scholarly journals Sensitivity of Limiting Hurricane Intensity to SST in the Atlantic from Observations and GCMs

2013 ◽  
Vol 26 (16) ◽  
pp. 5949-5957 ◽  
Author(s):  
James B. Elsner ◽  
Sarah E. Strazzo ◽  
Thomas H. Jagger ◽  
Timothy LaRow ◽  
Ming Zhao
Keyword(s):  
Climate Models ◽  
North Atlantic Ocean ◽  
Global Climate ◽  
Atmospheric Model ◽  
Global Climate Models ◽  
Lower Sensitivity ◽  
Hurricane Intensity ◽  
University Center ◽  
The North ◽  
Low Sensitivity

Abstract A statistical model for the intensity of the strongest hurricanes has been developed and a new methodology introduced for estimating the sensitivity of the strongest hurricanes to changes in sea surface temperature. Here, the authors use this methodology on observed hurricanes and hurricanes generated from two global climate models (GCMs). Hurricanes over the North Atlantic Ocean during the period 1981–2010 show a sensitivity of 7.9 ± 1.19 m s−1 K−1 (standard error; SE) when over seas warmer than 25°C. In contrast, hurricanes over the same region and period generated from the GFDL High Resolution Atmospheric Model (HiRAM) show a significantly lower sensitivity with the highest at 1.8 ± 0.42 m s−1 K−1 (SE). Similar weaker sensitivity is found using hurricanes generated from the Florida State University Center for Ocean–Atmospheric Prediction Studies (FSU-COAPS) model with the highest at 2.9 ± 2.64 m s−1 K−1 (SE). A statistical refinement of HiRAM-generated hurricane intensities heightens the sensitivity to a maximum of 6.9 ± 3.33 m s−1 K−1 (SE), but the increase is offset by additional uncertainty associated with the refinement. Results suggest that the caution that should be exercised when interpreting GCM scenarios of future hurricane intensity stems from the low sensitivity of limiting GCM-generated hurricane intensity to ocean temperature.

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.

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 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.

scholarly journals Future Australian Severe Thunderstorm Environments. Part II: The Influence of a Strongly Warming Climate on Convective Environments

2014 ◽  
Vol 27 (10) ◽  
pp. 3848-3868 ◽  
Author(s):  
John T. Allen ◽  
David J. Karoly ◽  
Kevin J. Walsh
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Vertical Wind Shear ◽  
Atmospheric Model ◽  
Global Climate Models ◽  
Historical Period ◽  
Severe Thunderstorm ◽  
Warming Climate ◽  
Eastern Australia

Abstract The influence of a warming climate on the occurrence of severe thunderstorm environments in Australia was explored using two global climate models: Commonwealth Scientific and Industrial Research Organisation Mark, version 3.6 (CSIRO Mk3.6), and the Cubic-Conformal Atmospheric Model (CCAM). These models have previously been evaluated and found to be capable of reproducing a useful climatology for the twentieth-century period (1980–2000). Analyzing the changes between the historical period and high warming climate scenarios for the period 2079–99 has allowed estimation of the potential convective future for the continent. Based on these simulations, significant increases to the frequency of severe thunderstorm environments will likely occur for northern and eastern Australia in a warmed climate. This change is a response to increasing convective available potential energy from higher continental moisture, particularly in proximity to warm sea surface temperatures. Despite decreases to the frequency of environments with high vertical wind shear, it appears unlikely that this will offset increases to thermodynamic energy. The change is most pronounced during the peak of the convective season, increasing its length and the frequency of severe thunderstorm environments therein, particularly over the eastern parts of the continent. The implications of this potential increase are significant, with the overall frequency of potential severe thunderstorm days per year likely to rise over the major population centers of the east coast by 14% for Brisbane, 22% for Melbourne, and 30% for Sydney. The limitations of this approach are then discussed in the context of ways to increase the confidence of predictions of future severe convection.

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.

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 Comparison of warming trends over the last century around Antarctica from three coupled models

1998 ◽  
Vol 27 ◽  
pp. 565-570 ◽  
Author(s):  
William M. Connolley ◽  
Siobhan P. O'Farrell
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Atmospheric Model ◽  
Global Climate Models ◽  
Large Temperature ◽  
Coupled Models ◽  
Temperature Changes ◽  
Model Variability ◽  
The Antarctic

We compare observed temperature variations in Antarctica with climate-model runs over the last century. The models used are three coupled global climate models (GCMs) — the UKMO, the CSIRO and the MPI forced by the CO2 increases observed over the last century, and an atmospheric model experiment forced with observed sea-surface temperatures and sea-ice extents over the last century. Despite some regions of agreement, in general the GCM runs appear to be incompatible with each other and with the observations, although the short observational record and high natural variability make verification difficult. One of the best places for a more detailed study is the Antarctic Peninsula where the density of stations is higher and station records are longer than elsewhere in Antarctica. Observations show that this area has seen larger temperature rises than anywhere else in Antarctica. None of the three GCMs simulate such large temperature changes in the Peninsula region, in either climate-change runs radiatively forced by CO2 increases or control runs which assess the level of model variability.

scholarly journals Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

2018 ◽  
Vol 22 (7) ◽  
pp. 3933-3950 ◽  
Author(s):  
Reinhard Schiemann ◽  
Pier Luigi Vidale ◽  
Len C. Shaffrey ◽  
Stephanie J. Johnson ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Global Climate ◽  
River Basins ◽  
Global Climate Models ◽  
Sensitivity Experiment ◽  
Scale Parameters ◽  
The North ◽  
Resolution Model ◽  
High Resolution Model

Abstract. Limited spatial resolution is one of the factors that may hamper applications of global climate models (GCMs), in particular over Europe with its complex coastline and orography. In this study, the representation of European mean and extreme precipitation is evaluated in simulations with an atmospheric GCM (AGCM) at different resolutions between about 135 and 25 km grid spacing in the mid-latitudes. The continent-wide root-mean-square error in mean precipitation in the 25 km model is about 25  % smaller than in the 135 km model in winter. Clear improvements are also seen in autumn and spring, whereas the model's sensitivity to resolution is very small in summer. Extreme precipitation is evaluated by estimating generalised extreme value distributions (GEVs) of daily precipitation aggregated over river basins whose surface area is greater than 50 000 km2. GEV location and scale parameters are measures of the typical magnitude and of the interannual variability of extremes, respectively. Median model biases in both these parameters are around 10 % in summer and around 20 % in the other seasons. For some river basins, however, these biases can be much larger and take values between 50 % and 100 %. Extreme precipitation is better simulated in the 25 km model, especially during autumn when the median GEV parameter biases are more than halved, and in the North European Plains, from the Loire in the west to the Vistula in the east. A sensitivity experiment is conducted showing that these resolution sensitivities in both mean and extreme precipitation are in many areas primarily due to the increase in resolution of the model orography. The findings of this study illustrate the improved capability of a global high-resolution model in simulating European mean and extreme precipitation.

Some Counterintuitive Dependencies of Tropical Cyclone Frequency on Parameters in a GCM

2012 ◽  
Vol 69 (7) ◽  
pp. 2272-2283 ◽  
Author(s):  
Ming Zhao ◽  
Isaac M. Held ◽  
Shian-Jiann Lin
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Global Climate ◽  
Atmospheric Model ◽  
Global Climate Models ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Mixing Rate ◽  
Continuous Increase ◽  
Mean Intensity ◽  
Two Parameters

Abstract High-resolution global climate models (GCMs) have been increasingly utilized for simulations of the global number and distribution of tropical cyclones (TCs), and how they might change with changing climate. In contrast, there is a lack of published studies on the sensitivity of TC genesis to parameterized processes in these GCMs. The uncertainties in these formulations might be an important source of uncertainty in the future projections of TC statistics. This study investigates the sensitivity of the global number of TCs in present-day simulations using the Geophysical Fluid Dynamics Laboratory High Resolution Atmospheric Model (GFDL HIRAM) to alterations in physical parameterizations. Two parameters are identified to be important in TC genesis frequency in this model: the horizontal cumulus mixing rate, which controls the entrainment into convective cores within the convection parameterization, and the strength of the damping of the divergent component of the horizontal flow. The simulated global number of TCs exhibits nonintuitive response to incremental changes of both parameters. As the cumulus mixing rate increases, the model produces nonmonotonic response in global TC frequency with an initial sharp increase and then a decrease. However, storm mean intensity rises monotonically with the mixing rate. As the strength of the divergence damping increases, the model produces a continuous increase of global number of TCs and hurricanes with little change in storm mean intensity. Mechanisms for explaining these nonintuitive responses are discussed.

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