scholarly journals Nitrogen restricts future treeline advance in the sub-arctic

2021 ◽  
Author(s):  
Adrian Gustafson ◽  
Paul A. Miller ◽  
Robert Björk ◽  
Stefan Olin ◽  
Benjamin Smith
Keyword(s):  
High Latitude ◽  
Vegetation Change ◽  
Nitrogen Availability ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Environmental Drivers ◽  
Climate Data ◽  
Plant Soil ◽  
Vegetation Models

Abstract. Arctic environmental change has induced shifts in high latitude plant community composition and stature with impli-cations for Arctic carbon cycling and energy exchange. Two major components of high latitude ecosystems undergoing change is the advancement of trees into treeless tundra and the increased abundance and size of shrubs. How future changes in key climatic and environmental drivers will affect distributions of major ecosystem types is an active area of research. Dynamic Vegetation Models (DVMs) offer a way to investigate multiple and interacting drivers of vegeta-tion distribution and ecosystem function. We employed the LPJ-GUESS DVM over a subarctic landscape in northern Sweden, Torneträsk. Using a highly resolved climate dataset we downscaled CMIP5 climate data from three Global Climate Models and two 21st century future scenarios (RCP2.6 and RCP8.5) to investigate future impacts of climate change on these ecosystems. We also performed three model experiments where we factorially varied drivers (climate, nitrogen deposition and [CO2]) to disentangle the effects of each on ecosystem properties and functions. We found that treelines could advance by between 45 and 195 elevational meters in the landscape until the year 2100, depending on the scenario. Temperature was a strong, but not the only, driver of vegetation change. Nitrogen availability was identi-fied as an important modulator of treeline advance. While increased CO2 fertilisation drove productivity increases it did not result in any range shifts of trees. Treeline advance was realistically simulated without any temperature depend-ence on growth, but biomass was overestimated. As nitrogen was identified as an important modulator of treeline ad-vance, we support the idea that accurately representing plant-soil interactions in models will be key to future predic-tions Arctic vegetation change.

scholarly journals Nitrogen restricts future sub-arctic treeline advance in an individual-based dynamic vegetation model

2021 ◽  
Vol 18 (23) ◽  
pp. 6329-6347
Author(s):  
Adrian Gustafson ◽  
Paul A. Miller ◽  
Robert G. Björk ◽  
Stefan Olin ◽  
Benjamin Smith
Keyword(s):  
High Latitude ◽  
Vegetation Change ◽  
Nitrogen Availability ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Environmental Drivers ◽  
Climate Data ◽  
Plant Soil ◽  
Vegetation Models

Abstract. Arctic environmental change induces shifts in high-latitude plant community composition and stature with implications for Arctic carbon cycling and energy exchange. Two major components of change in high-latitude ecosystems are the advancement of trees into tundra and the increased abundance and size of shrubs. How future changes in key climatic and environmental drivers will affect distributions of major ecosystem types is an active area of research. Dynamic vegetation models (DVMs) offer a way to investigate multiple and interacting drivers of vegetation distribution and ecosystem function. We employed the LPJ-GUESS tree-individual-based DVM over the Torneträsk area, a sub-arctic landscape in northern Sweden. Using a highly resolved climate dataset to downscale CMIP5 climate data from three global climate models and two 21st-century future scenarios (RCP2.6 and RCP8.5), we investigated future impacts of climate change on these ecosystems. We also performed model experiments where we factorially varied drivers (climate, nitrogen deposition and [CO2]) to disentangle the effects of each on ecosystem properties and functions. Our model predicted that treelines could advance by between 45 and 195 elevational metres by 2100, depending on the scenario. Temperature was a strong driver of vegetation change, with nitrogen availability identified as an important modulator of treeline advance. While increased CO2 fertilisation drove productivity increases, it did not result in range shifts of trees. Treeline advance was realistically simulated without any temperature dependence on growth, but biomass was overestimated. Our finding that nitrogen cycling could modulate treeline advance underlines the importance of representing plant–soil interactions in models to project future Arctic vegetation change.

scholarly journals Prediction of the Long-Term Potential Distribution of Cryptorhynchus lapathi (L.) under Climate Change

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 5 ◽  
Author(s):  
Ya Zou ◽  
Linjing Zhang ◽  
Xuezhen Ge ◽  
Siwei Guo ◽  
Xue Li ◽  
...  
Keyword(s):  
Climate Change ◽  
North America ◽  
Potential Distribution ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Prevention Measures ◽  
Climate Data ◽  
Effective Prevention ◽  
Future Global Warming

The poplar and willow borer, Cryptorhynchus lapathi (L.), is a severe worldwide quarantine pest that causes great economic, social, and ecological damage in Europe, North America, and Asia. CLIMEX4.0.0 was used to study the likely impact of climate change on the potential global distribution of C. lapathi based on existing (1987–2016) and predicted (2021–2040, 2041–2080, and 2081–2100) climate data. Future climate data were simulated based on global climate models from Coupled Model Inter-comparison Project Phase 5 (CMIP5) under the RCP4.5 projection. The potential distribution of C. lapathi under historical climate conditions mainly includes North America, Africa, Europe, and Asia. Future global warming may cause a northward shift in the northern boundary of potential distribution. The total suitable area would increase by 2080–2100. Additionally, climatic suitability would change in large regions of the northern hemisphere and decrease in a small region of the southern hemisphere. The projected potential distribution will help determine the impacts of climate change and identify areas at risk of pest invasion in the future. In turn, this will help design and implement effective prevention measures for expanding pest populations, using natural enemies, microorganisms, and physical barriers in very favorable regions to impede the movement and oviposition of C. lapathi.

scholarly journals Impacts of future climate change on potential yields of major crops in China

2014 ◽  
Vol 5 (1) ◽  
pp. 617-647
Author(s):  
Y. Yin ◽  
Q. Tang ◽  
X. Liu
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Climate ◽  
Future Climate Change ◽  
Yield Reduction ◽  
Climate Data ◽  
Potential Yield ◽  
Crop Models

Abstract. Climate change may affect crop development and yield, and consequently cast a shadow of doubt over China's food self-sufficiency efforts. In this study we used the model projections of a couple of global gridded crop models (GGCMs) to assess the effects of future climate change on the potential yields of the major crops (i.e. wheat, rice, maize and soybean) over China. The GGCMs were forced with the bias-corrected climate data from 5 global climate models (GCMs) under the Representative Concentration Pathways (RCP) 8.5 which were made available by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). The results show that the potential yields of rice may increase over a large portion of China. Climate change may benefit food productions over the high-altitude and cold regions where are outside current main agricultural area. However, the potential yield of maize, soybean and wheat may decrease in a large portion of the current main crop planting areas such as North China Plain. Development of new agronomic management strategy may be useful for coping with climate change in the areas with high risk of yield reduction.

Persistence of in-stream waterholes in ephemeral rivers of tropical northern Australia and potential impacts of climate change

2014 ◽  
Vol 65 (12) ◽  
pp. 1131 ◽  
Author(s):  
David McJannet ◽  
Steve Marvanek ◽  
Anne Kinsey-Henderson ◽  
Cuan Petheram ◽  
Jim Wallace
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Landsat Imagery ◽  
Global Climate Models ◽  
Aquatic Biota ◽  
Water Resource Development ◽  
Northern Australia ◽  
Climate Data ◽  
Ephemeral Rivers ◽  
Impacts Of Climate Change

Many northern Australian rivers have limited or non-existent dry season flow and rivers tend to dry to a series of pools, or waterholes, which become particularly important refugial habitat for aquatic biota during the periods between streamflow events. The present study developed techniques to identify in-stream waterholes across large and inaccessible areas of the Flinders and Gilbert catchments using Landsat imagery. Application of this technique to 400 scenes between 2003 and 2010 facilitated the identification of key waterhole refugia that are likely to persist during all years. Relationships for predicting total waterhole area from streamflow characteristics were produced for four river reaches. Using these relationships and streamflow predictions based upon climate data scaled using 15 global climate models, the potential impacts of future climate on waterhole persistence was assessed. Reductions in waterhole area of more than 60% were modelled in some years under drier scenarios and this represents a large reduction in available habitat for areas that already have limited in-stream refugia. Conversely, under wetter future climates the total area of waterholes increased. The approach developed here has applicability in other catchments, both in Australia and globally, and for assessing the impacts of changed flow resulting from water resource development.

scholarly journals A Comprehensive Intermediate-Term Drought Evaluation System and Evaluation of Climate Data Products over the Conterminous United States

2021 ◽  
Author(s):  
Zeyu Xue ◽  
Paul Ullrich
Keyword(s):  
Evaluation System ◽  
Climate Models ◽  
Global Climate ◽  
Statistical Significance ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Statistical Hypothesis ◽  
Climate Data ◽  
Statistical Hypothesis Testing

AbstractClimate models are frequently-used tools for adaptation planning in light of future uncertainty. However, not all climate models are equally trustworthy, and so model biases must be assessed to select models suitable for producing credible projections. Drought is a well-known and high-impact form of extreme weather, and knowledge of its frequency, intensity, and duration key for regional water management plans. Droughts are also difficult to assess in climate datasets, due to the long duration per event, relative to the length of a typical simulation. Therefore, there is a growing need for a standardized suite of metrics addressing how well models capture this phenomenon. In this study, we present a widely applicable set of metrics for evaluating agreement between climate datasets and observations in the context of drought. Two notable advances are made in our evaluation system: First, statistical hypothesis testing is employed for normalization of individual scores against the threshold for statistical significance. And second, within each evaluation region and dataset, principal feature analysis is used to select the most descriptive metrics among 11 metrics that capture essential features of drought. Our metrics package is applied to three characteristically distinct regions in the conterminous US and across several commonly employed climate datasets (CMIP5/6, LOCA and CORDEX). As a result, insights emerge into the underlying drivers of model bias in global climate models, regional climate models, and statistically downscaled models.

scholarly journals Sustainable City Planning: A Data-Driven Approach for Mitigating Urban Heat

2021 ◽  
Vol 6 ◽  
Author(s):  
Andrew MacLachlan ◽  
Eloise Biggs ◽  
Gareth Roberts ◽  
Bryan Boruff
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
Climate Models ◽  
Global Climate ◽  
Three Dimensional ◽  
Global Climate Models ◽  
Data Driven ◽  
Climate Data ◽  
Data Driven Approach ◽  
Urban Heat

Urban areas are expected to triple by 2030 in order to accommodate 60% of the global population. Anthropogenic landscape modifications expand coverage of impervious surfaces inducing the urban heat island (UHI) effect, a critical twenty first century challenge associated with increased economic expenditure, energy consumption, and adverse health impacts. Yet, omission of UHI measures from global climate models and metropolitan planning methodologies precludes effective sustainable development governance. We present an approach that integrates Earth observation and climate data with three-dimensional urban models to determine optimal tree placement (per square meter) within proposed urban developments to enable more effective localized UHI mitigation. Such data-driven planning decisions will enhance the future sustainability of our cities to align with current global urban development agendas.

scholarly journals Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA

2020 ◽  
Author(s):  
Alexis Kirsten Cooley ◽  
Heejun Chang
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Historical Record ◽  
Hydrologic Cycle ◽  
Global Climate Models ◽  
Future Climate ◽  
Trend Detection ◽  
Climate Indices ◽  
Climate Data ◽  
Expert Team

Abstract This study addresses how regional changes to precipitation may be identified by exploring the effect of temporal resolution on trend detection. Climate indices that summarize precipitation characteristics are used with Mann–Kendall monotonic testing to investigate precipitation trends in Portland, Oregon (OR) from 1977 to 2016. Observational records from rain gages are compared with downscaled global climate models to determine trends for the historic (1977–2005) and future (2006–2100) periods. Standard indices created by the Expert Team on Climate Change Detection and Indices (ETCCDI) are deployed. ETCCDI indices that summarize conditions at the annual level are generated alongside a limited number of ETCCDI indices summarized at the monthly level. For the future climate, the indices summarized at the annual level demonstrate trends indicative of an intensifying hydrologic cycle. The historical record depicted by annual indices does not show trends. The historical record is viewed differently by changing the indices to monthly summaries, which causes trend detection to increase and hallmark indicators of an intensifying hydrologic cycle to become apparent.

Environmental drivers of large, infrequent wildfires: the emerging conceptual model

2007 ◽  
Vol 31 (3) ◽  
pp. 287-312 ◽  
Author(s):  
Andrea Meyn ◽  
Peter S. White ◽  
Constanze Buhk ◽  
Anke Jentsch
Keyword(s):  
Conceptual Model ◽  
Climate Models ◽  
Global Climate ◽  
Climatic Conditions ◽  
Global Climate Models ◽  
Environmental Drivers ◽  
Fuel Moisture ◽  
Seasonally Dry ◽  
Additional Information ◽  
Human Management

Large, infrequent fires (LIFs) can have substantial impacts on both ecosystems and the economy. To better understand LIFs and to better predict the effects of human management and climate change on their occurrence, we must first determine the factors that produce them. Here, we review local and regional literature investigating the drivers of LIFs. The emerging conceptual model proposes that ecosystems can be typified based on climatic conditions that determine both fuel moisture and fuel amount. The concept distinguishes three ecosystem types: (1) biomass-rich, rarely dry ecosystems where fuel moisture rather than fuel amount limits LIFs; (2) biomass-poor, at least seasonally dry ecosystems where fuel amount rather than fuel moisture limits LIFs; and (3) biomass-poor, rarely dry ecosystems where both fuel amount and fuel moisture limit the occurrence of LIFs. Our main goal in this paper is to discuss the drivers of LIFs and the three mentioned ecosystem types in a global context. Further, we will discuss the drivers that are not included within the `fuels' versus `climate' discussion. Finally, we will address the question: what kinds of additional information are needed if models predicting LIFs are to be coupled with global climate models? As with all generalizations, there are local deviations and modifications due to processes such as disturbance interaction or human impact. These processes tend to obscure the general patterns of the occurrence of LIFs and are likely to cause much of the observed controversy and confusion in the literature.

The Global Character of the Flux of Downward Longwave Radiation

2012 ◽  
Vol 25 (7) ◽  
pp. 2329-2340 ◽  
Author(s):  
Graeme L. Stephens ◽  
Martin Wild ◽  
Paul W. Stackhouse ◽  
Tristan L’Ecuyer ◽  
Seiji Kato ◽  
...  
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Cloud Base ◽  
Climate Data ◽  
Clear Sky ◽  
Downward Longwave Radiation

Abstract Four different types of estimates of the surface downwelling longwave radiative flux (DLR) are reviewed. One group of estimates synthesizes global cloud, aerosol, and other information in a radiation model that is used to calculate fluxes. Because these synthesis fluxes have been assessed against observations, the global-mean values of these fluxes are deemed to be the most credible of the four different categories reviewed. The global, annual mean DLR lies between approximately 344 and 350 W m−2 with an error of approximately ±10 W m−2 that arises mostly from the uncertainty in atmospheric state that governs the estimation of the clear-sky emission. The authors conclude that the DLR derived from global climate models are biased low by approximately 10 W m−2 and even larger differences are found with respect to reanalysis climate data. The DLR inferred from a surface energy balance closure is also substantially smaller that the range found from synthesis products suggesting that current depictions of surface energy balance also require revision. The effect of clouds on the DLR, largely facilitated by the new cloud base information from the CloudSat radar, is estimated to lie in the range from 24 to 34 W m−2 for the global cloud radiative effect (all-sky minus clear-sky DLR). This effect is strongly modulated by the underlying water vapor that gives rise to a maximum sensitivity of the DLR to cloud occurring in the colder drier regions of the planet. The bottom of atmosphere (BOA) cloud effect directly contrast the effect of clouds on the top of atmosphere (TOA) fluxes that is maximum in regions of deepest and coldest clouds in the moist tropics.

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