scholarly journals « Idol with feet of clay »: reliable predictions of forest ecosystem functioning require flawless climate forcings

2021 ◽  
Author(s):  
M. Jourdan ◽  
C. François ◽  
N. Delpierre ◽  
N. Martin St-Paul ◽  
E. Dufrêne
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Impact Model ◽  
Climate Data ◽  
Large Spatial Scale ◽  
Large Variability ◽  
Model Predictions ◽  
Climate Forcings ◽  
The Impact ◽  
Impact Models

AbstractClimate change affects various aspects of the functioning of ecosystem, especially photosynthesis, respiration and carbon storage. We need accurate modelling approaches (impact models) to simulate the functioning, vitality and provision of ecosystem services of forests in a warmer world. These impact models require climate data as forcings, which are often produced by climate models comparing more or less well with observational climate data. The bias percentage of the climate forcings propagates throughout the modeling chain from the climate model to the impact model.In this study, we aimed to quantify these bias percentage, addressing three questions: (1) Do the impact model predictions vary when forcing it with different climate models, and how do the predictions under climate model vs. observational climate forcing differ? (2) Does the variability in the impact climate simulations caused by climate forcings fade out at large spatial scale? (3) How the fact of using simulated climatic data affects the process-based model predictions in the case of stressful events?To answer these questions, we present results obtained over the historical period (e.g. 1970-2010) with the CASTANEA ecophysiological forest model and use the data from three climate models. Our analysis focuses on French forests, studying European beech (Fagus sylvatica), temperate deciduous oaks (Quercus robur and Q. petraea), Scots pine (Pinus sylvestris) and spruce (Picea abies) monospecific stands.We show that prediction of photosynthesis, respiration and wood growth highly depends on the climate model used, whether debiased or not, and also on species and region considered. Overall, we observed an improvement of prediction after a monthly mean bias or monthly quantile mapping correction for three model considered, but not with the same success. Then we highlighted a large variability in the processes simulated by the impact model under different climate forcings when considering the plot (i.e. scale of a few hectares) scale. This variability fades out at larger scale (e.g. the scale of an ecological region, i.e. 100 km2), owing to an aggregation effect. Moreover, process predictions obtained under different climate forcings are more variable during driest years. These results highlight the necessity to quantify bias and uncertainties in climate forcings before predicting fluxes dynamics with process-based model.

scholarly journals Traiter l'incertitude des projections climatiques (essai)

2018 ◽  
Vol 169 (4) ◽  
pp. 203-209 ◽  
Author(s):  
Guillaume Evin ◽  
Nicolas Eckert ◽  
Benoît Hingray ◽  
Deborah Verfaillie ◽  
Samuel Morin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Climate Models ◽  
Climate Projections ◽  
Impact Model ◽  
Mountainous Region ◽  
Climate Change Projections ◽  
Sources Of Uncertainty ◽  
The Impact ◽  
Different Sources ◽  
Impact Models

Handling the uncertainty of climate change projections (essay) The uncertainties associated with climate projections cannot be ignored when those projections are used. They arise from uncertainties surrounding developments for greenhouse gases, uncertainties arising from the climate models and the impact models (hydrology, biodiversity etc.), as well as the natural variability of the climate. To describe these different sources of uncertainty, and take them into account is not easy for engineers and managers, who are more accustomed to reasoning in a determinist framework. This article aims to demonstrate that statistics offers a number of approaches which make it possible to use a given multiscenario, multimodel climate projection. The simplest propose a descriptive summary of the information, while the more complex approaches can test the significance of the expected changes, to separate the different causes of uncertainty and measure each one's contribution to the overall variability. The approach is illustrated by a set of projections for temperature, precipitation and mean snow depth for a mountainous region of the French Alps. It can be easily applied to any variable whose projections have been generated through an impact model which simulates the consequences of a set of climate projections for a given sector defined in social and ecosystem terms.

scholarly journals Half a degree Additional warming, Projections, Prognosis and Impacts (HAPPI): Background and Experimental Design

2016 ◽  
Author(s):  
Daniel Mitchell ◽  
Krishna AchutaRao ◽  
Myles Allen ◽  
Ingo Bethke ◽  
Piers Forster ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Greenhouse Gas Emission ◽  
Coupled Model ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Internal Climate Variability ◽  
Large Ensembles ◽  
Half Degree ◽  
The Impact

Abstract. The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the Half a degree Additional warming, Projections, Prognosis and Impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 °C and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of integrated assessment models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slice experiments are proposed, each a decade in length; the first being the most recent observed 10-year period (2006–2015), the second two being estimates of the a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the Representative Concentration Pathways 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.

scholarly journals Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design

2017 ◽  
Vol 10 (2) ◽  
pp. 571-583 ◽  
Author(s):  
Daniel Mitchell ◽  
Krishna AchutaRao ◽  
Myles Allen ◽  
Ingo Bethke ◽  
Urs Beyerle ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Greenhouse Gas Emission ◽  
Coupled Model ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Internal Climate Variability ◽  
Low Emission ◽  
Half Degree ◽  
The Impact

Abstract. The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios.Large ensembles of simulations (>  50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006–2015), the second two being estimates of a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.

scholarly journals Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Assessment Tool ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Data ◽  
The Future ◽  
The Impact ◽  
Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

scholarly journals The representation of solar cycle signals in stratospheric ozone. Part II: Analysis of global models

2017 ◽  
Author(s):  
Amanda C. Maycock ◽  
Katja Matthes ◽  
Susann Tegtmeier ◽  
Hauke Schmidt ◽  
Rémi Thiéblemont ◽  
...  
Keyword(s):  
Solar Cycle ◽  
Solar Irradiance ◽  
Climate Models ◽  
Climate Model ◽  
Stratospheric Ozone ◽  
Temperature Response ◽  
Solar Variability ◽  
High Latitudes ◽  
Stratospheric Temperature ◽  
The Impact

Abstract. The impact of changes in incoming solar irradiance on stratospheric ozone abundances should be included in climate model simulations to fully capture the atmospheric response to solar variability. This study presents the first systematic comparison of the solar-ozone response (SOR) during the 11 year solar cycle amongst different chemistry-climate models (CCMs) and ozone databases specified in climate models that do not include chemistry. We analyse the SOR in eight CCMs from the WCRP/SPARC Chemistry-Climate Model Initiative (CCMI-1) and compare these with three ozone databases: the Bodeker Scientific database, the SPARC/AC&C database for CMIP5, and the SPARC/CCMI database for CMIP6. The results reveal substantial differences in the representation of the SOR between the CMIP5 and CMIP6 ozone databases. The peak amplitude of theSOR in the upper stratosphere (1–5 hPa) decreases from 5 % to 2 % between the CMIP5 and CMIP6 databases. This difference is because the CMIP5 database was constructed from a regression model fit to satellite observations, whereas the CMIP6 database is constructed from CCM simulations, which use a spectral solar irradiance (SSI) dataset with relatively weak UV forcing. The SOR in the CMIP6 ozone database is therefore implicitly more similar to the SOR in the CCMI-1 models than to the CMIP5 ozone database, which shows a greater resemblance in amplitude and structure to the SOR in the Bodeker database. The latitudinal structure of the annual mean SOR in the CMIP6 ozone database and CCMI-1 models is considerably smoother than in the CMIP5 database, which shows strong gradients in the SOR across the midlatitudes owing to the paucity of observations at high latitudes. The SORs in the CMIP6 ozone database and in the CCMI-1 models show a strong seasonal dependence, including large meridional gradients at mid to high latitudes during winter; such seasonal variations in the SOR are not included in the CMIP5 ozone database. Sensitivity experiments with a global atmospheric model without chemistry (ECHAM6.3) are performed to assess the impact of changes in the representation of the SOR and SSI forcing between CMIP5 and CMIP6. The experiments show that the smaller amplitude of the SOR in the CMIP6 ozone database compared to CMIP5 causes a decrease in the modelled tropical stratospheric temperature response over the solar cycle of up to 0.6 K, or around 50 % of the total amplitude. The changes in the SOR explain most of the difference in the amplitude of the tropical stratospheric temperature response in the case with combined changes in SOR and SSI between CMIP5 and CMIP6. The results emphasise the importance of adequately representing the SOR in climate models to capture the impact of solar variability on the atmosphere. Since a number of limitations in the representation of the SOR in the CMIP5 ozone database have been identified, CMIP6 models without chemistry are encouraged to use the CMIP6 ozone database to capture the climate impacts of solar variability.

Evaluating the Consistency between Statistically Downscaled and Global Dynamical Model Climate Change Projections

2008 ◽  
Vol 21 (22) ◽  
pp. 6052-6059 ◽  
Author(s):  
B. Timbal ◽  
P. Hope ◽  
S. Charles
Keyword(s):  
Global Warming ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Specific Humidity ◽  
Model Output ◽  
Winter Rainfall ◽  
Direct Model ◽  
The Impact

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

scholarly journals Cluster-based analysis of multi-model climate ensembles

2018 ◽  
Vol 11 (6) ◽  
pp. 2033-2048 ◽  
Author(s):  
Richard Hyde ◽  
Ryan Hossaini ◽  
Amber A. Leeson
Keyword(s):  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Climate Models ◽  
Climate Model ◽  
Complex Data ◽  
Similar Data ◽  
Climate Data ◽  
Efficient Manner ◽  
Clustering Techniques ◽  
Insight Into

Abstract. Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model–observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry–climate model (CCM) output of tropospheric ozone – an important greenhouse gas – from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ∼ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ∼ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere – where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.

Influence of Ocean and Atmosphere Components on Simulated Climate Sensitivities

2013 ◽  
Vol 26 (1) ◽  
pp. 231-245 ◽  
Author(s):  
Michael Winton ◽  
Alistair Adcroft ◽  
Stephen M. Griffies ◽  
Robert W. Hallberg ◽  
Larry W. Horowitz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Model Simulation ◽  
Coupled Model ◽  
Ocean Model ◽  
Historical Simulation ◽  
Model Version ◽  
Salinity Response ◽  
The Impact

Abstract The influence of alternative ocean and atmosphere subcomponents on climate model simulation of transient sensitivities is examined by comparing three GFDL climate models used for phase 5 of the Coupled Model Intercomparison Project (CMIP5). The base model ESM2M is closely related to GFDL’s CMIP3 climate model version 2.1 (CM2.1), and makes use of a depth coordinate ocean component. The second model, ESM2G, is identical to ESM2M but makes use of an isopycnal coordinate ocean model. The authors compare the impact of this “ocean swap” with an “atmosphere swap” that produces the GFDL Climate Model version 3 (CM3) by replacing the AM2 atmospheric component with AM3 while retaining a depth coordinate ocean model. The atmosphere swap is found to have much larger influence on sensitivities of global surface temperature and Northern Hemisphere sea ice cover. The atmosphere swap also introduces a multidecadal response time scale through its indirect influence on heat uptake. Despite significant differences in their interior ocean mean states, the ESM2M and ESM2G simulations of these metrics of climate change are very similar, except for an enhanced high-latitude salinity response accompanied by temporarily advancing sea ice in ESM2G. In the ESM2G historical simulation this behavior results in the establishment of a strong halocline in the subpolar North Atlantic during the early twentieth century and an associated cooling, which are counter to observations in that region. The Atlantic meridional overturning declines comparably in all three models.

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.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

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