Projections of North American Snow from NA-CORDEX and their Uncertainties, with a Focus on Model Resolution

Abstract Snow is important for many physical, social, and economic sectors in North America. In a warming climate, the characteristics of snow will likely change in fundamental ways, therefore compelling societal need for future projections of snow. However many stakeholders require climate change information at finer resolutions that global climate models (GCMs) can provide. The North American Coordinated Regional Downscaling Experiment (NA-CORDEX) provides an ensemble of regional climate model (RCMs) simulations at two resolutions (~0.5º and ~0.25º) designed to help serve the climate impacts and adaptation communities. This is the first study to examine the differences in end-of-21st-century projections of snow from the NA-CORDEX RCMs and their driving GCMs. We find the broad patterns of change are similar across RCMs and GCMs: snow cover retreats, snow mass decreases everywhere except at high latitudes, and the duration of the snow covered season decreases. Regionally, the spatial details, magnitude, percent, and uncertainty of future changes varies between the GCM and RCM ensemble, but are similar between the two resolutions of the RCM ensembles. Increases in winter snow amounts at high latitudes is a robust response across all ensembles. Percent snow losses are found to be more substantial in the GCMs than the RCMs over most of North America, especially in regions with high-elevation topography. Specifically, percent snow losses decrease with increasing elevation as the model resolution becomes finer.

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Future rainfall and temperature changes in Brazil under global warming levels of 1.5ºC, 2ºC and 4ºC

Sustentabilidade em Debate ◽

10.18472/sustdeb.v11n3.2020.33933 ◽

2020 ◽

Vol 11 (3) ◽

pp. 57-90

Author(s):

Diego Jatobá dos Santos ◽

George Ulguim Pedra ◽

Marcelo Guatura Barbosa da Silva ◽

Carlos Augusto Guimarães Júnior ◽

Lincoln Muniz Alves ◽

...

Keyword(s):

Global Warming ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Climate Impacts ◽

Global Climate Models ◽

Northeastern Brazil ◽

Climate Indices ◽

Sea Ice Concentration

The present study analyzes the impacts of global warming of 1.5ºC, 2ºC, and 4ºC above pre-industrial levels in the Brazilian territory. Climate change projected among the different global warming levels has been analyzed for rainfall, temperature and extreme climate indices. The projections are derived from the global climate model HadGEM3-A, from the High-End cLimate Impacts and eXtremes (HELIX) international project, from the United Kingdom, forced by sea surface temperature and sea ice concentration of a subset of six CMIP5 (Coupled Model Intercomparison Project phase 5) global climate models and considering the RCP 8.5 (Representative Concentration Pathways) emissions scenario throughout the 21st century. Projections indicate robust differences in regional climate characteristics. These differences include changes: in the minimum and maximum air temperature close to the surface to all the country’s regions, in extremes of heat, particularly in northern Brazil, in the occurrence of heavy rainfall (Southern and Southeastern regions), and in the probability of droughts and rain deficits in some regions (Northern and Northeastern Brazil).

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Regions of intensification of extreme snowfall under future warming

Scientific Reports ◽

10.1038/s41598-021-95979-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lennart Quante ◽

Sven N. Willner ◽

Robin Middelanis ◽

Anders Levermann

Keyword(s):

Climate Change ◽

Global Warming ◽

Observational Data ◽

Climate Models ◽

Hydrological Cycle ◽

Global Climate ◽

Global Climate Models ◽

Average Intensity ◽

High Latitudes ◽

Future Warming

AbstractDue to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.

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The MJO-QBO Relationship in a GCM with Stratospheric Nudging

Journal of Climate ◽

10.1175/jcli-d-20-0636.1 ◽

2021 ◽

pp. 1-69

Author(s):

Zane Martin ◽

Clara Orbe ◽

Shuguang Wang ◽

Adam Sobel

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Global Climate Models ◽

Boreal Winter ◽

Quasi Biennial Oscillation ◽

Meridional Winds ◽

Goddard Institute ◽

Minimal Bias

AbstractObservational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

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

International Journal of Big Data Mining for Global Warming ◽

10.1142/s263053482150008x ◽

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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A prognostic pollen emissions model for climate models (PECM1.0)

10.5194/gmd-2017-105 ◽

2017 ◽

Author(s):

Matthew C. Wozniak ◽

Allison Steiner

Keyword(s):

United States ◽

Land Cover ◽

Climate Models ◽

Climate Model ◽

Vegetation Type ◽

Global Climate ◽

The United States ◽

Global Climate Models ◽

Pollen Counts ◽

Surface Pollen

Abstract. We develop a prognostic model of Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in a regional climate model (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model: (1) using a taxa-specific land cover database, phenology and emission potential, and (2) a PFT-based land cover, phenology and emission potential. The resulting surface concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model, however we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

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Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

Journal of Climate ◽

10.1175/jcli-d-18-0598.1 ◽

2019 ◽

Vol 32 (2) ◽

pp. 639-661 ◽

Cited By ~ 5

Author(s):

Y. Chang ◽

S. D. Schubert ◽

R. D. Koster ◽

A. M. Molod ◽

H. Wang

Keyword(s):

North America ◽

Bias Correction ◽

Climate Models ◽

Global Climate ◽

Storm Track ◽

Coupled Model ◽

Global Climate Models ◽

Forecast Skill ◽

Boreal Summer ◽

Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

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Assessing the impact of global warming on worldwide open field tomato cultivation through CSIRO-Mk3·0 global climate model

The Journal of Agricultural Science ◽

10.1017/s0021859616000654 ◽

2016 ◽

Vol 155 (3) ◽

pp. 407-420 ◽

Cited By ~ 7

Author(s):

R. S. SILVA ◽

L. KUMAR ◽

F. SHABANI ◽

M. C. PICANÇO

Keyword(s):

Climate Change ◽

Global Warming ◽

Open Field ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Current Model ◽

Global Climate Models ◽

The Impact

SUMMARYTomato (Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

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Back to the Future: Using Long-Term Observational and Paleo-Proxy Reconstructions to Improve Model Projections of Antarctic Climate

Geosciences ◽

10.3390/geosciences9060255 ◽

2019 ◽

Vol 9 (6) ◽

pp. 255 ◽

Cited By ~ 6

Author(s):

Thomas J. Bracegirdle ◽

Florence Colleoni ◽

Nerilie J. Abram ◽

Nancy A. N. Bertler ◽

Daniel A. Dixon ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Ice Core ◽

Global Climate Models ◽

Climate Projections ◽

Past Performance ◽

Wide Range ◽

Proxy Reconstructions

Quantitative estimates of future Antarctic climate change are derived from numerical global climate models. Evaluation of the reliability of climate model projections involves many lines of evidence on past performance combined with knowledge of the processes that need to be represented. Routine model evaluation is mainly based on the modern observational period, which started with the establishment of a network of Antarctic weather stations in 1957/58. This period is too short to evaluate many fundamental aspects of the Antarctic and Southern Ocean climate system, such as decadal-to-century time-scale climate variability and trends. To help address this gap, we present a new evaluation of potential ways in which long-term observational and paleo-proxy reconstructions may be used, with a particular focus on improving projections. A wide range of data sources and time periods is included, ranging from ship observations of the early 20th century to ice core records spanning hundreds to hundreds of thousands of years to sediment records dating back 34 million years. We conclude that paleo-proxy records and long-term observational datasets are an underused resource in terms of strategies for improving Antarctic climate projections for the 21st century and beyond. We identify priorities and suggest next steps to addressing this.

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Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

Journal of Climate ◽

10.1175/jcli-d-18-0104.1 ◽

2018 ◽

Vol 32 (1) ◽

pp. 195-212 ◽

Cited By ~ 9

Author(s):

Sicheng He ◽

Jing Yang ◽

Qing Bao ◽

Lei Wang ◽

Bin Wang

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Yangtze River Basin ◽

Global Climate Model ◽

Global Climate Models ◽

Global Ocean ◽

East China ◽

Cmip5 Models

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

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Assessing Future Spatio-Temporal Changes in Crop Suitability and Planting Season over West Africa: Using the Concept of Crop-Climate Departure

Climate ◽

10.3390/cli7090102 ◽

2019 ◽

Vol 7 (9) ◽

pp. 102 ◽

Cited By ~ 5

Author(s):

Temitope S. Egbebiyi ◽

Chris Lennard ◽

Olivier Crespo ◽

Phillip Mukwenha ◽

Shakirudeen Lawal ◽

...

Keyword(s):

West Africa ◽

Linear Correlation ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Models ◽

Crop Suitability ◽

Suitable Area ◽

The Impact ◽

Planting Season

The changing climate is posing significant threats to agriculture, the most vulnerable sector, and the main source of livelihood in West Africa. This study assesses the impact of the climate-departure on the crop suitability and planting month over West Africa. We used 10 CMIP5 Global climate models bias-corrected simulations downscaled by the CORDEX regional climate model, RCA4 to drive the crop suitability model, Ecocrop. We applied the concept of the crop-climate departure (CCD) to evaluate future changes in the crop suitability and planting month for five crop types, cereals, legumes, fruits, root and tuber and horticulture over the historical and future months. Our result shows a reduction (negative linear correlation) and an expansion (positive linear correlation) in the suitable area and crop suitability index value in the Guinea-Savanna and Sahel (southern Sahel) zone, respectively. The horticulture crop was the most negatively affected with a decrease in the suitable area while cereals and legumes benefited from the expansion in suitable areas into the Sahel zone. In general, CCD would likely lead to a delay in the planting season by 2–4 months except for the orange and early planting dates by about 2–3 months for cassava. No projected changes in the planting month are observed for the plantain and pineapple which are annual crops. The study is relevant for a short and long-term adaptation option and planning for future changes in the crop suitability and planting month to improve food security in the region.

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