Exploring the role of observational uncertainty and resolution mismatch in the application of bias adjustment methods

2020 ◽  
Author(s):  
Ana Casanueva ◽  
Sixto Herrera ◽  
Maialen Iturbide ◽  
Stefan Lange ◽  
Martin Jury ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Change Signal ◽  
Climate Indices ◽  
Bias Adjustment ◽  
Temperature And Precipitation ◽  
Observational Uncertainty ◽  
Distribution Mapping ◽  
Systematic Biases ◽  
The Impact

<p>Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many bias adjustment methods, which merely correct for deficiencies in the distribution, have been developed. Despite adjusting the desired features under historical simulations, their application in a climate change context is subject to additional uncertainties and modifications of the change signals, especially for climate indices which have not been tackled by the methods. In this sense, some of the commonly-used bias adjustment methods allow changes of the signals, which appear by construction in case of intensity-dependent biases; some others ensure the trends in some statistics of the original, raw models. Two relevant sources of uncertainty, often overlooked, which bring further uncertainties are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect).</p><p>In the present work, we assess the impact of these factors on the climate change signal of a set of climate indices of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature -empirical or parametric-, fitted parameters and preservation of the signals) for a case study in the Iberian Peninsula. The quantile trend-preserving methods (namely quantile delta mapping -QDM-, scaled distribution mapping -SDM- and the method from the third phase of ISIMIP -ISIMIP3) preserve better the raw signals for the different indices and variables (not all preserved by construction). However they rely largely on the reference dataset used for calibration, thus present a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20km) and low (approximately 120km) spatial resolutions.</p>

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

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

<p align="justify"><span>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 “present climate conditions” (1980-2014) while in the second set (amip-lfmip-rmLC) 30-year running mean of land-surface state from the corresponding ScenarioMIP experiment (O’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).</span></p><p align="justify"><span>In this analysis, we focus on the differences between amip-lfmip-rmLC and amip-lfmip-pdLC at the end of the 21st Century (2071–2100) 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.</span></p>

scholarly journals Evaluating quantile-based bias adjustment methods for climate change scenarios

2021 ◽  
Author(s):  
Fabian Lehner ◽  
Imran Nadeem ◽  
Herbert Formayer
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Climate Change Signal ◽  
Historical Period ◽  
Climate Change Scenarios ◽  
Statistical Bias ◽  
Bias Adjustment ◽  
Direct Model

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

scholarly journals High-Resolution Climate Change Impact Analysis on Medium-Sized River Catchments in Germany: An Ensemble Assessment

2013 ◽  
Vol 14 (4) ◽  
pp. 1175-1193 ◽  
Author(s):  
Irena Ott ◽  
Doris Duethmann ◽  
Joachim Liebert ◽  
Peter Berg ◽  
Hendrik Feldmann ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Impact Analysis ◽  
Climate Models ◽  
Global Climate Models ◽  
Climate Change Signal ◽  
Climate Modelling ◽  
Community Land Model ◽  
The Impact ◽  
River Catchments

Abstract The impact of climate change on three small- to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021–50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member ensemble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is established. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one realization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM–realization–RCM–HM) at least two members and allows the investigation of qualitative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer.

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 Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuadorean Andes

2014 ◽  
Vol 18 (2) ◽  
pp. 631-648 ◽  
Author(s):  
D. E. Mora ◽  
L. Campozano ◽  
F. Cisneros ◽  
G. Wyseure ◽  
P. Willems
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
High Peak ◽  
Annual Rainfall ◽  
Climate Change Signal ◽  
Perturbation Approach ◽  
Peak Flows ◽  
Ipcc Sres ◽  
The Impact

Abstract. Investigation was made on the climate change signal for hydrometeorological and hydrological variables along the Paute River basin, in the southern Ecuador Andes. An adjusted quantile perturbation approach was used for climate downscaling, and the impact of climate change on runoff was studied for two nested catchments within the basin. The analysis was done making use of long daily series of seven representative rainfall and temperature sites along the study area and considering climate change signals of global and regional climate models for IPCC SRES scenarios A1B, A2 and B1. The determination of runoff was carried out using a lumped conceptual rainfall–runoff model. The study found that the range of changes in temperature is homogeneous for almost the entire region with an average annual increase of approximately +2.0 &degC. However, the warmest periods of the year show lower changes than the colder periods. For rainfall, downscaled results project increases in the mean annual rainfall depth and the extreme daily rainfall intensities along the basin for all sites and all scenarios. Higher changes in extreme rainfall intensities are for the wetter region. These lead to changes in catchment runoff flows, with increasing high peak flows and decreasing low peak flows. The changes in high peak flows are related to the changes in rainfall extremes, whereas the decreases in the low peak flows are due to the increase in temperature and potential evapotranspiration together with the reduction in the number of wet days.

scholarly journals Unpacking future climate extremes and their sectoral implications in western Nepal

2021 ◽  
Vol 168 (1-2) ◽  
Author(s):  
Dipesh Chapagain ◽  
Sanita Dhaubanjar ◽  
Luna Bharati
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Extreme Temperature ◽  
Climate Extremes ◽  
Climate Indices ◽  
Climate Projections ◽  
Temperature And Precipitation ◽  
Extreme Precipitation Events ◽  
The Impact ◽  
Precipitation Events

AbstractExisting climate projections and impact assessments in Nepal only consider a limited number of generic climate indices such as means. Few studies have explored climate extremes and their sectoral implications. This study evaluates future scenarios of extreme climate indices from the list of the Expert Team on Sector-specific Climate Indices (ET-SCI) and their sectoral implications in the Karnali Basin in western Nepal. First, future projections of 26 climate indices relevant to six climate-sensitive sectors in Karnali are made for the near (2021–2045), mid (2046–2070), and far (2071–2095) future for low- and high-emission scenarios (RCP4.5 and RCP8.5, respectively) using bias-corrected ensembles of 19 regional climate models from the COordinated Regional Downscaling EXperiment for South Asia (CORDEX-SA). Second, a qualitative analysis based on expert interviews and a literature review on the impact of the projected climate extremes on the climate-sensitive sectors is undertaken. Both the temperature and precipitation patterns are projected to deviate significantly from the historical reference already from the near future with increased occurrences of extreme events. Winter in the highlands is expected to become warmer and dryer. The hot and wet tropical summer in the lowlands will become hotter with longer warm spells and fewer cold days. Low-intensity precipitation events will decline, but the magnitude and frequency of extreme precipitation events will increase. The compounding effects of the increase in extreme temperature and precipitation events will have largely negative implications for the six climate-sensitive sectors considered here.

scholarly journals Analyses of Climate Variations at Four Meteorological Stations on Remote Islands in the Croatian Part of the Adriatic Sea

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1044
Author(s):  
Ognjen Bonacci ◽  
Matko Patekar ◽  
Marco Pola ◽  
Tanja Roje-Bonacci
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Water Resources ◽  
Air Temperature ◽  
Climate Models ◽  
Adriatic Sea ◽  
Temperature And Precipitation ◽  
Intensive Exploitation ◽  
Increasing Trends ◽  
The Impact

The Mediterranean region is one of the regions in the world that is most vulnerable to the impact of imminent climate change. In particular, climate change has an adverse effect on both the ecosystem and socioeconomic system, influencing water availability for both human and environmental purposes. The most endangered water resources are along the coasts and on islands since they have relatively small volumes and are intensively exploited. We analyzed the time series of air temperature and precipitation measured at four meteorological stations (Komiža, Palagruža, Lastovo, and Biševo) located on small islands in the Croatian part of the Adriatic Sea in this study. The investigated time series extend from the 1950s to the present, being contemporaneous for approximately 50 years. Despite possessing discontinuity, they can be considered as representative for assessing climate change and variability in the scattered environment of the Croatian islands. The results showed increasing trends in the annual air temperature, while the annual cumulative precipitation did not show significant variations. In addition, the analyses of the monthly air temperature showed that statistically significant increasing trends occurred from April to August, suggesting a more severe impact during these months. These results are in accordance with regional and local studies and climate models. Although the climate variability during the analyzed period can be considered as moderate, the impact on water resources could be severe due to the combined effect of the increase in air temperature during warm periods and the intensive exploitation for tourism purposes.

scholarly journals The impact of climate change in wheat and barley yields in the Iberian Peninsula

2021 ◽  
Author(s):  
Virgílio A. Bento ◽  
Andreia F.S. Ribeiro ◽  
Ana Russo ◽  
Célia M. Gouveia ◽  
Rita M. Cardoso ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Crop Yields ◽  
Greenhouse Gas Emission ◽  
Hot Spot ◽  
Historical Period ◽  
Temperature And Precipitation ◽  
The Impact

<p>World food and drink production largely depends on wheat and barley crops, which are the basis of nutrition for both humans and animals. The Iberian Peninsula (IP), and particularly Spain, is responsible for a large percentage of farming areas dedicated to these two crops. Furthermore, the IP is known as a prominent climate change hot spot, with expected rising temperatures and a decrease in mean precipitation (with more extreme events). Thus, it is vital to understand the effects of climate change in wheat and barley yields in the IP.</p><p>Multiple linear regression (MLR) models were developed based on the relation between temperature and precipitation and both crop yields, with the aim of projecting these into the future. Three main objectives were pursued: (1) to establish the existence of a relationship between wheat and barley yields and temperature and precipitation, taking advantage of data from the EURO-CORDEX regional climate models (RCMs) forced with ERA-Interim; (2) to calibrate and validate MLR models using a selection of predictors from the same EURO-CORDEX RCMs; and (3) to apply these MLR models to EURO-CORDEX RCMs forced with global climate models (GCMs) for an historical period (1971-2000) and two future periods (2041-2070 and 2071-2100) according to two greenhouse gas emission scenarios (RCP4.5 and RCP8.5). Results show a dichotomic behaviour of wheat and barley future yields depending on the crop’s production region. Projections for the southern cluster of the IP show severe yield losses for both cereals, which may be a consequence of the increase in maximum temperatures in spring, particularly for RCP8.5 at the end of the 21st century. Conversely, projections for the northern cluster of the IP show an increase in yield output, which may be a result of the projected warming taking place within the early winter months.</p><p>This study reinforces the worth to implementing changes in the society to mitigate losses and to assess production gains/losses due to climate change. These may be implemented locally (different cultivar species), countrywide (implementing sustainable policies), or even globally (alleviate greenhouse gas emissions). This work was supported by project IMPECAF (PTDC/CTA-CLI/28902/2017), LEADING (PTDC/CTA-MET/28914/2017) and by IDL (UIDB/50019/2020).</p>

scholarly journals On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 799 ◽  
Author(s):  
Lorenzo Sangelantoni ◽  
Barbara Tomassetti ◽  
Valentina Colaiuda ◽  
Annalina Lombardi ◽  
Marco Verdecchia ◽  
...  
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Scale ◽  
Central Italy ◽  
Stress Index ◽  
Climate Change Signal ◽  
Correction Technique ◽  
The Impact

The response of Mediterranean small catchments hydrology to climate change is still relatively unexplored. Regional Climate Models (RCMs) are an established tool for evaluating the expected climate change impact on hydrology. Due to the relatively low resolution and systematic errors, RCM outputs are routinely and statistically post-processed before being used in impact studies. Nevertheless, these techniques can impact the original simulated trends and then impact model results. In this work, we characterize future changes of a small Apennines (Central Italy) catchment hydrology, according to two radiative forcing scenarios (Representative Concentration Pathways, RCPs, 4.5 and 8.5). We also investigate the impact of a widely used bias correction technique, the empirical Quantile Mapping (QM) on the original Climate Change Signal (CCS), and the subsequent alteration of the original Hydrological Change Signal (HCS). Original and bias-corrected simulations of five RCMs from Euro-CORDEX are used to drive the CETEMPS hydrological model CHyM. HCS is assessed by using monthly mean discharge and a hydrological-stress index. HCS shows a large spatial and seasonal variability where the summer results are affected by the largest decrease of mean discharge (down to −50%). QM produces a small alteration of the original CCS, which generates a generally wetter HCS, especially during the spring season.

scholarly journals Evaluating the impact of climate change on extreme temperature and precipitation events over the Kashmir Himalaya

2021 ◽  
Author(s):  
Shafkat Ahsan ◽  
M. Sultan Bhat ◽  
Akhtar Alam ◽  
Hakim Farooq ◽  
Hilal Ahmad Shiekh
Keyword(s):  
Climate Change ◽  
Extreme Temperature ◽  
Climate Indices ◽  
General Tendency ◽  
Kashmir Himalaya ◽  
Impact Of Climate Change ◽  
Extreme Climate ◽  
Temperature And Precipitation ◽  
The Future ◽  
The Impact

AbstractThe frequency and severity of climatic extremes is expected to escalate in the future primarily because of the increasing greenhouse gas concentrations in the atmosphere. This study aims to assess the impact of climate change on the extreme temperature and precipitation scenarios using climate indices in the Kashmir Himalaya. The analysis has been carried out for the twenty-first century under different representative concentration pathways (RCPs) through the Statistical Downscaling Model (SDSM) and ClimPACT2. The simulation reveals that the climate in the region will get progressively warmer in the future by increments of 0.36–1.48 °C and 0.65–1.07 °C in mean maximum and minimum temperatures respectively, during 2080s (2071–2100) relative to 1980–2010 under RCP8.5. The annual precipitation is likely to decrease by a maximum of 2.09–6.61% (2080s) under RCP8.5. The seasonal distribution of precipitation is expected to alter significantly with winter, spring, and summer seasons marking reductions of 9%, 5.7%, and 1.7%, respectively during 2080s under RCP8.5. The results of extreme climate evaluation show significant increasing trends for warm temperature-based indices and decreasing trends for cold temperature-based indices. Precipitation indices on the other hand show weaker and spatially incoherent trends with a general tendency towards dry regimes. The projected scenarios of extreme climate indices may result in large-scale adverse impacts on the environment and ecological resource base of the Kashmir Himalaya.

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