Evaluating sources of uncertainty in modelling the impact of probabilistic climate change on sub-arctic palsa mires

Abstract. We present an analysis of different sources of impact model uncertainty and combine this with probabilistic projections of climate change. Climatic envelope models describing the spatial distribution of palsa mires (mire complexes with permafrost peat hummocks) in northern Fennoscandia were calibrated for three baseline periods, eight state-of-the-art modelling techniques and 25 versions sampling the parameter uncertainty of each technique – a total of 600 models. The sensitivity of these models to changes in temperature and precipitation was analysed to construct impact response surfaces. These were used to assess the behaviour of models when extrapolated into changed climate conditions, so that new criteria, in addition to conventional model evaluation statistics, could be defined for determining model reliability. Impact response surfaces were also combined with climate change projections to estimate the risk of areas suitable for palsas disappearing during the 21st century. Structural differences in impact models appeared to be a major source of uncertainty, with 60% of the models giving implausible projections. Generalized additive modelling (GAM) was judged to be the most reliable technique for model extrapolation. Using GAM, it was estimated as very likely (>90% probability) that the area suitable for palsas is reduced to less than half the baseline area by the period 2030–2049 and as likely (>66% probability) that the entire area becomes unsuitable by 2080–2099 (A1B emission scenario). The risk of total loss of palsa area was reduced for a mitigation scenario under which global warming was constrained to below 2 °C relative to pre-industrial climate, although it too implied a considerable reduction in area suitable for palsas.

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Climate change impact evaluation in various regions in Europe on the base of ensemble modelling

10.5194/egusphere-egu2020-18052 ◽

2020 ◽

Author(s):

Jaromir Krzyszczak ◽

Piotr Baranowski ◽

Monika Zubik

Keyword(s):

Climate Change ◽

Response Surfaces ◽

Mitigation Strategies ◽

Climate Conditions ◽

Crop Models ◽

Climate Change Uncertainty ◽

Arable Fields ◽

Four Levels ◽

The Impact ◽

Adaptation Options

Climate change uncertainty largely complicates adaptation and risk management evaluation at the regional level, therefore new approaches for managing this uncertainty are still being developed. In this study three crop models (DNDC, WOFOST and DSSAT) were used to explore the utility of impact response surfaces (IRS) and adaptation response surfaces (ARS) methodologies (Pirttioja et al., 2015; Ruiz-Ramos et al., 2018).To build IRS, the sensitivity of modelled yield to systematic increments of changes in temperature (-1 to +6&#176;C) and precipitation (-30 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather. Four levels of CO2 (360, 447, 522 and 601 ppm) representing future conditions until 2070 were considered. In turn, to build ARS, adaptation options were: shortening or extending the crop cycle of the standard cultivar, sowing earlier or later than the standard date and additional irrigation. Preliminary data indicate that yields are declining with higher temperatures and decreased precipitation. Yield is more sensitive to changes in baseline temperature values and much less sensitive to changes in baseline precipitation values for arable fields in Finland, while for arable fields in Germany, ARS indicates yield sensitivity at a similar level for both variables. Also, our data suggests that some adaptation options provides increase of the yield up to 1500 kg/ha, which suggest that ARSs may be valuable tool for planning an effective adaptation treatments. This research shows how to analyze and assess the impact of adaptation strategies in the context of the high level of regional uncertainty in relation to future climate conditions. Developed methodology can be applied to other climatic zones to help in planning adaptation and mitigation strategies.This study has been partly financed from the funds of the Polish National Centre for Research and Development in frame of the project: MSINiN, contract number: BIOSTRATEG3/343547/8/NCBR/2017

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Land-surface feedbacks on temperature and precipitation in CMIP6-LS3MIP projections

10.5194/egusphere-egu21-12175 ◽

2021 ◽

Author(s):

Franco Catalano ◽

Andrea Alessandri ◽

Wilhelm May ◽

Thomas Reerink

Keyword(s):

Climate Change ◽

Soil Moisture ◽

Land Surface ◽

Hydrological Cycle ◽

Climate Change Signal ◽

Moisture Change ◽

Climate Conditions ◽

Temperature And Precipitation ◽

Systematic Biases ◽

The Impact

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) aims at diagnosing systematic biases in the land models of CMIP6 Earth System Models and assessing the role of land-atmosphere feedbacks on climate change. Two components of experiments have been designed: the first is devoted to the assessment of the systematic land biases in offline mode (LMIP) while the second component is dedicated to the analysis of the land feedbacks in coupled mode (LFMIP). Here we focus on the LFMIP experiments. In the LFMIP protocol (van den Hurk et al. 2016), which builds upon the GLACE-CMIP configuration, two sets of climate-sensitivity projections have been carried out in amip mode: in the first set (amip-lfmip-pdLC) the land feedbacks to climate change have been disabled by prescribing the soil-moisture states from a climatology derived from &#8220;present climate conditions&#8221; (1980-2014) while in the second set (amip-lfmip-rmLC) 30-year running mean of land-surface state from the corresponding ScenarioMIP experiment (O&#8217;Neill et al., 2016) is prescribed. The two sensitivity simulations span the period 1980-2100 with sea surface temperature and sea-ice conditions prescribed from the first member of historical and ScenarioMIP experiments. Two different scenarios are considered: SSP1-2.6 (f1) and SSP5-8.5 (f2).In this analysis, we focus on the differences between amip-lfmip-rmLC and amip-lfmip-pdLC at the end of the 21st Century (2071&#8211;2100) in order to isolate the impact of the soil moisture changes on surface climate change. The (2071-2100) minus (1985-2014) temperature change is positive everywhere over land and the climate change signal of precipitation displays a clear intensification of the hydrological cycle in the Northern Hemisphere. Warming and hydrological cycle intensification are larger in SSP5-8.5 scenario. Results show large differences in the feedbacks between wet, transition and semi-arid climates. In particular, over the regions with negative soil moisture change, the 2m-temperature increases significantly while the cooling signal is not significant over all the regions getting wetter. In agreement with Catalano et al. (2016), the larger effects on precipitation due to soil moisture forcing occur mostly over transition zones between dry and wet climates, where evaporation is highly sensitive to soil moisture. The sensitivity of both 2m-temperature and precipitation to soil moisture change is much stronger in the SSP5-8.5 scenario.

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The impact of climate change on the corn yield in Serbia

Acta agriculturae Serbica ◽

10.5937/aaser2050133p ◽

2020 ◽

Vol 25 (50) ◽

pp. 133-140

Author(s):

Gordana Petrović ◽

Darjan Karabašević ◽

Svetlana Vukotić ◽

Vuk Mirčetić ◽

Adriana Radosavac

Keyword(s):

Climate Change ◽

Climatic Factors ◽

Vegetation Period ◽

Research Process ◽

Climatic Conditions ◽

Corn Yield ◽

Climate Conditions ◽

Agricultural Plant ◽

Temperature And Precipitation ◽

The Impact

The aim of the paper is to show the impact of climate factors on the corn yield in Serbia. Contemporary climate reports show that climate is changing, and the emission of greenhouse gases is one of the main causes of climate change. In three different locations (West Bačka District, Šumadija District and Nišava District) different climatic conditions and corn yield were analyzed for the period from 1991 to 2011. In the research process, the model of multiple linear regression and Pearson coefficient of correlation was applied. Obtained results has shown that there is a high correlation between parameters of climate conditions and variance of corn yield. A small amount of precipitation quantity and high maximum values of temperatures in the vegetation period influenced the decrease in yield, which was particularly noticed during the period from 2000 to 2007. A lower yield of corn was established compared to the average yield in all three observed districts, in the Šumadija district, the yield was lower 48% in 2000 and 52% in 2007, in the West Bačka District, a yield was lower 40% in 2000 and 20% in 2007, and in the Nišava District, the yield was lower 65% in 2000 and 49% in 2007. There are perennial variations of climatic factors, especially temperature and precipitation quantity, which affect the realization of the economic profitability of growing agricultural plant species. Losses in agriculture can be higher in conditions of an unstable climate. It is necessary to more precisely predict climate change and create new hybrids and varieties for cultivation that will be adaptable to changed climate conditions. Adaptations of plants to climatic conditions changes will contribute to greater economy of agricultural production, and the provision of food for the world's population.

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Effect of baseline snowpack assumptions in the HySIM model in predicting future hydrological behaviour of a Himalayan catchment

Hydrology Research ◽

10.2166/nh.2018.069 ◽

2018 ◽

Vol 50 (2) ◽

pp. 691-708 ◽

Cited By ~ 2

Author(s):

Renji Remesan ◽

Sazeda Begam ◽

Ian P. Holman

Keyword(s):

Climate Change ◽

Model Performance ◽

High Elevation ◽

Climate Impact ◽

Snow Accumulation ◽

Response Surfaces ◽

Impact Response ◽

Discharge Data ◽

Climate Impact Assessment ◽

The Impact

Abstract Glaciers and snowpacks influence streamflow by altering the volume and timing of discharge. Without reliable data on baseline snow and ice volumes, properties and behaviour, initializing hydrological models for climate impact assessment is challenging. Two contrasting HySIM model builds were calibrated and validated against observed discharge data (2000–2008) assuming that snowmelt of the baseline permanent snowpack reserves in the high-elevation sub-catchment are either constrained (snowmelt is limited to the seasonal snow accumulation) or unconstrained (snowmelt is only energy-limited). We then applied both models within a scenario-neutral framework to develop impact response surface of hydrological response to future changes in annual temperature and precipitation. Both models had similar baseline model performance (NSE of 0.69–0.70 in calibration and 0.64–0.66 in validation), but the impact response surfaces differ in the magnitude and (for some combinations) direction of model response to climate change at low (Q10) and high (Q90) daily flows. The implications of historical data inadequacies in snowpack characterization for assessing the impacts of climate change and the associated timing of hydrological tipping points are discussed.

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Response of altitudinal vegetation belts of the Tianshan Mountains in northwestern China to climate change during 1989–2015

Scientific Reports ◽

10.1038/s41598-021-84399-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yong Zhang ◽

Lu-yu Liu ◽

Yi Liu ◽

Man Zhang ◽

Cheng-bang An

Keyword(s):

Climate Change ◽

Global Climate Change ◽

Global Climate ◽

Animal Husbandry ◽

Forest Tree ◽

Tianshan Mountains ◽

Northwestern China ◽

Temperature And Precipitation ◽

Altitudinal Belts ◽

The Impact

AbstractWithin the mountain altitudinal vegetation belts, the shift of forest tree lines and subalpine steppe belts to high altitudes constitutes an obvious response to global climate change. However, whether or not similar changes occur in steppe belts (low altitude) and nival belts in different areas within mountain systems remain undetermined. It is also unknown if these, responses to climate change are consistent. Here, using Landsat remote sensing images from 1989 to 2015, we obtained the spatial distribution of altitudinal vegetation belts in different periods of the Tianshan Mountains in Northwestern China. We suggest that the responses from different altitudinal vegetation belts to global climate change are different. The changes in the vegetation belts at low altitudes are spatially different. In high-altitude regions (higher than the forest belts), however, the trend of different altitudinal belts is consistent. Specifically, we focused on analyses of the impact of changes in temperature and precipitation on the nival belts, desert steppe belts, and montane steppe belts. The results demonstrated that the temperature in the study area exhibited an increasing trend, and is the main factor of altitudinal vegetation belts change in the Tianshan Mountains. In the context of a significant increase in temperature, the upper limit of the montane steppe in the eastern and central parts will shift to lower altitudes, which may limit the development of local animal husbandry. The montane steppe in the west, however, exhibits the opposite trend, which may augment the carrying capacity of pastures and promote the development of local animal husbandry. The lower limit of the nival belt will further increase in all studied areas, which may lead to an increase in surface runoff in the central and western regions.

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Projections for Mexico’s Tropical Rainforests Considering Ecological Niche and Climate Change

Forests ◽

10.3390/f12020119 ◽

2021 ◽

Vol 12 (2) ◽

pp. 119

Author(s):

Antonio Fidel Santos-Hernández ◽

Alejandro Ismael Monterroso-Rivas ◽

Diódoro Granados-Sánchez ◽

Antonio Villanueva-Morales ◽

Malinali Santacruz-Carrillo

Keyword(s):

Climate Change ◽

Ecological Niche ◽

Potential Distribution ◽

Tropical Rainforest ◽

Statistical Significance ◽

Climate Change Scenarios ◽

Climate Conditions ◽

Tropical Regions ◽

Temperature And Precipitation ◽

Ecological Importance

The tropical rainforest is one of the lushest and most important plant communities in Mexico’s tropical regions, yet its potential distribution has not been studied in current and future climate conditions. The aim of this paper was to propose priority areas for conservation based on ecological niche and species distribution modeling of 22 species with the greatest ecological importance at the climax stage. Geographic records were correlated with bioclimatic temperature and precipitation variables using Maxent and Kuenm software for each species. The best Maxent models were chosen based on statistical significance, complexity and predictive power, and current potential distributions were obtained from these models. Future potential distributions were projected with two climate change scenarios: HADGEM2_ES and GFDL_CM3 models and RCP 8.5 W/m2 by 2075–2099. All potential distributions for each scenario were then assembled for further analysis. We found that 14 tropical rainforest species have the potential for distribution in 97.4% of the landscape currently occupied by climax vegetation (0.6% of the country). Both climate change scenarios showed a 3.5% reduction in their potential distribution and possible displacement to higher elevation regions. Areas are proposed for tropical rainforest conservation where suitable bioclimatic conditions are expected to prevail.

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Structural safety and design under climate change

IABSE Congress, New York, New York 2019: The Evolving Metropolis ◽

10.2749/newyork.2019.1129 ◽

2019 ◽

Author(s):

Pietro Croce ◽

Paolo Formichi ◽

Filippo Landi

Keyword(s):

Climate Change ◽

Greenhouse Gas ◽

Structural Reliability ◽

Greenhouse Gas Emission ◽

Extreme Value ◽

Structural Safety ◽

Climate Projections ◽

Climate Conditions ◽

Impact Of Climate Change ◽

The Impact

The impact of climate change on climatic actions could significantly affect, in the mid-term future, the design of new structures as well as the reliability of existing ones designed in accordance to the provisions of present and past codes. Indeed, current climatic loads are defined under the assumption of stationary climate conditions but climate is not stationary and the current accelerated rate of changes imposes to consider its effects.Increase of greenhouse gas emissions generally induces a global increase of the average temperature, but at local scale, the consequences of this phenomenon could be much more complex and even apparently not coherent with the global trend of main climatic parameters, like for example, temperature, rainfalls, snowfalls and wind velocity.In the paper, a general methodology is presented, aiming to evaluate the impact of climate change on structural design, as the result of variations of characteristic values of the most relevant climatic actions over time. The proposed procedure is based on the analysis of an ensemble of climate projections provided according a medium and a high greenhouse gas emission scenario. Factor of change for extreme value distribution’s parameters and return values are thus estimated in subsequent time windows providing guidance for adaptation of the current definition of structural loads.The methodology is illustrated together with the outcomes obtained for snow, wind and thermal actions in Italy. Finally, starting from the estimated changes in extreme value parameters, the influence on the long-term structural reliability can be investigated comparing the resulting time dependent reliability with the reference reliability levels adopted in modern Structural codes.

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Bias correction of extreme values of high resolution climate simulations for risk analysis.

10.21203/rs.3.rs-264479/v1 ◽

2021 ◽

Author(s):

luis Augusto sanabria ◽

Xuerong Qin ◽

Jin Li ◽

Robert Peter Cechet

Keyword(s):

Climate Change ◽

Bias Correction ◽

Extreme Values ◽

The Body ◽

Future Climate ◽

Return Periods ◽

Climate Conditions ◽

Mitigation And Adaptation ◽

Climate Simulations ◽

The Impact

Abstract Most climatic models show that climate change affects natural perils' frequency and severity. Quantifying the impact of future climate conditions on natural hazard is essential for mitigation and adaptation planning. One crucial factor to consider when using climate simulations projections is the inherent systematic differences (bias) of the modelled data compared with observations. This bias can originate from the modelling process, the techniques used for downscaling of results, and the ensembles' intrinsic variability. Analysis of climate simulations has shown that the biases associated with these data types can be significant. Hence, it is often necessary to correct the bias before the data can be reliably used for further analysis. Natural perils are often associated with extreme climatic conditions. Analysing trends in the tail end of distributions are already complicated because noise is much more prominent than that in the mean climate. The bias of the simulations can introduce significant errors in practical applications. In this paper, we present a methodology for bias correction of climate simulated data. The technique corrects the bias in both the body and the tail of the distribution (extreme values). As an illustration, maps of the 50 and 100-year Return Period of climate simulated Forest Fire Danger Index (FFDI) in Australia are presented and compared against the corresponding observation-based maps. The results show that the algorithm can substantially improve the calculation of simulation-based Return Periods. Forthcoming work will focus on the impact of climate change on these Return Periods considering future climate conditions.

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FOOD SECURITY IN RUSSIA IN THE CONTEXT OF CLIMATE CHANGE

Economic and social problems of Russia The digital economy Current state and prospects ◽

10.31249/espr/2021.01.03 ◽

2021 ◽

pp. 45-65

Author(s):

Maria Polozhikhina ◽

Keyword(s):

Climate Change ◽

Food Security ◽

Agricultural Production ◽

Global Climate ◽

Point Of View ◽

Crimean Peninsula ◽

Climate Conditions ◽

Self Sufficiency ◽

The Impact ◽

The Crimean Peninsula

Climate conditions remain one of the main risk factors for domestic agriculture, and the consequences of global climate change are ambiguous in terms of prospects for agricultural production in Russia. This paper analyzes the impact of climate change on the country’s food security from the point of view of its self-sufficiency in grain primarily. Specific conditions prevailing on the Crimean peninsula are also considered.

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