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

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 twenty global climate models, which are from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The results show that, relative to the 1986–2005 mean, the multi-model ensemble projects a significant decrease in SWE for most regions, particularly over the Tibetan Plateau and western North America, but an increase in eastern Siberia. Seasonal SWE projections show an overall decreasing trend, with the greatest reduction in spring, which is linked to the stronger inverse partial correlation between the SWE and increasing temperature. Moreover, zonal mean annual SWE exhibits significant reductions in three Representative Concentration Pathways (RCP), a stronger linear relationship between SWE and temperature at mid–high latitudes suggests the reduction in SWE there is related to rising temperature. However, the rate of reduction in SWE declines gradually during the 21st century, indicating that the temperature may reach a threshold value that decreases the rate of SWE reduction. A large reduction in zonal maximum SWE (ZMSWE) between 30° and 40° N is evident in all 21st century for the three RCPs, while RCP8.5 alone indicates a further reduction at high latitudes in the late period of the century. This pattern implies that ZMSWE is affected not only by a terrain factor but also by the increasing temperature. In summary, our results show both a decreasing trend in SWE in the 21st century and a decline in the rate of SWE reduction over the 21st century despite rising temperatures.

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Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models with satellite-based SnowCCI data during 1982–2014

10.5194/tc-2021-195 ◽

2021 ◽

Author(s):

Kerttu Kouki ◽

Petri Räisänen ◽

Kari Luojus ◽

Anna Luomaranta ◽

Aku Riihelä

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Climate Models ◽

Snow Water Equivalent ◽

Global Climate ◽

Dominant Factor ◽

Large Variability ◽

Cold Temperatures ◽

Continental Scale ◽

Snow Water

Abstract. Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. Monitoring snow water equivalent (SWE) at continental scale is only possible from satellites, yet substantial uncertainties have been reported in NH SWE estimates. A recent bias-correction method significantly reduces the uncertainty of NH SWE estimation, which enables a more reliable analysis of the climate models' ability to describe the snow cover. We have intercompared the CMIP6 (Coupled Model Intercomparison Project Phase 6) and satellite-based NH SWE estimates north of 40° N for the period 1982–2014, and analyzed with a regression approach whether temperature (T) and precipitation (P) could explain the differences in SWE. We analyzed separately SWE in winter and SWE change rate in spring. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ data. The analysis shows that CMIP6 models tend to overestimate SWE, however, large variability exists between models. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. This is in line with the expectation that even too cold temperatures cannot cause too high SWE without precipitation. T contributes to SWE biases mainly in regions, where T is close to 0 °C in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. Furthermore, it is obvious from the results that biases in T or P can not explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as deficiencies in model parameterizations and possibly biases in the observational datasets, also contribute to SWE discrepancies. In particular, linear regression suggests that when the biases in T and P are eliminated, the models generally overestimate the snowmelt rate in spring.

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

Journal of Climate ◽

10.1175/jcli-d-12-00563.1 ◽

2013 ◽

Vol 26 (18) ◽

pp. 6904-6914 ◽

Cited By ~ 41

Author(s):

David E. Rupp ◽

Philip W. Mote ◽

Nathaniel L. Bindoff ◽

Peter A. Stott ◽

David A. Robinson

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Internal Variability ◽

Global Climate Models ◽

Anthropogenic Forcing ◽

Detection And Attribution ◽

Snow Cover Extent

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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Snowpack Changes in the Hindu Kush–Karakoram–Himalaya from CMIP5 Global Climate Models

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0196.1 ◽

2014 ◽

Vol 15 (6) ◽

pp. 2293-2313 ◽

Cited By ~ 22

Author(s):

Silvia Terzago ◽

Jost von Hardenberg ◽

Elisa Palazzi ◽

Antonello Provenzale

Keyword(s):

Snow Depth ◽

Climate Models ◽

Snow Water Equivalent ◽

Global Climate ◽

Control Period ◽

High Elevation ◽

Global Climate Models ◽

Spatial Average ◽

Hindu Kush ◽

Snow Water

Abstract The Hindu Kush, Karakoram, and Himalaya (HKKH) mountain ranges feed the most important Asian river systems, providing water to about 1.5 billion people. As a consequence, changes in snow dynamics in this area could severely impact water availability for downstream populations. Despite their importance, the amount, spatial distribution, and seasonality of snow in the HKKH region are still poorly known, owing to the limited availability of surface observations in this remote and high-elevation area. This work considers global climate models (GCM) participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and analyzes how they represent current and future snowpack in the HKKH region in terms of snow depth and snow water equivalent. It is found that models with high spatial resolution (up to 1.25°) simulate a spatial pattern of the winter snowpack in greater agreement with each other, with observations, with reanalysis datasets, and with the orographic features of the region, compared to most lower-resolution models. The seasonal cycle of snow depth displays a unimodal regime, with a maximum in February–March and almost complete melting in summer. The models generally indicate thicker [in Hindu Kush–Karakoram (HKK)] or comparable (in the Himalayas) snow depth and higher snow water equivalent compared to the reanalyses for the control period 1980–2005. Future projections, evaluated in terms of the ensemble mean of GCM simulations, indicate a significant reduction in the spatial average of snow depth over the HKK and an even stronger decrease in the Himalayas, where a reduction between 25% and 50% is expected by the end of the twenty-first century.

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Global surface air temperatures in CMIP6: Historical performance and future

10.5194/egusphere-egu2020-5436 ◽

2020 ◽

Author(s):

Xuewei Fan

Keyword(s):

21St Century ◽

Land Surface ◽

Climate Models ◽

Bayesian Model Averaging ◽

Global Climate ◽

Coupled Model ◽

Surface Air Temperature ◽

Global Climate Models ◽

Global Land ◽

Model Ensembles

<p>Surface air temperature outputs from 16 global climate models (GCMs) participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) were used to evaluate agreement with observations over the global land surface for the period 1901&#8211;2014. Projections of Bayesian model averaging (BMA) multi-model ensembles under four different Shared Socioeconomic Pathways (SSPs) were also examined. The results reveal that the majority of models reasonably capture the dominant features of the spatial changes in observed temperature with a pattern correlation typically greater than 0.98. However, most models underestimate annual temperature over northeastern North America and overestimate it over central Eurasia. In addition, most CMIP6 models overestimate the warming trend in most regions. The BMA multi-model ensembles show more agreement than individual models do in simulating the spatial patterns of the temperature, but with less spatial variability compared with the observations. In the 21st century, temperature is generally projected to increase over the global land surface under all four SSP scenarios. By the end of the 21st century, temperature is projected to increase by 1.35 &#176;C/100 yr, 3.61 &#176;C/100 yr, 6.39 &#176;C/100 yr and 8.03 &#176;C/100 yr under the SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios, respectively, with greater warming projected over the high latitudes of the northern hemisphere and weaker warming over the tropics and the southern hemisphere.</p>

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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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IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING

International Journal of Big Data Mining for Global Warming ◽

10.1142/s263053482150008x ◽

2021 ◽

Author(s):

SOURABH SHRIVASTAVA ◽

RAM AVTAR ◽

PRASANTA KUMAR BAL

Keyword(s):

Machine Learning ◽

North American ◽

Climate Models ◽

Global Climate ◽

Horizontal Resolution ◽

Summer Monsoon Rainfall ◽

Global Climate Models ◽

Himalayan Region ◽

Model Ensemble ◽

The North

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

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Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

Journal of Climate ◽

10.1175/jcli-d-18-0598.1 ◽

2019 ◽

Vol 32 (2) ◽

pp. 639-661 ◽

Cited By ~ 5

Author(s):

Y. Chang ◽

S. D. Schubert ◽

R. D. Koster ◽

A. M. Molod ◽

H. Wang

Keyword(s):

North America ◽

Bias Correction ◽

Climate Models ◽

Global Climate ◽

Storm Track ◽

Coupled Model ◽

Global Climate Models ◽

Forecast Skill ◽

Boreal Summer ◽

Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

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Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP-6 models

10.5194/esd-2020-80 ◽

2020 ◽

Author(s):

Anja Katzenberger ◽

Jacob Schewe ◽

Julia Pongratz ◽

Anders Levermann

Keyword(s):

Climate Change ◽

Global Warming ◽

Climate Models ◽

Monsoon Rainfall ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Future Climate Change ◽

Global Mean Temperature ◽

Almost All

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP-5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP-5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP-6 are of interest. Here, we analyse 32 models of the latest CMIP-6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with high agreement between the models and independent of the SSP; the multi-model mean for JJAS projects an increase of 0.33 mm/day and 5.3 % per degree of global warming. This is significantly higher than in the CMIP-5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP-6 simulations largely confirm the findings from CMIP-5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

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