Timing of human-induced climate change emergence from internal climate variability for hydrological impact studies

Abstract This study proposes a method to estimate the timing of human-induced climate change (HICC) emergence from internal climate variability (ICV) for hydrological impact studies based on climate model ensembles. Specifically, ICV is defined as the inter-member difference in a multi-member ensemble of a climate model in which human-induced climate trends have been removed through a detrending method. HICC is defined as the mean of multiple climate models. The intersection between HICC and ICV curves is defined as the time of emergence (ToE) of HICC from ICV. A case study of the Hanjiang River watershed in China shows that the temperature change has already emerged from ICV during the last century. However, the precipitation change will be masked by ICV up to the middle of this century. With the joint contributions of temperature and precipitation, the ToE of streamflow occurs about one decade later than that of precipitation. This implies that consideration for water resource vulnerability to climate should be more concerned with adaptation to ICV in the near-term climate (present through mid-century), and with HICC in the long-term future, thus allowing for more robust adaptation strategies to water transfer projects in China.

Download Full-text

Robust detection of forced warming in the presence of potentially large climate variability

10.5194/egusphere-egu21-9611 ◽

2021 ◽

Author(s):

Sebastian Sippel ◽

Nicolai Meinshausen ◽

Eniko Székely ◽

Erich Fischer ◽

Angeline G. Pendergrass ◽

...

Keyword(s):

Climate Change ◽

Climate Variability ◽

Climate Models ◽

Climate Model ◽

Decadal Variability ◽

Internal Variability ◽

Warming Trend ◽

Robust Detection ◽

Internal Climate Variability ◽

Detection And Attribution

Warming of the climate system is unequivocal and substantially exceeds unforced internal climate variability. Detection and attribution (D&A) employs spatio-temporal fingerprints of the externally forced climate response to assess the magnitude of a climate signal, such as the multi-decadal global temperature trend, while internal variability is often estimated from unforced (&#8220;control&#8221;) segments of climate model simulations (e.g. Santer et al. 2019). Estimates of the exact magnitude of decadal-scale internal variability, however, remain uncertain and are limited by relatively short observed records, their entanglement with the forced response, and considerable spread of simulated variability across climate models. Hence, a limitation of D&A is that robustness and confidence levels depend on the ability of climate models to correctly simulate internal variability (Bindoff et al., 2013).For example, the large spread in simulated internal variability across climate models implies that the observed 40-year global mean temperature trend of about 0.76&#176;C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5&#963;), corresponding to vanishingly small probabilities if taken at face value. But the observed trend would exceed the standard deviation of a few `high-variability' climate models `only' by a factor of about two, thus unlikely to be internally generated but not practically impossible given unavoidable climate system and observational uncertainties. This illustrates the key role of model uncertainty in the simulation of internal variability for D&A confidence estimates.Here we use a novel statistical learning method to extract a fingerprint of climate change that is robust towards model differences and internal variability, even of large amplitude. We demonstrate that externally forced warming is distinct from internal variability and detectable with high confidence on any state-of-the-art climate model, even those that simulate the largest magnitude of unforced multi-decadal variability. Based on the median of all models, it is extremely likely that more than 85% of the observed warming trend over the last 40 years is externally driven. Detection remains robust even if their main modes of decadal variability would be scaled by a factor of two. It is extremely likely that at least 55% of the observed warming trend over the last 40 years cannot be explained by internal variability irrespective of which climate model&#8217;s natural variability estimates are used.Our analysis helps to address this limitation in attributing warming to external forcing and provides a novel perspective for quantifying the magnitude of forced climate change even under uncertain but potentially large multi-decadal internal climate variability. This opens new opportunities to make D&A fingerprints robust in the presence of poorly quantified yet important features inextricably linked to model structural uncertainty, and the methodology may contribute to more robust detection and attribution of climate change to its various drivers.&#160;Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science.&#160;Nat Clim Change&#160;9(3), pp. 180-182.

Download Full-text

Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability

Hydrology and Earth System Sciences ◽

10.5194/hess-24-3251-2020 ◽

2020 ◽

Vol 24 (6) ◽

pp. 3251-3269 ◽

Cited By ~ 3

Author(s):

Chao Gao ◽

Martijn J. Booij ◽

Yue-Ping Xu

Keyword(s):

Climate Change ◽

Climate Variability ◽

River Basin ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Internal Climate Variability ◽

Low Flows ◽

Uncertainty Sources ◽

High Flows

Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.

Download Full-text

High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation in Morocco

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-5687-2013 ◽

2013 ◽

Vol 10 (5) ◽

pp. 5687-5737 ◽

Cited By ~ 3

Author(s):

Y. Tramblay ◽

D. Ruelland ◽

S. Somot ◽

R. Bouaicha ◽

E. Servat

Keyword(s):

Climate Change ◽

High Resolution ◽

Regional Climate Model ◽

Bias Correction ◽

Hydrological Model ◽

Climate Model ◽

Regional Climate ◽

Impact Studies ◽

Hydrological Impact ◽

Split Sample

Abstract. In the framework of the international CORDEX program, new regional climate model (RCM) simulations at high spatial resolutions are becoming available for the Mediterranean region (Med-CORDEX initiative). This study provides the first evaluation for hydrological impact studies of these high-resolution simulations. Different approaches are compared to analyze the climate change impacts on the hydrology of a catchment located in North Morocco, using a high-resolution RCM (ALADIN-Climate) from the Med-CORDEX initiative at two different spatial resolutions (50 km and 12 km) and for two different Radiative Concentration Pathway scenarios (RCP4.5 and RCP8.5). The main issues addressed in the present study are: (i) what is the impact of increased RCM resolution on present-climate hydrological simulations and on future projections? (ii) Are the bias-correction of the RCM model and the parameters of the hydrological model stationary and transferable to different climatic conditions? (iii) What is the climate and hydrological change signal based on the new Radiative Concentration Pathways scenarios (RCP4.5 and RCP8.5)? Results indicate that high resolution simulations at 12 km better reproduce the seasonal patterns, the seasonal distributions and the extreme events of precipitation. The parameters of the hydrological model, calibrated to reproduce runoff at the monthly time step over the 1984–2010 period, do not show a strong variability between dry and wet calibration periods in a differential split-sample test. However the bias correction of precipitation by quantile-matching does not give satisfactory results in validation using the same differential split-sample testing method. Therefore a quantile-perturbation method that does not rely on any stationarity assumption and produces ensembles of perturbed series of precipitation was introduced. The climate change signal under scenarios 4.5 and 8.5 indicates a decrease of respectively −30% to −57% in surface runoff for the mid-term (2041–2062), when for the same period the projections for precipitation are ranging between −15% and −19% and for temperature between +1.28°C and +1.87°C.

Download Full-text

Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-2657-2015 ◽

2015 ◽

Vol 12 (3) ◽

pp. 2657-2706 ◽

Cited By ~ 2

Author(s):

T. Olsson ◽

J. Jakkila ◽

N. Veijalainen ◽

L. Backman ◽

J. Kaurola ◽

...

Keyword(s):

Climate Change ◽

Seasonal Changes ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Control Period ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Catchment Scale ◽

Temperature And Precipitation

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional Climate Models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction of RCM temperature and precipitation for Finland is carried out using different versions of distribution based scaling (DBS) method. The DBS adjusted RCM data is used as input of a hydrological model to simulate changes in discharges in four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period (1961–2000) and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the SD of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

Download Full-text

Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe

Hydrology and Earth System Sciences ◽

10.5194/hess-19-1827-2015 ◽

2015 ◽

Vol 19 (4) ◽

pp. 1827-1847 ◽

Cited By ~ 89

Author(s):

M. A. Sunyer ◽

Y. Hundecha ◽

D. Lawrence ◽

H. Madsen ◽

P. Willems ◽

...

Keyword(s):

Climate Change ◽

Statistical Downscaling ◽

Extreme Precipitation ◽

Climate Change Impact ◽

Climate Models ◽

Climate Model ◽

Control Period ◽

Large Variability ◽

Impact Studies ◽

Change Impact

Abstract. Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.

Download Full-text

Anthropogenic climate change versus internal climate variability: Impacts on Alpine snow cover

10.5194/tc-2020-84 ◽

2020 ◽

Author(s):

Fabian Willibald ◽

Sven Kotlarski ◽

Adrienne Grêt-Regamey ◽

Ralf Ludwig

Keyword(s):

Climate Change ◽

Climate Variability ◽

Snow Cover ◽

Climate Model ◽

Regional Climate ◽

Anthropogenic Climate Change ◽

Swiss Alps ◽

Internal Climate Variability ◽

Annual Variability ◽

The Future

Abstract. Snow is a sensitive component of the climate system. In many parts of the world, water, stored as snow, is a vital resource for agriculture, tourism and the energy sector. As uncertainties in climate change assessments are still relatively large, it is important to investigate the interdependencies between internal climate variability and anthropogenic climate change and their impacts on snow cover. We use regional climate model data from a new single model large ensemble with 50 members (ClimEX LE) as driver for the physically based snow model SNOWPACK at eight locations across the Swiss Alps. We estimate the contribution of internal climate variability to uncertainties in future snow trends by applying a Mann-Kendall test for consecutive future periods of different lengths (between 30 and 100 years) until the end of the 21st century. Under RCP8.5, we find probabilities between 15 % and 50 % that there will be no significantly negative trend in future mean snow depths over a period of 50 years. While it is important to understand the contribution of internal climate variability to uncertainties in future snow trends, it is likely that the variability of snow depth itself changes with anthropogenic forcing. We find that relative to the mean, inter-annual variability of snow increases in the future. A decrease of future mean snow depths, superimposed by increases in inter-annual variability will exacerbate the already existing uncertainties that snow-dependent economies will have to face in the future.

Download Full-text

Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies

Hydrology Research ◽

10.2166/nh.2010.004 ◽

2010 ◽

Vol 41 (3-4) ◽

pp. 211-229 ◽

Cited By ~ 137

Author(s):

Wei Yang ◽

Johan Andréasson ◽

L. Phil Graham ◽

Jonas Olsson ◽

Jörgen Rosberg ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Climate Projections ◽

Impact Studies ◽

Emissions Scenarios ◽

Precipitation And Temperature

As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts. Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrological impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS-adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESA1B) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961–2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

Download Full-text

High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation of the ALADIN-Climate model in Morocco

Hydrology and Earth System Sciences ◽

10.5194/hess-17-3721-2013 ◽

2013 ◽

Vol 17 (10) ◽

pp. 3721-3739 ◽

Cited By ~ 81

Author(s):

Y. Tramblay ◽

D. Ruelland ◽

S. Somot ◽

R. Bouaicha ◽

E. Servat

Keyword(s):

Climate Change ◽

High Resolution ◽

Regional Climate Model ◽

Bias Correction ◽

Hydrological Model ◽

Climate Model ◽

Regional Climate ◽

Impact Studies ◽

Hydrological Impact ◽

Split Sample

Abstract. In the framework of the international CORDEX program, new regional climate model (RCM) simulations at high spatial resolutions are becoming available for the Mediterranean region (Med-CORDEX initiative). This study provides the first evaluation for hydrological impact studies of one of these high-resolution simulations in a 1800 km2 catchment located in North Morocco. Different approaches are compared to analyze the climate change impacts on the hydrology of this catchment using a high-resolution RCM (ALADIN-Climate) from the Med-CORDEX initiative at two different spatial resolutions (50 and 12 km) and for two different Radiative Concentration Pathway scenarios (RCP4.5 and RCP8.5). The main issues addressed in the present study are: (i) what is the impact of increased RCM resolution on present-climate hydrological simulations and on future projections? (ii) Are the bias-correction of the RCM model and the parameters of the hydrological model stationary and transferable to different climatic conditions? (iii) What is the climate and hydrological change signal based on the new Radiative Concentration Pathways scenarios (RCP4.5 and RCP8.5)? Results indicate that high resolution simulations at 12 km better reproduce the seasonal patterns, the seasonal distributions and the extreme events of precipitation. The parameters of the hydrological model, calibrated to reproduce runoff at the monthly time step over the 1984–2010 period, do not show a strong variability between dry and wet calibration periods in a differential split-sample test. However the bias correction of precipitation by quantile-matching does not give satisfactory results in validation using the same differential split-sample testing method. Therefore a quantile-perturbation method that does not rely on any stationarity assumption and produces ensembles of perturbed series of precipitation was introduced. The climate change signal under scenarios 4.5 and 8.5 indicates a decrease of respectively −30 to −57% in surface runoff for the mid-term (2041–2062), when for the same period the projections for precipitation are ranging between −15 and −19% and for temperature between +1.3 and +1.9 °C.

Download Full-text

Anthropogenic climate change versus internal climate variability: impacts on snow cover in the Swiss Alps

The Cryosphere ◽

10.5194/tc-14-2909-2020 ◽

2020 ◽

Vol 14 (9) ◽

pp. 2909-2924

Author(s):

Fabian Willibald ◽

Sven Kotlarski ◽

Adrienne Grêt-Regamey ◽

Ralf Ludwig

Keyword(s):

Climate Change ◽

Climate Variability ◽

Snow Cover ◽

Interannual Variability ◽

Climate Model ◽

Regional Climate ◽

Anthropogenic Climate Change ◽

Swiss Alps ◽

Internal Climate Variability ◽

The Future

Abstract. Snow is a sensitive component of the climate system. In many parts of the world, water stored as snow is a vital resource for agriculture, tourism and the energy sector. As uncertainties in climate change assessments are still relatively large, it is important to investigate the interdependencies between internal climate variability and anthropogenic climate change and their impacts on snow cover. We use regional climate model data from a new single-model large ensemble with 50 members (ClimEX LE) as a driver for the physically based snow model SNOWPACK at eight locations across the Swiss Alps. We estimate the contribution of internal climate variability to uncertainties in future snow trends by applying a Mann–Kendall test for consecutive future periods of different lengths (between 30 and 100 years) until the end of the 21st century. Under RCP8.5, we find probabilities between 10 % and 60 % that there will be no significant negative trend in future mean snow depths over a period of 50 years. While it is important to understand the contribution of internal climate variability to uncertainties in future snow trends, it is likely that the variability of snow depth itself changes with anthropogenic forcing. We find that relative to the mean, interannual variability of snow increases in the future. A decrease in future mean snow depths, superimposed by increases in interannual variability, will exacerbate the already existing uncertainties that snow-dependent economies will have to face in the future.

Download Full-text

An update of IPCC climate reference regions for subcontinental analysis of climate model data: Definition and aggregated datasets

10.5194/essd-2019-258 ◽

2020 ◽

Cited By ~ 2

Author(s):

Maialen Iturbide ◽

José Manuel Gutiérrez ◽

Lincoln Muniz Alves ◽

Joaquín Bedia ◽

Ezequiel Cimadevilla ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Climate Adaptation ◽

Regional Climate ◽

Future Climate Change ◽

Temperature And Precipitation ◽

Data Definition ◽

Definition Of ◽

Projected Changes

Abstract. Several sets of reference regions have been proposed in the literature for the regional synthesis of observed and model-projected climate change information. A popular example is the set of reference regions introduced in the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX) based on a prior coarser selection and then slightly modified for the 5th Assessment Report of the IPCC. This set was developed for reporting sub-continental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes (the typical resolution of the 5th Climate Model Intercomparison Projection, CMIP5, climate models was around 2º). These regions have been used as the basis for several popular spatially aggregated datasets, such as the seasonal mean temperature and precipitation in IPCC regions for CMIP5. Here we present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher model resolution (around 1º for CMIP6). As a result, the number of regions increased to 43 land plus 12 open ocean, better representing consistent regional climate features. The paper describes the rationale followed for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and shapefile together with companion R and Python notebooks to illustrate their use in practical problems (trimming data, etc.). We also describe the generation of a new dataset with monthly temperature and precipitation spatially aggregated in the new regions, currently for CMIP5 (for backwards consistency) and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter diagrams to offer guidance on the likely range of future climate change at the scale of reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository; https://github.com/SantanderMetGroup/ATLAS, doi:10.5281/zenodo.3688072 (Iturbide et al., 2020).

Download Full-text