulations while allowing for the associated uncertainty.


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