Detection and Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover

Abstract Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

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A Method for Detection and Attribution of Regional Precipitation Change Using Granger Causality Application to the United States Historical Record

10.21203/rs.3.rs-785460/v1 ◽

2021 ◽

Author(s):

Mark Risser ◽

William Collins ◽

Michael Wehner ◽

Travis O'Brien ◽

Christopher Paciorek ◽

...

Keyword(s):

United States ◽

Climate Models ◽

Water Cycle ◽

Global Climate ◽

Internal Variability ◽

The United States ◽

Historical Record ◽

Global Climate Models ◽

Detection And Attribution ◽

Observational Uncertainty

Abstract Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we propose a novel approach to regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is minimized. In a single framework, we simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. We use output from global climate models in a perfect-data sense to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day). In doing so, we turn an apparent limitation into an opportunity by using the diversity of responses to short-lived climate forcers across the CMIP6 multi-model ensemble to ensure our D&A is insensitive to structural uncertainty. Our framework is developed using a Pearl-causal perspective, but forthcoming research now underway will apply the framework to in situ measurements using a Granger-causal perspective. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.

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Quantifying the Uncertainty in Historical and Future Simulations of Northern Hemisphere Spring Snow Cover

Journal of Climate ◽

10.1175/jcli-d-16-0341.1 ◽

2016 ◽

Vol 29 (23) ◽

pp. 8647-8663 ◽

Cited By ~ 21

Author(s):

Chad W. Thackeray ◽

Christopher G. Fletcher ◽

Lawrence R. Mudryk ◽

Chris Derksen

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Climate Model ◽

Coupled Model ◽

Internal Variability ◽

Winter Precipitation ◽

First Century ◽

Internal Climate Variability ◽

Snow Cover Extent ◽

Twenty First Century

Abstract Projections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, internal variability estimated from CanESM-LE is substantial, but smaller than intermodel spread from CMIP5. Additionally, internal variability in NH extratropical land warming trends can affect SCE trends in the near future (R2 = 0.45), while variability in winter precipitation can also have a significant (but lesser) impact on SCE trends. On the other hand, a majority of the intermodel spread is driven by differences in simulated warming (dominant in March–May) and snow cover available for melt (dominant in June). The strong temperature–SCE linkage suggests that model uncertainty in projections of SCE could be potentially reduced through improved simulation of spring season warming over land.

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Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

10.21203/rs.3.rs-227967/v1 ◽

2021 ◽

Author(s):

Gavin D. Madakumbura ◽

Chad W. Thackeray ◽

Jesse Norris ◽

Naomi Goldenson ◽

Alex Hall

Keyword(s):

Machine Learning ◽

Model Uncertainty ◽

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Internal Variability ◽

Global Climate Models ◽

Human Influence ◽

Anthropogenic Forcing ◽

Global Datasets

Abstract Global climate models produce large increases in extreme precipitation when subject to anthropogenic forcing, but detecting this human influence in observations is challenging. Large internal variability makes the signal difficult to characterize. Models produce diverse precipitation responses to anthropogenic forcing, mirroring a variety of parameterization choices for subgrid-scale processes. And observations are inhomogeneously sampled in space and time, leading to multiple global datasets, each produced with a different homogenization technique. Thus, previous attempts to detect human influence on extreme precipitation have not incorporated internal variability or model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods, we find a physically interpretable anthropogenic signal that is detectable in all global datasets. Detection occurs even when internal variability and model uncertainty are taken into account. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in extreme precipitation.

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Uncertainty in Forced and Natural Arctic Solar Absorption Variations in CMIP6 Models

Journal of Climate ◽

10.1175/jcli-d-20-0244.1 ◽

2021 ◽

Vol 34 (3) ◽

pp. 931-948

Author(s):

Anne Sledd ◽

Tristan L’Ecuyer

Keyword(s):

Sea Ice ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Internal Variability ◽

Global Climate Models ◽

Shortwave Radiation ◽

The Arctic ◽

Solar Absorption ◽

Clear Sky

AbstractThe Arctic is rapidly changing, with increasingly dramatic sea ice loss and surface warming in recent decades. Shortwave radiation plays a key role in Arctic warming during summer months, and absorbed shortwave radiation has been increasing largely because of greater sea ice loss. Clouds can influence this ice–albedo feedback by modulating the amount of shortwave radiation incident on the Arctic Ocean. In turn, clouds impact the amount of time that must elapse before forced trends in Arctic shortwave absorption emerge from internal variability. This study determines whether the forced climate response of absorbed shortwave radiation in the Arctic has emerged in the modern satellite record and global climate models. From 18 years of satellite observations from CERES-EBAF, we find that recent declines in sea ice are large enough to produce a statistically significant trend (1.7 × 106 PJ or 3.9% per decade) in observed clear-sky absorbed shortwave radiation. However, clouds preclude any forced trends in all-sky absorption from emerging within the existing satellite record. Across 18 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), the predicted time to emergence of absorbed shortwave radiation trends varies from 8 to 39 and from 8 to 35 years for all-sky and clear-sky conditions, respectively, across two future scenarios. Furthermore, most models fail to reproduce the observed cloud delaying effect because of differences in internal variability. Contrary to observations, one-third of models suggest that clouds may reduce the time to emergence of absorbed shortwave trends relative to clear skies, an artifact that may be the result of inaccurate representations of cloud feedbacks.

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Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

Nature Communications ◽

10.1038/s41467-021-24262-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Gavin D. Madakumbura ◽

Chad W. Thackeray ◽

Jesse Norris ◽

Naomi Goldenson ◽

Alex Hall

Keyword(s):

Machine Learning ◽

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Internal Variability ◽

Global Climate Models ◽

Sampled Data ◽

Anthropogenic Forcing ◽

Precipitation Response ◽

Observational Record

Abstract The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.

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Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

10.21203/rs.3.rs-227967/v2 ◽

2021 ◽

Author(s):

Gavin D. Madakumbura ◽

Chad W. Thackeray ◽

Jesse Norris ◽

Naomi Goldenson ◽

Alex Hall

Keyword(s):

Machine Learning ◽

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Internal Variability ◽

Global Climate Models ◽

Sampled Data ◽

Anthropogenic Forcing ◽

Precipitation Response ◽

Observational Record

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Projected 21st century changes in snow water equivalent over Northern Hemisphere landmasses from the CMIP5 model ensemble

The Cryosphere ◽

10.5194/tc-9-1943-2015 ◽

2015 ◽

Vol 9 (5) ◽

pp. 1943-1953 ◽

Cited By ~ 19

Author(s):

H. X. Shi ◽

C. H. Wang

Keyword(s):

Northern Hemisphere ◽

21St Century ◽

Climate Models ◽

Snow Water Equivalent ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

The Tibetan Plateau ◽

Model Ensemble ◽

Snow Water

Abstract. Changes in snow water equivalent (SWE) over Northern Hemisphere (NH) landmasses are investigated for the early (2016–2035), middle (2046–2065) and late (2080–2099) 21st century using a multi-model ensemble from 20 global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The multi-model ensemble was found to provide a realistic estimate of observed NH mean winter SWE compared to the GlobSnow product. The multi-model ensemble projects significant decreases in SWE over the 21st century for most regions of the NH for representative concentration pathways (RCPs) 2.6, 4.5 and 8.5. This decrease is particularly evident over the Tibetan Plateau and North America. The only region with projected increases is eastern Siberia. Projected reductions in mean annual SWE exhibit a latitudinal gradient with the largest relative changes over lower latitudes. SWE is projected to undergo the largest decreases in the spring period where it is most strongly negatively correlated with air temperature. The reduction in snowfall amount from warming is shown to be the main contributor to projected changes in SWE during September to May over the NH.

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Future Changes in Northern Hemisphere Snowfall

Journal of Climate ◽

10.1175/jcli-d-12-00832.1 ◽

2013 ◽

Vol 26 (20) ◽

pp. 7813-7828 ◽

Cited By ~ 97

Author(s):

John P. Krasting ◽

Anthony J. Broccoli ◽

Keith W. Dixon ◽

John R. Lanzante

Keyword(s):

Twentieth Century ◽

Northern Hemisphere ◽

Climate Models ◽

Global Climate ◽

Noise Analysis ◽

Coupled Model ◽

Surface Air Temperature ◽

Global Climate Models ◽

First Century ◽

Twenty First Century

Abstract Using simulations performed with 18 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of the Northern Hemisphere snowfall under the representative concentration pathway (RCP4.5) scenario are analyzed for the period 2006–2100. These models perform well in simulating twentieth-century snowfall, although there is a positive bias in many regions. Annual snowfall is projected to decrease across much of the Northern Hemisphere during the twenty-first century, with increases projected at higher latitudes. On a seasonal basis, the transition zone between negative and positive snowfall trends corresponds approximately to the −10°C isotherm of the late twentieth-century mean surface air temperature, such that positive trends prevail in winter over large regions of Eurasia and North America. Redistributions of snowfall throughout the entire snow season are projected to occur—even in locations where there is little change in annual snowfall. Changes in the fraction of precipitation falling as snow contribute to decreases in snowfall across most Northern Hemisphere regions, while changes in total precipitation typically contribute to increases in snowfall. A signal-to-noise analysis reveals that the projected changes in snowfall, based on the RCP4.5 scenario, are likely to become apparent during the twenty-first century for most locations in the Northern Hemisphere. The snowfall signal emerges more slowly than the temperature signal, suggesting that changes in snowfall are not likely to be early indicators of regional climate change.

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Observation-based selection of climate models projects Arctic ice-free summers around 2035

Communications Earth & Environment ◽

10.1038/s43247-021-00214-7 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

David Docquier ◽

Torben Koenigk

Keyword(s):

Model Selection ◽

Sea Ice ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Arctic Sea Ice ◽

Global Climate Models ◽

The Arctic ◽

Arctic Sea ◽

Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

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Climate change in the High Mountain Asia simulated with CMIP6 models

10.5194/egusphere-egu21-8365 ◽

2021 ◽

Author(s):

Mickaël Lalande ◽

Martin Ménégoz ◽

Gerhard Krinner

Keyword(s):

High Resolution ◽

Snow Cover ◽

Climate Models ◽

Global Climate Models ◽

Cold Bias ◽

High Mountain ◽

The Tibetan Plateau ◽

Near Surface ◽

Model Biases ◽

Snow Cover Extent

<p>The High Mountains of Asia (HMA) region and the Tibetan Plateau (TP), with an average altitude of 4000 m, are hosting the third largest reservoir of glaciers and snow after the two polar ice caps, and are at the origin of strong orographic precipitation. Climate studies over HMA are related to serious challenges concerning the exposure of human infrastructures to natural hazards and the water resources for agriculture, drinking water, and hydroelectricity to whom several hundred million inhabitants of the Indian subcontinent are depending. However, climate variables such as temperature, precipitation, and snow cover are poorly described by global climate models because their coarse resolution is not adapted to the rugged topography of this region. Since the first CMIP exercises, a cold model bias has been identified in this region, however, its attribution is not obvious and may be different from one model to another. Our study focuses on a multi-model comparison of the CMIP6 simulations used to investigate the climate variability in this area to answer the next questions: (1) are the biases in HMA reduced in the new generation of climate models? (2) Do the model biases impact the simulated climate trends? (3) What are the links between the model biases in temperature, precipitation, and snow cover extent? (4) Which climate trajectories can be projected in this area until 2100? An analysis of 27 models over 1979-2014 still show a cold bias in near-surface air temperature over the HMA and TP reaching an annual value of -2.0 &#176;C (&#177; 3.2 &#176;C), associated with an over-extended relative snow cover extent of 53 % (&#177; 62 %), and a relative excess of precipitation of 139 % (&#177; 38 %), knowing that the precipitation biases are uncertain because of the undercatch of solid precipitation in observations. Model biases and trends do not show any clear links, suggesting that biased models should not be excluded in trend and projections analysis, although non-linear effects related to lagged snow cover feedbacks could be expected. On average over 2081-2100 with respect to 1995-2014, for the scenarios SSP126, SSP245, SSP370, and SSP585, the 9 available models shows respectively an increase in annual temperature of 1.9 &#176;C (&#177; 0.5 &#176;C), 3.4 &#176;C (&#177; 0.7 &#176;C), 5.2 &#176;C (&#177; 1.2 &#176;C), and 6.6 &#176;C (&#177; 1.5 &#176;C); a relative decrease in the snow cover extent of 10 % (&#177; 4.1 %), 19 % (&#177; 5 %), 29 % (&#177; 8 %), and 35 % (&#177; 9 %); and an increase in total precipitation of 9 % (&#177; 5 %), 13 % (&#177; 7 %), 19 % (&#177; 11 %), and 27 % (&#177; 13 %). Further analyses will be considered to investigate potential links between the biases at the surface and those at higher tropospheric levels as well as with the topography. The models based on high resolution do not perform better than the coarse-gridded ones, suggesting that the race to high resolution should be considered as a second priority after the developments of more realistic physical parameterizations.</p>

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