scholarly journals Attribution of Declining Western U.S. Snowpack to Human Effects

2008 ◽  
Vol 21 (23) ◽  
pp. 6425-6444 ◽  
Author(s):  
David W. Pierce ◽  
Tim P. Barnett ◽  
Hugo G. Hidalgo ◽  
Tapash Das ◽  
Céline Bonfils ◽  
...  
Keyword(s):  
Climate Variability ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Formal Model ◽  
Model Estimate ◽  
Economic Implications ◽  
Internal Climate Variability ◽  
Detection And Attribution

Abstract Observations show snowpack has declined across much of the western United States over the period 1950–99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D–A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean–atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D–A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols.

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

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

<p>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 (“control”) 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).</p><p>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°C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5σ), 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.</p><p>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’s natural variability estimates are used.</p><p>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.</p><p> </p><p>Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.</p><p>Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science. <em>Nat Clim Change</em> <strong>9</strong>(3), pp. 180-182.</p>

scholarly journals Mediterranean Precipitation Response to Greenhouse Gases and Aerosols

2018 ◽  
Author(s):  
Tao Tang ◽  
Drew Shindell ◽  
Bjørn H. Samset ◽  
Oliviér Boucher ◽  
Piers M. Forster ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Local Heating ◽  
Global Climate Models ◽  
Historical Period ◽  
Radiative Balance ◽  
Precipitation Decrease ◽  
The Mediterranean

Abstract. Atmospheric aerosols and greenhouse gases affect cloud properties, radiative balance and thus, the hydrological cycle. Observations show that precipitation has decreased in the Mediterranean since the 20th century, and many studies have investigated possible mechanisms. So far, however, the effects of aerosol forcing on Mediterranean precipitation remain largely unknown. Here we compare Mediterranean precipitation responses to individual forcing agents in a set of state-of-the-art global climate models (GCMs). Our analyses show that both greenhouse gases and aerosols can cause drying in the Mediterranean, and that precipitation is more sensitive to black carbon (BC) forcing than to well-mixed greenhouse gases (WMGHGs) or sulfate aerosol. In addition to local heating, BC appears to reduce precipitation by causing an enhanced positive North Atlantic Oscillation (NAO)/Arctic Oscillation (AO)-like sea level pressure (SLP) pattern, characterized by higher SLP at mid-latitudes and lower SLP at high-latitudes. WMGHGs cause a similar SLP change, and both are associated with a northward diversion of the jet stream and storm tracks, reducing precipitation in the Mediterranean while increasing precipitation in Northern Europe. Though the applied forcings were much larger, if forcings are scaled to those of the historical period of 1901–2010, roughly one-third (31 ± 17 %) of the precipitation decrease would be attributable to global BC forcing with the remainder largely attributable to WMGHGs whereas global scattering sulfate aerosols have negligible impacts. The results from this study suggest that future BC emissions may significantly affect regional water resources, agricultural practices, ecosystems, and the economy in the Mediterranean region.

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

2020 ◽  
Vol 24 (6) ◽  
pp. 3251-3269 ◽  
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.

scholarly journals The Sensitivity of the Hydrological Cycle to Internal Climate Variability versus Anthropogenic Climate Change

2016 ◽  
Vol 29 (10) ◽  
pp. 3661-3673 ◽  
Author(s):  
Ryan J. Kramer ◽  
Brian J. Soden
Keyword(s):  
Water Vapor ◽  
Time Scales ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Internal Variability ◽  
Radiative Cooling ◽  
Surface Warming ◽  
Internal Climate Variability ◽  
Radiative Processes ◽  
Global Mean

Abstract In response to rising CO2 concentrations, climate models predict that globally averaged precipitation will increase at a much slower rate than water vapor. However, some observational studies suggest that global-mean precipitation and water vapor have increased at similar rates. While the modeling results emphasize changes at multidecadal time scales where the anthropogenic signal dominates, the shorter observational record is more heavily influenced by internal variability. Whether the physical constraints on the hydrological cycle fundamentally differ between these time scales is investigated. The results of this study show that while global-mean precipitation is constrained by radiative cooling on both time scales, the effects of CO2 dominate on multidecadal time scales, acting to suppress the increase in radiative cooling with warming. This results in a smaller precipitation change compared to interannual time scales where the effects of CO2 forcing are small. It is also shown that intermodel spread in the response of atmospheric radiative cooling (and thus global-mean precipitation) to anthropogenically forced surface warming is dominated by clear-sky radiative processes and not clouds, while clouds dominate under internal variability. The findings indicate that the sensitivity of the global hydrological cycle to surface warming differs fundamentally between internal variability and anthropogenically forced changes and this has important implications for interpreting observations of the hydrological sensitivity.

scholarly journals Impact of Climate Change on the Hydrology of the Forested Watershed That Drains to Lake Erken in Sweden: An Analysis Using SWAT+ and CMIP6 Scenarios

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1803
Author(s):  
Inmaculada C. Jiménez-Navarro ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Julio Pérez-Sánchez ◽  
Javier Senent-Aparicio
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Water Discharge ◽  
Global Climate Models ◽  
Climate Change Scenarios ◽  
Case Scenario ◽  
Worst Case

Precipitation and temperature around the world are expected to be altered by climate change. This will cause regional alterations to the hydrological cycle. For proper water management, anticipating these changes is necessary. In this study, the basin of Lake Erken (Sweden) was simulated with the recently released software SWAT+ to study such alterations in a short (2026–2050), medium (2051–2075) and long (2076–2100) period, under two different climate change scenarios (SSP2-45 and SSP5-85). Seven global climate models from the latest projections of future climates that are available (CIMP 6) were compared and ensembled. A bias-correction of the models’ data was performed with five different methods to select the most appropriate one. Results showed that the temperature is expected to increase in the future from 2 to 4 °C, and precipitation from 6% to 20%, depending on the scenario. As a result, water discharge would also increase by about 18% in the best-case scenario and by 50% in the worst-case scenario, and the surface runoff would increase between 5% and 30%. The floods and torrential precipitations would also increase in the basin. This trend could lead to soil impoverishment and reduced water availability in the basin, which could damage the watershed’s forests. In addition, rising temperatures would result in a 65% reduction in the snow water equivalent at best and 92% at worst.

Detection and attribution of seasonal temperature changes in India with climate models in the CMIP5 archive

2015 ◽  
Vol 7 (1) ◽  
pp. 83-102 ◽  
Author(s):  
P. Sonali ◽  
D. Nagesh Kumar
Keyword(s):  
Climate Models ◽  
Internal Variability ◽  
Causative Factor ◽  
Temporal Variations ◽  
Seasonal Temperature ◽  
Temperature Changes ◽  
Model Simulations ◽  
Internal Climate Variability ◽  
Detection And Attribution ◽  
Attribution Analysis

This study analyzes the change in annual and seasonal maximum and minimum temperature (Tmax and Tmin) during the period 1950–2005 (i.e., second half of the 20th century). In-depth analyses have been carried out for all over India as well as for five temperature homogenous regions of India separately. First, the temporal variations of annual and seasonal Tmax and Tmin are analyzed, employing the trend free pre-whitening Mann-Kendall approach. Secondly, it is assessed whether the observations contain significant signals above the natural internal variability determined from a long ‘piControl’ experiment, using Monte Carlo simulation. Thirdly, fingerprint based formal detection and attribution analysis is used to determine the signal strengths of observed and model simulations with respect to different considered experiments. Finally, these signal strengths are compared to attribute the observed changes in Tmax and Tmin to different factors. All the model simulated datasets are retrieved from the CMIP5 archive. It is noticed that the emergence of observed trends is more pronounced in Tmin compared to Tmax. Although observed changes are not solely associated with one specific causative factor, most of the changes in Tmin lie above the bounds of natural internal climate variability.

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

2018 ◽  
Vol 49 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Mei-Jia Zhuan ◽  
Jie Chen ◽  
Ming-Xi Shen ◽  
Chong-Yu Xu ◽  
Hua Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Models ◽  
Climate Model ◽  
Climate Trends ◽  
Impact Studies ◽  
Internal Climate Variability ◽  
Temperature And Precipitation ◽  
Hydrological Impact ◽  
Near Term

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.

scholarly journals CMIP5 Scientific Gaps and Recommendations for CMIP6

2017 ◽  
Vol 98 (1) ◽  
pp. 95-105 ◽  
Author(s):  
R. J. Stouffer ◽  
V. Eyring ◽  
G. A. Meehl ◽  
S. Bony ◽  
C. Senior ◽  
...  
Keyword(s):  
Climate Variability ◽  
Climate Models ◽  
Climate Modeling ◽  
Coupled Model ◽  
Climate Science ◽  
Science Research ◽  
Radiative Forcings ◽  
Internal Climate Variability ◽  
Model Biases ◽  
Numerical Experimentation

Abstract The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP, that is, CMIP6:How does the Earth system respond to changes in forcing?What are the origins and consequences of systematic model biases?How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. These biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.

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