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

2016 ◽  
Vol 29 (23) ◽  
pp. 8647-8663 ◽  
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.

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

2013 ◽  
Vol 26 (18) ◽  
pp. 6904-6914 ◽  
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.

scholarly journals Quantifying Sources of Uncertainty in Projections of Future Climate*

2014 ◽  
Vol 27 (23) ◽  
pp. 8793-8808 ◽  
Author(s):  
Paul J. Northrop ◽  
Richard E. Chandler
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Internal Variability ◽  
Future Climate ◽  
First Century ◽  
Climate Projections ◽  
Large Contribution ◽  
Sources Of Uncertainty ◽  
Twenty First Century ◽  
Emissions Scenario

Abstract A simple statistical model is used to partition uncertainty from different sources, in projections of future climate from multimodel ensembles. Three major sources of uncertainty are considered: the choice of climate model, the choice of emissions scenario, and the internal variability of the modeled climate system. The relative contributions of these sources are quantified for mid- and late-twenty-first-century climate projections, using data from 23 coupled atmosphere–ocean general circulation models obtained from phase 3 of the Coupled Model Intercomparison Project (CMIP3). Similar investigations have been carried out recently by other authors but within a statistical framework for which the unbalanced nature of the data and the small number (three) of scenarios involved are potentially problematic. Here, a Bayesian analysis is used to overcome these difficulties. Global and regional analyses of surface air temperature and precipitation are performed. It is found that the relative contributions to uncertainty depend on the climate variable considered, as well as the region and time horizon. As expected, the uncertainty due to the choice of emissions scenario becomes more important toward the end of the twenty-first century. However, for midcentury temperature, model internal variability makes a large contribution in high-latitude regions. For midcentury precipitation, model internal variability is even more important and this persists in some regions into the late century. Implications for the design of climate model experiments are discussed.

scholarly journals Historical Northern Hemisphere snow cover trends and projected changes in the CMIP-6 multi-model ensemble

2020 ◽  
Author(s):  
Lawrence Mudryk ◽  
Maria Santolaria-Otín ◽  
Gerhard Krinner ◽  
Martin Ménégoz ◽  
Chris Derksen ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Research Programme ◽  
Fast Response ◽  
Model Ensemble ◽  
Near Surface ◽  
Snow Cover Extent ◽  
Snow Mass

Abstract. This paper presents an analysis of observed and simulated historical snow cover extent and snow mass, along with future snow cover projections from models participating in the 6th phase of the World Climate Research Programme Coupled Model Inter-comparison Project (CMIP-6). Where appropriate, the CMIP-6 output is compared to CMIP-5 results in order to assess progress (or absence thereof) between successive model generations. An ensemble of six products are used to produce a new time series of northern hemisphere snow extent anomalies and trends; a subset of four of these products are used for snow mass. Trends in snow extent over 1981–2018 are negative in all months, and exceed −50 × 103 km2 during November, December, March, and May. Snow mass trends are approximately −5 Gt/year or more for all months from December to May. Overall, the CMIP-6 multi-model ensemble better represents the snow extent climatology over the 1981–2014 period for all months, correcting a low bias in CMIP-5. Simulated snow extent and snow mass trends over the 1981–2014 period are slightly stronger in CMIP-6 than in CMIP-5, although large inter-model spread remains in the simulated trends for both variables. There is a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all scenarios. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8 % relative to the 1995–2014 level per °C of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast response components of the cryosphere such as sea ice and near surface permafrost.

scholarly journals Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections

2015 ◽  
Vol 28 (6) ◽  
pp. 2332-2348 ◽  
Author(s):  
G. Abramowitz ◽  
C. H. Bishop
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
First Century ◽  
Annual Rainfall ◽  
Model Code ◽  
Temperature And Precipitation ◽  
Ensemble Mean ◽  
Out Of Sample ◽  
Twenty First Century

Abstract Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This is typically done by collating model estimates from different research institutions internationally with the assumption that they constitute independent samples. Heuristically, however, several factors undermine this assumption: shared treatment of processes between models, shared observed data for evaluation, and even shared model code. Here, a “perfect model” approach is used to test whether a previously proposed ensemble dependence transformation (EDT) can improve twenty-first-century Coupled Model Intercomparison Project (CMIP) projections. In these tests, where twenty-first-century model simulations are used as out-of-sample “observations,” the mean-square difference between the transformed ensemble mean and “observations” is on average 30% less than for the untransformed ensemble mean. In addition, the variance of the transformed ensemble matches the variance of the ensemble mean about the “observations” much better than in the untransformed ensemble. Results show that the EDT has a significant effect on twenty-first-century projections of both surface air temperature and precipitation. It changes projected global average temperature increases by as much as 16% (0.2°C for B1 scenario), regional average temperatures by as much as 2.6°C (RCP8.5 scenario), and regional average annual rainfall by as much as 410 mm (RCP6.0 scenario). In some regions, however, the effect is minimal. It is also found that the EDT causes changes to temperature projections that differ in sign for different emissions scenarios. This may be as much a function of the makeup of the ensembles as the nature of the forcing conditions.

scholarly journals Northern Hemisphere Snow-Cover Trends (1967–2018): A Comparison between Climate Models and Observations

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 135 ◽  
Author(s):  
◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Climate Model ◽  
Coupled Model ◽  
Poor Performance ◽  
Four Seasons ◽  
Intercomparison Project ◽  
Autumn And Winter

Observed changes in Northern Hemisphere snow cover from satellite records were compared to those predicted by all available Coupled Model Intercomparison Project Phase 5 (“CMIP5”) climate models over the duration of the satellite’s records, i.e., 1967–2018. A total of 196 climate model runs were analyzed (taken from 24 climate models). Separate analyses were conducted for the annual averages and for each of the seasons (winter, spring, summer, and autumn/fall). A longer record (1922–2018) for the spring season which combines ground-based measurements with satellite measurements was also compared to the model outputs. The climate models were found to poorly explain the observed trends. While the models suggest snow cover should have steadily decreased for all four seasons, only spring and summer exhibited a long-term decrease, and the pattern of the observed decreases for these seasons was quite different from the modelled predictions. Moreover, the observed trends for autumn and winter suggest a long-term increase, although these trends were not statistically significant. Possible explanations for the poor performance of the climate models are discussed.

scholarly journals How Climate Change Affects Extremes in Maize and Wheat Yield in Two Cropping Regions

2015 ◽  
Vol 28 (12) ◽  
pp. 4653-4687 ◽  
Author(s):  
Caroline C. Ummenhofer ◽  
Hong Xu ◽  
Tracy E. Twine ◽  
Evan H. Girvetz ◽  
Heather R. McCarthy ◽  
...  
Keyword(s):  
Climate Model ◽  
Wheat Yield ◽  
Coupled Model ◽  
Growing Season ◽  
Maize Yield ◽  
First Century ◽  
Southeastern Australia ◽  
Marginal Areas ◽  
Twenty First Century ◽  
Yield Responses

Abstract Downscaled climate model projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were used to force a dynamic vegetation agricultural model (Agro-IBIS) and simulate yield responses to historical climate and two future emissions scenarios for maize in the U.S. Midwest and wheat in southeastern Australia. In addition to mean changes in yield, the frequency of high- and low-yield years was related to changing local hydroclimatic conditions. Particular emphasis was on the seasonal cycle of climatic variables during extreme-yield years and links to crop growth. While historically high (low) yields in Iowa tend to occur during years with anomalous wet (dry) growing season, this is exacerbated in the future. By the end of the twenty-first century, the multimodel mean (MMM) of growing season temperatures in Iowa is projected to increase by more than 5°C, and maize yield is projected to decrease by 18%. For southeastern Australia, the frequency of low-yield years rises dramatically in the twenty-first century because of significant projected drying during the growing season. By the late twenty-first century, MMM growing season precipitation in southeastern Australia is projected to decrease by 15%, temperatures are projected to increase by 2.8°–4.5°C, and wheat yields are projected to decline by 70%. Results highlight the sensitivity of yield projections to the nature of hydroclimatic changes. Where future changes are uncertain, the sign of the yield change simulated by Agro-IBIS is uncertain as well. In contrast, broad agreement in projected drying over southern Australia across models is reflected in consistent yield decreases for the twenty-first century. Climatic changes of the order projected can be expected to pose serious challenges for continued staple grain production in some current centers of production, especially in marginal areas.

scholarly journals Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble

2020 ◽  
Vol 14 (7) ◽  
pp. 2495-2514 ◽  
Author(s):  
Lawrence Mudryk ◽  
María Santolaria-Otín ◽  
Gerhard Krinner ◽  
Martin Ménégoz ◽  
Chris Derksen ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Research Programme ◽  
Fast Response ◽  
Model Ensemble ◽  
Near Surface ◽  
Snow Cover Extent ◽  
Snow Mass

Abstract. This paper presents an analysis of observed and simulated historical snow cover extent and snow mass, along with future snow cover projections from models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 6 (CMIP6). Where appropriate, the CMIP6 output is compared to CMIP5 results in order to assess progress (or absence thereof) between successive model generations. An ensemble of six observation-based products is used to produce a new time series of historical Northern Hemisphere snow extent anomalies and trends; a subset of four of these products is used for snow mass. Trends in snow extent over 1981–2018 are negative in all months and exceed -50×103 km2 yr−1 during November, December, March, and May. Snow mass trends are approximately −5 Gt yr−1 or more for all months from December to May. Overall, the CMIP6 multi-model ensemble better represents the snow extent climatology over the 1981–2014 period for all months, correcting a low bias in CMIP5. Simulated snow extent and snow mass trends over the 1981–2014 period are stronger in CMIP6 than in CMIP5, although large inter-model spread remains in the simulated trends for both variables. There is a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all CMIP6 Shared Socioeconomic Pathways. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8 % relative to the 1995–2014 level per degree Celsius of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast-response components of the cryosphere such as sea ice and near-surface permafrost extent.

Future Changes in Northern Hemisphere Snowfall

2013 ◽  
Vol 26 (20) ◽  
pp. 7813-7828 ◽  
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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