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 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.

scholarly journals Orographic resolution driving the improvements associated with horizontal resolution increase in the Northern Hemisphere winter mid-latitudes

2021 ◽  
Author(s):  
Paolo Davini ◽  
Federico Fabiano ◽  
Irina Sandu
Keyword(s):  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Radiation Budget ◽  
Model Errors ◽  
The North ◽  
Hemisphere Winter ◽  
The Impact

Abstract. In recent years much attention has been devoted to the investigation of the impact of increasing the horizontal resolution of global climate models. In the present work, a set of atmosphere-only idealized sensitivity simulations with EC-Earth3 have been designed to disentangle the relative roles of increasing the resolution of the resolved orography and of the atmospheric grid. Focusing on the winter Northern Hemisphere, it is shown that if the grid is refined while keeping the resolved orography unchanged, model biases are reduced only in some specific occasions. Conversely, increasing the resolved (or mean) orography is found to clearly reduce several important systematic model errors, including synoptic transient eddies, the North Atlantic jet stream variability and atmospheric blocking frequency and duration. From an analysis of the radiation budget it is concluded that the large changes in radiative fluxes caused by the resolution increase – something commonly observed in climate models – have a relevant impact on the atmospheric circulation, partially offsetting the benefits obtained from the increase in orographic resolution. These findings point to the necessity of always tuning climate models to fully exploit the benefits of high horizontal resolution.

scholarly journals A new world natural vegetation map for global change studies

2008 ◽  
Vol 80 (2) ◽  
pp. 397-408 ◽  
Author(s):  
David M. Lapola ◽  
Marcos D. Oyama ◽  
Carlos A. Nobre ◽  
Gilvan Sampaio
Keyword(s):  
Large Scale ◽  
New World ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Vegetation Classification ◽  
Natural Vegetation ◽  
Global Climate Models ◽  
Vegetation Map ◽  
Vegetation Maps

We developed a new world natural vegetation map at 1 degree horizontal resolution for use in global climate models. We used the Dorman and Sellers vegetation classification with inclusion of a new biome: tropical seasonal forest, which refers to both deciduous and semi-deciduous tropical forests. SSiB biogeophysical parameters values for this new biome type are presented. Under this new vegetation classification we obtained a consensus map between two global natural vegetation maps widely used in climate studies. We found that these two maps assign different biomes in ca. 1/3 of the continental grid points. To obtain a new global natural vegetation map, non-consensus areas were filled according to regional consensus based on more than 100 regional maps available on the internet. To minimize the risk of using poor quality information, the regional maps were obtained from reliable internet sources, and the filling procedure was based on the consensus among several regional maps obtained from independent sources. The new map was designed to reproduce accurately both the large-scale distribution of the main vegetation types (as it builds on two reliable global natural vegetation maps) and the regional details (as it is based on the consensus of regional maps).

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 Performance of CMIP3 and CMIP5 GCMs to Simulate Observed Rainfall Characteristics over the Western Himalayan Region

2017 ◽  
Vol 30 (19) ◽  
pp. 7777-7799 ◽  
Author(s):  
Jitendra Kumar Meher ◽  
Lalu Das ◽  
Javed Akhter ◽  
Rasmus E. Benestad ◽  
Abdelkader Mezghani
Keyword(s):  
Interannual Variability ◽  
Annual Cycle ◽  
Climate Models ◽  
Global Climate ◽  
India Meteorological Department ◽  
Rain Gauge ◽  
Global Climate Models ◽  
Himalayan Region ◽  
Western Himalayan Region ◽  
Cmip5 Gcms

Abstract The western Himalayan region (WHR) was subject to a significant negative trend in the annual and monsoon rainfall during 1902–2005. Annual and seasonal rainfall change over the WHR of India was estimated using 22 rain gauge station rainfall data from the India Meteorological Department. The performance of 13 global climate models (GCMs) from phase 3 of the Coupled Model Intercomparison Project (CMIP3) and 42 GCMs from CMIP5 was evaluated through multiple analysis: the evaluation of the mean annual cycle, annual cycles of interannual variability, spatial patterns, trends, and signal-to-noise ratio. In general, CMIP5 GCMs were more skillful in terms of simulating the annual cycle of interannual variability compared to CMIP3 GCMs. The CMIP3 GCMs failed to reproduce the observed trend, whereas approximately 50% of the CMIP5 GCMs reproduced the statistical distribution of short-term (30 yr) trend estimates than for the longer-term (99 yr) trends from CMIP5 GCMs. GCMs from both CMIP3 and CMIP5 were able to simulate the spatial distribution of observed rainfall in premonsoon and winter months. Based on performance, each model of CMIP3 and CMIP5 was given an overall rank, which puts the high-resolution version of the MIROC3.2 model [MIROC3.2 (hires)] and MIROC5 at the top in CMIP3 and CMIP5, respectively. Robustness of the ranking was judged through a sensitivity analysis, which indicated that ranks were independent during the process of adding or removing any individual method. It also revealed that trend analysis was not a robust method of judging performances of the models as compared to other methods.

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.

Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning

2019 ◽  
Vol 53 (1-2) ◽  
pp. 601-615 ◽  
Author(s):  
Lei Xu ◽  
Nengcheng Chen ◽  
Xiang Zhang ◽  
Zeqiang Chen ◽  
Chuli Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
North American ◽  
Model Ensemble ◽  
The North ◽  
Local Areas
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