Surface Albedo Feedback Estimates for the AR4 Climate Models

2006 ◽  
Vol 19 (3) ◽  
pp. 359-365 ◽  
Author(s):  
Michael Winton
Keyword(s):  
Climate Change ◽  
Standard Deviation ◽  
Surface Albedo ◽  
Climate Models ◽  
Climate Model ◽  
Intergovernmental Panel ◽  
Albedo Feedback ◽  
Mean Climate ◽  
Carbon Dioxide Co2 ◽  
The Impact

Abstract A technique for estimating surface albedo feedback (SAF) from standard monthly mean climate model diagnostics is applied to the 1% yr−1 carbon dioxide (CO2)-increase transient climate change integrations of 12 Intergovernmental Panel on Climate Change (IPCC) fourth assessment report (AR4) climate models. Over the 80-yr runs, the models produce a mean SAF at the surface of 0.3 W m−2 K−1 with a standard deviation of 0.09 W m−2 K−1. Relative to 2 × CO2 equilibrium run estimates from an earlier group of models, both the mean SAF and the standard deviation are reduced. Three-quarters of the model mean SAF comes from the Northern Hemisphere in roughly equal parts from the land and ocean areas. The remainder is due to Southern Hemisphere ocean areas. The SAF differences between the models are shown to stem mainly from the sensitivity of the surface albedo to surface temperature rather from the impact of a given surface albedo change on the shortwave budget.

scholarly journals Assessing Snow Albedo Feedback in Simulated Climate Change

2006 ◽  
Vol 19 (11) ◽  
pp. 2617-2630 ◽  
Author(s):  
Xin Qu ◽  
Alex Hall
Keyword(s):  
Climate Change ◽  
Standard Deviation ◽  
Northern Hemisphere ◽  
Surface Albedo ◽  
Climate Models ◽  
Attenuation Effect ◽  
Albedo Feedback ◽  
Snow Albedo ◽  
The Mean ◽  
Planetary Albedo

Abstract In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

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 The Impact of Climate Change on Material Degradation Criteria in Heritage over Iran: Regional Climate Model Evaluation

2020 ◽  
Vol 172 ◽  
pp. 02006
Author(s):  
Hamed Hedayatnia ◽  
Marijke Steeman ◽  
Nathan Van Den Bossche
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Salt Crystallization ◽  
Climate Modelling ◽  
Impact Of Climate Change ◽  
Climate Change Effects ◽  
The Impact

Understanding how climate change accelerates or slows down the process of material deterioration is the first step towards assessing adaptive approaches for the preservation of historical heritage. Analysis of the climate change effects on the degradation risk assessment parameters like salt crystallization cycles is of crucial importance when considering mitigating actions. Due to the vulnerability of cultural heritage in Iran to climate change, the impact of this phenomenon on basic parameters plus variables more critical to building damage like salt crystallization index needs to be analyzed. Regional climate modelling projections can be used to asses the impact of climate change effects on heritage. The output of two different regional climate models, the ALARO-0 model (Ghent University-RMI, Belgium) and the REMO model (HZG-GERICS, Germany), is analyzed to find out which model is more adapted to the region. So the focus of this research is mainly on the evaluation to determine the reliability of both models over the region. For model validation, a comparison between model data and observations was performed in 4 different climate zones for 30 years to find out how reliable these models are in the field of building pathology.

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

scholarly journals Atmospheric Blocking and Mean Biases in Climate Models

2010 ◽  
Vol 23 (23) ◽  
pp. 6143-6152 ◽  
Author(s):  
Adam A. Scaife ◽  
Tim Woollings ◽  
Jeff Knight ◽  
Gill Martin ◽  
Tim Hinton
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Model Data ◽  
Atmospheric Blocking ◽  
Intergovernmental Panel ◽  
Weather And Climate ◽  
Main Culprit

Abstract Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.

Timothy yield and nutritive value with a three-harvest system under the projected future climate in Canada

2014 ◽  
Vol 94 (2) ◽  
pp. 213-222 ◽  
Author(s):  
Qi Jing ◽  
Gilles Bélanger ◽  
Budong Qian ◽  
Vern Baron
Keyword(s):  
Climate Change ◽  
Nutritive Value ◽  
Climate Models ◽  
Phleum Pratense ◽  
Global Climate Models ◽  
Future Climate ◽  
Climate Scenarios ◽  
Intergovernmental Panel ◽  
Stochastic Weather Generator ◽  
The Impact

Jing, Q., Bélanger, G., Qian, B. and Baron, V. 2014. Timothy yield and nutritive value with a three-harvest system under the projected future climate in Canada. Can. J. Plant Sci. 94: 213–222. Timothy (Phleum pratense L.) is harvested twice annually in Canada but with projected climate change, an additional harvest may be possible. Our objective was to evaluate the impact on timothy dry matter (DM) yield and key nutritive value attributes of shifting from a two- to a three-harvest system under projected future climate conditions at 10 sites across Canada. Future climate scenarios were generated with a stochastic weather generator (AAFC-WG) using two global climate models under the forcing of two Intergovernmental Panel on Climate Change emission scenarios and, then, used by the CATIMO (Canadian Timothy Model) grass model to simulate DM yield and key nutritive value attributes. Under future climate scenarios (2040–2069), the additional harvest and the resulting three-harvest system are expected to increase annual DM yield (+0.46 to +2.47 Mg DM ha−1) compared with a two-harvest system across Canada but the yield increment will on average be greater in eastern Canada (1.88 Mg DM ha−1) and Agassiz (2.02 Mg DM ha−1) than in the prairie provinces of Canada (0.84 Mg DM ha−1). The DM yield of the first harvest in a three-harvest system is expected to be less than in the two-harvest system, while that of the second harvest would be greater. Decreases in average neutral detergent fibre (NDF) concentration (−19 g kg−1 DM) and digestibility (dNDF, −5 g kg−1 NDF) are also expected with the three-harvest system under future conditions. Our results indicate that timothy will take advantage of projected climate change, through taking a third harvest, thereby increasing annual DM production.

scholarly journals Assessment of the impacts of climate change on maize production in the Wami Ruvu basin of Tanzania

2016 ◽  
Vol 8 (1) ◽  
pp. 142-164 ◽  
Author(s):  
Philbert Luhunga ◽  
Ladslaus Chang'a ◽  
George Djolov
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Projections ◽  
Intergovernmental Panel ◽  
Lower Altitude ◽  
Rcp 8.5 ◽  
Maize Yields ◽  
The Impact ◽  
Impacts Of Climate Change

The IPCC (Intergovernmental Panel on Climate Change) assessment reports confirm that climate change will hit developing countries the hardest. Adaption is on the agenda of many countries around the world. However, before devising adaption strategies, it is crucial to assess and understand the impacts of climate change at regional and local scales. In this study, the impact of climate change on rain-fed maize (Zea mays) production in the Wami-Ruvu basin of Tanzania was evaluated using the Decision Support System for Agro-technological Transfer. The model was fed with daily minimum and maximum temperatures, rainfall and solar radiation for current climate conditions (1971–2000) as well as future climate projections (2010–2099) for two Representative Concentration Pathways: RCP 4.5 and RCP 8.5. These data were derived from three high-resolution regional climate models, used in the Coordinated Regional Climate Downscaling Experiment program. Results showed that due to climate change future maize yields over the Wami-Ruvu basin will slightly increase relative to the baseline during the current century under RCP 4.5 and RCP 8.5. However, maize yields will decline in the mid and end centuries. The spatial distribution showed that high decline in maize yields are projected over lower altitude regions due to projected increase in temperatures in those areas.

scholarly journals An Investigation of Parameter Sensitivity of Minimum Complexity Earth Simulator

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 95
Author(s):  
Jiewei Chen ◽  
Huijuan Cui ◽  
Yangyang Xu ◽  
Quansheng Ge
Keyword(s):  
Climate Change ◽  
Sensitivity Analysis ◽  
Climate Models ◽  
Climate Model ◽  
Greenhouse Gas Emission ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis ◽  
Output Measurement ◽  
Sensitivity Analysis Method ◽  
The Impact

Climate change, induced by human greenhouse gas emission, has already influenced the environment and society. To quantify the impact of human activity on climate change, scientists have developed numerical climate models to simulate the evolution of the climate system, which often contains many parameters. The choice of parameters is of great importance to the reliability of the simulation. Therefore, parameter sensitivity analysis is needed to optimize the parameters for the model so that the physical process of nature can be reasonably simulated. In this study, we analyzed the parameter sensitivity of a simple carbon-cycle energy balance climate model, called the Minimum Complexity Earth Simulator (MiCES), in different periods using a multi-parameter sensitivity analysis method and output measurement method. The results show that the seven parameters related to heat and carbon transferred are most sensitive among all 37 parameters. Then uncertainties of the above key parameters are further analyzed by changing the input emission and temperature, providing reference bounds of parameters with 95% confidence intervals. Furthermore, we found that ocean heat capacity will be more sensitive if the simulation time becomes longer, indicating that ocean influence on climate is stronger in the future.

The implications of soil erosion model conceptualization, bias-correction methods and climate model ensembles on soil erosion projections under climate change

2020 ◽  
Author(s):  
Joris de Vente ◽  
Joris Eekhout
Keyword(s):  
Climate Change ◽  
Soil Erosion ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Erosion Model ◽  
Distribution Mapping ◽  
Climate Model Ensembles ◽  
Model Ensembles ◽  
The Impact

<p>Climate models project increased extreme precipitation for the coming decades, which may lead to higher soil erosion in many locations worldwide. The impact of climate change on soil erosion is most often assessed by applying a soil erosion model forced by bias-corrected climate model output. A literature review among more than 100 papers showed that many studies use different soil erosion models, bias-correction methods and climate model ensembles. In this study, we assessed how these differences affect the outcome of climate change impact assessments on soil erosion. The study was performed in two contrasting Mediterranean catchments (SE Spain), where climate change is projected to lead to a decrease in annual precipitation sum and an increase in extreme precipitation, based on the RCP8.5 emission scenario. First, we assessed the impact of soil erosion model selection using the three most widely used model concepts, i.e. a model forced by precipitation (RUSLE), a model forced by runoff (MUSLE), and a model forced by precipitation and runoff (MMF). Depending on the model, soil erosion in the study area is projected to decrease (RUSLE) or increase (MUSLE and MMF). The differences between the model projections are inherently a result of their model conceptualization, such as a decrease of soil loss due to decreased annual precipitation sum (RUSLE) and an increase of soil loss due to increased extreme precipitation and, consequently, increased runoff (MUSLE). An intermediate result is obtained with MMF, where a projected decrease in detachment by raindrop impact is counteracted by a projected increase in detachment by runoff. Second, we evaluated the implications of three bias‐correction methods, i.e. delta change, quantile mapping and scaled distribution mapping. Scaled distribution mapping best reproduces the raw climate change signal, in particular for extreme precipitation. Depending on the bias‐correction method, soil erosion is projected to decrease (delta change) or increase (quantile mapping and scaled distribution mapping). Finally, we assessed the effect of climate model ensembles on soil erosion projections. We showed that individual climate models may project opposite changes with respect to the ensemble average, hence, climate model ensembles are essential in soil erosion impact assessments to account for climate model uncertainty. We conclude that in climate change impact assessments it is important to select a soil erosion model that is forced by both precipitation and runoff, which under climate change may have a contrasting effect on soil erosion. Furthermore, the impact of climate change on soil erosion can only accurately be assessed with a bias‐correction method that best reproduces the projected climate change signal, in combination with a representative ensemble of climate models.</p>

scholarly journals The Impact of Stratospheric Ozone Recovery on Tropopause Height Trends

2009 ◽  
Vol 22 (2) ◽  
pp. 429-445 ◽  
Author(s):  
Seok-Woo Son ◽  
Lorenzo M. Polvani ◽  
Darryn W. Waugh ◽  
Thomas Birner ◽  
Hideharu Akiyoshi ◽  
...  
Keyword(s):  
Climate Change ◽  
Southern Hemisphere ◽  
Climate Models ◽  
Stratospheric Ozone ◽  
Reliable Estimate ◽  
Intergovernmental Panel ◽  
The Future ◽  
Realistic Representation ◽  
High Vertical Resolution ◽  
The Impact

Abstract The evolution of the tropopause in the past, present, and future climate is examined by analyzing a set of long-term integrations with stratosphere-resolving chemistry climate models (CCMs). These CCMs have high vertical resolution near the tropopause, a model top located in the mesosphere or above, and, most important, fully interactive stratospheric chemistry. Using such CCM integrations, it is found that the tropopause pressure (height) will continue to decrease (increase) in the future, but with a trend weaker than that in the recent past. The reduction in the future tropopause trend is shown to be directly associated with stratospheric ozone recovery. A significant ozone recovery occurs in the Southern Hemisphere lower stratosphere of the CCMs, and this leads to a relative warming there that reduces the tropopause trend in the twenty-first century. The future tropopause trends predicted by the CCMs are considerably smaller than those predicted by the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) models, especially in the southern high latitudes. This difference persists even when the CCMs are compared with the subset of the AR4 model integrations for which stratospheric ozone recovery was prescribed. These results suggest that a realistic representation of the stratospheric processes might be important for a reliable estimate of tropopause trends. The implications of these finding for the Southern Hemisphere climate change are also discussed.

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