scholarly journals Land–Sea Thermal Contrast and Intensity of the North American Monsoon under Climate Change Conditions

2014 ◽  
Vol 27 (12) ◽  
pp. 4566-4580 ◽  
Author(s):  
Abraham Torres-Alavez ◽  
Tereza Cavazos ◽  
Cuauhtemoc Turrent
Keyword(s):  
North American ◽  
Climate Models ◽  
Global Climate ◽  
Coastal Region ◽  
Global Climate Models ◽  
North American Monsoon ◽  
Thermal Contrast ◽  
The North ◽  
The Future ◽  
The Mean

Abstract The hypothesis that global warming during the twenty-first century will increase the land–sea thermal contrast (LSTC) and therefore the intensity of early season precipitation of the North American monsoon (NAM) is examined. To test this hypothesis, future changes (2075–99 minus 1979–2004 means) in LSTC, moisture flux convergence (MFC), vertical velocity, and precipitation in the region are analyzed using six global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) under the representative concentration pathway 8.5 (RCP8.5) emission scenario. A surface LSTC index shows that the continent becomes warmer than the ocean in May in the North American Regional Reanalysis (NARR) and ECMWF Interim Re-Analysis (ERA-Interim) and in June in the mean ensemble of the GCMs (ens_GCMs), and the magnitude of the positive LSTC is greater in the reanalyses than in the ens_GCMs during the historic period. However, the reanalyses underestimate July–August precipitation in the NAM region, while the ens_GCMs reproduces the peak season surprisingly well but overestimates it the rest of the year. The future ens_GCMs projects a doubling of the magnitude of the positive surface LSTC and an earlier start of the continental summer warming in mid-May. Contrary to the stated hypothesis, however, the mean projection suggests a slight decrease of monsoon coastal precipitation during June–August (JJA), which is attributed to increased midtropospheric subsidence, a reduced midtropospheric LSTC, and reduced MFC in the NAM coastal region. In contrast, the future ens_GCMs produces increased MFC and precipitation over the adjacent mountains during JJA and significantly more rainfall over the entire NAM region during September–October, weakening the monsoon retreat.

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.

Towards Assessing NARCCAP Regional Climate Model Credibility for the North American Monsoon: Current Climate Simulations*

2013 ◽  
Vol 26 (22) ◽  
pp. 8802-8826 ◽  
Author(s):  
Melissa S. Bukovsky ◽  
David J. Gochis ◽  
Linda O. Mearns
Keyword(s):  
North American ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
North American Monsoon ◽  
The North ◽  
Current Climate ◽  
Depth Analysis

Abstract The authors examine 17 dynamically downscaled simulations produced as part of the North American Regional Climate Change Assessment Program (NARCCAP) for their skill in reproducing the North American monsoon system. The focus is on precipitation and the drivers behind the precipitation biases seen in the simulations of the current climate. Thus, a process-based approach to the question of model fidelity is taken in order to help assess confidence in this suite of simulations. The results show that the regional climate models (RCMs) forced with a reanalysis product and atmosphere-only global climate model (AGCM) time-slice simulations perform reasonably well over the core Mexican and southwest United States regions. Some of the dynamically downscaled simulations do, however, have strong dry biases in Arizona that are related to their inability to develop credible monsoon flow structure over the Gulf of California. When forced with different atmosphere–ocean coupled global climate models (AOGCMs) for the current period, the skill of the RCMs subdivides largely by the skill of the forcing or “parent” AOGCM. How the inherited biases affect the RCM simulations is investigated. While it is clear that the AOGCMs have a large influence on the RCMs, the authors also demonstrate where the regional models add value to the simulations and discuss the differential credibility of the six RCMs (17 total simulations), two AGCM time slices, and four AOGCMs examined herein. It is found that in-depth analysis of parent GCM and RCM scenarios can identify a meaningful subset of models that can produce credible simulations of the North American monsoon precipitation.

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 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.

Climate classifications from regional and global climate models: Performances for present climate estimates and expected changes in the future at high spatial resolution

2019 ◽  
Vol 228 ◽  
pp. 107-121 ◽  
Author(s):  
Francisco J. Tapiador ◽  
Raúl Moreno ◽  
Andrés Navarro ◽  
José Luis Sánchez ◽  
Eduardo García-Ortega
Keyword(s):  
Spatial Resolution ◽  
Climate Models ◽  
High Spatial Resolution ◽  
Global Climate ◽  
Global Climate Models ◽  
Present Climate ◽  
The Future

scholarly journals Late Paleogene emergence of a North American loess plateau

Geology ◽  
2020 ◽  
Vol 48 (3) ◽  
pp. 273-277 ◽  
Author(s):  
Majie Fan ◽  
Ran Feng ◽  
John W. Geissman ◽  
Christopher J. Poulsen
Keyword(s):  
Great Plains ◽  
North American ◽  
Global Climate ◽  
North American Monsoon ◽  
Spatiotemporal Pattern ◽  
The North ◽  
Data Compilation ◽  
Climate Simulations ◽  
Central Rocky Mountains ◽  
Latest Eocene

Abstract The relative roles of tectonics and global climate in forming the hydroclimate for widespread eolian deposition remain controversial. Oligocene loess has been previously documented in the interior of western United States, but its spatiotemporal pattern and causes remain undetermined. Through new stratigraphic record documentation and data compilation, we reveal the time transgressive occurrence of loess beginning in the latest Eocene in the central Rocky Mountains, that expands eastward to the Great Plains across the Eocene-Oligocene transition (EOT). Our climate simulations show that moderate uplift of the southern North America Cordillera initiated drying in the Cordilleran hinterland and immediate foreland, forming a potential dust source and sink, and global cooling at the EOT expanded the drying and eolian deposition eastward by causing retreat of the North American Monsoon. Therefore, the eolian deposition reflects continental aridification induced both by regional tectonism and global climate change during the late Paleogene.

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.

scholarly journals Toward Assessing NARCCAP Regional Climate Model Credibility for the North American Monsoon: Future Climate Simulations*

2015 ◽  
Vol 28 (17) ◽  
pp. 6707-6728 ◽  
Author(s):  
Melissa S. Bukovsky ◽  
Carlos M. Carrillo ◽  
David J. Gochis ◽  
Dorit M. Hammerling ◽  
Rachel R. McCrary ◽  
...  
Keyword(s):  
Climate Change ◽  
North American ◽  
Southern Oscillation ◽  
Monsoon Season ◽  
Regional Climate ◽  
Global Climate Models ◽  
North American Monsoon ◽  
The North ◽  
Climate Simulations ◽  
Projected Changes

Abstract This study presents climate change results from the North American Regional Climate Change Assessment Program (NARCCAP) suite of dynamically downscaled simulations for the North American monsoon system in the southwestern United States and northwestern Mexico. The focus is on changes in precipitation and the processes driving the projected changes from the regional climate simulations and their driving coupled atmosphere–ocean global climate models. The effect of known biases on the projections is also examined. Overall, there is strong ensemble agreement for a large decrease in precipitation during the monsoon season; however, this agreement and the magnitude of the ensemble-mean change is likely deceiving, as the greatest decreases are produced by the simulations that are the most biased in the baseline/current climate. Furthermore, some of the greatest decreases in precipitation are being driven by changes in processes/phenomena that are less credible (e.g., changes in El Niño–Southern Oscillation, when it is initially not simulated well). In other simulations, the processes driving the precipitation change may be plausible, but other biases (e.g., biases in low-level moisture or precipitation intensity) appear to be affecting the magnitude of the projected changes. The most and least credible simulations are clearly identified, while the other simulations are mixed in their abilities to produce projections of value.

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