scholarly journals Visualizing Current and Future Climate Boundaries of the Conterminous United States: Implications for Forests

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 280 ◽  
Author(s):  
Brice Hanberry ◽  
Jacob Fraser
Keyword(s):  
United States ◽  
Climate Models ◽  
Future Climate ◽  
Temperate Climate ◽  
Climate Projections ◽  
Subtropical Climate ◽  
Spatial And Temporal Scales ◽  
Ecological Boundaries ◽  
Current Climate ◽  
Plant Hardiness

Many potential geographic information system (GIS) applications remain unrealized or not yet extended to diverse spatial and temporal scales due to the relative recency of conversion from paper maps to digitized images. Here, we applied GIS to visualize changes in the ecological boundaries of plant hardiness zones and the Köppen-Trewartha classification system between current climate (1981–2010) and future climate (2070–2099), as well as changing climate within stationary state boundaries of the conterminous United States, which provide context for the future of forests. Three climate models at Representative Concentration Pathway (RCP) 8.5 were variable in climate projections. The greatest departure from the current climate in plant hardiness zones, which represent the coldest days, occurred where temperatures were coldest, whereas temperatures in the southeastern United States remained relatively stable. Most (85% to 99%) of the conterminous US increased by at least one plant hardiness zone (5.6 °C). The areal extent of subtropical climate types approximately doubled, expanding into current regions of hot temperate climate types, which shifted into regions of warm temperate climate types. The northernmost tier of states may generally develop the hottest months of the southernmost tier of states; Montana’s hottest month may become hotter than Arizona’s current hottest month. We applied these results to demonstrate the large magnitude of potential shifts in forested ecosystems at the end of the century. Shifts in ecological boundaries and climate within administrative boundaries may result in mismatches between climate and ecosystems and coupled human–environment systems.

scholarly journals An Analysis of Regional Climate Simulations for Western Australia’s Wine Regions—Model Evaluation and Future Climate Projections

2017 ◽  
Vol 56 (7) ◽  
pp. 2113-2138 ◽  
Author(s):  
Rebecca Firth ◽  
Jatin Kala ◽  
Thomas J. Lyons ◽  
Julia Andrys
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Global Climate Models ◽  
Future Climate ◽  
Climate Indices ◽  
Climate Projections ◽  
Model Errors ◽  
Current Climate ◽  
Temperature Indices

AbstractThe Weather Research and Forecasting (WRF) Model is evaluated as a regional climate model for the simulation of climate indices that are relevant to viticulture in Western Australia’s wine regions at a 5-km resolution under current and future climate. WRF is driven with ERA-Interim reanalysis for the current climate and three global climate models (GCMs) for both current and future climate. The focus of the analysis is on a selection of climate indices that are commonly used in climate–viticulture research. Simulations of current climate are evaluated against an observational dataset to quantify model errors over the 1981–2010 period. Changes to the indices under future climate based on the SRES A2 emissions scenario are then assessed through an analysis of future (2030–59) minus present (1970–99) climate. Results show that when WRF is driven with ERA-Interim there is generally good agreement with observations for all of the indices although there is a noticeable negative bias for the simulation of precipitation. The results for the GCM-forced simulations were less consistent. Namely, while the GCM-forced simulations performed reasonably well for the temperature indices, all simulations performed inconsistently for the precipitation index. Climate projections showed significant warming for both of the temperature indices and indicated potential risks to Western Australia’s wine growing regions under future climate, particularly in the north. There was disagreement between simulations with regard to the projections of the precipitation indices and hence greater uncertainty as to how these will be characterized under future climate.

scholarly journals Future Crop Yield Projections Using a Multi-model Set of Regional Climate Models and a Plausible Adaptation Practice in the Southeast United States

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1300
Author(s):  
D. W. Shin ◽  
Steven Cocke ◽  
Guillermo A. Baigorria ◽  
Consuelo C. Romero ◽  
Baek-Min Kim ◽  
...  
Keyword(s):  
United States ◽  
Crop Yield ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Large Temperature ◽  
Climate Projections ◽  
Southeast United States ◽  
The Future

Since maize, peanut, and cotton are economically valuable crops in the southeast United States, their yield amount changes in a future climate are attention-grabbing statistics demanded by associated stakeholders and policymakers. The Crop System Modeling—Decision Support System for Agrotechnology Transfer (CSM-DSSAT) models of maize, peanut, and cotton are, respectively, driven by the North American Regional Climate Change Assessment Program (NARCCAP) Phase II regional climate models to estimate current (1971–2000) and future (2041–2070) crop yield amounts. In particular, the future weather/climate data are based on the Special Report on Emission Scenarios (SRES) A2 emissions scenario. The NARCCAP realizations show on average that there will be large temperature increases (~2.7 °C) and minor rainfall decreases (~−0.10 mm/day) with pattern shifts in the southeast United States. With these future climate projections, the overall future crop yield amounts appear to be reduced under rainfed conditions. A better estimate of future crop yield amounts might be achievable by utilizing the so-called weighted ensemble method. It is proposed that the reduced crop yield amounts in the future could be mitigated by altering the currently adopted local planting dates without any irrigation support.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

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>

Soil moisture and the water cycle in the UK Climate Projections

2020 ◽  
Author(s):  
Jennifer Pirret ◽  
Fai Fung ◽  
John. F.B. Mitchell ◽  
Rachel McInnes
Keyword(s):  
Soil Moisture ◽  
Plant Growth ◽  
Water Balance ◽  
Land Surface ◽  
Climate Models ◽  
Water Cycle ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Projections ◽  
Plant Cultivation

<p>Soil moisture is a key environmental factor for plant cultivation: too little and plant growth is restricted due to drought conditions; too much and soil becomes water-logged. It is important to understand how well climate models can represent current soil moisture processes as well as how soil moisture will respond to a changing climate, to inform adaptation of plant cultivation to future climate change. We explore current and future climate soil moisture conditions alongside water cycle processes such as evaporation and run-off in the latest UK Climate Projections (UKCP). Three model ensembles are available: UKCP Global, Regional and Local, with horizontal resolutions of 60km, 12km and 2.2km respectively. These each contain the Joint UK Land Environment Simulator (JULES) model as their land surface component. This suite of models offers the opportunity to understand the effects of parameter uncertainty and spatial resolution. Firstly, we assess the performance of the Global and Regional simulations by evaluating results from the baseline period (1981-2010) in terms of soil moisture (and the overall water balance) by comparing it to observations and to JULES driven by observations. Secondly, we assess how the water balance responds to a high future greenhouse gas concentration pathway. We find that soil moisture is likely to be lower in the summer and early autumn and spends a longer time below levels optimal for plant growth. The potential drivers of this change are explored, including future changes in precipitation and evaporation.</p>

The use of the multi-model ensemble in probabilistic climate projections

2007 ◽  
Vol 365 (1857) ◽  
pp. 2053-2075 ◽  
Author(s):  
Claudia Tebaldi ◽  
Reto Knutti
Keyword(s):  
Climate Models ◽  
Future Climate ◽  
Climate Projections ◽  
Model Design ◽  
Model Ensemble ◽  
Making Sense ◽  
Probabilistic Climate Projections ◽  
Regional Temperature ◽  
Structural Uncertainties ◽  
Model Ensembles

Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an ‘ensemble of opportunity’, are discussed in detail.

scholarly journals Will Future Climate Favor More Erratic Wildfires in the Western United States?

2013 ◽  
Vol 52 (11) ◽  
pp. 2410-2417 ◽  
Author(s):  
Lifeng Luo ◽  
Ying Tang ◽  
Shiyuan Zhong ◽  
Xindi Bian ◽  
Warren E. Heilman
Keyword(s):  
United States ◽  
Climate Models ◽  
Regional Climate ◽  
Fire Behavior ◽  
Regional Climate Models ◽  
Future Climate ◽  
Lower Atmosphere ◽  
Western United States ◽  
Mountainous Regions ◽  
Increased Risk

AbstractWildfires that occurred over the western United States during August 2012 were fewer in number but larger in size when compared with all other Augusts in the twenty-first century. This unique characteristic, along with the tremendous property damage and potential loss of life that occur with large wildfires with erratic behavior, raised the question of whether future climate will favor rapid wildfire growth so that similar wildfire activity may become more frequent as climate changes. This study addresses this question by examining differences in the climatological distribution of the Haines index (HI) between the current and projected future climate over the western United States. The HI, ranging from 2 to 6, was designed to characterize dry, unstable air in the lower atmosphere that may contribute to erratic or extreme fire behavior. A shift in HI distribution from low values (2 and 3) to higher values (5 and 6) would indicate an increased risk for rapid wildfire growth and spread. Distributions of Haines index are calculated from simulations of current (1971–2000) and future (2041–70) climate using multiple regional climate models in the North American Regional Climate Change Assessment Program. Despite some differences among the projections, the simulations indicate that there may be not only more days but also more consecutive days with HI ≥ 5 during August in the future. This result suggests that future atmospheric environments will be more conducive to erratic wildfires in the mountainous regions of the western United States.

scholarly journals The Influence of Atmospheric Blocking on Extreme Winter Minimum Temperatures in North America

2016 ◽  
Vol 29 (12) ◽  
pp. 4361-4381 ◽  
Author(s):  
Kirien Whan ◽  
Francis Zwiers ◽  
Jana Sillmann
Keyword(s):  
United States ◽  
North America ◽  
Extreme Events ◽  
Climate Models ◽  
Primary Source ◽  
The United States ◽  
Climate Projections ◽  
Cold Temperatures ◽  
Climate Conditions ◽  
The Difference

Abstract Regional climate models (RCMs) are the primary source of high-resolution climate projections, and it is of crucial importance to evaluate their ability to simulate extreme events under current climate conditions. Many extreme events are influenced by circulation features that occur outside, or on the edges of, RCM domains. Thus, it is of interest to know whether such dynamically controlled aspects of extremes are well represented by RCMs. This study assesses the relationship between upstream blocking and cold temperature extremes over North America in observations, reanalysis products (ERA-Interim and NARR), and RCMs (CanRCM4, CRCM5, HIRHAM5, and RCA4). Generalized extreme value distributions were fitted to winter minimum temperature (TNn) incorporating blocking frequency (BF) as a covariate, which is shown to have a significant influence on TNn. The magnitude of blocking influence in the RCMs is consistent with observations, but the spatial extent varies. CRCM5 and HIRHAM5 reproduce the pattern of influence best compared to observations. CanRCM4 and RCA4 capture the influence of blocking in British Columbia and the northeastern United States, but the extension of influence that is seen in observations and reanalysis into the southern United States is not evident. The difference in the 20-yr return value (20RV) of TNn between high and low BF in the Pacific Ocean indicates that blocking is associated with a decrease of up to 15°C in the 20RV over the majority of the United States and in western Canada. In northern North America the difference in the 20RV is positive as blocking is associated with warmer extreme cold temperatures. The 20RVs are generally simulated well by the RCMs.

scholarly journals Impacts of climate change on the seasonality of low flows in 134 catchments in the River Rhine basin using an ensemble of bias-corrected regional climate simulations

2013 ◽  
Vol 10 (5) ◽  
pp. 6807-6845
Author(s):  
M. C. Demirel ◽  
M. J. Booij ◽  
A. Y. Hoekstra
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Future Climate ◽  
Low Flow ◽  
River Rhine ◽  
Emission Scenarios ◽  
Climate Scenarios ◽  
Low Flows ◽  
Current Climate ◽  
The Future

Abstract. The impacts of climate change on the seasonality of low flows are analysed for 134 sub-catchments covering the River Rhine basin upstream of the Dutch–German border. Three seasonality indices for low flows are estimated, namely seasonality ratio (SR), weighted mean occurrence day (WMOD) and weighted persistence (WP). These indices are related to the discharge regime, timing and variability in timing of low flow events respectively. The three indices are estimated from: (1) observed low flows; (2) simulated low flows by the semi distributed HBV model using observed climate; (3) simulated low flows using simulated inputs from seven climate scenarios for the current climate (1964–2007); (4) simulated low flows using simulated inputs from seven climate scenarios for the future climate (2063–2098) including different emission scenarios. These four cases are compared to assess the effects of the hydrological model, forcing by different climate models and different emission scenarios on the three indices. The seven climate scenarios are based on different combinations of four General Circulation Models (GCMs), four Regional Climate Models (RCMs) and three greenhouse gas emission scenarios. Significant differences are found between cases 1 and 2. For instance, the HBV model is prone to overestimate SR and to underestimate WP and simulates very late WMODs compared to the estimated WMODs using observed discharges. Comparing the results of cases 2 and 3, the smallest difference is found in the SR index, whereas large differences are found in the WMOD and WP indices for the current climate. Finally, comparing the results of cases 3 and 4, we found that SR has decreased substantially by 2063–2098 in all seven subbasins of the River Rhine. The lower values of SR for the future climate indicate a shift from winter low flows (SR > 1) to summer low flows (SR < 1) in the two Alpine subbasins. The WMODs of low flows tend to be earlier than for the current climate in all subbasins except for the Middle Rhine and Lower Rhine subbasins. The WP values are slightly larger, showing that the predictability of low flow events increases as the variability in timing decreases for the future climate. From comparison of the uncertainty sources evaluated in this study, it is obvious that the RCM/GCM uncertainty has the largest influence on the variability in timing of low flows for future climate.

scholarly journals A mathematical approach to understanding emergent constraints

2018 ◽  
Vol 9 (3) ◽  
pp. 999-1012 ◽  
Author(s):  
Femke J. M. M. Nijsse ◽  
Henk A. Dijkstra
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Future Climate ◽  
Future Climate Change ◽  
Mathematical Approach ◽  
Empirical Relationships ◽  
Current Climate ◽  
Emergent Constraints

Abstract. One of the approaches to constrain uncertainty in climate models is the identification of emergent constraints. These are physically explainable empirical relationships between a particular simulated characteristic of the current climate and future climate change from an ensemble of climate models, which can be exploited using current observations. In this paper, we develop a theory to understand the appearance of such emergent constraints. Based on this theory, we also propose a classification for emergent constraints, and applications are shown for several idealized climate models.

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