scholarly journals Projections of Future Soil Temperature and Water Content for Three Southern Quebec Forested Sites

2012 ◽  
Vol 25 (21) ◽  
pp. 7690-7701 ◽  
Author(s):  
Daniel Houle ◽  
Ariane Bouffard ◽  
Louis Duchesne ◽  
Travis Logan ◽  
Richard Harvey
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Climate Models ◽  
Black Spruce ◽  
Global Climate ◽  
Model Performance ◽  
Growing Season ◽  
Global Climate Models ◽  
Soil Conditions ◽  
Eastern Canada

The impacts of climate change on future soil temperature Ts and soil moisture Ms of northern forests are uncertain. In this study, the authors first calibrated Ts and Ms models [Forest Soil Temperature Model (ForSTeM) and Forest Hydrology Model (ForHyM), respectively] using long-term observations of Ts and Ms at different depths measured at three forest sites in eastern Canada. The two models were then used to project Ts and Ms for the period 1971–2100 using historical and future climate scenarios generated by one regional and five global climate models. Results indicate good model performance by ForSTeM and ForHyM in predicting observed Ts and Ms values at various depths for the three sites. Projected annual-mean Ts at these sites increased between 1.1° and 1.9°C and between 1.9° and 3.3°C from the present 30-yr averages (1971–2000) to the periods 2040–69 and 2070–99, respectively. Increases as high as 5.0°C were projected at the black spruce site during the growing season (June) for the period 2070–99. Changes in annual-mean Ms were relatively small; however, seasonally Ms is projected to increase in April, because of earlier snowmelt, and to decrease during the growing season, mainly because of higher evapotranspiration rates. Soil moisture in the growing season could be reduced by 20%–40% for the period 2070–99 compared to the reference period. The projected warmer and drier soil conditions in the growing season could have significant impacts on forests growth and biogeochemical cycles.

scholarly journals Varying soil moisture-atmosphere feedbacks explain divergent temperature extremes and precipitation projections in Central Europe

2018 ◽  
Author(s):  
Martha M. Vogel ◽  
Jakob Zscheischler ◽  
Sonia I. Seneviratne
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Central Europe ◽  
Climate Models ◽  
Global Climate ◽  
Summer Precipitation ◽  
Global Climate Models ◽  
Temperature Extremes ◽  
Extreme Temperatures

Abstract. The frequency and intensity of climate extremes is expected to increase in many regions due to anthropogenic climate change. In Central Europe extreme temperatures are projected to change more strongly than global mean temperatures and soil moisture-temperature feedbacks significantly contribute to this regional amplification. Because of their strong societal, ecological and economic impacts, robust projections of temperature extremes are needed. Unfortunately, in current model projections, temperature extremes in Central Europe are prone to large uncertainties. In order to understand and potentially reduce uncertainties of extreme temperatures projections in Europe, we analyze global climate models from the CMIP5 ensemble for the business-as-usual high-emission scenario (RCP8.5). We find a divergent behavior in long-term projections of summer precipitation until the end of the 21st century, resulting in a trimodal distribution of precipitation (wet, dry and very dry). All model groups show distinct characteristics for summer latent heat flux, top soil moisture, and temperatures on the hottest day of the year (TXx), whereas for net radiation and large-scale circulation no clear trimodal behavior is detectable. This suggests that different land-atmosphere coupling strengths may be able to explain the uncertainties in temperature extremes. Constraining the full model ensemble with observed present-day correlations between summer precipitation and TXx excludes most of the very dry and dry models. In particular, the very dry models tend to overestimate the negative coupling between precipitation and TXx, resulting in a too strong warming. This is particularly relevant for global warming levels above 2 °C. The analysis allows for the first time to substantially reduce uncertainties in the projected changes of TXx in global climate models. Our results suggest that long-term temperature changes in TXx in Central Europe are about 20 % lower than projected by the multi-model median of the full ensemble. In addition, mean summer precipitation is found to be more likely to stay close to present-day levels. These results are highly relevant for improving estimates of regional climate-change impacts including heat stress, water supply and crop failure for Central Europe.

One stomatal model to rule them all? Evaluating competing hypotheses to regulate the exchange of carbon and water against experimental data

2020 ◽  
Author(s):  
Manon Sabot ◽  
Martin De Kauwe ◽  
Belinda Medlyn ◽  
Andy Pitman
Keyword(s):  
Stomatal Conductance ◽  
Land Surface ◽  
Model Comparison ◽  
Climate Models ◽  
Global Climate ◽  
Model Performance ◽  
Vapour Pressure Deficit ◽  
Global Climate Models ◽  
Model Complexity ◽  
Coupled Climate Models

<p>Nearly 2/3 of the annual global evapotranspiration (ET) over land arises from the vegetation. Yet, coupled-climate models only attribute between 22% – 58% of the annual terrestrial ET to plants. In coupled-climate models, the exchange of carbon and water between the terrestrial biosphere and the atmosphere is simulated by land-surface models (LSMs). Within those LSMs, stomatal conductance (g<sub>s</sub>) models allow plants to regulate their transpiration and carbon uptake, but most are empirically linked to climate, soil moisture availabilty, and CO<sub>2</sub>. Therefore, how and which g<sub>s</sub> schemes are implemented within LSMs is a key source of model uncertainty. This uncertainty has led to considerable investment in theory development in the recent years; multiple alternative hypotheses of optimal leaf-level regulation of gas exchange have been proposed as solutions to reduce existing model biases. However, a systematic inter-model evaluation is lacking (i.e. inter-model comparison within a single framework is needed to understand how different mechanistic assumptions across these new g<sub>s</sub> models affect plant behaviour). Here, we asked how, and under what conditions, nine novel optimal g<sub>s</sub> models differ from one another. The models were trained to match under average conditions before being subjected to: (i) a dry-down, (ii) high vapour pressure deficit, and (iii) elevated CO<sub>2</sub>. These experiments allowed us to identify the models’ specific responses and sensitivities. To further assess whether the models’ responses were realistic, we tested them against photosynthetic and hydraulic field data measured along mesic-xeric gradients in Europe and Australia. Finally, we evaluated model performance versus model complexity and the amount of information taken in by each model, which enables us to make recommendations regarding the use of stomatal conductance schemes in global climate models.</p>

Initialization and Potential Predictability of Soil Moisture in the Canadian Seasonal to Interannual Prediction System

2018 ◽  
Vol 31 (13) ◽  
pp. 5205-5224 ◽  
Author(s):  
Reinel Sospedra-Alfonso ◽  
William J. Merryfield
Keyword(s):  
Soil Moisture ◽  
Climate Models ◽  
Initial Conditions ◽  
Soil Depth ◽  
Global Climate ◽  
Global Climate Models ◽  
Prediction System ◽  
Potential Predictability ◽  
Temporal Variance ◽  
The Tropics

The initialization and potential predictability of soil moisture in CanCM4 hindcasts during 1981–2010 is assessed. CanCM4 is one of the two global climate models employed by the Canadian Seasonal to Interannual Prediction System (CanSIPS) providing operational multiseasonal forecasts for Environment and Climate Change Canada (ECCC). Soil moisture forecast initialization in CanSIPS is determined by the response of the land component to forcing from data-constrained model atmospheric fields. We evaluate hindcast initial conditions for soil moisture and its atmospheric forcings against observation-based datasets. Although model values of soil moisture variability compare relatively well with a blend of two reanalysis products, there is significant disagreement in the tropics and arid regions linked to biases in precipitation, as well as in snow-covered regions, likely the result of biases in the timing of snow onset and melt. The temporal variance of initial soil moisture anomalies is typically larger in regions of considerable precipitation variability and in cold continental areas of shallow soil depth. Appreciable variance of initial conditions, combined with persistence of the initial anomalies and the model’s ability to represent future climate variations, lead to potentially predictable soil moisture variance exceeding 60% of the total variance for up to 3–4 months in the tropics and 6–7 months in the mid- to high latitudes during hemispheric winter. Potential predictability at longer leads is primarily found in the tropics and extratropical areas of ENSO-teleconnected influences. We use lagged partial correlations to show that ENSO-teleconnected precipitation in CanCM4 is a likely source of potential predictability of soil moisture up to 1-yr lead in CanSIPS hindcasts.

scholarly journals Considerations in the Selection of Global Climate Models for Regional Climate Projections: The Arctic as a Case Study*

2011 ◽  
Vol 24 (6) ◽  
pp. 1583-1597 ◽  
Author(s):  
James E. Overland ◽  
Muyin Wang ◽  
Nicholas A. Bond ◽  
John E. Walsh ◽  
Vladimir M. Kattsov ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Regional Scale ◽  
Model Performance ◽  
Global Climate Models ◽  
The Arctic ◽  
Climate Projections ◽  
Scale Evaluation ◽  
Continental Scale ◽  
Societal Relevance

Abstract Climate projections at regional scales are in increased demand from management agencies and other stakeholders. While global atmosphere–ocean climate models provide credible quantitative estimates of future climate at continental scales and above, individual model performance varies for different regions, variables, and evaluation metrics—a less than satisfying situation. Using the high-latitude Northern Hemisphere as a focus, the authors assess strategies for providing regional projections based on global climate models. Starting with a set of model results obtained from an “ensemble of opportunity,” the core of this procedure is to retain a subset of models through comparisons of model simulations with observations at both continental and regional scales. The exercise is more one of model culling than model selection. The continental-scale evaluation is a check on the large-scale climate physics of the models, and the regional-scale evaluation emphasizes variables of ecological or societal relevance. An additional consideration is given to the comprehensiveness of processes included in the models. In many but not all applications, different results are obtained from a reduced set of models compared to relying on the simple mean of all available models. For example, in the Arctic the top-performing models tend to be more sensitive to greenhouse forcing than the poorer-performing models. Because of the mostly unexplained inconsistencies in model performance under different selection criteria, simple and transparent evaluation methods are favored. The use of a single model is not recommended. For some applications, no model may be able to provide a suitable regional projection. The use of model evaluation strategies, as opposed to relying on simple averages of ensembles of opportunity, should be part of future synthesis activities such as the upcoming Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

scholarly journals Estimating the potential impact of climate change on sunflower yield in the Konya province of Turkey

2021 ◽  
pp. 1-13
Author(s):  
Hudaverdi Gurkan ◽  
Vakhtang Shelia ◽  
Nilgun Bayraktar ◽  
Y. Ersoy Yildirim ◽  
Nebi Yesilekin ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Model Performance ◽  
Global Climate Models ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Adaptation To Climate Change ◽  
Impact Of Climate Change ◽  
The Impact

Abstract The impact of climate change on agricultural productivity is difficult to assess. However, determining the possible effects of climate change is an absolute necessity for planning by decision-makers. The aim of the study was the evaluation of the CSM-CROPGRO-Sunflower model of DSSAT4.7 and the assessment of impact of climate change on sunflower yield under future climate projections. For this purpose, a 2-year sunflower field experiment was conducted under semi-arid conditions in the Konya province of Turkey. Rainfed and irrigated treatments were used for model analysis. For the assessment of impact of climate change, three global climate models and two representative concentration pathways, i.e. 4.5 and 8.5 were selected. The evaluation of the model showed that the model was able to simulate yield reasonably well, with normalized root mean square error of 1.3% for the irrigated treatment and 17.7% for the rainfed treatment, a d-index of 0.98 and a modelling efficiency of 0.93 for the overall model performance. For the climate change scenarios, the model predicted that yield will decrease in a range of 2.9–39.6% under rainfed conditions and will increase in a range of 7.4–38.5% under irrigated conditions. Results suggest that temperature increases due to climate change will cause a shortening of plant growth cycles. Projection results also confirmed that increasing temperatures due to climate change will cause an increase in sunflower water requirements in the future. Thus, the results reveal the necessity to apply adequate water management strategies for adaptation to climate change for sunflower production.

scholarly journals Comparison of CMIP6 and CMIP5 Model Performance in Simulating Historical Precipitation and Temperature in Bangladesh: A Preliminary Study

2021 ◽  
Author(s):  
Mohammad Kamruzzaman ◽  
Shamsuddin Shahid ◽  
ARM Towfiqul Islam ◽  
Syewoon Hwang ◽  
Jaepil Cho ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Model Performance ◽  
Coupled Model ◽  
Skill Score ◽  
Global Climate Models ◽  
Added Value ◽  
Lower Performance ◽  
Mean Square Errors ◽  
Cmip5 Gcms

Abstract The relative performance of global climate models (GCMs) of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. The multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skilful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, a significant improvement in CMIP6 MME compared to CMIP5 MME was noticed in simulating rainfall over Bangladesh at annual and seasonal scales. CMIP6 MME also showed significant reduction in maximum and minimum temperature biases over Bangladesh. However, systematic wet and cold biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlation with observed data compared to CMIP5 GCMs, but higher difference in terms of standard deviations and centered root mean square errors, indicating better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables for different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature compared to CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is incompatible with the climate models used in this research.

scholarly journals Hydroclimate variability in Scandinavia over the last millennium – insights from a climate model-proxy data comparison

2017 ◽  
Author(s):  
Kristina Seftigen ◽  
Hugues Goosse ◽  
Francois Klein ◽  
Deliang Chen
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Model Performance ◽  
Circulation Model ◽  
Global Climate Models ◽  
Last Millennium ◽  
Atmospheric Variability ◽  
Out Of Sample

Abstract. The integration of climate proxy information with General Circulation Model (GCM) results offers considerable potential for deriving greater understanding of the mechanisms underlying climate variability, as well as unique opportunities for out-of-sample evaluations of model performance. In this study, we combine insights from a new tree-ring hydroclimate reconstruction from Scandinavian with projections from a suite of forced transient simulations of the last millennium and historical intervals from the CMIP5 and PMIP3 archives. Model simulations and proxy reconstruction data are found to broadly agree on the modes of atmospheric variability that produces droughts/pluvials in the region. But despite these dynamical similarities, large differences between simulated and reconstructed hydroclimate time series remain. We find simulated interannual components of variability to be overestimated, while the multidecadal/longer timescale components generally are too weak. Specifically, summertime moisture variability and temperature are weakly negatively associated at inter-annual timescales but positively correlated at decadal timescales, revealed from observational and proxy evidences. On this background, the CMIP5/PMIP3 simulated timescale dependent relationship between regional precipitation and temperature is considerably biased, because the short-term negative association is overestimated, and the long-term relationship is significantly underestimated. The lack of adequate understanding for mechanisms linking temperature and moisture supply on longer timescales has important implication for future projections. Weak multidecadal variability in models also implies that inference about future persistent droughts and pluvials based on the latest generation global climate models will likely underestimate the true risk of these events.

scholarly journals Increased Drought Risk in South Asia under Warming Climate: Implications of Uncertainty in Potential Evapotranspiration Estimates

2020 ◽  
Vol 21 (12) ◽  
pp. 2979-2996 ◽  
Author(s):  
Saran Aadhar ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
South Asia ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Global Climate Models ◽  
Drought Risk ◽  
Severe Drought ◽  
Considerable Uncertainty ◽  
Warming Climate

AbstractObserved and projected changes in potential evapotranspiration (PET) and drought are not well constrained in South Asia. Using five PET estimates [Thornthwaite (PET-TH), Hargreaves–Samani (PET-HS), Penman–Monteith (PET-PM), modified Penman–Monteith (PET-MPM), and energy (PET-EN)] for the observed (1979–2018, from ERA5) and future warming climate, we show that significant warming has occurred in South Asia during 1979–2018. PET changes show considerable uncertainty depending on the method used. For instance, PET-TH has increased significantly while all the other four methods show a decline in PET in the majority of South Asia during the observed period of 1979–2018. The increase in PET-TH is substantially higher than PET-HS, PET-PM, and PET-MPM due to a higher (3–4 times) sensitivity of PET-TH to warming during the observed period. Under the 1.5°, 2.0°, and 2.5°C warming worlds, global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5 GCMs) project increases in PET and drought frequency over the majority of the regions. Drought estimates based on PET-EN and PET-MPM are consistent with soil moisture–based drought estimates and project a substantial increase in the frequency of severe droughts under warming climate in South Asia. In addition, the projected frequency of severe drought based on PET-TH, which is an outlier, is about 5 times higher than PET-EN and PET-MPM. Methods to estimate PET contribute the most in the overall uncertainty of PET and drought projections in South Asia, primarily due to PET-TH. Drought estimates based on PET-TH are not reliable for the observed and projected future climate. Therefore, future drought projections should be either based on PET-EN/PET-MPM or soil moisture.

scholarly journals Future Arctic marine access: analysis and evaluation of observations, models, and projections of sea ice

2012 ◽  
Vol 6 (5) ◽  
pp. 3963-3998 ◽  
Author(s):  
T. S. Rogers ◽  
J. E. Walsh ◽  
T. S. Rupp ◽  
L. W. Brigham ◽  
M. Sfraga
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Model Performance ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Sea Ice Extent ◽  
State And Local

Abstract. There is an emerging need for regional applications of sea ice projections to provide more accuracy and greater detail to scientists, national, state and local planners, and other stakeholders. The present study offers a prototype for a comprehensive, interdisciplinary study to bridge observational data, climate model simulations, and user needs. The study's first component is an observationally-based evaluation of Arctic sea ice trends during 1980–2008, with an emphasis on seasonal and regional differences relative to the overall pan-Arctic trend. Regional sea ice los has varied, with a significantly larger decline of winter maximum (January–March) extent in the Atlantic region than in other sectors. A lead-lag regression analysis of Atlantic sea ice extent and ocean temperatures indicates that reduced sea ice extent is associated with increased Atlantic Ocean temperatures. Correlations between the two variables are greater when ocean temperatures lag rather than lead sea ice. The performance of 13 global climate models is evaluated using three metrics to compare sea ice simulations with the observed record. We rank models over the pan-Arctic domain and regional quadrants, and synthesize model performance across several different studies. The best performing models project reduced ice cover across key access routes in the Arctic through 2100, with a lengthening of seasons for marine operations by 1–3 months. This assessment suggests that the Northwest and Northeast Passages hold potential for enhanced marine access to the Arctic in the future, including shipping and resource development opportunities.

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