scholarly journals Projected Changes in European and North Atlantic Seasonal Wind Climate Derived from CMIP5 Simulations

2019 ◽  
Vol 32 (19) ◽  
pp. 6467-6490 ◽  
Author(s):  
Kimmo Ruosteenoja ◽  
Timo Vihma ◽  
Ari Venäläinen
Keyword(s):  
North Atlantic ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Near Surface ◽  
Wind Speeds ◽  
The North ◽  
Seasonal Wind ◽  
Projected Changes

Abstract Future changes in geostrophic winds over Europe and the North Atlantic region were studied utilizing output data from 21 CMIP5 global climate models (GCMs). Changes in temporal means, extremes, and the joint distribution of speed and direction were considered. In concordance with previous research, the time mean and extreme scalar wind speeds do not change pronouncedly in response to the projected climate change; some degree of weakening occurs in the majority of the domain. Nevertheless, substantial changes in high wind speeds are identified when studying the geostrophic winds from different directions separately. In particular, in northern Europe in autumn and in parts of northwestern Europe in winter, the frequency of strong westerly winds is projected to increase by up to 50%. Concurrently, easterly winds become less common. In addition, we evaluated the potential of the GCMs to simulate changes in the near-surface true wind speeds. In ocean areas, changes in the true and geostrophic winds are mainly consistent and the emerging differences can be explained (e.g., by the retreat of Arctic sea ice). Conversely, in several GCMs the continental wind speed response proved to be predominantly determined by fairly arbitrary changes in the surface properties rather than by changes in the atmospheric circulation. Accordingly, true wind projections derived directly from the model output should be treated with caution since they do not necessarily reflect the actual atmospheric response to global warming.

scholarly journals Understanding Intermodel Variability in Future Projections of a Sahelian Storm Proxy and Southern Saharan Warming

2021 ◽  
Vol 34 (2) ◽  
pp. 509-525
Author(s):  
David P. Rowell ◽  
Rory G. J. Fitzpatrick ◽  
Lawrence S. Jackson ◽  
Grace Redmond
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Vertical Wind Shear ◽  
Global Climate Models ◽  
Tropospheric Temperature ◽  
Storm Activity ◽  
Convective Systems ◽  
The North ◽  
Projected Changes

AbstractProjected changes in the intensity of severe rain events over the North African Sahel—falling from large mesoscale convective systems—cannot be directly assessed from global climate models due to their inadequate resolution and parameterization of convection. Instead, the large-scale atmospheric drivers of these storms must be analyzed. Here we study changes in meridional lower-tropospheric temperature gradient across the Sahel (ΔTGrad), which affect storm development via zonal vertical wind shear and Saharan air layer characteristics. Projected changes in ΔTGrad vary substantially among models, adversely affecting planning decisions that need to be resilient to adverse risks, such as increased flooding. This study seeks to understand the causes of these projection uncertainties and finds three key drivers. The first is intermodel variability in remote warming, which has strongest impact on the eastern Sahel, decaying toward the west. Second, and most important, a warming–advection–circulation feedback in a narrow band along the southern Sahara varies in strength between models. Third, variations in southern Saharan evaporative anomalies weakly affect ΔTGrad, although for an outlier model these are sufficiently substantive to reduce warming here to below that of the global mean. Together these uncertain mechanisms lead to uncertain southern Saharan/northern Sahelian warming, causing the bulk of large intermodel variations in ΔTGrad. In the southern Sahel, a local negative feedback limits the contribution to uncertainties in ΔTGrad. This new knowledge of ΔTGrad projection uncertainties provides understanding that can be used, in combination with further research, to constrain projections of severe Sahelian storm activity.

scholarly journals 20th-century sea-ice variations from observational data

2001 ◽  
Vol 33 ◽  
pp. 444-448 ◽  
Author(s):  
John E. Walsh ◽  
William L. Chapman
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
North Atlantic ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Empirical Orthogonal Functions ◽  
Global Climate Models ◽  
The Past ◽  
Ice Coverage

AbstractIn order to extend diagnoses of recent sea-ice variations beyond the past few decades, a century-scale digital dataset of Arctic sea-ice coverage has been compiled. For recent decades, the compilation utilizes satellite-derived hemispheric datasets. Regional datasets based primarily on ship reports and aerial reconnaissance are the primary inputs for the earlier part of the 20th century. While the various datasets contain some discrepancies, they capture the same general variations during their period of overlap. The outstanding feature of the time series of total hemispheric ice extent is a decrease that has accelerated during the past several decades. The decrease is greatest in summer and weakest in winter, contrary to the seasonality of the greenhouse changes projected by most global climate models. The primary spatial modes of sea-ice variability diagnosed in terms of empirical orthogonal functions, also show a strong seasonality. The first winter mode is dominated by an opposition of anomalies in the western and eastern North Atlantic, corresponding to the well-documented North Atlantic Oscillation. The primary summer mode depicts an anomaly of the same sign over nearly the entire Arctic and captures the recent trend of sea-ice coverage.

scholarly journals Pan-Arctic and Regional Sea Ice Predictability: Initialization Month Dependence

2014 ◽  
Vol 27 (12) ◽  
pp. 4371-4390 ◽  
Author(s):  
J. J. Day ◽  
S. Tietsche ◽  
E. Hawkins
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Regional Analysis ◽  
Early Summer ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Sea Ice Extent ◽  
The North ◽  
Arctic Sea

Abstract Seasonal-to-interannual predictions of Arctic sea ice may be important for Arctic communities and industries alike. Previous studies have suggested that Arctic sea ice is potentially predictable but that the skill of predictions of the September extent minimum, initialized in early summer, may be low. The authors demonstrate that a melt season “predictability barrier” and two predictability reemergence mechanisms, suggested by a previous study, are robust features of five global climate models. Analysis of idealized predictions with one of these models [Hadley Centre Global Environment Model, version 1.2 (HadGEM1.2)], initialized in January, May and July, demonstrates that this predictability barrier exists in initialized forecasts as well. As a result, the skill of sea ice extent and volume forecasts are strongly start date dependent and those that are initialized in May lose skill much faster than those initialized in January or July. Thus, in an operational setting, initializing predictions of extent and volume in July has strong advantages for the prediction of the September minimum when compared to predictions initialized in May. Furthermore, a regional analysis of sea ice predictability indicates that extent is predictable for longer in the seasonal ice zones of the North Atlantic and North Pacific than in the regions dominated by perennial ice in the central Arctic and marginal seas. In a number of the Eurasian shelf seas, which are important for Arctic shipping, only the forecasts initialized in July have continuous skill during the first summer. In contrast, predictability of ice volume persists for over 2 yr in the central Arctic but less in other regions.

scholarly journals The Atlantic inflow across the Greenland-Scotland ridge in global climate models (CMIP5)

Elem Sci Anth ◽  
2019 ◽  
Vol 7 ◽  
Author(s):  
Céline Heuzé ◽  
Marius Årthun
Keyword(s):  
Heat Transport ◽  
North Atlantic ◽  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
The Arctic ◽  
Nordic Seas ◽  
The North ◽  
The North Atlantic

Oceanic heat transport from the North Atlantic to the Arctic through the Nordic Seas is a key component of the climate system that has to be modelled accurately in order to predict, for example, future Arctic sea ice changes or European climate. Here we quantify biases in the climatological state and dynamics of the transport of oceanic heat into the Nordic Seas across the Greenland-Scotland ridge in 23 state-of-the-art global climate models that participated in the Climate Model Intercomparison Project phase 5. The mean poleward heat transport, its seasonal cycle and interannual variability are inconsistently represented across these models, with a vast majority underestimating them and a few models greatly overestimating them. The main predictor for these biases is the resolution of the model via its representation of the Greenland-Scotland ridge bathymetry: the higher the resolution, the larger the heat transport through the section. The second predictor is the large-scale ocean circulation, which is also connected to the bathymetry: models with the largest heat transport import water from the European slope current into all three straits of the Greenland-Scotland ridge, whereas those with a weak transport import water from the Labrador Sea. The third predictor is the spatial pattern of their main atmospheric modes of variability (North Atlantic Oscillation, East Atlantic and Scandinavian patterns), where the models with a weak inflow have their atmospheric low-pressure centre shifted south towards the central Atlantic. We argue that the key to a better representation of the large-scale oceanic heat transport from the North Atlantic to the Arctic in global models resides not only in higher resolution, but also in a better bathymetry and representation of the complex ocean-ice-atmosphere interactions.

scholarly journals Temperature domination of AMOC weakening due to freshwater hosing in two GCMs

2019 ◽  
Vol 54 (1-2) ◽  
pp. 273-286 ◽  
Author(s):  
Rosalind K. Haskins ◽  
Kevin I. C. Oliver ◽  
Laura C. Jackson ◽  
Richard A. Wood ◽  
Sybren S. Drijfhout
Keyword(s):  
North Atlantic ◽  
Climate Models ◽  
North Atlantic Ocean ◽  
Global Climate ◽  
Mixed Layer Depth ◽  
Global Climate Models ◽  
Meridional Overturning Circulation ◽  
Ice Melt ◽  
The North ◽  
The North Atlantic

Abstract Anthropogenic climate change is projected to lead to a weakening of the Atlantic meridional overturning circulation (AMOC). One of the mechanisms contributing to this is ice melt leading to a freshening of the North Atlantic Ocean. We use two global climate models to investigate the role of temperature and salinity in the weakening of the AMOC resulting from freshwater forcing. This study finds that freshwater hosing reduces the strength of the AMOC, but in some situations it is not through reduced density from freshening, but a reduction in density from subsurface warming. When the freshwater is mixed down it directly reduces the density of the North Atlantic, weakening the strength of the AMOC. As the AMOC weakens, the mixed layer depth reduces and surface properties are less effectively mixed down. A buoyant surface cap forms, blocking atmospheric fluxes. This leads to the development of a warm anomaly beneath the surface cap, which becomes the primary driver of AMOC weakening. We found that the mean North Atlantic salinity anomaly can be used as a proxy for AMOC weakening because it describes the extent of this surface cap.

North Atlantic Oscillation

2017 ◽  
Author(s):  
Edward Hanna ◽  
Thomas E. Cropper
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
The Arctic ◽  
Energy Output ◽  
Jet Stream ◽  
The North ◽  
The North Atlantic

Many variations in the weather in the European and North Atlantic regions are linked with changes in the North Atlantic Oscillation (NAO). The NAO is measured using a south-minus-north index of atmospheric surface pressure variation across the North Atlantic and is closely connected with changes in the North Atlantic atmospheric polar jet stream and wider changes in atmospheric circulation. The physical, human, and biological impacts of NAO changes extend well beyond weather and climate, with major economic, social, and environmental effects. The NAO index based on barometric pressure records now extends as far back as 1850, based on recent work. Although there are few significant overall trends in monthly or seasonal NAO (i.e., for the whole record), there are many shorter-term multidecadal variations. A prominent increase in the NAO between the 1960s and 1990s was widely noted in previous work and was thought to be related to human-induced greenhouse gas forcing. However, since then this trend has reversed, with a significant decrease in the summer NAO since the 1990s and a striking increase in variability of the winter—especially December—NAO that has resulted in four of the six highest and two of the five lowest NAO Decembers occurring during 2004–2015 in the 116-year record, with accompanying more variable year-to-year winter weather conditions over the United Kingdom. These NAO changes are related to an increasing trend in the Greenland Blocking Index (GBI; equals high pressure over Greenland) in summer and a significantly more variable GBI in December. Such NAO and related jet stream and blocking changes are not generally present in the current generation of global climate models, although recent process studies offer insights into their possible causes. Several plausible climate forcings and feedbacks, including changes in the sun’s energy output and the Arctic amplification of global warming with accompanying reductions in sea ice, may help explain the recent NAO changes. Recent research also suggests significant skill in being able to make seasonal NAO predictions and therefore long-range weather forecasts for up to several months ahead for northwest Europe. However, global climate models remain unclear on longer-term NAO predictions for the remainder of the 21st century.

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

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.

scholarly journals Ensemble models on palaeoclimate to predict India's groundwater challenge

2013 ◽  
Vol 2 (3) ◽  
Author(s):  
Partha Sarathi Datta
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Humid Climate ◽  
Ensemble Models ◽  
North West ◽  
The Past ◽  
The North ◽  
14C Age

In many parts of the world, freshwater crisis is largely due to increasing water consumption and pollution by rapidly growing population and aspirations for economic development, but, ascribed usually to the climate. However, limited understanding and knowledge gaps in the factors controlling climate and uncertainties in the climate models are unable to assess the probable impacts on water availability in tropical regions. In this context, review of ensemble models on δ18O and δD in rainfall and groundwater, 3H- and 14C- ages of groundwater and 14C- age of lakes sediments helped to reconstruct palaeoclimate and long-term recharge in the North-west India; and predict future groundwater challenge. The annual mean temperature trend indicates both warming/cooling in different parts of India in the past and during 1901–2010. Neither the GCMs (Global Climate Models) nor the observational record indicates any significant change/increase in temperature and rainfall over the last century, and climate change during the last 1200 yrs BP. In much of the North-West region, deep groundwater renewal occurred from past humid climate, and shallow groundwater renewal from limited modern recharge over the past decades. To make water management to be more responsive to climate change, the gaps in the science of climate change need to be bridged.

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

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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